Resource Allocation in RIS-Aided Wireless Communications: From Optimization to Continual Learning

The quest for physical-layer technologies pivotal to beyond-fifth-generation systems has begun, with Reconfigurable Intelligent Surfaces (RIS) being identified as a promising candidate. RIS can collect wireless signals from a transmitter and passively beamform them toward the receiver, enhancing performance and energy efficiency by reconfiguring the wireless propagation environment. To fully harness the potential of RIS, the development of corresponding resource allocation algorithms for RIS-aided wireless systems is essential.

Due to the unique properties of discrete amplitude coefficients and the passive nature of RIS, existing resource allocation algorithms are not well-suited for RIS-aided wireless systems, impacting both system performance and algorithm efficiency. Additionally, the computational complexity of RIS resource allocation makes it difficult to ensure real-time implementation. To address these issues, this thesis proposes advanced resource allocation algorithms for RIS-aided wireless communications.

Firstly, optimization-based resource allocation algorithms are explored, focusing on the wireless mobile edge computing scenario. Conventional methods struggle with feasibility issues due to RIS’s passive nature and face difficulties with discrete variables arising from transmission and reflection coefficients. To overcome these challenges, the hidden relationship between RIS phase shifts and quality-of-service constraints is exploited and converted into an explicit form for optimization, improving both the feasibility of the algorithms and system performance. For the discrete variables, an efficient smoothing-based method is proposed to decrease convergence error, in contrast to the conventional penalty-based method, which often results in undesired stationary points and local optima. These solutions provide robust frameworks for addressing the specific feasibility and discrete variable issues inherent in RISs, ensuring effective and efficient resource allocation.

Next, to better support the real-time implementation of resource allocation algorithms for RIS-aided wireless systems, the focus is shifted to the deep learning-based method. The limitations of traditional deep learning-based methods are critically analyzed, as they typically perform well only when the training data distribution matches the actual one. However, due to the nonstationary nature of wireless channels, this assumption is often not realizable in real-world scenarios. To address this issue, a novel continual learning paradigm is proposed. This approach enables deep neural networks to continuously learn from new data while preventing catastrophic forgetting, adapting to changes in channel conditions and ensuring robust performance in nonstationary environments. This new paradigm effectively addresses the challenge of resource allocation in RIS-aided wireless systems using traditional deep learning-based methods.

[1] K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang. “The Roadmap to 6G: AI Empowered Wireless Networks”. In: IEEE Commun. Mag. 57.8 (Aug. 2019), pp. 84–90.

[2] X. Yuan, Y.-J. A. Zhang, Y. Shi, W. Yan, and H. Liu. “Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities”. In: IEEE Wireless Commun. 28.2 (Apr. 2021), pp. 136–143.

[3] Q. Wu and R. Zhang. “Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming”. In: IEEE Trans. Wireless Commun. 18.11 (Nov. 2019), pp. 5394–5409.

[4] X. Pei, H. Yin, L. Tan, L. Cao, Z. Li, K. Wang, K. Zhang, and E. Björnson. “RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials”. In: IEEE Trans. Commun. 69.12 (Dec. 2021), pp. 8627–8640.

[5] T. Jiang and W. Yu. “Interference Nulling Using Reconfigurable Intelligent Surface”. In: IEEE J. Sel. Areas Commun. 40.5 (May 2022), pp. 1392–1406.

[6] Q. Wu and R. Zhang. “Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network”. In: IEEE Commun. Mag. 58.1 (Jan. 2020), pp. 106–112.

[7] Y. Liu, X. Mu, J. Xu, R. Schober, Y. Hao, H. V. Poor, and L. Hanzo. “STAR: Simultaneous Transmission and Reflection for 360° Coverage by Intelligent Surfaces”. In: IEEE Wireless Commun. 28.6 (Dec. 2021), pp. 102–109.

[8] S. Zhang, H. Zhang, B. Di, Y. Tan, M. Di Renzo, Z. Han, H. V. Poor, and L. Song. “Intelligent Omni-Surfaces: Ubiquitous Wireless Transmission by Reflective-Refractive Metasurfaces”. In: IEEE Trans. Wireless Commun. 21.1 (Jan. 2022), pp. 219–233.

