Resource Allocation Algorithm for Reconfigurable Intelligent Surface-Assisted Device-to-Device Communication Network
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摘要:
针对非正交多址（NOMA）和终端直通（D2D）技术在提高异构蜂窝网络容量的同时所带来的严重干扰问题，提出一种高效的资源分配算法以提升系统和速率. 首先，构建智能超表面（RIS）辅助的异构蜂窝NOMA-D2D通信网络模型，在用户信干噪比、发射功率以及RIS相移的单位膜等约束下，建立以最大化D2D用户和速率为目标的优化问题；该问题属于混合整数非线性问题，难以直接求解，为此，将其解耦为D2D用户-蜂窝用户（CU）匹配、D2D用户功率控制与RIS反射相移优化3个子问题；再此基础上，采用二分图最大匹配算法完成D2D簇信道分配，并提出一种基于深度确定性梯度策略（DDPG）的深度强化学习算法，实现对D2D发射功率与RIS相移矩阵的联合优化. 仿真结果表明，在相同条件下，所提算法相较于博弈算法、随机相移算法和无RIS辅助算法，D2D链路和速率的平均值分别提升了1.96%、10.64%和14.29%，验证了其在干扰抑制与频谱效率改善方面的有效性.
Abstract:To address the severe interference problem caused by non-orthogonal multiple access (NOMA) and device-to-device (D2D) technologies in enhancing the capacity of heterogeneous cellular networks, an efficient resource allocation algorithm was proposed to improve the system and rate. Firstly, a reconfigurable intelligent surface (RIS)-assisted heterogeneous cellular NOMA-D2D communication network model was constructed. Under the constraints of signal-to-noise ratio (SNR) of users, transmission power, and unit membrane of RIS phase shift, an optimization problem aiming to maximize the number of D2D users and rate was established. However, this problem was a mixed integer nonlinear problem and was difficult to solve directly. Therefore, it was decomposed into three sub-problems: D2D user-cellular user (CU) matching, D2D user power control, and RIS reflection phase shift optimization. Based on this, the D2D cluster channel allocation was completed by using the bipartite graph maximum matching algorithm, and a deep reinforcement learning algorithm based on a deep deterministic gradient strategy (DDPG) was proposed to jointly optimize the D2D transmission power and RIS phase shift matrix. Simulation results show that under the same conditions, the average value of the D2D link and rate of the proposed algorithm increases by 1.96%, 10.64%, and 14.29%, respectively, compared with that of the game algorithm, random phase shift algorithm, and RIS-assisted-free algorithm, verifying its effectiveness in interference suppression and spectral efficiency improvement.
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图 2 D2D通信簇和CU的信道分配示意
Figure 2. Signal channel allocation for D2D communication clusters and CU
图 4 D2D链路的SINR、和速率与D2D用户最大发射功率的关系
Figure 4. Variation of SINR and sum rate of D2D links with maximum transmission power of D2D users
图 5 D2D链路的SINR、和速率与D2D用户数目的关系
Figure 5. Variation of SINR and sum rate of D2D links with number of D2D users
图 6 D2D链路的SINR、和速率与RIS单元数目的关系
Figure 6. Variation of SINR and sum rate of D2D links with number of RIS units
图 7 D2D链路的SINR、和速率与蜂窝用户发射功率的关系
Figure 7. Variation of SINR and sum rate of D2D links with transmission power of cellular users
图 8 D2D链路的SINR、和速率与蜂窝用户数目的关系
Figure 8. Variation of SINR and sum rate of D2D links with number of cellular users
表 1 仿真参数
Table 1. Simulation parameters
参数 数值 蜂窝小区半径/m 500 路径损耗指数 2 噪声功率谱密度/（dBm·Hz−1） −174 系统带宽/MHz 1 D2D用户最小SINR/dB 10 蜂窝用户最小SINR/dB 10 
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[1] LIU X Y, XU B, WANG X, et al. Impacts of sensing energy and data availability on throughput of energy harvesting cognitive radio networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(1): 747-759. [2] ANSARI R I, CHRYSOSTOMOU C, ALI HASSAN S, et al. 5G D2D networks: techniques, challenges, and future prospects[J]. IEEE Systems Journal, 2018, 12(4): 3970-3984. [3] JAMEEL F, HAMID Z, JABEEN F, et al. A survey of device-to-device communications: research issues and challenges[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3): 2133-2168. [4] CAI Y, KE C H, NI Y Y, et al. Power allocation for NOMA in D2D relay communications[J]. China Communications, 2021, 18(1): 61-69. [5] LI B S, ZHANG H L, JI H, et al. Energy-aware resource allocation scheme for device-to-device communication based on NOMA underlaying cellular networks[C]//2017 IEEE 17th International Conference on Communication Technology (ICCT). Chengdu: IEEE, 2018: 513-517. [6] JI P S, JIA J, CHEN J, et al. Joint optimization for RIS-assisted multicast D2D communications[J]. Computer Networks, 2022, 211: 108977. [7] 鞠宏浩, 程楷钧, 邓彩连, 等. 