Performance Evaluation of Uplink Cell-free Massive MIMO Network under Weichselberger Rician Fading Channel

Abstract

Cell-Free Massive Multiple Input Multiple Output (CF M-MIMO) is one of the promising technologies for future wireless communication such as (5G) and beyond fifth-generation (B5G) networks. It is a type of network technology that uses a massive number of distributed antennas to serve a large number of users at the same time. There are no predefined cells in this network, and users are not assigned to a specific base station or cell. Its ability to provide high spectral efficiency (SE) and energy efficiency (EE), as well as improved coverage and interference management, compared to traditional cellular networks. However, estimating the channel with high performance and low computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. One of the high-performance channel estimators is a phase-aware minimum mean square error (MMSE). This channel estimator has high computational complexity. To address the shortcomings of the existing estimator, this thesis proposed an efficient channel estimator that is a phase-aware element-wise minimum mean square error (PA-EW-MMSE) with QR decomposition and precoding matrix at the user side. The focus of this thesis is on the uplink (UL) phase of CF M-MIMO, where both the access point (AP) and user equipment (UE) has multiple antennas, and the communication channel is subject to Weichselberger Rician fading. The investigated channel model consists of a line of sight (LoS) and non-line of sight (NLoS) components. To account for the effects of mobility and phase noise on the phase shifts, the phase of the LoS component is modeled as a random variable that is uniformly distributed. The closed form uplink SE with phase MMSE and proposed estimator are evaluated for MMSE combining. The EE and area throughput are also calculated from the SE. The complexity of the MMSE and the proposed channel estimator are compared. The simulation results show that the proposed estimator achieved the best SE, EE, and area throughput performance with a substantial reduction in the complexity of the computation.