Beamforming for Cell-Free Downlink Massive MIMO Systems

Abstract

With the release of fifth-generation (5G) wireless communication networks, we can experience a significant improvement in reliability, data rate, and energy efficiency. These improvements are achieved by a key technology called massive multiple-input multiple-output (mMIMO). The mMIMO system exploits the benefits of MIMO sys tems by deploying a large number of antennas at the base station (BS) and concurrently serving a large number of users. As we advance, there is a need for ubiquitous and seamless connectivity to support several existing and new applications. One candi date technology that can facilitate such connectivity is cell-free massive MIMO (CF mMIMO). In CF-mMIMO, hundreds of access points (APs) are deployed in a large geographical, where a set of APs beamform the signal to the intended user and enhance the received signal-to-interference-noise ratio (SINR). Since a user is supported by a set of APs, the beam weights at the AP antennas have to be optimized suitably. In this work, westudy the existing beamforming optimization techniques for CF-mMIMO sys tems, from the basic ones like equal power allocation (EPA) to the advanced ones like weighted minimum mean squared error (WMMSE). We observe that WMMSE has su perior performance but requires a higher computational complexity. Thus, we propose using the interior point method (IPM) to solve the beamforming optimization problem. Using simulations, we corroborate that the proposed IPM-based beamforming requires lesser time complexity and converges faster than the existing WMMSE algorithm at the cost of a marginal drop in the achievable throughput.