Lane-level autonomous driving relies on high-accuracy vehicle positioning. Among different vehicle positioning approaches, direction of arrival (DOA) based solutions are competitive as they avoid measuring delay information. However, a large distance between the vehicle and road side unit (RSU) compared to the antenna array aperture limits the positioning performance of the existing methods. To address this issue, in this paper, we propose an iterative positioning method using two collaborative RSUs and the spatial geometry, where the DOAs and the positions of vehicles are iteratively estimated. Numerical simulations demonstrate that the proposed method can achieve a positioning performance of millimeter-grade, improving the positioning performance by one to two orders of magnitude, compared to state-of-the-art methods.

Achieving joint communication and imaging via uplink transmission presents significant challenges due to the unknown communication signal and the coupling of communication and sensing echoes. In this paper, a joint uplink communication and imaging system with only one RF chain is proposed, where a reconfigurable intelligent surface (RIS) is used to assist the base station (BS) to achieve joint signal detection and imaging (JSDI). Aiming to enhance the transmission gain in the desired directions and generate the required radiation pattern in the imaging region of interest (RoI), a RIS phase optimization problem is formulated, which is high dimensional and non-convex. We transform the original problem into a more tractable form by introducing auxiliary variables. Then, a backpropagation (BP) based Batch Gradient Descent (BGD) for both continuous and discrete phase cases is developed. Numerical results show that the proposed RIS phase optimization method achieves a controllable trade-off between communication and imaging performance and reveals a favorable range of the weighting factor where imaging performance can be significantly improved without causing a severe loss in symbol detection. Additionally, the echo decoupling problem is tackled using a Bayesian approach with factor graph techniques, which involve joint maximum a posteriori (MAP) probability estimation and adaptive sparse Bayesian learning (SBL). The proposed decoupling method asymptotically approaches the lower bound of communication and imaging. Numerical results also show that communication performance can be enhanced by utilizing imaging echoes compared to other benchmark communication systems.

This work addresses the challenges of communication signal detection and direction of arrival (DOA) estimation in integrated sensing and communications (ISAC) systems with hardware imperfections. Conventional signal processing techniques often fail to effectively manage the complex nonlinearities caused by hardware imperfections, such as those introduced by power amplifiers and local oscillators. Recently, deep neural networks (DNNs) have been employed to mitigate the hardware imperfections, which however require a substantial amount of pilot signals for training, leading to unacceptable overhead and impracticality in fast time-varying channels. In this work, we employ an NN to characterize the nonlinear system, and propose a novel iterative approach to joint NN-based nonlinear system model learning, signal detection and DOA estimation. Instead of relying on pilot signals for NN learning, the proposed approach utilizes communication data signals as virtual training samples, enabling more accurate nonlinear model learning, which subsequently enhances signal detection and DOA estimation. A Bayesian framework is applied to the joint problem, wherein the NN parameters, the communication signals and the DOAs are jointly obtained by developing a message passing based inference algorithm. In particular, we impose sparse priors on the weights of the NN, so that overfitting can be better handled, resulting in significant improvement in system modeling performance. Extensive simulation results show that, compared to the state-of-the-art approaches, the proposed one delivers significantly better performance.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for future 6G wireless communications. However, the passive nature of RIS and the high-dimensional cascaded channels pose significant challenges for channel estimation (CE), particularly in practical scenarios where decomposition dictionaries cannot be predefined. This paper proposes a novel three-stage joint CE and positioning (JCEP) framework for RIS-assisted communication systems. It first performs the initial CE based on a predefined row dictionary that exploits the structural properties of cascaded channels, and then conducts positioning based on the initial CE results. Finally, it refines the CE results by incorporating the positioning output to construct customized column dictionaries. The framework employs a unitary approximate message passing sparse Bayesian learning (UAMP-SBL) based channel estimator that adapts to both initial and CE refinement stages. For positioning, we design a graph attention network (GAT) to achieve robust positioning performance in dynamic environments. Furthermore, in the CE refinement, we introduce a location-aware dictionary design that leverages position priors to reduce computational overhead. Additionally, we employ meta-learning to enable rapid adaptation to new environments. Extensive simulations show that our framework achieves superior performance in CE and positioning accuracy with low complexity.

Holographic surface based communication technologies are anticipated to play a significant role in the next generation of wireless networks. The existing reconfigurable holographic surface (RHS)-based scheme only utilizes the reconstruction process of the holographic principle for beamforming, where the channel state information (CSI) is needed. However, channel estimation for CSI acquirement is a challenging task in metasurface based communications. In this study, inspired by both the recording and reconstruction processes of holography, we develop a novel holographic communication scheme by introducing recordable and reconfigurable metasurfaces (RRMs), where channel estimation is not needed thanks to the recording process. Then we analyze input-output mutual information and outage probability of the RRM-based communication system and compare it with the existing RHS based system. Our results show that, without channel estimation, the proposed scheme achieves performance comparable to that of the RHS scheme with perfect CSI, suggesting a promising alternative for future wireless communication networks.

This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoFs) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoFs range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions.

Reconfigurable intelligent surface (RIS) has emerged as a promising wireless communication technology in the 6G era. Its ability to adaptively reflect signals offers improved coverage and low energy consumption. Existing channel estimation methods for RIS primarily rely on sparse signal recovery techniques with large overcomplete dictionaries, which results in prohibitive computational complexity. To address this issue, we employ the vision transformer (ViT) model for adaptive user positioning and propose a novel user position based dictionary design approach, to effectively reduce dictionary size and solve the off-grid problem. This design approach is incorporated into a unified framework, where user positioning and channel estimation are performed jointly for integrated sensing and communications. A modified unitary approximate message passing sparse Bayesian learning algorithm with an early stopping scheme is proposed to address potential overfitting issues in channel estimation. Extensive simulation results demonstrate the effectiveness and robustness of our proposed framework.