[9] D. Tse and P. Viswanath. Fundamentals of Wireless Communication. Cambridge, U.K.: Cambridge Univ. Press, 2005.

[10] Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He. “An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel”. In: IEEE Trans. Signal Process. 59.9 (Sept. 2011), pp. 4331–4340.

[11] S. Sesia, I. Toufik, and M. Baker. LTE-The UMTS Long Term Evolution: From Theory to Practice. Hoboken, NJ, USA: Wiley, 2011.

[12] L. Liang, H. Ye, G. Yu, and G. Y. Li. “Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks”. In: Proc. IEEE 108.2 (Feb. 2020), pp. 341–356.

[13] H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos. “Learning to Optimize: Training Deep Neural Networks for Interference Management”. In: IEEE Trans. Signal Process. 66.20 (Oct. 2018), pp. 5438–5453.

[14] F. Hussain, S. A. Hassan, R. Hussain, and E. Hossain. “Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges”. In: IEEE Commun. Surveys Tuts. 22.2 (2nd Quart. 2020), pp. 1251–1275.

[15] W. Lee, O. Jo, and M. Kim. “Intelligent Resource Allocation in Wireless Communications Systems”. In: IEEE Commun. Mag. 58.1 (Jan. 2020), pp. 100–105.

[16] Y. Wang, Y. Li, Q. Shi, and Y.-C. Wu. “ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks”. In: IEEE Trans. Wireless Commun. 23.6 (June 2024), pp. 5330–5344. issn: 1558-2248.

[17] A. Careem, M. Ahamed, and A. Dutta. “Real-time Prediction of Non-stationary Wireless Channels”. In: IEEE Trans. Wireless Commun. 19.12 (Dec. 2020), 7836–7850.

[18] X. Cheng, Z. Huang, and L. Bai. “Channel Nonstationarity and Consistency for Beyond 5G and 6G: A Survey”. In: IEEE Commun. Surveys Tuts. 24.3 (3rd Quart. 2022), pp. 1634–1669.

[19] H. Sun, W. Pu, X. Fu, T.-H. Chang, and M. Hong. “Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective”. In: IEEE Trans. Signal Process. 70 (2022), pp. 1900–1917.

[20] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief. “A Survey on Mobile Edge Computing: The Communication Perspective”. In: IEEE Commun. Surveys Tuts. 19.4 (4th Quart. 2017), pp. 2322–2358.

[21] Y. Qi, Y. Zhou, Y.-F. Liu, L. Liu, and Z. Pan. “Traffic-Aware Task Offloading Based on Convergence of Communication and Sensing in Vehicular Edge Computing”. In: IEEE Internet Things J. 8.24 (Dec. 2021), pp. 17762–17777.

[22] Y. Peng, X. Tang, Y. Zhou, J. Li, Y. Qi, L. Liu, and H. Lin. “Computing and Communication Cost-Aware Service Migration Enabled by Transfer Reinforcement Learning for Dynamic Vehicular Edge Computing Networks”. In: IEEE Trans. Mob. Comput. 23.1 (Jan. 2024), pp. 257–269.

[23] X. Hu, C. Masouros, and K.-K. Wong. “Reconfigurable Intelligent Surface Aided Mobile Edge Computing: From Optimization-Based to Location-Only Learning-Based Solutions”. In: IEEE Trans. Commun. 69.6 (June 2021), pp. 3709–3725.

[24] F. Zhou and R. Q. Hu. “Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks”. In: IEEE Trans. Wireless Commun. 19.5 (May 2020), pp. 3170–3184.

[25] H. Chen, D. Zhao, Q. Chen, and R. Chai. “Joint Computation Offloading and Radio Resource Allocations in Small-Cell Wireless Cellular Networks”. In: IEEE Trans. Green Commun. Netw. 4.3 (Sept. 2020), pp. 745–758.

[26] Z. Chu, P. Xiao, M. Shojafar, D. Mi, J. Mao, and W. Hao. “Intelligent Reflecting Surface Assisted Mobile Edge Computing for Internet of Things”. In: IEEE Wireless Commun. Lett. 10.3 (Mar. 2021), pp. 619–623.

