# Guiding AI-Generated Digital Content with Wireless Perception

Jiacheng Wang, Hongyang Du, Dusit Niyato, , Zehui Xiong, Jiawen Kang,
Shiwen Mao , and Xuemin (Sherman) Shen J. Wang, H. Du and D. Niyato are with
the School of Computer Science and Engineering, Nanyang Technological
University, Singapore (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>dniyato@ntu.edu.sg).Z. Xiong is with the Pillar of
Information Systems Technology and Design, Singapore University of Technology
and Design, Singapore (e-mail: zehui_xiong@sutd.edu.sg)J. Kang is with the
School of Automation, Guangdong University of Technology, China. (e-mail:
kavinkang@gdut.edu.cn)S. Mao is with the Department of Electrical and Computer
Engineering, Auburn University, Auburn, USA(e-mail: smao@ieee.org)X. Shen is
with the Department of Electrical and Computer Engineering, University of
Waterloo, Canada (e-mail: sshen@uwaterloo.ca).

###### Abstract

Recent advances in artificial intelligence (AI), coupled with a surge in
training data, have led to the widespread use of AI for digital content
generation, with ChatGPT serving as an representative example. Despite the
increased efficiency and diversity, the inherent instability of AI models
poses a persistent challenge in guiding these models to produce the desired
content for users. In this paper, we introduce an integration of wireless
perception (WP) with AI-generated content (AIGC) and propose a unified WP-AIGC
framework to improve the quality of digital content production. The framework
employs a novel multi-scale perception technology to read user’s posture,
which is difficult to describe accurately in words, and transmits it to the
AIGC model as skeleton images. Based on these images and user’s service
requirements, the AIGC model generates corresponding digital content. Since
the production process imposes the user’s posture as a constraint on the AIGC
model, it makes the generated content more aligned with the user’s
requirements. Additionally, WP-AIGC can also accept user’s feedback, allowing
adjustment of computing resources at edge server to improve service quality.
Experiments results verify the effectiveness of the WP-AIGC framework,
highlighting its potential as a novel approach for guiding AI models in the
accurate generation of digital content.

###### Index Terms:

Wireless perception, AI-generated content, resource adjustment, quality of
service

##  I Introduction

The spectacular growth of various types of data, upgradation of hardware, and
the advancement of artificial intelligence (AI) algorithms, has led to the
emergence of AI generated content (AIGC), which can imitate human behavior to
create digital content [1]. Specifically, AIGC refers to the artificial
intelligence enabled methods (able to automatically produce, manipulate, and
modify multi-modal digital content) and the corresponding generated content
[2]. Due to the ability of automatically producing various kinds of high-
quality digital content, the AIGC is gaining increasing attention, especially
with the rapid integration of the physical world and virtual digital world.

At the function and application levels, AIGC enables autonomous content
creation through AI [3], and boosts the development of various applications.
Taking the virtual interactive game in Metaverse as an example, AIGC can
generate avatars and create the corresponding scenarios according to users’
requirements, thereby constructing a complete virtual world for users to
explore. During this process, the user’s needs, e.g., prompts, can be
transmitted to the AIGC model through various ways such as voice and text [4].
Yet, some information is challenging to convey through words, such as the
user’s posture in the physical world. One feasible solution is to use cameras,
such as Kinect, to capture the user’s image, which is then combined with the
user’s requirements and fed into the AIGC model to generate digital content.
Nevertheless, prolonged use of the camera may raise privacy concerns,
particularly with the rise of face scan payment. Meanwhile, the image
transmission can consume significant transmission resources, such as
bandwidth.

Figure 1: The overall structure of WP-AIGC. During the operation process, the
CSI extracted from wireless signal is first processed, and then a pre-trained
network is used to transform the processed CSI into a skeleton image. On this
basis, the skeleton image, along with the user’s requirements (e.g., prompts),
is passed to the AIGC model to generate digital content, which is finally
presented to the user in the form of images or other media. Users assess the
generated digital content based on their experience, such as whether the
virtual character’s posture matches their actual posture, and provide feedback
to WP-AIGC. Based on feedback received, WP-AIGC adjusts the computing
resources at edge server to further enhance the quality of services (QoS).

