####  VII-C1 Motivations

GDM-based models are characterized by the ability to gradually add Gaussian
noise to the training data, and then learn to restore the original data from
the noise through a backsampling process. The process is similar to a receiver
in a wireless communication system, which is required to recover the
transmitted signal from the noisy received signal.

Thus, in [179], a GDM-based wireless communication channel denoising model is
designed, which can be used as a new module to predict and remove channel
noise after channel equalization, thus improving the overall performance. In
particular, it relies entirely on the forward diffusion process without any
received signal. When applied to a semantic communication system based on
joint source channel coding (JSCC), whether in Rayleigh fading channels or
additive white Gaussian noise (AWGN) channels, the GDM-based channel denoising
model can effectively reduce the distance between the transmitted signal and
the received signal.

####  VII-C2 Case Study: GDM-based Channel Denoising Model

Figure 23: The joint GDM and JSCC system architecture, where GDM is trained
using a specialized noise schedule [179].

As shown in Fig. 23, the joint GDM and JSCC architecture is summarized, where
the training process is as follows.

  * •

Conditional Distribution of The Received Signals: Real-valued and complex-
valued symbols are transformed and transmitted in the wireless channel, where
the transformation combines the effects of Rayleigh fading gain and additive
white Gaussian noise. The received signal is then processed through an MMSE
equalizer to produce an equalized complex signal. Study conditional
distributions of real-valued vectors using known signal and channel state
information. Based on the noise impact and channel state, the signal is
reparameterized and a GDM-based channel denoising model is trained to obtain
noise estimates.

  * •

Training Algorithm of GDM: In the training process of GDM, the original source
signal is first represented in a new parameterized form. At the beginning of
training, the Kullback-Leibler divergence [145] is mainly used to optimize the
variational upper bound of the negative log-likelihood. During training, the
optimal value of a key hyper-parameter is required to be determined. Next, the
optimization objective for a series of loss functions is simplified by re-
parameterization and re-weighting methods. Finally, the overall loss function
is minimized, effectively recovering the original source signal.

Figs. 5 and 6 in [179] show PSNR obtained by three schemes in terms of the SNR
over the AWGN channel and Rayleigh fading channel environments. To achieve
optimal performance, both GDM-based JSCC scheme and JSCC scheme are required
to be retrained for a given SNR. It shows that for different values of SNR,
the GDM-based JSCC scheme is superior to others. For example, over Rayleigh
fading channel with SNR of 20 dB, compared with the JSCC scheme, the GDM-based
JSCC scheme can obtain about 1.06 dB gain.

##  VIII Future Directions

This section elucidates potential research avenues warranting further
examination.

###  VIII-A Space-air-ground Integrated Network

The Space-Air-Ground Integrated Network (SAGIN) is a promising paradigm for
future wireless networks, characterized by its three-dimensional coverage,
high capacity, and reliable communications [180, 181, 182]. However, the
optimization of SAGIN is a complex task due to the high dimensionality of the
network configuration, the heterogeneity of the network elements, and the
dynamic nature of the network environment [183, 184]. GDMs, with their ability
in complex data distribution modeling, could be a powerful tool for optimizing
SAGIN.

  * •

Dynamic Network Environment Modeling and Prediction: The dynamic nature of the
SAGIN environment poses a significant challenge for its optimization [181,
185]. GDMs can be used to model and predict these dynamic network
environments. This would allow for more efficient resource allocation, network
scheduling, and routing strategies, as the predictions could provide valuable
insights into future network states [186].

  * •

Synthetic Network Scenario Generation: Testing and validating network
optimization algorithms require a variety of network scenarios [187]. GDMs can
generate synthetic network scenarios that closely mimic real-world conditions,
providing a robust platform for testing and validating these algorithms.

  * •

Network Scheduling and Routing: SAGIN involves a variety of network elements,
each with its unique characteristics and requirements [188, 189]. GDMs can
capture these unique characteristics and model the complex interactions
between different network elements, facilitating more efficient network
scheduling and routing strategies.

###  VIII-B Extremely Large-Scale MIMO

Extremely Large-Scale MIMO (XL-MIMO) is an emerging technology that is
expected to play a pivotal role in the 6G of wireless mobile networks [190,
191]. XL-MIMO offers vast spatial degrees of freedom by deploying an extremely
large number of antennas, leading to significant enhancements in spectral
efficiency and spatial degrees of freedom. However, implementing XL-MIMO
introduces new challenges, including the need for more flexible hardware
designs, a much larger number of antennas, smaller antenna spacing, new
electromagnetic characteristics, and near-field-based signal processing
schemes [192, 193]. GDMs can be instrumental in addressing these challenges
and optimizing the performance of XL-MIMO systems. Here are some potential
research directions:

  * •

Hybrid Channel Estimation and Modeling: XL-MIMO systems involve a large number
of antennas, leading to high-dimensional data [194], and also the co-existence
of near-field and far-field channels within the coverage of cellular networks.
Especially, in the near-field channel, the channel response vectors depend on
both the distance and direction between the transceiver of each antenna
element, unlike the far-field channel. Therefore, the increased “huge”
complexity for near-field channel estimation may not be resolved with the
conventional approaches. GDMs can be used to model and estimate such hybrid
channel state information efficiently. They can exploit the inherent graph
structure in the spatial domain, where antennas can be considered as nodes and
the spatial correlation between antennas as edges. This can lead to more
accurate and efficient channel estimation methods.

  * •

Signal Processing: The signal processing in XL-MIMO systems can be complex due
to the large number of antennas and the near-field communication
characteristics. Especially, in the latter case, the interference caused by
multi-user transmissions can be effectively mitigated by utilizing the higher
degree of freedom existing in the distance and direction of near-field channel
response vectors. GDMs can be used to develop efficient signal processing
algorithms that can handle high-dimensional data and exploit the spatial
correlation in the antenna array. This can lead to improved performance in
terms of data rate and reliability.

  * •

Hardware Design and Implementation: XL-MIMO systems involve different hardware
designs, such as uniform linear array (ULA)-based, uniform planar array
(UPA)-based, and continuous aperture phased (CAP)-based XL-MIMO. GDMs can be
used to model and analyze these different designs, helping to understand their
characteristics and interrelationships. This can guide the design and
implementation of XL-MIMO systems.

###  VIII-C Integrated Sensing and Communications

The ISAC unifies wireless sensing and communication systems to efficiently
employ limited resources for mutual benefits [195]. It is a key element in
future wireless systems, supporting various applications like autonomous
driving and indoor localization [40, 196]. The GDM can be utilized in ISAC
systems for both data processing and generation. As a processing technique, it
can classify and recover ISAC-related data. Moreover, it can generate
synthetic ISAC data, a vital function for boosting the training efficiency of
neural networks within the ISAC systems. Specifically, GDM has applications in
various aspects of the ISAC system.

  * •

ISAC Data Generation: The GDM can be used to generate samples for ISAC network
training. For example, in indoor localization based on received signal
strength indication (RSSI), the authors in [197] proposed a GAN for RSSI data
augmentation. This network generates fake RSSI based on a small set of real
collected labeled data. Using these data, the experimental results show that
overall localization accuracy of the system has improved by 15.36%. Compared
to GAN, GDM has stronger inference capabilities, which enable it to generate
better fake data, thereby further enhancing system performance.

