H. Kokkoniemi-Tarkkanen et al., “Enabling safe wireless harbor automation
via 5G URLLC,” in Proc. IEEE 5G World Forum (5GWF), 2019, pp.

[62]
O. Tirkkonen, S. R. Khosravirad, P. Baracca, L. Zhou, U. Parts, D. Korpi, and
M. Uusitalo, “Optimized survival mode to guarantee QoS for time-critical
services,” in Proc. IEEE Int. Commun. Conf. (ICC), Montreal, Canada,
Jun. 2021.

[63]
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Predictive ultra-reliable
communication: A survival analysis perspective,” IEEE Commun. Lett.,
vol. 25, no. 4, pp. 1221–1225, 2021.

[64]
J. Demel, C. Bockelmann, and A. Dekorsy, “Burst error analysis of
scheduling algorithms for 5G NR URLLC periodic deterministic
communication,” in Proc. IEEE Veh. Technol. Conf. (VTC-Spring), 2020,
pp. 1–6.

[65]
J. Gebert and A. Wich, “Alternating transmission of packets in dual
connectivity for periodic deterministic communication utilising survival
time,” in Proc. European Conf. Netw. Commun. (EuCNC), 2020, pp.

[66]
S. Paris, P. Kela, D. Laselva, and Q. Zhao, “Addressing reliability
needs of industrial applications in 5G NR with network coding,” in
Proc. IEEE Veh. Technol. Conf. (VTC-Spring), 2020, pp. 1–6.

[67]
S. Kaul, M. Gruteser, V. Rai, and J. Kenney, “Minimizing age of
information in vehicular networks,” in Proc. IEEE Conf. Sensor, Mesh,
and Ad Hoc Commun. and Netw., 2011, pp. 350–358.

[68]
M. K. Abdel-Aziz, C. Liu, S. Samarakoon, M. Bennis, and W. Saad,
“Ultra-reliable low-latency vehicular networks: Taming the age of
information tail,” in Proc. IEEE Global Commun. Conf. (GLOBECOM),
2018, pp. 1–7.

[69]
X. Zhang, Q. Zhu, and H. V. Poor, “Analyses for age of information
supporting URLLC over multimedia wireless networks,” in Proc. IEEE
Int. Workshop Signal Process. Advances Wireless Commun. (SPAWC), 2020, pp.

[70]
I. Kadota, A. Sinha, and E. Modiano, “Optimizing age of information in
wireless networks with throughput constraints,” in Proc. IEEE Int.
Conf. Comput. Commun. (INFOCOM), 2018, pp. 1844–1852.

[71]
Y. Hsu, E. Modiano, and L. Duan, “Age of information: Design and
analysis of optimal scheduling algorithms,” in Proc. IEEE Int. Symp.
Inf. Theory (ISIT), 2017, pp. 561–565.

[72]
A. Kosta, N. Pappas, and V. Angelakis, 2017.

[73]
P. Popovski, F. Chiariotti, K. Huang, A. E. Kalør, M. Kountouris, N. Pappas,
and B. Soret, “A perspective on time towards wireless 6G,” 2021.

[74]
3GPP TSG GERAN 46, “USF capacity evaluation for MTC,” Tech. Rep., May

[75]
A. Azari and G. Miao, “Network lifetime maximization for cellular-based M2M
networks,” IEEE Access, vol. 5, pp. 18 927–18 940, Sept. 2017.

[76]
X. Chen, D. W. K. Ng, W. Yu, E. G. Larsson, N. Al-Dhahir, and R. Schober,
“Massive access for 5G and beyond,” arXiv preprint
arXiv:2002.03491, 2020.

[77]
Ericsson, Huawei, NSN, and et al., “A choice of future M2M access
technologies for mobile network operators,” Tech. Rep., March 2014.

[78]
3GPP TS 45.820, “Cellular system support for ultra-low complexity and low
throughput Internet of Things (CIoT),” Tech. Rep., 2015, (Rel. 13).

[79]
W. Yang et al., “Narrowband wireless access for low-power massive
internet of things: A bandwidth perspective,” IEEE Wireless Commun.,
vol. 24, no. 3, pp. 138–145, May 2017.

[80]
A. I. Sulyman, S. M. Oteafy, and H. S. Hassanein, “Expanding the
cellular-IoT umbrella: An architectural approach,” IEEE Wireless
Commun., vol. 24, no. 3, pp. 66–71, 2017.

[81]
A. Azari, “Serving IoT communications over cellular networks: Challenges and
solutions in radio resource management for massive and critical IoT
communications,” Ph.D. dissertation, KTH Royal Institute of Technology,

[82]
G. Forecast, “Cisco visual networking index: global mobile data traffic
forecast update, 2017-2022,” Update, vol. 2017, p. 2022, 2019.

[83]
R. Ratasuk, N. Mangalvedhe, and A. Ghosh, “Overview of LTE enhancements for
cellular IoT,” in Proc. IEEE Int. Symp. Pers. Indoor Mobile
Radio-commun. (PIMRC), 2015, pp. 2293–2297.

[84]
A. Azari et al., “Lifetime-aware scheduling and power control for M2M
communications in LTE,” in Proc. IEEE Veh. Technol. Conf.
(VTC-Spring), 2015.

[85]
M. Mehaseb, Y. Gadallah, A. Elhamy, and H. El-Hennawy, “Classification of
LTE uplink scheduling techniques: An M2M perspective,” IEEE
Communications Surveys and Tutorials, no. 99, Nov. 2015.

[86]
Nokia Networks, “LTE-M – optimizing LTE for the Internet of things,”
Tech. Rep., 2015.

[87]
R. Ratasuk, B. Vejlgaard, N. Mangalvedhe, and A. Ghosh, “NB-IoT system for
M2M communication,” in Proc. IEEE Wireless Commun. Netw. Conf.
(WCNC), Aug. 2016.

[88]
Y. P. E. Wang et al., “A primer on 3GPP narrowband Internet of
Things,” IEEE Communications Magazine, vol. 55, no. 3, pp. 117–123,
March 2017.

[89]
J. Schlienz and D. Raddino, “Narrowband Internet of Things,” Rohde and
Schwarz, Tech. Rep., Aug. 2016.

[90]
P. A. Maldonado et al., “Narrowband IoT data transmission procedures
for massive machine-type communications,” IEEE Network, vol. 31,
no. 6, pp. 8–15, Nov 2017.

[91]
G. Association et al., “LTE-M deployment guide to basic feature set
requirements,” 2018.

[92]
A. K. Sultania, P. Zand, C. Blondia, and J. Famaey, “Energy modeling and
evaluation of NB-IoT with PSM and eDRX,” in Proc. IEEE Global
Commun. Conf. Workshops (GC Workshops), 2018, pp. 1–7.

[93]
A. K. Sultania, C. Blondia, and J. Famaey, “Optimizing the energy-latency
trade-off in NB-IoT with PSM and eDRX,” IEEE Internet Things J.,

[94]
B. Ray, “LTE eDRX and PSM Explained for LTE-M1,”
accessed: 2021-06-09.

[95]
X. Lin, A. Adhikary, and Y. P. E. Wang, “Random access preamble design and
detection for 3GPP narrowband IoT systems,” IEEE Wireless
Communications Letters, vol. 5, no. 6, pp. 640–643, Dec. 2016.

[96]
T. Kim, D. M. Kim, N. Pratas, P. Popovski, and D. K. Sung, “An enhanced access
reservation protocol with a partial preamble transmission mechanism in
NB-IoT systems,” IEEE Communications Letters, June 2017.

[97]
C. Yu et al., “Uplink scheduling and link adaptation for narrowband
Internet of Things systems,” IEEE Access, vol. 5, pp. 1724–1734,
Feb. 2017.

[98]
A. Adhikary, X. Lin, and Y. P. E. Wang, “Performance evaluation of NB-IoT
coverage,” in Proc. IEEE Veh. Technol. Conf. (VTC-Fall), Sept. 2016.

[99]
A. Azari, G. Miao, C. Stefanovic, and P. Popovski, “Latency-energy tradeoff
based on channel scheduling and repetitions in NB-IoT systems,”
Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2018.

[100]
É. Morin, M. Maman, R. Guizzetti, and A. Duda, “Comparison of the device
lifetime in wireless networks for the Internet of Things,” IEEE
Access, vol. 5, pp. 7097–7114, April 2017.

[101]
A. Azari, P. Popovski, C. Stefanovic, and C. Cavdar, “Energy-efficient and
reliable IoT access without radio resource reservation,” IEEE Trans.
Green Commun. Netw., 2021.

[102]
M. Lauridsen et al., “Interference measurements in the european 868
MHz ISM band with focus on LoRa and SigFox,” in Proc. IEEE
Wireless Commun. Netw. Conf. (WCNC), March 2017.

[103]
M. Lauridsen, “Studies on mobile terminal energy consumption for LTE,
5G,” Ph.D. dissertation, Aalborg University, 2015.

[104]
M. Centenaro et al., “Long-range communications in unlicensed bands:
The rising stars in the IoT and smart city scenarios,” IEEE Wireless
Communications, vol. 23, no. 5, pp. 60–67, Nov. 2016.

[105]
“NB-IoT vs. LoRa vs. Sigfox,”
<https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox>, accessed:

[106]
Semtech, “LoRa modulation basics,” Tech. Rep., 2014. [Online]. Available:

[107]
N. Blenn and F. Kuipers, “LoRaWAN in the wild: Measurements from the things
network,” arXiv preprint arXiv:1706.03086, June 2017.

[108]
D. Bankov, E. Khorov, and A. Lyakhov, “Mathematical model of LoRaWAN channel
access,” in Proc.IEEE 18th Int. Symposium World of Wireless, Mobile
and Multimedia Netw., June 2017.

[109]
——, “On the limits of LoRaWAN channel access,” in Proc. IEEE Int.
Conf. Eng. Telecom., Nov. 2016.

[110]
M. Lauridsen et al., “Coverage comparison of GPRS, NB-IoT, LoRa, and
SigFox in a 7800 km$^2$ area,” in Proc. IEEE Veh. Technol. Conf.
(VTC-Spring), 2017.

[111]
F. Adelantado et al., “Understanding the limits of LoRaWAN,”
IEEE Commun. Mag, vol. 55, no. 9, pp. 34–40, 2017.

[112]
F. C. Commission, “Use of spectrum bands above 24GHz for mobile radio
services,” Federal Communications Commission, Tech. Rep. 219, 2016.

[113]
M. R. Akdeniz, Y. Liu, M. K. Samimi, S. Sun, S. Rangan, T. S.
Rappaport, and E. Erkip, “Millimeter wave channel modeling and cellular
capacity evaluation,” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp.
1164–1179, 2014.

