Large Multiuser MIMO Detection: Algorithms and Architectures

TL;DR

This thesis develops low-complexity detection algorithms for large MIMO and MU-MIMO systems, analyzes their performance, and designs hardware architectures, demonstrating significant gains and tradeoffs in massive MIMO communications.

Contribution

It introduces novel low-complexity detection schemes for large MIMO systems, including channel matrix puncturing and joint modulation classification, with hardware implementations and performance analysis.

Findings

Proposed near-optimal low-complexity detection algorithms.

Derived bounds on capacity, diversity gain, and error probability.

Verified performance gains through simulations and hardware design.

Abstract

In this thesis, we investigate the problem of efficient data detection in large MIMO and high order MU-MIMO systems. First, near-optimal low-complexity detection algorithms are proposed for regular MIMO systems. Then, a family of low-complexity hard-output and soft-output detection schemes based on channel matrix puncturing targeted for large MIMO systems is proposed. The performance of these schemes is characterized and analyzed mathematically, and bounds on capacity, diversity gain, and probability of bit error are derived. After that, efficient high order MU-MIMO detectors are proposed, based on joint modulation classification and subspace detection, where the modulation type of the interferer is estimated, while multiple decoupled streams are individually detected. Hardware architectures are designed for the proposed algorithms, and the promised gains are verified via simulations. Finally, we map the studied search-based detection schemes to low-resolution precoding at the transmitter side in massive MIMO and report the performance-complexity tradeoffs.