  * •

ISAC Data Processing: Apart from data generation, GAI models are also commonly
used to process ISAC data [198]. For instance, given that the GAN-based semi-
supervised learning can handle unlabeled and labeled data, the authors in
[199] introduced a complement generator that uses a limited amount of
unlabeled data to generate samples for training the discriminator. Building on
this, they further adjust the number of probability outputs and utilize
manifold regularization to stabilize the learning process, enhancing the human
activity recognition performance in both semi-supervised and supervised
scenarios.

###  VIII-D Movable Antenna System

The future of wireless communication networks is expected to be shaped
significantly by the integration of movable antennas [200, 201]. Movable or
fluid antennas, unlike conventional fixed-position antennas, have the
capability of flexible movement and can be deployed at positions with more
favorable channel conditions to achieve higher spatial diversity gains [202].
This flexibility enables better coverage and adaptability to changing
environmental conditions. By strategically relocating the antenna, it becomes
possible to mitigate signal blockage or interference caused by various
obstacles, including buildings and vegetation. Therefore, the movable antennas
can reap the full diversity in the given spatial region [202]. The complex and
dynamic nature of wireless environments, characterized by high-dimensional
configurations and non-linear relationships, necessitates sophisticated models
like GDMs that can capture such high-dimensional and complex structures.

  * •

Optimization of Antenna Positioning: GDMs can be used to optimize the
positioning of movable antennas in real time. By modeling the wireless
environment and the effects of different antenna positions, GDMs can generate
optimal antenna positions that maximize signal strength and minimize
interference.

  * •

Dynamic Resource Allocation: GDMs can be applied to the dynamic resource
allocation problem in movable antennas. By modeling the resource demands and
availability in the network, GDMs can generate optimal resource allocation
strategies that balance the needs of different network users and maximize
network efficiency [203].

  * •

Predictive Maintenance: Based on historical data, GDMs can be used to predict
potential failures in movable antennas. By modeling antenna performance and
failure patterns, GDMs can generate predictions about future failures,
allowing for proactive maintenance and minimizing network downtime.

  * •

Integration with Reinforcement Learning: As demonstrated in Section III, the
integration of GDMs with reinforcement learning techniques can be further
explored in the context of movable antennas. This can lead to more robust and
efficient resource slicing and scheduling strategies, enhancing the
performance of 5G networks [204] and autonomous vehicles [205].

##  IX Conclusions

In this tutorial, the transformative potential of GDMs in the realm of
intelligent network optimization has been thoroughly explored. The unique
strengths of GDMs, including their broad applicability and capability to model
complex data distributions, were studied. We highlighted their potential in
enhancing the DRL algorithms and providing solutions in several key
intelligent network scenarios, such as incentive mechanism design, SemCom, IoV
networks, channel estimation, error correction coding, and channel denoising.
These explorations demonstrated the practicality and efficacy of GDMs in real-
world applications. The tutorial concluded by emphasizing the research
directions of GDMs in shaping the future of intelligent network optimization
and encouraging further exploration in this promising field.