Wireless perception is a suitable alternative to cameras for providing AIGC
perception support [5]. The conventional wireless perception demands the
deployment of numerous sensor nodes. However, the popularity of smart wireless
devices, penetration of wireless networks, and the advances in integrated
sensing and communications (ISAC) technologies make the ubiquitous user
perception possible. In particular, when the idea of crowdsensing is brought
in, more sensors are combined to complete a task, which can further boost the
perception quality [6]. Here, different sensors may contribute differently to
the same perception task. Hence, it is essential to adjust the importance of
different sensors to improve perception reliability and coverage.

Building on the above discussion, we present the first wireless perception
based AIGC (WP-AIGC) framework, which seamlessly integrates wireless
perception with AIGC, affording users with better digital content generation
services. Again, taking the virtual game as an example, the proposed framework
is illustrated in Fig. 1. As can be seen, in the physical world, the wireless
perception is used to ‘read’ user’s posture and form the skeleton image.
Subsequently, the AIGC model generate the avatar and the corresponding scene
according to the user’s application requirements and the formed skeleton
image. In WP-AIGC, the number of sensors affects the perception accuracy,
i.e., the similarity between the formed skeleton and the user’s actual pose,
while the inference steps of AIGC affects the quality of generated content. To
balance the perception accuracy and content quality, WP-AIGC receives user’s
feedback on satisfaction with generated content and adjusts the computing
resources at edge server occupied by perception and AIGC accordingly, thereby
enhancing user experience. Overall, the main contributions of this paper are
as follows:

  * •

We propose WP-AIGC, the first framework integrating wireless perception and
AIGC for providing virtual digital services to users. The WP-AIGC includes
wireless perception, AIGC based content generation, and a feedback interface,
which receives user’s feedback and optimizes computing resource at the edge
server to enhance the user experience.

  * •

We propose the concept of multi-scale wireless perception, which refers to
perform large-scale and small-scale perception on users in sequence. Rather
than being independent, the perception of different scales assists each other
by sharing the perception results, thereby improving the overall perception
performance.

  * •

Leveraging the collected wireless perception data and the AIGC model, we
provide a practical and compelling use case to verify the feasibility of the
proposed framework, lighting the way of providing virtual service via the
combination of wireless perception and AIGC.

##  II AIGC and wireless perception technologies

In this section, we provide a comprehensive review of AIGC and wireless
perception technologies.

###  II-A AIGC

The development of AIGC can be divided into three main stages. The initial
stage (around 1950s to 1990s) is characterized by limited technologies, and
only small-scale experiments are feasible, resulting in products such as the
‘Illiac Suite’ [7]. In the second stage (around 1990s to 2010s), the
breakthroughs in deep learning and the transformation of internet services
pushed AIGC into practical applications, taking advantage of the accumulation
of user data. We are now in the third stage (around 2010s to present), with
the emergence of new and better AI models leading to the more powerful and
intelligent AIGC, capable of producing a variety of digital content that
imitates or even exceeds human creations.

Basic to advanced, AIGC functions includes repair, enhance, edit and generate.
Specifically, repair refers to fix missing content, such as using diffusion
models [8] to recover missing pixels in a picture. Besides that, AIGC can also
enhance the image quality by increasing the contrast, improving pixel quality
and clarity, as shown in Fig. 2. The content editing and generation are more
advanced features of AIGC. Concretely, editing means that AIGC is capable of
conducting operations, such modifying and replacing, on the specified content.
Such a function can be used to handle the sensitive content, such as replacing
sensitive people or things without changing the overall layout and style of
the picture [9]. The content generation is the function of AIGC that
distinguishes it from other AI models. Benefiting from a large amount of
training data, algorithm progresses and hardware upgrades, the AIGC now can
generate not only images, but also videos, codes, and manuscripts, which can
greatly improve productivity of digital content. According to the above
described definition and functions, several features of AIGC can be summarized
as follows.

  * •

Automatic. Given a specified task or order, AIGC can automatically produce
digital content and present it in various forms, such as pictures and videos,
which is more productive than traditional ways that often requires human
involvement, such as professionally generated content (PGC) and user generated
content (UGC).

  * •

Interactive. Due to the massive training data and human-like way of thinking,
AIGC can better understand the user’s thoughts or instructions through input,
making AIGC can interact with people in a more natural way than other AI
models. Such a feature is expected to further improve the interaction
experience between humans and machines.

  * •

Creativity. Different from the traditional AI model with limited output space
in most cases (such as a classifier), AIGC have diverse answers to the same
question, demonstrating its certain creative ability that can promote the
diversification of digital content.

