Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications

Abstract

Transmit antenna selection plays an important role in large-scale multiple-input multiple-output (MIMO) communications, but optimal large-scale MIMO antenna selection is a technical challenge. Exhaustive search is often employed in antenna selection, but it cannot be efficiently implemented in large-scale MIMO communication systems due to its prohibitive high computation complexity. This paper proposes a low-complexity interactive multiple-parameter optimization method for joint transmit antenna selection and beamforming in large-scale MIMO communication systems. The objective is to jointly maximize the channel outrage capacity and signal-to-noise (SNR) performance and minimize the mean square error in transmit antenna selection and minimum variance distortionless response (MVDR) beamforming without exhaustive search. The effectiveness of all the proposed methods is verified by extensive simulation results. It is shown that the required antenna selection processing time of the proposed method does not increase along with the increase of selected antennas, but the computation complexity of conventional exhaustive search method will significantly increase when large-scale antennas are employed in the system. This is particularly useful in antenna selection for large-scale MIMO communication systems.

Overview of frequency diverse array in radar and navigation applications

“…Then, the receiver feedbacks the performance information, which tells the SINR performance is improved or worsen, to the transmitter. The feedback scheme is similar to the one‐bit feedback used widely in distributed wireless transmits beamforming literature [101–104]. This allows the transmitter to implement cognitive optimisation strategies to mitigate clutter and non‐target scatterers, especially for some range‐dependent strong interferences.…”

Antenna Selection and Power Allocation in Massive MIMO

“…In [1], an antenna selection technique has been reviewed with considering different criterion such that channel capac-ity and bit error rate seeking both cost and hardware complexity reduction and It has proven that the performance is improved with the increase of number of receive antennas. The main target is to improve the spectral and transmit-energy efficiency [1][2][3][4][5][6][7] and it is obtained by increasing the number of antennas and RF chains. However, system complexity and hardware energy consumption is increased with the reduced number of RF chains in the antenna selection process.…”

“…Selection metric considers the effect of the norm of each channel column and correlation between columns while attaining low computational complexity. While in [6] the problem of joint multicast beam forming and antenna selection for multiple co-channel multicast groups was the motivation of the work and the proposed algorithm has achieved a reduction in a number of required antennas that are essential to produce the wanted signal level along with less transmission power. The performance is satisfactory and has presented less complexity along with improvement in other attributes.…”

“…The Simulation has proven that the proposed technique fulfill an impressive feedback overhead reduction over the conventional beamforming techniques (33%-50%) within the same target sum rate needs. In [1], [6], MIMO can improve the spectral efficiency (SE) jointly with the energy efficiency (EE). Antenna selection process, that is necessary for Ma-MIMO system in both transmitting and receiving end.…”

Adaptive Antenna Selection and Power Allocation in Downlink Massive MIMO Systems

“…Additionally, the number of antenna at a BS cannot be subjectively increased due to the physical area in a practical system [9]. Furthermore, with regards to the energy resource in the cellular networks, the power allocation algorithms required minimizing the power consumption and maximizing the achievable data rate [7], [8]. The subspace projection for RF is adaptive to spatial correlations and able to mitigate the interference for different user clusters [10].…”