the advantages of low cost, high dynamics, and deployment flexibility.
However, UAV communication faces many threats such as being susceptible to
interference, inability to cover large areas, and unstable communication. The
combination of blockchain and AI provides new research ideas for alleviating
these threats. For example, under the UAV Internet system, reference [253]
introduced a security scheme for information transmission between UAVs, which
integrated blockchain and DL. This scheme applied a zero-knowledge-proof-based
blockchain to maintain the security and privacy of data dissemination between
UAVs. Moreover, the DL-inspired miner selection algorithm in [253] can obtain
the optimal miner node strategy, thereby shortening the block generation time
and transaction submission time. To overcome the security challenges of cross-
domain UAVs’ authentication, a novel secure identity authentication approach
by leveraging blockchain-backed FL was provided in [254]. In this approach, FL
only shared the data model uploaded by the authenticated UAV instead of
directly sharing the original data. The authors of [254] made full use of
multi-signature smart contracts to practice distributed cross-domain UAVs’
identity authentication. And, to surmount the single point failure, these
multi-signature smart contracts were also employed to perform aggregations of
global model updates. In the IoT environment, the work of [255] proposed a
distributed dynamic resource management and pricing system that integrated
blockchain-as-a-service (BaaS) and UAV-authorized MEC. Specifically, MEC
servers are installed on both the ground BSs and the UAVs acting as the air
BSs to process some blockchain tasks. BaaS combined blockchain and cloud
computing so that resource management and pricing can be handled on BSs with
MEC servers in [255]. Then, in the case of incomplete information, the
interaction process of resource management and pricing between BSs and peers
of the proposed system was expressed as a stochastic Stackelberg game with
multiple leaders.

Additionally, UAV swarms or low earth orbit (LEO) satellites are extremely
vulnerable to security threats. Therefore, reference [256] elucidated a
blockchain- and FL-assisted knowledge sharing and collaborative learning
scheme. Specifically, this scheme considered the influence of the number of
miners, block transfer, and the mobility of UAVs/LEOs, the authors of [256]
derived the probability of regular forks and optimized the energy consumption
of PoW computation for blocks. More importantly, in [256], an advanced FL-
enabled algorithm was used to complete the resource allocation of mobile
mining. And, the coordination gains of blockchain and FL for UAV swarms was
illustrated. The work of [257] integrated blockchain, AI, and UAV swarms to
design an autonomous detection framework for infectious diseases. Here, UAV
swarms can expand coverage and lessen human participation. This detection
framework applied a lightweight blockchain and two-stage security
authentication mechanism to remote areas where the network is scarce to
degrade the burden of UAVs. Then, a DL-inspired algorithm was provided to
autonomously detect disease prevalence in [257]. The security of RF signal
transmission between UAVs and the accuracy of identification and detection are
challenged. Consequently, a UAV recognition and detection architecture by
combining blockchain, deep DNN, and edge computing was elucidated in [258].
This architecture applied blockchain to protect the security of data
transmission. Moreover, the deep DNN was adopted for training by using the
collected RF signal data from UAVs in different flight modes. Then, the
trained model was downloaded to edge devices to identify UAVs and detect their
flight modes. There are trade-off problems in terms of quantity, energy
consumption, coverage area, and height when installing BSs on the UAV side
(UAV-BS). To address this deployment problem of UAV-BS, the study of [259]
demonstrated a blockchain- and ML-guided smart placement scheme for UAV-BS.
More interestingly, in 6G networks, the work in [260] combined blockchain and
FL can provide a new idea for UAV-assisted construction of disaster response
systems.

##  IV Open Issues, Research Challenges, and Future Work

Integrating blockchain and AI in 6G wireless communications is currently a hot
research topic. In the previous sections, we focused on investigating the
possibilities of combining blockchain and AI. Moreover, we extensively
discussed the integration of blockchain and AI for wireless communications,
involving secure services and IoT smart applications. Research on integrating
blockchain and AI for wireless communications is still emerging, but future
works need to address some questions and challenges. In this section, based on
extensive research works in current literatures, we summarize the possible
open issues and research challenges for integrating blockchain and AI in 6G
wireless communications. The purpose is to provide beneficial inspirations and
references for future innovation research. We also explore potential research
directions in the future.

###  IV-A Towards Blockchain

In recent years, the research and application of blockchain has begun to grow
explosively. Blockchain is considered to be the key technology leading the
current information internet to the value internet. Although blockchain has
great potential for 6G networks, there are still some challenges that limit
its widespread application in 6G networks.

As the number of transactions increases significantly, scalability is the
biggest hurdle limiting the widespread adoption of blockchain technology in 6G
networks. For example, the Bitcoin and Ethereum systems process an average of
7 to 20 transactions per second, far behind the Visa system that handles tens
of thousands of transactions per second. Moreover, the multi-copy feature of
the blockchain requires a large amount of additional storage space, which
increases storage costs. This will result in limited space utilization, making
it difficult to support large-scale applications. There have been many
researchers improving the scalability of blockchains by using techniques such
as sidechains, lightning networks, sharding, pruning, and directed acyclic
graphs. However, these methods still have their own problems, such as how to
properly divide the tiles, and which transactions to prune. In addition, the
blockchain is a high-energy-consumption industry. As an illustration, the
blockchain system based on the PoW consensus mechanism relies on the computing
power contributed by the blockchain nodes. However, only part of the computing
power has been rewarded, and other computing power is doing useless work,
which wastes a lot of resources. This problem of high energy consumption
problem affects the popularization and application of blockchain in 6G.

Security attack is the most important problem faced by the blockchain so far,
such as Bitcoin’s 51% attack and botnet attack. The asymmetric encryption
mechanism of the blockchain will become more and more fragile with the
development of mathematics, cryptography, and computing technology. Security
issues are also a great threat to the further application of blockchain to 6G
networks. The data transactions recorded on the blockchain are open and
transparent, which is beneficial for data sharing and verification, but not
conducive to the privacy protection of user information. As more and more
personal data is stored in blockchain-powered 6G networks, privacy leakage
becomes another key issue.

###  IV-B Towards AI

In recent years, AI, especially DL, has achieved great success in computer
vision, natural language processing, speech recognition, and other fields. The
researchers expect to apply AI to all levels of the 6G system, thereby
generating an intelligent communication system, realizing the true
interconnection of everything, and meeting people’s ever-changing demands for
data transmission rates. However, there are still many challenges and unsolved
issues in implementing and managing complex intelligent communication systems.

Inefficient data management schemes and high overhead of information exchange
among communication participants are key bottlenecks in the development of AI
technology. The AI-based solutions, such as ML methods, usually require large
amounts of training data, which need to be collected and implemented on a
centralized server with sufficient storage and computing resources.
Nevertheless, current wireless communication systems do not have access to
massive amounts of data to train models. In heterogeneous networks,
aggregating data from different sources to train models is also an open
problem. In 6G networks, users may have different service quality requirements
in different scenarios. For example, in video streaming applications, users
demand high throughput and low latency at the cost of security. However, in
payment softwares, users require high security, even at the expense of
throughput. In this direction, designing a cross-layer, action-based AI
protocol for different applications is a key issue to satisfy various service
demands while balancing the network resources of 6G networks.

Notably, in the current era of rapid development of AI, data security and
privacy issues are receiving increasing attention. 6G network realizes
interconnected intelligence by supporting AI functions, and adopts a
centralized network architecture, which is vulnerable to hacker attacks.
Moreover, 6G network needs to collect a large amount of user data for training
through billions of devices. The training data involves a large amount of
personal information, so AI can easily lead to privacy leakage of user data.
Using distributed technology to design AI-enabled 6G network architectures can
actualize a decentralized security and trustworthy mechanism. Without sending
all data to the cloud computing center, the distributed technology processes
data where it is generated, which can alleviate the problem of privacy leakage
to a certain extent. Nevertheless, communication needs to exchange the
knowledge information perceived by both parties and update the knowledge bases
of both parties collaboratively, which also leads to the risk of privacy
leakage of local data. How to develop an efficient coordination mechanism
among communication participants without causing any private data leakage
remains an urgent problem to be solved.

###  IV-C Towards Blockchain- and AI-assisted Wireless Communications

Integrating blockchain and AI brings new opportunities to 6G networks, as well
as some open issues and research challenges. For example, the natural conflict
between blockchain and AI, the processing of a large amounts of data, and the
collaborative optimization of multiple systems and multiple indicators.
Furthermore, the effectiveness and feasibility of blockchain- and AI-assisted
wireless communications still need to be verified by large-scale practice of
wireless communication networks.

There are some conflicts with the combination of blockchain and AI. For
example, the execution results of smart contracts in blockchains are often
deterministic. While, the execution results of AI algorithms are usually
uncertain, random, and unpredictable in most cases. The contradiction between
blockchain and AI poses certain challenges for AI embedded in blockchain to
optimize the execution decisions of 6G networks. Therefore, in future
research, a new solution is required to deal with the contradiction between
the certainty of smart contracts and the randomness of AI algorithms. The new
solution can handle approximate calculations for smart contracts and design
consensus protocols for each participating node of blockchain. The purpose of
the new solution is to output the decision results under the 6G network with
specific certainty, high accuracy, and high precision. With the explosive
growth of data in 6G networks, the processing of large amounts of data in
blockchain- and AI-assisted wireless communication systems is a terribly large
challenge. Typically, blockchain is applied to securely collect and store
large amounts of data, and AI uses these data for model training processing.
The massive unlabeled and unclassified datasets are intractable for AI
training. At the same time, the blockchain also presents a potential
bottlenecks in storing these large-scale distributed data. For example, the
data recorded on the blockchain is open and transparent. The blockchain-based
storage method is beneficial for data sharing and verification, but not
conducive to data privacy protection. Furthermore, at present, most works in
related kinds of literature often only optimize a single performance index in
a single wireless communication system. The collaborative optimization of
multiple performance indexes of multiple wireless communication systems is
ignored. However, with the maturity and in-depth research of blockchain and AI
technologies in the future, the solutions proposed for the performance of
different wireless communication systems can be combined with each other.
Thereby, the goal of co-optimizing multiple performances of multiple systems
can be achieved.