  * •

Diversity. The AIGC not only supports multi-modal input, but the generated
digital content can also be presented in different forms, which is one of the
most important characteristics that distinguish AIGC from other AI models. As
a result, AIGC can assist in a variety of digital content production in
different fields.

In Fig. 2, the definition, features and some applications of AIGC are
presented. It is clear that AIGC would significantly improve the content and
information production efficiency in the near future, thereby revolutionizing
the traditional digital content production and consumption mode.

Figure 2: The definition, features, and the corresponding applications of
AIGC.

###  II-B Wireless Perception

Benefiting from the ISAC technologies, using ubiquitous wireless communication
signals for physical world perception gains considerable traction. The
underlying principle of this approach is that the physical environment can
impact the propagation of wireless communication signal, resulting in the
received signal naturally containing information about the environment [10].
By utilizing signal processing techniques, the features of the physical
environment can be extracted, so as to realize the perception of the physical
environment.

In the realm of wireless perception, research mainly concentrates on the
detection, localization, and recognition of users as the primary target of
perception. Specifically, user detection refers to the determination of the
presence or absence of a user in a specific area through the analysis of
fluctuations in the wireless signal. The main steps often include signal noise
reduction, feature extraction and determination. The localization refers to
finding the coordinates of a user relative to a reference object in physical
space. This process mainly contains two key steps, including signal parameters
estimation and user localization, which is typically realized by constructing
geometric constraints based on the estimated parameters. The obtained user’s
location can be used for indoor navigation, emergency evacuation, etc.
Furthermore, recognizing the user’s state, behavior, gesture, and even
identity is also crucial. Here, recognition is often accomplished by
extracting and classifying the signal patterns. Taking user’s physical
activities recognition as an example, a common approach is to extract the
time-frequency characteristics of signal fluctuations, and use machine
learning, such as support vector machines, to achieve activity classification.
We can observe that wireless perception has two notable advantages.

  * •

The first is the wide coverage. Theoretically, wireless perception can be
applied at anywhere covered by wireless signals. However, wireless signals may
differ across different situations, resulting in varying perception
capabilities.

  * •

The second is that users are not required to bear any device during the
perception process, eliminating both the discomfort associated with prolonged
device wear and the need to be concerned with charging.

Based on foregoing discussion, with particular emphasis on the role of AIGC,
the operating principle and advantages of wireless perception, it is natural
to consider using the output of wireless sensing as the input of AIGC, thus
providing users with more automatic and diverse mixed reality service. Yet,
integrating these two technologies poses important technical challenges, which
are discussed next.

##  III The proposed framework

This section presents the proposed WP-AIGC framwork, including research
challenges and implementation processes.

###  III-A Research Challenges

####  III-A1 Multi-scale wireless perception

Different scales of wireless perception contribute differently to the
generated content. For instance, in Fig. 2, the user’s location in the
physical world determines the virtual character’s position in the boxing ring,
while behavior facilitates virtual character construction. Therefore, multi-
scale perception is vital for WP-AIGC. This involves perceiving various types
of information related to the user using the same set of data, while
considering the computational resources consumed by each perception task.
Furthermore, these tasks must be mutually reinforcing to enable multi-scale
perception of targets with limited data.

####  III-A2 Balance of computing resources

Both perception and AIGC require computing resources, and hence another key
issue is to balance the resource consumption. Allocating more resources to
perception can increase perception accuracy but reduce the resources available
to AIGC, leading to lower quality content generation. Conversely, excessive
resource consumption by AIGC may result in insufficient perception accuracy,
leading to the generation of content that fails to meet users’ application
requirements. In practice, the requirements for generated content vary among
individual users. Therefore effective resource balance depends on user
feedback.

####  III-A3 Content quality assessment

The evaluation of content quality is not only directly related to the QoS of
the framework, but also can guide the allocation of computing resources. The
content quality evaluation should consider two major aspects. The first is the
accuracy of the generated content, which depends on the wireless perception
accuracy and whether the user’s requirements are accurately transmitted to
AIGC. The second aspect is the quality of content presentation, such as
whether images are clear or videos are smooth. The evaluation of the first
aspect depends more on user feedback, while the second aspect can be analyzed
through numerical analysis.