[27] H. Xie, M. Xia, P. Wu, S. Wang, and H. V. Poor. “Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication”. In: IEEE Trans. Wireless Commun. 22.12 (Dec. 2023), pp. 9517–9532.

[28] B. L. Ng, J. S. Evans, S. V. Hanly, and D. Aktas. “Distributed Downlink Beamforming With Cooperative Base Stations”. In: IEEE Trans. Inf. Theory 54.12 (Dec. 2008), pp. 5491–5499.

[29] L. Liu, Y. Zhou, W. Zhuang, J. Yuan, and L. Tian. “Tractable Coverage Analysis for Hexagonal Macrocell-Based Heterogeneous UDNs With Adaptive Interference-Aware CoMP”. In: IEEE Trans. Wireless Commun. 18.1 (Jan. 2019), pp. 503–517.

[30] L. Liu, Y. Zhou, V. Garcia, L. Tian, and J. Shi. “Load Aware Joint CoMP Clustering and Inter-Cell Resource Scheduling in Heterogeneous Ultra Dense Cellular Networks”. In: IEEE Trans. Veh. Technol. 67.3 (Mar. 2018), pp. 2741–2755.

[31] Q. Cai, Y. Zhou, L. Liu, Y. Qi, Z. Pan, and H. Zhang. “Collaboration of Heterogeneous Edge Computing Paradigms: How to Fill the Gap between Theory and Practice”. In: IEEE Wireless Commun. 31.1 (Feb. 2024), pp. 110–117.

[32] J. T. Chapman, J. Andreoli-Fang, M. Chauvin, E. C. Reyes, Z. Lu, D. Liu, J. Padden, and A. Bernstein. “Low latency techniques for mobile backhaul over DOCSIS”. In: Proc. IEEE Wireless Commun. Netw. Conf. 2018.

[33] L. Su, C. Yang, and S. Han. “The Value of Channel Prediction in CoMP Systems with Large Backhaul Latency”. In: IEEE Trans. Commun. 61.11 (Nov. 2013), pp. 4577–4590.

[34] K. Raaen and I. Kjellmo. “Measuring Latency in Virtual Reality Systems”. In: Proc. Int. Conf. Entertainment Comput. 2015, pp. 457–462.

[35] Y. Wang, M. Sheng, X. Wang, L. Wang, and J. Li. “Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling”. In: IEEE Trans. Commun. 64.10 (Oct. 2016), pp. 4268–4282.

[36] Z. Chu, P. Xiao, M. Shojafar, D. Mi, W. Hao, J. Shi, and F. Zhou. “Utility Maximization for IRS Assisted Wireless Powered Mobile Edge Computing and Caching (WP-MECC) Networks”. In: IEEE Trans. Commun. 71.1 (Jan. 2023), pp. 457–472.

[37] K. Li, M. Tao, and Z. Chen. “Exploiting Computation Replication for Mobile Edge Computing: A Fundamental Computation-Communication Tradeoff Study”. In: IEEE Trans. Wireless Commun. 19.7 (July 2020), pp. 4563–4578.

[38] Q.-U.-A. Nadeem, H. Alwazani, A. Kammoun, A. Chaaban, M. Debbah, and M.-S. Alouini. “Intelligent Reflecting Surface-Assisted Multi-User MISO Communication: Channel Estimation and Beamforming Design”. In: IEEE Open J. Commun. Soc. 1 (May 2020), pp. 661–680.

[39] S. Abeywickrama, R. Zhang, Q. Wu, and C. Yuen. “Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization”. In: IEEE Trans. Commun. 68.9 (Sept. 2020), pp. 5849–5863.

[40] W. Zhang, Y. Wen, K. Guan, D. Kilper, H. Luo, and D. O. Wu. “Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel”. In: IEEE Trans. Wireless Commun. 12.9 (Sept. 2013), pp. 4569–4581.

[41] S. Bi and Y. J. Zhang. “Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading”. In: IEEE Trans. Wireless Commun. 17.6 (June 2018), pp. 4177–4190.