Observing the existing literature, blockchain- and AI-assisted wireless
communications are still in the infant stage. Many works apply blockchain to
create a trusted environment for wireless communications, and provide
prediction, optimization, identification, detection, and decision-making for
wireless communication systems through AI algorithms. Few works have really
deeply integrated blockchain and AI into wireless communications. Whether we
look at the current technical indicators of blockchains or the actual
implementation of AI and 6G networks, there are still many uncertainties to
truly realize the integration and implementation of blockchain and AI
technologies for 6G networks. The potential outcomes of the fusion of
blockchain and AI for 6G networks are also difficult to assess. Therefore,
while actively investigating the integration of blockchain and AI for 6G
networks, we must also look at it rationally and focus on practical
implementation. In the future, we will continue to take an organic combination
and flexible and innovative approach to truly realize the practice and
exploration of the integration of blockchain and AI in 6G wireless
communications.

###  IV-D Future Work

In future work, we still need to tackle the technical barriers of blockchain
and AI. We can deeply investigate the matching and joint optimization of
blockchain- and AI-powered 6G networks in terms of performance indicators,
security, stability, etc. We can also further research and ensure the healthy
and sustainable development of blockchain and AI technologies in 6G networks.
In addition, the Federal Communications Commission, at the 2018 US Mobile
World Congress, emphasized that 6G can introduce blockchain technology into
spectrum sharing [262]. Research institutions such as the Institute of
Electrical and Electronics Engineers and the France’s Spectrum Regulator have
also begun to explore the application of blockchain to manage spectrum [263].
Thus, we will focus on the fusion of blockchain and AI to obtain smarter and
more distributed dynamic wireless resource allocation. 6G can combine emerging
advanced technologies such as cloud computing, edge computing, and big data to
promote the development of blockchain and AI technologies. Accordingly, the
mutual promotion and integration of blockchain, AI, and 6G is also one of the
key research directions in the future. With the further improvement of
blockchain and AI, academia and industry will continue to transform theory
into technology and put it into practice. We believe that future integration
of blockchain and AI will be more in-depth, and the scenarios applied to 6G
networks will be more abundant. Deploying Blockchain and AI in 6G networks
will bring more surprises and possibilities to our lives.

##  V Conclusion

This survey reviewed the latest progress of blockchain and AI for 6G wireless
communications. We began our comprehensive survey with a basic overview of
blockchain and AI. Specifically, we briefly described the concepts,
characteristics, and categories of blockchain and AI. The recent developments
in applying blockchain and AI to wireless communications, respectively, were
also showcased. To thoroughly explore the possibility of combining blockchain
and AI, we started with two aspects of blockchain-assisted AI and AI-aided
blockchain. We also highlighted the motivations for integrating blockchain
with AI for 6G wireless communications. Next, we then extensively discussed
the simultaneous deployment of blockchain and AI in 6G wireless communication
systems, involving secure services and IoT smart applications. In particular,
a comprehensive exploration of the widely popular secure services supported by
blockchain and AI was conducted, spotlighting spectrum management, computation
allocation, content caching, and security and privacy services. In addition,
we also covered the latest achievements of blockchain and AI empowerment in
various IoT smart applications. We made an exhaustive analysis from four
scenarios: smart healthcare, smart transportation, smart grid, and UAV.
Finally, we have pointed out several open issues, research challenges, and
potential research directions toward blockchain and AI for 6G networks.

In summary, this survey attempts to comprehensively explore the technologies
related to blockchain and AI for wireless communications. Although the
research on integrating blockchain and AI for 6G networks is still in its
infancy, it is obvious that blockchain and AI will significantly uplift the
performance of various services and applications in 6G networks. We believe
our study will shed valuable insights into the research challenges associated
with blockchain- and AI-assisted 6G networks as well as motivate interested
researchers and practitioners to devote more research efforts to this
promising area.

