Channel State Information Estimation Research Articles

Joint Channel Estimation and Feedback for mm-Wave System Using Federated Learning

Due to the high propagation path loss in the millimeter wave (mm-wave) system, it is necessary to use the large antenna array for obtaining the high beamforming gain in mm-wave systems. In order to reduce the cost and complexity of hardware, an architecture called hybrid precoding is proposed, which divides the precoding operation into the analog domain and the digital domain. However, this poses a challenge for channel estimation in mm-wave systems. In addition, due to the great amount of users and antennas, the feedback overhead of channel estimation is also an issue that needs to be considered. In the letter, we consider the mm-wave massive multiple-input-multiple-output (MIMO) system and propose a federated learning (FL)-base channel state information (CSI) estimation and feedback (FCEF) scheme, where each user trains the local model through the local dataset and exchanges the model parameters with the base station (BS). Thanks to upload model parameters rather than huge channel data, the FCEF scheme has lower transmission overhead than centralized learning (CL). The architecture of the proposed FCEF model is described in the letter, which consists of a CSI estimation and compression (CEC) network and a CSI recovery (CR) network. Numerical results demonstrate that the proposed FCEF scheme has good performance for CSI estimation and feedback in mm-wave MIMO systems, which is close to that of the CL-based scheme while ensuring lower transmission overhead.

Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface Under Imperfect CSI

This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks. In the presence of eavesdroppers, the downlink communication from the base station to the legitimate users is safeguarded by the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), where three practical operating protocols, namely energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. The joint optimization of power allocation, active and passive beamforming is investigated to maximize the secrecy energy efficiency (SEE), taking into account the imperfect channel state information (CSI) of all channels. For ES, by approximating the semi-infinite constraints with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula> -procedure and general sign-definiteness, the problem is solved by an alternating optimization framework. Besides, the proposed algorithm is extended to the MS protocol by solving a mixed-integer non-convex problem. While for TS, a two-layer iterative method is proposed. Simulation results show that: 1) The proposed STAR-RIS assisted NOMA networks are able to provide up to 33.6% higher SEE than conventional RIS counterparts; 2) TS and ES protocols are generally preferable for low and high power domain, respectively; 3) The accuracy of CSI estimation and the bit resolution power consumption are crucial to reap the SEE benefits offered by STAR-RIS.

Performance Analysis of Cell-Free Massive MIMO System with Network-Assisted Full-Duplex under Time-Shifting Pilot Scheme

A network-assisted full-duplex (NAFD) system based on a cell-free (CF) massive multiple-input, multiple-output (MIMO) framework has been proposed to satisfy the demands of higher data transmission rates and efficient communication. However, pilot contamination may occur due to the reuse of pilot sequences in a massive MIMO. With this consideration, we raise an asynchronous channel estimation method based on an uplink and downlink time-shifting pilot-sending scheme, which is able to avoid pilot sequence reuse when obtaining channel state information (CSI), while the data signals could be transmitted simultaneously at the same frequency. The transmission processes of the proposed method above are divided into three phases, including pilot phase, estimation phase, and data phase, in chronological order. When the uplink is in pilot phase, the corresponding downlink is in data phase and vice versa. After the channel state information estimation, both uplinks and downlinks are in data phase. The maximum ratio combination (MRC) receiver and the maximum ratio transmission (MRT) precoding are adopted in the uplink and downlink. The closed-form expressions are derived based on large-scale random matrix theory. We compared our asymptotic results with practical results in simulation, and find that they are well matched. Moreover, the proposed method is superior to the normal time-division duplex (TDD) system.

