Large Multiuser MIMO Detection: Algorithms and Architectures

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.

PhD dissertation - Hadi Sarieddeen

References in corpus (6)

• Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems
• Throughput Analysis of Massive MIMO Uplink with Low-Resolution ADCs
• Massive MIMO with Non-Ideal Arbitrary Arrays: Hardware Scaling Laws and Circuit-Aware Design
• Uplink Achievable Rate for Massive MIMO with Low-Resolution ADC
• Spatio-Temporal Waveform Design for Multi-user Massive MIMO Downlink with 1-bit Receivers
• Achievable Uplink Rates for Massive MIMO with Coarse Quantization