For base stations (BSs) with integrated sensing and communication (ISAC) capabilities, interference from neighboring BSs not only degrades their communication performance but also increases sensing errors. While beamforming is commonly employed to suppress interference from undesired directions, this approach overlooks the potential sensing gain offered by target-reflected interference signals. In this paper, we investigate a scenario where a BS employs adaptive beamforming to leverage target-reflected interference for enhanced sensing performance. Notably, we deliberately preserve the line-of-sight interference component to enable accurate estimate of the interference symbols, enabling interference utilization without prior knowledge of the pilot of interference signals. Simulation results validate the effectiveness of the proposed approach in improving sensing performance through beamforming-enabled exploitation of interference.

This work investigates communication signal detection and direction of arrival (DOA) estimation for an integrated sensing and communications (ISAC) system with multiple hardware imperfections, including power amplifier nonlinearity, in-phase and quadrature phase imbalance, and phase-gain error (PGE). Conventional signal processing techniques struggle with the complex nonlinearities arising from these imperfections. Recently, deep neural networks (DNNs) have been employed to mitigate hardware impairments; however, they require a substantial number of pilot signals for training, leading to significant overhead, making them impractical in many applications. In this work, we design a signal flow inspired neural network (NN) to characterize the nonlinear ISAC system. Then, we propose a Bayesian method to jointly estimate the parameters of the PGE, the communication signals, and the DOAs by developing a message-passing inference algorithm based on the NN. Extensive simulation results demonstrate that the proposed method provides efficient and robust signal detection and DOA estimation performance under PGE, and significantly outperforms state-of-the-art ones.

In perceptive mobile networks (PMNs), using 5G New Radio (NR) signals for direct sensing poses a significant challenge to practical implementation due to the high computational complexity involved in estimating sensing parameters. In this paper, an efficient sensing method is proposed to incorporate both downlink active sensing and passive sensing to estimate multiple sensing parameters, including delays, angle of arrival (AoA), angle of departure (AoD) and Doppler. In particular, it exploits the synchronization signal blocks (SSBs) to facilitate sensing with multiple remote radio units (RRUs). To reduce the computational complexity of direct sensing, a sparse model is developed to decouple multiple sensing parameter estimation, enabling efficient sensing method design. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we propose an efficient method to achieve parameter estimation and association with corresponding RRUs. This method is further extended to general scenarios involving multiple path components with the same delay. Extensive simulations demonstrate the effectiveness of the proposed method, showing that it outperforms existing ones in terms of sensing accuracy and complexity.

Most recently, deep learning (DL) has been used for inverse synthetic aperture radar (ISAR) imaging to overcome some shortcomings of the compressive sensing (CS)-based algorithms. Although DL-based ISAR imaging methods have effectively improved imaging quality, their performance remains sensitive to noise and sparse aperture. Moreover, many of these methods minimize the mean square error (MSE), which can result in overly smooth reconstructions and lose image details, such as weak scatterers. To solve these problems, an enhanced ISAR imaging method based on AU-Net is proposed in this letter. We design an AU-Net imaging network to improve the imaging accuracy of the model by introducing the attention mechanism to U-Net. Moreover, the combined loss function is designed by adding the$L1$and$L2$regularization terms, which further improves the recovery performance of the network for weak scatterers. Extensive simulations and experimental results validate the effectiveness and superiority of the proposed method compared to the existing algorithms.

Mitigation of interference between base stations (BSs) is a significant challenge in integrated sensing and communication (ISAC) systems, particularly in noncooperative deployments. This letter investigates the scenario where an ISAC-enabled BS experiences interference from downlink (DL) transmission of another noncooperative BS (NBS). We observe that target-reflected interference contains valuable information, motivating its exploitation to enhance sensing capability. However, precise symbol estimation of NBS signals is infeasible without pilot information. To address this, we propose a novel iterative reconstruction-elimination algorithm (IREA) that derives a phase-ambiguous estimate of NBS signals through an efficient one-dimensional search, thereby enabling both interference mitigation and target information extraction from the reflected interference signals. Simulations demonstrate significant improvements in target detection and localization performance through our interference exploitation method.

Due to the utilization of large antenna arrays at base stations (BSs) and the operations of wireless communications in high frequency bands, mobile terminals often find themselves in the near-field of the array aperture. In this work, we address the signal processing challenges of integrated near-field localization and communication in uplink transmission of an integrated sensing and communication (ISAC) system, where the BS performs joint near-field localization and signal detection (JNFLSD). We show that JNFLSD can be formulated as a matrix factorization (MF) problem with proper structures imposed on the factor matrices. Then, leveraging the variational inference (VI) and unitary approximate message passing (UAMP), we develop a low complexity Bayesian approach to MF, called UAMP-MF, to handle a generic MF problem. We then apply the UAMP-MF algorithm to solve the JNFLSD problem, where the factor matrix structures are fully exploited. Extensive simulation results are provided to demonstrate the superior performance of the proposed method.

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green’s function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method.

We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas. Leveraging variational inference (VI) and unitary approximate message passing (UAMP), we develop a Bayesian approach to MF with an efficient message passing implementation, called UAMP-MF. With proper priors imposed on the factor matrices, UAMP-MF can be used to solve a range of problems formulated as MF, such as dictionary learning, compressive sensing with matrix uncertainty, robust principal component analysis, etc. Numerical examples are provided to show that UAMP-MF significantly outperforms state-of-the-art algorithms in terms of computational complexity, recovery accuracy and robustness.

In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/matrix variables rather than scalar ones in message passing, resulting in a loop-free Bayesian network that improves convergence. Numerical results show that GBF-HVMP significantly outperforms state-of-the-art methods in terms of NMSE performance and computational complexity.

Dynamic spectrum access (DSA) is a key technology in cognitive radios, where secondary users (SUs) opportunistically access spectral holes of primary users (PUs) (i.e., channels unoccupied by PUs). The existing DSA schemes often use the listen-before-talk (LBT) mechanism to avoid transmission collisions with PUs. However, LBT-based schemes may not be able to achieve high utilization efficiency of spectral holes as SUs need to perform spectrum sensing over multiple spectrum holes heavily. To address this issue, in this work, we propose a new mechanism called listen-after-collision (LAC), where an SU accesses a channel of PUs without spectrum sensing, and it performs spectrum sensing only after a transmission collision occurs. Moreover, a deep$Q$-network with an improved Thompson sampling algorithm (DQN-iTSA) is proposed to predict both the availabilities and the time lengths of spectral holes to avoid unacceptable transmission collisions and also to determine the order of the channels for sensing by jointly considering the characteristics of spectral holes and the channel qualities of SU transmissions. Extensive simulation results are provided to demonstrate the superior performance of DQN-iTSA, which shows that DQN-iTSA achieves the highest throughput among the compared methods.