HERA: High-Efficiency Resource Allocation Scheme for Newly Joined Mobile Terminal in Heterogeneous Wireless Network

Objectives:To design an efficient resource allocation design for provisioning service of varied traffic classes to Multi-Mode Mobile Terminals (MMTs) in Heterogeneous Wireless Networks (HWNs). In HWNs, users prerequisite certain Quality of Service (QoS); thus, for meeting QoS, theMMTs are handoff to the new network. However, handoff MMTs might induce interference with the ongoing communication of existing MMTs. As resources are shared, resource allocation becomes a challenging task. Methods:This study presents a High-Efficiency Resource Allocation optimization model for HWNs. The HERA scheme first employs Channel State Information (CSI) estimation for mitigating interference and establishing channel availability. Second, using game theory optimal resource is allocated to MMTs and proves the existence of Nash equilibrium (NE) for spectrum resource allocation to MMTs in HWNs. Finally, a hybrid resource allocation algorithm combining contentionless and contention-based is designed to provide an improved resource allocation mechanism. Findings:The experiment outcome shows the HERA scheme allocates resources more efficiently in comparison with Access Fairness Resource Allocation (AFRA)(1), Joint Resource Allocation with Power Optimization (JRA-PO)(2), Resource Allocation and Node Placement (RANP)(3), and Existing Resource Allocation (ERA)(4). The HERA improves throughput by 19.07% and reduces collision by 42.96% in comparison with ERA(4) . Novelty: existing model predominantly focused on either addressing interference or maximizing throughput by leveraging either contention or contention-less resource allocation mechanism. However, in this study both contention-less and contention-based mechanisms are merged to maximize throughput and reduce collision. Keywords: Interference; Quality of service; Resource allocation; Softcomputing; Spectrum utilization; Heterogeneous communication network

Adaptive Power Control for Cooperative Covert Communication With Partial Channel State Information

In jammer-assisted covert communication scenarios, jamming signals also degrade the communication between legitimate users. Considering that it is difficult for a transmitter to obtain all the required channel state information (CSI), we propose an adaptive power control strategy with partial CSI, which decreases the outage probability while bringing more uncertainty to a warden. Specifically, we adopt an upper confidence limit to estimate the unknown CSI from the jammer to the receiver. Under warden’s optimal detector, we analyze the maximum covert throughput by optimizing the upper confidence limit. A closed-form expression of the optimized upper confidence limit is derived in certain cases. The numerical results validate the effectiveness of the proposed strategy and guide the estimation of unknown CSI.

Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models.

Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently accurate DL CSI estimation. Specifically, to reduce the DL CSI estimation overhead, the training sequence is designed based on the eigenvectors of the transmit correlation matrix. To this end, the achievable sum rate (ASR) maximization and the mean square error (MSE) of CSI estimation with short CT are investigated using the proposed training sequence design. Furthermore, this article examines the effect of channel hardening in an FDD massive-MIMO system. The results demonstrate that in high correlation scenarios, a large loss in channel hardening is obtained. The results reveal that increasing the correlation level reduces the MSE but does not increase the ASR. However, exploiting the spatial correction structure is still very essential for the FDD massive-MIMO systems under limited CT. This finding holds for all the physical correlation models considered.

A Computationally Efficient Gradient Algorithm for Downlink Training Sequence Optimization in FDD Massive MIMO Systems

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date very challenging. Although advanced iterative algorithms have been developed to address this challenge, they exhibit slow convergence speed and thus deliver high latency and computational complexity. To overcome this challenge, we propose a computationally efficient conjugate gradient-descent (CGD) algorithm based on the Riemannian manifold in order to optimize the DL training sequence at base station (BS), while improving the convergence rate to provide a fast CSI estimation for an FDD m-MIMO system. To this end, the sum rate and the computational complexity performances of the proposed training solution are compared with the state-of-the-art iterative algorithms. The results show that the proposed training solution maximizes the achievable sum rate performance, while delivering a lower overall computational complexity owing to a faster convergence rate in comparison to the state-of-the-art iterative algorithms.

Sparse Bayesian Learning Aided Estimation of Doubly-Selective MIMO Channels for Filter Bank Multicarrier Systems

Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasi-static single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.