[114]
S. K. Yoo, S. L. Cotton, R. W. Heath, and Y. J. Chun, “Measurements of the 60
GHz UE to eNB channel for small cell deployments,” IEEE Wireless
Commun. Lett., vol. 6, no. 2, pp. 178–181, 2017.

[115]
D. Chizhik, J. Du, R. Feick, M. Rodriguez, G. Castro, and R. A. Valenzuela,
“Path loss and directional gain measurements at 28 GHz for
non-line-of-sight coverage of indoors with corridors,” IEEE Trans.
Antennas Propag., vol. 68, no. 6, pp. 4820–4830, 2020.

[116]
J. Du, D. Chizhik, R. Feick, G. Castro, M. Rodríguez, and R. A.
Valenzuela, “Suburban residential building penetration loss at 28 GHz for
fixed wireless access,” IEEE Wireless Commun. Lett., vol. 7, no. 6,
pp. 890–893, 2018.

[117]
B. P. S. Sahoo, C. Chou, C. Weng, and H. Wei, “Enabling
millimeter-wave 5G networks for massive IoT applications: A closer look
at the issues impacting millimeter-waves in consumer devices under the 5G
framework,” IEEE Commun. Lett., vol. 8, no. 1, pp. 49–54, 2019.

[118]
T. K. Vu, C.-F. Liu, M. Bennis, M. Debbah, M. Latva-Aho, and C. S. Hong,
“Ultra-reliable and low latency communication in mmwave-enabled massive
MIMO networks,” IEEE Commun. Lett., vol. 21, no. 9, pp. 2041–2044,

[119]
O. Semiari, W. Saad, M. Bennis, and M. Debbah, “Integrated millimeter wave and
sub-6 GHz wireless networks: A roadmap for joint mobile broadband and
ultra-reliable low-latency communications,” IEEE Wireless Commun.,
vol. 26, no. 2, pp. 109–115, 2019.

[120]
G. Berardinelli, N. H. Mahmood, I. Rodriguez, and P. Mogensen, “Beyond
5G wireless IRT for Industry 4.0: Design principles and spectrum
aspects,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2018,
pp. 1–6.

[121]
M. S. Ibrahim, S. R. Khosravirad, J. Mazgula, H. Viswanathan, and N. D.
Sidiropoulos, “Beam selection for Ultra-Reliable Low-Latency
communication in industrial environment with beamforming repeaters,” in
Proc. IEEE Int. Commun. Conf. (ICC), Montreal, Canada, Jun. 2021.

[122]
J. Mazgula, J. Sapis, U. S. Hashmi, and H. Viswanathan, “Ultra reliable low
latency communications in mmWave for factory floor automation,”
Journal of the Indian Institute of Science, pp. 1–12, 2020.

[123]
M. Wu, D. Wuebben, A. Dekorsy, P. Baracca, V. Braun, and H. Halbauer,
“Hardware impairments in millimeter wave communications using OFDM and
SC-FDE,” in Proc. 20th Int. ITG Workshop on Smart Antenn., 2016, pp.

[124]
V. Potdar, A. Sharif, and E. Chang, “Wireless sensor networks: A
survey,” in Proc. Int. Conf. Adv. Inform. Netw. Appl. Workshops,
2009, pp. 636–641.

[125]
“3GPP TR 22.866; enhanced relays for energy efficiency and extensive
coverage,” 3GPP, Tech. Rep., Feb. 2019.

[126]
M. S. Mekala and P. Viswanathan, “A survey: Smart agriculture IoT with
cloud computing,” in Proc. Int. conf. Microelectronic Devices,
Circuits Syst. (ICMDCS), 2017, pp. 1–7.

[127]
M. Mahmood, W. Seah, and I. Welch, “Reliability in wireless sensor networks:
Survey and challenges ahead,” Computer Networks, 01 2015.

[128]
S. Murugaanandam and V. Ganapathy, “Reliability-based cluster head selection
methodology using fuzzy logic for performance improvement in WSNs,”
IEEE Access, vol. PP, pp. 1–1, 07 2019.

[129]
J. Zhang, J. Long, C. Zhang, and G. Zhao, “A delay-aware and reliable data
aggregation for cyber-physical sensing,” Sensors (Basel), vol. 17, p.
395, 02 2017.

[130]
S. Lindsey, C. Raghavendra, and K. M. Sivalingam, “Data gathering
algorithms in sensor networks using energy metrics,” IEEE Trans. on
Parallel and Distributed Systems, vol. 13, no. 9, pp. 924–935, 2002.

[131]
J. Kruse, S. Mandelli, and S. R. Khosravirad, “QoS-aware wireless sensor
networks: Reliability and low-latency for heterogeneous Industry 4.0,” in
Proc. IEEE VTC Spring, 2021, pp. 1–5.

[132]
E. Dahlman, S. Parkvall, and J. Skold, 4G, LTE-advanced Pro and the Road
to 5G.1em plus 0.5em minus 0.4emAcademic Press, 2016.

[133]
“3GPP TS 23.303; proximity-based services (prose),” 3GPP, Tech. Spec., Mar.

[134]
“3GPP TR 36.746; study on further enhancements to LTE device to device
(D2D), user equipment (UE) to network relays for Internet of Things
(IoT) and wearables;,” 3GPP, Tech. Rep., Apr. 2017.

[135]
“3GPP TR 38.874; nr; study on integrated access and backhaul,” 3GPP, Tech.
Rep., Nov. 2018.

[136]
N. H. Mahmood, H. Alves, O. A. López, M. Shehab, D. P. M. Osorio, and
M. Latva-aho, “Six key enablers for machine type communication in 6G,”
arXiv preprint arXiv:1903.05406, 2019.

[137]
R. Adeogun, G. Berardinelli, P. Mogensen, I. Rodriguez, and M. Razzaghpour,
“Towards 6G in-X subnetworks with sub-millisecond communication cycles and
extreme reliability,” IEEE Access, 2020.

[138]
L. Xu, R. Collier, and G. M. P. O?Hare, “A survey of clustering
techniques in WSNs and consideration of the challenges of applying such to
5G IoT scenarios,” IEEE Internet Things J., vol. 4, no. 5, pp.
1229–1249, Oct 2017.

[139]
V. N. Swamy, S. Suri, P. Rigge, M. Weiner, G. Ranade, A. Sahai, and B. Nikolic,
“Cooperative communication for high-reliability low-latency wireless
control,” in Proc. IEEE Int. Commun. Conf. (ICC), June 2015, pp.

[140]
P. Zand, S. Chatterjea, K. Das, and P. Havinga, “Wireless industrial
monitoring and control networks: The journey so far and the road ahead,”
Journal of sensor and actuator networks, vol. 1, no. 2, pp. 123–152,

[141]
N. Brahmi, O. N. C. Yilmaz, K. W. Helmersson, S. A. Ashraf, and J. Torsner,
“Deployment strategies for ultra-reliable and low-latency communication in
factory automation,” in Proc. IEEE GLOBECOM Workshops, Dec. 2015, pp.

[142]
B. M. Hochwald, T. L. Marzetta, and V. Tarokh, “Multiple-antenna channel
hardening and its implications for rate feedback and scheduling,” IEEE
Trans. Inf. Theory, vol. 50, no. 9, pp. 1893–1909, Sep. 2004.

[143]
S. A. Ayoughi, W. Yu, S. R. Khosravirad, and H. Viswanathan,
“Interference mitigation for ultrareliable low-latency wireless
communication,” IEEE J. Sel. Areas Commun., pp. 1–1, 2019.

[144]
S. R. Khosravirad, H. Viswanathan, and W. Yu, “Exploiting diversity for
ultra-reliable and low-latency wireless control,” IEEE Trans. Wireless
Commun., vol. 20, no. 1, pp. 316–331, 2021.

[145]
E. Arikan, “Channel polarization: A method for constructing capacity-achieving
codes for symmetric binary-input memoryless channels,” IEEE Trans.
Inf. Theory, vol. 55, no. 7, pp. 3051–3073, July 2009.

[146]
R. Gallager, “Low-density parity-check codes,” IRE Trans. Inf. Theory,
vol. 8, no. 1, pp. 21–28, 1962.

[147]
D. J. MacKay and R. M. Neal, “Near Shannon limit performance of low density
parity check codes,” Electronics letters, vol. 33, no. 6, pp.
457–458, 1997.

[148]
T. J. Richardson and R. L. Urbanke, “The capacity of low-density parity-check
codes under message-passing decoding,” IEEE Trans. Inf. Theory,
vol. 47, no. 2, pp. 599–618, 2001.

[149]
M. Shirvanimoghaddam, M. S. Mohammadi, R. Abbas, A. Minja, C. Yue, B. Matuz,
G. Han, Z. Lin, W. Liu, Y. Li et al., “Short block-length codes for
ultra-reliable low latency communications,” IEEE Commun. Mag.,
vol. 57, no. 2, pp. 130–137, 2018.

[150]
F. Abbasi, H. Mahdavifar, and E. Viterbo, “Polar coded repetition for
low-capacity channels,” arXiv preprint arXiv:2010.16144, 2020.

[151]
M. Shirvanimoghaddam and S. Johnson, “Raptor codes in the low SNR
regime,” IEEE Trans. Commun., vol. 64, no. 11, pp. 4449–4460, 2016.

[152]
H. Chen, R. Abbas, P. Cheng, M. Shirvanimoghaddam, W. Hardjawana, W. Bao,
Y. Li, and B. Vucetic, “Ultra-reliable low latency cellular networks: Use
cases, challenges and approaches,” IEEE Commun. Mag., vol. 56,
no. 12, pp. 119–125, 2018.

[153]
A. Shokrollahi, “Raptor codes,” IEEE Trans. Inf. Theory, vol. 52,
no. 6, pp. 2551–2567, 2006.

[154]
A. Gudipati and S. Katti, “Strider: Automatic rate adaptation and collision
handling,” SIGCOMM-Comput. Commun. Review, vol. 41, no. 4, pp.
158–169, 2011.

[155]
H. Cui, C. Luo, K. Tan, F. Wu, and C. W. Chen, “Seamless rate adaptation for
wireless networking,” in Proc. ACM Int. Conf. Modeling, Analysis
Simulation Wireless Mobile Syst., 2011, pp. 437–446.

[156]
M. Shirvanimoghaddam, Y. Li, and B. Vucetic, “Near-capacity adaptive
analog fountain codes for wireless channels,” IEEE Commun. Lett.,
vol. 17, no. 12, pp. 2241–2244, 2013.