## References

  * [1] S. Dang, O. Amin, B. Shihada, and M.-S. Alouini, “What should 6G be?” _Nat. Electron._ , vol. 3, no. 1, pp. 20–29, Jan. 2020.
  * [2] P. Yang, Y. Xiao, M. Xiao, and S. Li, “6G wireless communications: Vision and potential techniques,” _IEEE Netw._ , vol. 33, no. 4, pp. 70–75, Jul. 2019.
  * [3] F. Tariq, M. R. Khandaker, K.-K. Wong, M. A. Imran, M. Bennis, and M. Debbah, “A speculative study on 6G,” _IEEE Wireless Commun._ , vol. 27, no. 4, pp. 118–125, Aug. 2020.
  * [4] W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The road towards 6G: A comprehensive survey,” _IEEE Open J. Comm. Soc._ , vol. 2, pp. 334–366, Feb. 2021.
  * [5] Z. Zhang, Y. Xiao, Z. Ma, M. Xiao, Z. Ding, X. Lei, G. K. Karagiannidis, and P. Fan, “6G wireless networks: Vision, requirements, architecture, and key technologies,” _IEEE Veh. Technol. Mag._ , vol. 14, no. 3, pp. 28–41, Sept. 2019.
  * [6] D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, D. Niyato, O. Dobre, and H. V. Poor, “6G internet of things: A comprehensive survey,” _IEEE Internet Things J._ , vol. 9, no. 1, pp. 359–383, Jan. 2022\.
  * [7] W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” _IEEE Netw._ , vol. 34, no. 3, pp. 134–142, May 2019.
  * [8] M. Z. Chowdhury, M. Shahjalal, S. Ahmed, and Y. M. Jang, “6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions,” _IEEE Open J. Comm. Soc._ , vol. 1, pp. 957–975, Jul. 2020.
  * [9] N. Kato, B. Mao, F. Tang, Y. Kawamoto, and J. Liu, “Ten challenges in advancing machine learning technologies toward 6G,” _IEEE Wireless Commun._ , vol. 27, no. 3, pp. 96–103, Jun. 2020.
  * [10] H. Yang, A. Alphones, Z. Xiong, D. Niyato, J. Zhao, and K. Wu, “Artificial-intelligence-enabled intelligent 6G networks,” _IEEE Netw._ , vol. 34, no. 6, pp. 272–280, Nov. 2020.
  * [11] S. Khan, A. Hussain, S. Nazir, F. Khan, A. Oad, and M. D. Alshehri, “Efficient and reliable hybrid deep learning-enabled model for congestion control in 5G/6G networks,” _Comput. Commun._ , vol. 182, pp. 31–40, Jan. 2022.
  * [12] J. Guo, Y. Zuo, C.-K. Wen, and S. Jin, “User-centric online gossip training for autoencoder-based CSI feedback,” _IEEE J. Sel. Topics Signal Process._ , vol. 16, no. 3, pp. 559–572, Apr. 2022.
  * [13] J. Guo, C.-K. Wen, and S. Jin, “CAnet: Uplink-aided downlink channel acquisition in FDD massive MIMO using deep learning,” _IEEE Trans. Commun._ , vol. 70, no. 1, pp. 199–214, Jan. 2022.
  * [14] M. Latva-aho, K. Leppänen, F. Clazzer, and A. Munari, “Key drivers and research challenges for 6G ubiquitous wireless intelligence (White Paper),” 6G Flagship, University of Oulu, Oulu, 2019. [Online]. Available: http://urn.fi/urn:isbn:9789526223544.
  * [15] ITU-T, “Framework for evaluating intelligence levels of future networks including IMT-2020,” Y.3173, Feb. 2020. [Online]. Available: https://www.itu.int/rec/T-REC-Y.3173/en.
  * [16] J. Leng, M. Zhou, J. L. Zhao, Y. Huang, and Y. Bian, “Blockchain security: A survey of techniques and research directions,” _IEEE Trans. Serv. Comput._ , vol. 15, no. 4, pp. 2490–2510, Jul.-Aug. 2022.
  * [17] M. Wang, T. Zhu, T. Zhang, J. Zhang, S. Yu, and W. Zhou, “Security and privacy in 6G networks: New areas and new challenges,” _Digit. Commun. Netw._ , vol. 6, no. 3, pp. 281–291, Aug. 2020.
  * [18] M. Conti, E. S. Kumar, C. Lal, and S. Ruj, “A survey on security and privacy issues of bitcoin,” _IEEE Commun. Surv. Tutor._ , vol. 20, no. 4, pp. 3416–3452, May 2018.
  * [19] T. Hewa, G. Gür, A. Kalla, M. Ylianttila, A. Bracken, and M. Liyanage, “The role of blockchain in 6G: Challenges, opportunities and research directions,” in _2020 2nd 6G Wireless Summit (6G SUMMIT)_. Levi, Finland: IEEE, Mar. 2020, pp. 1–5.
  * [20] Y. Zhu, G. Zheng, and K.-K. Wong, “Blockchain-empowered decentralized storage in air-to-ground industrial networks,” _IEEE Trans. Ind. Informat._ , vol. 15, no. 6, pp. 3593–3601, Jun. 2019.
  * [21] M. Ylianttila, R. Kantola, A. Gurtov, L. Mucchi, I. Oppermann, Z. Yan, T. H. Nguyen, F. Liu, T. Hewa, M. Liyanage _et al._ , “6G white paper: Research challenges for trust, security and privacy,” _arXiv preprint arXiv:2004.11665_ , 2020.
  * [22] J. Wang, X. Ling, Y. Le, Y. Huang, and X. You, “Blockchain-enabled wireless communications: a new paradigm towards 6G,” _Natl. Sci. Rev._ , vol. 8, no. 9, p. nwab069, Sept. 2021.
  * [23] M. Wu, K. Wang, X. Cai, S. Guo, M. Guo, and C. Rong, “A comprehensive survey of blockchain: From theory to IoT applications and beyond,” _IEEE Internet Things J._ , vol. 6, no. 5, pp. 8114–8154, Oct. 2019.
  * [24] D. C. Nguyen, P. N. Pathirana, M. Ding, and A. Seneviratne, “Blockchain for 5G and beyond networks: A state of the art survey,” _J. Netw. and Comput. Appl._ , vol. 166, p. 102693, 2020.
  * [25] K. Yue, Y. Zhang, Y. Chen, Y. Li, L. Zhao, C. Rong, and L. Chen, “A survey of decentralizing applications via blockchain: The 5G and beyond perspective,” _IEEE Commun. Surv. Tutor._ , vol. 23, no. 4, pp. 2191–2217, Sept. 2021.
  * [26] M. Tahir, M. H. Habaebi, M. Dabbagh, A. Mughees, A. Ahad, and K. I. Ahmed, “A review on application of blockchain in 5G and beyond networks: Taxonomy, field-trials, challenges and opportunities,” _IEEE Access_ , vol. 8, pp. 115 876–115 904, Jun. 2020.
  * [27] S. A. Bhat, I. B. Sofi, and C.-Y. Chi, “Edge computing and its convergence with blockchain in 5G and beyond: security, challenges, and opportunities,” _IEEE Access_ , vol. 8, pp. 205 340–205 373, Nov. 2020\.
  * [28] P. Sharma, S. Jain, S. Gupta, and V. Chamola, “Role of machine learning and deep learning in securing 5G-driven industrial IoT applications,” _Ad Hoc Netw._ , vol. 123, p. 102685, Dec. 2021.
  * [29] Y. Sun, J. Liu, J. Wang, Y. Cao, and N. Kato, “When machine learning meets privacy in 6G: A survey,” _IEEE Commun. Surv. Tutor._ , vol. 22, no. 4, pp. 2694–2724, Jul. 2020.
  * [30] V. P. Rekkas, S. Sotiroudis, P. Sarigiannidis, S. Wan, G. K. Karagiannidis, and S. K. Goudos, “Machine learning in beyond 5G/6G networks—state-of-the-art and future trends,” _Electronics_ , vol. 10, no. 22, p. 2786, Nov. 2021.
  * [31] Y. Liu, X. Yuan, Z. Xiong, J. Kang, X. Wang, and D. Niyato, “Federated learning for 6G communications: Challenges, methods, and future directions,” _China Commun._ , vol. 17, no. 9, pp. 105–118, Sept. 2020.
  * [32] M. Lin and Y. Zhao, “Artificial intelligence-empowered resource management for future wireless communications: A survey,” _China Commun._ , vol. 17, no. 3, pp. 58–77, Mar. 2020.
  * [33] K. Salah, M. H. U. Rehman, N. Nizamuddin, and A. Al-Fuqaha, “Blockchain for AI: Review and open research challenges,” _IEEE Access_ , vol. 7, pp. 10 127–10 149, Jan. 2019.
  * [34] M. Shafay, R. W. Ahmad, K. Salah, I. Yaqoob, R. Jayaraman, and M. Omar, “Blockchain for deep learning: review and open challenges,” _Cluster Computing_ , pp. 1–25, Mar. 2022.
  * [35] R. Wang, M. Luo, Y. Wen, L. Wang, K.-K. Raymond Choo, and D. He, “The applications of blockchain in artificial intelligence,” _Secur. Commun. Netw._ , vol. 2021, Sept. 2021.
  * [36] B. Xing and T. Marwala, “The synergy of blockchain and artificial intelligence,” _Available at SSRN 3225357_ , Aug. 2018.
  * [37] K. D. Pandl, S. Thiebes, M. Schmidt-Kraepelin, and A. Sunyaev, “On the convergence of artificial intelligence and distributed ledger technology: A scoping review and future research agenda,” _IEEE Access_ , vol. 8, pp. 57 075–57 095, Mar. 2020.
  * [38] T. N. Dinh and M. T. Thai, “AI and blockchain: A disruptive integration,” _Computer_ , vol. 51, no. 9, pp. 48–53, Sept. 2018.
  * [39] S. K. Singh, S. Rathore, and J. H. Park, “BlockIoTIntelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence,” _Future Gener. Comput. Syst._ , vol. 110, pp. 721–743, Sept. 2020.
  * [40] A. A. Hussain and F. Al-Turjman, “Artificial intelligence and blockchain: A review,” _T. Emerg. Telecommun. T._ , vol. 32, no. 9, p. e4268, Apr. 2021\.
  * [41] Q. Yang, Y. Zhao, H. Huang, Z. Xiong, J. Kang, and Z. Zheng, “Fusing blockchain and AI with metaverse: A survey,” _IEEE Open J. Comp. Soc._ , vol. 3, pp. 122–136, Jul. 2022.
  * [42] B. K. Mohanta, D. Jena, U. Satapathy, and S. Patnaik, “Survey on iot security: Challenges and solution using machine learning, artificial intelligence and blockchain technology,” _Internet of Things_ , vol. 11, p. 100227, Sept. 2020\.
  * [43] P. Tagde, S. Tagde, T. Bhattacharya, P. Tagde, H. Chopra, R. Akter, D. Kaushik, and M. H. Rahman, “Blockchain and artificial intelligence technology in e-health,” _Environ. Sci. Pollut. Res._ , vol. 28, pp. 52 810–52 831, Sept. 2021.
  * [44] S. S. Gill, S. Tuli, M. Xu, I. Singh, K. V. Singh, D. Lindsay, S. Tuli, D. Smirnova, M. Singh, U. Jain _et al._ , “Transformative effects of iot, blockchain and artificial intelligence on cloud computing: Evolution, vision, trends and open challenges,” _Internet of Things_ , vol. 8, p. 100118, Dec. 2019.
  * [45] A. Dhar Dwivedi, R. Singh, K. Kaushik, R. Rao Mukkamala, and W. S. Alnumay, “Blockchain and artificial intelligence for 5g-enabled internet of things: Challenges, opportunities, and solutions,” _T. Emerg. Telecommun. T._ , p. e4329, Jul. 2021.
