QoS-Based Linear Transceiver Optimization for Full-Duplex Multiuser Communications - 2018 PROJECT TITLE :QoS-Based Linear Transceiver Optimization for Full-Duplex Multiuser Communications - 2018ABSTRACT:In this Project, we have a tendency to think about a multiuser wireless system with one full duplex (FD) base station (BS) serving a collection of 0.5 duplex (HD) mobile users. To deal with the in-band self-interference (SI) and co-channel interference, we have a tendency to formulate a top quality-of-service (QoS) based linear transceiver style drawback. The matter jointly optimizes the downlink (DL) and uplink (UL) beamforming vectors of the BS and also the transmission powers of UL users so as to provide both the DL and UL users with guaranteed signal-to-interference-plus-noise ratio performance, using a minimum UL and DL transmission add power. The considered system model not only takes into account noise caused by nonideal RF circuits, analog/digital SI cancellation however also constrains the common signal power at the input of the analog-to-digital converter (ADC) for avoiding signal distortion due to finite ADC precision. The formulated style problem is not convex and difficult to unravel generally. We 1st show that for a special case with a worst case SI channel estimation error, the QoS-based mostly linear transceiver design drawback is globally solvable by a polynomial time bisection algorithm. For the general case, we propose a suboptimal algorithm based mostly on alternating optimization (AO). The AO algorithm is certain to converge to a Karush-Kuhn-Tucker solution. To boost the computational potency of the AO algorithm, we have a tendency to more develop a fastened-point method by extending the classical uplink-downlink duality in HD systems to the FD system. Simulation results are presented to demonstrate the performance of the proposed algorithms and therefore the comparison with HD systems. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest PRUNE: Dynamic and Decidable Dataflow for Signal Processing on Heterogeneous Platforms - 2018 Quantized Spectral Compressed Sensing: Cramer–Rao Bounds and Recovery Algorithms - 2018