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—In this paper, we present a novel approach based on compressive sensing theory to estimate and mitigate asyn-chronous narrow-band interference (NBI) in orthogonal frequency division multiplexing systems with multiple transmit and/or multiple receive antennas. We consider the practical scenarios where one or multiple asynchronous NBI signals experience fast(More)
—In this paper, we design MMSE-optimal training sequences for multiuser MIMO-OFDM systems with an arbitrary number of transmit antennas and an arbitrary number of training symbols. It addresses spectrally-efficient uplink transmission scenarios where the users overlap in time and frequency and are separated using spatial processing at the base station. The(More)
—In this paper, we propose a new framework for the design of sparse finite impulse response (FIR) equalizers. We start by formulating greedy and convex-optimization-based solutions for sparse FIR linear equalizer tap vectors given a maximum allowable loss in the decision-point signal-to-noise ratio. Then, we extend our formulation to decision feedback(More)
We propose two novel algorithms based on compressed-sensing theory to estimate and cancel narrow band interference (NBI) in orthogonal frequency division multiplexing (OFDM) systems. Simulation results demonstrate the effectiveness of our proposed algorithms in estimating the NBI frequency support and approaching the performance with no NBI.
We consider a multiuser multiple-input multiple-output (MU-MIMO) system that uses orthogonal frequency division multiplexing (OFDM). Several receivers are developed for data detection of MU-MIMO transmissions where two users share the same OFDM time and frequency resources. The receivers have partial state information about the MU-MIMO transmission with(More)