## References

  * [1] M. Jovanovic and M. Campbell, “Generative artificial intelligence: Trends and prospects,” _Computer_ , vol. 55, no. 10, pp. 107–112, Oct. 2022.
  * [2] P. Korzynski, G. Mazurek, A. Altmann, J. Ejdys, R. Kazlauskaite, J. Paliszkiewicz, K. Wach, and E. Ziemba, “Generative artificial intelligence as a new context for management theories: Analysis of ChatGPT,” _Central Eur. Manag. J._ , 2023.
  * [3] R. Peres, M. Schreier, D. Schweidel, and A. Sorescu, “On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice,” _Int. J. Res. Mark._ , 2023.
  * [4] C. van Dun, L. Moder, W. Kratsch, and M. Röglinger, “ProcessGAN: Supporting the creation of business process improvement ideas through generative machine learning,” _Decis. Support Syst._ , vol. 165, p. 113880, 2023.
  * [5] Accenture, “2023 technology vision report,” https://www.accenture.com/us-en/insights/technology/technology-trends-2023.
  * [6] B. Ni, D. L. Kaplan, and M. J. Buehler, “Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model,” _Chem_ , 2023.
  * [7] R. Srinivasan and K. Uchino, “Biases in generative art: A causal look from the lens of art history,” in _Proc. ACM Conf. Fair. Account. Transp._ , 2021, pp. 41–51.
  * [8] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” _Proc. Adv. Neural Inf. Process. Syst._ , vol. 30, 2017.
  * [9] O. AI, “Gpt-4 technical report,” _arXiv preprint arXiv:2303.08774_ , 2023\.
  * [10] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” _Commun. ACM_ , vol. 63, no. 11, pp. 139–144, Nov. 2020.
  * [11] D. P. Kingma, M. Welling _et al._ , “An introduction to variational autoencoders,” _Found. Trends Mach. Learn._ , vol. 12, no. 4, pp. 307–392, Apr. 2019.
  * [12] D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in _Proc. Int. Conf. Mach. Learn._ , Lille, France, Jul. 2015, pp. 1530–1538.
  * [13] J. Zhao, M. Mathieu, and Y. LeCun, “Energy-based generative adversarial networks,” in _Proc. Int. Conf. Mach. Learn._ , 2017.
  * [14] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in _Proc. Int. Conf. Mach. Learn._ , 2015, pp. 2256–2265.
  * [15] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in _Proc. Int. Conf. Learn. Represent._ , 2021.
  * [16] S. Peng and S. Peng, _Stochastic differential equations_. Springer, 2019.
  * [17] H. Cao, C. Tan, Z. Gao, G. Chen, P.-A. Heng, and S. Z. Li, “A survey on generative diffusion model,” _arXiv preprint arXiv:2209.02646_ , 2022.
  * [18] S. AI, “Stable diffusion,” https://stability.ai/.
  * [19] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” _Adv. Neural Inf. Process. Syst._ , vol. 33, pp. 6840–6851, 2020.
  * [20] J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” _Proc. Int. Conf. Learn. Represent._ , 2020.
  * [21] X. Li, J. Thickstun, I. Gulrajani, P. S. Liang, and T. B. Hashimoto, “Diffusion-lm improves controllable text generation,” _Adv. Neural Inf. Process. Syst._ , vol. 35, pp. 4328–4343, 2022.
  * [22] G. Mittal, J. Engel, C. Hawthorne, and I. Simon, “Symbolic music generation with diffusion models,” _Proc. Int. Society Music Inf. Retr. Conf._ , 2021\.
  * [23] R. Huang, Z. Zhao, H. Liu, J. Liu, C. Cui, and Y. Ren, “Prodiff: Progressive fast diffusion model for high-quality text-to-speech,” in _Proc. ACM Int. Conf. Multimedia_ , 2022, pp. 2595–2605.
  * [24] C. Niu, Y. Song, J. Song, S. Zhao, A. Grover, and S. Ermon, “Permutation invariant graph generation via score-based generative modeling,” in _Proc. Int. Conf. Artif. Intell. Stat._ PMLR, 2020, pp. 4474–4484.
  * [25] C. Vignac, I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard, “DiGress: Discrete denoising diffusion for graph generation,” _arXiv preprint arXiv:2209.14734_ , 2022.
  * [26] X. Chen, J. He, X. Han, and L.-P. Liu, “Efficient and degree-guided graph generation via discrete diffusion modeling,” _arXiv preprint arXiv:2305.04111_ , 2023.
  * [27] X. Peng, J. Guan, Q. Liu, and J. Ma, “MolDiff: Addressing the atom-bond inconsistency problem in 3D molecule diffusion generation,” _arXiv preprint arXiv:2305.07508_ , 2023.
  * [28] M. A. Ketata, C. Laue, R. Mammadov, H. Stark, M. Wu, G. Corso, C. Marquet, R. Barzilay, and T. S. Jaakkola, “DiffDock-PP: Rigid protein-protein docking with diffusion models,” in _Proc. Int. Conf. Learn. Represent._ , 2023.
  * [29] L. Huang, H. Zhang, T. Xu, and K.-C. Wong, “MDM: Molecular diffusion model for 3D molecule generation,” _arXiv preprint arXiv:2209.05710_ , 2022.
  * [30] C. Lee, J. Kim, and N. Park, “CoDi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis,” _arXiv preprint arXiv:2304.12654_ , 2023.
  * [31] A. Kotelnikov, D. Baranchuk, I. Rubachev, and A. Babenko, “TabDDPM: Modelling tabular data with diffusion models,” _arXiv preprint arXiv:2209.15421_ , 2022.
  * [32] N. Neifar, A. Ben-Hamadou, A. Mdhaffar, and M. Jmaiel, “DiffECG: A generalized probabilistic diffusion model for ECG signals synthesis,” _arXiv preprint arXiv:2306.01875_ , 2023.
  * [33] L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, Y. Shao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,” _arXiv preprint arXiv:2209.00796_ , 2022.
  * [34] F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” _IEEE Trans. Pattern Anal. Mach. Intell._ , to appear, 2023.
  * [35] M. Reuss, M. Li, X. Jia, and R. Lioutikov, “Goal-conditioned imitation learning using score-based diffusion policies,” _arXiv preprint arXiv:2304.02532_ , 2023.
  * [36] Y. Li, Y. Lu, R. Zhang, B. Ai, and Z. Zhong, “Deep learning for energy efficient beamforming in MU-MISO networks: A GAT-based approach,” _IEEE Wireless Commun. Lett._ , vol. 12, no. 7, pp. 1264–1268, 2023.
  * [37] C. Lu, H. Chen, J. Chen, H. Su, C. Li, and J. Zhu, “Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning,” _arXiv preprint arXiv:2304.12824_ , 2023.
  * [38] H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, and D. I. Kim, “AI-generated incentive mechanism and full-duplex semantic communications for information sharing,” _IEEE J. Sel. Areas Commun._ , to appear, 2023.
  * [39] B. Du, H. Du, H. Liu, D. Niyato, P. Xin, J. Yu, M. Qi, and Y. Tang, “YOLO-based semantic communication with generative AI-aided resource allocation for digital twins construction,” _arXiv preprint arXiv:2306.14138_ , 2023.
