Integrated sensing and communication (ISAC) is poised to redefine the landscape of wireless networks by seamlessly combining data transmission and environmental sensing. However, ISAC systems remain susceptible to eavesdropping, especially under uncertainty in eavesdroppers’ channel state information, which can lead to secrecy outages. On the other hand, diverse and complex sensing performance requirements further complicate resource optimization, often requiring custom solutions for each scenario. To this end, this paper introduces a unified optimization framework that holistically addresses both the worst-case user secrecy rate and the sum secrecy rate across multiple users. Besides putting the two commonly used objectives into a single but flexible objective function, the framework accurately controls secrecy outage probabilities while accommodating a broad spectrum of sensing constraints. To solve such a general problem, we integrate the sensing requirements into the objective function through an auxiliary variable. This enables efficient alternating optimization and the proposed approach is theoretically guaranteed to converge to at least a stationary point of the original problem. Extensive simulation results show that the proposed framework consistently achieves higher optimized secrecy rates under various sensing constraints compared to existing methods. These results underscore the proposed unified framework’s superiority and versatility in secure ISAC systems.

Tensor train (TT) representation has achieved tremendous success in visual data completion tasks, especially when it is combined with tensor folding. However, folding an image or video tensor breaks the original data structure, leading to local information loss as nearby pixels may be assigned into different dimensions and become far away from each other. In this paper, to fully preserve the local information of the original visual data, we explore not folding the data tensor, and at the same time adopt graph information to regularize local similarity between nearby entries. To overcome the high computational complexity introduced by the graph-based regularization in the TT completion problem, we propose to break the original problem into multiple sub-problems with respect to each TT core fiber, instead of each TT core as in traditional methods. Furthermore, to avoid heavy parameter tuning, a sparsity-promoting probabilistic model is built based on the generalized inverse Gaussian (GIG) prior, and an inference algorithm is derived under the mean-field approximation. Experiments on both synthetic data and real-world visual data show the superiority of the proposed methods.

Utilizing complex-valued neural networks (CVNNs) in wireless communication tasks has received growing attention for their ability to provide natural and effective representation of complex-valued signals and data. However, existing studies typically employ complex-valued versions of simple neural network architectures. Not only they merely scratch the surface of the extensive range of modern deep learning techniques, theoretical understanding of the superior performance of CVNNs is missing. To this end, this paper aims to fill both the theoretical and practice gap of employing CVNNs in wireless communications. In particular, we provide a comprehensive description on the various operations in CVNNs and theoretically prove that when the output dimension is large, the CVNN requires fewer layers than its real-valued counterpart to achieve a given approximation error of a continuous complex-valued function. Furthermore, to advance CVNNs in the field of wireless communications, this paper focuses on the transformer model, which represents a more sophisticated deep learning architecture and has been shown to have excellent performance in wireless communications but only in its real-valued form. In this aspect, we propose a fundamental paradigm of complex-valued transformers for wireless communications, including the complex-valued embedding module, encoding module, decoding module, and output projection module. Leveraging this structure, we develop customized complex-valued transformers for three representative applications in wireless communications: channel estimation, user activity detection, and joint design of pilot, feedback quantization, and precoder. These applications utilize transformers with varying levels of sophistication and span a variety of tasks, ranging from regression to classification, supervised to unsupervised learning, and specific module design to end-to-end design. Experimental results verify the theoretical advantage and effectiveness of the complex-valued transformers for the above three applications compared to other traditional real-valued neural network-based methods.

Joint activity detection and channel estimation is a crucial task in grant-free random access for massive machine-type communications. To enhance communication quality, intelligent reflecting surfaces (IRSs) have been proposed as a promising technology by controlling the propagation environment with passive reflecting elements. However, due to their passive nature and the non-Gaussian device-IRS-BS composite channels, IRSs introduce significant challenges for joint activity detection and channel estimation. To this end, this paper establishes an accurate statistical model for the composite channel, demonstrating that it follows a variance-gamma (VG) distribution. Based on the exact channel statistics, this paper employs a Bernoulli-VG prior and extends the standard approximate message passing algorithm to learn the Gamma-distributed channel variance within an expectation-maximization framework. Additionally, this paper introduces a novel approach to enforce consistency in device activity status across all base station antennas by transforming the activity detection task into the estimation of a dedicated active probability for each device. Extensive simulations validate the proposed VG channel model and demonstrate significant improvement due to imposing consistent activity probability across multiple antennas.