Communication-Efficient Distributed SGD Using Random Access for Over-the-Air Computation

In this paper, we study communication-efficient distributed stochastic gradient descent (SGD) with data sets of users distributed over a certain area and communicating through wireless channels. Since the time for one iteration in the proposed approach is independent of the number of users, it is well-suited to scalable distributed SGD. Furthermore, since the proposed approach is based on preamble-based random access, which is widely adopted for machine-type communication (MTC), it can be easily employed for training models with a large number of devices where MTC is used for their connectivity. For fading channel, we show that noncoherent combining can be used. As a result, no channel state information (CSI) estimation is required. For over-the-air computation, the proposed approach takes advantage of random access, where the access probability is to encode the norm of local gradient vector (without using any additional bits) and the preamble is to encode the quantized normalized gradient vector. From analysis and simulation results, we can confirm that the proposed approach is not only scalable, but also provides improved performance as the number of devices increases. From analysis and simulation results, we can confirm that the proposed approach is not only scalable, but also provides improved performance as the number of devices increases.

A low complexity burst channel estimation algorithm for FDD massive MIMO systems

To fully attain array gains of massive multiple-input multiple-output (MIMO) and its energy and spectral efficiency, deriving channel state information (CSI) at the base station (BS) side is essential. However, CSI estimation of frequency-division duplex (FDD) based massive MIMO is a challenging task owning to the required pilots, which are proportional to the number of antennas at the BS side. Therefore, the pilot overhead should be inevitably mitigated in the process of downlink channel estimation of FDD technique. In this paper, we propose a novel compressed sensing (CS) algorithm which takes advantage of correlation between the received and transmitted signals to estimate the channel with high precision, and moreover, to reduce the computational complexity imposed on the BS side. The main idea behind the proposed algorithm is to sort the specific number of maximum correlations as a common support in each iteration of the algorithm. Simulation results indicate that the proposed algorithm is capable of estimating downlink channel better than the counterpart algorithms in terms of mean square error (MSE) and the computational complexity. Meanwhile, the complexity of the proposed method linearly grows up when the number of BS antennas increases.

Efficient jamming attack against MIMO transceiver

This work addresses the issue of point-to-point intelligent jamming against MIMO architecture-based systems. The receiver uses an optimal Maximum Likelihood (ML) detector to extract the data stream from the received signal. Analyzing the receiver performance in the presence of a jamming signal, optimal attack strategies based on the pairwise error probability parameter as an objective function are designed, enabling the achievement of maximum degradation of the service quality. To perform the worst-case jamming of a legitimate transceiver, we assume that the jammer can eavesdrop on the Channel State Information (CSI). Depending on the jamming power budget constraint and the potential of jamming systems to estimate the CSI, different scenarios are envisaged, including imperfect CSI estimation cases. By minimizing the trace of Positive Semi-Definite (PSD) matrix products, the optimal jamming approaches that ensure maximum degradation of the legitimate link are designed. The disruption efficiency of the proposed approaches is comparable to the employment of the brute-force attack, but with a higher power level that is proportional to the number of jamming antennas in the case of partial knowledge of CSI, and inversely to the statistical expectation of the square root of the Wishart matrix's eigenvalues in the case of complete knowledge of CSI. To justify the relevance of the suggested policy, extensive simulations of the effect of these interventions are presented.

A Unified Framework for Precoding and Pilot Design for FDD Symbol-Level Precoding

Large-scale antenna array techniques are key enablers for modern wireless communication systems. Channel state information (CSI) is indispensable for large-scale multi-antenna systems, but is challenging to obtain. To tackle this issue, in this paper we propose a unified precoding and pilot design frame-work, that allows minimal and precoding-sensitive modified CSI (mCSI) to be collected. This results in a significant reduction in the CSI overheads and complexity compared to classical physical CSI (pCSI) estimation. Based on this unified framework, we further propose an intelligent pilot (IP) approach that senses and selects the mCSI to be collected. The IP approach utilizes a compressive sensing formulation to attach sensing and selection of significant mCSI to precoding optimization. We apply the above techniques to the multi-user frequency division duplexing (FDD) downlink as an example. Our study shows that the advantages of the IP approach are three-fold. First, in contrast to the pCSI, precoding-sensitive information is only captured, which reduces the training and feedback overheads. Second, the precoders are optimized directly based on the mCSI, which avoids recovering the pCSI of high-dimension. Third, since the mCSI of reduced dimension is utilized, the scale of the problem to optimize the precoder is also reduced and thus it is much easier to solve.

