Cell-Free Massive MIMO Networks

QC 20250416

Abstract

The increasing demand for wireless data traffic poses a significant challenge for current cellular networks, requiring each new technology generation to enhance network capacity and coverage, and spectral efficiency (SE) per connected device. Massive multiple-input multiple-output (MIMO) technology has emerged as a key component of 5G and leverages a large number of antennas at each access point (AP) to spatially multiplex many user equipments (UEs) over the same time-frequency resources. Looking beyond 5G, the new cell-free massive MIMO technology has gained considerable attention due to its ability to exploit spatial macro diversity and achieve higher interference resilience. Unlike traditional cellular networks, the cell-free architecture consists of a dense deployment of distributed APs that collaboratively serve UEs across a large coverage area without predefined cell boundaries. This architecture improves the mobile network coverage and aims to provide a more uniform quality of service throughout the network. However, the primary challenges of cell-free massive MIMO include the high computational complexity required for signal processing and the substantial fronthaul capacity needed for information exchange between APs. Moreover, another major challenge is handover management to cope with changing channel conditions and UE mobility; since in a cell-free network handover needs to consider how to dynamically evolve the serving set of APs to each UE, which is more complicated than in a cellular network where each UE is served by a single AP and handover means changing the serving AP.

In this doctoral thesis, we provide distributed solutions to research problems related to power allocation and mobility management to address some of the inherent challenges of the cell-free network architecture. Additionally, we introduce a new method for characterizing unknown interference in wireless networks. Moreover, we propose efficient optimization procedures in the context of multicast beamforming optimization and establish a novel method for rank reduction in conjunction with semidefinite relaxation (SDR).

For the problem related to power allocation, a distributed machine learning-based solution that provides a good trade-off between SE performance and applicability for implementation in large-scale networks is developed with reduced fronthaul requirements and computational complexity as compared to a centralized solution, where the power allocation for all APs is computed at a central processor. The solution is divided in a way that enables each AP, or group of APs, to separately decide on the power coefficients to the UEs based on the locally available information at the AP without exchanging information with the other APs, however, still attempting to achieve a network wide optimization objective. 

Regarding mobility management, a new soft handover procedure is devised for updating the serving sets of APs and assigning pilot signals to each UE in a dynamic scenario considering UE mobility. The algorithm is tailored to reduce the required number of handovers per UE and changes in pilot assignment. Numerical results show that our proposed solution identifies the essential refinements since it can deliver comparable SE to the case when the AP-UE association is completely redone.

As for interference modeling, we developed a new Bayesian-based technique to model the distribution of the unknown interference arising from scheduling variations in neighbouring cells. The method is shown to provide accurate statistical modeling of the unknown interference power and an effective tool for robust rate allocation in the uplink with a guaranteed target outage performance. The method was later extended to account for the unknown interference of neighbouring clusters in a cell-free network architecture.

Many wireless communication applications require sending the same data to multiple UEs; for example, in streaming live events, distributing software updates, or training of federated learning models. Physical-layer multicasting presents an efficient transmission topology to exploit the beamforming capabilities at the transmitting nodes and broadcast nature of the wireless channel to satisfy the demand for the same content from several UEs. The uniform service quality and improved coverage of the cell-free network architecture are particularly suitable for this transmission topology. In this regard, we propose a novel successive elimination algorithm coupled with SDR to extract a near-global optimal rank-1 beamforming solution to the max-min fairness (MMF) multicast problem in a cell-free massive MIMO network. A specifically tailored optimization algorithm is then designed, leveraging the alternating direction method of multipliers (ADMM) and offering significant improvements in computational requirements.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-362462