Massive MIMO in Real Propagation Environments: Do All Antennas Contribute Equally?

Abstract

Abstract-Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy consumption. Here we investigate the possibilities to reduce the required number of RF chains, by performing antenna selection. While this approach is not a very effective strategy for theoretical independent Rayleigh fading channels, a substantial reduction in the number of RF chains can be achieved for real massive MIMO channels, without significant performance loss. We evaluate antenna selection performance on measured channels at 2.6 GHz, using a linear and a cylindrical array, both having 128 elements. Sum-rate maximization is used as the criterion for antenna selection. A selection scheme based on convex optimization is nearly optimal and used as a benchmark. The achieved sum-rate is compared with that of a very simple scheme that selects the antennas with the highest received power. The power-based scheme gives performance close to the convex optimization scheme, for the measured channels. This observation indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF chains and performing antenna selection using simple algorithms.

A Genetic Algorithm-Based Antenna Selection Approach for Large-but-Finite MIMO Networks

“…Therefore, the optimal set of antennas can indeed be selected via exhaustive search in the cases with few antennas. However, as the network size grows, which is of interest in the next generation of wireless networks, we need to design efficient algorithms to derive the (sub)optimal antenna selection with low complexity 1 . In this paper, we propose a GA-based antenna selection approach as explained in Algorithm 1.…”

“…To address the demands on the next generation of wireless networks, the main strategy persuaded recently is the network densification [1]- [4]. One of the promising techniques to densify the network is to use many transmit antennas and/or receive terminals.…”

“…Thus, it is interesting to analyze MIMO networks in the presence of large but finite number of antennas. Particularly, antenna selection algorithms, e.g., [1]- [4], in which only a set of antennas are activated based on the channel quality, are appropriate schemes to utilize the diversity of large MIMO systems with high perantenna power and few radio-frequency (RF) chains.…”

“…Moreover, [10]- [12] and [13] develop different selection schemes to maximize the capacity in spatially correlated and uncorrelated conditions, respectively. Finally, e.g., [1]- [4] have recently studied the problem in massive MIMO networks.…”

“…The problem setup of the paper is different from, e.g., [1]- [13], because we consider continuous and bursty data communication scenarios and concentrate on the cases with different channel/CSI models. Moreover, the proposed GAbased algorithm has not been presented before.…”