A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input&#x2013;multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.

Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

SummaryMultiple‐input multiple‐output (MIMO) technology is much significant for achieving high data rates while considering the multiuser communication. For this purpose, the estimation of Channel State Information (CSI) is performed by the pilot. But, the estimated CSI cannot be applied for downlinks while processing an extremely high speed mobile data as it gets outdated because of the rapid channel variation. Estimating the channel in the MIMO systems is highly difficult on using non‐stationary channel characteristics under the high mobility environments. Channel estimation with received signal‐to‐noise ratio (SNR) feedback ensures an effective performance in the practical wireless MIMO systems. This paper plans to develop an intelligent deep learning strategy to solve the channel estimation problem in MIMO system. The main goal of the proposed model is to estimate the MIMO channel coefficients at a transmitter based on the received SNR feedback information from a receiver using the swarm intelligence‐based deep ensemble learning machine (SI‐DELM). For both the scenarios, the SI‐DELM is developed, in which the convolution neural network (NN) with three classifiers is used. Here, the fully connected layers of CNN are replaced by the NN, recurrent neural network (RNN), and deep neural network (DNN). In the SI‐DEL‐based channel estimation, the hyper parameter tuning is performed by the adaptive iteration assisted mixture ratio‐based cat swarm optimization (AIMR‐CSO) for minimizing the mean square error (MSE) and bit error rate (BER) of the estimated channel. Finally, by performing numerical simulations, the efficiency and better performance of the suggested model is revealed by conducting the comparison between proposed and conventional models.

ML-Based Massive MIMO Channel Prediction: Does It Work on Real-World Data?

Accurate channel state information (CSI) acquisition is hindered by CSI estimation errors, compression, feedback, and processing delays. We propose a machine learning (ML)-based massive multiple-input multiple-output (mMIMO) channel predictor (CP), which can work on the estimated channel and the compressed version of the estimated channel as well. While existing work has evaluated the performance of ML algorithms by only using the artificially generated channel realizations, this letter reports the results of the ML algorithm using the real-world channel realizations from a measurement campaign performed at Nokia Bell-Labs. The results corroborate the validity of the proposed ML-based CP.

Machine Learning Empowered Accurate CSI Prediction for Large-Scale 5G Networks

Wi-Fi networks rely on channel estimation to ensure their performance. The computational complexity and dependability of fifth generation telecommunication networks have significantly improved using supervised learning. In this paper, we develop a channel estimation model that uses a machine learning approach and the study uses multipath channel simulations for the estimation of channel state information (CSI) over arbitrary transceiver antennas. The simulation is conducted to test the efficacy of the model against various machine learning channel estimation models. The results of simulation show that the proposed model obtains increased channel estimation quality than other methods. Further, the bit error rate is recorded low among other methods using the machine learning model. Thus, it is seen that the proposed method achieves a reduced mismatch rate of 1.26 × 10 − 1.5 than other methodson Doppler frequency during channel estimation, where the mismatch rate is higher in existing methods.

CSI Feedback With Model-Driven Deep Learning of Massive MIMO Systems

In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter. However, the feedback overhead becomes exorbitant with the increasing number of antennas. In this letter, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network (ST-Net) is designed in FISTA-Net based on the attention mechanism, which can choose the threshold adaptively. Simulation results show that the proposed TSLR CSI feedback scheme and FISTA-Net outperform the existing algorithms in various scenarios.