[157]
J. Perry, P. A. Iannucci, K. E. Fleming, H. Balakrishnan, and D. Shah, “Spinal
codes,” ACM SIGCOM Comp. Commun. Review, vol. 42, no. 4, pp. 49–60,

[158]
R. Abbas, M. Shirvanimoghaddam, T. Huang, Y. Li, and B. Vucetic,
“Novel design for short analog fountain codes,” IEEE Commun. Lett.,
vol. 23, no. 8, pp. 1306–1309, 2019.

[159]
Z. Huang, J. Jiao, K. Zhang, S. Wu, and Q. Zhang, “A novel weight
coefficient PEG algorithm for ultra-reliable short length analog fountain
codes,” IEEE Commun. Lett., vol. 8, no. 1, pp. 125–128, 2019.

[160]
K. Zhang, J. Jiao, Z. Huang, S. Wu, and Q. Zhang, “Finite
block-length analog fountain codes for ultra-reliable low latency
communications,” IEEE Trans. Commun., vol. 68, no. 3, pp. 1391–1404,

[161]
X. Xu, S. Wu, D. Dong, J. Jiao, and Q. Zhang, “High performance short Polar
codes: A concatenation scheme using Spinal codes as the outer code,”
IEEE Access, vol. 6, pp. 70 644–70 654, 2018.

[162]
R. Abbas, M. Shirvanimoghaddam, T. Huang, Y. Li, and B. Vucetic, “Performance
analysis of short analog Fountain codes,” in Proc. IEEE Global.
Commun. Conf., 2019, pp. 1–6.

[163]
Y. Polyanskiy, H. V. Poor, and S. Verdú, “Channel coding rate in the
finite blocklength regime,” IEEE Trans. Inf. Theory, vol. 56, no. 5,
pp. 2307–2359, 2010.

[164]
M. P. Fossorier and S. Lin, “Soft-decision decoding of linear block codes
based on ordered statistics,” IEEE Trans. Inf. Theory, vol. 41,
no. 5, pp. 1379–1396, 1995.

[165]
M. Shirvanimoghaddam, Y. Li, and B. Vucetic, “Near-capacity adaptive analog
fountain codes for wireless channels,” IEEE Commun. Lett., vol. 17,
no. 12, pp. 2241–2244, Dec. 2013.

[166]
T. Marshall Jr., “Coding of real-number sequences for error correction: A
digital signal processing problem,” IEEE J. Sel. Areas Commun.,
vol. 2, no. 2, pp. 381–392, March 1984.

[167]
J. K. Wolf, “Redundancy, the discrete Fourier transform, and impulse noise
cancellation,” IEEE Trans. Commun., vol. 31, no. 3, pp. 458–461,
March 1983.

[168]
R. E. Blahut, Algebraic Codes for Data Transmission.1em plus
0.5em minus 0.4emNew York: Cambridge University Press, 2003.

[169]
G. Takos and C. N. Hadjicostis, “Determination of the number of errors in DFT
codes subject to low-level quantization noise,” IEEE Trans. Signal
Process., vol. 56, no. 3, pp. 1043–1054, 2008.

[170]
G. Rath and C. Guillemot, “Subspace algorithms for error localization with
quantized DFT codes,” IEEE Trans. Commun., vol. 52, no. 12, pp.
2115–2124, December 2004.

[171]
M. Vaezi and F. Labeau, “Generalized and extended subspace algorithms for
error correction with quantized DFT codes,” IEEE Trans. Commun.,
vol. 62, no. 2, pp. 410–422, 2014.

[172]
M. Vaezi, Distributed lossy source coding using BCH-DFT codes.1em plus 0.5em minus 0.4emPh.D. dissertation, McGill University, 2014.

[173]
A. Gabay, M. Kieffer, and P. Duhamel, “Joint source-channel coding using real
BCH codes for robust image transmission,” IEEE Trans. Signal
Process., vol. 16, no. 6, pp. 1568–1583, June 2007.

[174]
G. Rath and C. Guillemot, “Subspace-based error and erasure correction with
DFT codes for wireless channels,” IEEE Trans. Signal Process.,
vol. 52, no. 11, pp. 3241–3252, November 2004.

[175]
V. K. Goyal, J. Kovačević, and J. A. Kelner, “Quantized frame
expansions with erasures,” Appl. Comput. Harmon Anal., vol. 10,
no. 3, pp. 203–233, 2001.

[176]
M. Vaezi and F. Labeau, “Systematic DFT frames: Principle, eigenvalues
structure, and applications,” IEEE Trans. Signal Process., vol. 61,
no. 15, pp. 3774–3885, August 2013.

[177]
M. P. C. Fossorier and S. Lin, “Soft decision decoding of linear block codes
based on ordered statistics,” in Proc. IEEE Int. Symp. Inf. Theory
(ISIT), Jun 1994, pp. 395–.

[178]
C. Yue, M. Shirvanimoghaddam, B. Vucetic, and Y. Li, “A revisit to ordered
statistics decoding: Distance distribution and decoding rules,” IEEE
Trans. Inf. Theory, pp. 1–1, 2021.

[179]
C. Choi and J. Jeong, “Fast soft decision decoding of linear block codes using
partial syndrome search,” in Proc. IEEE Int. Symp. Inf. Theory
(ISIT), 2020, pp. 384–388.

[180]
C. Yue, M. Shirvanimoghaddam, Y. Li, and B. Vucetic, “Segmentation-discarding
ordered-statistic decoding for linear block codes,” in Proc. IEEE
Global Commun. Conf. (GLOBECOM), 2019, pp. 1–6.

[181]
C. Yue, M. Shirvanimoghaddam, G. Park, O.-S. Park, B. Vucetic, and Y. Li,
“Probability-based ordered-statistics decoding for short block codes,”
IEEE Commun. Lett., pp. 1–1, 2021.

[182]
M. P. Fossorier, M. Mihaljevic, and H. Imai, “Reduced complexity iterative
decoding of low-density parity check codes based on belief propagation,”
IEEE Trans. Commun., vol. 47, no. 5, pp. 673–680, 1999.

[183]
J. Chen, A. Dholakia, E. Eleftheriou, M. P. Fossorier, and X.-Y. Hu,
“Reduced-complexity decoding of LDPC codes,” IEEE Trans. Commun.,
vol. 53, no. 8, pp. 1288–1299, 2005.

[184]
X. Wu, M. Jiang, and C. Zhao, “Decoding optimization for 5G LDPC codes by
machine learning,” IEEE Access, vol. 6, pp. 50 179–50 186, 2018.

[185]
H. Cui, F. Ghaffari, K. Le, D. Declercq, J. Lin, and Z. Wang, “Design of
high-performance and area-efficient decoder for 5G LDPC codes,”
IEEE Trans. Circuits Syst. I, 2020.

[186]
K. Sun and M. Jiang, “A hybrid decoding algorithm for low-rate LDPC codes in
5G,” in Proc. IEEE Int. Conf. Wireless Commun. Signal Process.
(WCSP), 2018, pp. 1–5.

[187]
K. Le Trung, F. Ghaffari, and D. Declercq, “An adaptation of min-sum decoder
for 5G low-density parity-check codes,” in Proc. IEEE Int Symp
Circuits Syst. (ISCAS), 2019, pp. 1–5.

[188]
K. Chen, K. Niu, and J. Lin, “List successive cancellation decoding of Polar
codes,” Electronics letters, vol. 48, no. 9, pp. 500–501, 2012.

[189]
K. Niu and K. Chen, “Stack decoding of polar codes,” Electronics
letters, vol. 48, no. 12, pp. 695–697, 2012.

[190]
B. Li, H. Shen, and D. Tse, “An adaptive successive cancellation list decoder
for Polar codes with cyclic redundancy check,” IEEE Commun. Lett.,
vol. 16, no. 12, pp. 2044–2047, 2012.

[191]
K. Niu and K. Chen, “CRC-aided decoding of Polar codes,” IEEE Commun.
Lett., vol. 16, no. 10, pp. 1668–1671, 2012.

[192]
N. H. Mahmood, S. Böcker, A. Munari, F. Clazzer, I. Moerman, K. Mikhaylov,
O. Lopez, O.-S. Park, E. Mercier, H. Bartz et al., “Critical and
Massive Machine Type Communication Towards 6G,” 6G Channel, 2020.

[193]
M. Vaezi, Z. Ding, and H. V. Poor, Multiple Access Techniques for 5G
Wireless Networks and Beyond.1em plus 0.5em minus 0.4emSpringer, 2019.

[194]
Cisco, “The zettabyte era: Trends and analysis,” [Online; accessed
September 2019], 2017,

[195]
3GPP TR 38.812, “Study on non-orthogonal multiple access (NOMA) for NR,”
[Online]. Available:

[196]
F. Kara and H. Kaya, “BER performances of downlink and uplink NOMA in the
presence of SIC errors over fading channels,” IET Commun., vol. 12,
no. 15, pp. 1834–1844, 2018.

[197]
W. Shin, H. Yang, M. Vaezi, J. Lee, and H. V. Poor, “Relay-aided NOMA in
uplink cellular networks,” IEEE Signal Process. Lett., vol. 24,
no. 12, pp. 1842–1846, 2017.

[198]
M. Zeng, W. Hao, O. A. Dobre, and H. V. Poor, “Energy-efficient power
allocation in uplink mmWave massive MIMO with NOMA,” IEEE Trans. Veh.
Technol., vol. 68, no. 3, pp. 3000–3004, 2019.

[199]
S. Schiessl, M. Skoglund, and J. Gross, “NOMA in the uplink: Delay analysis
with imperfect CSI and finite-length coding,” IEEE Trans. Wireless
Commun., no. 6, pp. 3879–3893, 2020.

[200]
Y. Yuan, “Industry Perspective: 5G non-orthogonal multiple access study,”
IEEE Commun. Mag., vol. 25, no. 5, pp. 4–6, October 2018.

[201]
Y. Chen, A. Bayesteh, Y. Wu, B. Ren, S. Kang, S. Sun, Q. Xiong, C. Qian, B. Yu,
Z. Ding et al., “Toward the standardization of non-orthogonal
multiple access for next generation wireless networks,” IEEE Commun.
Mag., vol. 56, no. 3, pp. 19–27, 2018.

[202]
L. Ping, L. Liu, K. Wu, and W. K. Leung, “Interleave division
multiple-access,” IEEE Trans. Wireless Commun., vol. 5, no. 4, pp.
938–947, 2006.