[42] Y. Ye, L. Shi, X. Chu, R. Q. Hu, and G. Lu. “Resource Allocation in Backscatter-Assisted Wireless Powered MEC Networks With Limited MEC Computation Capacity”. In: IEEE Trans. Wireless Commun. 21.12 (Dec. 2022), pp. 10678–10694.

[43] X. Yang, S. Hua, Y. Shi, H. Wang, J. Zhang, and K. B. Letaief. “Sparse optimization for green edge AI inference”. In: J. Commun. Inf. Netw. 5.1 (Mar. 2020), pp. 1–15.

[44] L. Lovasz. “Randomized Algorithms in Combinatorial Optimization”. In: Comb. Optim., vol. 20, DIMACS Ser. Discrete Math. Theor. Comput. Sci. 20 (1995), pp. 153–179.

[45] F. Neumann. “Combinatorial Optimization and the Analysis of Randomized Search Heuristics”. PhD thesis. Christian-Albrechts Universität Kiel, 2006.

[46] E. Visotsky and U. Madhow. “Optimum Beamforming Using Transmit Antenna Arrays”. In: Proc. IEEE Veh. Technol. Conf. May 1999.

[47] L. Du, S. Shao, G. Yang, J. Ma, Q. Liang, and Y. Tang. “Capacity Characterization for Reconfigurable Intelligent Surfaces Assisted Multiple-Antenna Multicast”. In: IEEE Trans. Wireless Commun. 20.10 (Oct. 2021), pp. 6940–6953.

[48] Z. Liu, Z. Li, M. Wen, Y. Gong, and Y.-C. Wu. “STAR-RIS-Aided Mobile Edge Computing: Computation Rate Maximization With Binary Amplitude Coefficients”. In: IEEE Trans. Commun. 71.7 (July 2023), pp. 4313–4327.

[49] A. Andresen and V. Spokoiny. “Convergence of an Alternating Maximization Procedure”. In: The J. Mach. Learn. Res. 17.1 (Apr. 2016), pp. 2229–2281.

[50] E. Björnson, M. Bengtsson, and B. Ottersten. “Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure [Lecture Notes]”. In: IEEE Signal Process. Mag. 31.4 (July 2014), pp. 142–148.

[51] S. Akila. “l0 Sparse Signal Processing and Model Selection with Applications”. PhD thesis. UNSW Sydney, 2012.

[52] I. Pólik and T. Terlaky. Interior Point Methods for Nonlinear Optimization. Berlin, Germany: Springer, 2010.

[53] R. Gribonval and M. Nielsen. “Sparse Representations in Unions of Bases”. In: IEEE Trans. Inf. Theory 49.12 (Dec. 2003), pp. 3320–3325.

[54] Z. Li, S. Wang, Q. Lin, Y. Li, M. Wen, Y.-C. Wu, and H. V. Poor. “Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods”. In: Network 2.3 (2022), pp. 398–418.

[55] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen. “Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication”. In: IEEE Trans. Wireless Commun. 18.8 (Nov. 2019), pp. 4157–4170.

[56] Y. Chen, M. Wen, E. Basar, Y.-C. Wu, L. Wang, and W. Liu. “Exploiting Reconfigurable Intelligent Surfaces in Edge Caching: Joint Hybrid Beamforming and Content Placement Optimization”. In: IEEE Trans. Wireless Commun. 20.12 (Dec. 2021), pp. 7799–7812.

[57] M. T. Heath. Scientific Computing: An Introductory Survey, Revised Second Edition. Philadelphia, PA, USA: SIAM, 2018.

[58] S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.

[59] Y. Li, M. Xia, and Y.-C. Wu. “First-Order Algorithm for Content-Centric Sparse Multicast Beamforming in Large-Scale C-RAN”. In: IEEE Trans. Wireless Commun. 17.9 (Sept. 2018), pp. 5959–5974.

[60] T. Lipp and S. Boyd. “Variations and Extension of the Convex-Concave Procedure”. In: Optim. Eng. 17.2 (2016), pp. 263–287.