####  III-A4 Mutual enhancement between perception and AIGC

The proposed framework involves perception and AIGC, which are independent but
require mutual promotion to enhance the overall performance of the framework.
From the perspective of promoting AIGC through perception, with a certain
level of perception accuracy and data refresh rate, the fewer resources
occupied by perception, the more resources AIGC can use, resulting in more
accurate and higher quality content generation. Similarly, improving AIGC
efficiency can provide perception with more computing resources and thus
enhance its performance to some extent. Besides, there are other more in-depth
approaches that can be adopted, such as adjusting perception algorithms and
strategies by evaluating the accuracy of the generated content.

###  III-B The Proposed WP-AIGC

As shown in Fig. 1, the proposed WP-AIGC framework can be divided into three
main parts. The first part is the multi-scale perception of user in physical
world, which employs a series of techniques such as signal processing and
machine learning to transform wireless signals into user skeleton image. The
second part is the digital content generation, which creates corresponding
digital content based on user needs and skeleton image. The third part is the
feedback control, which adjusts the allocation of computing resources at edge
server according to user feedback, thereby achieving a balance in performance.

####  III-B1 Multi-scale sensing

A straightforward method for multi-scale perception is to use different
algorithms to process the data, to realize various perception purposes. Yet,
during this process, the perception algorithms operate independently and as
such are unable to promote each another. To improve perception accuracy, we
propose to perceive users in order of large-scale to small-scale, and enhance
the perception accuracy by considering the results of large-scale perception
in the process of small-scale perception, as shown in Fig. 3.

Figure 3: The multi-scale perception and training process of the network that
maps signals to skeleton image. Here, TX is the wireless signal transmitter,
and RX is the receiver. The green dashed lines are the transmission links and
the orange concentric circles represent the Fresnel zone formed by TX and RX4.
During training, wireless signals and video signals are collected
synchronously. The CSI amplitude and phase vectors are then extracted from
large to small scales and fed to the network for predicting the skeleton.
Simultaneously, the human skeleton extracted from the video frames is used as
the ground truth to optimize the skeleton predicted by the wireless signals,
so as to completing the training. During the operation, the trained network
can directly convert CSI into skeleton images without the assistance of a
camera.

Specifically, assuming there are several multi-antenna wireless devices with
known locations participating in the perception, where one of them plays as
transmitter and the rest are receiver. Based on the received signal, the
channel state information (CSI) is extracted and the phase error is eliminated
via conjugate multiplication operation.

As the direct signal and static objects introduced reflections do not contain
information about the user, and hence the elimination of such signals is
performed after phase error correction. After that, we use the matrix pencil
algorithm to estimate the angle of arrival (AoA) and time of flight (ToF) of
the user induced reflection, and then utilize the estimation results to build
the constraints and determine the user’s location (large-scale information).
On this basis, we weight the CSI of links based on the distance between the
user and the link (closer links receive higher weights) and sum the amplitude
and phase of the CSI for all links, to get the amplitude and phase vectors,
respectively.

The orientation of the user relative to the link affects the accuracy of
small-scale perception. Based on the user’s location, therefore, further
analysis is conducted on the Fresnel zone formed by the surrounding links of
the user. The Fresnel zone [11] consists of a series of concentric ellipses.
When an object moves perpendicular to the elliptical boundary, it passes
through more ellipses, causing greater fluctuations in the received signal.
Conversely, when it moves parallel to the boundary, the signal fluctuations
are smaller. Thus, by analyzing the signal fluctuations of different links,
the user’s orientation can be determined. Leveraging the obtained orientation,
the amplitude and phase of the CSI of the links are weighted and summed, to
obtain another pair of vector for for user pose perception (small-scale).

####  III-B2 Signal to skeleton conversion

Using the obtained vectors, a neural network is trained to map the extracted
amplitude and phase vectors to human skeletal images. To this end, we
simultaneously use the camera to capture the video stream when collecting CSI
training data, and extract the human skeleton from the video frame as the
ground truth for the training process. Considering temporal and spatial
correlations of user’s state, we aggregate multiple amplitude and phase
vectors as inputs and use the convolutional neural networks as basic blocks to
construct the encoder and decoder to realize mapping process.

Concretely, The encoder network consists of three layers of 3x3 convolutions
with 2x2 strides, each followed by a 1x1 convolutional layer with a stride of
1x1. The ReLu activation functions are applied after each layer and a fully
connected layer is utilized after the final convolutional layer to directly
convert images. The decoder network consists of a total of seven layers, where
the first two layers use 1x1 kernels with a stride of 1x1, and the subsequent
5 layers use 3x3 convolutions with a stride of 1x1. The network is implemented
using TensorFlow and trained over 64 epochs with a batch size of 32, with a
learning rate of 0.001. Notably, the encoder network and decoder network were
jointly trained. During the training phase, the network takes aggregated
amplitude and phase vectors as inputs and produces a corresponding predicted
human skeletal image. Then, the neural network is optimized under the
supervision of skeleton extracted from video frames. The training objective is
to minimize the difference between the predicted skeleton and the
corresponding ground truth skeleton, with the loss function defined as the
average euclidean distance error.