  * [40] X. Cheng, D. Duan, S. Gao, and L. Yang, “Integrated sensing and communications (ISAC) for vehicular communication networks (VCN),” _IEEE Internet Things J._ , vol. 9, no. 23, pp. 23 441–23 451, 2022.
  * [41] W. Yang, H. Du, Z. Q. Liew, W. Y. B. Lim, Z. Xiong, D. Niyato, X. Chi, X. S. Shen, and C. Miao, “Semantic communications for future internet: Fundamentals, applications, and challenges,” _IEEE Commun. Surv. Tut._ , to appear, 2023.
  * [42] L.-M. Ang, K. P. Seng, G. K. Ijemaru, and A. M. Zungeru, “Deployment of IoV for smart cities: Applications, architecture, and challenges,” _IEEE Access_ , vol. 7, pp. 6473–6492, 2018.
  * [43] H. Zhou, H. Zhou, J. Li, K. Yang, J. An, and X. Shen, “Heterogeneous ultra-dense networks with traffic hotspots: A unified handover analysis,” _IEEE Internet Things J._ , to appear, 2023.
  * [44] Y. Lin, Z. Gao, H. Du, D. Niyato, J. Kang, A. Jamalipour, and X. S. Shen, “A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse,” _arXiv preprint arXiv:2305.11911_ , 2023.
  * [45] H. Zhou, B. Liu, T. H. Luan, F. Hou, L. Gui, Y. Li, Q. Yu, and X. Shen, “Chaincluster: Engineering a cooperative content distribution framework for highway vehicular communications,” _IEEE Trans. Intell. Transp Syst._ , vol. 15, no. 6, pp. 2644–2657, Jun. 2014.
  * [46] R. Zhang, K. Xiong, X. Tian, Y. Lu, P. Fan, and K. B. Letaief, “Inverse reinforcement learning meets power allocation in multi-user cellular networks,” in _IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_ , 2022, pp. 1–2.
  * [47] H. Du, D. Niyato, J. Kang, Z. Xiong, K.-Y. Lam, Y. Fang, and Y. Li, “Spear or shield: Leveraging generative AI to tackle security threats of intelligent network services,” _arXiv preprint arXiv:2306.02384_ , 2023.
  * [48] A. Ajay, Y. Du, A. Gupta, J. Tenenbaum, T. Jaakkola, and P. Agrawal, “Is conditional generative modeling all you need for decision-making?” _arXiv preprint arXiv:2211.15657_ , 2022.
  * [49] M. Janner, Y. Du, J. B. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” _arXiv preprint arXiv:2205.09991_ , 2022.
  * [50] Z. Wang, J. J. Hunt, and M. Zhou, “Diffusion policies as an expressive policy class for offline reinforcement learning,” _arXiv preprint arXiv:2208.06193_ , 2022.
  * [51] H. Chen, C. Lu, C. Ying, H. Su, and J. Zhu, “Offline reinforcement learning via high-fidelity generative behavior modeling,” _arXiv preprint arXiv:2209.14548_ , 2022.
  * [52] H.-C. Wang, S.-F. Chen, and S.-H. Sun, “Diffusion model-augmented behavioral cloning,” _arXiv preprint arXiv:2302.13335_ , 2023.
  * [53] A. Kazerouni, E. K. Aghdam, M. Heidari, R. Azad, M. Fayyaz, I. Hacihaliloglu, and D. Merhof, “Diffusion models for medical image analysis: A comprehensive survey,” _arXiv preprint arXiv:2211.07804_ , 2022.
  * [54] C. Zhang, C. Zhang, M. Zhang, and I. S. Kweon, “Text-to-image diffusion model in generative AI: A survey,” _arXiv preprint arXiv:2303.07909_ , 2023\.
  * [55] A. Ulhaq, N. Akhtar, and G. Pogrebna, “Efficient diffusion models for vision: A survey,” _arXiv preprint arXiv:2210.09292_ , 2022.
  * [56] H. Zou, Z. M. Kim, and D. Kang, “Diffusion models in NLP: A survey,” _arXiv preprint arXiv:2305.14671_ , 2023.
  * [57] Y. Li, K. Zhou, W. X. Zhao, and J.-R. Wen, “Diffusion models for non-autoregressive text generation: A survey,” _arXiv preprint arXiv:2303.06574_ , 2023.
  * [58] L. Lin, Z. Li, R. Li, X. Li, and J. Gao, “Diffusion models for time series applications: A survey,” _arXiv preprint arXiv:2305.00624_ , 2023.
  * [59] W. Luo, “A comprehensive survey on knowledge distillation of diffusion models,” _arXiv preprint arXiv:2304.04262_ , 2023.
  * [60] M. Zhang, M. Qamar, T. Kang, Y. Jung, C. Zhang, S.-H. Bae, and C. Zhang, “A survey on graph diffusion models: Generative AI in science for molecule, protein and material,” _arXiv preprint arXiv:2304.01565_ , 2023.
  * [61] C. Zhang, C. Zhang, S. Zheng, M. Zhang, M. Qamar, S.-H. Bae, and I. S. Kweon, “Audio diffusion model for speech synthesis: A survey on text to speech and speech enhancement in generative AI,” _arXiv preprint arXiv:2303.13336_ , 2023.
  * [62] Z. Guo, J. Liu, Y. Wang, M. Chen, D. Wang, D. Xu, and J. Cheng, “Diffusion models in bioinformatics: A new wave of deep learning revolution in action,” _arXiv preprint arXiv:2302.10907_ , 2023.
  * [63] W. Fan, C. Liu, Y. Liu, J. Li, H. Li, H. Liu, J. Tang, and Q. Li, “Generative diffusion models on graphs: Methods and applications,” _arXiv preprint arXiv:2302.02591_ , 2023.
  * [64] H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, H. Huang, and S. Mao, “Generative AI-aided optimization for AI-generated content (AIGC) services in edge networks,” _arXiv preprint arXiv:2303.13052_ , 2023.
  * [65] A. Lou and S. Ermon, “Reflected diffusion models,” _Proc. Int. Conf. Mach. Learn._ , 2023.
  * [66] Q. Zhang, J. Song, X. Huang, Y. Chen, and M.-Y. Liu, “DiffCollage: Parallel generation of large content with diffusion models,” in _Proc. IEEE Conf. Comput. Vis. Pattern Recognit._ , 2023, pp. 10 188–10 198.
  * [67] H. Ni, C. Shi, K. Li, S. X. Huang, and M. R. Min, “Conditional image-to-video generation with latent flow diffusion models,” in _Proc. IEEE Conf. Comput. Vis. Pattern Recognit._ , 2023, pp. 18 444–18 455.
  * [68] W. Enkelmann, “Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences,” _Computer Vision, Graphics, and Image Processing_ , vol. 43, no. 2, pp. 150–177, Feb. 1988.
  * [69] J. Ho, T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet, “Video diffusion models,” _arXiv:2204.03458_ , 2022.
  * [70] P. Yu, S. Xie, X. Ma, B. Jia, B. Pang, R. Gao, Y. Zhu, S.-C. Zhu, and Y. Wu, “Latent diffusion energy-based model for interpretable text modeling.” in _Proc. Int. Conf. Mach. Learn._ , 2022.
  * [71] S. Gong, M. Li, J. Feng, Z. Wu, and L. Kong, “Diffuseq: Sequence to sequence text generation with diffusion models,” _Proc. Int. Conf. Learn. Represent._ , 2022.
  * [72] H. Zhang, X. Liu, and J. Zhang, “Diffusum: Generation enhanced extractive summarization with diffusion,” _Assoc. Comput. Linguist._ , 2023.
  * [73] M. Reid, V. J. Hellendoorn, and G. Neubig, “Diffuser: Diffusion via edit-based reconstruction,” 2023.
  * [74] L. Ruan, Y. Ma, H. Yang, H. He, B. Liu, J. Fu, N. J. Yuan, Q. Jin, and B. Guo, “Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation,” in _Proc. IEEE Conf. Comput. Vis. Pattern Recognit._ , 2023, pp. 10 219–10 228.
