Pilot Decontamination in CMT-based Massive MIMO Networks

Pilot contamination problem in massive MIMO networks operating in time-division duplex (TDD) mode can limit their expected capacity to a great extent. This paper addresses this problem in cosine modulated multitone (CMT) based massive MIMO networks; taking advantage of their so-called blind equalization property. We extend and apply the blind equalization technique from single antenna case to multi-cellular massive MIMO systems and show that it can remove the channel estimation errors (due to pilot contamination effect) without any need for cooperation between different cells or transmission of additional training information. Our numerical results advocate the efficacy of the proposed blind technique in improving the channel estimation accuracy and removal of the residual channel estimation errors caused by the users of the other cells.Comment: Accepted in ISWCS 201

A Two-Stage 2D Channel Extrapolation Scheme for TDD 5G NR Systems

Recently, channel extrapolation has been widely investigated in frequency division duplex (FDD) massive MIMO systems. However, in time division duplex (TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation problem also arises due to the hopping uplink pilot pattern, which has not been fully researched yet. This paper addresses this gap by formulating a channel extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating imperfection factors. A novel two-stage two-dimensional (2D) channel extrapolation scheme in both frequency and time domain is proposed, designed to mitigate the negative effects of imperfection factors and ensure high-accuracy channel estimation. Specifically, in the channel estimation stage, we propose a novel multi-band and multi-timeslot based high-resolution parameter estimation algorithm to achieve 2D channel extrapolation in the presence of imperfection factors. Then, to avoid repeated multi-timeslot based channel estimation, a channel tracking stage is designed during the subsequent time instants, in which a sparse Markov channel model is formulated to capture the dynamic sparsity of massive MIMO-OFDM channels under the influence of imperfection factors. Next, an expectation-maximization (EM) based compressive channel tracking algorithm is designed to jointly estimate unknown imperfection and channel parameters by exploiting the high-resolution prior information of the delay/angle parameters from the previous timeslots. Simulation results underscore the superior performance of our proposed channel extrapolation scheme over baselines

Channel Estimation in Massive Multi-User MIMO Systems Based on Low-Rank Matrix Approximation

In recent years, massive Multi-User Multi-Input Multi-Output (MU-MIMO) system has attracted significant research interests in mobile communication systems. It has been considered as one of the promising technologies for 5G mobile wireless networks. In massive MU-MIMO system, the base station (BS) is equipped with a very large number of antenna elements and simultaneously serves a large number of single-antenna users. Compared to traditional MIMO system with fewer antennas, massive MU-MIMO system can offer many advantages such as significant improvements in both spectral and power efficiencies. However, the channel estimation in massive MU-MIMO system is particularly challenging due to large number of channel matrix entries to be estimated within a limited coherence time interval. This problem occurs in a single-cell case where both dimensions of the channel matrix grow large. Also, It happens in the multi-cell setting due to the pilot contamination effect. In this thesis, the problem of channel estimation in both single-cell and multi-cell time division duplex (TDD) massive MU-MIMO systems is studied. Thus, two-channel estimation namely “nuclear norm (NN)” and “iterative weighted nuclear norm (IWNN)” approximation techniques are proposed to solve the channel estimation problem in both systems. First, channel estimation in a single-cell TDD massive MU-MIMO system is formulated as a convex nuclear norm optimization problem with regularization parameter γ. In this study, the regularization parameter γ is selected based on the cross-validation (CV) curve method. The simulation results in terms of the normalized mean square error (NMSE) and uplink achievable sum-rate (ASR) are provided to show the effectiveness of the NN proposed scheme compared to the conventional least square (LS) estimator. Then, the IWNN approximation is proposed to improve the performance of the NN method. Thus, the channel estimation in a single-cell TDD massive MU-MIMO system is formulated as a weighted nuclear norm optimization problem. The simulation results show the effectiveness of the IWNN estimation approach compared to the standard NN and conventional LS estimation methods in terms of the NMSE and ASR. Second, both previous estimation techniques are extended to apply in a multi-cell TDD massive MU-MIMO system to mitigate pilot contamination effect. The simulation results in terms of the NMSE and uplink ASR show that the IWNN scheme outperforms the NN and LS estimations in the presence of high pilot contamination effect. Finally, a novel channel estimation scheme namely “Approximate minimum mean square error (AMMSE)” is proposed to reduce the computational complexity of the minimum mean square error (MMSE) estimator which was proposed for multi-cell TDD massive MU-MIMO system. Furthermore, a brief analysis of the computational complexity regarding the number of multiplications of the proposed AMMSE estimator is provided. It has been shown that the complexity of the proposed AMMSE estimator is reduced compared to the conventional MMSE estimator. The simulation results in terms of the NMSE and the uplink ASR performances show the proposed AMMSE estimation performance is almost the same as the conventional MMSE estimator under two different scenarios: noise-limited and pilot contamination

A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels

In this paper, we establish a general framework on the reduced dimensional channel state information (CSI) estimation and pre-beamformer design for frequency-selective massive multiple-input multiple-output MIMO systems employing single-carrier (SC) modulation in time division duplex (TDD) mode by exploiting the joint angle-delay domain channel sparsity in millimeter (mm) wave frequencies. First, based on a generic subspace projection taking the joint angle-delay power profile and user-grouping into account, the reduced rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived for spatially correlated wideband MIMO channels. Second, the statistical pre-beamformer design is considered for frequency-selective SC massive MIMO channels. We examine the dimension reduction problem and subspace (beamspace) construction on which the RR-MMSE estimation can be realized as accurately as possible. Finally, a spatio-temporal domain correlator type reduced rank channel estimator, as an approximation of the RR-MMSE estimate, is obtained by carrying out least square (LS) estimation in a proper reduced dimensional beamspace. It is observed that the proposed techniques show remarkable robustness to the pilot interference (or contamination) with a significant reduction in pilot overhead

Channel estimation for massive MIMO TDD systems assuming pilot contamination and flat fading

Channel estimation is crucial for massive massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. This paper presents a simple and practical channel estimator for multi-cell MU massive MIMO time division duplex (TDD) systems with pilot contamination in flat Rayleigh fading channels, i.e., the gains of the channels follow the Rayleigh distribution. We also assume uncorrelated antennas. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the cross-cell large-scale channel coefficients. This estimator performs asymptotically as well as the minimum mean square error (MMSE) estimator with respect to the number of antennas. An approximate analytical mean square error (MSE) expression is also derived for the proposed estimator

Channel estimation for massive MIMO TDD systems assuming pilot contamination and flat fading

Channel estimation is crucial for massive massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. This paper presents a simple and practical channel estimator for multi-cell MU massive MIMO time division duplex (TDD) systems with pilot contamination in flat Rayleigh fading channels, i.e., the gains of the channels follow the Rayleigh distribution. We also assume uncorrelated antennas. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the cross-cell large-scale channel coefficients. This estimator performs asymptotically as well as the minimum mean square error (MMSE) estimator with respect to the number of antennas. An approximate analytical mean square error (MSE) expression is also derived for the proposed estimator201

A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems

[EN] Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. 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