####  III-B3 Digital content generation

After converting wireless signals into skeleton image, the InstructPix2Pix
[12], which can edit images based on human instructions, is utilized to
generate corresponding virtual digital content based on the user’s requests
and the obtained skeletal images.

The construction of InstructPix2Pix mainly consists of two steps: generating
training data and training the model. During the first step, a fine-tuned
GPT-3 is used to generate instructions and edited captions, and then
StableDiffusion [13] is combined with PrompttoPrompt [14] to generate paired
images based on the paired instructions and captions. In the second step,
based on the generated pairs of images and corresponding instructions, a
network is trained to predict the noise added to the noisy latent given the
image conditioning and the text instruction. During the training process, the
available weights of the InstructPix2Pix model are initialized with a pre-
trained stable diffusion checkpoint. Meanwhile, to enable the trained model to
perform conditional or unconditional denoising with respect to either or both
conditional inputs, 5% of the sample images, 5% of the sample instructions,
and 5% of the sample images with instructions are randomly set to an empty
set. Based on such capability, two scale guidance parameters are further
introduced to trade off how strongly the generated samples correspond with the
input image and how strongly they correspond with the edit instruction. After
completion of the training, WP-AIGC utilizes this network to generate digital
content, with the skeleton image from wireless perception as input and user’s
service requirements as editing instructions to meet desired specifications.

####  III-B4 Computing resource adjustment

Besides the above mentioned modules, the WP-AIGC framework features an
interface for collecting user feedback, which is used to adjust computational
resources and improve QoS. During the initial stages of operation, the WP-AIGC
allocates computational resources according to a default ratio that considers
factors such as the number of devices involved in perception and the minimum
number of inference steps required. Meanwhile, Total Variation (TV) and
Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) are used to
monitor the quality of the generated images and ensure that they meet the
basic criteria [2]. Here, the TV characterizes the smoothness of the images,
while BRISQUE quantifies the potential loss of “naturalness” in the images. On
this basis, WP-AIGC optimizes the available resources once it receives
feedback from users. For example, if the feedback reveals a disparity between
the posture of the generated virtual character and user’s actual posture, then
WP-AIGC increases the resources allocated to perception to enhance perception
accuracy.

##  IV Case study

To validate WP-AIGC, we conducted tests in a practical scenario. Specifically,
we built a wireless perception platform based on the 801.11ac protocol. This
platform consists of a signal transmitter and five receivers for signal
reception and CSI extraction. The receivers forwarded the extracted CSI to the
edge server, which used multi-scale perception and a pre-trained neural
network to convert the CSI into the user’s skeletal images in a non-real-time
manner. On this basis, the skeleton image and user’s service requests are fed
to AIGC as guidance to generate corresponding content.

To begin with, we validate the advancement of the proposed multi-scale
perception by comparing the skeleton images generated with and without the use
of multi-scale perception technology. As illustrated in Fig 4, the overall
similarity between the skeletons generated by the two methods and the real
human pose is relatively close, but there are still certain differences in
details, such as the red parts in the skeletal images. Particularly, in the
case that the arms are close to the torso, such as the pose corresponding to
the third row of images, it is difficult to distinguish the signals reflected
from the torso and those from the arms. In this situation, multi-scale
perception produces better results.

Figure 4: Comparison of experimental results of different perception methods.

Following the validation of multi-scale perception, we conduct an analysis on
image generation, using boxing as an example. The results are presented in Fig
5, which reveals that, firstly, the model is capable of generating images
based on skeleton image and user’s instruction, affirming the effectiveness of
the entire WP-AIGC architecture. Secondly, under a constant skeleton data and
instruction set, an increase in the number of inference steps leads to a
significant improvement in the image quality. Such an improvement manifests
itself in two key aspects: enhanced clarity, as indicated by the virtual
character’s facial features; and augmented realism, as demonstrated by the
more natural color scheme of the boxing gloves corresponding to the character
in the image located at the fourth row and third column. Hence, the abundance
of computing resources allocated to AIGC is expected to correspondingly
improve the generated content quality.

