大规模多输入多输出（Massive Multiple-Input Multiple-Output，Massive MIMO）技术能够有效提高系统频谱效率和能量效率，是无线移动通信系统中最具有潜力的技术之一。用户数量的激增和网络规模的日益庞大化带来了能源消耗总量与碳排放量的增加，从而引起严重的环境污染问题，以绿色通信为目标的能量采集（Energy Harvesting，EH）技术受到了越来越多的关注。本文研究基于能量采集技术，包括可再生能源采集技术和无线信息与功率同时传输（Simultaneous Wireless Information and Power Transfer，SWIPT）技术的 Massive MIMO 系统资源优化问题。具体研究内容如下：

   （1）针对进行可再生能源采集的 Massive MIMO 系统，建立了可再生能源和电网混合供电的多用户 Massive MIMO 系统模型，提出了能量效率最大化的离线和在线功率优化算法。具体的优化算法为：a）在已知信道状态信息（Channel State Information，CSI）和能量采集动态过程的理想假设下，以能量效率最大化为目标对功率优化问题进行数学建模，由于所建模问题具有非凸特性，利用分数规划方法将其转化为凸问题，从而通过Lagrange 对偶法得到最优功率分配，并将其作为性能上限的基准，称为离线优化算法。b）由于实际应用时的 CSI 和能量采集动态过程无法提前预知，进一步，基于统计思想设计了马尔可夫预测和时隙检测两种在线算法。其中，马尔可夫预测算法利用状态转移概率预测 CSI 和能量采集动态信息；而时隙检测算法则是利用当前特定时隙的状态信息并结合动态规划思想去实现能量效率最大化，具有比马尔可夫预测算法更低的实现复杂度。仿真结果表明，所提在线算法能以较低的实现复杂度接近理想的离线算法的性能；而且，与已有算法相比，所提算法不仅能够提高系统的能量效率，还可以有效降低电网能源的消耗。

    （2）针对使用 SWIPT 技术的无线中继无人机（Unmanned Aerial Vehicle，UAV）协作通信系统，首先建立了基于 SWIPT 技术的中继协作系统模型，其中,中继 UAV 采用NOMA（Non-Orthogonal Multiple Access，NOMA）技术实现多用户服务。在上述系统模型下，以系统频谱效率最大化为目标，以 UAV 功率分割比、UAV 的发射功率以及 UAV的轨迹为优化变量对优化问题进行建模，利用交替优化以及差分凸函数方法进行求解并设计优化算法。仿真结果表明，与已有算法相比，所提算法能够在合理利用周围电磁能源的前提下有效提升系统的频谱效率。

     Massive multiple-input multiple-output (Massive MIMO) technology can effectively improve the spectral efficiency and energy efficiency of the system, which is one of the most promising technologies in wireless mobile communication system. The surge in the number of users and the increasing scale of the network have brought about an increase in total energy consumption and carbon emissions, resulting in serious environmental pollution, causingserious environmental pollution problems. Energy Harvesting (EH) for green communications
has attracted more and more attention. In this paper, Massive MIMO system resource optimization is studied based on EH technology, including renewable energy harvesting
technology and Simultaneous Wireless Information and Power Transfer (SWIPT) technology. Specific research contents are as follows:
      (1) Aiming at the Massive MIMO system for renewable energy harvesting, a multi-user Massive MIMO system model with hybrid power supply from renewable energy and power grid was established, and offline and online power optimization algorithms to maximize energy efficiency were proposed. The specific optimization algorithms are as follows: a) given the assumption of channel state information (CSI) and the dynamic information of EH, the power optimization problem is modeled mathematically with the goal of maximizing energy efficiency. Since the offline problem is non-convex, the fractional programming method is used to transform it into convex problem, thus, the optimal power allocation is obtained by Lagrange duality method, which is called offline algorithm, and use it as the benchmark of performance
upper limit. b) In fact, the dynamic information of CSI and EH process cannot be obtained in advance, furthermore, Markov prediction and time slot detection algorithms based on statistical
ideas are designed. Among them, Markov prediction algorithm uses state transition probability to predict CSI and EH dynamic information, the time slot detection algorithm uses the current
specific time slot state information and combines the idea of dynamic programming to maximize the energy efficiency, and has lower complexity than the Markov prediction algorithm. The simulation results show that, the proposed online algorithm can approach the performance of the idea offline algorithm with the low implementation complexity. Moreover, compared with the existing algorithms, the proposed algorithm can not only improves the energy efficiency of the system, but also can effectively reduce the energy consumption of the grid.
        (2) Aiming at the cooperative communication system of wireless relay Unmanned Aerial Vehicle (UAV) using SWIPT technology, a cooperative relay system model based on SWIPT
technology was established. Among them, the relay UAV uses NOMA (Non-Orthogonal Multiple Access) technology for muti-user service. Under the above system model, aiming at
maximizing the spectrum efficiency of the system, the optimization problem was modeled by taking UAV power partition ratio, transmitted power of the UAV and the trajectory of the UAV
as optimization variables, and the optimization algorithm was designed by using alternate optimization and differential convex function method. Compared with the existing algorithms,
the simulation results show that the proposed algorithm can effectively the spectral efficiency of the system under the premise of rational use of the surrounding electromagnetic energy。
 

[1] You X, Wang C X, Huang J, et al. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts[J]. Science China Information Sciences, 2021, 64(1): 1-74.

