Karl Nieman (The University of Texas at Austin) "Space-Time-Frequency Methods for Interference-Limited Communication Systems", (2014), Advisor: Brian Evans The communication performance in Wi-Fi, cellular, smart grid, underwater acoustic, and many other modern systems, is limited by non-Gaussian noise/interference instead of Gaussian noise. This PhD dissertation develops new multi-dimensional signal processing methods to improve performance of communication systems in three application areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, the dissertation addresses impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with hundreds of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the power line. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128-antenna) multiple-input multi-output (MIMO) systems. This framework is used in the world's first real-time 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. For all three applications, I developed real-time testbeds as proofs of concept of the proposed methods.