Distributed Channel Estimation Research Articles

Distributed Spatially Non-Stationary Channel Estimation for Extremely-Large Antenna Systems

This paper aims to develop a distributed channel estimation (CE) algorithm for spatially non-stationary (SNS) channels in extremely large aperture array systems, addressing the issues of high communication cost and computational complexity associated with traditional centralized algorithms. However, SNS channels differ from conventional spatially stationary channels, presenting new challenges such as varying sparsity patterns for different antennas. To overcome these challenges, we propose a novel distributed CE algorithm accompanied by a simple yet effective hard thresholding scheme. The proposed algorithm is not only suitable for uniform antenna arrays but also for irregularly deployed antennas. Simulation results demonstrate the advantages of the proposed algorithm in terms of estimation accuracy, communication cost, and computational complexity.

Role of Deep Learning in Wireless Communications

Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave (mmWave) initial alignment to illustrate that a data-driven design that bypasses explicit channel modelling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning based approach can potentially provide significant system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques is in choosing the appropriate neural network architecture to match the underlying problem structure.

Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO

This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex massive multiple-input multiple-output system in which a base station (BS) serves multiple mobile users, but with rate-limited feedback from the users to the BS. A key observation is that the multiuser channel estimation and feedback problem can be thought of as a distributed source coding problem. In contrast to the traditional approach where the channel state information (CSI) is estimated and quantized at each user independently, this paper shows that a joint design of pilots and a new DNN architecture, which maps the received pilots directly into feedback bits at the user side then maps the feedback bits from all the users directly into the precoding matrix at the BS, can significantly improve the overall performance. This paper further proposes robust design strategies with respect to channel parameters and also a generalizable DNN architecture for varying number of users and number of feedback bits. Numerical results show that the DNN-based approach with short pilot sequences and very limited feedback overhead can already approach the performance of conventional linear precoding schemes with full CSI.

Robust distributed Lorentzian adaptive filter with diffusion strategy in impulsive noise environment

Many works of literature support the potential of distributed channel estimation resorting to the traditional LMS algorithm and its variants. But these conventional LMS algorithms fail in an impulsive noise environment, which is undeniable in many communication systems. Hence in this paper, we study distributed channel estimation with robust cost functions. Most of the robust adaptive algorithms are less efficient in terms of convergence rate. To deal with this, we propose the use of the window-based Lorentzian norm in a distributed framework to gain the merit of improved convergence rate in terms of both distribution and data reuse. The performance of the proposed algorithm is validated using simulation results. Our contribution in this work is the application of Lorentzian norm in sensor networks with diffusion cooperation and stability analysis of the same.

The Impact of Nonidentical Estimation Error on Performance of Scheduled STBC With MV Power Allocation in CR-MIMO Systems

This correspondence paper studies the impact of nonidentically distributed channel estimation error on performance of a scheduled space-time block coding with mean-value power allocation in cognitive-radio multiple-input multiple-output system. Using the derived probability density function, the exact closed-form expressions of the proposed system for outage probability, ergodic capacity, and symbol-error rate (SER) with both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rm M$</tex-math></inline-formula> -ary quadrature amplitude modulation and phase shift keying modulation are derived. Using asymptotic analysis, this paper quantifies the diversity order for both outage probability and SER in the presence of nonidentically distributed error. From the analysis results, this paper shows that the diversity order is enhanced by the multiuser diversity as the number of user terminals increases regardless of outage probability and SER, while the spatial diversity is eliminated.

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.