Conventional auto-correlation based detectors often require the knowledge of a covariance matrix, which is usually replaced with its corresponding sample covariance matrix by dividing the received signal vector into a number of sub-vectors. This can lead to performance loss due to the deviation of the sample covariance matrix from the population covariance matrix. In this work, the received signal is used as a single signal vector and the use of sample covariance matrices is avoided. Taking advantage of oversampling, we obtain a truncated approximate covariance matrix of primary signals, which leads to a new approximate log-likelihood-ratio-test (aLLRT) detector with low complexity. In addition, a noise power estimator is also proposed by exploiting oversampling, which is incorporated into the new detector for practical implementation. Theoretical analyses for the false-alarm and detection probabilities of the proposed detector are conducted, and their accurate expressions are obtained via performing a nonlinear transformation to the test-statistic of the proposed detector. Numerical results show that, compared to state-of-the-art detectors, the proposed detector improves the detection probability by at least 10%.

Conventional sparse representation methods cannot effectively characterize the nonlinear effects caused by nonideal space-time factors of multiple-input–multiple-output (MIMO) radar system and scenes with complex nonuniform clutter. In addition, a single dictionary is employed for both target and clutter representation, making their separability rather low, thus leading to the degradation of target detection performance. In this article, we propose a deep learning-based dictionary construction approach to achieve dictionaries of target and clutter with high separability and excellent nonlinear correction ability, where the nonlinear characteristics of the received signal are effectively represented and corrected using a complex-valued convolutional autoencoder (CVCAE) network. With the criteria of minimizing the reconstruction and nonlinear correction errors as well as the correlation of target and clutter dictionaries, we jointly learn a CVCAE-based nonlinear correction model for the received signal with nonlinearity and sparse representation for target and clutter in the corrected linear space. An iterative algorithm is proposed to jointly search the solutions to the resultant optimization issue. To acquire the optimal complete dictionaries, an allocation model of the transceiving space-time resources is constructed under the least squares (LSs) criterion and tackled using convex optimization. Extensive experimental results conducted on the measured Mountain-Top data set demonstrate the effectiveness and superiority of the proposed method compared to state-of-the-art methods.

Attaining centimeter-level vehicle positioning is a fundamental requirement for lane-level autonomous driving. Pursuing this objective from the perspective of direction-of-arrival (DOA) estimation is promising, which has emerged as a prominent research topic. In order to simultaneously meet the demands of low complexity and high accuracy, it is crucial for DOA-based solutions to address pressing challenges, including the model mismatch problem and reliable positioning in scenarios with limited samples. This article explores a novel vehicle positioning scheme employing DOAs obtained from collaborative base stations (BSs) or roadside unit (RSU). To cope with the actual propagation scenarios and avoid nonrandom systematic error, the exact spatial geometry (ESG) for DOA estimation is adopted. Under the ESG model, a two-stage unitary approximate message passing (UAMP)-based DOA estimation method is proposed. With DOAs estimated at multiple collaborative BSs/RSUs, the locations of vehicles are finally obtained with cross-localization criterion. Numerical simulations are provided to show that the proposed method is effective and delivers competitive performance. Furthermore, inspired by intriguing simulation results, we design a DOA subset selection mechanism that enhances the reliability of positioning performance.

Grant-free random access is promising in achieving massive connectivity with sporadic transmissions in massive machine-type communications (mMTCs) for Internet of Things (IoT) applications, where the handshaking between the access point (AP) and users is skipped, leading to high multiple access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this article, to further reduce the overhead and latency, we investigate the problem of grant-free random access without the use of pilot signals in a millimeter-wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation, and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging a unitary approximate message passing-based matrix factorization (UAMP-MF) algorithm, we design a message passing-based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme.

Massive machine-type communications (mMTCs) features a massive number of low-cost user equipment (UE) with sparse activity. Tailor-made for these features, grant-free random access (GF-RA) serves as an efficient access solution for massive machine-type communication (mMTC). However, most existing GF-RA schemes rely on strict synchronization, which incurs excessive coordination burden for the low-cost UEs. In this work, we propose a receiver design for asynchronous GF-RA, and address the joint user-activity detection (UAD) and channel estimation (CE) problem in the presence of asynchronization-induced intersymbol interference. Specifically, the delay profile is exploited at the receiver to distinguish different UEs. However, a sample correlation problem in this receiver design impedes the factorization of the joint likelihood function, which complicates the UAD and CE problem. To address this correlation problem, we design a partially uni-directional (PUD) factor graph representation for the joint likelihood function. Building on this PUD factor graph, we further propose a PUD message passing-based sparse Bayesian learning (SBL) algorithm for asynchronous UAD and CE (PUDMP-SBL-aUADCE). Our theoretical analysis shows that the PUDMP-SBL-aUADCE algorithm exhibits higher signal-to-interference-and-noise ratio (SINR) in the asynchronous case than in the synchronous case, i.e., the proposed receiver design can exploit asynchronization to suppress multiuser interference. In addition, considering potential timing error from the low-cost UEs, we investigate the impacts of imperfect delay profile, and reveal the advantages of adopting the SBL method in this case. Finally, extensive simulation results are provided to demonstrate the performance of the PUDMP-SBL-aUADCE algorithm.

This article considers a challenging scenario of machine-type communications, where we assume Internet of Things (IoT) devices send short packets sporadically to an access point (AP) and the devices are not synchronized in the packet level. High-transmission efficiency and low latency are concerned. Motivated by the great potential of multiple-input-multiple-output nonorthogonal multiple access (MIMO-NOMA) in massive access, we design a grant-free MIMO-NOMA scheme, and in particular differential modulation is used so that expensive channel estimation at the receiver (AP) can be bypassed. The receiver at AP needs to carry out active device detection and multidevice data detection. The active user detection is formulated as the estimation of the common support of sparse signals, and a message-passing-based sparse Bayesian learning (SBL) algorithm is designed to solve the problem. Due to the use of differential modulation, we investigate the problem of noncoherent multidevice data detection, and develop a message-passing-based Bayesian data detector, where the constraint of differential modulation is exploited to drastically improve the detection performance, compared to the conventional noncoherent detection scheme. Simulation results demonstrate the effectiveness of the proposed active device detector and noncoherent multidevice data detector.