  * [75] Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, “DiffWave: A versatile diffusion model for audio synthesis,” in _Proc. Int. Conf. Learn. Represent._ , 2021.
  * [76] J. Liu, C. Li, Y. Ren, F. Chen, and Z. Zhao, “Diffsinger: Singing voice synthesis via shallow diffusion mechanism,” in _Proc. AAAI Conf. Artif. Intell._ , vol. 36, no. 10, 2022, pp. 11 020–11 028.
  * [77] S. Rouard and G. Hadjeres, “CRASH: Raw audio score-based generative modeling for controllable high-resolution drum sound synthesis,” _Proc. Int. Society Music Inf. Retr. Conf._ , 2021.
  * [78] A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen, “GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models,” in _Int. Conf. Mach. Learn._ , 2022, pp. 16 784–16 804.
  * [79] OpenAI, “Dall·e 2,” https://openai.com/dall-e-2.
  * [80] B. T. Google Research, “Imagen,” https://imagen.research.google/.
  * [81] J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, “A review on generative adversarial networks: Algorithms, theory, and applications,” _IEEE Trans. Knowl. Data Eng._ , vol. 35, no. 4, pp. 3313–3332, Apr. 2021.
  * [82] H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, X. S. Shen, and H. V. Poor, “Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks,” _IEEE Netw._ , no. 99, pp. 1–8, 2023\.
  * [83] A. Zappone, M. Di Renzo, M. Debbah, T. T. Lam, and X. Qian, “Model-aided wireless artificial intelligence: Embedding expert knowledge in deep neural networks for wireless system optimization,” _IEEE Veh. Technol. Mag._ , vol. 14, no. 3, pp. 60–69, Mar. 2019.
  * [84] X. Lin, N. B. Shroff, and R. Srikant, “A tutorial on cross-layer optimization in wireless networks,” _IEEE J. Sel. Areas Commun._ , vol. 24, no. 8, pp. 1452–1463, Aug. 2006.
  * [85] Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, and A. Jamalipour, “Deep generative model and its applications in efficient wireless network management: A tutorial and case study,” _arXiv preprint arXiv:2303.17114_ , 2023.
  * [86] I. Osband, C. Blundell, A. Pritzel, and B. Van Roy, “Deep exploration via bootstrapped DQN,” _Adv. Neural Inf. Process. Syst._ , vol. 29, 2016.
  * [87] T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in _Proc. Int. Conf. Mach. Learn._ PMLR, 2018, pp. 1861–1870.
  * [88] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” _arXiv preprint arXiv:1707.06347_ , 2017\.
  * [89] A. Goldsmith, _Wireless communications_. Cambridge university press, 2005.
  * [90] B. Zheng and R. Zhang, “Intelligent reflecting surface-enhanced OFDM: Channel estimation and reflection optimization,” _IEEE Wireless Commun. Lett._ , vol. 9, no. 4, pp. 518–522, Apr. 2019.
  * [91] S. Desale, A. Rasool, S. Andhale, and P. Rane, “Heuristic and meta-heuristic algorithms and their relevance to the real world: A survey,” _Int. J. Comput. Eng. Res. Trends_ , vol. 351, no. 5, pp. 2349–7084, May 2015.
  * [92] W. Yu, W. Rhee, S. Boyd, and J. M. Cioffi, “Iterative water-filling for gaussian vector multiple-access channels,” _IEEE Trans. Inf. Theory_ , vol. 50, no. 1, pp. 145–152, Jan. 2004.
  * [93] A. Feriani and E. Hossain, “Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial,” _IEEE Commun. Surv. Tut._ , vol. 23, no. 2, pp. 1226–1252, Feb. 2021.
  * [94] Y. Yu, T. Wang, and S. C. Liew, “Deep-reinforcement learning multiple access for heterogeneous wireless networks,” _IEEE J. Sel. Areas Commun._ , vol. 37, no. 6, pp. 1277–1290, Jun. 2019.
  * [95] H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, D. I. Kim _et al._ , “Enabling AI-generated content (AIGC) services in wireless edge networks,” _arXiv preprint arXiv:2301.03220_ , 2023.
  * [96] H. Zhou, Y. Wu, Y. Hu, and G. Xie, “A novel stable selection and reliable transmission protocol for clustered heterogeneous wireless sensor networks,” _Comput. Commun._ , vol. 33, no. 15, pp. 1843–1849, 2010.
  * [97] W.-K. Ching and M. K. Ng, “Markov chains,” _Models, algorithms and applications_ , 2006.
  * [98] X. Xu, B. Shen, S. Ding, G. Srivastava, M. Bilal, M. R. Khosravi, V. G. Menon, M. A. Jan, and M. Wang, “Service offloading with deep Q-network for digital twinning-empowered internet of vehicles in edge computing,” _iEEE Trans. Industr. Inform._ , vol. 18, no. 2, pp. 1414–1423, Feb. 2020\.
  * [99] M. Ohira, K. Takano, and Z. Ma, “A novel deep-Q-network-based fine-tuning approach for planar bandpass filter design,” _IEEE Microw. Wireless Compon. Lett._ , vol. 31, no. 6, pp. 638–641, Jun. 2021.
  * [100] A. Iqbal, M.-L. Tham, and Y. C. Chang, “Double deep Q-network-based energy-efficient resource allocation in cloud radio access network,” _IEEE Access_ , vol. 9, pp. 20 440–20 449, 2021.
  * [101] H. Hasselt, “Double Q-learning,” _Proc. Adv. Neural Inf. Process. Syst._ , vol. 23, 2010.
  * [102] S. Vimal, M. Khari, R. G. Crespo, L. Kalaivani, N. Dey, and M. Kaliappan, “Energy enhancement using multiobjective ant colony optimization with double Q learning algorithm for IoT based cognitive radio networks,” _Comput. Commun._ , vol. 154, pp. 481–490, 2020.
  * [103] N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y.-C. Liang, and D. I. Kim, “Applications of deep reinforcement learning in communications and networking: A survey,” _IEEE Commun. Surv. Tut._ , vol. 21, no. 4, pp. 3133–3174, Apr. 2019.
  * [104] Y. Xu, W. Xu, Z. Wang, J. Lin, and S. Cui, “Load balancing for ultradense networks: A deep reinforcement learning-based approach,” _IEEE Internet Things J._ , vol. 6, no. 6, pp. 9399–9412, Jun. 2019.
  * [105] R. Zhang, K. Xiong, W. Guo, X. Yang, P. Fan, and K. B. Letaief, “Q-learning-based adaptive power control in wireless RF energy harvesting heterogeneous networks,” _IEEE Syst. J._ , vol. 15, no. 2, pp. 1861–1872, 2021.
  * [106] X. Tian, K. Xiong, R. Zhang, P. Fan, D. Niyato, and K. B. Letaief, “Sum rate maximization in muti-cell muti-user networks: An inverse reinforcement learning-based approach,” _IEEE Wireless Commun. Lett._ , pp. 1–1, 2023\.
  * [107] R. Zhang, K. Xiong, Y. Lu, B. Gao, P. Fan, and K. B. Letaief, “Joint coordinated beamforming and power splitting ratio optimization in MU-MISO SWIPT-enabled hetnets: A multi-agent DDQN-based approach,” _IEEE J. Sel. Areas Commun._ , vol. 40, no. 2, pp. 677–693, 2022.