[2] Jamil S, Abbas M S, Umair M, et al. A Review of Techniques and Challenges in Green Communication[C]//2020 International Conference on Information Science and Communication Technology (ICISCT). IEEE, 2020: 1-6.

[3] Bohli A, Bouallegue R. How to meet increased capacities by future green 5G networks: a survey[J]. IEEE Access, 2019, 7: 42220-42237.

[4] Ku M L, Li W, Chen Y, et al. Advances in energy harvesting communications: Past, present, and future challenges[J]. IEEE Communications Surveys & Tutorials, 2015, 18(2): 1384-1412.

[5] 范梦兰, 张杭, 李炯. RF 能量采集全双工认知无线网络中协作频谱感知参数优化[J]. 通信技术, 2017, 50(5): 928-932.

[6] Fettweis G, Zimmermann E. ICT energy consumption-trends and

challenges[C]//Proceedings of the 11th international symposium on wireless personal multimedia communications. (Lapland, 2008, 2(4): 6).

[7] Héliot F, Imran M A, Tafazolli R. Energy-efficient power allocation for point-to-point MIMO systems over the Rayleigh fading channel[J]. IEEE Wireless Communications Letters, 2012, 1(4): 304-307.

[8] Zogović N. Energy Efficiency Versus Reliability Tradeoff Improvement in Low-Power Wireless Communications[J]. IEEE Systems Journal, 2020, 14(3): 3173-3184.

[9] Guo B, Guan Q, Yu F R, et al. Energy-efficient topology control with selective diversity in cooperative wireless ad hoc networks: A game-theoretic approach[J]. IEEE Transactions on Wireless Communications, 2014, 13(11): 6484-6495.

[10]Kim S, Yu H. Energy-efficient HARQ-IR for massive MIMO systems[J]. IEEE

Transactions on Communications, 2018, 66(9): 3892-3901.

[11]Zappone A, Sanguinetti L, Bacci G, et al. Energy-efficient power control: A look at 5G wireless technologies[J]. IEEE Transactions on Signal Processing, 2015, 64(7): 1668-1683.

[12]Zhou Z, Zhang C, Wang J, et al. Energy-efficient resource allocation for energy harvestingbased cognitive machine-to-machine communications[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(3): 595-607.

[13]Zhang Y, Du J, Chen Y, et al. Optimal hybrid beamforming design for millimeter-wave massive multi-user MIMO relay systems[J]. IEEE Access, 2019, 7: 157212-157225.

[14]Gregori M, Payaró M. On the optimal resource allocation for a wireless energy harvesting node considering the circuitry power consumption[J]. IEEE Transactions on Wireless Communications, 2014, 13(11): 5968-5984.

[15]刘壮, 李曦, 纪红. 应用于 MIMO-OFDMA 下行链路的分组调度算法[J]. 北京航空

航天大学学报, 2014 (10): 1451-1456.

[16]Zlatanov N, Schober R, Hadzi-Velkov Z. Asymptotically optimal power allocation for energy harvesting communication networks[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 7286-7301.

[17]Wang Z, Wang X, Aggarwal V. Transmission with energy harvesting nodes in frequencyselective fading channels[J]. IEEE Transactions on Wireless Communications, 2015, 15(3): 1642-1656.

[18]马礼, 朱大文, 马东超, 等. 一种无线传感器网络能量感知路由协议[J]. 计算机工程, 2017, 43(12): 124-129.

[19]Rezgui G, Belmega E V, Chorti A. Mitigating jamming attacks using energy harvesting[J]. IEEE wireless communications letters, 2018, 8(1): 297-300.

[20]In C, Kim H M, Choi W. Achievable rate-energy region in two-way decode-and-forward energy harvesting relay systems[J]. IEEE Transactions on Communications, 2019, 67(6): 3923-3935.

[21]Ashraf M, Jang J W, Han J K, et al. Capacity maximizing adaptive power splitting protocol for cooperative energy harvesting communication systems[J]. IEEE Communications Letters, 2018, 22(5): 902-905.

[22]Hu J, Heng W, Zhang G, et al. Energy efficient resource allocation in timesharing multiuser systems with hybrid energy harvesting transmitter[J]. China Communications, 2017, 14(8):

83-92.

[23]Zhao M, Zhao J, Zhou W, et al. Energy efficiency optimization in relay-assisted networks with energy harvesting relay constraints[J]. China Communications, 2015, 12(2): 84-94.