  * [46] Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. M. Leung, “Blockchain and machine learning for communications and networking systems,” _IEEE Commun. Surv. Tutor._ , vol. 22, no. 2, pp. 1392–1431, Feb. 2020.
  * [47] F. Jameel, U. Javaid, W. U. Khan, M. N. Aman, H. Pervaiz, and R. Jäntti, “Reinforcement learning in blockchain-enabled IIoT networks: A survey of recent advances and open challenges,” _Sustainability_ , vol. 12, no. 12, p. 5161, Jun. 2020.
  * [48] Y. Wu, Z. Wang, Y. Ma, and V. C. Leung, “Deep reinforcement learning for blockchain in industrial IoT: A survey,” _Comput. Netw._ , vol. 191, p. 108004, May 2021.
  * [49] A. Miglani and N. Kumar, “Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review,” _Comput. Commun._ , vol. 178, pp. 37–63, Oct. 2021.
  * [50] A. El Azzaoui, S. K. Singh, Y. Pan, and J. H. Park, “Block5GIntell: Blockchain for AI-enabled 5G networks,” _IEEE Access_ , vol. 8, pp. 145 918–145 935, Aug. 2020.
  * [51] M. Dibaei, X. Zheng, Y. Xia, X. Xu, A. Jolfaei, A. K. Bashir, U. Tariq, D. Yu, and A. V. Vasilakos, “Investigating the prospect of leveraging blockchain and machine learning to secure vehicular networks: A survey,” _IEEE Trans. Intell. Transp. Syst._ , Feb. 2022.
  * [52] Y. Yuan, T. Zhou, A. Zhou, Y. Duan, and F. Wang, “Blockchain technology: from data intelligence to knowledge automation,” _Acta Autom. Sin._ , vol. 43, no. 9, pp. 1485–1490, Sept. 2017.
  * [53] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” _White Paper_ , May 2008. [Online]. Available: http://nakamotoinstitute.org/bitcoin/.
  * [54] S. Aggarwal and N. Kumar, “Blockchain 2.0: smart contracts,” in _Advances in Computers_. Elsevier, 2021, vol. 121, pp. 301–322.
  * [55] D. D. F. Maesa and P. Mori, “Blockchain 3.0 applications survey,” _J. Parallel Distrib. Comput._ , vol. 138, pp. 99–114, Apr. 2020.
  * [56] F. Tschorsch and B. Scheuermann, “Bitcoin and beyond: A technical survey on decentralized digital currencies,” _IEEE Commun. Surv. Tutor._ , vol. 18, no. 3, pp. 2084–2123, Mar. 2016.
  * [57] W. Wang, D. T. Hoang, P. Hu, Z. Xiong, D. Niyato, P. Wang, Y. Wen, and D. I. Kim, “A survey on consensus mechanisms and mining strategy management in blockchain networks,” _IEEE Access_ , vol. 7, pp. 22 328–22 370, Jan. 2019\.
  * [58] A. Kiayias, A. Russell, B. David, and R. Oliynykov, “Ouroboros: A provably secure proof-of-stake blockchain protocol,” in _Proc. 37th Annual International Cryptology Conference (CRYPTO)_. Santa Barbara, CA, USA, Aug. 2017, pp. 357–388.
  * [59] D. Larimer, “Delegated proof-of-stake (DPoS).” Blacksburg, VA, USA, 2014.
  * [60] M. Castro and B. Liskov, “Practical byzantine fault tolerance and proactive recovery,” _ACM Trans. Comput. Syst. (TOCS)_ , vol. 20, no. 4, pp. 398–461, Nov. 2002.
  * [61] G. Wood _et al._ , “Ethereum: A secure decentralised generalised transaction ledger,” _Ethereum project yellow paper_ , vol. 151, no. 2014, pp. 1–32, 2014.
  * [62] O. Dib, K.-L. Brousmiche, A. Durand, E. Thea, and E. B. Hamida, “Consortium blockchains: Overview, applications and challenges,” _Int. J. Adv. Telecommun._ , vol. 11, no. 1&2, pp. 51–64, 2018.
  * [63] Y. Zuo, S. Jin, S. Zhang, and Y. Zhang, “Blockchain storage and computation offloading for cooperative mobile-edge computing,” _IEEE Internet Things J._ , vol. 8, no. 11, pp. 9084–9098, Jun. 2021.
  * [64] X. Ling, Y. Le, J. Wang, Z. Ding, and X. Gao, “Practical modeling and analysis of blockchain radio access network,” _IEEE Trans. Commun._ , vol. 69, no. 2, pp. 1021–1037, Feb. 2021.
  * [65] Y. Zuo, S. Jin, and S. Zhang, “Computation offloading in untrusted MEC-aided mobile blockchain IoT systems,” _IEEE Trans. Wireless Commun._ , vol. 20, no. 12, pp. 8333–8347, Dec. 2021.
  * [66] Y. Zuo, S. Jin, S. Zhang, Y. Han, and K.-K. Wong, “Delay-limited computation offloading for MEC-assisted mobile blockchain networks,” _IEEE Trans. Commun._ , vol. 69, no. 12, pp. 8569–8584, Dec. 2021.
  * [67] X. Ling, P. Chen, J. Wang, and Z. Ding, “Data broker: Dynamic multi-hop routing protocol in blockchain radio access network,” _IEEE Commun. Lett._ , vol. 25, no. 12, pp. 4000–4004, Dec. 2021.
  * [68] Y. Zuo, S. Jin, and S. Zhang, “Blockchain storage, computation offloading, and user association for heterogeneous cellular networks,” _IEEE Internet Things J._ , vol. 9, no. 11, pp. 8191–8204, Jun. 2022.
  * [69] Y. Zuo, S. Zhang, Y. Han, and S. Jin, “Computation resource allocation in mobile blockchain-enabled edge computing networks,” in _2020 IEEE/CIC International Conference on Communications in China (ICCC)_. Chongqing, China, Aug. 2020, pp. 617–622.
  * [70] Y. Zuo, S. Jin, and S. Zhang, “Computation offloading and user association for blockchain-enabled heterogeneous cellular networks,” in _2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)_. Norman, OK, USA, Sept. 2021, pp. 01–06.
  * [71] K. Kotobi and S. G. Bilén, “Blockchain-enabled spectrum access in cognitive radio networks,” in _2017 Wireless Telecommunications Symposium (WTS)_ , Apr. 2017, pp. 1–6.
  * [72] K. Kotobi and S. G. Bilen, “Secure blockchains for dynamic spectrum access: A decentralized database in moving cognitive radio networks enhances security and user access,” _IEEE Veh. Technol. Mag._ , vol. 13, no. 1, pp. 32–39, Mar. 2018.
  * [73] S. Bayhan, A. Zubow, and A. Wolisz, “Spass: Spectrum sensing as a service via smart contracts,” in _2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)_ , Oct. 2018, pp. 1–10.
  * [74] S. Bayhan, A. Zubow, P. Gawłowicz, and A. Wolisz, “Smart contracts for spectrum sensing as a service,” _IEEE Trans. on Cogn. Commun. Netw._ , vol. 5, no. 3, pp. 648–660, Sept. 2019.
  * [75] M. B. H. Weiss, K. Werbach, D. C. Sicker, and C. E. C. Bastidas, “On the application of blockchains to spectrum management,” _IEEE Trans. on Cogn. Commun. Netw._ , vol. 5, no. 2, pp. 193–205, Jun. 2019.
  * [76] J. Qiu, D. Grace, G. Ding, J. Yao, and Q. Wu, “Blockchain-based secure spectrum trading for unmanned-aerial-vehicle-assisted cellular networks: An operator’s perspective,” _IEEE Internet Things J._ , vol. 7, no. 1, pp. 451–466, Jan. 2020.
  * [77] M. Grissa, A. A. Yavuz, and B. Hamdaoui, “TrustSAS: A trustworthy spectrum access system for the 3.5 GHz CBRS band,” in _IEEE INFOCOM 2019 - IEEE Conference on Computer Communications_ , Apr. 2019, pp. 1495–1503.
  * [78] T. Maksymyuk, J. Gazda, L. Han, and M. Jo, “Blockchain-based intelligent network management for 5G and beyond,” in _2019 3rd International Conference on Advanced Information and Communications Technologies (AICT)_ , Jul. 2019, pp. 36–39.
  * [79] H. Zhang, S. Leng, and H. Chai, “A blockchain enhanced dynamic spectrum sharing model based on proof-of-strategy,” in _2020 IEEE International Conference on Communications (ICC)_ , Jun. 2020, pp. 1–6.
  * [80] M. Jiang, Y. Li, Q. Zhang, G. Zhang, and J. Qin, “Decentralized blockchain-based dynamic spectrum acquisition for wireless downlink communications,” _IEEE Trans. Signal Process._ , vol. 69, pp. 986–997, Jan. 2021.
  * [81] Z. Li, Z. Yang, S. Xie, W. Chen, and K. Liu, “Credit-based payments for fast computing resource trading in edge-assisted internet of things,” _IEEE Internet Things J._ , vol. 6, no. 4, pp. 6606–6617, Aug. 2019.
  * [82] D. Chatzopoulos, M. Ahmadi, S. Kosta, and P. Hui, “FlopCoin: A cryptocurrency for computation offloading,” _IEEE Trans. Mobile Comput._ , vol. 17, no. 5, pp. 1062–1075, May 2018.
  * [83] W. Sun, J. Liu, Y. Yue, and P. Wang, “Joint resource allocation and incentive design for blockchain-based mobile edge computing,” _IEEE Trans. Wireless Commun._ , vol. 19, no. 9, pp. 6050–6064, Sept. 2020.
  * [84] S. Wang, D. Ye, X. Huang, R. Yu, Y. Wang, and Y. Zhang, “Consortium blockchain for secure resource sharing in vehicular edge computing: A contract-based approach,” _IEEE Trans. Netw. Sci. Eng._ , vol. 8, no. 2, pp. 1189–1201, Jun. 2021.
  * [85] J. Kang, R. Yu, X. Huang, M. Wu, S. Maharjan, S. Xie, and Y. Zhang, “Blockchain for secure and efficient data sharing in vehicular edge computing and networks,” _IEEE Internet Things J._ , vol. 6, no. 3, pp. 4660–4670, Jun. 2019.
  * [86] J. Sun, X. Yao, S. Wang, and Y. Wu, “Blockchain-based secure storage and access scheme for electronic medical records in IPFS,” _IEEE Access_ , vol. 8, pp. 59 389–59 401, Mar. 2020.
  * [87] Y. Xu, J. Ren, Y. Zhang, C. Zhang, B. Shen, and Y. Zhang, “Blockchain empowered arbitrable data auditing scheme for network storage as a service,” _IEEE Trans. Serv. Comput._ , vol. 13, no. 2, pp. 289–300, Apr. 2020.
  * [88] K. Fan, Y. Ren, Y. Wang, H. Li, and Y. Yang, “Blockchain-based efficient privacy preserving and data sharing scheme of content-centric network in 5G,” _IET Commun._ , vol. 12, pp. 527–532, Feb. 2018.
  * [89] A. El Gamal and H. El Gamal, “A single coin monetary mechanism for distributed cooperative interference management,” _IEEE Wireless Commun. Lett._ , vol. 8, no. 3, pp. 757–760, Jun. 2019.
  * [90] Y. Sun, L. Zhang, G. Feng, B. Yang, B. Cao, and M. A. Imran, “Blockchain-enabled wireless internet of things: Performance analysis and optimal communication node deployment,” _IEEE Internet Things J._ , vol. 6, no. 3, pp. 5791–5802, Jun. 2019.
