This paper develops a new genetic algorithm based resource allocation (GA-RA) technique for energy-efficient throughout maximization in multi-user massive multiple-input multiple-output (MU-mMIMO) systems using orthogonal frequency division multiplexing (OFDM) based transmission. We employ a hybrid precoding (HP) architecture with three stages: (i) radio frequency (RF) beamformer, (ii) baseband (BB) precoder, (iii) resource allocation (RA) block. First, a single RF beamformer block is built for all subcarriers via the slow time-varying angle-of-departure (AoD) information. For enhancing the energy efficiency, the RF beamformer aims to reduce the hardware cost/complexity and total power consumption via a low number of RF chains. Afterwards, the reduced-size effective channel state information (CSI) is utilized in the design of a distinct BB precoder and RA block for each subcarrier. The BB precoder is developed via regularized zero-forcing technique. Finally, the RA block is built via the proposed GA-RA technique for throughput maximization by allocating the power and subcarrier resources. The illustrative results show that the throughput performance in the MU-mMIMO-OFDM systems is greatly enhanced via the proposed GA-RA technique compared to both equal RA (EQ-RA) and particle swarm optimization based RA (PSO-RA). Moreover, the performance gain ratio increases with the increasing number of subcarriers, particularly for low transmission powers.
翻译:本文开发了一种新的基于基因算法的资源分配(GA-RA)技术,在使用正方位频率分多路传输(OFDM)传输(OFDM)的多用户大规模多投入多输出(MU-MMIMIMO)系统中实现最大最大化的整个过程中,在使用多用户大规模多投入多输出(MU-MMIMIMO)系统的过程中,为节能开发一种新的基于资源配置的基于资源配置(GA-RA)技术。我们采用了一种混合的预编码(HP)结构,分为三个阶段:(一) 无线电频率(RF) 光线条;(二) 基础带(BBB) 预代码;(三) 资源分配(RA) 区块。首先,通过慢时间移动角度移动角度(AODD),为所有子包车(RARA-RA-RA-RA)信息配置常规化技术开发一个单一的RF BRB前端区块。最后,通过快速递增压(RA-RA-RB)技术,通过快速递增压(OMMU),通过拟议的压(ODU-RA-RO-R-R)所有冲压(O-RO-RU-S-R-R-R)技术,通过推进)系统生成,通过最大变压(A-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SL-SL-SLL)生成,通过拟议的进度,所有最大性能,通过拟议的升级-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SAL-SB-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-