[61] K.-G. Nguyen, Q.-D. Vu, L.-N. Tran, and M. Juntti. “Energy Efficiency Fairness for Multi-Pair Wireless-Powered Relaying Systems”. In: IEEE J. Sel. Areas Commun. 37.2 (Feb. 2019), pp. 357–373.

[62] Z.-Q. Luo, W.-K. Ma, A. M.-C. So, Y. Ye, and S. Zhang. “Semidefinite Relaxation of Quadratic Optimization Problems”. In: IEEE Signal Process. Mag. 27.3 (May 2010), pp. 20–34.

[63] Q. Wu and R. Zhang. “Weighted Sum Power Maximization for Intelligent Reflecting Surface Aided SWIPT”. In: IEEE Wireless Commun. Lett. 9.5 (May 2020), pp. 586–590.

[64] T. Bai, C. Pan, Y. Deng, M. Elkashlan, A. Nallanathan, and L. Hanzo. “Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing”. In: IEEE J. Sel. Areas Commun. 38.11 (Nov. 2020), pp. 2666–2682.

[65] R. Pinto Antonioli, I. M. Braga, G. Fodor, Y. C. B. Silva, A. L. F. de Almeida, and W. C. Freitas. “On the Energy Efficiency of Cell-Free Systems With Limited Fronthauls: Is Coherent Transmission Always the Best Alternative?” In: IEEE Trans. Wireless Commun. 21.10 (Oct. 2022), pp. 8729–8743.

[66] H. Q. Ngo, L.-N. Tran, T. Q. Duong, M. Matthaiou, and E. G. Larsson. “On the Total Energy Efficiency of Cell-Free Massive MIMO”. In: IEEE Trans. Green Commun. Netw. 2.1 (Mar. 2018), pp. 25–39.

[67] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, et al. “A View of Cloud Computing”. In: Commun. ACM 53.4 (2010), pp. 50–58.

[68] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications”. In: IEEE Commun. Surveys Tuts. 17.4 (4th Quart. 2015), pp. 2347–2376.

[69] X. Hu, K.-K. Wong, and K. Yang. “Wireless Powered Cooperation-Assisted Mobile Edge Computing”. In: IEEE Trans. Commun. 17.4 (Apr. 2018), pp. 2375–2388.

[70] C. You, K. Huang, H. Chae, and B.-H. Kim. “Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading”. In: IEEE Trans. Wireless Commun. 16.3 (Mar. 2017), pp. 1397–1411.

[71] Z. Li, S. Wang, M. Wen, and Y.-C. Wu. “Secure Multicast Energy-Efficiency Maximization With Massive RISs and Uncertain CSI: First-Order Algorithms and Convergence Analysis”. In: IEEE Trans. Wireless Commun. 21.9 (Sept. 2022), pp. 6818–6833.

[72] Y. Yang, Y. Gong, and Y.-C. Wu. “Intelligent-Reflecting-Surface-Aided Mobile Edge Computing With Binary Offloading: Energy Minimization for IoT Devices”. In: IEEE Internet Things J. 9.15 (Aug. 2022), pp. 12973–12983.

[73] J. Xu, Y. Liu, X. Mu, and O. A. Dobre. “STAR-RISs: Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces”. In: IEEE Commun. Lett. 25.9 (Sept. 2021), pp. 3134–3138.

[74] B. O. Zhu, K. Chen, N. Jia, L. Sun, J. Zhao, T. Jiang, and Y. Feng. “Dynamic Control of Electromagnetic Wave Propagation with the Equivalent Principle Inspired Tunable Metasurface”. In: Sci. Rep. 4.1 (2014), pp. 1–7.

[75] H. Zhang, S. Zeng, B. Di, Y. Tan, M. Di Renzo, M. Debbah, Z. Han, H. V. Poor, and L. Song. “Intelligent Omni-Surfaces for Full-Dimensional Wireless Communications: Principles, Technology, and Implementation”. In: IEEE Commun. Mag. 60.2 (Feb. 2022), pp. 39–45.

[76] S. Zeng, H. Zhang, B. Di, Y. Liu, M. D. Renzo, Z. Han, H. V. Poor, and L. Song. “Intelligent Omni-Surfaces: Reflection-Refraction Circuit Model, Full-Dimensional Beamforming, and System Implementation”. In: IEEE Trans. Wireless Commun. 70.11 (Nov. 2022), pp. 7711–7727.