  * [91] Z. Liu, L. Gao, Y. Liu, X. Guan, K. Ma, and Y. Wang, “Efficient QoS support for robust resource allocation in blockchain-based femtocell networks,” _IEEE Trans. Ind. Informat._ , vol. 16, no. 11, pp. 7070–7080, Nov. 2020\.
  * [92] A. P. Singh, N. R. Pradhan, A. K. Luhach, S. Agnihotri, N. Z. Jhanjhi, S. Verma, Kavita, U. Ghosh, and D. S. Roy, “A novel patient-centric architectural framework for blockchain-enabled healthcare applications,” _IEEE Trans. Ind. Informat._ , vol. 17, no. 8, pp. 5779–5789, Aug. 2021.
  * [93] D. C. Nguyen, P. N. Pathirana, M. Ding, and A. Seneviratne, “BEdgeHealth: A decentralized architecture for edge-based IoMT networks using blockchain,” _IEEE Internet Things J._ , vol. 8, no. 14, pp. 11 743–11 757, Jul. 2021\.
  * [94] A. Yazdinejad, G. Srivastava, R. M. Parizi, A. Dehghantanha, K.-K. R. Choo, and M. Aledhari, “Decentralized authentication of distributed patients in hospital networks using blockchain,” _IEEE J. Biomed. Health Inform._ , vol. 24, no. 8, pp. 2146–2156, Aug. 2020.
  * [95] S. Xia, F. Lin, Z. Chen, C. Tang, Y. Ma, and X. Yu, “A bayesian game based vehicle-to-vehicle electricity trading scheme for blockchain-enabled internet of vehicles,” _IEEE Trans. Veh. Technol._ , vol. 69, no. 7, pp. 6856–6868, Jul. 2020.
  * [96] Y. Yin, Y. Li, B. Ye, T. Liang, and Y. Li, “A blockchain-based incremental update supported data storage system for intelligent vehicles,” _IEEE Trans. Veh. Technol._ , vol. 70, no. 5, pp. 4880–4893, May 2021.
  * [97] D. Chattaraj, B. Bera, A. K. Das, S. Saha, P. Lorenz, and Y. Park, “Block-CLAP: Blockchain-assisted certificateless key agreement protocol for internet of vehicles in smart transportation,” _IEEE Trans. Veh. Technol._ , vol. 70, no. 8, pp. 8092–8107, Aug. 2021.
  * [98] J. Wang, L. Wu, K.-K. R. Choo, and D. He, “Blockchain-based anonymous authentication with key management for smart grid edge computing infrastructure,” _IEEE Trans. Ind. Informat._ , vol. 16, no. 3, pp. 1984–1992, Mar. 2020.
  * [99] B. Bera, S. Saha, A. K. Das, and A. V. Vasilakos, “Designing blockchain-based access control protocol in IoT-enabled smart-grid system,” _IEEE Internet Things J._ , vol. 8, no. 7, pp. 5744–5761, Apr. 2021.
  * [100] A. Jindal, G. S. Aujla, N. Kumar, and M. Villari, “GUARDIAN: Blockchain-based secure demand response management in smart grid system,” _IEEE Trans. Serv. Comput._ , vol. 13, no. 4, pp. 613–624, Jul.-Aug. 2020\.
  * [101] K. Gai, Y. Wu, L. Zhu, K.-K. R. Choo, and B. Xiao, “Blockchain-enabled trustworthy group communications in UAV networks,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 7, pp. 4118–4130, Jul. 2021.
  * [102] Y. Tan, J. Liu, and N. Kato, “Blockchain-based key management for heterogeneous flying ad hoc network,” _IEEE Trans. Ind. Informat._ , vol. 17, no. 11, pp. 7629–7638, Nov. 2021.
  * [103] L. Jiang, B. Chen, S. Xie, S. Maharjan, and Y. Zhang, “Incentivizing resource cooperation for blockchain empowered wireless power transfer in UAV networks,” _IEEE Trans. Veh. Technol._ , vol. 69, no. 12, pp. 15 828–15 841, Dec. 2020.
  * [104] J. Guo, C.-K. Wen, S. Jin, and G. Y. Li, “Overview of deep learning-based CSI feedback in massive MIMO systems,” _IEEE Trans. Commun._ , vol. 70, no. 12, pp. 8017–8045, Dec. 2022.
  * [105] N. J. Nilsson, _The quest for artificial intelligence_. Cambridge University Press, 2009.
  * [106] P. H. Winston, _Artificial intelligence_. Addison-Wesley Longman Publishing Co., Inc., 1992.
  * [107] A. Barr, E. A. Feigenbaum, and P. R. Cohen, _The handbook of artificial intelligence_. William Kaufmann, 1981, vol. 1.
  * [108] Q. Mao, F. Hu, and Q. Hao, “Deep learning for intelligent wireless networks: A comprehensive survey,” _IEEE Commun. Surv. Tutor._ , vol. 20, no. 4, pp. 2595–2621, Jun. 2018.
  * [109] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” _Nature_ , vol. 521, no. 7553, pp. 436–444, May 2015.
  * [110] I. Goodfellow, Y. Bengio, and A. Courville, _Deep learning_. MIT press, 2016.
  * [111] L. Bottou and O. Bousquet, “The tradeoffs of large scale learning,” _In Proc. Advances in Neural Information Processing Systems_ , vol. 20, pp. 161–168, 2007.
  * [112] G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” _Neural Comput._ , vol. 18, no. 7, pp. 1527–1554, Jul. 2006\.
  * [113] Q. Xue, Y.-J. Liu, Y. Sun, J. Wang, L. Yan, G. Feng, and S. Ma, “Beam management in ultra-dense mmwave network via federated reinforcement learning: An intelligent and secure approach,” _IEEE Trans. on Cogn. Commun. Netw._ , vol. 9, no. 1, pp. 185–197, Feb. 2023.
  * [114] K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “Deep reinforcement learning: A brief survey,” _IEEE Signal Process. Mag._ , vol. 34, no. 6, pp. 26–38, Nov. 2017.
  * [115] Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel, “Handwritten digit recognition with a back-propagation network,” _In Proc. Advances in Neural Information Processing Systems_ , vol. 2, pp. 396–404, 1989.
  * [116] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” _Proceedings of the IEEE_ , vol. 86, no. 11, pp. 2278–2324, Nov. 1998.
  * [117] Z. C. Lipton, J. Berkowitz, and C. Elkan, “A critical review of recurrent neural networks for sequence learning,” _arXiv preprint arXiv:1506.00019_ , 2015.
  * [118] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” _In Proc. Advances in Neural Information Processing Systems_ , vol. 27, 2014.
  * [119] J.-M. Kang, C.-J. Chun, and I.-M. Kim, “Deep-learning-based channel estimation for wireless energy transfer,” _IEEE Commun. Lett._ , vol. 22, no. 11, pp. 2310–2313, Nov. 2018.
  * [120] H. Huang, J. Yang, H. Huang, Y. Song, and G. Gui, “Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system,” _IEEE Trans. Veh. Technol._ , vol. 67, no. 9, pp. 8549–8560, Sept. 2018.
  * [121] H. He, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmWave massive MIMO systems,” _IEEE Wireless Commun. Lett._ , vol. 7, no. 5, pp. 852–855, Oct. 2018.
  * [122] M. Borgerding, P. Schniter, and S. Rangan, “AMP-inspired deep networks for sparse linear inverse problems,” _IEEE Trans. Signal Process._ , vol. 65, no. 16, pp. 4293–4308, Aug. 2017.
  * [123] D. Neumann, T. Wiese, and W. Utschick, “Learning the MMSE channel estimator,” _IEEE Trans. Signal Process._ , vol. 66, no. 11, pp. 2905–2917, Jun. 2018.
  * [124] H. Ye, G. Y. Li, and B.-H. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” _IEEE Wireless Commun. Lett._ , vol. 7, no. 1, pp. 114–117, Feb. 2018.
  * [125] N. Samuel, T. Diskin, and A. Wiesel, “Deep MIMO detection,” in _2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)_. Sapporo, Japan: IEEE, Jul. 2017, pp. 1–5.
  * [126] H. He, C.-K. Wen, S. Jin, and G. Y. Li, “A model-driven deep learning network for MIMO detection,” in _2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)_. Anaheim, CA, USA: IEEE, Nov. 2018, pp. 584–588.
  * [127] J. Liao, J. Zhao, F. Gao, and G. Y. Li, “A model-driven deep learning method for massive MIMO detection,” _IEEE Commun. Lett._ , vol. 24, no. 8, pp. 1724–1728, Aug. 2020.
  * [128] Q. Cao, F. Li, T. Li, W. Ji, and Y. Liang, “Adaptive signal detection method based on model-driven for massive MIMO systems,” in _2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)_. Changsha, China: IEEE, Oct. 2021, pp. 1–5.
  * [129] C.-K. Wen, W.-T. Shih, and S. Jin, “Deep learning for massive MIMO CSI feedback,” _IEEE Wireless Commun. Lett._ , vol. 7, no. 5, pp. 748–751, Oct. 2018.
  * [130] T. Wang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based CSI feedback approach for time-varying massive MIMO channels,” _IEEE Wireless Commun. Lett._ , vol. 8, no. 2, pp. 416–419, Apr. 2019.
  * [131] Z. Liu, L. Zhang, and Z. Ding, “Exploiting bi-directional channel reciprocity in deep learning for low rate massive MIMO CSI feedback,” _IEEE Wireless Commun. Lett._ , vol. 8, no. 3, pp. 889–892, Jun. 2019.
  * [132] J. Guo, J. Wang, C.-K. Wen, S. Jin, and G. Y. Li, “Compression and acceleration of neural networks for communications,” _IEEE Wireless Commun._ , vol. 27, no. 4, pp. 110–117, Aug. 2020.
  * [133] P. K. Sangdeh, H. Pirayesh, A. Mobiny, and H. Zeng, “LB-SciFi: Online learning-based channel feedback for MU-MIMO in wireless LANs,” in _2020 IEEE 28th International Conference on Network Protocols (ICNP)_. Madrid, Spain: IEEE, Oct. 2020, pp. 1–11.
  * [134] T. Gruber, S. Cammerer, J. Hoydis, and S. ten Brink, “On deep learning-based channel decoding,” in _2017 51st Annual Conference on Information Sciences and Systems (CISS)_. Baltimore, MD, USA: IEEE, Mar. 2017, pp. 1–6.
  * [135] S. Cammerer, T. Gruber, J. Hoydis, and S. Ten Brink, “Scaling deep learning-based decoding of polar codes via partitioning,” in _2017 IEEE global communications conference (GLOBECOM)_. Singapore: IEEE, Dec. 2017, pp. 1–6.
  * [136] F. Liang, C. Shen, and F. Wu, “An iterative BP-CNN architecture for channel decoding,” _IEEE J. Sel. Topics Signal Process._ , vol. 12, no. 1, pp. 144–159, Feb. 2018.
  * [137] E. Nachmani, Y. Be’ery, and D. Burshtein, “Learning to decode linear codes using deep learning,” in _2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)_. Monticello, IL, USA: IEEE, Sept. 2016, pp. 341–346.
  * [138] E. Nachmani, E. Marciano, L. Lugosch, W. J. Gross, D. Burshtein, and Y. Be’ery, “Deep learning methods for improved decoding of linear codes,” _IEEE J. Sel. Topics Signal Process._ , vol. 12, no. 1, pp. 119–131, Feb. 2018.
