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arXiv 2201.08068cs.ITcs.NImath.IT

面向具有多天线用户的大规模 MIMO 系统的功率分配算法

Power Allocation Algorithms for Massive MIMO Systems with Multi-Antenna Users

Evgeny Bobrov, Boris Chinyaev, Viktor Kuznetsov, Dmitrii Minenkov, Daniil Yudakov

AI总结:

针对多天线用户大规模 MIMO 中渐近正则化迫零预编码,本文简化频谱效率表达式,证明 TPC 下等功率分配最优,并提出结合 WF 的低复杂度 IM 以处理 PAPC,仿真显示显著增益且计算时间与 EP 相近。

摘要:

现代 5G 无线蜂窝网络采用大规模多输入多输出(MIMO)技术。这一概念要求在基站处使用天线阵列,以同时服务许多自身具有若干天线的移动设备。在该领域中,预编码(波束赋形)问题发挥着重要作用。在下行链路期间,预编码的一个重要部分是在发射符号之间分配功率的功率分配问题。本文考虑一类渐近表现为正则化迫零的预编码的功率分配问题。在若干现实假设下,我们简化了频谱效率泛函,并获得了其易处理表达式。我们证明,在总功率约束(TPC)下,等功率分配为该简化泛函提供最优解。我们提出低复杂度的 Intersection Methods(IM,交集方法),用于在每天线功率约束(PAPC)情形下改进等功率分配。在使用 Quadriga 的仿真中,所提出的 IM 方法与被广泛研究的 Water Filling(WF,注水法)相结合,在频谱效率方面表现出显著增益,同时使用与参考 Equal Power(EP,等功率)方案相近的计算时间。

英文摘要:

Modern 5G wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This concept entails using an antenna array at a base station to concurrently service many mobile devices that have several antennas on their side. In this field, a significant role is played by the precoding (beamforming) problem. During downlink, an important part of precoding is the power allocation problem that distributes power between transmitted symbols. In this paper, we consider the power allocation problem for a class of precodings that asymptotically work as regularized zero-forcing. Under some realistic assumptions, we simplify the spectral efficiency functional and obtain tractable expressions for it. We prove that equal power allocation provides optimum for the simplified functional with total power constraint (TPC). We propose low-complexity Intersection Methods (IM) that improve equal power allocation in the case of per-antenna power constraints (PAPC). On simulations using Quadriga, the proposed IM method in combination with widely-studied Water Filling (WF) shows a significant gain in spectral efficiency while using a similar computing time as the reference Equal Power (EP) solution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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