The recently emerged movable antenna (MA) shows great potential in leveraging spatial degrees of freedom for enhancing the performance of wireless systems. However, resource allocation in MA-aided systems faces unique challenges due to the non-convex and coupled constraints on antenna positions. This paper systematically reveals the challenges brought by the minimum MA separation constraints, and proposes a penalty framework for resource allocation under such new constraints in MA-aided systems. By introducing auxiliary variables, the proposed framework separates the non-convex and coupled antenna distance constraints from the movable region constraint. This enables the resulting problem be efficiently solved by alternating optimization, where the optimization of the original variables resembles that in conventional resource allocation problem while the optimization with respect to the auxiliary variables is achieved in closed-form solutions. To illustrate the effectiveness of the proposed framework, we present three case studies: capacity maximization, latency minimization, and regularized zero-forcing precoding. Simulation results demonstrate that the proposed optimization framework consistently outperforms state-of-the-art schemes.

A great amount of endeavor has recently been devoted to activity detection for massive machine-type communications in cell-free multiple-input multiple-output (MIMO) systems. However, as the number of antennas at the access points (APs) increases, the Rayleigh distance that separates the near-field and far-field regions also expands, rendering the conventional assumption of sole far-field propagation impractical. To address this challenge, this paper establishes a covariance-based formulation that can effectively capture the statistical property of hybrid near-far field channels. Based on this formulation, we theoretically reveal that increasing the proportion of near-field channels enhances the detection performance. Furthermore, we propose a distributed algorithm, where each AP performs local activity detection and only exchanges the detection results to the central processing unit, thus significantly reduces the computational complexity and the communication overhead. Not only with convergence guarantee, the proposed algorithm is unified in the sense that it can handle single-cell or cell-free systems with either near-field or far-field devices as special cases. Simulation results validate the theoretical analyses and demonstrate the superior performance of the proposed approach compared with existing methods.

This work considers a spatially non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationarity, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and develop a novel dynamic alternating maximum a posteriori (DA-MAP) method to solve the problem. DA-MAP contains four core modules: channel estimation module, VR detection module, grid update module, and temporal processing module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computationally intensive matrix inverse in each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulation results demonstrate that the proposed method achieves significant improvements in channel tracking performance with low computational overhead.

This paper addresses the challenge of large model (LM)-embedded wireless network for handling the trade-off problem of model accuracy and network latency. To guarantee a high-quality of users’ service, the network latency should be minimized while maintaining an acceptable inference accuracy. To meet this requirement, LM quantization is proposed to reduce the latency. However, the excessive quantization may destroy the accuracy of LM inference. To this end, a promising fluid antenna (FA) technology is investigated for enhancing the transmission capacity, leading to a lower network latency in the LM-embedded multiple-input multiple-output (MIMO) network. To design the FA-assisted LM-embedded network with the lower latency and higher accuracy requirements, the latency and peak signal to-noise ratio (PSNR) are considered in the objective function. Then, an efficient optimization algorithm is proposed under the block coordinate descent framework. Simulation results are provided to show the convergence behavior of the proposed algorithm, and the performance gains from the proposed FA-assisted LM-embedded network over the other benchmark networks in terms of network latency and PSNR.

