Channel Estimation Error Research Articles

Outage constrained transmission design for NOMA-based integrated sensing and communication systems

This paper investigates a framework for integrated sensing and communication (ISAC) based on non-orthogonal multiple access (NOMA). In this framework, a dual-function base station (BS) utilizes NOMA technology to send superimposed signals to various users, and this superimposed signal also acts on target sensing simultaneously. Considering the channel estimation error, ensuring the communication performance, and sensing performance requirements, a transmit power optimization problem of ISAC system using NOMA is studied. Specifically, in the statistical channel state information (CSI) error model, the total system communicate power is minimized while ensuring all single users' rate outage probability (OP) constraints and the requirements for the beampattern gains of all single radar targets. Unfortunately, the proposed problem is challenging to solve and non-convex. But we have devised a feasible way to deal with this problem. First, we use Bernstein inequality to transform the rate OP constraint, and this challenging non-convex problem is then successfully solved using a method based on semi-definite relaxation (SDR). The numerical outcomes demonstrate that the system's overall transmission power will increase due to the channel estimation error. The numerical findings also show that the ISAC system performs better with NOMA assistance than with OMA when comparing the NOMA and OMA schemes.

Performance of Rate Splitting Multiple Access-based Visible Light Communications with Residual Interference and Channel Estimation Errors

Performance of Rate Splitting Multiple Access-based Visible Light Communications with Residual Interference and Channel Estimation Errors

Codebook-Based IRS System: Impact of Channel Estimation Errors and Pilot Power Adaptation on Codeword Selection and Data Rate

Codebook-Based IRS System: Impact of Channel Estimation Errors and Pilot Power Adaptation on Codeword Selection and Data Rate

Channel Error Estimation Algorithm for Multichannel in Azimuth HRWS SAR System Based on a 3-D Deep Learning Scheme

Channel Error Estimation Algorithm for Multichannel in Azimuth HRWS SAR System Based on a 3-D Deep Learning Scheme

Machine learning assisted adaptive LDPC coded system design and analysis

AbstractThis paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

Two-Timescale Transmission Design and RIS Optimization for Integrated Localization and Communications

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked. To control the RIS, channel state information (CSI) is needed, which entails significant pilot overhead. To reduce this overhead and the need for frequent RIS reconfiguration, we propose a novel framework for integrated localization and communications, where RIS configurations are fixed during location coherence intervals, while BS precoders are optimized every channel coherence interval. This framework leverages accurate location information obtained with the aid of several RISs as well as novel RIS optimization and channel estimation methods. Performance in terms of localization accuracy, channel estimation error, and achievable rate demonstrates the effectiveness of the proposed approach.

Robust Integrated Data and Energy Transfer Aided by Intelligent Reflecting Surfaces: Successive Target Migration Optimization Toward Energy Sustainability

Intelligent reflecting surfaces (IRSs) can actively adjust the wireless environment. However, accurate channel estimation on IRS-aided communication systems is difficult to obtain. Therefore, we study a robust beamforming design for an IRS-aided integrated data and energy transfer (IDET) with imperfect channel state information (CSI). Against the uncertain channel estimation error, we robustly design the transmit beamformers of the transmitter and the passive reflecting beamformer of the IRS to minimize the transmit power by satisfying both the wireless data transfer (WDT) and wireless energy transfer (WET) requirements for realising energy-sustainability in 6G. A successive target migration optimization (STMO) algorithm is proposed to obtain a robust design. The transmit covariance matrices are optimized by relaxing rank-one constraints, when a passive reflecting beamformer is given. Then, the target to minimize the transmit power is migrated to maximize the QoS requirements of energy users due to the fixed transmit power. A local optimal reflecting beamformer is obtained for improving the attainable WET performance, when the transmit covariance matrices are given. Finally, we prove that the rank-one transmit beamformers can always be found, which have the same WET and WDT performance as the transmit covariance matrices. The numerical results demonstrate the advantage of our design.

Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements

In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with semi-passive elements that can receive the signal in the active sensing mode. Ultimately, our goal is to minimize the channel estimation error using the received signal at the base station and additional information acquired from a small number of active sensors at the IRS. Unlike recent works on channel estimation with semi-passive elements that require both uplink and downlink training signals to estimate the UE-IRS and IRS-BS links, we only use uplink training signals to estimate all the links. To compute the minimum mean squared error (MMSE) estimates of all the links, we propose a novel variational inference-sparse Bayesian learning (VI-SBL) channel estimator that performs approximate posterior inference on the channel using VI with the mean-field approximation under the SBL framework. The simulation results show that VI-SBL outperforms the state-of-the-art baselines for IRS with passive reflecting elements in terms of the channel estimation accuracy and training overhead. Furthermore, VI-SBL with semi-passive elements is shown to be more spectral- and energy-efficient than the baselines with passive reflecting elements.

Preset Conditional Generative Adversarial Network for Massive MIMO Detection

In recent years, extensive research has been conducted to obtain better detection performance by combining massive multiple-input multiple-output (MIMO) signal detection with deep neural network (DNN). However, spatial correlation and channel estimation errors significantly affect the performance of DNN-based detection methods. In this study, we consider applying conditional generation adversarial network (CGAN) model to massive MIMO signal detection. First, we propose a preset conditional generative adversarial network (PC-GAN). We construct the dataset with the channel state information (CSI) as a condition preset in the received signal, and train the detector without direct involvement of CSI, which effectively resists the impact of imperfect CSI on the detection performance. Then, we propose a noise removal and preset conditional generative adversarial network (NR-PC-GAN) suitable for low-signal-to-noise ratio (SNR) communication scenarios. The noise in the received signal is removed to improve the detection performance of the detector. The numerical results show that PC-GAN performs well in spatially correlated and imperfect channels. The detection performance of NR-PC-GAN is far superior to the other algorithms in low-SNR scenarios.

Performance evaluation of L‐maximal ratio combining diversity with estimation error for Shadowed Beaulieu‐Xie fading channels

SummaryTo provide the increasing demand for wireless technologies, high capacity with lower latency is vital. New emerging technologies like 5G, millimeter wave, THz, and so forth are able to characterize such complex fading and shadowed environments. Spatial diversity is one of the techniques to minimize fading in the wireless communication channel. The performance of the wireless communication system can be further optimized by using estimators at the receivers. The influence of imperfect channel estimation on the performance over shadowed Beaulieu‐Xie (SBX) fading channels with L‐maximal ratio combining (MRC) diversity is studied. The probability density function (PDF) of the received output signal‐to‐noise ratio (SNR) with L‐MRC over SBX fading channels is derived. Further PDF of the received output SNR, outage probability, and average bit error rate (ABER) using the derived PDF is evaluated for L‐MRC diversity with channel estimation error over SBX fading channels. ABER expression for Gray‐coded rectangular quadrature amplitude modulation over the SBX channel is also evaluated. The study also includes how the system performs under the effect of different shadowing and fading parameters.

Analysis of Coded Slotted ALOHA With Energy Harvesting Nodes for Perfect and Imperfect Packet Recovery Scenarios

We analyze the performance of Coded Slotted ALOHA (CSA) protocols in scenarios where users are equipped with limited batteries that are recharged through Energy Harvesting (EH). First, we assume a Perfect Packet Recovery Scenario (PPRS) for which the received packets are decoded with no errors when there is no interference. We introduce Battery Outage Probability (BOP) as an extra performance metric; and, we derive the optimal EH-CSA transmission policies, which offer the maximum attainable traffic load while maintaining an asymptotically negligible Packet Loss Ratio (PLR), under specific rate and BOP constraints. We extend our study to Imperfect Packet Recovery Scenario (IPRS) where impairments at the physical layer, including channel estimation and channel decoding errors, will distort messages being passed through the iterative Successive Interference Cancellation (SIC) process. The distorted messages being passed through the SIC process potentially lead to error propagation. In order to track the error propagation process, we define the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accumulated Noise plus Interference Power</i> (ANIP), and analytically track the evolution of its probability distribution. We employ our results to evaluate the bit error rates for different transmission policies for the case of IPRS. We also demonstrate the advantages of the optimal transmission policies through numerical examples for both PPRS and IPRS. Our results show that the optimal EH-CSA policies outperform the policies optimized for standard CSA without EH considerations, and the schemes that are optimal for PPRS are not necessarily optimal for the IPRS case. Furthermore, the EH-CSA optimal policies strictly outperform standard CRDSA when the system is required to support higher traffic loads.

Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication

The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.

Expectation-maximization vector approximate message passing-based frequency-domain turbo equalization for underwater acoustic communications.

Channel equalization plays a crucial role in single-carrier underwater acoustic (UWA) communications. Recently, a frequency-domain turbo equalization (FDTE) scheme enabled by the vector approximate message passing (VAMP) algorithm, was proposed, and it outperformed classic linear minimum mean square error FDTE at acceptable complexity cost. The operation of the VAMP-FDTE requires knowledge of noise power, which is predetermined before the equalization starts. In practice, however, it is difficult to obtain prior knowledge of noise power due to factors of unknown channel estimation errors and dynamic underwater environments. Motivated by this fact, we propose an enhanced VAMP-FDTE scheme, which learns the noise power knowledge during the equalization process via the expectation-maximization (EM) algorithm. The EM-based noise power estimation makes use of intermediate results of the VAMP-FDTE and, thus, only incurs a small extra computational overhead. The improved VAMP-FDTE, named EM-VAMP-FDTE, was tested by experimental data collected in shallow-sea horizontal UWA communication trials with MIMO configuration. It showed better performance than the existing VAMP-FDTE scheme, attributed to the online noise power learning.

Reliability performance analysis for OTFS modulation based integrated sensing and communication

In this paper, we develop an orthogonal time frequency space (OTFS) modulation based integrated (radar) sensing and communication (ISAC) technique, namely OTFS-ISAC, for the high mobility multiuser system. In particular, at the transmitter, it can alternate the modes between sensing and communication using time division multiplexing so that it takes advantage of radar assistance to design the OTFS frame structure for reducing the overhead of guard symbols. At the receiver, based on the embedded pilot-aided channel estimation, it employs zero-forcing (ZF) equalization. For performance analysis, we derive the error probability of channel estimation based on radar sensing, and find the outage probability of downlink communication. Then, we introduce an adaptive power allocation scheme at the transmitter to optimize the outage probability of the communication link. Our simulations show that, by properly selecting N, M, and the pilot symbol strength, the proposed OTFS-ISAC scheme can reduce the overhead of guard symbols while achieving promising reliability performance of the downlink transmission with relative low channel estimation error.

Generalized transmit laser selection for vertical underwater wireless optical communications over Gamma-Gamma turbulence channels.

Underwater wireless optical communication (UWOC) is a potential technology for high-speed and reliable underwater data transmission. In underwater environment, ocean turbulence has a strong impact on the performance of UWOC systems. Thus, transmission laser selection (TLS) is proposed as an effective technique for fading mitigation in turbulence channels. In this paper, we present a vertical UWOC system with generalized TLS (GTLS) in multi-layer cascaded Gamma-Gamma ocean turbulence channels. With GTLS, the transmitter is equipped with N laser sources and the nth source is selected for transmission. For the ideal case, the optimal laser source is selected, but in practice, a sub-optimal or worse source may be selected due to channel estimation and/or feedback errors. For the GTLS system, we derive an exact closed-form expression for outage probability. Furthermore, based on the outage probability expression, the diversity order and asymptotical diversity order expression are analyzed. Finally, we give simulation results to validate our analytical results. Numerical results show that the GTLS system performs better than the system without GTLS (i.e. N = 1). The number of cascade channel layers, the number of laser sources and the selection of source index significantly affect the performance of the GTLS system.