  * [139] S. Dörner, S. Cammerer, J. Hoydis, and S. Ten Brink, “Deep learning based communication over the air,” _IEEE J. Sel. Topics Signal Process._ , vol. 12, no. 1, pp. 132–143, Feb. 2017.
  * [140] H. Ye, G. Y. Li, B.-H. F. Juang, and K. Sivanesan, “Channel agnostic end-to-end learning based communication systems with conditional GAN,” in _2018 IEEE Globecom Workshops (GC Wkshps)_. Abu Dhabi, United: IEEE, Dec. 2018, pp. 1–5.
  * [141] M. Damrath and P. A. Hoeher, “Low-complexity adaptive threshold detection for molecular communication,” _IEEE Trans. Nanobiosci._ , vol. 15, no. 3, pp. 200–208, Apr. 2016.
  * [142] N. Farsad and A. Goldsmith, “Sliding bidirectional recurrent neural networks for sequence detection in communication systems,” in _2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. Calgary, AB, Canada: IEEE, Apr. 2018, pp. 2331–2335.
  * [143] S. Mohamed, J. Dong, A. Junejo _et al._ , “Model-based: End-to-end molecular communication system through deep reinforcement learning auto encoder,” _IEEE Access_ , vol. 7, pp. 70 279–70 286, May 2019.
  * [144] Y. Zhao, I. G. Niemegeers, and S. H. De Groot, “Power allocation in cell-free massive MIMO: A deep learning method,” _IEEE Access_ , vol. 8, pp. 87 185–87 200, May 2020.
  * [145] C. D’Andrea, A. Zappone, S. Buzzi, and M. Debbah, “Uplink power control in cell-free massive MIMO via deep learning,” in _2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)_. Le gosier, Guadeloupe: IEEE, Dec. 2019, pp. 554–558.
  * [146] N. Rajapaksha, K. S. Manosha, N. Rajatheva, and M. Latva-Aho, “Deep learning-based power control for cell-free massive MIMO networks,” in _ICC 2021-IEEE International Conference on Communications_. Montreal, QC, Canada: IEEE, Jun. 2021, pp. 1–7.
  * [147] W. Cui, K. Shen, and W. Yu, “Spatial deep learning for wireless scheduling,” _IEEE J. Sel. Areas Commun._ , vol. 37, no. 6, pp. 1248–1261, Jun. 2019.
  * [148] S. Xu, P. Liu, R. Wang, and S. S. Panwar, “Realtime scheduling and power allocation using deep neural networks,” in _2019 IEEE Wireless Communications and Networking Conference (WCNC)_. Marrakesh, Morocco: IEEE, Apr. 2019, pp. 1–5.
  * [149] X. Li, J. Fang, W. Cheng, H. Duan, Z. Chen, and H. Li, “Intelligent power control for spectrum sharing in cognitive radios: A deep reinforcement learning approach,” _IEEE Access_ , vol. 6, pp. 25 463–25 473, Apr. 2018\.
  * [150] Y. S. Nasir and D. Guo, “Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,” _IEEE J. Sel. Areas Commun._ , vol. 37, no. 10, pp. 2239–2250, Oct. 2019.
  * [151] J. Tan, Y.-C. Liang, L. Zhang, and G. Feng, “Deep reinforcement learning for joint channel selection and power control in D2D networks,” _IEEE Trans. Wireless Commun._ , vol. 20, no. 2, pp. 1363–1378, Feb. 2021.
  * [152] A. Sadeghi, G. Wang, and G. B. Giannakis, “Deep reinforcement learning for adaptive caching in hierarchical content delivery networks,” _IEEE Trans. on Cogn. Commun. Netw._ , vol. 5, no. 4, pp. 1024–1033, Dec. 2019.
  * [153] J. Ren, H. Wang, T. Hou, S. Zheng, and C. Tang, “Federated learning-based computation offloading optimization in edge computing-supported internet of things,” _IEEE Access_ , vol. 7, pp. 69 194–69 201, Jun. 2019.
  * [154] X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning,” _IEEE Netw._ , vol. 33, no. 5, pp. 156–165, Sept. 2019\.
  * [155] Y. He, C. Liang, F. R. Yu, and Z. Han, “Trust-based social networks with computing, caching and communications: A deep reinforcement learning approach,” _IEEE Trans. Netw. Sci. Eng._ , vol. 7, no. 1, pp. 66–79, Jan. 2020.
  * [156] Y. He, N. Zhao, and H. Yin, “Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach,” _IEEE Trans. Veh. Technol._ , vol. 67, no. 1, pp. 44–55, Jan. 2018.
  * [157] A. Ndikumana, N. H. Tran, T. M. Ho, Z. Han, W. Saad, D. Niyato, and C. S. Hong, “Joint communication, computation, caching, and control in big data multi-access edge computing,” _IEEE Trans. Mobile Comput._ , vol. 19, no. 6, pp. 1359–1374, Jun. 2020.
  * [158] Y. Xu, W. Xu, Z. Wang, J. Lin, and S. Cui, “Load balancing for ultradense networks: A deep reinforcement learning-based approach,” _IEEE Internet Things J._ , vol. 6, no. 6, pp. 9399–9412, Dec. 2019.
  * [159] N. Zhao, Y.-C. Liang, D. Niyato, Y. Pei, M. Wu, and Y. Jiang, “Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks,” _IEEE Trans. Wireless Commun._ , vol. 18, no. 11, pp. 5141–5152, Nov. 2019.
  * [160] F. B. Mismar and B. L. Evans, “Deep Q-learning for self-organizing networks fault management and radio performance improvement,” in _2018 52nd Asilomar Conference on Signals, Systems, and Computers_. Pacific Grove, CA, USA: IEEE, Oct. 2018, pp. 1457–1461.
  * [161] J. Ye and Y.-J. A. Zhang, “DRAG: Deep reinforcement learning based base station activation in heterogeneous networks,” _IEEE Trans. Mobile Comput._ , vol. 19, no. 9, pp. 2076–2087, Sept. 2020.
  * [162] J. Liu, B. Krishnamachari, S. Zhou, and Z. Niu, “DeepNap: Data-driven base station sleeping operations through deep reinforcement learning,” _IEEE Internet Things J._ , vol. 5, no. 6, pp. 4273–4282, Dec. 2018.
  * [163] J. Wu, P. Yu, L. Feng, F. Zhou, W. Li, and X. Qiu, “3D aerial base station position planning based on deep Q-network for capacity enhancement,” in _2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)_. Arlington, VA, USA: IEEE, Apr. 2019, pp. 482–487.
  * [164] J. D. Harris and B. Waggoner, “Decentralized and collaborative AI on blockchain,” in _2019 IEEE International Conference on Blockchain (Blockchain)_. Atlanta, GA, USA, Jul. 2019, pp. 368–375.
  * [165] G. Zhang, T. Li, Y. Li, P. Hui, and D. Jin, “Blockchain-based data sharing system for AI-powered network operations,” _J. Commun. Inf. Netw._ , vol. 3, no. 3, pp. 1–8, Sept. 2018.
  * [166] K. Wang, J. Dong, Y. Wang, and H. Yin, “Securing data with blockchain and AI,” _IEEE Access_ , vol. 7, pp. 77 981–77 989, Jun. 2019.
  * [167] C. H. Liu, Q. Lin, and S. Wen, “Blockchain-enabled data collection and sharing for industrial IoT with deep reinforcement learning,” _IEEE Trans. Ind. Informat._ , vol. 15, no. 6, pp. 3516–3526, Jun 2019.
  * [168] G. J. Mendis, Y. Wu, J. Wei, M. Sabounchi, and R. Roche, “A blockchain-powered decentralized and secure computing paradigm,” _IEEE Trans. Emerg. Topics Comput._ , vol. 9, no. 4, pp. 2201–2222, Oct.-Dec. 2021.
  * [169] Q. Wang, Y. Guo, X. Wang, T. Ji, L. Yu, and P. Li, “AI at the edge: Blockchain-empowered secure multiparty learning with heterogeneous models,” _IEEE Internet Things J._ , vol. 7, no. 10, pp. 9600–9610, Oct. 2020.
  * [170] C. Korkmaz, H. E. Kocas, A. Uysal, A. Masry, O. Ozkasap, and B. Akgun, “Chain FL: Decentralized federated machine learning via blockchain,” in _2020 Second International Conference on Blockchain Computing and Applications (BCCA)_. Antalya, Turkey, Nov. 2020, pp. 140–146.
  * [171] X. Lin, J. Li, J. Wu, H. Liang, and W. Yang, “Making knowledge tradable in edge-AI enabled IoT: A consortium blockchain-based efficient and incentive approach,” _IEEE Trans. Ind. Informat._ , vol. 15, no. 12, pp. 6367–6378, Dec. 2019.
  * [172] R. Gupta, D. Reebadiya, S. Tanwar, N. Kumar, and M. Guizani, “When blockchain meets edge intelligence: Trusted and security solutions for consumers,” _IEEE Netw._ , vol. 35, no. 5, pp. 272–278, Sept. 2021.
  * [173] J. Weng, J. Weng, J. Zhang, M. Li, Y. Zhang, and W. Luo, “DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive,” _IEEE Trans. Depend. Sec. Comput._ , vol. 18, no. 5, pp. 2438–2455, Sept.-Oct. 2021.
  * [174] M. Shayan, C. Fung, C. J. M. Yoon, and I. Beschastnikh, “Biscotti: A blockchain system for private and secure federated learning,” _IEEE Trans. Parallel Distrib. Syst._ , vol. 32, no. 7, pp. 1513–1525, Jul. 2021.
  * [175] L. Feng, Y. Zhao, S. Guo, X. Qiu, W. Li, and P. Yu, “Blockchain-based asynchronous federated learning for internet of things,” _IEEE Trans. Comput._ , vol. 99, pp. 1–9, Apr. 2021.
  * [176] M. Nassar, K. Salah, M. H. ur Rehman, and D. Svetinovic, “Blockchain for explainable and trustworthy artificial intelligence,” _Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery_ , vol. 10, no. 1, p. e1340, Oct. 2020.
  * [177] K. Sarpatwar, R. Vaculin, H. Min, G. Su, T. Heath, G. Ganapavarapu, and D. Dillenberger, “Towards enabling trusted artificial intelligence via blockchain,” in _Policy-based autonomic data governance_. Springer, Apr. 2019, pp. 137–153.
  * [178] J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang, “Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory,” _IEEE Internet Things J._ , vol. 6, no. 6, pp. 10 700–10 714, Dec. 2019.
  * [179] X. Bao, C. Su, Y. Xiong, W. Huang, and Y. Hu, “FLChain: A blockchain for auditable federated learning with trust and incentive,” in _2019 5th International Conference on Big Data Computing and Communications (BIGCOM)_. QingDao, China, Aug. 2019, pp. 151–159.
  * [180] M. Liu, F. R. Yu, Y. Teng, V. C. M. Leung, and M. Song, “Performance optimization for blockchain-enabled industrial internet of things (IIoT) systems: A deep reinforcement learning approach,” _IEEE Trans. Ind. Informat._ , vol. 15, no. 6, pp. 3559–3570, Jun. 2019.
