Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion.
翻译:大规模多输出多重输出( MIMO) 是提高5G和偏差无线网络光谱和能效的关键技术。 用于可移植的分析, MIMO以往的大部分工作都侧重于系统性能, 富含隔热环境下的复杂高山频道脉冲反应。 相反, 本文根据双散射频道模式, 调查每个用户的上行点光谱效率( SE) 。 我们通过利用基于不完善频道状态信息的最大比例( MR) 组合技术, 得出了上行点电子SE 的封闭式表达。 我们进一步研究了每个基站天线数量的功能以及每个无线电频道可用的散射器数量。 然后我们为上行点数据传输制定并解决了全部能源优化问题, 目的是同时满足所有数据能量有限的用户对上行点的所需数据效率。 值得注意的是, 我们提议的算法可以应对在至少一个用户被低端SEE- Geo系统服务时出现的拥堵问题。 此外, 我们进一步研究了SE- AS 系统所需的超低端用户的SE- 系统效率。