Antenna Selection for Full-Duplex Distributed Antenna Systems

Zhan Liu,Suili Feng

doi:10.1109/access.2019.2941797

Abstract

Full-duplex (FD) distributed antenna system (DAS) can take advantage of both FD and DAS to dramatically improve system capacity. The challenges of designing such a system are self-interference at the base station (BS) and multiuser interference, as well as hardware cost, computational complexity and signaling overhead, especially for dense antenna deployments. To address these problems mentioned above, in this paper, we investigate an antenna selection strategy at the BS for FD DAS including FD-capable BS antennas and half-duplex (HD) users. In particular, we separately optimize the receive and transmit antenna selection problems to maximize the achievable sum rate. To reduce the computational complexity and signaling overhead, each user is restricted to selecting only the BS antennas in its virtual cell. The optimization problem is a nonconvex integer programming problem, for which it is difficult to find a globally optimal solution. Using variable relaxation and successive convex approximation, we present an iterative antenna selection algorithm based on difference of convex functions (D.C.) programming to obtain a suboptimal solution. The simulation results demonstrate that the proposed antenna selection algorithm can provide significant performance gain over various reference algorithms.

Highlights

With the development of mobile communications, distributed antenna systems (DASs), known as cloud radio access networks (C-RANs), are a promising technology for supporting high user rates and satisfying the requirements of the user’s various wireless access [1]–[4]
In DAS, plenty of low-power base station (BS) antennas are deployed in a geographically distributed manner and connected to a centralized baseband unit (BBU) via fiber optics; BS antennas are responsible for data transmission only, while the BBU is in charge of baseband processing for the whole system [1]
In [9], the authors compared the average sum rate with maximum ratio combining (MRC)/maximal ratio transmission (MRT) (MRC for the uplink and MRT for the downlink) and zero-forcing (ZF)/MRT (ZF for the uplink and MRT for the downlink) under two different power constraints, i.e., the per-remote radio heads (RRHs) transmit power constraint and the total downlink transmit power constraint; the results showed that in all cases, selecting the nearest RRH with multiple antennas for transmission results in superior performance compared to the scheme in which all the RRHs participated

Summary

INTRODUCTION

With the development of mobile communications, distributed antenna systems (DASs), known as cloud radio access networks (C-RANs), are a promising technology for supporting high user rates and satisfying the requirements of the user’s various wireless access [1]–[4]. In [18], the authors compared the downlink performance under two different power constraints, and it was shown that in the case of the total transmit power constraint, for multiple antenna remote radio heads (RRHs) using maximal ratio transmission (MRT), the selection transmission scheme where only one RRH with the best channel participated in transmission outperforms the all-RRH participation scheme, while the opposite result was obtained under the per-RRH transmit power constraint. It was shown in [19] that, in a multicell scenario, selection transmission using a single BS antenna outperforms MRT using all antennas because selection transmission causes less intercell interference than does MRT. Notations: Boldface capital and lowercase letters represent matrices and vectors, respectively; AH and AT are the Hermitian transpose and the transpose of A, respectively; IN denotes an N × N identity matrix; x ∼ CN (μ, σ 2) is a complex Gaussian distribution with mean μ and variance σ 2; diag(X) is a diagonal matrix with the same diagonal elements as matrix X; diag(x) is a diagonal matrix with the diagonal elements from vector x; CN×M denotes the set of N × M complex matrices; x is the Euclidean norm of vector x; |x| denotes the absolute value of complex scalar x; and | | denotes the cardinality of set

SYSTEM MODEL AND PROBLEM FORMULATION

1: Initialization

CONCLUSION