Channel State Information Estimation Research Articles

Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems

Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there’s no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches.

2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two-dimensional (2D) beam-based statistical channel model (BSCM) in massive multi-input multi-output (MIMO) systems. The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple received pilot signals. A received signal model showing the relation between the statistical properties of the received pilot signals and the BDCPMs is derived. On the basis of the received signal model, we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without the estimates of instantaneous CSI is proposed. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., estimation of the angle domain channel power matrix. Simulation results show that the proposed method has good convergence and can obtain sparse BDCPMs. Compared with the regularized multiple measurement vector focal underdetermined system solver (RM-FOCUSS) algorithm, the proposed algorithm obtains overall more accurate statistical CSI with much lower complexity and brings significant performance gains when used in channel estimation.

Federated Generative-Adversarial-Network-Enabled Channel Estimation

Accurately estimating channel state information is essential for meeting the quality-of-service requirements of modern applications and scenarios. Deep learning techniques have proven effective in acquiring channel conditions with low pilot overhead in massive connectivity scenarios. However, accessing channel data brings new challenges related to transmission overhead, privacy concerns, scalability, heterogeneous network support, and adaptability to dynamic environments. We propose a federated generative-adversarial-network-enabled channel estimator to address these challenges. We refine the coarse least-squares estimation results for their low complexity and fast convergence. To ensure accuracy, we designed a double U-shaped network. The Lipschitz continuous function is applied to discriminators for spectral normalization. We then propose a federated learning framework to utilize the training process. The local generator parameters are updated at the center, reducing communication overhead and privacy concerns. To deal with nonindependent and identically distributed datasets, the discriminators dynamically push away the predictions by dynamic regularization to obtain a more robust aggregated generative model at the center. Furthermore, we propose a motivation scheme that benefits users participating in the training process, encouraging them to join and take advantage of edge/cloud computing capabilities. Numerical results demonstrate that the proposed federated generative adversarial network-enabled channel estimator provides high estimation accuracy and reduces the burden on pilots. The proposed dynamic regularization terms and motivation scheme boost performance efficiently with low communication cost and high participation.

Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems

AbstractSo far, various data‐driven approaches have been presented to obtain channel state information (CSI) in millimeter wave multiple‐input‐multiple‐output wireless networks. In almost all previous works, training and testing channels were assumed to have the same distribution, which may not be the case in practice. In this article, we address this challenge by proposing a learning framework that is a combination of a recurrent neural network (RNN) model and a deep neural network (DNN) for estimating CSI in a dynamic wireless communication environment. Furthermore, we use federated learning to train the learning‐based channel estimation model. More specifically, we introduce a two‐stage downlink pilot transmission procedure, where in the initial stage, long frame length downlink pilot signals are used to train the introduced RNN‐DNN model. Following that, users will receive shorter‐frame‐length pilot signals that can be used for CSI estimation. To speed up the training procedure of the proposed network, we first generate a pre‐trained model and then modify it according to the collected data samples. Simulation results demonstrate that, when the channel distribution is unavailable, the proposed approach performs significantly better than the most recent channel estimation algorithms in terms of estimation performance and computational complexity.

Multi‐user beamforming with a reconfigurable intelligent surface using stereo camera images

AbstractThis paper proposes and experimentally evaluates, a reconfigurable intelligent surface (RIS) control system for multiple users using stereo camera images. The aim is to enable fast and fair received signal strength (RSS) beamforming for multiple users. In the proposed system, a stereo camera is mounted on top of the RIS. Human bodies are recognized from the obtained images. Subsequently, the beam directions and the phase of the RIS are calculated for the estimated positions. The stereo camera‐based scheme enables fast beamforming because channel state information estimation is omitted. Moreover, identical RSS beamforming is enabled by calculating the weighted sums of the RIS phases for multiple users utilizing distance and angle information of users from the RIS obtained using the stereo camera. Experiments are conducted in an anechoic chamber using RIS operating at 28 GHz. The efficacy of the RIS control system using stereo camera images and its ability to increase the RSS fairly for multiple users is validated via simulations and experiments.