[77] B. Wu and B. Ghanem. “lp-Box ADMM: A Versatile Framework for Integer Programming”. In: IEEE Trans. Pattern Anal. Mach. Intell. 41.7 (July 2019), pp. 1695–1708.

[78] W. Murray and K.-M. Ng. “An Algorithm for Nonlinear Optimization Problems with Binary Variables”. In: Comput. Optim. Appl. 47.2 (2010), pp. 257–288.

[79] M. Borchardt. “An Exact Penalty Approach for Solving a Class of Minimization Problems with Boolean Variables”. In: Optim. 19.6 (1988), pp. 829–838.

[80] X. Mu, Y. Liu, L. Guo, J. Lin, and R. Schober. “Simultaneously Transmitting and Reflecting (STAR) RIS Aided Wireless Communications”. In: IEEE Trans. Wireless Commun. 21.5 (May 2022), pp. 3083–3098.

[81] T. Zhang, S. Wang, Y. Zhuang, C. You, M. Wen, and Y.-C. Wu. “Reconfigurable Intelligent Surface Assisted OFDM Relaying: Subcarrier Matching With Balanced SNR”. In: IEEE Trans. on Veh. Tech. 72.2 (Feb. 2023), pp. 2216–2230.

[82] R. Horst and H. Tuy. Global Optimization: Deterministic Approaches. Springer Science & Business Media, 2013.

[83] A. V. Fiacco and G. P. McCormick. Nonlinear Programming: Sequential Unconstrained Minimization Techniques. SIAM, 1990.

[84] D. P. Bertsekas. “Nonlinear Programming”. In: J. Oper. Res. Soc. 48.3 (1997), pp. 334–334.

[85] W. Murray and K.-M. Ng. “Algorithms for Global Optimization and Discrete Problems Based on Methods for Local Optimization”. In: Handbook of Global Optimization. Springer, 2002, pp. 87–113.

[86] K. Shen and W. Yu. “Fractional Programming for Communication Systems—Part I: Power Control and Beamforming”. In: IEEE Trans. Signal Process. 66.10 (May 2018), pp. 2616–2630.

[87] K. Shen and W. Yu. “Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching”. In: IEEE Trans. Signal Process. 66.10 (May 2018), pp. 2631–2644.

[88] Y. Ma, Y. Shen, X. Yu, J. Zhang, S. Song, and K. B. Letaief. “A Low-Complexity Algorithmic Framework for Large-Scale IRS-Assisted Wireless Systems”. In: Proc. IEEE Glob. Commun. Conf. Workshops. 2020.

[89] M. Razaviyayn, M. Hong, and Z.-Q. Luo. “A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization”. In: SIAM J. Optim. 23.2 (2013), p. 1126.

[90] H. Guo, Y.-C. Liang, J. Chen, and E. G. Larsson. “Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks”. In: IEEE Trans. Wireless Commun. 19.5 (May 2020), pp. 3064–3076.

[91] S. Zhang and R. Zhang. “Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication”. In: IEEE J. Sel. Areas Commun. 38.8 (Aug. 2020), pp. 1823–1838.

[92] S. Hua, Y. Zhou, K. Yang, Y. Shi, and K. Wang. “Reconfigurable Intelligent Surface for Green Edge Inference”. In: IEEE Trans. Green Commun. Netw. 5.2 (2021), pp. 964–979.

[93] Q. Wu and R. Zhang. “Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts”. In: IEEE Trans. Commun. 68.3 (Mar. 2020), pp. 1838–1851.

[94] Q. Wu and R. Zhang. “Joint Active and Passive Beamforming Optimization for Intelligent Reflecting Surface Assisted SWIPT Under QoS Constraints”. In: IEEE J. Sel. Areas Commun. 38.8 (Aug. 2020), pp. 1735–1748.

[95] H. V. Cheng and W. Yu. “Degree-of-Freedom of Modulating Information in the Phases of Reconfigurable Intelligent Surface”. In: IEEE Trans. Inf. Theory 70.1 (Jan. 2024), pp. 170–188.

