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Practical Deployment Aspects of Cell-Free Massive MIMO Networks

Time: Wed 2023-05-24 13.15

Location: Ka-301, Kistagången 16, Kista

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Language: English

Subject area: Computer Science

Doctoral student: Mahmoud Zaher , Kommunikationssystem, CoS

Opponent: Professor Mikael Sternad, Uppsala University

Supervisor: Professor Emil Björnson, Kommunikationssystem, CoS; Associate professor Marina Petrova, Kommunikationssystem, CoS

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QC 20230503


The ever-growing demand of wireless traffic poses a challenge for current cellular networks. Each new generation must find new ways to boost the network capacity and spectral efficiency (SE) per device. A pillar of 5G is massive multiple-input-multiple-output (MIMO) technology. Through utilizing a large number of antennas at each transmitting node, massive MIMO has the ability to multiplex several user equipments (UEs) on the same time-frequency resources via spatial multiplexing. Looking beyond 5G, cell-free massive MIMO has attracted a lot of attention for its ability to utilize spatial macro diversity and higher resilience to interference. The cell-free architecture is based on a large number of distributed access points (APs) jointly serving the UEs within a coverage area without creating artificial cell boundaries. It provides a promising solution that is focused on delivering uniform service quality throughout the mobile network. The main challenges of the cell-free network architecture lie in the computational complexity for signal processing and the huge fronthaul requirements for information exchange among the APs.

In this thesis, we tackle some of the inherent problems of the cell-free network architecture by providing distributed solutions to the power allocation and mobility management problems. We then introduce a new method for characterizing unknown interference in wireless networks.

For the problem of power allocation, a distributed 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. The problem 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 handover procedure is devised for updating the serving sets of APs and assigned pilot 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.

Finally, we developed a new technique based on a Bayesian approach to model the distribution of the unknown interference arising from scheduling variations in neighbouring cells. The method is shown to provide accurate modelling for the unknown interference power and an effective tool for robust rate allocation in the uplink with a guaranteed target outage performance.