A great amount of endeavor has recently been devoted to activity detection for massive machine-type communications in cell-free massive MIMO. However, in practice, as the number of antennas at the access points (APs) increases, the Rayleigh distance that separates the near-field and far-field regions also expands, rendering the conventional assumption of far-field propagation alone impractical. To address this challenge, this paper considers a hybrid near-far field activity detection in cell-free massive MIMO, and establishes a covariance-based formulation, which facilitates the development of a distributed algorithm to alleviate the computational burden at the central processing unit (CPU). Specifically, each AP performs local activity detection for the devices and then transmits the detection result to the CPU for further processing. In particular, a novel coordinate descent algorithm based on the Sherman-Morrison-Woodbury update with Taylor expansion is proposed to handle the local detection problem at each AP. Moreover, we theoretically analyze how the hybrid near-far field channels affect the detection performance. Simulation results validate the theoretical analysis and demonstrate the superior performance of the proposed approach compared with existing approaches.

Spatial-spectral radio map estimation (RME) from sparsely deployed sensors can be viewed as a tensor learning problem. Among tensor models, the block-term decomposition (BTD) model is especially effective for RME, enabling accurate recovery of the radio map as well as emitter-level spatial loss field (SLF) and power spectral density (PSD). However, selecting the appropriate tensor and block-term ranks in the BTD model is crucial yet challenging, as the ranks indicate the number and properties of emitters, which are typically unknown in practice. Inappropriate settings of these ranks leads to overfitting and underfitting of the model. Existing optimization-based BTD learning methods either unrealistically assume that these ranks are known or require parameter tuning. To address these issues, we propose a Bayesian BTD model that incorporates sparsitypromoting priors on the factor matrices for rank learning, eliminating the need for parameter tuning while learning all other parameters. Extensive numerical experiments showcase the effectiveness of the proposed method.

Understanding abnormal events in videos is a vital and challenging task that has garnered significant attention in a wide range of applications. Although current video understanding Multi-modal Large Language Models (MLLMs) are capable of analyzing general videos, they often struggle to handle anomalies due to the spatial and temporal sparsity of abnormal events, where the redundant information always leads to suboptimal outcomes. To address these challenges, exploiting the representation and generalization capabilities of Vison Language Models (VLMs) and Large Language Models (LLMs), we propose VA-GPT, a novel MLLM designed for summarizing and localizing abnormal events in various videos. Our approach efficiently aligns effective tokens between visual encoders and LLMs through two key proposed modules: Spatial Effective Token Selection (SETS) and Temporal Effective Token Generation (TETG). These modules enable our model to effectively capture and analyze both spatial and temporal information associated with abnormal events, resulting in more accurate responses and interactions. Furthermore, we construct an instruction-following dataset specifically for fine-tuning video-anomaly-aware MLLMs, and introduce a cross-domain evaluation benchmark based on XD-Violence dataset. Our proposed method outperforms existing state-of-the-art methods on various benchmarks.

Inferring properties of graph-structured data, e.g., the solubility of molecules, essentially involves learning the implicit mapping from graphs to their properties. This learning process is often costly for graph property learners like Graph Convolutional Networks (GCNs). To address this, we propose a paradigm called Graph Nonparametric Teaching (GraNT) that reinterprets the learning process through a novel nonparametric teaching perspective. Specifically, the latter offers a theoretical framework for teaching implicitly defined (i.e., nonparametric) mappings via example selection. Such an implicit mapping is realized by a dense set of graph-property pairs, with the GraNT teacher selecting a subset of them to promote faster convergence in GCN training. By analytically examining the impact of graph structure on parameter-based gradient descent during training, and recasting the evolution of GCNs—shaped by parameter updates—through functional gradient descent in nonparametric teaching, we show for the first time that teaching graph property learners (i.e., GCNs) is consistent with teaching structure-aware nonparametric learners. These new findings readily commit GraNT to enhancing learning efficiency of the graph property learner, showing significant reductions in training time for graph-level regression (-36.62%), graph-level classification (-38.19%), node-level regression (-30.97%) and node-level classification (-47.30%), all while maintaining its generalization performance.

Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of required data for training robust model. To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator. Specifically, we firstly incorporate a pretrained Vision Transformer (ViT) to extract the latent features. On top of ViT, we propose a semantic class adapter (SCA) and an optimization-oriented unrolling algorithm (OptOU), both with a quite small number of trainable parameters. SCA is proposed to fuse ViT-extracted features to consolidate the basis of the subsequent automatic annotation. OptOU consists of multiple cascading layers and each layer contains an optimization formulation to align its output with the ground truth as closely as possible, though which OptOU acts as being interpretable rather than learning-based blackbox nature. In addition, training SCA and OptOU requires only a single pre-annotated RGB seed image, owing to their small volume of learnable parameters. Extensive experiments clearly demonstrate that the proposed LAM can generate high-fidelity annotations (almost 100% in mIoU) for multiple real-world datasets (i.e., Camvid, Cityscapes, and Apolloscapes) and CARLA simulation dataset.

To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progresses, the amount of data fitted by the AD model expands, which helps to improve the AD model generalization substantially. However, such ever-expanding data is a double-edged sword for the AD model. Specifically, as the fitted data volume grows to exceed the AD model’s fitting capacities, the AD model is prone to under-fitting. To address this issue, we propose to use a pretrained Large Vision Models (LVMs) as backbone coupled with downstream perception head to understand AD semantic information. This design can not only surmount the aforementioned under-fitting problem due to LVMs’ powerful fitting capabilities, but also enhance the perception generalization thanks to LVMs’ vast and diverse training data. On the other hand, to mitigate vehicles’ computational burden of training the perception head while running LVM backbone, we introduce a Posterior Optimization Trajectory (POT)-Guided optimization scheme (POTGui) to accelerate the convergence. Concretely, we propose a POT Generator (POTGen) to generate posterior (future) optimization direction in advance to guide the current optimization iteration, through which the model can generally converge within 10 epochs. Extensive experiments demonstrate that the proposed method improves the performance by over 66.48% and converges faster over 6 times, compared to the existing state-of-the-art approaches.

Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization due to domain-shift. Federated Learning (FL) has emerged as a promising paradigm for enhancing the generalization of AD models through privacy-preserving distributed learning. However, these FL AD models face significant temporal catastrophic forgetting when deployed in dynamically evolving environments, where continuous adaptation causes abrupt erosion of historical knowledge. This paper proposes Federated Exponential Moving Average (FedEMA), a novel framework that addresses this challenge through two integral innovations: (I) Server-side model’s historical fitting capability preservation via fusing current FL round’s aggregation model and a proposed previous FL round’s exponential moving average (EMA) model; (II) Vehicle-side negative entropy regularization to prevent FL models’ possible overfitting to EMA-introduced temporal patterns. Above two strategies empower FedEMA a dual-objective optimization that balances model generalization and adaptability. In addition, we conduct theoretical convergence analysis for the proposed FedEMA. Extensive experiments both on Cityscapes dataset and Camvid dataset demonstrate FedEMA’s superiority over existing approaches, showing 7.12% higher mean Intersectionover-Union (mIoU).

Deep learning-based Autonomous Driving (AD) perception models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD model (known as FedAD system), conventional models often struggle with under-fitting as the amount of accumulated training data progressively increases. To address this issue, instead of conventional small models, employing Large Vision Models (LVMs) in FedAD is a viable option for better learning of representations from a vast volume of data. However, implementing LVMs in FedAD introduces three challenges:(I)the extremely high communication overheads associated with transmitting LVMs between participating vehicles and a central server;(II)lack of computing resource to deploy LVMs on each vehicle;(III)the performance drop due to LVM focusing on shared features but overlooking local vehicle characteristics. To overcome these challenges, we propose pFedLVM, a LVM-Driven, Latent Feature-Based Personalized Federated Learning framework. In this approach, the LVM is deployed only on central server, which effectively alleviates the computational burden on individual vehicles. Furthermore, the exchange between central server and vehicles are the learned features rather than the LVM parameters, which significantly reduces communication overhead. In addition, we utilize both shared features from all participating vehicles and individual characteristics from each vehicle to establish a personalized learning mechanism. This enables each vehicle’s model to learn features from others while preserving its personalized characteristics, thereby outperforming globally shared models trained in general FL. As a demonstration of the proposed pFedLVM, this paper focuses on the semantic segmentation (SSeg) task. Extensive experiments demonstrate that pFedLVM outperforms the existing state-of-the-art approach by 18.47%, 25.60%, 51.03% and 14.19% in terms of mIoU, mF1, mPrecision and mRecall, respectively.

Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous driving (AD). However, inference model trained from data in a particular geographical region faces poor generalization when applied in other regions due to inter-city data domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization by collaborative privacy-preserving training over distributed datasets from different cities. Unfortunately, it suffers from slow convergence because the data from different cities are with disparate statistical properties. Going beyond existing HFL methods, we propose a Gaussian heterogeneous HFL algorithm (FedGau) to address inter-city data heterogeneity so that convergence can be accelerated. In the proposed FedGau algorithm, both single RGB image and RGB dataset are modelled as Gaussian distributions for aggregation weight design. This approach not only differentiates each RGB image by respective statistical distribution, but also exploits the statistics of dataset from each city in addition to the conventionally considered data volume. With the proposed approach, the convergence is accelerated by 35.5%-40.6% compared to existing state-of-the-art (SOTA) HFL methods. On the other hand, to reduce the involved communication resource, we further introduce a novel performance-aware adaptive resource scheduling (AdapRS) policy. Unlike the traditional static resource scheduling policy that exchanges a fixed number of models between two adjacent aggregations, AdapRS adjusts the number of model aggregation at different levels of HFL so that unnecessary communications are minimized. Extensive experiments demonstrate that AdapRS saves 29.65% communication overhead compared to conventional static resource scheduling policy while maintaining almost the same performance.

Wireless resource allocation is a critical component in modern communication systems, and deep neural networks (DNNs) have shown great promise in addressing this challenge. However, the conventional DNNs assume that testing data follows the same distribution as that of the training data, which is incongruent with the dynamic nature of real-world wireless environments. This paper introduces a new training algorithm designed specifically for dynamic wireless environments where channel distribution exhibits variability. This method helps DNNs adapt to new environments while preserving previously learned information. The proposed approach distinguishes itself by updating the DNN parameters in the null space of the low-rank covariance of previous data, which reduces memory needs and boosts training efficiency. Additionally, to counter the problem of DNNs hitting their model capacity during continuous adaptation, a selective forgetting mechanism is proposed. This mechanism allows DNNs to discard the unimportant knowledge over time, freeing up model capacity for more effective adaptation. The effectiveness of the algorithm is validated by integrating it with graph neural networks and multilayer perceptrons for weighted sum-rate maximization. Through a comprehensive evaluation that includes synthetic and ray-tracing-based datasets, superior performance is demonstrated compared to existing methods.

The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To achieve this, we utilize the multi-port communication theory, which considers impedance matching among the source, transmission medium, and load to facilitate efficient power transfer. Specifically, we first investigate the impact of insertion loss, mutual coupling, and other factors on the performance of multi-port matching networks. Next, to further improve system performance, we explore two important deep unfolding designs for the multi-port matching networks: beamforming and power control, respectively. For the hybrid beamforming, we develop a deep unfolding framework, i.e., projected gradient descent (PGD)-Net based on unfolding projected gradient descent. For the power control, we design a deep unfolding network, graph neural network (GNN) aided alternating optimization (AO)-Net, which considers the interaction between different ports in optimizing power allocation. Numerical results verify the necessity of considering insertion loss in the dynamic metasurface antenna (DMA) performance analysis. Besides, the proposed PGD-Net based hybrid beamforming approaches approximate the conventional model-based algorithm with very low complexity. Moreover, our proposed power control scheme has a fast run time compared to the traditional weighted minimum mean squared error (WMMSE) method.

In continual learning, networks confront a trade-off between stability and plasticity when trained on a sequence of tasks. To bolster plasticity without sacrificing stability, we propose a novel training algorithm called LRFR. This approach optimizes network parameters in the null space of the past tasks’ feature representation matrix to guarantee the stability. Concurrently, we judiciously select only a subset of neurons in each layer of the network while training individual tasks to learn the past tasks’ feature representation matrix in low-rank. This increases the null space dimension when designing network parameters for subsequent tasks, thereby enhancing the plasticity. Using CIFAR-100 and TinyImageNet as benchmark datasets for continual learning, the proposed approach consistently outperforms state-of-the-art methods.

