Software-Radio Strategies for Heterogeneous Communication Networks

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Research developing Multiple-input-Multiple-Output (MIMO) wireless communications has experienced tremendous growth over the past two decades due to the rate and diversity gains achievable using multiple antennas at both transmit and receive sides. The current standards capture only a fraction of the efficient spectral benefits of MIMO techniques, as they use only a small number of antennas and attain spectral efficiencies of only around ten bps/Hz. Such improvement of traditional MIMO systems triggers researchers to examine the use of a massive number of antennas at the communication ends to obtain higher spectral efficiencies in addition to higher diversity orders in wireless transmissions.***Machine Learning (ML) algorithms have shown their promising capacities recently. In previous years, the body of work on ML methods for communication systems has conspicuously grown. This research attempts to find an understanding of the feasibility of combining ML technologies with traditional wireless communication theories to address the current and future challenges in wireless communication, such as MIMO equalization, estimation and detection techniques, configuring uplink and downlink channels and scheduling beamforming in massive MIMO systems.***Beamforming is a technique that allows the base stations to transmit targeted beams of information to the users, decreasing the interference and using the wireless spectrum more efficiently. Hybrid beamforming is one of the most promising approaches for reducing the hardware cost and training overhead in massive MIMO, especially for mm-wave bands. In this research program, we aim to develop algorithms that can be used to reduce the energy consumption and hardware cost of the wireless systems while enhancing the spectral efficiency.***A common feature of the radio resource allocations for multi-user-MIMO systems is that the limited number of radio resources should be distributed among many users. The idea of the radio resource allocation that will be investigated in this research at any network is to distribute the limited resources effectively, i.e., total transmit energy and available spectrum, among users to meet users' service requirements.***Finally, although massive MIMO and mm-wave techniques have recently received a considerable amount of interest in the research community, their RF front-end implementation, their implication on the system performance as well as the effectiveness of the mitigation techniques have not yet been thoroughly investigated. Therefore, this research program investigates analysis, modelling and low complexity suppression of RF non-idealities using novel/modified digital signal processing algorithms.***In this research, we focus mainly on the impact of multi-antennas signalling strategies on system capacity and the optimum, in the sense of capacity maximization and error minimization, allocation of the spatial degree of freedom to diversity, multiplexing, and interference cancellation.**