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

SummaryIn this article, we propose two deep neural network (DNN) architectures for an intelligent reflecting surface (IRS)‐assisted network with a single transmitter and multiple receivers. The first DNN is designed to obtain an optimal beamforming weight vector that maximizes the achievable sum rate over the IRS‐aided network. The beamforming vector is designed based on a pilot‐based channel estimation procedure, which requires only a small fraction of the available number of reflecting surfaces on the IRS to be active. The obtained vector is fed to the second DNN, which is designed for detecting information symbols from the transmitter. Our simulation study shows that the achievable rate obtained using the designed beamforming vector approaches the maximum achievable rate quickly, as the number of active elements increases. The impact of the number of active elements and the beamforming vector on the performance of the detector is also studied, in terms of the bit error rate (BER). We show that the BER performance of the second DNN is close to that of the optimal maximum likelihood detector. Further, we study the effect of the channel estimation errors on the performance of the DNNs. Moreover, we study the effect of the hyperparameters of the architectures on the training time and performance, in terms of root mean squared error and accuracy.

Performance of GFDM system with channel estimation error over FTR mmWave channels for 5G and beyond communication

Millimeter-wave (mm-Wave) communication are the building block of the beyond 5G (B5G) generations. For mm-Wave communication, the fluctuating two-ray (FTR) fading channels serve as an appropriate model for the small-scale fading. Generalized frequency division multiplexing (GFDM) is envisioned as one of the potential candidate to fulfil the requirements of B5G communication systems. This paper presents the performance analysis of a GFDM system over the FTR fading environment. In this analysis, both GFDM and space–time coded (STC) GFDM system has been considered, and an exact closed-form expression of the average symbol error rate (ASER) is derived for the μ-QAM modulation technique under imperfect channel estimation condition. The simulation results demonstrate excellent agreement with the numerical results obtained for GFDM and STC-GFDM systems with different channel and system parameters such as m, Kf, Δ, TA, RA, ϱA2 and μ. An asymptotic analysis is also done for both systems.

Low-complex channel estimation in extra-large scale MIMO with the spherical wave properties

This paper investigates the low-complex linear minimum mean squared error (LMMSE) channel estimation in an extra-large scale MIMO system with the spherical wave model (SWM). We model the extra-large scale MIMO channels using the SWM in the terahertz (THz) line-of-sight propagation, in which the transceiver is a uniform circular antenna array. On this basis, for the known channel covariance matrix (CCM), a low-complex LMMSE channel estimation algorithm is proposed by exploiting the spherical wave properties (SWP). Meanwhile, for the unknown CCM, a similar low-complex LMMSE channel estimation algorithm is also proposed. Both theoretical and simulation results show that the proposed algorithm has lower complexity without reducing the accuracy of channel estimation.

PLS performance analysis of the vertical UWOC system with perfect and imperfect CSI.

Although underwater wireless optical communication (UWOC) receives much interest lately, security issues associated with it get little attention. In this work, it is the first attempt to investigate the physical layer security (PLS) performance of the vertical UWOC system with perfect and imperfect channel state information (CSI). Specifically speaking, the communication between two legitimate peers in the presence of an external eavesdropper is studied from the information-theoretic security perspective. Assuming that turbulence-induced fading over the vertical UWOC links is respectively subject to cascaded lognormal (LN) and Gamma-Gamma (GG) distributions for weak and moderate/strong turbulence conditions, and the angular pointing error is randomized by the Beckmann distribution, the composite cascaded statistical fading models are derived with the comprehensive effects of path loss, underwater turbulence, angular pointing errors, and channel estimation error. On the basis of these models, analysis frameworks of the probability of strictly positive secrecy capacity (SPSC), secrecy outage probability (SOP), and average secrecy capacity (ASC) are further obtained for this UWOC system, which are confirmed by Monte Carlo (MC) simulations. Furthermore, the effects including the number of layers, the level of channel estimation error, the link distance, the location of the eavesdropper, the quality of the main and eavesdropping channels on this system are analyzed for different water conditions. The presented results give valuable insights into the practical aspects of deployment of UWOC networks.

Securing Multiuser Underlay Cognitive Transmissions With Hardware Impairments and Channel Estimation Errors

Securing Multiuser Underlay Cognitive Transmissions With Hardware Impairments and Channel Estimation Errors