We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods.

Spatiotemporal spectrum sensing of multiple primary users (PUs) sharing the same channels with unknown and irregular coverage presents significant challenges. The receive signal strength (RSS) levels at secondary users (SUs) vary greatly due to path propagation loss and shadowing. Moreover, due to security concerns and limited energy at SUs, the reported RSS measurements to a fusion center are noisy and incomplete. These challenges seriously impact the performance of existing cooperative spectrum sensing (CSS) techniques. In this work, to address these issues, we propose a robust graph learning based CSS (RoGL-CSS) detector, after revealing the low rank property of the expectation of RSS matrix and formulating a graph learning problem. By solving the graph learning problem with the alternating direction method of multipliers (ADMM), a probability matrix (graph) representing the correlations among SUs is acquired, which is applied to recover the expectation of RSSs from corrupted measurements and select SUs for CSS. Specifically, a robust RSS recovery algorithm with a learned probability matrix is adopted, and the SUs with recovered RSSs of high correlations are collected for implementing CSS. Numerical results are provided to demonstrate the superiority of the proposed detector compared to state-of-the-art detectors. At a false-alarm probability of 10%, with measurement missing rate 20% and outlier rate 30%, RoGL-CSS achieves performance improvement of at least 16% and 9% in detection probability, compared to other detectors for scenarios of two and five PUs, respectively.

We consider the problem of recovering an unknown signal from general nonlinear measurements obtained through a generalized linear model (GLM). Based on the unitary transform approximate message passing (UAMP) and expectation propagation, a unitary transform based generalized AMP (GUAMP) algorithm is proposed for general measurement matrices, in particular highly correlated matrices. Experimental results on quantized compressed sensing demonstrate that the proposed GUAMP significantly outperforms state-of-the-art Generalized AMP (AMP) and generalized vector AMP (GVAMP) under correlated matrices.

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates the sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as the unknown identity of active users and multiuser interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference-based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

This work deals with directional of arrival (DOA) estimation with a large antenna array. We first develop a novel signal model with a sparse system transfer matrix using an inverse discrete Fourier transform (DFT) operation, which leads to the formulation of a structured block sparse signal recovery problem with a sparse sensing matrix. This enables the development of a low complexity message passing based Bayesian algorithm with a factor graph representation. Simulation results demonstrate the superior performance of the proposed method.

Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc. To unleash the full potentials of RIS-aided communications, acquiring accurate channel state information is crucial, which however is very challenging. For RIS-aided multiple-input and multiple-output (MIMO) communications, the existing channel estimation methods have computational complexity growing rapidly with the number of RIS units$N$(e.g., in the order of$N^{2}$or$N^{3}$) and/or have special requirements on the matrices involved (e.g., the matrices need to be sparse for algorithm convergence to achieve satisfactory performance), which hinder their applications. In this work, instead of using the conventional signal model in the literature, we derive a new signal model obtained through proper vectorization and reduction operations. Then, leveraging the unitary approximate message passing (UAMP), we develop a more efficient channel estimator that has complexity linear with$N$and does not have special requirements on the relevant matrices, thanks to the robustness of UAMP. These facilitate the applications of the proposed algorithm to a general RIS-aided MIMO system with a larger$N$. Moreover, extensive numerical results show that the proposed estimator delivers much better performance and/or requires significantly less number of training symbols, thereby leading to notable reductions in both training overhead and latency.

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes.

The orthogonal-time-frequency-space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To harvest the time and frequency diversity promised by OTFS, some promising detectors, especially message passing based ones, have been developed by taking advantage of the sparsity of the channel in the delay-Doppler domain. However, when the number of channel paths is relatively large or fractional Doppler shifts have to be considered, the complexity of existing detectors is a concern, and the existing message passing based detectors suffer from performance loss. In this work, we investigate the design of OTFS detectors based on the approximate message passing (AMP). In particular, leveraging the unitary AMP (UAMP), we design new detectors that enjoy the structure of the channel matrix and allow efficient implementation. In addition, the estimation of noise variance is incorporated into the UAMP-based detectors. Thanks to the robustness of UAMP relative to AMP, the UAMP-based detectors deliver superior performance, and outperform state-of-the-art detectors significantly. We also investigate iterative joint detection and decoding in a coded OTFS system, where the OTFS detectors are integrated into a powerful turbo receiver, leading to considerable performance gains.

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted multi-user wireless communication system and present a joint channel estimation and signal recovery algorithm in this paper. Specifically, we propose a bidirectional approximate message passing algorithm that applies the Taylor series expansion and Gaussian approximation to simplify the sum-product algorithm in the formulated problem. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed algorithms.

Recent research shows that distributed radar has great potential in recognizing and countering deception jamming. In this letter, we investigate how to use deception bistatic range measurements to estimate the target location and deception ranges, and propose a message passing based method for target localization under range deception jamming in distributed MIMO radar. Firstly, the a posteriori distribution of the target location is derived, but its maximization is intractable. Then we represent the joint distribution of the relevant variables as a factor graph model and a highly efficient message passing algorithm is developed, where the target location and deception ranges are estimated iteratively. The Cramer-Rao bound is also derived and numerical simulations are provided to demonstrate the superiority of the proposed method.

