Advanced MIMO Techniques for Future Wireless Communications

  • Fred D Wiffen

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


Multi-user multiple input multiple output (MU-MIMO) technologies exploit the spatial do- main to serve multiple users on the same time-frequency resource, achieving unrivalled spectral efficiencies. This thesis investigates and proposes novel signal processing-based solutions to practical challenges associated with two MU-MIMO technologies that are expected to play a key role in future wireless systems – massive MIMO and distributed MIMO cloud radio access networks (C-RAN).
The first part of this thesis addresses the problem of peak-to-average-power ratio (PAPR) reduction, for improving the operating power efficiency of the large number of power amplifiers used in massive MIMO transmitters. It begins by using Bussgang’s theory to derive a statistical signal model for the distortion introduced by conventional clipping-based PAPR reduction. This model is then used to develop a practical and effective PAPR reduction scheme that uses spatial filtering to eliminate the effects of clipping distortion from the signals received by the users, and can incorporate active constellation extension for improved performance.
The remainder of the thesis focuses on lossy data compression for MIMO C-RAN – reducing the quantity of signal data such that low capacity fronthaul connections can be used. For the massive MIMO uplink, transform coding is shown to be effective at exploiting the inherent sparsity in the received signals to achieve efficient data compression. This transform coding approach is then adapted for distributed MIMO, using jointly optimised rate allocations to account for correlations between the signals received at different remote receivers.
The final part of the thesis shows that distributed dimension reduction can be applied to distributed MIMO to produce a reduced dimension MIMO system that preserves many of the benefits provided by deploying a large number of antennas. Combined with simple scalar quantization, this represents an efficient fronthaul data compression/reduction strategy for both the distributed MIMO uplink and downlink.
Date of Award23 Mar 2021
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorAngela Doufexi (Supervisor), Mark A Beach (Supervisor), Andrew R Nix (Supervisor), Zubeir Bocus (Supervisor) & Woon Hau Chin (Supervisor)

Cite this