Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

As one of the key technologies in the fifth generation of mobile communications, massive multi-input multi-output (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radio-frequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users, and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm

TAS Strategies for Incremental Cognitive MIMO Relaying: New Results and Accurate Comparison

In this paper, we thoroughly elaborate on the impact different transmit-antenna selection (TAS) strategies induce in terms of the outage performance of incremental cognitive multiple-input multiple-output (MIMO) relaying systems employing receive maximum-ratio combining (MRC). Our setup consists of three multi-antenna secondary nodes: a transmitter, a receiver and a decode-and-forward (DF) relay node acting in a half-duplex incremental relaying mode whereas the primary transmitter and receiver are equipped with a single antenna. Only a statistical channel-state information (CSI) is acquired by the secondary system transmitting nodes to adapt their transmit power. In this context, our contribution is fourfold. First, we focus on two TAS strategies that are driven by maxim

Enhanced Transmit-Antenna Selection Schemes for Multiuser Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems are a core technology designed to achieve the performance objectives defined for 5G wireless communications. They achieve high spectral efficiency, reliability, and diversity gain. However, the many radio frequency chains required in base stations equipped with a high number of transmit antennas imply high hardware costs and computational complexity. Therefore, in this paper, we investigate the use of a transmit-antenna selection scheme, with which the number of required radio frequency chains in the base station can be reduced. This paper proposes two efficient transmit-antenna selection (TAS) schemes designed to consider a trade-off between performance and computational complexity in massive MIMO systems. The spectral efficiency and computational complexity of the proposed schemes are analyzed and compared with existing TAS schemes, showing that the proposed algorithms increase the TAS performance and can be used in practical systems. Additionally, the obtained results enable a better understanding of how TAS affects massive MIMO systems