Efficient Capacity-Based Joint Quantized Precoding and Transmit Antenna Selection Using Cross-Entropy Method for Multiuser MIMO Systems

Jung-Chieh Chen,Kuan-Yuen Liao,Yi-Syun Yang,Chih-Peng Li,Min-Han Chiu

doi:10.1155/2012/965834

Abstract

The current paper considers the joint precoding and transmit antenna selection to reduce hardware cost, such as radio-frequency chains, associated with antennas in the downlink of multiuser multiple-input multiple-output systems with limited feedback. The joint precoding and transmit antenna selection algorithm requires an exhaustive search of all possible combinations and permutations to find the optimum solution at the transmitter, thus resulting in extremely high computational complexity. To reduce the computational load while still maximizing channel capacity, the cross-entropy (CE) method is adopted to determine the suboptimum solution. Compared with the conventional genetic algorithm and random search method, the CE method provides better performance under the same computational complexity, as shown by the simulation results.

Highlights

Studies have shown that the capacity of multiple-input multiple-output (MIMO) systems equipped with multiple antennas at both the transmitter and receiver sides increases almost linearly with the minimum number of transmit and receive antennas [1, 2]
Similar to [13], our multiuser MIMO (MU-MIMO) system is based on the following assumptions: (1) the base station (BS) is equipped with NT transmit antennas, whereas each user has NR receive antennas; (2) both the BS and the users know a codebook of 2L precoding vectors, where L is the number of feedback bits; (3) each user has perfect knowledge of its own channel state information (CSI) that can be used to select and find the index of the best precoding vector from the Grassmannian codebook [16]
In the parameters used in the proposed CE method, the smoothing parameter θ is 0.8

Summary

Introduction

Studies have shown that the capacity of multiple-input multiple-output (MIMO) systems equipped with multiple antennas at both the transmitter and receiver sides increases almost linearly with the minimum number of transmit and receive antennas [1, 2]. Rather than directly sending the quantized version of the estimated CSI at the receiver back to the transmitter, a predetermined finite set of precoding vectors, referred to as the “codebook,” is selected based on predefined criteria and is fed back to the transmitter As both the transmitter and the receiver know the codebook, only the index of the selected code word is delivered to the transmitter, reducing the feedback rate. To lower the hardware complexity and achieve superior performance in the MU-MIMO systems simultaneously, the overall system performance is greatly expected to benefit from the combination of the precoders and the transmit antenna subset selection [10,11,12,13]. GA is a metaheuristic search method that is suitable for solving optimization problems It encodes each candidate solution (called an individual) into a bit string (called a chromosome) and associates it with an objective function. Simulation results show that the proposed algorithm is superior to the GA method in terms of average capacity and BER performance under the same complexity

System Model and Problem Definition

RF chain

The Proposed CE-Based Approach

Simulation Results

Conclusion