[203]
Hughes, “ACMA-OFDM for uplink NOMA, 3GPP TSG RAN WG1 92b,R1‑1804171,”
April 2016, [Online]. Available:

[204]
Intel Corporation, “Multiple access schemes for new radio interface, 3GPP
TSG-RAN WG1 84b,R1-162385,” April 2016, [Online]. Available:

[205]
S. Hu, B. Yu, C. Qian, Y. Xiao, Q. Xiong, C. Sun, and Y. Gao, “Nonorthogonal
interleave-grid multiple access scheme for industrial Internet of things in
5G network,” IEEE Trans. Ind. Informat., vol. 14, no. 12, pp.
5436–5446, 2018.

[206]
H. Nikopour, E. Yi, A. Bayesteh, K. Au, M. Hawryluck, H. Baligh, and J. Ma,
“SCMA for downlink multiple access of 5G wireless networks,” in
Proc. IEEE Global Commun. Conf. (GLOBECOM), 2014, pp. 3940–3945.

[207]
Nokia, Alcatel-Lucent, “Non-orthogonal multiple access for New Radio, 3GPP
TSG-RAN WG1 85,R1-165019,” May 2016, [Online]. Available:

[208]
LG Electronics, “Considerations on DL/UL multiple access for NR, 3GPP
TSG-RAN WG1 84b,R1-162517,” April 2016, [Online]. Available:

[209]
Z. Yuan, G. Yu, W. Li, Y. Yuan, X. Wang, and J. Xu, “Multi-user shared access
for Internet of things,” in Proc. IEEE Veh. Technol. Conf.
(VTC-Spring), 2016, pp. 1–5.

[210]
R. Hoshyar, F. P. Wathan, and R. Tafazolli, “Novel low-density signature for
synchronous CDMA systems over AWGN channel,” IEEE Trans. Signal
Process., vol. 56, no. 4, pp. 1616–1626, 2008.

[211]
R. Hoshyar, R. Razavi, and M. Al-Imari, “LDS-OFDM an efficient multiple
access technique,” in Proc. IEEE Veh. Technol. Conf. (VTC-Spring),
2010, pp. 1–5.

[212]
C. S. et al., “Pattern Division Multiple Access (PDMA). In: Vaezi M.,
Ding Z., Poor H. (eds) Multiple Access Techniques for 5G Wireless Networks
and Beyond. Springer, Cham,” 2019.

[213]
ETRI, “Low code rate and signature based multiple access scheme for New
Radio, 3GPP TSG RAN WG1 85,R1‑164869,” May 2016, [Online]. Available:

[214]
Qualcomm, “resource spread multiple access (RSMA), 3GPP TSG RAN WG1
85,R1‑164688,” May 2016, [Online]. Available:

[215]
R1-163510 , “Candidate NR multiple access schemes,” Tech. Rep., April
2016, 3GPP TSG RAN WG1 Meeting 84, Korea.

[216]
3GPP TSG RAN WG1, “New uplink non-orthogonal multiple access schemes for
NR,” August 2016, [Online]. Available:

[217]
S. Tang, L. Hao, and Z. Ma, “Low complexity joint MPA detection for downlink
MIMO-SCMA,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2016, pp.

[218]
Z. Ma and J. Bao, Sparse Code Multiple Access (SCMA).1em plus
0.5em minus 0.4emCham: Springer International Publishing, 2019, pp.

[219]
W.-C. Sun, Y.-C. Su, Y.-L. Ueng, and C.-H. Yang, “An LDPC-coded SCMA receiver
with multi-user iterative detection and decoding,” IEEE Trans.
Circuits Syst., vol. 66, no. 9, pp. 3571–3584, 2019.

[220]
J. Liu, G. Wu, S. Li, and O. Tirkkonen, “On fixed-point implementation of
Log-MPA for SCMA signals,” IEEE Wireless Commun. Lett., vol. 5,
no. 3, pp. 324–327, 2016.

[221]
A. Ghaffari, M. Léonardon, A. Cassagne, C. Leroux, and Y. Savaria, “Toward
high-performance implementation of 5G SCMA algorithms,” IEEE
Access, vol. 7, pp. 10 402–10 414, 2019.

[222]
T. M. Cover and J. A. Thomas, Elements of Information Theory.1em plus 0.5em minus 0.4emNew York: John Wiley & Sons, 2006.

[223]
A. El Gamal and Y. H. Kim, Network Information Theory.1em plus
0.5em minus 0.4emCambridge University Press, 2011.

[224]
M. Vaezi and H. V. Poor, “NOMA: An information-theoretic perspective,” in
Multiple Access Techniques for 5G Wireless Networks and Beyond.1em plus 0.5em minus 0.4emSpringer, 2019, pp. 167–193.

[225]
W. Shin, M. Vaezi, B. Lee, D. J. Love, J. Lee, and H. V. Poor,
“Non-orthogonal multiple access in multi-cell networks: Theory,
performance, and practical challenges,” IEEE Commun. Mag., vol. 55,
no. 10, pp. 176–183, 2017.

[226]
3GPP TD RP-150496, “Study on Downlink Multiuser Superposition
Transmission,” March 2015., [Online]. Available:

[227]
Y. Yuan, Z. Yuan, G. Yu, C.-h. Hwang, P.-k. Liao, A. Li, and K. Takeda,
“Non-orthogonal transmission technology in LTE evolution,” IEEE
Commun. Mag., vol. 54, no. 7, pp. 68–74, 2016.

[228]
K. Y. Benjebbour A., Saito K., “Experimental Trials on Non-Orthogonal
Multiple Access,” in Multiple Access Techniques for 5G Wireless
Networks and Beyond.1em plus 0.5em minus 0.4emSpringer, 2019,
pp. 587–607.

[229]
S. Vanka, S. Srinivasa, Z. Gong, P. Vizi, K. Stamatiou, and M. Haenggi,
“Superposition coding strategies: Design and experimental evaluation,”
IEEE Trans. Wireless Commun., vol. 11, no. 7, pp. 2628–2639, 2012.

[230]
Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A. Li, and K. Higuchi,
“Non-orthogonal multiple access (NOMA) for cellular future radio access,”
in Proc. IEEE Veh. Technol. Conf. (VTC-Spring), 2013, pp. 1–5.

[231]
L. Dai, B. Wang, Y. Yuan, S. Han, C.-L. I, and Z. Wang, “Non-orthogonal
multiple access for 5G: Solutions, challenges, opportunities, and future
research trends,” IEEE Commun. Mag., vol. 53, no. 9, pp. 74–81,

[232]
Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo,
“Nonorthogonal multiple access for 5G and beyond,” Proc. IEEE,
vol. 105, no. 12, pp. 2347–2381, 2017.

[233]
M. Vaezi, R. Schober, Z. Ding, and H. V. Poor, “Non-orthogonal multiple
access: Common myths and critical questions,” IEEE Wireless Commun.,
vol. 26, no. 5, pp. 174–180, 2019.

[234]
M. Taherzadeh, H. Nikopour, A. Bayesteh, and H. Baligh, “SCMA codebook
design,” in Proc. IEEE Veh. Technol. Conf. (VTC-Fall), 2014, pp.

[235]
A. Benjebbour, Y. Kishiyama, Y. Okumura, C.-H. Hwang, and I.-K. Fu, “Outdoor
experimental trials of advanced downlink NOMA using smartphone-sized
devices,” in Proc. IEEE Veh. Technol. Conf. (VTC-Spring), 2018, pp.

[236]
Q. Wu, W. Chen, D. W. K. Ng, and R. Schober, “Spectral and energy-efficient
wireless powered IoT networks: NOMA or TDMA?” IEEE Trans. Veh.
Technol., vol. 67, no. 7, pp. 6663–6667, July 2018.

[237]
Z. Ding, R. Schober, and H. V. Poor, “A general MIMO framework for
NOMA downlink and uplink transmission based on signal alignment,”
IEEE Trans. Wireless Commun., vol. 15, no. 6, pp. 4438–4454, June

[238]
T. Lv, Y. Ma, J. Zeng, and P. T. Mathiopoulos, “Millimeter-wave NOMA
transmission in cellular M2M communications for Internet of Things,”
IEEE Internet Things J., vol. 5, no. 3, pp. 1989–2000, 2018.

[239]
M. A. Almasi, R. Amiri, M. Vaezi, and H. Mehrpouyan, “Lens-based millimeter
wave reconfigurable antenna NOMA,” in Proc. IEEE Int. Commun. Conf.
Workshops (ICC Workshops), 2019, pp. 1–5.

[240]
R. Jia, X. Chen, C. Zhong, D. W. K. Ng, H. Lin, and Z. Zhang, “Design of
non-orthogonal beamspace multiple access for cellular Internet-of-Things,”
IEEE J. Sel. Topics Signal Process, vol. 13, no. 3, pp. 538–552,

[241]
M. Liu, T. Song, and G. Gui, “Deep cognitive perspective: Resource allocation
for NOMA-based heterogeneous IoT with imperfect SIC,” IEEE Internet
Things J., vol. 6, no. 2, pp. 2885–2894, 2018.

[242]
K. N. Doan, M. Vaezi, W. Shin, H. V. Poor, H. Shin, and T. Q. Quek, “Power
allocation in cache-aided NOMA systems: Optimization and deep reinforcement
learning approaches,” IEEE Trans. Commun., vol. 68, no. 1, pp.
630–644, 2020.

[243]
G. Gui, H. Huang, Y. Song, and H. Sari, “Deep learning for an effective
nonorthogonal multiple access scheme,” IEEE Trans. Veh. Technol.,
vol. 67, no. 9, pp. 8440–8450, 2018.

[244]
X. Zhang and M. Vaezi, “Deep learning based precoding for the MIMO Gaussian
wiretap channel,” in Proc. IEEE Global Commun. Conf. Workshops (GC
Workshops), 2019, pp. 1–6.

[245]
B. Clerckx, Y. Mao, R. Schober, E. Jorswieck, D. J. Love, J. Yuan, L. Hanzo,
G. Y. Li, E. G. Larsson, and G. Caire, “Is NOMA efficient in multi-antenna
networks? A critical look at next generation multiple access techniques,”
arXiv preprint arXiv:2101.04802, 2021.

[246]
H. Weingarten, Y. Steinberg, and S. S. Shamai, “The capacity region of the
Gaussian multiple-input multiple-output broadcast channel,” IEEE
Trans. Inf. Theory, vol. 52, no. 9, pp. 3936–3964, 2006.

[247]
Y. Qi and M. Vaezi, “Secure transmission in MIMO-NOMA networks,” IEEE
Commun. Lett., vol. 24, no. 12, pp. 2696–2700, 2020.

[248]
M. Vaezi and Y. Zhang, “Radio access network evolution,” in Cloud
Mobile Networks.1em plus 0.5em minus 0.4emSpringer, 2017, pp.

