Performance of regression-based precoding for multi-user massive MIMO-OFDM systems

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

In 3D massive MIMO (multiple-input multiple-output) systems, especially in time division duplex (TDD) mode, a base station (BS) relies on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO processing. Accordingly, multi-dimensional parameter estimation of MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the joint estimation of elevation and azimuth angles as well as the delay parameters for 3D massive MIMO orthogonal frequency division multiplexing (OFDM) systems under a parametric channel modeling. To be specific, we introduce a matrix-based joint parameter estimation method, and analytically characterize its performance for massive MIMO OFDM systems. Results show that antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance, and the characterized MSEs match well with the simulated ones. Also, the parametric channel estimation outperforms the MMSE-based channel estimation in terms of the correlation between the estimated channel and the real channel.

A robust receiver for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems is proposed. We are interested in the scenario when only a limited number of observations in both time and frequency domains are available. For this scenario, perfect channel state information is impossible to obtain and the receiver suffers from statistical information mismatch. To overcome this limitation, we first propose the optimum receiver by performing jointly channel and data estimation. For statistical information mismatch, we construct a finite set of covariance matrices and derive a model-selection scheme based on Bayesian Model Selection. Finally, the sliding-window scheme is used in order to enhance the model selection accuracy. Simulation results are presented, showing that the proposed scheme outperforms the conventional scheme under imperfect channel knowledge.

This paper investigates the gains harvested through base station cooperation in the up-link for a multi-user (MU) Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, operating in a real indoor environment. The base stations perform joint detection using an iterative receiver that carries out multi-user detection and channel estimation via soft information from the single-user decoders. Performance evaluation is carried out using real channels from an indoor dynamic dual MIMO link measurement campaign. The measured scenario represent a real life situation where two users communicate with two base stations, each with two antennas, in an environment resembling a shopping mall or an airport terminal. System performance is evaluated in terms of both Bit-Error Rate (BER) vs. Signal-to-Interference Ratio (SIR) and Cumulative Distribution Functions (CDF) for the instantaneous BER. Also, the impact of using soft information in the channel estimation is analyzed.

A new blind channel estimation approach for orthogonally coded multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems is proposed. The main idea of our technique is, using specific properties of the orthogonal space-time block codes (OSTBCs), to estimate the finite impulse response (FIR) channel parameters in the time domain instead of doing this in the frequency domain independently for each subcarrier. This results in a substantially improved parsimony of the channel parametric model as compared to the direct per subcarrier channel estimation methods and allows coherent processing across the subcarriers. It is shown that using the semidefinite relaxation (SDR) technique, our channel estimation problem can be approximated as a convex semidefinite programming (SDP) problem and, hence, can be solved efficiently using modern convex optimization methods. Our simulations validate the performance and/or computational complexity advantages of the proposed method as compared to some current state-of-the-art blind MIMO-OFDM channel estimation methods.

Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels

In this new information age, high data rate and strong reliability features our wireless communication systems and is becoming the dominant factor for a successful deployment of commercial networks. MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing), a new wireless broadband technology, has gained great popularity for its capability of high rate transmission and its robustness against multi-path fading and other channel impairments. A major challenge to MIMO-OFDM systems is how to obtain the channel state information accurately and promptly for coherent detection of information symbols and channel synchronization. In the first part, this dissertation formulates the channel estimation problem for MIMO-OFDM systems and proposes a pilot-tone based estimation algorithm. A complex equivalent base-band MIMO-OFDM signal model is presented by matrix representation. By choosing equally-spaced and equally-powered pilot tones from sub-carriers in one OFDM symbol, a down-sampled version of the original signal model is obtained. Furthermore, this signal model is transformed into a linear form solvable for the LS (least-square) estimation algorithm. Based on the resultant model, a simple pilot-tone design is proposed in the form of a unitary matrix, whose rows stand for different pilot-tone sets in the frequency domain and whose columns represent distinct transmit antennas in the spatial domain. From the analysis and synthesis of the pilot-tone design in this dissertation, our estimation algorithm can reduce the computational complexity inherited in MIMO systems by the fact that the pilot-tone matrix is essentially a unitary matrix, and is proven an optimal channel estimator in the sense of achieving the minimum MSE (mean squared error) of channel estimation for a fixed power of pilot tones. In the second part, this dissertation addresses the wireless location problem in WiMax (worldwide interoperability for microwave access) networks, which is mainly based on the MIMO-OFDM technology. From the measurement data of TDOA (time difference of arrival), AOA (angle of arrival) or a combination of those two, a quasi-linear form is formulated for an LS-type solution. It is assumed that the observation data is corrupted by a zero-mean AWGN (additive white Gaussian noise) with a very small variance. Under this assumption, the noise term in the quasi-liner form is proved to hold a normal distribution approximately. Hence the ML (maximum-likelihood) estimation and the LS-type solution are equivalent. But the ML estimation technique is not feasible here due to its computational complexity and the possible nonexistence of the optimal solution. Our proposed method is capable

This paper proposes a new channel estimation method for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in the presence of intersymbol interference (ISI). The proposed method uses special training sequences (TS) mainly for two reasons. Firstly, to obtain a desirable crest-factor of the transmitted training signal. Secondly, to eliminate the influence of ISI on the channel estimation performance. By using the recommended training sequences, one can avoid the ill-conditioned least square (LS) problem. To illustrate the proposed method, we assume that the system does not use a guard interval (GI). Simulation results show that the proposed method outperforms conventional methods with respect to the mean square error (MSE) of the estimated channel by more than 20 dB if the signal-to-noise ratio (SNR) is high.