  * [181] J. Yun, Y. Goh, and J.-M. Chung, “DQN-based optimization framework for secure sharded blockchain systems,” _IEEE Internet Things J._ , vol. 8, no. 2, pp. 708–722, Jan. 2021.
  * [182] C. Qiu, X. Ren, Y. Cao, and T. Mai, “Deep reinforcement learning empowered adaptivity for future blockchain networks,” _IEEE Open J. Comm. Soc._ , vol. 2, pp. 99–105, Jul. 2021.
  * [183] J. Zhang, Z. Hong, X. Qiu, Y. Zhan, S. Guo, and W. Chen, “SkyChain: A deep reinforcement learning-empowered dynamic blockchain sharding system,” in _49th International Conference on Parallel Processing-ICPP_ , Aug. 2020, pp. 1–11.
  * [184] F. Bravo-Marquez, S. Reeves, and M. Ugarte, “Proof-of-learning: a blockchain consensus mechanism based on machine learning competitions,” in _2019 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPCON)_. Newark, CA, USA, Apr. 2019, pp. 119–124.
  * [185] C. Chenli, B. Li, Y. Shi, and T. Jung, “Energy-recycling blockchain with proof-of-deep-learning,” in _2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)_. Seoul, Korea (South), May 2019, pp. 19–23.
  * [186] A. Baldominos and Y. Saez, “Coin.AI: A proof-of-useful-work scheme for blockchain-based distributed deep learning,” _Entropy_ , vol. 21, no. 8, p. 723, Jul. 2019.
  * [187] J. Chen, K. Duan, R. Zhang, L. Zeng, and W. Wang, “An AI based super nodes selection algorithm in blockchain networks,” _arXiv preprint arXiv:1808.00216_ , Aug. 2018.
  * [188] W. Chen, Z. Zheng, J. Cui, E. Ngai, P. Zheng, and Y. Zhou, “Detecting ponzi schemes on ethereum: Towards healthier blockchain technology,” in _2018 world wide web conference_ , Apr. 2018, pp. 1409–1418.
  * [189] M. Salimitari, M. Joneidi, and M. Chatterjee, “AI-enabled blockchain: An outlier-aware consensus protocol for blockchain-based IoT networks,” in _2019 IEEE Global Communications Conference (GLOBECOM)_. Waikoloa, HI, USA, Dec. 2019, pp. 1–6.
  * [190] W. Liang, L. Xiao, K. Zhang, M. Tang, D. He, and K.-C. Li, “Data fusion approach for collaborative anomaly intrusion detection in blockchain-based systems,” _IEEE Internet Things J._ , vol. 9, no. 16, pp. 14 741–14 751, Aug. 2022.
  * [191] F. Scicchitano, A. Liguori, M. Guarascio, E. Ritacco, and G. Manco, “A deep learning approach for detecting security attacks on blockchain.” in _ITASEC_ , 2020, pp. 212–222.
  * [192] M. Mudassir, S. Bennbaia, D. Unal, and M. Hammoudeh, “Time-series forecasting of bitcoin prices using high-dimensional features: a machine learning approach,” _Neural Comput. Appl._ , pp. 1–15, Jul. 2020.
  * [193] H. J. Singh and A. S. Hafid, “Prediction of transaction confirmation time in ethereum blockchain using machine learning,” in _International Congress on Blockchain and Applications_. Springer, Jun. 2019, pp. 126–133.
  * [194] S. Mohammadi and E. Rabieinejad, _Prediction forks in the blockchain using machine learning_ , Dec. 2020.
  * [195] Q. Pan, J. Wu, J. Li, W. Yang, and Z. Guan, “Blockchain and AI empowered trust-information-centric network for beyond 5G,” _IEEE Netw._ , vol. 34, no. 6, pp. 38–45, Nov. 2020.
  * [196] K. Zhang, Y. Zhu, S. Maharjan, and Y. Zhang, “Edge intelligence and blockchain empowered 5G beyond for the industrial internet of things,” _IEEE Netw._ , vol. 33, no. 5, pp. 12–19, Sept. 2019.
  * [197] S. Rathore, J. H. Park, and H. Chang, “Deep learning and blockchain-empowered security framework for intelligent 5G-enabled IoT,” _IEEE Access_ , vol. 9, pp. 90 075–90 083, Jun. 2021.
  * [198] S. Hu, Y.-C. Liang, Z. Xiong, and D. Niyato, “Blockchain and artificial intelligence for dynamic resource sharing in 6G and beyond,” _IEEE Wireless Commun._ , vol. 28, no. 4, pp. 145–151, Aug. 2021.
  * [199] T. Maksymyuk, J. Gazda, M. Liyanage, L. Han, B. Shubyn, B. Strykhaliuk, O. Yaremko, M. Jo, and M. Dohler, “AI-enabled blockchain framework for dynamic spectrum management in multi-operator 6G networks,” in _Future Intent-Based Networking_. Springer, 2022, pp. 322–338.
  * [200] Y. Lu, X. Huang, K. Zhang, S. Maharjan, and Y. Zhang, “Blockchain and federated learning for 5G beyond,” _IEEE Netw._ , vol. 35, no. 1, pp. 219–225, Jan. 2020.
  * [201] ——, “Communication-efficient federated learning and permissioned blockchain for digital twin edge networks,” _IEEE Internet Things J._ , vol. 8, no. 4, pp. 2276–2288, Feb. 2020.
  * [202] F. Guo, F. R. Yu, H. Zhang, H. Ji, M. Liu, and V. C. Leung, “Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing,” _IEEE Trans. Wireless Commun._ , vol. 19, no. 3, pp. 1689–1703, Mar. 2020.
  * [203] G. Gür, “Expansive networks: Exploiting spectrum sharing for capacity boost and 6G vision,” _J. Commun. Networks_ , vol. 22, no. 6, pp. 444–454, Dec. 2020.
  * [204] C. Qiu, F. R. Yu, H. Yao, C. Jiang, F. Xu, and C. Zhao, “Blockchain-based software-defined industrial internet of things: A dueling deep Q-learning approach,” _IEEE Internet Things J._ , vol. 6, no. 3, pp. 4627–4639, Jun. 2019.
  * [205] Y. He, Y. Wang, C. Qiu, Q. Lin, J. Li, and Z. Ming, “Blockchain-based edge computing resource allocation in IoT: a deep reinforcement learning approach,” _IEEE Internet Things J._ , vol. 8, no. 4, pp. 2226–2237, Feb. 2021.
  * [206] G. Manogaran, S. Mumtaz, C. X. Mavromoustakis, E. Pallis, and G. Mastorakis, “Artificial intelligence and blockchain-assisted offloading approach for data availability maximization in edge nodes,” _IEEE Trans. Veh. Technol._ , vol. 70, no. 3, pp. 2404–2412, Mar. 2021.
  * [207] D. C. Nguyen, P. N. Pathirana, M. Ding, and A. Seneviratne, “Secure computation offloading in blockchain based IoT networks with deep reinforcement learning,” _IEEE Trans. Netw. Sci. Eng._ , vol. 8, no. 4, pp. 3192–3208, Oct. 2021.
  * [208] H. Liao, Y. Mu, Z. Zhou, M. Sun, Z. Wang, and C. Pan, “Blockchain and learning-based secure and intelligent task offloading for vehicular fog computing,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 7, pp. 4051–4063, Jul. 2020.
  * [209] H. Liao, Z. Wang, Z. Zhou, Y. Wang, H. Zhang, S. Mumtaz, and M. Guizani, “Blockchain and semi-distributed learning-based secure and low-latency computation offloading in space-air-ground-integrated power IoT,” _IEEE J. Sel. Topics Signal Process._ , vol. 16, no. 3, pp. 381–394, Apr. 2022.
  * [210] C. Qiu, H. Yao, X. Wang, N. Zhang, F. R. Yu, and D. Niyato, “AI-Chain: blockchain energized edge intelligence for beyond 5G networks,” _IEEE Netw._ , vol. 34, no. 6, pp. 62–69, Nov. 2020.
  * [211] Y. Qian, Y. Jiang, L. Hu, M. S. Hossain, M. Alrashoud, and M. Al-Hammadi, “Blockchain-based privacy-aware content caching in cognitive internet of vehicles,” _IEEE Netw._ , vol. 34, no. 2, pp. 46–51, Mar. 2020.
  * [212] Y. Dai, D. Xu, K. Zhang, S. Maharjan, and Y. Zhang, “Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks,” _IEEE Trans. Veh. Technol._ , vol. 69, no. 4, pp. 4312–4324, Apr. 2020.
  * [213] M. Li, F. R. Yu, P. Si, W. Wu, and Y. Zhang, “Resource optimization for delay-tolerant data in blockchain-enabled IoT with edge computing: A deep reinforcement learning approach,” _IEEE Internet Things J._ , vol. 7, no. 10, pp. 9399–9412, Oct. 2020.
  * [214] L. Cui, X. Su, Z. Ming, Z. Chen, S. Yang, Y. Zhou, and W. Xiao, “CREAT: Blockchain-assisted compression algorithm of federated learning for content caching in edge computing,” _IEEE Internet Things J._ , vol. 9, no. 16, pp. 14 151–14 161, Aug. 2022.
  * [215] R. Zhang, F. R. Yu, J. Liu, T. Huang, and Y. Liu, “Deep reinforcement learning (DRL)-based device-to-device (D2D) caching with blockchain and mobile edge computing,” _IEEE Trans. Wireless Commun._ , vol. 19, no. 10, pp. 6469–6485, Oct. 2020.
  * [216] N. Waheed, X. He, M. Ikram, M. Usman, S. S. Hashmi, and M. Usman, “Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures,” _ACM Computing Surveys (CSUR)_ , vol. 53, no. 6, pp. 1–37, Nov. 2021.
  * [217] N. Dhieb, H. Ghazzai, H. Besbes, and Y. Massoud, “Scalable and secure architecture for distributed IoT systems,” in _2020 IEEE Technology & Engineering Management Conference (TEMSCON)_, Novi, MI, USA, Jun. 2020, pp. 1–6.
  * [218] X. Wang, S. Garg, H. Lin, M. J. Piran, J. Hu, and M. S. Hossain, “Enabling secure authentication in industrial IoT with transfer learning empowered blockchain,” _IEEE Trans. Ind. Informat._ , vol. 17, no. 11, pp. 7725–7733, Nov. 2021.
  * [219] Y. Tian, T. Li, J. Xiong, M. Z. A. Bhuiyan, J. Ma, and C. Peng, “A blockchain-based machine learning framework for edge services in IIoT,” _IEEE Trans. Ind. Informat._ , vol. 18, no. 3, pp. 1918–1929, Mar. 2022.
  * [220] H. Lin, S. Garg, J. Hu, G. Kaddoum, M. Peng, and M. S. Hossain, “Blockchain and deep reinforcement learning empowered spatial crowdsourcing in software-defined internet of vehicles,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 6, pp. 3755–3764, Jun. 2021.
  * [221] R. Kumar, P. Kumar, R. Tripathi, G. P. Gupta, N. Kumar, and M. M. Hassan, “A privacy-preserving-based secure framework using blockchain-enabled deep-learning in cooperative intelligent transport system,” _IEEE Trans. Intell. Transp. Syst._ , vol. 23, no. 9, pp. 16 492–16 503, Sept. 2022\.
  * [222] C. Zhang, W. Li, Y. Luo, and Y. Hu, “AIT: An AI-enabled trust management system for vehicular networks using blockchain technology,” _IEEE Internet Things J._ , vol. 8, no. 5, pp. 3157–3169, Mar. 2021.
