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High throughput, energy efficiency, and connectivity of a massive number of users are the<br/>main requirements of existing wireless communication systems. The three basic and commonly<br/>used techniques that can fulfil the spectral and energy efficiency requirements of these<br/>networks include finding new frequency resources, deploying ultra-dense networks, and using<br/>multiple antenna systems. In recent years, Multiple Input Multiple Output (MIMO)<br/>systems have appeared as a promising, reliable, and spatially effectual solution to the growing<br/>shortage of radio frequency communications bands. These systems, when employed for<br/>spatial multiplexing, provide a rise in capacity without the requirement for extra spectrum<br/>and power. Their improved data rates and performance have made them increasingly prominent<br/>in modern wireless devices. But with the decreased bit error rate, increased diversity,<br/>and array gains of MIMO systems, comes an increased complexity in the receiver design.<br/>An optimal design of transmitter and receiver is required to enjoy the advantages presented<br/>by these systems.<br/>For fifth generation and beyond communication systems, massive MIMO systems are<br/>considered a cutting edge technology. It is a promising technique for providing orders of<br/>magnitude improvements in throughput, coverage, energy, and spectral efficiency. Having<br/>several antennas at the base station, the massive MIMO system achieves prominent performance<br/>advantages. The problem with massive MIMO systems is that the signal processing<br/>complexity increases exponentially with a large system limit. It becomes challenging to<br/>extract the individual signals from the composite signal, thus making the optimal detectors<br/>prohibitively complex. For both MIMO and massive MIMO systems, linear and non-linear<br/>detectors are proposed in the literature for symbol detection. Linear detectors like Zero<br/>Forcing and Minimum Mean Squared Error (MMSE) are computationally less complex than<br/>non-linear detectors like Vertical Bell-Labs Layered Space-Time. However, they suffer from<br/>high bit error rate performance. The drawback of linear detectors is that they perform matrix<br/>inversion operations, which are not hardware friendly. To address this issue and reduce<br/>the computational complexity of linear detectors even further, approximate detectors, e.g.,<br/>Gauss-Seidel, Neumann Series, etc., are proposed in the literature. However, they show less<br/>satisfactory performances.<br/>The effectiveness of massive MIMO detectors relies mostly on the channel state information.<br/>Therefore, the base station has to be aware of it to ensure good quality of communication.<br/>The majority of research done in the literature considers perfect channel state information at the receiver for evaluation of signal to noise and interference ratio, symbol<br/>error rate, and bit error rate performances. Practically, it is hard to attain perfect channel<br/>state information because of feedback delays and imperfections in information extraction,<br/>resulting in imperfect channel state information. Also, an infeasible number of pilot signals,<br/>reciprocity errors, and electromagnetic coupling between a large number of antennas at base<br/>stations with inadequate spacing impact the overall efficiency. There is a paucity of work<br/>in the literature covering analysis of massive MIMO detectors under imperfect channel state<br/>information. Little information is present on the bit error rate analysis of detectors under<br/>imperfect channel state information.<br/>Firstly, in this thesis, detectors for MIMO systems are investigated, and an algorithm based<br/>on local search is presented to improve the performance of existing detectors. The proposed<br/>algorithm yields improved bit error rate performance of linear detectors. Secondly, a hybrid<br/>detector consisting of linear and approximate detector is proposed for massive MIMO<br/>systems. The proposed detector, a hybrid Neumann Series based MMSE assisted detector,<br/>is simulated in a Rayleigh fading channel and evaluated along with approximate message<br/>passing algorithm having Ternary and Gaussian distribution threshold functions. Simulation<br/>results confirm that the proposed hybrid Neumann Series based MMSE assisted detection<br/>algorithm performs significantly better than the mentioned detection schemes. Finally, the<br/>performance of approximate detectors, i.e., Gauss-Seidel and Neumann Series, is evaluated<br/>under imperfect channel state information. The results are compared with the linear detectors<br/>under imperfect channel state information. It is noted with the help of Monte-Carlo numerical<br/>simulations, that under imperfect channel state information, the Gauss-Seidel detector<br/>gives comparative bit error rate performance as the linear detectors with lower computational<br/>complexity. |