[249]
A. Laya, L. Alonso, and J. Alonso-Zarate, “Is the random access channel of
LTE and LTE-A suitable for M2M communications? A survey of alternatives,”
IEEE Commun. Surveys Tuts., vol. 16, no. 1, pp. 4–16, 2013.

[250]
L. Liu, E. G. Larsson, W. Yu, P. Popovski, C. Stefanovic, and E. De Carvalho,
“Sparse signal processing for grant-free massive connectivity: A future
paradigm for random access protocols in the internet of things,” IEEE
Signal Process. Mag., vol. 35, no. 5, pp. 88–99, 2018.

[251]
D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for
compressed sensing,” Proc. National Academy Sci., vol. 106, no. 45,
pp. 18 914–18 919, 2009.

[252]
L. Liu and W. Yu, “Massive connectivity with massive MIMO — Part II:
Achievable rate characterization,” IEEE Trans. Signal Process.,
vol. 66, no. 11, pp. 2947–2959, 2018.

[253]
M. Ke, Z. Gao, Y. Wu, X. Gao, and R. Schober, “Compressive sensing-based
adaptive active user detection and channel estimation: Massive access meets
massive MIMO,” IEEE Trans. Signal Process., vol. 68, pp. 764–779,

[254]
S. Li, W. Zhang, Y. Cui, H. V. Cheng, and W. Yu, “Joint design of measurement
matrix and sparse support recovery method via deep auto-encoder,” IEEE
Signal Process. Lett., vol. 26, no. 12, pp. 1778–1782, 2019.

[255]
Y. Cui, S. Li, and W. Zhang, “Jointly sparse signal recovery and support
recovery via deep learning with applications in MIMO-based grant-free random
access,” IEEE J. Sel. Areas Commun., 2020.

[256]
J. Ding and J. Choi, “Comparison of preamble structures for grant-free random
access in massive MIMO systems,” IEEE Wireless Commun. Lett.,
vol. 9, no. 2, pp. 166–170, 2019.

[257]
Y. Polyanskiy, “A perspective on massive random-access,” in Proc. IEEE
Int. Symp. Inf. Theory (ISIT), 2017, pp. 2523–2527.

[258]
O. Ordentlich and Y. Polyanskiy, “Low complexity schemes for the random
access Gaussian channel,” in Proc. IEEE Int. Symp. Inf. Theory
(ISIT), 2017, pp. 2528–2532.

[259]
A. Fengler, G. Caire, P. Jung, and S. Haghighatshoar, “Massive MIMO unsourced
random access,” arXiv preprint arXiv:1901.00828, 2019.

[260]
R. Calderbank and A. Thompson, “CHIRRUP: a practical algorithm for unsourced
multiple access,” Information and Inference: A Journal of the IMA,
vol. 9, no. 4, pp. 875–897, 2020.

[261]
G. Pathak, J. Gutierrez, and S. U. Rehman, “Security in low powered wide area
networks: Opportunities for software defined network-supported solutions,”
Electron., vol. 9, no. 8, 2020. [Online]. Available:

[262]
P. A. Networks, “IoT threat report 2020,” May 2020. [Online]. Available:

[263]
C. Kolias, G. Kambourakis, A. Stavrou, and J. Voas, “DDoS in the IoT:
Mirai and other botnets,” Comput., vol. 50, no. 7, pp. 80–84, 2017.

[264]
M. Wazid, A. K. Das, V. Odelu, N. Kumar, M. Conti, and M. Jo, “Design of
secure user authenticated key management protocol for generic IoT
networks,” IEEE Int. of Things, vol. 5, no. 1, pp. 269–282, 2018.

[265]
X. Yang, “LoRaWAN: Vulnerability analysis and practical expoitation,”
Master's Thesis, TU Delft Electrical Engineering, Mathematics and Computer
Science, 2017.

[266]
E. Aras, G. S. Ramachandran, P. Lawrence, and D. Hughes, “Exploring the
security vulnerabilities of LoRa,” in Proc. IEEE Int. Conf. on
Cybern (CYBCONF), 2017, pp. 1–6.

[267]
G. Ikrissi and T. Mazri, “A study of smart campus environment and its security
attacks,” ISPRS - Int. Arch. of the Photogramm., Remote Sens. and
Spat. Inf. Sci., vol. XLIV-4/W3-2020, pp. 255–261, 11 2020.

[268]
F. L. Coman, K. M. Malarski, M. N. Petersen, and S. Ruepp, “Security issues in
Internet of things: Vulnerability analysis of LoRaWAN, Sigfox and
NB-IoT,” in Global IoT Summit (GIoTS), 2019, pp. 1–6.

[269]
S. Chacko and M. D. Job, “Security mechanisms and vulnerabilities in
LPWAN,” IOP Conf. Series: Mater. Sci. and Eng., vol. 396, p.
012027, aug 2018. [Online]. Available:

[270]
Y. Gao, Y. Su, W. Yang, S. Chen, S. Nepal, and D. C. Ranasinghe, “Building
secure SRAM PUF key generators on resource constrained devices,” in
Proc. IEEE Int. Conf. on Perv. Comput. and Commun. Wksp. (PerCom
Workshops), 2019, pp. 912–917.

[271]
E. Aras, N. J. Small, G. S. Ramachandran, S. Delbruel, W. Joosen, and
D. Hughes, “Selective jamming of LoRaWAN using commodity hardware,”
CoRR, vol. abs/1712.02141, 2017. [Online]. Available:

[272]
X. Yang, E. Karampatzakis, C. Doerr, and F. Kuipers, “Security vulnerabilities
in LoRaWAN,” in Proc. IEEE/ACM Int. Conf. on IoT Design and Impl.
(IoTDI), 2018, pp. 129–140.

[273]
G. Bernardinetti, F. Mancini, and G. Bianchi, “Disconnection attacks against
LoRaWAN 1.0.X ABP devices,” in Med. Commun. and Comput. Netw.
Conf. (MedComNet), 2020, pp. 1–8.

[274]
B. Xu, W. Wang, Q. Hao, Z. Zhang, P. Du, T. Xia, H. Li, and X. Wang, “A
security design for the detecting of buffer overflow attacks in IoT
device,” IEEE Access, vol. PP, pp. 1–1, 11 2018.

[275]
S. Shen, K. Zhang, Y. Zhou, and S. Ci, “Security in edge-assisted Internet
of things: challenges and solutions,” Sci. China Inf. Sci., vol. 63,
no. 12, p. 220302, 2020. [Online]. Available:

[276]
H. A. Abdul-Ghani, D. Konstantas, and M. Mahyoub, “A comprehensive IoT
attacks survey based on a building-blocked reference model,” Int. J.
of Adv. Comput. Sci. and Appl., vol. 9, 2018.

[277]
M. Davis, The Undecidable: Basic Papers on Undecidable Propositions,
Unsolvable Problems, and Computable Functions.1em plus 0.5em minus
0.4emDover Publication, 1965.

[278]
P. Rost, C. Mannweiler, D. S. Michalopoulos, C. Sartori, V. Sciancalepore,
N. Sastry, O. Holland, S. Tayade, B. Han, D. Bega, D. Aziz, and H. Bakker,
“Network slicing to enable scalability and flexibility in 5G mobile
networks,” IEEE Commun. Mag., vol. 55, no. 5, pp. 72–79, 2017.

[279]
L. Wallgren, S. Raza, and T. Voigt, “Routing attacks and countermeasures in
the RPL-based Internet of things,” Int. J. of Distrib. Sens.
Netw., vol. 9, no. 8, p. 794326, 2013. [Online]. Available:

[280]
D. Rupprecht, K. Kohls, T. Holz, and C. Pöpper, “Breaking LTE on layer
two,” in Proc. IEEE Symp. on Security and Privacy (SP), 2019, pp.

[281]
D. Rupprecht, K. Kohls, T. Holz, and C. Pöpper, “IMP4GT: Impersonation
attacks in 4G networks,” in Proc. ISOC Netw. and Distrib. Syst.
Security Symp. (NDSS).1em plus 0.5em minus 0.4emISOC, Feb.

[282]
D. E. Kouicem, A. Bouabdallah, and H. Lakhlef, “Internet of things security: A
top-down survey,” Comp. Netw., vol. 141, pp. 199–221, 2018.
[Online]. Available:

[283]
P. Hu, “A system architecture for software-defined industrial Internet of
things,” in IEEE Int. Conf. on Ubiquitous Wireless Broadband (ICUWB),
2015, pp. 1–5.

[284]
O. Flauzac, C. González, A. Hachani, and F. Nolot, “SDN based architecture
for IoT and improvement of the security,” in Proc. IEEE Int. Conf.
on Adv. Inf. Netw. and Appl. Wksp., 2015, pp. 688–693.

[285]
A. Molina Zarca, J. B. Bernabe, R. Trapero, D. Rivera, J. Villalobos,
A. Skarmeta, S. Bianchi, A. Zafeiropoulos, and P. Gouvas, “Security
management architecture for NFV/SDN-aware IoT systems,” IEEE
Int. of Things, vol. 6, no. 5, pp. 8005–8020, 2019.

[286]
A. Yazdinejad, R. M. Parizi, A. Dehghantanha, Q. Zhang, and K.-K. R. Choo, “An
energy-efficient SDN controller architecture for IoT networks with
blockchain-based security,” IEEE Trans. Services Comput., vol. 13,
no. 4, pp. 625–638, 2020.

[287]
B. Al-Hayajneh and McAndrew, “Improving Internet of things (IoT) security
with software-defined networking (SDN),” Comp., vol. 9, p. 8, 02

[288]
N. Ravi and S. M. Shalinie, “Learning-driven detection and mitigation of
DDoS attack in IoT via SDN-cloud architecture,” IEEE Int. of
Things, vol. 7, no. 4, pp. 3559–3570, 2020.

[289]
T. G. Nguyen, T. V. Phan, B. T. Nguyen, C. So-In, Z. A. Baig, and
S. Sanguanpong, “Search: A collaborative and intelligent NIDS architecture
for SDN-based cloud IoT networks,” IEEE Access, vol. 7, pp.
107 678–107 694, 2019.

[290]
M. Casado, M. J. Freedman, J. Pettit, J. Luo, N. McKeown, and S. Shenker,
“Ethane: Taking control of the enterprise,” SIGCOMM Comput. Commun.
Rev., vol. 37, no. 4, p. 1–12, Aug. 2007. [Online]. Available:

[291]
N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford,
S. Shenker, and J. Turner, “Openflow: Enabling innovation in campus
networks,” SIGCOMM Comput. Commun. Rev., vol. 38, no. 2, p. 69–74,
Mar. 2008. [Online]. Available: <https://doi.org/10.1145/1355734.1355746>

[292]
P. Bull, R. Austin, E. Popov, M. Sharma, and R. Watson, “Flow based security
for IoT devices using an SDN gateway,” in Proc. IEEE Int. Conf. on
Future Int. of Things and Cloud (FiCloud), 2016, pp. 157–163.