Activity detection is an important task in the next generation Internet-of-things (IoT) networks. Existing algorithms mostly require precise information about the network, such as large-scale fading, noise variance, and small-scale fading statistics. Acquiring such information would take a significant overhead and their estimated values might not be accurate. This problem is even more severe in cell-free networks as more parameters are acquired. Therefore, this paper sets out to investigate this problem without the above mentioned information. In order to handle so many unknown parameters, this paper employs a Bayesian approach, where they are endowed with prior distributions as regularizations. Together with the likelihood function, a maximum a posteriori (MAP) estimator is derived. Simulations demonstrate that the proposed method outperforms state-of-the-art methods especially under imprecise information.

We investigate the learning of implicit neural representation (INR) using an overparameterized multilayer perceptron (MLP) via a novel nonparametric teaching perspective. The latter offers an efficient example selection framework for teaching nonparametrically defined (viz. non-closed-form) target functions, such as image functions defined by 2D grids of pixels. To address the costly training of INRs, we propose a paradigm called Implicit Neural Teaching (INT) that treats INR learning as a nonparametric teaching problem, where the given signal being fitted serves as the target function. The teacher then selects signal fragments for iterative training of the MLP to achieve fast convergence. By establishing a connection between MLP evolution through parameter-based gradient descent and that of function evolution through functional gradient descent in nonparametric teaching, we show *for the first time* that teaching an overparameterized MLP is consistent with teaching a nonparametric learner. This new discovery readily permits a convenient drop-in of nonparametric teaching algorithms to broadly enhance INR training efficiency, demonstrating 30%+ training time savings across various input modalities.

Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model faces poor generalization issue due to inter-city domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization, but suffers from slow convergence rate because of vehicles’ surrounding heterogeneity across cities. Going beyond existing HFL works that have deficient capabilities in complex tasks, we propose a rapid-converged heterogeneous HFL framework (FedRC) to address the inter-city data heterogeneity and accelerate HFL model convergence rate. In our proposed FedRC framework, both single RGB image and RGB dataset are modelled as Gaussian distributions in HFL aggregation weight design. This approach not only differentiates each RGB sample instead of typically equalizing them, but also considers both data volume and statistical properties rather than simply taking data quantity into consideration. Extensive experiments on the TriSU task using across-city datasets demonstrate that FedRC converges faster than the state-of-the-art benchmark by 38.7%, 37.5%, 35.5%, and 40.6% in terms of mIoU, mPrecision, mRecall, and mF1, respectively. Furthermore, qualitative evaluations in the CARLA simulation environment confirm that the proposed FedRC framework delivers top-tier performance.

Wireless power transfer (WPT) is recognized as a promising technique to alleviate the energy limitation of wireless devices (WDs) under mobile-edge computing (MEC) scenario in the upcoming Internet of Things (IoT) era. In wireless-powered MEC networks, WDs can utilize the harvested energy to handle the computation tasks. Furthermore, reconfigurable intelligent surface (RIS) can also play a significant role in MEC systems due to its capability to enhance the channel quality. In this article, we investigate an RIS-assisted wireless-powered MEC system where each WD follows the binary offloading policy. The objective is to minimize the total energy consumption of WDs by jointly optimizing the WPT time, the RIS phase shifts for WPT, the binary mode selection, the CPU frequencies for local computation, the RIS phase shifts for offloading, and the offloading times and powers. A gradient ascent-based algorithm with linear complexity with respect to the number of RIS reflecting elements is proposed to optimize the transmit power and RIS phases shifts design. On the other hand, a penalty-based algorithm with linear complexity with respect to the number of WDs is proposed to solve the offloading decision and time allocation. Numerical results are presented to demonstrate the effectiveness of the proposed system and algorithms.