无人机空地网络研究综述[J]. 西南交通大学学报, 2024, 59(4): 877-889.JU Honghao, CHENG Kaijun, DENG Cailian, et al. A survey on air-ground networks of unmanned aerial vehicles[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 877-889. [8] SELIM M M, TOMASIN S. Performance analysis of RIS-assisted downlink NOMA wireless systems under D2D interference[J]. Digital Signal Processing, 2024, 144: 104269. [9] ALEXANDROPOULOS G C, CROZZOLI M, PHAN-HUY D T, et al. Smart wireless environments enabled by RISs: deployment scenarios and two key challenges[C]//2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit). Grenoble: IEEE, 2022: 1-6. [10] FANG J J, ZHANG C, WU Q Q, et al. Improper Gaussian signaling for IRS assisted multiuser SWIPT systems with hardware impairments[J]. IEEE Transactions on Vehicular Technology, 2023, 72(10): 13024-13038. [11] XU F M, YE Z J, CAO H Y, et al. Sum-rate optimization for IRS-aided D2D communication underlaying cellular networks[J]. IEEE Access, 2022, 10: 48499-48509. [12] AO S Y, NIU Y, HAN Z, et al. Resource allocation for RIS-assisted device-to-device communications in heterogeneous cellular networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(9): 11741-11755. [13] ZHANG C Y, CHEN W Y, HE C L, et al. Throughput maximization for intelligent reflecting surface-aided device-to-device communications system[J]. Journal of Communications and Information Networks, 2020, 5(4): 403-410. [14] MNIH V, KORAY K, SILVER D, et al. Playing Atari with deep reinforcement learning[J]. Computer Science, 2013, 12(19): 1-9. [15] VINYALS O, BABUSCHKIN I, CZARNECKI W M, et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning[J]. Nature, 2019, 575(7782): 350-354. [16] SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484-489. [17] HUANG C W, MO R H, YUEN C. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1839-1850. [18] JOSHI N, BUDHIRAJA I, GARG D, et al. Deep reinforcement learning based rate enhancement scheme for RIS assisted mobile users underlaying UAV[J]. Alexandria Engineering Journal, 2024, 91: 1-11. [19] 朱政宇, 侯庚旺, 黄崇文, 等. 基于并行CNN的RIS辅助D2D保密通信系统资源分配算法[J]. 通信学报, 2022, 43(3): 172-179.ZHU Zhengyu, HOU Gengwang, HUANG Chongwen, et al. Systems resource allocation algorithm for RIS-assisted D2D secure communication based on parallel CNN[J]. Journal on Communications, 2022, 43(3): 172-179. [20] GUO L, JIA J, ZOU Y X, et al. Resource allocation for multiple RISs assisted NOMA empowered D2D communication: a MAMP-DQN approach[J]. Ad Hoc Networks, 2023, 146: 103163. [21] LIU Y W, QIN Z J, ELKASHLAN M, et al. Nonorthogonal multiple access for 5G and beyond[J]. Proceedings of the IEEE, 2017, 105(12): 2347-2381. [22] 李翠然, 杨茜, 谢健骊, 等. 基于SWIPT的能量收集WSN吞吐量性能分析及优化[J]. 西南交通大学学报, 2024, 59(5): 1014-1022.LI Cuiran, YANG Qian, XIE Jianli, et al. Throughput performance analysis and optimization of energy harvesting wireless sensor network based on simultaneous wireless information and power transfer[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1014-1022. [23] 赵太飞, 林亚茹, 马倩文, 等. 无人机编队中无线紫外光隐秘通信的能耗均衡算法[J]. 电子与信息学报, 2020, 42(12): 2969-2975.ZHAO Taifei, LIN Yaru, MA Qianwen, et al. Energy balance algorithm for wireless ultraviolet secret communication in UAV formation[J]. Journal of Electronics & Information Technology, 2020, 42(12): 2969-2975. [24] WEST D B. Introduction to Graph Theory[M]. 2nd ed. Upper Saddle River: Prentice Hall, 2001. [25] 覃琦超, 翟雷. D2D通信中信道分配与功率优化联合算法[J]. 现代电子技术, 2020, 43(23): 11-14, 19.QIN Qichao, ZHAI Lei. Joint algorithms of channel allocation and power optimization in D2D communication[J]. Modern Electronics Technique, 2020, 43(23): 11-14,19. [26] LI S C, LIN S Y, CAI L, et al. Joint resource allocation and computation offloading with time-varying fading channel in vehicular edge computing[J]. IEEE Transactions on Vehicular Technology, 2020, 69(3): 3384-3398. -
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