[293]
R. Vilalta, R. Ciungu, A. Mayoral, R. Casellas, R. Martinez, D. Pubill,
J. Serra, R. Munoz, and C. Verikoukis, “Improving security in Internet of
things with software defined networking,” in Proc. IEEE Global Commun.
Conf. (GLOBECOM), 2016, pp. 1–6.

[294]
B. Arshdeep and M. Vijay, “Blockchain platform for industrial Internet of
things,” J. of Softw. Eng. and Appl., vol. 09, pp. 533–546, 01 2016.

[295]
M. A. Khan and K. Salah, “IoT security: Review, blockchain solutions, and
open challenges,” Future Gener. Comput. Syst., vol. 82, pp. 395–411,
2018. [Online]. Available:

[296]
A. Chaer, K. Salah, C. Lima, P. P. Ray, and T. Sheltami, “Blockchain for 5G:
Opportunities and challenges,” in Proc. IEEE Global Commun. Conf.
(GLOBECOM), 2019, pp. 1–6.

[297]
B. Xu, N. Wang, T. Chen, and M. Li, “Empirical evaluation of rectified
activations in convolutional network,” arXiv:1505.00853, 2015.

[298]
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521,
no. 7553, pp. 436–444, 2015.

[299]
C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless
networking: A survey,” IEEE Commun. Surveys Tuts., vol. 21, no. 3,
pp. 2224–2287, 2019.

[300]
M. A. Alsheikh, D. Niyato, S. Lin, H.-P. Tan, and Z. Han, “Mobile big data
analytics using deep learning and apache spark,” IEEE Netw., vol. 30,
no. 3, pp. 22–29, 2016.

[301]
P. Georgiev, S. Bhattacharya, N. D. Lane, and C. Mascolo, “Low-resource
multi-task audio sensing for mobile and embedded devices via shared deep
neural network representations,” Proc. ACM Interact. Mob. Wearable
Ubiquitous Technol., vol. 1, no. 3, pp. 1–19, 2017.

[302]
X. Zhang and M. Vaezi, “Multi-objective DNN-based precoder for MIMO
communications,” IEEE Trans. Commun., 2021.

[303]
T. J. O'Shea, T. Erpek, and T. C. Clancy, “Deep learning based MIMO
communications,” arXiv preprint arXiv:1707.07980, 2017.

[304]
Z. Zhang and A. Z. Kouzani, “Implementation of DNNs on IoT devices,”
Neural. Comput. Appl., vol. 32, no. 5, pp. 1327–1356, 2020.

[305]
R. Hadidi, J. Cao, M. S. Ryoo, and H. Kim, “Toward collaborative inferencing
of deep neural networks on Internet-of-Things devices,” IEEE Trans.
Cogn. Commun. Netw., vol. 7, no. 6, pp. 4950–4960, 2020.

[306]
L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model compression and hardware
acceleration for neural networks: A comprehensive survey,” Proc.
IEEE, vol. 108, no. 4, pp. 485–532, 2020.

[307]
Z. Xiong, A. D. Liveris, and S. Cheng, “Distributed source coding for sensor
networks,” IEEE Signal Process. Mag., vol. 21, no. 5, pp. 80–94,

[308]
S. S. Pradhan and K. Ramchandran, “Distributed source coding using syndromes
(DISCUS): Design and construction,” IEEE Trans. Inf. Theory,
vol. 49, no. 3, pp. 626–643, 2003.

[309]
M. Vaezi and F. Labeau, “Distributed source-channel coding based on
real-field BCH codes,” IEEE Trans. Signal Process., vol. 62, no. 5,
pp. 1171–1184, 2014.

[310]
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,”
ACM comput. surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009.

[311]
R. Chalapathy and S. Chawla, “Deep learning for anomaly detection: A
survey,” arXiv preprint arXiv:1901.03407, 2019.

[312]
B. B. Zarpelão, R. S. Miani, C. T. Kawakani, and S. C. de Alvarenga, “A
survey of intrusion detection in Internet of things,” J. Netw.
Comput. Appl., vol. 84, pp. 25–37, 2017.

[313]
G. Aceto, D. Ciuonzo, A. Montieri, and A. Pescapé, “Mobile encrypted
traffic classification using deep learning: Experimental evaluation, lessons
learned, and challenges,” IEEE Trans. Netw. Service Manage., vol. 16,
no. 2, pp. 445–458, 2019.

[314]
T. Chen, X. Liu, B. Xia, W. Wang, and Y. Lai, “Unsupervised anomaly detection
of industrial robots using sliding-window convolutional variational
autoencoder,” IEEE Access, vol. 8, pp. 47 072–47 081, 2020.

[315]
D. Azzalini, L. Bonali, and F. Amigoni, “A minimally supervised approach based
on variational autoencoders for anomaly detection in autonomous robots,”
IEEE Robot. Autom. Lett., 2021.

[316]
S. Nazir, S. Patel, and D. Patel, “Autoencoder based anomaly detection for
SCADA networks,” Int. J. Artif. Intell. Mach. Learn. (IJAIML), 2020.

[317]
C. Yin, S. Zhang, J. Wang, and N. N. Xiong, “Anomaly detection based on
convolutional recurrent autoencoder for IoT time series,” IEEE Trans.
Syst. Man Cybern. Syst., 2020.

[318]
M. Savic, M. Lukic, D. Danilovic, Z. Bodroski, D. Bajovic, I. Mezei,
D. Vukobratovic, S. Skrbic, and D. Jakovetic, “Deep learning anomaly
detection for cellular IoT with applications in smart logistics,”
arXiv preprint arXiv:2102.08936, 2021.

[319]
A.-H. Muna, N. Moustafa, and E. Sitnikova, “Identification of malicious
activities in industrial Internet of things based on deep learning
models,” J. Inf. Secur. Appl., vol. 41, pp. 1–11, 2018.

[320]
Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and
Y. Elovici, “N-baiot—network-based detection of IoT botnet attacks using
deep autoencoders,” IEEE Pervasive Comput., vol. 17, no. 3, pp.
12–22, 2018.

[321]
S. J. Lee, P. D. Yoo, A. T. Asyhari, Y. Jhi, L. Chermak, C. Y. Yeun, and
K. Taha, “IMPACT: Impersonation attack detection via edge computing using
deep autoencoder and feature abstraction,” IEEE Access, vol. 8, pp.
65 520–65 529, 2020.

[322]
C. Kolias, G. Kambourakis, A. Stavrou, and S. Gritzalis, “Intrusion detection
in 802.11 networks: empirical evaluation of threats and a public dataset,”
IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 184–208, 2015.

[323]
R.-H. Hwang, M.-C. Peng, and C.-W. Huang, “Detecting IoT malicious traffic
based on autoencoder and convolutional neural network,” in Proc. IEEE
Global Commun. Conf. Workshops (GC Workshops), 2019, pp. 1–6.

[324]
M. Al-Qatf, Y. Lasheng, M. Al-Habib, and K. Al-Sabahi, “Deep learning
approach combining sparse autoencoder with SVM for network intrusion
detection,” IEEE Access, vol. 6, pp. 52 843–52 856, 2018.

[325]
Y. Li, “Deep reinforcement learning: An overview,” arXiv preprint
arXiv:1701.07274, 2017.

[326]
S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for
robotic manipulation with asynchronous off-policy updates,” in Proc.
IEEE int. conf. Robot. Autom. (ICRA), 2017, pp. 3389–3396.

[327]
S. Chinchali, P. Hu, T. Chu, M. Sharma, M. Bansal, R. Misra, M. Pavone, and
S. Katti, “Cellular network traffic scheduling with deep reinforcement
learning,” in Proc. AAAI Conf. Artif. Intell., vol. 32, no. 1, 2018.

[328]
H. Li, K. Ota, and M. Dong, “Deep reinforcement scheduling for mobile
crowdsensing in fog computing,” ACM Trans. Internet Technol.,
vol. 19, no. 2, pp. 1–18, 2019.

[329]
M. Liu, F. R. Yu, Y. Teng, V. C. 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, 2019.

[330]
H. Mao, M. Alizadeh, I. Menache, and S. Kandula, “Resource management with
deep reinforcement learning,” in Proc. ACM workshop hot topics netw.,
2016, pp. 50–56.

[331]
Y. Li, F. Qi, Z. Wang, X. Yu, and S. Shao, “Distributed edge computing
offloading algorithm based on deep reinforcement learning,” IEEE
Access, vol. 8, pp. 85 204–85 215, 2020.

[332]
F. Bu and X. Wang, “A smart agriculture IoT system based on deep
reinforcement learning,” Future Gener. Comput. Syst., vol. 99, pp.
500–507, 2019.

[333]
A. Azari, F. Ghavimi, M. Ozger, R. Jantti, and C. Cavdar, “Machine learning
assisted handover and resource management for cellular connected drones,” in
Proc. IEEE Veh. Technol. Conf. (VTC-Spring), 2020, pp. 1–7.

[334]
L. Clavier et al., “Experimental evidence for heavy tailed interference
in the IoT,” IEEE Commun. Lett., 2020.

[335]
S. K. Sharma and X. Wang, “Collaborative distributed Q-learning for RACH
congestion minimization in cellular IoT networks,” IEEE Commun.
Lett., vol. 23, no. 4, pp. 600–603, 2019.

[336]
A. Azari, “Energy-efficient scheduling and grouping for machine-type
communications over cellular networks,” Ad Hoc Netw. J., vol. 43, pp.
16–29, 2016.

[337]
A. Azari and C. Cavdar, “Self-organized low-power IoT networks: A
distributed learning approach,” Proc. IEEE Global Commun. Conf.
(GLOBECOM), 2018.

[338]
D.-T. Ta, K. Khawam, S. Lahoud, C. Adjih, and S. Martin, “LoRa-MAB: Toward
an intelligent resource allocation approach for LoRAWAN,” in Proc.
IEEE Global Commun. Conf. (GLOBECOM), 2019, pp. 1–6.

[339]
R. M. Sandoval, A.-J. Garcia-Sanchez, and J. Garcia-Haro, “Optimizing and
updating LoRa communication parameters: A machine learning approach,”
IEEE Trans. Netw. Service Manag, vol. 16, no. 3, pp. 884–895, 2019.

