Mudassir Masood

Efficient Coordinated Recovery of Sparse Channels in Massive MIMO

This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

Sparse Reconstruction Using Distribution Agnostic Bayesian Matching Pursuit

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator.

Receiver-Based Recovery of Clipped OFDM Signals for PAPR Reduction: A Bayesian Approach

Clipping is one of the simplest peak-to-average power ratio reduction schemes for orthogonal frequency division multiplexing (OFDM). Deliberately clipping the transmission signal degrades system performance, and clipping mitigation is required at the receiver for information restoration. In this paper, we acknowledge the sparse nature of the clipping signal and propose a low-complexity Bayesian clipping estimation scheme. The proposed scheme utilizes a priori information about the sparsity rate and noise variance for enhanced recovery. At the same time, the proposed scheme is robust against inaccurate estimates of the clipping signal statistics. The undistorted phase property of the clipped signal, as well as the clipping likelihood, is utilized for enhanced reconstruction. Furthermore, motivated by the nature of modern OFDM-based communication systems, we extend our clipping reconstruction approach to multiple antenna receivers and multi-user OFDM.We also address the problem of channel estimation from pilots contaminated by the clipping distortion. Numerical findings are presented that depict favorable results for the proposed scheme compared to the established sparse reconstruction schemes.

Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems

By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.

Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery

This paper presents a novel narrowband interference (NBI) mitigation scheme for single carrier-frequency division multiple access systems. The proposed NBI cancellation scheme exploits the frequency-domain sparsity of the unknown signal and adopts a low complexity Bayesian sparse recovery procedure. At the transmitter, a few randomly chosen data locations are kept data free to sense the NBI signal at the receiver. Furthermore, it is noted that in practice, the sparsity of the NBI signal is destroyed by a grid mismatch between the NBI sources and the system under consideration. Toward this end, first, an accurate grid mismatch model is presented that is capable of assuming independent offsets for multiple NBI sources, and second, the sparsity of the unknown signal is restored prior to reconstruction using a sparsifying transform. To improve the spectral efficiency of the proposed scheme, a data-aided NBI recovery procedure is outlined that relies on adaptively selecting a subset of data-points and using them as additional measurements. Numerical results demonstrate the effectiveness of the proposed scheme for NBI mitigation.

Support agnostic Bayesian matching pursuit for block sparse signals

A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator.

A Minorization–Maximization Algorithm for Maximizing the Secrecy Rate of the MIMOME Wiretap Channel

We consider physical layer security in a multi-input multi-output multi-eavesdropper wiretap channel and present an exact solution to the problem of secrecy rate maximization. A system model with multiple multi-antenna eavesdroppers and multiple multi-antenna full-duplex receivers is considered, which is general enough such that models existing in the literature may be considered as special cases. In particular, we perform joint beamforming and artificial noise optimization in an effort to maximize the achievable secrecy rate. The optimization is performed in the presence of artificial noise generated by both transmitter and legitimate receivers. The resulting optimization problem is non-convex and difficult to solve. We develop a minorization–maximization algorithm to solve the problem exactly and the results can therefore be used to benchmark existing methods. Numerical results are presented to demonstrate the efficacy of the proposed approach.

Bayesian narrowband interference mitigation in SC-FDMA

This paper presents a novel narrowband interference (NBI) mitigation scheme for SC-FDMA systems. The proposed scheme exploits the frequency domain sparsity of the unknown NBI signal and adopts a low complexity Bayesian sparse recovery procedure. In practice, however, the sparsity of the NBI is destroyed by a grid mismatch between NBI sources and SC-FDMA system. Towards this end, an accurate grid mismatch model is presented and a sparsifying transform is utilized to restore the sparsity of the unknown signal. Numerical results are presented that depict the suitability of the proposed scheme for NBI mitigation.

Distributive estimation of frequency selective channels for massive MIMO systems

We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, where our major concern is complexity. A low complexity distributed LMMSE algorithm is proposed that attains near optimal channel impulse response (CIR) estimates from noisy observations at receive antenna array. In proposed method, every antenna estimates the CIRs of its neighborhood followed by recursive sharing of estimates with immediate neighbors. At each step, every antenna calculates the weighted average of shared estimates which converges to near optimal LMMSE solution. The simulation results validate the near optimal performance of proposed algorithm in terms of mean square error (MSE).

Image denoising via collaborative support-agnostic recovery

In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.