  * [223] W. Zhang, Q. Lu, Q. Yu, Z. Li, Y. Liu, S. K. Lo, S. Chen, X. Xu, and L. Zhu, “Blockchain-based federated learning for device failure detection in industrial IoT,” _IEEE Internet Things J._ , vol. 8, no. 7, pp. 5926–5937, Apr. 2021.
  * [224] S. Otoum, I. Al Ridhawi, and H. Mouftah, “Securing critical IoT infrastructures with blockchain-supported federated learning,” _IEEE Internet Things J._ , vol. 9, no. 4, pp. 2592–2601, Feb. 2022.
  * [225] Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Blockchain and federated learning for privacy-preserved data sharing in industrial IoT,” _IEEE Trans. Ind. Informat._ , vol. 16, no. 6, pp. 4177–4186, Jun. 2020.
  * [226] P. Zhang, Y. Hong, N. Kumar, M. Alazab, M. D. Alshehri, and C. Jiang, “BC-EdgeFL: Defensive transmission model based on blockchain assisted reinforced federated learning in IIoT environment,” _IEEE Trans. Ind. Informat._ , vol. 18, no. 5, pp. 3551–3561, May 2022.
  * [227] H. Liu, S. Zhang, P. Zhang, X. Zhou, X. Shao, G. Pu, and Y. Zhang, “Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing,” _IEEE Trans. Veh. Technol._ , vol. 70, no. 6, pp. 6073–6084, Jun. 2021.
  * [228] P. Bhattacharya, S. Tanwar, U. Bodkhe, S. Tyagi, and N. Kumar, “BinDaaS: Blockchain-based deep-learning as-a-service in healthcare 4.0 applications,” _IEEE Trans. Netw. Sci. Eng._ , vol. 8, no. 2, pp. 1242–1255, Apr. 2021.
  * [229] A. Z. Al-Marridi, A. Mohamed, and A. Erbad, “Reinforcement learning approaches for efficient and secure blockchain-powered smart health systems,” _Comput. Netw._ , vol. 197, p. 108279, Oct. 2021.
  * [230] T. Veeramakali, R. Siva, B. Sivakumar, P. Senthil Mahesh, and N. Krishnaraj, “An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model,” _J. Supercomput._ , vol. 77, no. 9, pp. 9576–9596, Feb. 2021.
  * [231] R. Kumar, A. A. Khan, J. Kumar, N. A. Golilarz, S. Zhang, Y. Ting, C. Zheng, W. Wang _et al._ , “Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging,” _IEEE Sensors J._ , vol. 21, no. 14, pp. 16 301–16 314, Jul. 2021.
  * [232] S. Otoum, I. Al Ridhawi, and H. T. Mouftah, “Preventing and controlling epidemics through blockchain-assisted AI-enabled networks,” _IEEE Netw._ , vol. 35, no. 3, pp. 34–41, May 2021.
  * [233] B. Mallikarjuna, G. Shrivastava, and M. Sharma, “Blockchain technology: A DNN token-based approach in healthcare and COVID-19 to generate extracted data,” _Expert Syst._ , p. e12778, Jul. 2021.
  * [234] X. Guo, M. A. Khalid, I. Domingos, A. L. Michala, M. Adriko, C. Rowel, D. Ajambo, A. Garrett, S. Kar, X. Yan _et al._ , “Smartphone-based DNA diagnostics for malaria detection using deep learning for local decision support and blockchain technology for security,” _Nat. Electron._ , vol. 4, no. 8, pp. 615–624, Aug. 2021.
  * [235] R. Gupta, A. Shukla, and S. Tanwar, “BATS: A blockchain and AI-empowered drone-assisted telesurgery system towards 6G,” _IEEE Trans. Netw. Sci. Eng._ , vol. 8, no. 4, pp. 2958–2967, Oct. 2021.
  * [236] I. Al Ridhawi, M. Aloqaily, A. Boukerche, and Y. Jararweh, “Enabling intelligent IoCV services at the edge for 5G networks and beyond,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 8, pp. 5190–5200, Aug. 2021.
  * [237] V. Hassija, V. Gupta, S. Garg, and V. Chamola, “Traffic jam probability estimation based on blockchain and deep neural networks,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 7, pp. 3919–3928, Jul. 2021.
  * [238] K. Tiba, R. M. Parizi, Q. Zhang, A. Dehghantanha, H. Karimipour, and K.-K. R. Choo, “Secure blockchain-based traffic load balancing using edge computing and reinforcement learning,” in _Blockchain Cybersecurity, Trust and Privacy_. Springer, Mar. 2020, pp. 99–128.
  * [239] Y. Song, Y. Fu, F. R. Yu, and L. Zhou, “Blockchain-enabled internet of vehicles with cooperative positioning: A deep neural network approach,” _IEEE Internet Things J._ , vol. 7, no. 4, pp. 3485–3498, Apr. 2020.
  * [240] D. Zhang, F. R. Yu, and R. Yang, “Blockchain-based distributed software-defined vehicular networks: A dueling deep Q-learning approach,” _IEEE Trans. on Cogn. Commun. Netw._ , vol. 5, no. 4, pp. 1086–1100, Dec. 2019.
  * [241] X. Jiang, F. R. Yu, T. Song, and V. C. Leung, “Intelligent resource allocation for video analytics in blockchain-enabled internet of autonomous vehicles with edge computing,” _IEEE Internet Things J_ , vol. 9, no. 16, pp. 14 260–14 272, Aug. 2022.
  * [242] Y. Fu, C. Li, F. R. Yu, T. H. Luan, and Y. Zhang, “An autonomous lane-changing system with knowledge accumulation and transfer assisted by vehicular blockchain,” _IEEE Internet Things J._ , vol. 7, no. 11, pp. 11 123–11 136, Nov. 2020.
  * [243] S. R. Pokhrel and J. Choi, “Federated learning with blockchain for autonomous vehicles: Analysis and design challenges,” _IEEE Trans. Commun._ , vol. 68, no. 8, pp. 4734–4746, Aug. 2020.
  * [244] Y. Lu, X. Huang, K. Zhang, S. Maharjan, and Y. Zhang, “Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles,” _IEEE Trans. Veh. Technol._ , vol. 69, no. 4, pp. 4298–4311, Apr. 2020.
  * [245] H. Chai, S. Leng, Y. Chen, and K. Zhang, “A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 7, pp. 3975–3986, Jul. 2021.
  * [246] N. M. Kumar, A. A. Chand, M. Malvoni, K. A. Prasad, K. A. Mamun, F. Islam, and S. S. Chopra, “Distributed energy resources and the application of AI, IoT, and blockchain in smart grids,” _Energies_ , vol. 13, no. 21, p. 5739, Nov. 2020.
  * [247] M. Keshk, B. Turnbull, N. Moustafa, D. Vatsalan, and K.-K. R. Choo, “A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks,” _IEEE Trans. Ind. Informat._ , vol. 16, no. 8, pp. 5110–5118, Aug. 2020.
  * [248] Z. Wang, M. Ogbodo, H. Huang, C. Qiu, M. Hisada, and A. B. Abdallah, “AEBIS: AI-enabled blockchain-based electric vehicle integration system for power management in smart grid platform,” _IEEE Access_ , vol. 8, pp. 226 409–226 421, Dec. 2020.
  * [249] M. A. Ferrag and L. Maglaras, “DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids,” _IEEE Trans. Eng. Manag._ , vol. 67, no. 4, pp. 1285–1297, Nov. 2020.
  * [250] F. Jamil, N. Iqbal, S. Ahmad, D. Kim _et al._ , “Peer-to-peer energy trading mechanism based on blockchain and machine learning for sustainable electrical power supply in smart grid,” _IEEE Access_ , vol. 9, pp. 39 193–39 217, Feb. 2021.
  * [251] G. Gao, C. Song, A. Bandara, M. Shen, F. Yang, W. Posdorfer, D. Tao, and Y. Wen, “FogChain: a blockchain-based peer-to-peer solar power trading system powered by fog AI,” _IEEE Internet Things J._ , vol. 9, no. 7, pp. 5200–5215, Apr. 2022.
  * [252] Z. Li, J. Xu, Y. Xie, J. Jiang, Y. Zhu, and X. Yang, “Integration of blockchain and machine learning for microgrids,” in _2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)_ , Chengdu, China, Apr. 2021, pp. 211–216.
  * [253] M. Singh, G. S. Aujla, and R. S. Bali, “A deep learning-based blockchain mechanism for secure internet of drones environment,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 7, pp. 4404–4413, Jul. 2021.
  * [254] C. Feng, B. Liu, K. Yu, S. K. Goudos, and S. Wan, “Blockchain-empowered decentralized horizontal federated learning for 5G-enabled UAVs,” _IEEE Trans. Ind. Informat._ , vol. 18, no. 5, May 2022.
  * [255] A. Asheralieva and D. Niyato, “Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-enabled mobile edge computing,” _IEEE Internet Things J._ , vol. 7, no. 3, pp. 1974–1993, Mar. 2020.
  * [256] S. R. Pokhrel, “Blockchain brings trust to collaborative drones and LEO satellites: an intelligent decentralized learning in the space,” _IEEE Sensors J._ , vol. 21, no. 22, pp. 25 331–25 339, Nov. 2021.
  * [257] A. Islam, T. Rahim, M. Masuduzzaman, and S. Y. Shin, “A blockchain-based artificial intelligence-empowered contagious pandemic situation supervision scheme using internet of drone things,” _IEEE Wireless Commun._ , vol. 28, no. 4, pp. 166–173, Aug. 2021.
  * [258] A. Gumaei, M. Al-Rakhami, M. M. Hassan, P. Pace, G. Alai, K. Lin, and G. Fortino, “Deep learning and blockchain with edge computing for 5G-enabled drone identification and flight mode detection,” _IEEE Netw._ , vol. 35, no. 1, pp. 94–100, Jan. 2021.
  * [259] A. Aftab, N. Ashraf, H. K. Qureshi, S. A. Hassan, and S. Jangsher, “BLOCK-ML: blockchain and machine learning for UAV-BSs deployment,” in _2020 IEEE 92nd Vehicular Technology Conference (VTC-Fall)_ , Victoria, BC, Canada, Nov. 2020, pp. 1–5.
  * [260] S. R. Pokhrel, “Federated learning meets blockchain at 6G edge: A drone-assisted networking for disaster response,” in _Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond_ , Sept. 2020, pp. 49–54.
  * [261] Y.-C. Liang, _Dynamic spectrum management: From cognitive radio to blockchain and artificial intelligence_. Springer Nature, 2020.
  * [262] J. Rosenworcel, “The FCC should use blockchain to manage wireless spectrum (2018-03-20),” May 2020, [Online]. Available: https://www.wired.com/story/the-fcc-should-use-blockchain-to-manage-wireless-spectrum.
  * [263] M. B. Weiss, K. Werbach, D. C. Sicker, and C. E. C. Bastidas, “On the application of blockchains to spectrum management,” _IEEE Trans. Cognit. Commun. Netw._ , vol. 5, no. 2, pp. 193–205, Jun. 2019.