[340]
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas,
“Communication-efficient learning of deep networks from decentralized
data,” in Proc. Artif. Intell. Statist., 2017, pp. 1273–1282.

[341]
M. Vaezi and Y. Zhang, Cloud Mobile Networks From RAN to EPC.1em plus 0.5em minus 0.4emSpringer, 2017.

[342]
A. Checko, H. L. Christiansen, Y. Yan, L. Scolari, G. Kardaras, M. S. Berger,
and L. Dittmann, “Cloud RAN for mobile networks—A technology overview,”
IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 405–426, 2014.

[343]
B. Liu, L. Wang, M. Liu, and C.-Z. Xu, “Federated imitation learning: A novel
framework for cloud robotic systems with heterogeneous sensor data,”
IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 3509–3516, 2020.

[344]
B. Yin, H. Yin, Y. Wu, and Z. Jiang, “FDC: A secure federated deep learning
mechanism for data collaborations in the Internet of Things,” IEEE
Trans. Cogn. Commun. Netw., vol. 7, no. 7, pp. 6348–6359, 2020.

[345]
L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning
for internet of things: Recent advances, taxonomy, and open challenges,”
arXiv preprint arXiv:2009.13012, 2020.

[346]
Z. Du, C. Wu, T. Yoshinaga, K.-L. A. Yau, Y. Ji, and J. Li, “Federated
learning for vehicular Internet of things: Recent advances and open
issues,” IEEE Open J. Comput. Soc, vol. 1, pp. 45–61, 2020.

[347]
D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor,
“Federated learning for Internet of Things: A comprehensive survey,”
arXiv preprint arXiv:2104.07914, 2021.

[348]
E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, “Exploiting linear
structure within convolutional networks for efficient evaluation,”
arXiv preprint arXiv:1404.0736, 2014.

[349]
N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, and F. Kawsar, “An
early resource characterization of deep learning on wearables, smartphones
and Internet-of-things devices,” in Proc. Iint. workshop Internet
things Appl., 2015, pp. 7–12.

[350]
M. Jaderberg, A. Vedaldi, and A. Zisserman, “Speeding up convolutional neural
networks with low rank expansions,” arXiv preprint arXiv:1405.3866,

[351]
S. Han, J. Pool, J. Tran, and W. J. Dally, “Learning both weights and
connections for efficient neural networks,” arXiv preprint
arXiv:1506.02626, 2015.

[352]
Y. Guo, A. Yao, and Y. Chen, “Dynamic network surgery for efficient DNNs,”
arXiv preprint arXiv:1608.04493, 2016.

[353]
Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel, “Optimal
brain damage,” in Proc. Neural Inform. Process. Syst. (NIPS), vol. 2,
1989, pp. 598–605.

[354]
V. Vanhoucke, A. Senior, and M. Z. Mao, “Improving the speed of neural
networks on CPUs,” in Deep Learning and Unsupervised Feature
Learning Workshop (NIPS Conference), 2011.

[355]
Y. Gong, L. Liu, M. Yang, and L. Bourdev, “Compressing deep convolutional
networks using vector quantization,” arXiv preprint arXiv:1412.6115,

[356]
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng, “Quantized convolutional neural
networks for mobile devices,” in Proc. IEEE Conf. Comput. Vis. Pattern
Recognit. (CVPR), 2016, pp. 4820–4828.

[357]
M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep
neural networks with binary weights during propagations,” arXiv
preprint arXiv:1511.00363, 2015.

[358]
W. Tang, G. Hua, and L. Wang, “How to train a compact binary neural network
with high accuracy?” in Proc. AAAI Conf. Artif. Intell., vol. 31,
no. 1, 2017.

[359]
S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural
networks with pruning, trained quantization and huffman coding,” arXiv
preprint arXiv:1510.00149, 2015.

[360]
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2:
Inverted residuals and linear bottlenecks,” in Proc. IEEE Conf.
Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 4510–4520.

[361]
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer,
“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB
model size,” arXiv preprint arXiv:1602.07360, 2016.

[362]
J. Lin, W.-M. Chen, Y. Lin, J. Cohn, C. Gan, and S. Han, “Mcunet: Tiny deep
learning on IoT devices,” arXiv preprint arXiv:2007.10319, 2020.

[363]
G. T. 22.822, “Study on using satellite access in 5G,” 2018. [Online].

[364]
O. Liberg, S. E. Löwenmark, S. Euler, B. Hofström, T. Khan, X. Lin, and
J. Sedin, “Narrowband Internet of Things for non-terrestrial networks,”
IEEE Commun. Stand. Mag., vol. 4, no. 4, pp. 49–55, 2020.

[365]
G. Geraci, A. Garcia-Rodriguez, M. M. Azari, A. Lozano, M. Mezzavilla,
S. Chatzinotas, Y. Chen, S. Rangan, and M. Di Renzo, “What will the future
of UAV cellular communications be? a flight from 5G to 6G,” arXiv
preprint arXiv:2105.04842, 2021.

[366]
E. Vinogradov, H. Sallouha, S. De Bast, M. M. Azari, and S. Pollin, “Tutorial
on UAV: A blue sky view on wireless communication,” arXiv preprint
arXiv:1901.02306, 2019.

[367]
M. M. Azari, F. Rosas, K.-C. Chen, and S. Pollin, “Ultra reliable UAV
communication using altitude and cooperation diversity,” IEEE Trans.
Commun., vol. 66, no. 1, pp. 330–344, 2017.

[368]
C. Zhan and H. Lai, “Energy minimization in Internet-of-Things system based
on rotary-wing UAV,” IEEE Wireless Commun. Lett., vol. 8, no. 5,
pp. 1341–1344, 2019.

[369]
S. Fu, Y. Tang, Y. Wu, N. Zhang, H. Gu, C. Chen, and M. Liu, “Energy-efficient
UAV enabled data collection via wireless charging: A reinforcement learning
approach,” IEEE Internet Things J., 2021.

[370]
J. Li, H. Zhao, H. Wang, F. Gu, J. Wei, H. Yin, and B. Ren, “Joint
optimization on trajectory, altitude, velocity, and link scheduling for
minimum mission time in UAV-aided data collection,” IEEE Internet
Things J., vol. 7, no. 2, pp. 1464–1475, 2020.

[371]
S. Fu, Y. Tang, N. Zhang, L. Zhao, S. Wu, and X. Jian, “Joint unmanned aerial
vehicle (UAV) deployment and power control for Internet of Things
networks,” IEEE Trans. Veh. Technol., vol. 69, no. 4, pp. 4367–4378,

[372]
O. A. Saraereh, A. Alsaraira, I. Khan, and P. Uthansakul, “Performance
evaluation of UAV-enabled LoRa networks for disaster management
applications,” Sensors, vol. 20, no. 8, p. 2396, 2020.

[373]
H. Bayerlein, M. Theile, M. Caccamo, and D. Gesbert, “UAV path planning for
wireless data harvesting: A deep reinforcement learning approach,”
arXiv preprint arXiv:2007.00544, 2020.

[374]
Y. Cao, L. Zhang, and Y.-C. Liang, “Deep reinforcement learning for channel
and power allocation in UAV-enabled IoT systems,” in Proc. IEEE
Global Commun. Conf. (GLOBECOM), 2019, pp. 1–6.

[375]
W. Alshrafi, U. Engel, and T. Bertuch, “Compact controlled reception pattern
antenna for interference mitigation tasks of global navigation satellite
system receivers,” IET Microwaves, Antennas & Propagation, vol. 9,
no. 6, pp. 593–601, 2014.

[376]
G. Han et al., “A survey on mobile anchor node assisted localization in
wireless sensor networks,” IEEE Commun. Surveys Tuts., vol. 18,
no. 3, pp. 2220–2243, 2016.

[377]
S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molisch, H. V. Poor,
and Z. Sahinoglu, “Localization via ultra-wideband radios: a look at
positioning aspects for future sensor networks,” IEEE Signal Process.
Mag., vol. 22, no. 4, pp. 70–84, 2005.

[378]
A. Zanella, “Best practice in RSS measurements and ranging,” IEEE
Commun. Surveys Tuts., vol. 18, no. 4, pp. 2662–2686, 2016.

[379]
A. Al-Hourani, S. Kandeepan, and A. Jamalipour, “Modeling air-to-ground path
loss for low altitude platforms in urban environments,” in Proc. IEEE
Global Commun. Conf. (GLOBECOM), 2014, pp. 2898–2904.

[380]
M. M. Azari, H. Sallouha, A. Chiumento, S. Rajendran, E. Vinogradov, and
S. Pollin, “Key technologies and system trade-offs for detection and
localization of amateur drones,” IEEE Commun. Mag., vol. 56, no. 1,
pp. 51–57, 2018.

[381]
H. Sallouha, M. M. Azari, A. Chiumento, and S. Pollin, “Aerial anchors
positioning for reliable RSS-based outdoor localization in urban
environments,” IEEE Wireless Commun. Lett., 2018.

[382]
H. Sallouha, M. M. Azari, and S. Pollin, “Energy-constrained UAV trajectory
design for ground node localization,” in Proc. IEEE Global Commun.
Conf. (GLOBECOM), 2018, pp. 1–7.

[383]
J. Park, H. Lee, S. Eom, and I. Lee, “UAV-aided wireless powered
communication networks: Trajectory optimization and resource allocation for
minimum throughput maximization,” IEEE Access, vol. 7, pp.
134 978–134 991, 2019.

[384]
Q. Wu, Y. Zeng, and R. Zhang, “Joint trajectory and communication design for
multi-UAV enabled wireless networks,” IEEE Trans. Wireless Commun.,
vol. 17, no. 3, pp. 2109–2121, 2018.

[385]
W. Feng, J. Tang, Y. Yu, J. Song, N. Zhao, G. Chen, K.-K. Wong, and
J. Chambers, “UAV-enabled SWIPT in IoT networks for emergency
communications,” IEEE Wireless Commun., vol. 27, no. 5, pp. 140–147,

[386]
C. Jeong and S. H. Chae, “Simultaneous wireless information and power transfer
for multi-user UAV-enabled IoT networks,” IEEE Internet Things
J., 2020.

[387]
F. Huang, J. Chen, H. Wang, G. Ding, Y. Gong, and Y. Yang,
“Multiple-UAV-assisted SWIPT in Internet of Things: User association
and power allocation,” IEEE Access, vol. 7, pp. 124 244–124 255,

[388]
J.-M. Kang and C.-J. Chun, “Joint trajectory design, Tx power allocation,
and Rx power splitting for UAV-enabled multicasting SWIPT systems,”
IEEE Systems Journal, vol. 14, no. 3, pp. 3740–3743, 2020.