  * [108] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski _et al._ , “Human-level control through deep reinforcement learning,” _Nature_ , vol. 518, no. 7540, pp. 529–533, 2015.
  * [109] T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized experience replay,” _arXiv preprint arXiv:1511.05952_ , 2015.
  * [110] M. Hausknecht and P. Stone, “Deep recurrent Q-learning for partially observable MDPs,” in _AAAI Fall Symposium Series_ , 2015.
  * [111] R. Zhang, K. Xiong, Y. Lu, P. Fan, D. W. K. Ng, and K. B. Letaief, “Energy efficiency maximization in RIS-assisted SWIPT networks with rsma: A PPO-based approach,” _IEEE J. Sel. Areas Commun._ , vol. 41, no. 5, pp. 1413–1430, 2023.
  * [112] R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” _Reinforcement Learning_ , pp. 5–32, 1992.
  * [113] M. Hessel, J. Modayil, H. Van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver, “Rainbow: Combining improvements in deep reinforcement learning,” in _Proc. AAAI Conf. Artif. Intell._ , vol. 32, no. 1, 2018.
  * [114] C. Wang, L. Liu, C. Jiang, S. Wang, P. Zhang, and S. Shen, “Incorporating distributed DRL into storage resource optimization of space-air-ground integrated wireless communication network,” _IEEE J. Sel. Top. Signal Process._ , vol. 16, no. 3, pp. 434–446, Mar. 2021.
  * [115] Y. Li, X. Hu, Y. Zhuang, Z. Gao, P. Zhang, and N. El-Sheimy, “Deep reinforcement learning (DRL): Another perspective for unsupervised wireless localization,” _IEEE Internet Things J._ , vol. 7, no. 7, pp. 6279–6287, Jul. 2019.
  * [116] J. Tang, A. Mihailovic, and H. Aghvami, “Constructing a DRL decision making scheme for multi-path routing in all-IP access network,” in _Proc. IEEE Global Commun. Conf._ IEEE, 2022, pp. 3623–3628.
  * [117] Y. Zhang, Y. Lu, R. Zhang, B. Ai, and D. Niyato, “Deep reinforcement learning for secrecy energy efficiency maximization in ris-assisted networks,” _IEEE Trans. Veh. Technol._ , pp. 1–6, 2023.
  * [118] N. M. Ashraf, R. R. Mostafa, R. H. Sakr, and M. Rashad, “Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm,” _Plos one_ , vol. 16, no. 6, p. e0252754, 2021.
  * [119] J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine, “D4RL: Datasets for deep data-driven reinforcement learning,” _arXiv preprint arXiv:2004.07219_ , 2020.
  * [120] E. Zhang, Y. Lu, W. Wang, and A. Zhang, “LAD: Language augmented diffusion for reinforcement learning,” _arXiv preprint arXiv:2210.15629_ , 2022.
  * [121] H. Wang, Y. Wu, S. Guo, and L. Wang, “PDPP: Projected diffusion for procedure planning in instructional videos,” _arXiv preprint arXiv:2303.14676_ , 2023.
  * [122] J. Brehmer, J. Bose, P. De Haan, and T. Cohen, “EDGI: Equivariant diffusion for planning with embodied agents,” _arXiv preprint arXiv:2303.12410_ , 2023.
  * [123] Y. Cao, E. Rizk, S. Vlaski, and A. H. Sayed, “Multi-agent adversarial training using diffusion learning,” in _ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2023, pp. 1–5.
  * [124] Z. Liang, Y. Mu, M. Ding, F. Ni, M. Tomizuka, and P. Luo, “AdaptDiffuser: Diffusion models as adaptive self-evolving planners,” _arXiv preprint arXiv:2302.01877_ , 2023.
  * [125] T. Pearce, T. Rashid, A. Kanervisto, D. Bignell, M. Sun, R. Georgescu, S. V. Macua, S. Z. Tan, I. Momennejad, K. Hofmann _et al._ , “Imitating human behaviour with diffusion models,” _arXiv preprint arXiv:2301.10677_ , 2023\.
  * [126] F. Vargas, T. Reu, and A. Kerekes, “Expressiveness remarks for denoising diffusion models and samplers,” _arXiv preprint arXiv:2305.09605_ , 2023\.
  * [127] R. Liu, H. Liu, D. Kwak, Y. Xiang, C. Borcea, B. Nath, and L. Iftode, “Balanced traffic routing: Design, implementation, and evaluation,” _Ad Hoc Networks_ , vol. 37, pp. 14–28, 2016.
  * [128] D. Watson, W. Chan, J. Ho, and M. Norouzi, “Learning fast samplers for diffusion models by differentiating through sample quality,” in _Int. Conf. Learn. Represent._ , 2021.
  * [129] S. Hong, G. Lee, W. Jang, and S. Kim, “Improving sample quality of diffusion models using self-attention guidance,” _arXiv preprint arXiv:2210.00939_ , 2022.
  * [130] Z. Lyu, X. Xu, C. Yang, D. Lin, and B. Dai, “Accelerating diffusion models via early stop of the diffusion process,” _arXiv preprint arXiv:2205.12524_ , 2022.
  * [131] M. Dai, L. Luo, J. Ren, H. Yu, and G. Sun, “PSACCF: Prioritized online slice admission control considering fairness in 5G/B5G networks,” _IEEE Trans. Netw. Sci. Eng._ , vol. 9, no. 6, pp. 4101–4114, Jun. 2022.
  * [132] 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._ , 2017, pp. 3389–3396.
  * [133] N. Khaneja, T. Reiss, C. Kehlet, T. Schulte-Herbrüggen, and S. J. Glaser, “Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms,” _J. Magn. Reson._ , vol. 172, no. 2, pp. 296–305, Feb. 2005.
  * [134] S. Zhao, M. Gong, T. Liu, H. Fu, and D. Tao, “Domain generalization via entropy regularization,” _Adv. Neural Inf. Process. Syst._ , vol. 33, pp. 16 096–16 107, 2020.
  * [135] G. Tesauro _et al._ , “Temporal difference learning and TD-Gammon,” _Commun. ACM_ , vol. 38, no. 3, pp. 58–68, 1995.
  * [136] K. Cobbe, C. Hesse, J. Hilton, and J. Schulman, “Leveraging procedural generation to benchmark reinforcement learning,” in _Proc. Int. Conf. Mach. Learn._ , vol. 119. PMLR, 13–18 Jul 2020, pp. 2048–2056.
  * [137] A. Raffin, “Rl baselines zoo,” https://github.com/araffin/rl-baselines-zoo, 2018.
  * [138] E. Aubry, T. Silverston, A. Lahmadi, and O. Festor, “Crowdout: A mobile crowdsourcing service for road safety in digital cities,” in _2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)_ , 2014, pp. 86–91.
  * [139] Y. Liu, K. Wang, Y. Lin, and W. Xu, “$\mathsf{LightChain}$: A lightweight blockchain system for industrial Internet of Things,” _IEEE Trans. Indust. Inform._ , vol. 15, no. 6, pp. 3571–3581, June. 2019.
  * [140] R. Zeng, C. Zeng, X. Wang, B. Li, and X. Chu, “A comprehensive survey of incentive mechanism for federated learning,” _arXiv preprint arXiv:2106.15406_ , 2021.
  * [141] D. Yang, G. Xue, J. Zhang, A. Richa, and X. Fang, “Coping with a smart jammer in wireless networks: A stackelberg game approach,” _IEEE Trans. Wireless Commun._ , vol. 12, no. 8, pp. 4038–4047, Aug. 2013.
  * [142] X. Chen, Y. Deng, G. Zhu, D. Wang, and Y. Fang, “From resource auction to service auction: An auction paradigm shift in wireless networks,” _IEEE Wirel. Commun._ , vol. 29, no. 2, pp. 185–191, Apr. 2022.
