Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey

Abstract

In recent times, the rapid growth in mobile subscriptions and the associated demand for high data rates fuels the need for a robust wireless network design to meet the required capacity and coverage. Deploying massive numbers of cellular base stations (BSs) over a geographic area to fulfill high-capacity demands and broad network coverage is quite challenging due to inter-cell interference and significant rate variations. Cell-free massive MIMO (CF-mMIMO), a key enabler for 5G and 6G wireless networks, has been identified as an innovative technology to address this problem. In CF-mMIMO, many irregularly scattered single access points (APs) are linked to a central processing unit (CPU) via a backhaul network that coherently serves a limited number of mobile stations (MSs) to achieve high energy efficiency (EE) and spectral gains. This paper presents key areas of applications of CF-mMIMO in the ubiquitous 5G, and the envisioned 6G wireless networks. First, a foundational background on massive MIMO solutions-cellular massive MIMO, network MIMO, and CF-mMIMO is presented, focusing on the application areas and associated challenges. Additionally, CF-mMIMO architectures, design considerations, and system modeling are discussed extensively. Furthermore, the key areas of application of CF-mMIMO such as simultaneous wireless information and power transfer (SWIPT), channel hardening, hardware efficiency, power control, non-orthogonal multiple access (NOMA), spectral efficiency (SE), and EE are discussed exhaustively. Finally, the research directions, open issues, and lessons learned to stimulate cutting-edge research in this emerging domain of wireless communications are highlighted.

Machine Learning aided Channel Estimation for Cell-Free Networks using a novel pilot assignment algorithm

“…DRAFT where T is the training size, and R is the number of realizations used for training, ĥDFF is the output of neural network, ĥactual is the actual channel, w and b are weights and biases respectively. These weights and biases are regularly updated by minimizing the loss function [34] in (11) . The data set for the proposed network is gathered from [34], [35].…”

“…There are different ways of signal processing between AP and CPU as explained in literature [10], where centralized processing with MMSE estimation proves to be optimal for CFMM network. Two future research directions [11] and areas of concern for the CFMM networks are addressed in this paper i.e channel estimation and pilot contamination. In wireless communication, quality of transmitted signal is severely affected by various parameters such as scattering and multi-path propagation.…”

Towards Cell-Free Massive MIMO: A Measurement-Based Analysis

“…The research work on cell-free massive MIMO has increased tremendously over the last five years, see, e.g., [1], [2], [5], [6] and the references therein, as it is considered an enabling technology for beyond 5G systems. While anticipated key characteristics are already known through extensive analysis and simulation, empirical validation is missing in most instances.…”

Towards Cell-Free Massive MIMO: A Measurement-Based Analysis

“…The research work on cell-free massive MIMO has increased tremendously over the last five years, see e.g. [1], [2], [5], [6] and the references therein, as it is considered an enabling technology for beyond 5G systems. While anticipated key characteristics are already known through extensive analysis and simulation, empirical validation is largely missing.…”