Short-Packet Backscatter Assisted Wireless-Powered Relaying With NOMA: Mode Selection With Performance Estimation

Backscatter-assisted wireless-powered relaying has emerged as a promising approach to extend the transmission range of energy-constrained devices, e.g., sensors, with improved energy efficiency. Mode selection adapting to the dynamic channel conditions has a great impact on the performance of this hybrid relaying paradigm. However, designing an effective mode selection is challenging as the instantaneous channel state information (CSI) may not be available and is subject to errors and delays. Furthermore, instant CSI estimation incurs continual communication and computation overhead that burdens the operation of energy-constrained devices. This work considers the mode selection problem in a downlink cooperative scenario, wherein a hybrid relay aids the finite blocklength non-orthogonal transmissions from a source to a pair of destinations. To eliminate the need for instantaneous CSI estimation, we exploit the statistical system information to indicate the mode selection. In particular, we first derive the analytical expressions of average end-to-end block error rates in both active and passive relaying modes, based on which we can obtain the reward gap of the system. We then propose a bandit learning-based mode selection that exploits the reward gap to assess the upper confidence bound. The proposed mode selection method is light-weight and requires no knowledge of instantaneous CSI. Simulation results are presented to validate our analytical results and expose the efficiency of the proposed mode selection over conventional approaches. Besides, we demonstrate that the proposed approach is robust to estimation errors of the reward gap.

Full-duplex cell-free mMIMO networks with coarse ADCs/DACs over Ricean fading for beyond 5G

In this article, we explore a practical full-duplex (FD) cell-free massive multiple-input multiple-output (mMIMO) network equipped with coarse analog-to-digital converters/digital-to-analog converters (ADCs/DACs) at the access points (APs) over Ricean fading channels for beyond 5G communication. First, we suppose that the minimum mean square error scheme is applied to estimate channel state information. The accurate sum achievable rate expressions for both uplink (UL) and downlink (DL) are obtained in closed-form by performing the maximum ratio combining and conjugate beamforming at the APs, respectively. Then, the expressions of energy efficiency for UL and DL can be obtained in closed-form based on the established power dissipation model. Finally, the numerical results are conducted to demonstrate our analytical results about the spectral efficiency of considered system. It is also found that when the total number of coarse ADCs/DACs precision is fixed, employing the higher-precision ADCs at the receive end of APs can notably improve the FD sum rate of proposed networks. In addition, the simulation results also show the impacts of number of antennas at each AP and resolution of coarse ADCs/DACs on the energy efficiency (EE) performance. It is revealed that the moderate quantization bits can contribute to the optimal EE.

Simple and Robust Log-Likelihood Ratio Calculation of Coded MPSK Signals in Wireless Sensor Networks for Healthcare

The simple and robust log-likelihood ratio (LLR) computation of coded Multiple Phase Shift Keying (MPSK) signals in Wireless Sensor Networks (WSNs) is considered under both phase noncoherent and Rayleigh fading channels for healthcare applications. We first simplify the optimal LLR for phase noncoherent channel, the estimation of the instantaneous channel state information (CSI) for both the fading amplitude and the additive white Gaussian noise (AWGN) is successfully avoided, and the complexity-intensive process for zero-order Bessel function of the first kind is also perfectly eliminated. Furthermore, we also develop the simplified LLR under Rayleigh fading channel. Correspondingly, the variance estimation for both AWGN and the statistical characteristic of the fading amplitude is no longer required, and the complicated process for implementation of the exponential function is also successfully avoided. Compared to the calculation of optimal LLR with full complexity, the proposed method is implementation-friendly, which is practically desired for energy-limited WSNs. The simulations are developed in the context of low-density parity-check (LDPC) codes, and the corresponding results show that the detection performance is extremely close to that of the full-complexity LLR metrics. That is, the performance degradation is efficiently prevented, whereas complexity reduction is also successfully achieved.