  * [143] J. Kang, Z. Xiong, D. Niyato, D. Ye, D. I. Kim, and J. Zhao, “Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory,” _IEEE Trans. Veh. Technol._ , vol. 68, no. 3, pp. 2906–2920, 2019.
  * [144] Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, C. Miao, Xuemin, Shen, and A. Jamalipour, “Blockchain-empowered lifecycle management for AI-Generated Content (AIGC) products in edge networks,” _arXiv preprint arXiv:2303.02836_ , 2023.
  * [145] A. Kumar, “Model complexity,” https://vitalflux.com/model-complexity-overfitting-in-machine-learning/.
  * [146] Microsoft, “The relationshio between model size and performance,” https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering/llm-models.
  * [147] H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and X. Shen, “Semantic communications for wireless sensing: RIS-aided encoding and self-supervised decoding,” _IEEE J. Sel. Areas Commun._ , to appear, 2023.
  * [148] H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, M. Guizani, and D. I. Kim, “Rethinking wireless communication security in semantic internet of things,” _IEEE Wireless Commun. Mag._ , to appear, 2023.
  * [149] J. Kang, H. Du, Z. Li, Z. Xiong, S. Ma, D. Niyato, and Y. Li, “Personalized saliency in task-oriented semantic communications: Image transmission and performance analysis,” _IEEE J. Sel. Areas Commun._ , vol. 41, no. 1, pp. 186–201, Jan. 2022.
  * [150] Y. Lin, Z. Gao, Y. Tu, H. Du, D. Niyato, J. Kang, and H. Yang, “A Blockchain-based Semantic Exchange Framework for Web 3.0 toward Participatory Economy,” _IEEE Commun Mag_ , 2023.
  * [151] Y. Lin, Z. Gao, H. Du, D. Niyato, J. Kang, R. Deng, and X. S. Shen, “A unified blockchain-semantic framework for wireless edge intelligence enabled web 3.0,” _IEEE Wirel Commun_ , 2023.
  * [152] Y. Lin, H. Du, D. Niyato, J. Nie, J. Zhang, Y. Cheng, and Z. Yang, “Blockchain-aided secure semantic communication for AI-generated content in metaverse,” _IEEE Open J. Comput. Soc._ , vol. 4, pp. 72–83, 2023.
  * [153] H. Zhang, S. Shao, M. Tao, X. Bi, and K. B. Letaief, “Deep learning-enabled semantic communication systems with task-unaware transmitter and dynamic data,” _IEEE J. Sel. Areas Commun._ , vol. 41, no. 1, pp. 170–185, 2022\.
  * [154] A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy, “Deep variational information bottleneck,” _arXiv preprint arXiv:1612.00410_ , 2016.
  * [155] H. Du, J. Liu, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and D. I. Kim, “Attention-aware resource allocation and QoE analysis for metaverse xURLLC services,” _IEEE J. Sel. Areas Commun._ , to appear, 2023.
  * [156] Y. Liu, H. Yu, S. Xie, and Y. Zhang, “Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks,” _IEEE Trans. Veh. Technol._ , vol. 68, no. 11, pp. 11 158–11 168, 2019\.
  * [157] L. Yan, Z. Qin, R. Zhang, Y. Li, and G. Y. Li, “Qoe-aware resource allocation for semantic communication networks,” in _Proc. IEEE Global Commun. Conf._ IEEE, 2022, pp. 3272–3277.
  * [158] R. Zhang, K. Xiong, H. Du, D. Niyato, J. Kang, X. Shen, and H. V. Poor, “Generative AI-enabled vehicular networks: Fundamentals, framework, and case study,” _arXiv preprint arXiv:2304.11098_ , 2023.
  * [159] H. Zhou, W. Xu, J. Chen, and W. Wang, “Evolutionary V2X technologies toward the internet of vehicles: Challenges and opportunities,” _Proc. IEEE_ , vol. 108, no. 2, pp. 308–323, Feb. 2020.
  * [160] W. Duan, J. Gu, M. Wen, G. Zhang, Y. Ji, and S. Mumtaz, “Emerging technologies for 5G-IoV networks: Applications, trends and opportunities,” _IEEE Net._ , vol. 34, no. 5, pp. 283–289, 2020.
  * [161] H. Zhou, W. Xu, J. Chen, and W. Wang, “Evolutionary v2x technologies toward the Internet of Vehicles: Challenges and opportunities,” _Proc. IEEE_ , vol. 108, no. 2, pp. 308–323, 2020.
  * [162] L. Liang, G. Y. Li, and W. Xu, “Resource allocation for D2D-enabled vehicular communications,” _IEEE Trans. Commun._ , vol. 65, no. 7, pp. 3186–3197, 2017.
  * [163] S. Huang, Z. Wang, P. Li, B. Jia, T. Liu, Y. Zhu, W. Liang, and S.-C. Zhu, “Diffusion-based generation, optimization, and planning in 3D scenes,” in _Proc. IEEE CVPR_ , June 2023, pp. 16 750–16 761.
  * [164] L. Liang, H. Ye, and G. Y. Li, “Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,” _IEEE J. Sel. Areas Commun._ , vol. 37, no. 10, pp. 2282–2292, 2019.
  * [165] K. Dovelos, M. Matthaiou, H. Q. Ngo, and B. Bellalta, “Channel estimation and hybrid combining for wideband terahertz massive mimo systems,” _IEEE J. Sel. Areas Commun._ , vol. 39, no. 6, pp. 1604–1620, Jun. 2021.
  * [166] Y. Liu, Z. Tan, H. Hu, L. J. Cimini, and G. Y. Li, “Channel estimation for OFDM,” _IEEE Commun. Surv. Tutor._ , vol. 16, no. 4, pp. 1891–1908, Apr. 2014.
  * [167] S. J. Nawaz, K. I. Ahmed, M. N. Patwary, and N. M. Khan, “Superimposed training-based compressed sensing of sparse multipath channels,” _IET Commun._ , vol. 6, no. 18, pp. 3150–3156, 2012.
  * [168] Y. Liao, Y. Hua, X. Dai, H. Yao, and X. Yang, “Chanestnet: A deep learning based channel estimation for high-speed scenarios,” in _ICC 2019-2019 IEEE international conference on communications (ICC)_. IEEE, 2019, pp. 1–6.
  * [169] M. Arvinte and J. I. Tamir, “MIMO channel estimation using score-based generative models,” _IEEE Trans. Wireless Commun._ , 2022.
  * [170] Y. Song and S. Ermon, “Improved techniques for training score-based generative models,” _Adv. Neural Inf. Process. Syst._ , vol. 33, pp. 12 438–12 448, 2020.
  * [171] G. Lin, A. Milan, C. Shen, and I. Reid, “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2017, pp. 1925–1934.
  * [172] E. Balevi, A. Doshi, A. Jalal, A. Dimakis, and J. G. Andrews, “High dimensional channel estimation using deep generative networks,” _IEEE J. Sel. Areas Commun._ , vol. 39, no. 1, pp. 18–30, 2020.
  * [173] P. Schniter and A. Sayeed, “Channel estimation and precoder design for millimeter-wave communications: The sparse way,” in _2014 48th Asilomar conference on signals, systems and computers_. IEEE, 2014, pp. 273–277.
  * [174] A. Jalal, M. Arvinte, G. Daras, E. Price, A. G. Dimakis, and J. Tamir, “Robust compressed sensing mri with deep generative priors,” _Adv. Neural Inf. Process. Syst._ , vol. 34, pp. 14 938–14 954, 2021.
