Consistent-coverage Oriented AP Deployment Optimization in Cell Free and Legacy Coexistence Network
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摘要: 为了解决传统蜂窝网络中用户体验剧烈波动的问题，无蜂窝和传统蜂窝共存网络将大量接入点(Access Point, AP)部署到传统蜂窝网络中，显著改善边缘用户和盲区的覆盖信号质量。因此用户在覆盖区域的任何位置均获得良好、一致的用户体验，即一致覆盖是提升共存网络性能的首要目标。而AP部署方案是共存网络中用户传输速率和覆盖的决定性因素，该文提出了面向一致覆盖的AP部署优化方法。首先根据共存网络的联合传输模型推导得到用户的下行可达速率，然后以最大化平均吞吐量为目标，将AP部署建模为比率和规划问题，并基于分式规划和引入辅助变量将其转换为凸优化问题，进而通过迭代求解AP的最优位置。仿真结果表明，相比传统蜂窝网络，所提方案可显著提高边缘和盲区的平均吞吐量。
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关键词:
- 无线通信 /
- 无蜂窝和传统蜂窝共存网络 /
- 一致覆盖 /
- 联合传输 /
- AP部署
Abstract: To address the issue of dramatic fluctuations in user experience in legacy cellular networks, cell-free and legacy coexistence networks deploy Access Points (APs) into cellular networks, which can significantly improve the coverage signal quality of edge users and blind areas. Therefore, a good and consistent user experience at any location in the coverage area, i.e. consistent-coverage is the primary goal to improve the performance of coexistence networks. As the AP deployment is the determinant of user transmission rate and coverage in coexistence networks, a consistent-coverage oriented AP deployment optimization problem is designed. Firstly, the expression of the downlink achievable rate of each user is derived based on the joint transmission model of coexistence network. Secondly, a ratio sum optimization problem is proposed to maximize the average throughput. Finally, the non-convex problem is transformed into a convex optimization problem by using the fractional programming and the introduction of auxiliary variables, where the AP deployment scheme is obtained by the iterative solution. Compared with the legacy cellular networks, the simulation results demonstrate that the proposed scheme can significantly increase average throughput of the edge and blind areas. -
1 算法整体流程
初始化：可行解${{\boldsymbol{X}}^0}$，迭代收敛精度$\delta \ge 0$，迭代次数$t = 0$，最
大迭代次数${T_{\max }}$(1) while $ {R_t} - {R_{t - 1}} \ge \delta $或$t < {T_{\max }}$do (2) 对于给定的${{\boldsymbol{X}}_t}$，根据式(14)更新${\lambda _t}$ (3) 根据式(21)和式(22)更新$ {{\boldsymbol{C}}_k} $和$ {{\boldsymbol{D}}_k} $ (4) 根据式(23)更新$ {\boldsymbol{E}} $ (5) 利用单纯形法求解P5，更新${{\boldsymbol{X}}_{t + 1}}$ (6) 迭代次数$t = t + 1$ (7) end while (8) 将${\boldsymbol{X}}$中最大的前$L$个值并将其置为1，其他置为0，输出${\boldsymbol{X}}$ 
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表 1 部分仿真参数
参数名称 符号 数值 蜂窝数/BS数 $M$ 4 用户数 $K$ 300 基站天线数 ${N_{\mathrm{t}}}$ 64 预定义AP位置数 $N$ 500 栅格数 $S$ 900 系统带宽(MHz) $B$ 20
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[1] FREDRIK J. Ericsson mobility report[R]. 2022. [2] NGUYEN V M and KOUNTOURIS M. Performance limits of network densification[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(6): 1294–1308. doi: 10.1109/JSAC.2017.2687638. [3] 章嘉懿. 去蜂窝大规模MIMO系统研究进展与发展趋势[J]. 重庆邮电大学学报:自然科学版, 2019, 31(3): 285–292. doi: 10.3979/j.issn.1673-825X.2019.03.001.ZHANG Jiayi. Overview of cell-free massive MIMO system[J]. Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition, 2019, 31(3): 285–292. doi: 10.3979/j.issn.1673-825X.2019.03.001. [4] LIU Pei, LUO Kai, CHEN Da, et al. Spectral efficiency analysis of cell-free massive MIMO systems with zero-forcing detector[J]. IEEE Transactions on Wireless Communications, 2020, 19(2): 795–807. doi: 10.1109/TWC.2019.2948841. [5] KIM T, KIM H, CHOI S, et al. How will cell-free systems be deployed?[J]. IEEE Communications Magazine, 2022, 60(4): 46–51. doi: 10.1109/MCOM.001.2100533. [6] WANG Kehao, LIU Pei, LIU Kezhong, et al. Joint beamforming and phase-shifting design for energy efficiency in RIS-assisted MISO communication with statistical CSI[J]. Physical Communication, 2023, 59: 102080. doi: 10.1016/j.phycom.2023.102080. [7] WANG Zhe, ZHANG Jiayi, AI Bo, et al. Uplink performance of cell-free massive MIMO with multi-antenna users over jointly-correlated Rayleigh fading channels[J]. IEEE Transactions on Wireless Communications, 2022, 21(9): 7391–7406. doi: 10.1109/TWC.2022.3158353. [8] NGUYEN L D, DUONG T Q, NGO H Q, et al. Energy efficiency in cell-free massive MIMO with zero-forcing precoding design[J]. IEEE Communications Letters, 2017, 21(8): 1871–1874. doi: 10.1109/LCOMM.2017.2694431. [9] LIU Heng, ZHANG Jiayi, JIN Shi, et al. Graph coloring based pilot assignment for cell-free massive MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2020, 69(8): 9180–9184. doi: 10.1109/TVT.2020.3000496. [10] NAYEBI E and RAO B D. Access point location design in cell-free massive MIMO systems[C]. Proceedings of 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2018: 985–989. doi: 10.1109/ACSSC.2018.8645382. [11] ZHU Yihang, CALLEBAUT G, ÇALIK H, et al. Energy efficient access point placement for distributed massive MIMO[J]. Network, 2022, 2(2): 288–310. doi: 10.3390/network2020019. [12] GOPAL G R, NAYEBI E, VILLARDI G P, et al. Modified vector quantization for small-cell access point placement with inter-cell interference[J]. IEEE Transactions on Wireless Communications, 2022, 21(8): 6387–6401. doi: 10.1109/TWC.2022.3148996. [13] 戴琼海, 付长军, 季向阳. 压缩感知研究[J]. 计算机学报, 2011, 34(3): 425–434. doi: 10.3724/SP.J.1016.2011.00425.DAI Qionghai, FU Changjun, and JI Xiangyang. Research on compressed sensing[J]. Chinese Journal of Computers, 2011, 34(3): 425–434. doi: 10.3724/SP.J.1016.2011.00425. [14] SHEN Kaiming and YU Wei. Fractional programming for communication systems—Part I: Power control and beamforming[J]. IEEE Transactions on Signal Processing, 2018, 66(10): 2616–2630. doi: 10.1109/TSP.2018.2812733. [15] ZHANG Weizhong, ZHANG Lijun, JIN Zhongming, et al. Sparse learning with stochastic composite optimization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1223–1236. doi: 10.1109/TPAMI.2016.2578323. [16] NGO H Q, ASHIKHMIN A, YANG Hong, et al. Cell-free massive MIMO versus small cells[J]. IEEE Transactions on Wireless Communications, 2017, 16(3): 1834–1850. doi: 10.1109/TWC.2017.2655515. -
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