[96] Y. Li, S. Zhang, X. Ren, J. Zhu, J. Huang, P. He, K. Shen, Z. Yao, J. Gong, T.-H. Chang, Q. Shi, and Z.-Q. Luo. “Real-World Wireless Network Modeling and Optimization: From Model/Data-Driven Perspective”. In: Chinese J. Electron. 31.6 (Nov. 2022), pp. 991–1012.

[97] J. Zhang, W. Xia, M. You, G. Zheng, S. Lambotharan, and K.-K. Wong. “Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration”. In: IEEE Trans. Wireless Commun. 19.6 (June 2020), pp. 3738–3752.

[98] J. Guo and C. Yang. “Learning Power Allocation for Multi-Cell-Multi-User Systems with Heterogeneous Graph Neural Networks”. In: IEEE Trans. Wireless Commun. 21.2 (Feb. 2022), pp. 884–897.

[99] Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief. “Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis”. In: IEEE J. Sel. Areas Commun. 39.1 (Jan. 2021), pp. 101–115.

[100] J. Guo, C.-K. Wen, and S. Jin. “Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-Cell Massive MIMO Systems”. In: IEEE J. Sel. Areas Commun. 39.7 (July 2021), pp. 1872–1884.

[101] J. M. J. Huttunen, D. Korpi, and M. Honkala. “DeepTx: Deep Learning Beamforming With Channel Prediction”. In: IEEE Trans. Wireless Commun. 22.3 (Mar. 2023), pp. 1855–1867.

[102] X. He, L. Huang, J. Wang, and Y. Gong. “Learn to Optimize RIS Aided Hybrid Beamforming with Out-of-Distribution Generalization”. In: IEEE Trans. Veh. Technol. (early access, Mar. 2024).

[103] T. Van Luong, N. Shlezinger, C. Xu, T. M. Hoang, Y. C. Eldar, and L. Hanzo. “Deep Learning Based Successive Interference Cancellation for the Non-Orthogonal Downlink”. In: IEEE Trans. Veh. Technol. 71.11 (Nov. 2022), pp. 11876–11888.

[104] Z. Liu, Y. Zeng, W. Zhang, and Y. Gong. “Trajectory Design for UAV Communications with No-Fly Zones by Deep Reinforcement Learning”. In: Proc. IEEE Int. Conf. Commun. 2021.

[105] Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief. “LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples”. In: IEEE Trans. Wireless Commun. 19.1 (Jan. 2020), pp. 665–679.

[106] Y. Yuan, G. Zheng, K.-K. Wong, B. Ottersten, and Z.-Q. Luo. “Transfer Learning and Meta Learning-Based Fast Downlink Beamforming Adaptation”. In: IEEE Trans. Wireless Commun. 20.3 (Mar. 2021), pp. 1742–1755.

[107] I. Nikoloska and O. Simeone. “Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks”. In: IEEE Trans. Wireless Commun. 22.1 (Jan. 2023), pp. 457–470.

[108] H. Zhou, W. Xia, H. Zhao, J. Zhang, Y. Ni, and H. Zhu. “Continual Learning-Based Fast Beamforming Adaptation in Downlink MISO Systems”. In: IEEE Wireless Commun. Lett. 12.1 (Jan. 2023), pp. 36–39.

[109] M. Akrout, A. Feriani, F. Bellili, A. Mezghani, and E. Hossain. “Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study”. In: Proc. IEEE Int. Conf. Commun. 2023.

[110] Q. Hou, M. Lee, G. Yu, and Y. Cai. “Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments”. In: IEEE Trans. Commun. 71.9 (Sept. 2023), pp. 5259–5273.

[111] R. M. French. “Catastrophic Forgetting in Connectionist Networks”. In: Trends Cogn. Sci. 3.4 (1999), pp. 128–135.

[112] R. Ratcliff. “Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions.” In: Psychol. Rev. 97.2 (1990), pp. 285–308.

[113] Y. Shi, L. Lian, Y. Shi, Z. Wang, Y. Zhou, L. Fu, L. Bai, J. Zhang, and W. Zhang. “Machine Learning for Large-Scale Optimization in 6G Wireless Networks”. In: IEEE Commun. Surveys Tuts. 25.4 (4th Quart. 2023), pp. 2088–2132.