Reconfigurable intelligent surface (RIS) has the capability to significantly enhance physical layer security by reconfiguring the propagation in wireless communications. However, due to the cascaded channel brought by the RIS and the hostile nature of potential eavesdroppers, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas and there exists spatial correlation at the RIS due to closely spaced RIS elements, the random channel matrices are complicatedly coupled with the phase shift and other wireless resources in the outage probabilistic constraint, making their optimizations intractable. To date, there has been no systematic and feasible approach to address such a challenge. To fill this gap, this paper for the first time reveals an analytical transformation for handling the intractable outage probabilistic constraint. It is theoretically established that when the maximum tolerable outage probability is smaller than a threshold around 0.4, which generally holds in practice, the proposed transformation is exact and suffers no performance loss. As an illustrative example of the developed constraint transformation, the secure energy efficiency maximization is selected as the objecitve function and the resultant resource optimization is handled by the alternating maximization framework. Numerical results are presented to show the rapid convergence behavior of the proposed algorithm and unveil that the proposed probabilistic constraint transformation has superiority over the Bernstein-Type Inequality approximation. Compared with several baseline schemes (e.g., random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS), the proposed scheme significantly boosts the performance, underscoring the significance of appropriately managing the probabilistic constraint outage and optimizing RIS phase shifts for secure transmission against multi-antenna eavesdroppers.

A great amount of endeavour has recently been devoted to activity detection for cell-free massive multiple-input multiple-output (MIMO) systems, where multiple access points (APs) jointly identify the active devices from a large number of potential devices. In practice, the APs and the central processing unit (CPU) are connected by capacity-limited fronthauls and the signals at the APs need to be compressed/quantized before they are forwarded to the CPU. However, existing approaches treat the compression/quantization and activity detection as separate tasks, which makes it difficult to achieve global system optimality. To tackle the above problem, this paper proposes an augmented model-driven end-to-end learning framework which jointly optimizes the compression modules, quantization modules at the APs, and the decompression module and detection module at the CPU. Specifically, deep unfolding is leveraged for designing the detection module in order to inherit the domain knowledge derived from the optimization algorithm, and other modules are constructed by judiciously designed neural network architectures for improving the learning capability. Furthermore, we design an enhanced scheme so that the proposed framework is adaptable to different compression rates. We demonstrate numerically that the proposed framework significantly reduces the computational complexity and achieves better detection performance than the conventional approaches. Moreover, it costs a much smaller number of bits on the fronthauls while still maintaining the detection performance.

This paper investigates a covariance-based approach for intelligent reflecting surface (IRS) aided activity detection in massive machine-type communications (mMTC). In the conventional scenario without IRS, the covariance-based approach, which exploits the probability density function (PDF) of the received signals at the base station (BS), has been demonstrated to outperform the compressed sensing approach. However, when taking the impact of the IRS into account, due to the newly introduced cascaded channels, it is difficult to obtain the exact PDF of the received signals at the BS. To tackle this challenge, we propose an approximation for the intended PDF with tunable parameters in the covariance matrix of the received signals. Based on the proposed tractable reformulation, an analytic framework is established to reveal the guideline for the phase shift design. Moreover, to determine the optimal correlation parameters, a deep unfolding approach is further leveraged by regarding them as trainable parameters. Simulation results validate the theoretical analysis and demonstrate the superior performance of the proposed learning approach.

In mobile edge computing (MEC) systems, the wireless channel condition is a critical factor affecting both the communication power consumption and computation rate of the offloading tasks. This paper exploits the idea of cooperative transmission and employing reconfigurable intelligent surface (RIS) in MEC to improve the channel condition and maximize computation efficiency (CE). The resulting problem couples various wireless resources in both uplink and downlink, which calls for the joint design of the user association, receive/downlink beamforming vectors, transmit power of users, task partition strategies for local computing and offloading, and uplink/downlink phase shifts at the RIS. To tackle the challenges brought by the combinatorial optimization problem, the group sparsity structure of the beamforming vectors determined by user association is exploited. Furthermore, while the CE does not explicitly depend on the downlink phase shifts, instead of simply finding a feasible solution, we exploit the hidden relationship between them and convert this relationship into an explicit form for optimization. Then the resulting problem is solved via the alternating maximization framework, and the nonconvexity of each subproblem is handled individually. Simulation results show that cooperative transmission and RIS deployment can significantly improve the CE and demonstrate the importance of optimizing the downlink phase shifts with an explicit form.