[24]Lu X, Wang P, Niyato D, et al. Wireless networks with RF energy harvesting: A contemporary survey[J]. IEEE Communications Surveys & Tutorials, 2014, 17(2): 757-789.

[25]Hua M, Li C, Huang Y, et al. Throughput maximization for UAV-enabled wireless power transfer in relaying system[C]//2017 9th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2017: 1-5.

[26]Yin S, Zhao Y, Li L, et al. UAV-assisted cooperative communications with power-splitting information and power transfer[J]. IEEE Transactions on Green Communications and Networking, 2019, 3(4): 1044-1057.

[27]Yin S, Zhao Y, Li L. UAV-assisted cooperative communications with time-sharing SWIPT[C]//2018 IEEE International Conference on Communications (ICC). IEEE, 2018: 1-6.

[28]Zhang J, Zeng Y, Zhang R. Spectrum and energy efficiency maximization in UAV-enabled mobile relaying[C]//2017 IEEE International Conference on Communications (ICC). IEEE, 2017: 1-6.

[29]Hong X, Liu P, Zhou F, et al. Resource allocation for secure UAV-assisted SWIPT systems[J]. IEEE Access, 2019, 7: 24248-24257.

[30]Marzetta T L. Massive MIMO: an introduction[J]. Bell Labs Technical Journal, 2015, 20: 11-22.

[31]曹自平, 王楚, 袁明, 等. 环境能量采集技术的研究现状及发展趋势[J]. 南京邮电大学学报 (自然科学版), 2016.

[32]Ding Z, Yang Z, Fan P, et al. On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users[J]. IEEE signal processing letters, 2014, 21(12): 1501-1505.

[33]王钢, 许尧, 周若飞, 等. 无线网络中的功率域非正交多址接入技术[J]. 无线电通信技术, 2019, 45(4): 329-336.

[34]Dai L, Wang B, Ding Z, et al. A survey of non-orthogonal multiple access for 5G[J]. IEEE communications surveys & tutorials, 2018, 20(3): 2294-2323.

[35]Ngo H Q, Larsson E G, Marzetta T L. Energy and spectral efficiency of very large multiuser MIMO systems[J]. IEEE Transactions on Communications, 2013, 61(4): 1436-1449.

[36]Björnson E, Sanguinetti L, Hoydis J, et al. Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer?[J]. IEEE Transactions on wireless communications, 2015, 14(6): 3059-3075.

[37]Wu S, Shin Y, Kim J Y, et al. Probability of packet loss in energy harvesting nodes with cognitive radio capabilities[J]. IEEE Communications Letters, 2016, 20(5):978-981.

[38]Yates R D, Mahdavi-Doost H. Energy harvesting receivers: Packet sampling and decoding policies[J]. IEEE Journal on Selected Areas in Communications, 2015, 33(3): 558-570.

[39]Siddiqui A M, Musavian L, Aïssa S, et al. Weighted Tradeoff Between Spectral Efficiency and Energy Efficiency in Energy Harvesting Systems[C]//2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2019: 1-6.

[40]Ashraf M, Vandendorpe L. Rate Maximizing Online Power Allocation at Energy

Harvesting Transmitter for Multiple Receivers[C]//ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020: 1-7.

[41]He P, Zhao L, Zhou S, et al. Optimal recursive power allocation for energy harvesting system with multiple antennas[J]. IEEE Transactions on Vehicular Technology, 2014, 64(10): 4525-4536.

[42]欧阳键, 庄毅, 薛羽, 等. 非对称衰落信道下无人机中继传输方案及性能分析[J]. 航空学报, 2013 (1): 130-140.

[43]Yin S, Qu Z, Zhang L. Wireless information and power transfer in cooperative communications with power splitting[C]//2015 IEEE Global Communications Conference (GLOBECOM). IEEE, 2015: 1-6.

[44]Huberman S, Le-Ngoc T. Full-duplex MIMO precoding for sum-rate maximization with sequential convex programming[J]. IEEE Transactions on Vehicular Technology, 2014, 64(11): 5103-5112.

[45]林敏, 魏恒, 欧阳键, 等. 无人飞行器中继双跳无线链路中的优化设计及性能分析

[J]. 系统工程与电子技术, 2015, 37(6): 1391-1398.

[46]Yin S, Zhao Y, Li L, et al. UAV-assisted cooperative communications with time-sharing information and power transfer[J]. IEEE Transactions on Vehicular Technology, 2019, 69(2): 1554-1567.

[47]胡莹, 黄永明, 俞菲, 等. 多用户大规模 MIMO 系统能效资源分配算法[J]. 电子与信息学报, 2015, 37(9): 2198-2203.

[48]Boyd S, Boyd S P, Vandenberghe L. Convex optimization[M]. Cambridge university press, 2004.