The spatially and temporally correlated sea clutter with phase information is valuable for marine radar applications. The major difficulty of coherent sea clutter modeling is the generation of the continuous phases. This article presents a new phase retrieval approach for modeling the correlated complex sea clutter based on unsupervised neural networks. The unsupervised short-term and long-term neural networks have been developed for the phase retrieval on different term scales. Both these networks have the same input layer and feature extraction module, and however, the number of output neurons is different. The amplitude sea clutter series and the desired Doppler spectrum are fed into the network in parallel, and their features are extracted by two parallel bidirectional long short-term memory (Bi-LSTM) networks which sufficiently utilize the correlations of sea clutter data. These features are concatenated and fused by a residual network (ResNet). The phases can be successfully obtained by constraining to the desired Doppler spectrum and the given amplitudes of sea clutter series. This proposed approach has been verified by the measured Ice Multiparameter Imaging X-Band (IPIX) radar data, and it can precisely model the complex sea clutter with specified statistic characteristics and Doppler properties. The amplitude root mean square error (RMSE) between the obtained and measured Doppler spectra is only 1.5065 with the interval between adjacent frames equals to 32. The RMSE of Doppler central frequency and spectrum width is 6.9306 and 1.2293 Hz, respectively. It shows robustness with the change of range resolution and interval.

The orthogonal time frequency space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To enable efficient OTFS detection in the delay-Doppler (DD) domain, the DD domain channels need to be acquired accurately. To achieve the low latency requirement in future wireless communications, the time duration of the OTFS block should be small, therefore fractional Doppler shifts have to be considered to avoid significant modelling errors due to the assumption of integer Doppler shifts. However there lack investigations on the estimation of OTFS channels with fractional Doppler shifts in the literature. In this work, we develop a channel estimator for OTFS with particular attention to fractional Doppler shifts, and both bi-orthogonal waveform and rectangular waveform are considered. Instead of estimating the DD domain channel directly, we estimate the channel gains and (fractional) Doppler shifts that parameterize the DD domain channel. The estimation is formulated as a structured sparse signal recovery problem with a Bayesian treatment. Based on a factor graph representation of the problem, an efficient message passing algorithm is developed to recover the structured sparse signal (thereby the OTFS channel). The Cramer-Rao Lower Bound (CRLB) for the estimation is developed and the effectiveness of the algorithm is demonstrated through simulations.

With recent advances on the dense low-Earth orbit (LEO) constellation, the LEO satellite network has become one promising solution for providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli–Rician message passing with expectation–maximization (BR-MP-EM) algorithm is proposed for this terrestrial–satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation–maximization (EM) method is employed in the outer iterations to update the hyperparameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

In this letter, a novel factor graph approach to target localization in distributed MIMO radars is proposed. To achieve robust localization in the presence of outliers, target localization can be formulated as a least absolute deviation (LAD) problem, which, however, is difficult to solve. We then reformulate the LAD problem as a reweighted least square (LS) one, which is converted to a product of some functions, enabling the use of factor graph techniques. Based on a factor graph representation, a highly efficient message passing algorithm is developed, where the target location is estimated in an iterative way. Comparisons with state-of-the-art methods show that the proposed method is superior in terms of computational complexity, robustness and accuracy.

Given the requirements of increased data rate and massive connectivity in the Internet-of-things (IoT) applications of the fifth-generation communication systems (5G), non-orthogonal multiple access (NOMA) was shown to be capable of supporting more users than OMA. As a further potential enhancement, the faster-than-Nyquist (FTN) signaling is also capable of increasing the symbol rate. Since NOMA and FTN signaling impose non-orthogonalities from different perspectives, it is possible to achieve further increased spectral efficiency by exploiting both. Hence we investigate the FTN-NOMA uplink in the context of random access. Although random access schemes reduce the signaling overheads as well as latency, they require the base station to identify active users before performing data detection. As both inter-symbol and inter-user interferences exist, performing optimal detection requires a prohibitively high complexity. Moreover, in typical mobile communication environments, the channel envelope of users fluctuates violently, which imposes challenges on the receiver design. To tackle this problem, we propose a joint user activity tracking and data detection algorithm based on the factor graph framework, which relies on a sophisticated amalgam of expectation maximization (EM) and hybrid message passing algorithms. The complexity of the algorithm advocated only increases linearly with the number of active users. Our simulation results show that the proposed algorithm is effective in tracking user activity and detecting data symbols in dynamic random access systems.

Cognitive radio (CR) is a promising technology to address the challenge of spectrum scarcity due to the massive number of objects in the Internet of Things (IoT). Equipping IoT objects with CR capability can also alleviate interference situations and achieve seamless connectivity in IoT. This paper deals with CR spectrum sensing and proposes a new eigenvalue-based detector by exploiting the summation of multiple large eigenvalues of the covariance matrix of received signals. By analyzing the distribution of the sum of the dependent large eigenvalues, we derive an approximate but explicit expression for the theoretical performance of the proposed detector. The theoretical analysis of the proposed detector is validated and its superior performance is demonstrated with real world signals. It is shown that the proposed detector outperforms the existing eigenvalue-based detectors and is more robust against noise uncertainty.

This paper deals with spectrum sensing for cognitive radio-based Internet of Things (IoT) systems and their coexistence with Long Term Evolution (LTE) systems. Due to the sparsity of the covariance matrix of IoT/LTE signals, we reveal that the likelihood ratio test approximates to energy detection (ED) at low signal to noise ratio. However, the noise (power) uncertainty can degrade the performance of ED severely, especially when low-cost IoT devices are employed for spectrum sensing. To tackle this issue, we derive the relationship among noise power, total power, and autocorrelation coefficient of received signals, and propose an unbiased estimator of noise power without the knowledge of the presence/absence of IoT/LTE signals. We then design a new ED with multiple estimates of noise power from historical and current sensing data, and analyze its theoretical performance. Numerical results are provided to verify the theoretical results and demonstrate the superior performance of the proposed detector. It is shown that, by exploiting sufficient historical sensing data, the performance of the proposed ED can closely approach that of the ideal ED.

Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a passive target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph-based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large-scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.