[389]
M. M. Azari, F. Rosas, and S. Pollin, “Cellular connectivity for UAVs:
Network modeling, performance analysis, and design guidelines,” IEEE
Trans. Wireless Commun., vol. 18, no. 7, pp. 3366–3381, 2019.

[390]
“3rd generation partnership project; technical specification group radio
access network; study on enhanced lte support for aerial vehicles (release
15),” Tech. Rep., Jan. 2018. [Online]. Available:

[391]
M. M. Azari, F. Rosas, A. Chiumento, and S. Pollin, “Coexistence of
terrestrial and aerial users in cellular networks,” in Proc. IEEE
Global Commun. Conf. Workshops (GC Workshops), 2017, pp. 1–6.

[392]
M. M. Azari, G. Geraci, A. Garcia-Rodriguez, and S. Pollin, “UAV-to-UAV
communications in cellular networks,” IEEE Trans. Wireless Commun.,
vol. 19, no. 9, pp. 6130–6144, 2020.

[393]
B. Soret, I. Leyva-Mayorga, S. Cioni, and P. Popovski, “5G satellite
networks for IoT: offloading and backhauling,” arXiv preprint
arXiv:2011.05202, 2020.

[394]
Y. Shi, Y. Xia, and Y. Ga, “Joint gateway selection and resource allocation
for cross-tier communication in space-air-ground integrated IoT networks,”
IEEE Access, 2020.

[395]
H. Dai, H. Bian, C. Li, and B. Wang, “UAV-aided wireless communication design
with energy constraint in space-air-ground integrated green IoT networks,”
IEEE Access, vol. 8, pp. 86 251–86 261, 2020.

[396]
B. Zhao, J. Liu, Z. Wei, and I. You, “A deep reinforcement learning based
approach for energy-efficient channel allocation in satellite Internet of
things,” IEEE Access, vol. 8, pp. 62 197–62 206, 2020.

[397]
G. Cui, X. Li, L. Xu, and W. Wang, “Latency and energy optimization for mec
enhanced SAT-IoT networks,” IEEE Access, vol. 8, pp.
55 915–55 926, 2020.

[398]
A. Azari et al., “Grant-free radio access for short-packet
communications over 5G networks,” in Proc. IEEE Global Commun. Conf.
(GLOBECOM), 2017.

[399]
H. Tabassum et al., “Non-orthogonal multiple access (NOMA) in cellular
uplink and downlink: Challenges and enabling techniques,” arXiv
preprint arXiv:1608.05783, 2016.

[400]
Z. Ding et al., “A survey on non-orthogonal multiple access for 5G
networks: Research challenges and future trends,” IEEE J. Sel. Areas
Commun., vol. 35, no. 10, pp. 2181–2195, July 2017.

[401]
O. L. A. López, H. Alves, R. D. Souza, S. Montejo-Sánchez, E. M. G.
Fernández, and M. Latva-aho, “Massive wireless energy transfer: Enabling
sustainable IoT towards 6G era,” arXiv preprint arXiv:1912.05322,

[402]
J. Ding, D. Qu, H. Jiang, and T. Jiang, “Success probability of grant-free
random access with massive MIMO,” IEEE Internet Things J, vol. 6,
no. 1, pp. 506–516, 2019.

[403]
X. Jiang et al., “Low-latency networking: Where latency lurks and how
to tame it,” Proceedings of the IEEE, vol. 107, no. 2, pp. 280–306,
Feb 2019.

[404]
R. Abbas et al., “A novel analytical framework for massive Grant-Free
NOMA,” IEEE Trans. Commun., vol. 67, no. 3, pp. 2436–2449, 2019.

[405]
N. Ye et al., “Deep learning aided grant-free NOMA towards reliable
low-latency access in tactile Internet of things,” IEEE Trans. Ind.
Informat., 2019.

[406]
R1-1808304, “Discussion on the reliability enhancement for grant-free
transmission,” Tech. Rep., Aug. 2018, 3GPP TSG-RAN1 Meeting 94, Gothenburg,

[407]
H. Jiang et al., “Distributed layered grant-free non-orthogonal
multiple access for massive MTC,” in Proc. IEEE Int. Symp. Pers.
Indoor Mobile Radio-commun. (PIMRC), 2018.

[408]
A. Azari, M. Ozger, and C. Cavdar, “Risk-aware resource allocation for
URLLC: Challenges and strategies with machine learning,” IEEE
Commun. Mag, vol. 57, no. 3, pp. 42–48, March 2019.

[409]
Z. Li et al., “2D time-frequency interference modeling using
stochastic geometry for performance evaluation in low-power wide-area
networks,” arXiv preprint arXiv:1606.04791, May 2016.

[410]
Y. Zhang, K. Peng, Z. Chen, and J. Song, “SIC vs. JD: Uplink NOMA
techniques for M2M random access,” in Proc. IEEE Int. Commun. Conf.
(ICC), May 2017.

[411]
M. Masoudi, A. Azari, E. A. Yavuz, and C. Cavdar, “Grant-free radio access
IoT networks: Scalability analysis in coexistence scenarios,” in
Proc. IEEE Int. Commun. Conf. (ICC), 2018, pp. 1–7.

[412]
R. Abbas, M. Shirvanimoghaddam, Y. Li, and B. Vucetic, “Grant-free massive
NOMA: Outage probability and throughput,” arXiv preprint
arXiv:1707.07401, 2017.

[413]
D. D. Wcntzloff, A. Alghaihab, and J. Im, “Ultra-low power receivers for IoT
applications: A review,” in Proc. IEEE Custom Integr. Circuits Conf.,
2020, pp. 1–8.

[414]
H. Khodr, N. Kouzayha, M. Abdallah, J. Costantine, and Z. Dawy, “Energy
efficient IoT sensor with RF wake-up and addressing capability,”
IEEE sensors lett., vol. 1, no. 6, pp. 1–4, 2017.

[415]
T. Sanislav, G. D. Mois, S. Zeadally, and S. C. Folea, “Energy harvesting
techniques for Internet of things (IoT),” IEEE Access, vol. 9, pp.
39 530–39 549, 2021.

[416]
A. Haridas, V. S. Rao, R. V. Prasad, and C. Sarkar, “Opportunities and
challenges in using energy-harvesting for NB-IoT,” ACM Sigbed
Review, vol. 15, no. 5, pp. 7–13, 2018.

[417]
Y. Liu, Y. Deng, M. Elkashlan, A. Nallanathan, J. Yuan, and R. K. Mallik,
“RACH in self-powered NB-IoT networks: Energy availability and
performance evaluation,” arXiv preprint arXiv:2011.11723, 2020.

[418]
M. Eshaghi and R. Rashidzadeh, “An energy harvesting solution for IoT
devices in 5G networks,” in Proc. IEEE Canadian Conf. Electr.
Comput. Eng. (CCECE).1em plus 0.5em minus 0.4emIEEE, 2020, pp.

[419]
N. Kaina, M. Dupré, G. Lerosey, and M. Fink, “Shaping complex microwave
fields in reverberating media with binary tunable metasurfaces,”
Scientific reports, vol. 4, no. 1, pp. 1–8, 2014.

[420]
Z. Zhao, M. Peng, Z. Ding, W. Wang, and H.-H. Chen, “Denoise-and-forward
network coding for two-way relay MIMO systems,” IEEE Trans. Veh.
Technol., vol. 63, no. 2, pp. 775–788, 2013.

[421]
G. Liu, F. R. Yu, H. Ji, V. C. Leung, and X. Li, “In-band full-duplex
relaying: A survey, research issues and challenges,” IEEE
Communications Surveys & Tutorials, vol. 17, no. 2, pp. 500–524, 2015.

[422]
G. Bauch and J. S. Malik, “Cyclic delay diversity with bit-interleaved coded
modulation in orthogonal frequency division multiple access,” IEEE
Trans. Wireless Comm., vol. 5, no. 8, pp. 2092–2100, 2006.

[423]
F. Fang, H. Zhang, J. Cheng, S. Roy, and V. C. Leung, “Joint user scheduling
and power allocation optimization for energy-efficient noma systems with
imperfect csi,” IEEE J. Sel. Areas Commun., vol. 35, no. 12, pp.
2874–2885, 2017.

[424]
D.-T. Do, M. Vaezi, and T.-L. Nguyen, “Wireless powered cooperative relaying
using NOMA with imperfect CSI,” in Proc. IEEE Global Commun. Conf.
Workshops (GC Workshops).1em plus 0.5em minus 0.4emIEEE, 2018,
pp. 1–6.

[425]
S. Arzykulov, T. A. Tsiftsis, G. Nauryzbayev, and M. Abdallah, “Outage
performance of cooperative underlay CR-NOMA with imperfect CSI,” IEEE
Commun. Lett., vol. 23, no. 1, pp. 176–179, 2018.

[426]
Y. Qi, X. Zhang, and M. Vaezi, “Over-the-air implementation of NOMA: New
experiments, existing literature and future directions,” submitted
for publication, 2021.

[427]
S. Shao, P. Hailes, T.-Y. Wang, J.-Y. Wu, R. G. Maunder, B. M. Al-Hashimi, and
L. Hanzo, “Survey of turbo, LDPC, and polar decoder ASIC
implementations,” IEEE Communications Surveys & Tutorials, vol. 21,
no. 3, pp. 2309–2333, 2019.

[428]
B. Zhou and W. Saad, “Joint status sampling and updating for minimizing age
of information in the Internet of Things,” IEEE Trans. Commun.,
vol. 67, no. 11, pp. 7468–7482, 2019.

[429]
B. Wang, S. Feng, and J. Yang, “When to preempt? age of information
minimization under link capacity constraint,” IEEE J. Commun. Netw.,
vol. 21, no. 3, pp. 220–232, 2019.

[430]
B. Zhou, W. Saad, M. Bennis, and P. Popovski, “Risk-aware optimization of age
of information in the Internet of Things,” in Proc. IEEE Int.
Commun. Conf. (ICC), 2020, pp. 1–6.

[431]
F. Farha, H. Ning, K. Ali, L. Chen, and C. Nugent, “SRAM-PUF-based
entities authentication scheme for resource-constrained IoT devices,”
IEEE Int. of Things, vol. 8, no. 7, pp. 5904–5913, 2021.

[432]
J. Yoo, D. Kim, H. Park, M. Shim, C. Lee, and C. Kim, “Physically unclonable
function using ring oscillator collapse in 0.5 V near-threshold voltage for
low-power Internet of Things,” in Proc. IEEE Int. Conf. on Consum.
Electron. Asia (ICCE-Asia), 2018, pp. 206–212.