Adaptive received signal strength-based localization and channel state information estimation model for indoor multiple-input multiple-output visible light communication using the distance vector (Erratum)

Adaptive received signal strength-based localization and channel state information estimation model for indoor multiple-input multiple-output visible light communication using the distance vector (Erratum)

Adaptive received signal strength-based localization and channel state information estimation model for indoor multiple-input multiple-output visible light communication using the distance vector

Adaptive received signal strength-based localization and channel state information estimation model for indoor multiple-input multiple-output visible light communication using the distance vector

Pilot-Partitioning Protocol and Anti-Jamming Methods in Distributed Massive MIMO Systems

Accurate channel state information (CSI) is crucial for effective beamforming in wireless communication systems. Unfortunately, the estimation of CSI might not be accurate due to pilot contamination, which happens when the transmission of pilots from the users to access points (APs) is interfered. In systems with a large number of users, non-orthogonal pilot training can increase pilot contamination resulting in incorrect CSI estimation. To address this issue, we propose a pilot-partitioning protocol for distributed massive multiple-input multiple-output (MIMO) systems. The protocol decomposes long-length orthogonal pilots into short-length sub-pilots, transmits them in consecutive slots, and uses the channel correlation among consecutive slots to regenerate orthogonal pilots. In addition, we consider the impact of adversarial transmissions, such as jamming attacks, on CSI estimation. To protect the system against jamming attacks, we propose an AP selection framework to eliminate ineffective APs where the estimated CSI is strongly distorted by jammers. Furthermore, we develop power control strategies for achieving the net throughput maximization and user fairness. Our numerical results show that the proposed designs improve system performance by up to 6.47 times compared to non-optimized scenarios.

Machine learning based low-complexity channel state information estimation

In 5G communications, the acquisition of accurate channel state information (CSI) is of great importance to the hybrid beamforming of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. In classical mmWave MIMO channel estimation methods, the exploitation of inherent sparse or low-rank structures has demonstrated to improve the performance. However, most high-accurate CSI estimators incur a high computational complexity and require the prior channel information, which hence present the major challenges in the practical deployment. In this work, we leverage machine learning to design the low-complexity and high-performance channel estimator. To be specific, we first formulate the CSI estimation, in the case of sparse structure, as one classical least absolute shrinkage and selection operator problem. In order to reduce the time complexity of existing compressed sensing (CS) methods, we then approximate the original optimization problem to another one, by imposing the other low-rank constraint that was barely considered by CS. We thus solve this new approximated problem and attain the near-optimal solution of the original problem. One new method excludes any prior channel information, and greatly improves the estimation performance, which only incurs a low time complexity. Simulation results demonstrate the superiority of our proposed method both in the estimation accuracy and time complexity.

Robust Beamforming for Sensing Assisted Integrated Communication and Jamming Systems

This paper investigates the robust beamforming design problem in a sensing assisted integrated communication and jamming (ICAJ) system, which aims to minimize the transmission energy consumption in a time duration while ensuring the communication requirements for multiple legitimate users and jamming demands for the suspicious network with multiple communication groups. To achieve these goals, we first design a sensing-communication and jamming (SCJ) protocol. Then, a robust beamforming design problem with both communication and jamming channel state information (CSI) estimation error is formulated as a non-convex optimization. To solve it, a joint optimization (JO) algorithm is developed to jointly optimize the beamforming matrices of communication and jamming, by leveraging the General S-procedure, Schur' complement and General Sign-definiteness techniques. Simulation results show that the proposed SCJ protocol with JO algorithm has superior performance, as compared to benchmark methods.

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback.

Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

Data-Aided CSI Estimation Using Affine-Precoded Superimposed Pilots in Orthogonal Time Frequency Space Modulated MIMO Systems

An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.