This paper studies the influence of signal detection schemes on the energy efficiency (EE) of uplink multiple-input-multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). Assuming equal transmission rates for all users, we derive the optimal power allocation and their analytical approximations for zero-forcing (ZF) and ZF successive interference cancellation (ZF-SIC) receivers. Both the cases with perfect channel state information (CSI) and with imperfect CSI are considered. The EE with different receivers is compared. The results indicate that for uplink massive MIMO systems with low-resolution ADCs, the radio-frequency circuit power consumption can be significant because a large number of antennas are required to compensate for the loss due to quantization errors while the number of base station antennas needed with the ZF-SIC receiver is significantly smaller than that with the ZF receiver. Meanwhile, the increase of power consumption of signal processing with ZF-SIC can be moderate, due to the fact that the receiver weights are reused in a coherent block and the dimensionality is reduced. Consequently, the ZF-SIC receiver is able to improve the overall EE for massive MIMO systems with practical ADCs. We also conduct an approximation analysis for a multi-cell scenario with the pilot contamination and inter-cell-interference considered.

This paper considers the issue of passive localization based on time of arrival (TOA) measurement in the presence of receiver detection failures. In passive localization, the signal sent from the transmitter is reflected or relayed by "passive" target and then received at several distributed receivers. The target's position can be determined by collecting range mea- surements from all receivers. With a linearized model for range measurements, we build a factor graph model and implement Gaussian message passing algorithm to obtain target location and detect link failures. The Cramer-rao bound (CRB) is also derived to evaluate the performance of proposed algorithm. Simulation results verify the effectiveness of proposed factor graph approach.

In this study, the authors investigate the use of combined belief propagation (BP), mean field (MF) and expectation propagation (EP) message passing to achieve joint channel estimation and decoding (JCED) for orthogonal frequency division multiplexing where the channel sparsity is exploited, and a low‐complexity BP–MF–EP‐based JCED receiver is designed. Moreover, comparisons in message updating of state‐of‐the‐art message passing‐based JCED receivers are provided to illustrate the merits of the proposed one. In addition, message passing schedules are optimised to achieve better system performance. Simulation results verify the superiority of the proposed combined message passing receiver in terms of both bit‐error‐rate performance and convergence speed.

Cooperative spectrum sensing has been studied to combat the hidden terminal problem by exploiting the spatial diversity in cognitive radio (CR) networks. This paper concerns blind cooperative spectrum sensing with soft fusion, where thea prioriknowledge of channels and primary signals is unavailable, and soft information is transmitted from each secondary user (SU) to a fusion center for detection. We first introduce the Quade test to design a blind detector. Then, a new detector with both lower computational complexity and lower overhead is derived, where only the estimated power and the variance of the instantaneous power at each SU are required at the fusion center. The analytical expressions for the detection performance, in terms of false-alarm probability and detection probability, are derived for the proposed detector. Simulation results are provided to validate the theoretical analyses and demonstrate the superior performance of proposed detector compared to the state-of-the-art detectors. It is also shown that, with the increase of the number of hidden terminals in the CR, the proposed detector can maintain high detection performance while the conventional detectors exhibit rapid performance degradation.

The rapid development of mobile communications requires even higher spectral efficiency. Non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Among several NOMA schemes, it has been shown that sparse code multiple access (SCMA) is able to achieve better performance. In this paper, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity increases exponentially with the product of the number of users, the number of antennas and the channel length. To tackle this challenge, we propose near optimal low-complexity iterative receivers based on factor graph. By introducing auxiliary variables, a stretched factor graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named stretch-BP-EP, is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named conv-BP-EP. We further consider cooperative network and propose two distributed cooperative detection schemes to exploit the diversity gain, namely, belief consensus-based algorithm and the Bregman alternative direction method of multipliers (ADMM)-based method. Simulation results verify the superior performance of the proposed conv-BP-EP receiver compared with other methods. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the diversity gain. Moreover, Bregman ADMM method outperforms the belief consensus-based algorithm in noisy inter-user links.

This letter deals with message passing receiver design for joint channel estimation and decoding in MIMO-OFDM with unknown noise variance. The conventional factor graph representation for the system involves observation factors, which are functions of a number of variables in the form of multiplication and summation. In this work, by introducing some auxiliary variables, we further break each of the observation factors into several factors, which enables the use of hybrid mean field (MF), belief propagation (BP), and expectation propagation (EP) message passing to tackle the observation factors. It turns out that our approach is much more efficient than the existing approaches, leading to remarkable performance improvement as shown by simulation results.

Linear signal estimation based on sample covariance matrices (SCMs) can perform poorly if the training data are limited and the SCMs are ill-conditioned. Diagonal loading (DL) may be used to improve robustness in the face of limited training data. This paper introduces two leave-one-out cross-validation schemes for choosing the DL factor. One scheme repeatedly splits the training data with respect to time, while the other repeatedly splits the out-of-training data with respect to space. We derive computationally efficient implementations and compare them with the oracle choice in terms of the mean squared error.

In this letter, with combined belief propagation (BP), mean field (MF), and expectation propagation (EP), an iterative receiver is designed for joint phase noise estimation, equalization, and decoding in a coded communication system. The presence of the phase noise results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (Soft-in EKS). In this letter, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the Soft-in EKS, leading to a significant performance improvement with similar complexity as demonstrated by simulation results.

This letter deals with turbo equalization for coded data transmission over intersymbol interference (ISI) channels. We propose a message-passing algorithm that uses the expectation propagation rule to convert messages passed from the demodulator and decoder to the equalizer and computes messages returned by the equalizer by using a partial Gaussian approximation (PGA). We exploit the specific structure of the ISI channel model to compute the latter messages from the beliefs obtained using a Kalman smoother/equalizer. Doing so leads to a significant complexity reduction compared to the initial PGA implementation. Results from Monte Carlo simulations show that the proposed approach leads to a significant performance improvement compared to state-of-the-art turbo equalizers and allows for trading performance with complexity.

In this paper, we address the design of message-passing receiver for massive multiple-input multiple-output orthogonal frequency division multiplex (MIMO-OFDM) systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. In particular, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization-based method is derived to learn the channel power-delay profiles. The proposed scheme is assessed in 3D massive MIMO-OFDM systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.