Energy‐aware cooperative and distributed channel estimation schemes for wireless sensor networks

SummaryIt is a fact that energy consumption is a key issue in wireless sensor networks. In order to improve the energy efficiency of network utilization, distributed power control methods are advocated. However, the sensors are deployed randomly, which results in the channel gains that are difficult to be obtained; thus, how to estimate channel gains accurately to increase node communication efficiency and reduce energy consumption is the motivation of our work. In this paper, a cooperative and distributed algorithm based on diffusion least mean square (LMS) is firstly proposed to estimate channel gain by utilizing the equality and collaboration of sensor nodes. Its objective is to enhance the accuracy and convergence of the estimation process. Because the presented globe‐based scheme is unrealistic for practical utilization, two distributed estimation algorithms are investigated for different network scenarios, namely, exchange diffusion combine LMS and exchange combine diffusion LMS. Simulation results demonstrate the effectiveness of our methods. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Symbol Error Rate Analysis of Joint Diversity System With Channel Estimation Error

In this paper, we investigate the impact of channel estimation error on symbol error rate (SER) for joint diversity (i.e., combining space–time block coding and user scheduling) in multiple-input–multiple-output systems with a channel estimation error. Under a Gaussian distributed channel estimation error, the exact expressions of the joint diversity for SER with both M-ary quadrature amplitude modulation (QAM) and phase-shift keying (PSK) modulation are derived. Using probability density function approximation, we quantify the diversity order and signal-to-noise ratio (SNR) gain of SER for joint diversity in the presence of a channel estimation error. From the analysis results, we show that the diversity order is enhanced as the number of user terminals increases, regardless of the modulation type. This is because user scheduling provides multiuser diversity. Moreover, the SNR gain decreases as the number of user terminals increases. This is because the peak effective SNR in joint diversity decreases as the number of user terminals increases.

Blind Channel and Source Estimation in Networked Systems

In this paper, we study the blind channel and source estimation in sensor networks, where the channels are modeled by FIR filters and the source signal is deterministic. Distributed estimation algorithms for networked systems under noise-free and noisy measurements are developed, which blindly identify the multiple channels, followed by the source signal estimation. The key to the proposed algorithms lies in the adaptation of the blind system identification technique for the distributed channel estimation. In the presence of measurement noises, conventional blind identification methods cannot be straightforwardly realized in distributed environments. Instead, two stable distributed algorithms are introduced, which can avoid trivial solutions for the blind identification problem. Convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.

A General Robust Linear Transceiver Design for Multi-Hop Amplify-and-Forward MIMO Relaying Systems

In this paper, linear transceiver design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with Gaussian distributed channel estimation errors is investigated. Commonly used transceiver design criteria including weighted mean-square-error (MSE) minimization, capacity maximization, worst-MSE/MAX-MSE minimization and weighted sum-rate maximization, are considered and unified into a single matrix-variate optimization problem. A general robust design algorithm is proposed to solve the unified problem. Specifically, by exploiting majorization theory and properties of matrix-variate functions, the optimal structure of the robust transceiver is derived when either the covariance matrix of channel estimation errors seen from the transmitter side or the corresponding covariance matrix seen from the receiver side is proportional to an identity matrix. Based on the optimal structure, the original transceiver design problems are reduced to much simpler problems with only scalar variables whose solutions are readily obtained by iterative water-filling algorithm. A number of existing transceiver design algorithms are found to be special cases of the proposed solution. The differences between our work and the existing related work are also discussed in detail. The performance advantages of the proposed robust designs are demonstrated by simulation results.

Distributed Estimation of Channel Gains in Wireless Sensor Networks

We consider the problem of distributed channel estimation in a sensor network which employs a random sleep strategy to conserve energy. If the network nodes are randomly placed at unknown positions, some prior information about the channel gains can be obtained due to the path loss effect. When considered from a single node perspective this prior information is uninformative because there are on the order of links to estimate, while there are on the order of parameters to specify the unknown node positions. However, from a network wide channel estimation perspective, there are on the order of channel gains, but these are heavily influenced by only an order of position parameters. We show that expectation propagation (EP) can provide a distributed channel gain estimation algorithm which makes effective use of this prior information together with standard channel training methods. Exploiting prior information significantly improves estimate performance, as is evidenced by comparison with the prior-information-blind diffusion LMS algorithm. Provided simulation results affirm this conclusion even when shadowing is included and path loss exponents are mismatched or unknown. As communication and computation are both expensive at sensor nodes, we detail the message passing, computation, and memory requirements of both algorithms.