Simultaneous Secure and Covert Transmissions Against Two Attacks Under Practical Assumptions

In practice, Internet-of-Things (IoT) applications may face two different hostile attacks i.e., overhearing the broadcasted data and detecting the presence of data communication. Previous works have assumed that only one of these attacks is present in the network and have addressed these attacks separately. In this paper, we investigate the security of a simple but practical IoT-based point-to-point communication which is facing with the above mentioned two attacks under some realistic assumptions. In other words, we aim to protect the data against two adversary nodes, namely Eve (who tries to capture the broadcasted data) and warden Willie (who tries to detect the presence of data communication between legitimate nodes). To tackle these attacks, we jointly employ the information-theoretic security and covert communication techniques. To hide information from Willies, we force the source to transmit its message in the selected time slot and the multiple-antennas cooperative jammer to inject jamming during all time slots contentiously which leads to deceive Eves and Willies jointly. To enhance the performance of this security design, we formulate an optimization problem, with the goal of maximizing the secrecy rate subject to power constraints at the source and jammer, while satisfying a covert communication requirement. Since the mentioned optimization problem is non-convex, we propose an algorithm to solve it. Then to obtain further insights, we extend our proposed system model to non-colluding and colluding Willies. Next, we study a practical communication scenario, where the source has uncertainty about Willie’s location and knows the channel state information (CSI) of Eves imperfectly. Our numerical results highlight that a more accurate estimation of Willie’s location has a more positive efficacy on the secrecy performance compared to the case with a more accuracy of Eves’ CSI estimation.

A time-varying acoustic channel-aware topology control mechanism for cooperative underwater sonar detection network

Topology control plays a vital role in cooperative underwater sonar detection networks (USDNs) to ensure detection applications. By incorporating spatio-temporal uncertainties in acoustic channel and limited resources in underwater networks, a large number of node deployment schemes have been proposed to overcome these difficulties through topology control. However, the channel uncertainty has not been solved fundamentally. In order to effectively deal with the time-space-frequency uncertainty of the acoustic channel, this paper proposes a time-varying acoustic channel-aware (TVAC) topology control mechanism to deploy sonar nodes in USDNs. Initially, a time-varying acoustic channel state information (CSI) estimation algorithm and a prediction algorithm are proposed by applying three sub-modules: a feature stitching deep neural network-based temperature prediction model that we designed, a deep neural network-based sound velocity estimation model, and a BELLHOP-based propagation loss calculation model. After that, a TVAC topology control mechanism is adopted to adaptively deploy nodes based on the CSI estimation and prediction results. Using the improved weighted set covering algorithm, the current CSI is adopted to determine the working modes of sonar nodes, and the predicted CSI is adopted to obtain the cycle of topology update. What is more, we develop an offline–online CSI prediction model to improve the accuracy and stability of node working modes management. Simulation results show that compared with other topology control schemes, TVAC can obtain superior coverage performance, full connectivity, and prolong the network lifetime with guaranteed coverage and connectivity.

Performance Analysis and Optimization of RSMA Enabled UAV-Aided IBL and FBL Communication With Imperfect SIC and CSI

In this work, we investigate rate-splitting multiple access (RSMA) for a multiuser downlink wireless network consisting of an unmanned aerial vehicle (UAV)-assisted base station (BS) that serves multiple ground users (GUs) simultaneously. Considering two different transmission regimes, namely infinite blocklength (IBL), and finite blocklength (FBL), we analyze the performance of the considered network under the effect of imperfections in channel state information (CSI) estimation and successive interference cancellation (SIC) with the probabilistic line of sight fading channels. For IBL transmission, we derive the closed-form expressions of the outage probability, throughput, and achievable ergodic rate at each GU. Furthermore, for short packet communication, the closed-form expressions of block error rate (BLER), goodput, and achievable ergodic rate are determined with FBL transmission. Moreover, for the FBL regime we also formulate an optimization problem that jointly optimizes the 3D-position of the UAV, power allocated to each user, and common rate distribution at each user to maximize the ergodic sum rate subject to the practical constraints such as maximum tolerable BLER, minimum private and total rate at each user. We then propose an alternating optimization-based iterative algorithm to solve the problem. Monte-Carlo simulations are used to verify the accuracy of derived analytical results and demonstrate the trade-off between transmit power and achievable BLER. In addition to this, the effectiveness of RSMA in UAV-assisted communication under IBL and FBL transmission regimes is also observed compared to non-orthogonal multiple access.