In MIMO applications, the minimum mean square error parallel interference cancellation (MMSE-PIC) based Soft-Input Soft-Output (SISO) detector has been widely adopted because of its low complexity and good bit error rate (BER) performance. In this paper, we firstly propose to use a Gaussian model based MMSE detection algorithm to implement MMSE-PIC with low complexity. This algorithm, which can detect a length-Nrreceived data block by a single Hermitian matrix (sized Nt× Nt) inversion, is especially preferable in Massive MIMO up-link applications where the number of transmit antennas Ntfrom each end terminal is much less than the number of receive antennas Nrin the Base Station. Then we derive a new method to calculate the matrix inversion by a linear combination of two matrices, which reduces the complexity from O(Nt3) to O(Nt2). At last, in order to improve the system performance for the first pass when there is no a priori information available, a self-iteration method is proposed and thus a system performance gain of 1dB to 2dB is achieved at the cost of modest complexity increase.

Power-line communications (PLC) commonly employs orthogonal frequency-division multiplexing (OFDM) as the modulation technique, and impulsive noise has a significant negative impact on its performance. Using the property of null subcarriers in OFDM and the fact that impulsive noise is sparse, we formulate a minimization problem to detect and estimate the impulsive noise. In previous works, ℓ1-norm minimization was employed to achieve this task. In this paper, we propose to use a smoothed ℓ0-norm minimization algorithm for impulsive noise detection instead. Simulation results show that this approach is promising as it achieves comparable performance as ℓ1-norm minimization but with much lower complexity.

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K|A| + K log2K), where |A| denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the known-channel bound.

This paper considers the coded OFDM system and instead of discarding the cyclic prefix (CP) at the receiver, we utilize the CP observation for joint detection, decoding and channel estimation. In particular, detection and decoding are performed iteratively between an equalizer and a soft-input soft-output (SISO) decoder based on the turbo principle, and the expectation-maximization (EM) algorithm is employed in the equalizer for joint detection and channel estimation via message passing. Models for the CP observation, non-CP observation and the time correlation of the time-varying channel are presented in Forney-style factor graphs (FFGs), and a scheduling scheme is proposed to pass messages between the graphs. Simulation results show that with unknown channel impulse response (CIR), the performance of the proposed algorithm approaches the case where CIR is perfectly known and through proper exploitation of the CP, the proposed algorithm outperforms the conventional algorithm (i.e. CP is discarded) with known CIR, as well as the alternative algorithm in the literature (where CP is exploited) with unknown CIR.

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms.

A contention‐based opportunistic feedback protocol has been proposed in the literature, to achieve multiuser diversity for the downlink transmission of a multiuser wireless system, with the assumption that the incoming data for the active users are infinitely backlogged. However, this assumption is not valid if the authors consider the finite‐length queuing effect in practical systems. By taking not only the channel states, but also the queuing states into account, the authors propose an improved opportunistic feedback protocol with multiuser diversity, and analyse its performance with adaptive modulation and coding. Additionally, instead of solving a system of equations directly, a simple iterative method is proposed to obtain the stationary distribution of the system, where the system behaviour is modelled by a two‐dimensional finite‐state Markov chain. Numerical results demonstrate that the authors proposed protocol is superior to the protocol in the literature in terms of the average throughput, the average spectral efficiency and the average packet delay, when considering the queuing dynamics.

This letter introduces condition number-constrained approximation to matrices used for signal estimation and detection. Under a Frobenius norm criterion, the closed-form solution to the optimal approximation is derived, which can be found efficiently for arbitrary condition number constraints. The resulting approximation techniques are applied to the imperfectly estimated covariance and channel matrices used for estimating transmit signals in communication systems. With an appropriately chosen value of condition number, the robustness of the linear and decision-feedback estimators (DFE) against model mismatch can be significantly improved.

Impulsive noise is one of the main channel impairments in orthogonal frequency division multiplexing (OFDM)-based power line communications (PLC). Clipping nonlinearites such as conventional clipping, blanking, joint blanking/clipping, and deep clipping have been proposed to mitigate the impulsive noise. However, OFDM systems result in high peak amplitude after inverse discrete Fourier transform (IDFT) at the transmitter, which leads to large peak-to-average power ratio (PAPR). High peak amplitude can be falsely clipped by the impulsive noise clipper at the receiver leading to poor system performance. In this paper we propose a joint peak amplitude clipping and impulsive noise clipping nonlinearities to improve the bit error rate (BER) performance.

This paper investigates the MMSE-based frequency domain equalization (FDE) algorithms in turbo equalization systems. As opposed to the conventional FDE systems where the cyclic prefix (CP) is discarded at the receiver, we take advantage of the redundancy and make use of all the observed signals for equalization purpose. First, we interpret the conventional frequency domain equalizer as a Forney-style factor graph (FFG), and accordingly an equalization algorithm is derived based on the Gaussian message passing (GMP) technique. Second, the normally discarded CP part is presented similarly using an FFG, and an algorithm that integrates both of the FFGs is proposed. As a result, two extrinsic messages about the data symbols are obtained rather than one, and they are merged together based on a symbol-wise combination. Third, with approximations made on two covariance matrices, the complexity of the proposed equalization algorithm is maintained at the same order as that of the conventional FDE algorithm, i.e. O(Nlog2N) per block per iteration. Simulations results verify that, a gain of around 0.7dB is achieved compared with the conventional algorithm at 1/4 CP ratio, for both 16QAM and 64QAM system with Gray mapping over AWGN or ISI channels.

An iterative frequency domain equalization approach for coded single-carrier block transmissions over frequency selective channels is developed by using the recently proposed generalized approximate message passing (GAMP) algorithm. Compared with the low-complexity iterative frequency domain linear minimum mean square error (FD-LMMSE) equalization, the proposed approach can achieve significant performance gain with slight complexity increase.

In OFDM systems, cyclic prefix (CP) insertion and removal enables the use of a set of computationally efficient single-tap equalizers at the receiver. Due to the extra transmission time and energy, the CP causes a loss in both spectrum efficiency and power efficiency. On the other hand, as a repetition of part of the data, the CP brings extra information and can be exploited for detection. Therefore, instead of discarding the CP observation as in the conventional OFDM system, we utilize all the received signals in a soft-input soft-output equalizer of a turbo equalization OFDM system. First, the models for both the CP part and the non-CP part of observation are presented in a Forney-style factor graph (FFG). Then based on the computation rules of the FFG and the Gaussian message passing (GMP) technique, we develop an equalization algorithm. With proper approximation, the complexity of the proposed algorithm is reduced to \changedmathcal O(2RNlog2N+4RGlog2G+2RG) per data block for R iterations, where N is the length of the data block and G is equal to P+L-1 with P the length of the CP and L the maximum delay spread of the channel. To justify the performance improvement, SNR analysis is provided. Simulation results show that the proposed approach achieves a significant gain over the conventional approach and the turbo equalization system converges within two iterations.