[114] W.-J. Hsu, T. Spyropoulos, K. Psounis, and A. Helmy. “Modeling Time-Variant User Mobility in Wireless Mobile Networks”. In: Proc. IEEE Int. Conf. Comput. Commun. 2007.

[115] J. Rodríguez-Piñeiro, Z. Huang, X. Cai, T. Domínguez-Bolaño, and X. Yin. “Geometry-Based MPC Tracking and Modeling Algorithm for Time-Varying UAV Channels”. In: IEEE Trans. Wireless Commun. 20.4 (Apr. 2021), pp. 2700–2715.

[116] C. F. López and C.-X. Wang. “Novel 3-D Non-Stationary Wideband Models for Massive MIMO Channels”. In: IEEE Trans. Wireless Commun. 17.5 (May 2018), pp. 2893–2905.

[117] Z. Lian, L. Jiang, C. He, and D. He. “A Non-Stationary 3-D Wideband GBSM for HAP-MIMO Communication Systems”. In: IEEE Trans. Veh. Technol. 68.2 (Feb. 2019), pp. 1128–1139.

[118] Z. Li and D. Hoiem. “Learning without Forgetting”. In: IEEE Trans. Pattern Anal. Mach. Intell. 40.12 (Dec. 2018), pp. 2935–2947.

[119] A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny. “Efficient Lifelong Learning with A-GEM”. In: Proc. Int. Conf. Learn. Represent. 2019.

[120] S. Wang, X. Li, J. Sun, and Z. Xu. “Training Networks in Null Space of Feature Covariance for Continual Learning”. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2021.

[121] G. Saha, I. Garg, and K. Roy. “Gradient Projection Memory for Continual Learning”. In: Proc. Int. Conf. Learn. Represent. 2021.

[122] Y. Kong, L. Liu, Z. Wang, and D. Tao. “Balancing Stability and Plasticity Through Advanced Null Space in Continual Learning”. In: Proc. Eur. Conf. Comput. Vis. 2022.

[123] Z. Liu, Y. Li, Y. Gong, and Y.-C. Wu. “Learning a Low-Rank Feature Representation: Achieving Better Trade-Off between Stability and Plasticity in Continual Learning”. In: Proc. Int. Conf. Acoust., Speech, Signal Process. 2024.

[124] A. Abbasi, P. Nooralinejad, V. Braverman, H. Pirsiavash, and S. Kolouri. “Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations”. In: Proc. Conf. Lifelong Learn. Agents. 2022.

[125] D. Callan. “When is “Rank” Additive?” In: The College Math. J. 29.2 (Mar. 1998), pp. 145–147.

[126] Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell. “Rethinking the Value of Network Pruning”. In: Proc. Int. Conf. Learn. Represent. 2019.

[127] J. Frankle and M. Carbin. “The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks”. In: Proc. Int. Conf. Learn. Represent. 2019.

[128] M. Lin, Q. Chen, and S. Yan. “Network In Network”. In: Proc. Int. Conf. Learn. Represent. 2013.

[129] W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li. “Learning Structured Sparsity in Deep Neural Networks”. In: Proc. Adv. Neural Inf. Process. Systs. 2016.

[130] K. He, X. Zhang, S. Ren, and J. Sun. “Deep Residual Learning for Image Recognition”. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2016.

[131] Y. Li, Z. Chen, Y. Wang, C. Yang, B. Ai, and Y.-C. Wu. “Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection”. In: IEEE Trans. Wireless Commun. 22.5 (May 2023), pp. 3432–3446.

[132] Y. Li and Y.-F. Liu. “HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints”. In: IEEE Trans. Commun. (early access, Apr. 2024).

[133] D. Lopez-Paz and M. Ranzato. “Gradient Episodic Memory for Continual Learning”. In: Proc. Adv. Neural Inf. Process. Systs. 2017.

[134] A. Alkhateeb. “DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications”. In: Proc. Inf. Theory Appl. Workshop. Feb. 2019.