Adaptive Transceiver Design for High-Capacity Multi-Modal Free-Space Optical Communications With Commercial Devices and Atmospheric Turbulence

In this paper, to achieve higher robustness against atmospheric turbulence for high-capacity free-space optical (FSO) communications, an adaptive multi-modal FSO transceiver has been designed and experimentally demonstrated. We show that based on the dynamically estimated channel state information, modulation formats and power for different transmit modes can be adaptively allocated at the transmitter side. Meanwhile, at the receiver side, we show that the most suitable multi-input/multi-output decoder can be selected and employed to meet the requirement of forward error correction at the minimal expense of power consumption. By employing time-division multiplexed transmitter and receiver emulation and a spatial light modulator for turbulence emulation, an aggregate data rate of 590 Gbit/s/wavelength has been achieved when suffering from strong atmospheric turbulence, verifying the feasibility of the proposed adaptive transceiver over a turbulent FSO link. Moreover, to demonstrate practical applicability, all key devices such as transponder, multiplexer, and demultiplexer are commercially available in this work.

Massive MIMO Evolution Toward 3GPP Release 18

Since the introduction of fifth-generation new radio (5G-NR) in Third Generation Partnership Project (3GPP) Release 15, swift progress has been made to evolve 5G with 3GPP Release 18 emerging. A critical aspect is the design of massive multiple-input multiple-output (MIMO) technology. In this line, this paper makes several important contributions: We provide a comprehensive overview of the evolution of standardized massive MIMO features from 3GPP Release 15 to 17 for both time/frequency-division duplex operation across frequency range in (FR)-1 and FR 2-1. We analyze the progress on channel state information (CSI) frameworks, beam management frameworks and present enhancements for uplink CSI. We shed light on emerging 3GPP Release 18 problems requiring imminent attention. These include advanced codebook design and sounding reference signal design for coherent joint transmission (CJT) with multiple transmission/reception points (multi TRPs). We discuss advancements in uplink demodulation reference signal design, enhancements for mobility to provide accurate CSI estimates, and unified transmission configuration indicator framework tailored for FR 2-1 bands. For each concept, we provide system level simulation results to highlight their performance benefits. Via field trials in an outdoor environment at Shanghai Jiaotong University, we demonstrate the gains of multi-TRP CJT relative to single TRP at 3.7 GHz.

Unsupervised LoS/NLoS identification in mmWave communication using two-stage machine learning framework

Identification of line-of-sight (LoS)/ non-LoS (NLoS) condition in millimeter wave (mmWave) communication is important for localization and unobstructed transmission between a base station (BS) and a user. A sudden obstruction in a link between a BS and a user can result in poorly received signal strength or termination of communication. Channel features obtained by the estimation of channel state information (CSI) of a user at the BS can be used for identifying LoS/NLoS condition. With the assumption of labeled CSI, existing machine learning (ML) methods have achieved satisfactory performance for LoS/NLoS identification. However, in a real communication environment, labeled CSI is not available. In this paper, we propose a two-stage unsupervised ML based LoS/NLoS identification framework to address the lack of labeled data. We conduct experiments for the outdoor scenario by generating data from the NYUSIM simulator. We compare the performance of our method with the supervised deep neural network (SDNN) in terms of accuracy and receiver characteristic curves. The proposed framework can achieve an accuracy of 87.4% and it outperforms SDNN. Further, we compare the performance of our method with other state-of-the-art LoS/NLoS identification schemes in terms of accuracy, recall, precision, and F1-score.

A low‐complexity transmit combining method for downlink of massive multiuser multiple input multiple output systems

AbstractMultiuser multiple input multiple output (MU‐MIMO) systems can provide high spectral efficiency (SE) that is achieved with transmit/receive combining schemes at the base station (BS) for downlink (DL) and uplink (UL). So far, various combining schemes for DL were proposed, with the regularized zero forcing (RZF) and Tomlinson–Harashima precoding (THP) being usually considered. In a case of massive MU‐MIMO systems, with a large number of antennas, the matrix inversion operation used in the RZF and THP schemes causes high computational complexity that significantly aggravates implementation and raises its costs. The authors propose a low‐complexity transmit combining scheme for the massive MU‐MIMO systems with time division duplex (TDD), devised in a similar manner as the THP scheme, based on the gram‐Schmidt orthogonalization, and show that it achieves slightly better SE than the THP scheme for the estimated channel state information (CSI), but with a much lower complexity than the THP and RZF schemes.