MIMO Radar and Cellular Coexistence: A Power-Efficient Approach Enabled by Interference Exploitation - 2018 PROJECT TITLE :MIMO Radar and Cellular Coexistence: A Power-Efficient Approach Enabled by Interference Exploitation - 2018ABSTRACT:We propose a unique approach to enable the coexistence between Multi-Input-Multi-Output (MIMO) radar and downlink multiuser multi-input single-output Communication system. By exploiting the constructive multiuser interference (MUI), the proposed approach tradeoff useful MUI power for reducing the transmit power, to obtain a power economical transmission. This Project focuses on 2 optimization issues: a) Transmit power minimization at the bottom station (BS), whereas guaranteeing the receive signal-to-interference-and-noise ratio (SINR) level of downlink users and also the interference-to-noise ratio level to radar; b) Minimization of the interference from BS to radar for a given requirement of downlink SINR and transmit power budget. To scale back the computational overhead of the proposed scheme in practice, an algorithm primarily based on gradient projection is designed to solve the power minimization drawback. Additionally, we investigate the tradeoff between the performance of radar and Communication, and analytically derive the key metrics for MIMO radar within the presence of the interference from the BS. Finally, a robust power minimization downside is formulated to ensure the effectiveness of the proposed methodology in the case of imperfect channel state information. Numerical results show that the proposed methodology achieves a significant power saving compared to conventional approaches, whereas obtaining a favorable performance-complexity tradeoff. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation - 2018 Mitigating Quantization Effects on Distributed Sensor Fusion: A Least Squares Approach - 2018