Figure 5: The influence of AIGC steps on the generated content. The image on
the left are the input skeleton image, while the image on the right represents
the corresponding generated content. As can be seen, more steps trigger more
natural content, as denoted by the green boxes.

However, in practice, the overall computing resources are often limited,
necessitating a balance between the resources allocated to perception and
AIGC. Thereby, we explore the relationship between perception accuracy
(evaluated by the structure similarity between the generated skeleton and the
real pose) and image quality (measured using TV and BRISQUE) under the
constraint of content refresh rate at 1 Hz. Specifically, assuming a
processing time of 0.1 seconds for one perception link and 0.05 seconds for
generating skeleton images, then the time for AIGC inference in one time slot
is given by $\left({1{\text{ - }}\\#\left({links}\right)\times
0.1-0.05}\right)s$, where $\\#\left({links}\right)$ is the the number of
links. Based on above assumptions, the relationship between perception
accuracy and image quality is illustrated in Fig. 6

Figure 6: Effects of link quantity on structural similarity, TV, and BRISQUE.

The experimental results show that increasing the perceptual link improves the
perception accuracy, i.e., the similarity is increased. However, it reduces
the computing resources of AIGC, resulting in an increase in TV and BRISQUE,
that is, a decrease in image quality. In contrast, reducing the perceptual
links lowers the perceptual accuracy, but improves the image quality. Given
that user’s demands for perceptual accuracy and image quality may vary across
different applications, such results evidently show that it is crucial to
obtain user feedback and adjust computational resources in situations where
resources are limited. Meanwhile, the result provides a reference for the
initial allocation of resources.

##  V Future Directions

###  V-A The AIGC Model Selection Strategy

Nowadays, various models are available for generating virtual digital content,
each of which excels in different domains and consumes different amounts of
computing and communication. Therefore, it is crucial to design an AIGC model
selection mechanism that carefully considers user requirements, resource
availability, and other relevant factors. Furthermore, in situations where
historical data is available, one can leverage user feedback to gain insights
into user preferences and customize the AIGC to the user. For example, if a
user likes to switch virtual characters while playing virtual game, then we
should choose an AIGC model that excels in generating virtual characters from
the pool of models capable of generating virtual game content to further
enhance QoS.

###  V-B Edge Computing Resource Optimization

In deploying AIGC in mobile networks, the edge computing resource optimization
needs to be considered. The resources here include computing resources
required for digital content generation, storage space occupied, and
transmission resources consumed for content delivery, such as bandwidth and
transmitting power [2]. The joint resource optimization model design process
must ensure content quality and minimize response time delays, while also
preventing overload of the AIGC model due to excessive tasks. Additionally,
the impact on other network services must be taken into account. One potential
approach is to utilize deep reinforcement learning to allocate various network
resources.

###  V-C Guiding AIGC with Other Promising Techniques

Regarding the proposed WP-AIGC, further research can be conducted on how to
expand the range that users can move around and the number of users, so as to
improve the system’s practicality. Besides, AIGC can be combined with other
promising technologies, such as eye-tracking and brain-computer interfaces
[15], to guide or even control AIGC in generating more complex digital content
that better meets user’s requirements. However, signals such as human brain
waves are weaker, and are also influenced by human emotions and thoughts,
making them contain more information. Therefore, when using brain waves to
guide the AIGC, the difficulty lies in accurately inferring user needs based
on raw signals and efficiently transmitting these needs to the AIGC model.
Meanwhile, ensuring the security of both the original signal and the generated
content is crucial, and blockchain can be introduced here to achieve these
goals.

##  VI Conclusions

In this article, we have proposed the WP-AIGC framework which leverages the
multi-scale wireless perception technology to sense users in the physical
world. It then sends the perception results to the AIGC model, which generates
digital content according to the user’s service requests. The effectiveness of
the proposed framework has also been verified through experiments. Unlike most
existing works that take descriptive text as input, WP-AIGC takes perception
results as input, which can more accurately convey the user’s behavioral and
status information in physical space to AIGC, resulting in a tighter
connection between the generated content and the user. Additionally, in
designing WP-AIGC, we also considered the issue of balancing computing
resources. To our knowledge, WP-AIGC is the first framework to combine
wireless perception with AIGC technology to achieve digital content
generation. For the future work, we will further investigate specific
technical issues in the process of combining perception with AIGC, thereby
further unleashing the productivity of AIGC.

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