Abstract
The increasing demand for connectivity and throughput, combined with the tight latency requirements of current communication systems, and the existing spectrum limitations, has triggered a paradigm shift towards non-orthogonal signal transmissions where multiple information streams are transmitted using the same time/frequency resources. Despite the promising theoretical gains of such transmissions, the complexity and/or latency requirements of the corresponding receiver processing techniques that are required to translate these gains into throughput make their realization impractical, especially for large numbers of mutually interfering information streams. In addition to the processing complexity/latency increase related to systems’ high dimensionality, the interference matrix of some of the recently proposed non-orthogonal transmission schemes can either be ill-determined or even rank-deficient, making their detection even more challenging. These requirements combined with the saturating speed of processors, motivates a timely requirement for massively parallel processing of such non-orthogonal transmissions. In this context, this thesis introduces a generic, massively parallel and near-optimal processing framework that applies to both well- and ill-determined non-orthogonal systems. In contrast to known approaches, this framework enables practical large uplink multi-user MIMO systems with numbers of concurrently transmitting users that exceed the number of receive antennas by a factor of two or more. In contrast to traditional approaches, the proposed framework, does not require sparse signal transmissions for the detection of non-orthogonal multiple access (NOMA) schemes since it is not based on the “Message Passing” algorithm. Consequently, the proposed framework can enable more efficient NOMA approaches that support more users than existing systems, with better detection performance and practical complexity requirements. In comparison to state-of-the-art detectors for NOMA schemes and non-orthogonal signal waveforms(e.g., Spectrally efficient FDM) the proposed scheme can be up to an order of magnitude less complex and can provide throughput gains of up to 60%.This thesis also introduces a massively parallel soft-input soft-output (SISO) detection design for large MIMO systems capable of bridging the gap between theoretical capacity and achievable throughput, with a processing complexity that can be an order of magnitude lower than that of highly optimized sequential SISO detectors, and a processing latency similar to that of highly sub-optimal, linear, SISO detection approaches. Finally, a massively parallel processing framework is presented that enables extreme grant-free non-orthogonal multiple access. This framework allows reliable and low-overhead user identification and reliable detection/decoding with complexity requirements that can be orders of magnitude lower than existing schemes.