  * [175] E. Biglieri, _Coding for wireless channels_. Springer Science & Business Media, 2005.
  * [176] Y. Choukroun and L. Wolf, “Error correction code transformer,” _Adv. Neural Inf. Process. Syst._ , vol. 35, pp. 38 695–38 705, 2022.
  * [177] Z. Chen, J. Qing, T. Xiang, W. L. Yue, and J. H. Zhou, “Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding,” in _Proc. IEEE/CVF CVPR_ , June 2023, pp. 22 710–22 720.
  * [178] Y. Choukroun and L. Wolf, “Denoising diffusion error correction codes,” _arXiv preprint arXiv:2209.13533_ , 2022.
  * [179] T. Wu, Z. Chen, D. He, L. Qian, Y. Xu, M. Tao, and W. Zhang, “CDDM: Channel denoising diffusion models for wireless communications,” _arXiv preprint arXiv:2305.09161_ , 2023.
  * [180] N. Cheng, W. Xu, W. Shi, Y. Zhou, N. Lu, H. Zhou, and X. Shen, “Air-ground integrated mobile edge networks: Architecture, challenges, and opportunities,” _IEEE Commun. Mag._ , vol. 56, no. 8, pp. 26–32, Aug. 2018\.
  * [181] X. Cao, B. Yang, C. Yuen, and Z. Han, “Hap-reserved communications in space-air-ground integrated networks,” _IEEE Trans. Veh. Tech._ , vol. 70, no. 8, pp. 8286–8291, Aug. 2021.
  * [182] H. Du, D. Niyato, Y.-A. Xie, Y. Cheng, J. Kang, and D. I. Kim, “Performance analysis and optimization for jammer-aided multiantenna UAV covert communication,” _IEEE J. Sel. Areas Commun._ , vol. 40, no. 10, pp. 2962–2979, Oct. 2022.
  * [183] D. Li, S. Wu, J. Jiao, N. Zhang, and Q. Zhang, “Age-oriented transmission protocol design in space-air-ground integrated networks,” _IEEE Trans. Wireless Commun._ , vol. 21, no. 7, pp. 5573–5585, Jul. 2022.
  * [184] Z. Jia, M. Sheng, J. Li, and Z. Han, “Toward data collection and transmission in 6G space-air-ground integrated networks: Cooperative HAP and LEO satellite schemes,” _IEEE Internet Things J._ , vol. 9, no. 13, pp. 10 516–10 528, Sept. 2021.
  * [185] H. Cui, J. Zhang, Y. Geng, Z. Xiao, T. Sun, N. Zhang, J. Liu, Q. Wu, and X. Cao, “Space-air-ground integrated network (SAGIN) for 6G: Requirements, architecture and challenges,” _China Commun._ , vol. 19, no. 2, pp. 90–108, Feb. 2022.
  * [186] N. Cheng, W. Quan, W. Shi, H. Wu, Q. Ye, H. Zhou, W. Zhuang, X. Shen, and B. Bai, “A comprehensive simulation platform for space-air-ground integrated network,” _IEEE Wireless Commun._ , vol. 27, no. 1, pp. 178–185, Jan. 2020\.
  * [187] J. Liu, Y. Shi, Z. M. Fadlullah, and N. Kato, “Space-air-ground integrated network: A survey,” _IEEE Commun. Surveys Tuts._ , vol. 20, no. 4, pp. 2714–2741, Apr. 2018.
  * [188] J. Ye, S. Dang, B. Shihada, and M.-S. Alouini, “Space-air-ground integrated networks: Outage performance analysis,” _IEEE Trans. Wireless Commun._ , vol. 19, no. 12, pp. 7897–7912, Dec. 2020.
  * [189] W. Mao, K. Xiong, Y. Lu, P. Fan, and Z. Ding, “Energy consumption minimization in secure multi-antenna UAV-assisted MEC networks with channel uncertainty,” _IEEE Trans. Wireless Commun._ , pp. 1–1, 2023.
  * [190] Z. Wang, J. Zhang, H. Du, E. Wei, B. Ai, D. Niyato, and M. Debbah, “Extremely large-scale MIMO: Fundamentals, challenges, solutions, and future directions,” _IEEE Wireless Commun._ , 2023.
  * [191] H. Du, J. Zhang, J. Cheng, and B. Ai, “Millimeter wave communications with reconfigurable intelligent surfaces: Performance analysis and optimization,” _IEEE Trans. Commun._ , vol. 69, no. 4, pp. 2752–2768, Apr. 2021.
  * [192] Z. Wang, J. Zhang, B. Ai, C. Yuen, and M. Debbah, “Uplink performance of cell-free massive MIMO with multi-antenna users over jointly-correlated Rayleigh fading channels,” _IEEE Trans. Wireless Commun._ , vol. 21, no. 9, pp. 7391–7406, Sept. 2022.
  * [193] H. Du, J. Zhang, K. Guan, D. Niyato, H. Jiao, Z. Wang, and T. Kürner, “Performance and optimization of reconfigurable intelligent surface aided THz communications,” _IEEE Trans. Commun._ , vol. 70, no. 5, pp. 3575–3593, May 2022.
  * [194] Z. Wang, J. Zhang, H. Q. Ngo, B. Ai, and M. Debbah, “Uplink precoding design for cell-free massive MIMO with iteratively weighted MMSE,” _IEEE Trans. Commun._ , vol. 71, no. 3, pp. 1646–1664, Mar. 2023.
  * [195] Y. Cui, F. Liu, X. Jing, and J. Mu, “Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges,” _IEEE Netw._ , vol. 35, no. 5, pp. 158–167, May. 2021.
  * [196] J. Wang, Z. Tian, X. Yang, and M. Zhou, “TWPalo: Through-the-wall passive localization of moving human with Wi-Fi,” _Computer Commun._ , vol. 157, pp. 284–297, 2020.
  * [197] W. Njima, M. Chafii, A. Chorti, R. M. Shubair, and H. V. Poor, “Indoor localization using data augmentation via selective generative adversarial networks,” _IEEE Access_ , vol. 9, pp. 98 337–98 347, 2021.
  * [198] X. Chen, H. Li, C. Zhou, X. Liu, D. Wu, and G. Dudek, “Fido: Ubiquitous fine-grained wifi-based localization for unlabelled users via domain adaptation,” in _Proc. Web Conf._ , 2020, pp. 23–33.
  * [199] C. Xiao, D. Han, Y. Ma, and Z. Qin, “CsiGAN: Robust channel state information-based activity recognition with gans,” _IEEE Internet Things J._ , vol. 6, no. 6, pp. 10 191–10 204, 2019.
  * [200] W. K. New, K.-K. Wong, H. Xu, K.-F. Tong, and C.-B. Chae, “Fluid antenna system: New insights on outage probability and diversity gain,” _IEEE Trans. Wireless Commun._ , to appear, 2023.
  * [201] M. Khammassi, A. Kammoun, and M.-S. Alouini, “A new analytical approximation of the fluid antenna system channel,” _IEEE Trans. Wireless Commun._ , to appear, 2023.
  * [202] A. Shojaeifard, K.-K. Wong, K.-F. Tong, Z. Chu, A. Mourad, A. Haghighat, I. Hemadeh, N. T. Nguyen, V. Tapio, and M. Juntti, “MIMO evolution beyond 5g through reconfigurable intelligent surfaces and fluid antenna systems,” _Proc. IEEE_ , vol. 110, no. 9, pp. 1244–1265, Sept. 2022.
  * [203] L. Tlebaldiyeva, G. Nauryzbayev, S. Arzykulov, A. Eltawil, and T. Tsiftsis, “Enhancing QoS through fluid antenna systems over correlated nakagami-m fading channels,” in _Proc. IEEE Wireless Commun. Netw. Conf._ IEEE, 2022, pp. 78–83.
  * [204] Y. Zhao, F. Zhou, L. Feng, W. Li, and P. Yu, “Madrl-based 3d deployment and user association of cooperative mmwave aerial base stations for capacity enhancement,” _Chinese J. Electron._ , vol. 32, no. 2, pp. 283–294, 2023\.
  * [205] Y. Lin, Z. Gao, H. Du, J. Kang, D. Niyato, Q. Wang, J. Ruan, and S. Wan, “DRL-based adaptive sharding for blockchain-based federated learning,” _IEEE Trans. Commun._ , 2023.