Channel estimation for MIMO orthogonal frequency division multiplexing (MIMO-OFDM) systems has been improved observably based on compressive sensing (CS) techniques. Recently, some block-structured CS methods have been utilized in MIMO-OFDM systems. However, the block-coherence which characterizes the performance of a block-structured measurement matrix is still not optimized. In this paper, we proposed a two-step scheme to optimize the pilot locations and power of different transmitting antennas, in order to minimize the block-coherence of the aggregate system matrix. The selection of pilot locations and power can be obtained by two genetic algorithms. Then we introduce a block version of the orthogonal matching pursuit (OMP) algorithm, termed BOMP, to obtain the channel estimation consistent with the block-sparse model. Simulation results demonstrate that the proposed pilot allocation scheme with superimposed pilots design and block-structured CS method performs very well in MIMO-OFDM systems.

Optimization on semi-blind channel estimation for MIMO-OFDM systems

We propose a new resource allocation algorithm to support user fairness for downlink coordinated multipoint (CoMP) transmission with joint processing in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The proposed algorithm utilizes successive convex approximation (SCA) to dynamically allocate the transmit powers between multiple CoMP base stations transmitting to multiple co-channel user terminals (UTs). The aim is to minimize the total transmit power subject to per-antenna power and per-UT rate constraints. Simulation results compare our SCA-based algorithm with iterative waterfilling.

This study deals with semi-blind (SB) channel estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using maximum likelihood (ML) technique. For the ML cost optimisation function, new expectation maximisation (EM) algorithms for the channel taps estimation are introduced. Different approximation/simplification approaches are proposed for the algorithm's computational cost reduction. The first approach consists of decomposing the MIMO-OFDM system into parallel multiple-input single-output OFDM systems. The EM algorithm is then applied to estimate the MIMO channel in a parallel way. The second approach takes advantage of the SB context to reduce the EM cost from exponential to linear complexity by reducing the size of the search space. Finally, the last proposed approach uses a parallel interference cancellation technique to decompose the MIMO-OFDM system into several single-input multiple-output OFDM systems. The latter are identified in a parallel scheme and with a reduced complexity. The performance of the proposed approaches are discussed, assessed through numerical experiments and compared with respect to the Cramer Rao Bound and to other EM-based solutions reported in the literature.

A linear channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, based on a two-dimensional Slepian expansion, is presented. The estimator is meant to be part of an iterative receiver. We consider both estimation based on pilots only and on pilots and data, the latter considered as a reference for the case when feedback from decoders is exploited. Performances are analyzed via computer simulations comparing the relative minimum square error (RMMSE) of an analogous one-dimensional estimator and the proposed extension.

Accurate channel estimation is crucial for millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Conventional grid-based compressive sensing approaches encounter the base mismatch problem, which degrades channel estimation accuracy. To address this issue, this letter proposes a novel gridless channel estimation strategy tailored for mmWave MIMO-OFDM systems. Specifically, the channel estimation problem is formulated as a joint sparse signal recovery problem by exploiting the inherent sparsity in the angle-delay domain of the mmWave channel. We then introduce a Hankel-Toeplitz block model-based new atomic norm minimization (NANM) algorithm with multiple measurement vectors (MMV), representing the formulated problem as a semi-definite programming (SDP) problem with structured sparsity. To efficiently solve the SDP problem, we employ a low-complexity alternating direction multiplier method (ADMM). Simulation results verify that the proposed method significantly enhances channel estimation accuracy with reduced pilot overhead, compared with conventional channel estimation techniques.

In this paper, the training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems under the minimum mean square error (MMSE) criterion is addressed. The optimal training sequence for channel estimation in spatially correlated MIMO-OFDM systems was not known for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNRs. The current paper presents a necessary and sufficient condition for the optimal training sequence, and reformulates the training design problem as a convex optimization problem whose optimal solution is efficiently solved. In addition, tight upper bounds for MMSE and resulting low complexity iterative algorithms with the closed-form expression in iterations to find the optimum training sequence are derived. Simulation results confirm the superiority of the proposed design over the existing one in terms of both MSE estimation and BER performance. The proposed methods are also shown to be robust with respect to the spatial correlation mismatch at the transmitter.