In a wireless network, multi-user diversity can be employed to improve system throughput performance by scheduling the channel to the user with the best instantaneous channel state information (CSI). However, the overhead induced by polling CSIs of a large number of users can overshadow the multi-user diversity gain. In our previous work, a user identification approach (UIDA) was proposed to reduce the system overhead. In this paper, by allowing a small degree of outage to occur, we simplify the UIDA to reduce the overhead further, and present the throughput and outage analysis of the simplified UIDA over Rayleigh fading channels. Computer simulations based on IEEE 802.11a systems show that the simplified UIDA achieves considerable throughput improvement.

In this paper, we first develop a state-space system model for channel estimation, then propose a recursive channel estimation approach using the Gaussian message passing (GMP) technique, which is equivalent to the LS (least squares) approach. By introducing two different approximations to a GMP updating rule, the computational complexity of this recursive approach is significantly reduced, leading to two low-complexity unbiased FFT (fast Fourier transform)-based channel estimators. The proposed estimators are very suitable for turbo equalization, where the estimates of the data symbols available from the decoder are exploited as a virtual training sequence for channel estimation (In contrast, a direct use of the LS estimator incurs high complexity since the matrix inversion involved needs to be calculated online). We apply the proposed channel estimators in turbo equalization of quasi-static and time-varying frequency selective channels, and simulation results demonstrate their effectiveness.

We present a graph-based channel estimation approach for SC-IFDE (single-carrier transmissions with iterative frequency domain equalization) without CP (cyclic prefix) over doubly selective channels using the recently developed Gaussian message passing (GMP) technique. A direct application of the GMP updating rules in the FFG (Forney-style factor graph) of the SC-IFDE system model incurs high complexity. Approximate updating rules are therefore developed to overcome this problem. The proposed GMP-based channel estimation approach has similar complexity as the low-complexity Kalman-filtering based frequency domain channel estimation approach in the literature, but significantly outperforms the latter due to its enhanced capability in capturing the time correlation information of doubly selective channels through bidirectional processing.

We study superposition coded modulation (SCM) with iterative linear minimum-mean-square-error (LMMSE) detection. We show that SCM offers an attractive solution for highly complicated transmission environments with severe interference. We analyze the impact of signaling schemes on the performance of iterative LMMSE detection. We prove that among all possible signaling methods, SCM maximizes the output signal-to-noise/ interference ratio (SNIR) in the LMMSE estimates during iterative detection. Numerical examples are used to demonstrate that SCM outperforms other signaling methods when iterative LMMSE detection is applied to multi-user/multi-antenna/multipath channels.

In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message passing (GMP) technique is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform inter-tap interference cancellation. Simulation results demonstrate the effectiveness of the proposed detection and channel estimation algorithms.

In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key is a segment-by-segment frequency domain equalization (FDE) strategy under the assumption that channel is approximately static within a short segment. Guard gaps (for cyclic prefixing or zero padding) are not required between adjacent segments, which avoids the power and spectral overheads due to the use of cyclic prefix (CP) in the conventional FDE technique. A low-complexity bi-directional channel estimation algorithm is also developed to exploit correlation information of time-varying channels. Simulation results are provided to demonstrate the efficiency of the proposed algorithms.

In this paper, we study the iterative detection problem for a coded system with multi-ary modulation. We show that, with iterative linear minimum-mean-square-error (LMMSE) detection, superposition coded modulation (SCM) can provide performance superior to that with other traditional signaling schemes used in trellis coded modulation (TCM) and bit-interleaved coded modulation (BICM). This finding provides a useful guideline for system design considering inter-symbol interference (ISI) and other forms of interference. Simulation results are provided to illustrate the efficiency of the iterative LMMSE detection with different signaling schemes.

In this paper, a vector-form factor graph representation is derived for intersymbol interference (ISI) channels. The resultant graphs have a tree-structure that avoids the short cycle problem in existing graph approaches. Based on a joint Gaussian approximation, we establish a connection between the LLR (log-likelihood ratio) estimator for a linear system driven by binary inputs and the LMMSE (linear minimum mean-square error) estimator for a linear system driven by Gaussian inputs. This connection facilitates the application of the recently proposed Gaussian message passing technique to the cycle-free graphs for ISI channels. We also show the equivalence between the proposed approach and the Wang-Poor approach based on the LMMSE principle. An attractive advantage of the proposed approach is its intrinsic parallel structure. Simulation results are provided to demonstrate this property.

This paper is concerned with the low-cost iterative equalization/detection principles for coded linear systems with cyclic prefixes. Turbo frequency-domain-equalization (FDE) is applied to systems that may contain the joint effect of multiple-access interference (MAI), cross-antenna interference (CAI) and inter-symbol interference (ISI). We develop an SNR-variance evolution technique for the performance evaluation of the proposed systems. Numerical results in various channel environments demonstrate excellent agreement between the predicted and simulated system performance.

We propose a low-cost detection strategy for multi-user multiple-input-multiple-output (MIMO) systems with inter-symbol interference (ISI). The cyclic prefix (CP) technique is assumed. The proposed detection algorithm is derived in a very concise manner based on some elegant properties of circulant matrices. We show that multi-user detection and equalization can be carried out jointly and efficiently.

This paper is concerned with the iterative detection principles for coded linear systems with cyclic prefixes. We derive a matrix-form low-cost fast Fourier transform (FFT) based iterative LMMSE-APP detector and propose an evolution technique for the performance evaluation of the proposed detector. Numerical results show a good match between simulation and evolution prediction.

This paper presents a factor graph approach to turbo equalization. Unlike the existing linear MMSE turbo equalization methods, which operate with truncated windows (sliding or extending window), the proposed is a full-window approach with low complexity. This approach supports a high-speed parallel implementation technique, which makes it an attractive option in practice