Recently, intelligent reflecting surface (IRS) emerges as an effective technique for saving power consumption by customizing the wireless propagation environment. On the other hand, the symbol level precoding (SLP) technique provides a clever solution to interference exploitation by converting the multiuser interference (MUI) into a beneficial part of the desired signal. In this paper, we propose to jointly exploit IRS and SLP to cope with the power control and interference management issues in a heterogeneous network (HetNet). Considering the possible coordination between the macro base station (MBS) and the pico base station (PBS), we propose two corresponding schemes to manage the inter-cell and intra-cell interference. For both proposed schemes, the power minimization problems are studied by jointly optimizing the precoding matrices at the MBS and PBS as well as reflecting coefficients at the IRS. Due to the non-convexity of these problems, the precoding matrices and reflecting coefficients are optimized alternately. We propose two Lagrangian based algorithms to obtain the optimal solutions of the precoding matrices, where the precoding matrix of the MBS always yields a closed-form. A multiple-gradient descent algorithm based on the Riemannian manifold (MGD-RM) is proposed as well to enhance the received signal quality of each MUE and PUE for the reflecting design. Simulation results manifest a significant performance gain achieved by our proposed HetNet over benchmarks.

In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation. Unfortunately, the iterative nature of the commonly applied optimization-based algorithms cannot meet the low latency requirements due to the high computational complexity. For real-time implementations, deep learning-based approaches, especially the graph neural networks (GNNs), have been demonstrated with good scalability and generalization performance due to the permutation equivariance (PE) property. However, the current architectures are only equipped with the node-update mechanism, which prohibits the applications to a more general setup, where the unknown variables are also defined on the graph edges. To fill this gap, we propose an edge-update mechanism, which enables GNNs to handle both node and edge variables and prove its PE property with respect to both transmitters and receivers. Simulation results on typical radio resource management problems demonstrate that the proposed method achieves higher sum rate but with much shorter computation time than state-of-the-art methods and generalizes well on different numbers of base stations and users, different noise variances, interference levels, and transmit power budgets.

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to multiple macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. Furthermore, to explore the potential advantage of common streams in RSMA systems, we develop a “Hybrid RSMA” scheme in which the MBS only employs RSMA to encode the backhaul messages while each MCU’s message is directly encoded without rate-splitting. We formulate an optimization problem to maximize the system’s spectral efficiency (SE) by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we first propose an iterative centralized algorithm based on successive convex approximation (SCA). Then, we further develop an efficient decentralized algorithm that can be executed in parallel at the MBS and each SBS based on the local channel state information with fewer signaling exchanges. Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. Particularly, “Hybrid RSMA” has a greater performance improvement than “RSMA” in the underloaded system.

Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the physical layer security by reconfiguring the wireless propagation environment. However, due to the hostile nature of potential eavesdroppers and the cascaded channel brought by the RIS, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas, the design of the optimal phase-shift, power allocation, and secure transmission data rate are intractable due to the couplings of random channel matrices in the outage probability. To overcome these challenges, this paper for the first time reveals an analytical transformation for handling the outage probabilistic constraint in the secure energy efficiency maximization problem due to multi-antenna eavesdropper. The resultant problem is readily handled under the alternating maximization framework. Simulation results unveil that the proposed probabilistic constraint transformation and the associated optimization algorithm provide superior secure energy efficiency over the baseline schemes of random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS.

This paper investigates a covariance-based learning approach for intelligent reflecting surface (IRS) aided activity detection in massive machine-type communications (mMTC). In the conventional scenario without IRS, the covariance-based approach has been demonstrated to outperform the compressed sensing approach, as the covariance-based approach can well exploit the probability density function (PDF) of the received signals at the base station (BS). However, when taking the impact of the IRS into account, due to the newly introduced cascaded channels, it is quite difficult to obtain the exact PDF of the received signals at the BS. To tackle this challenge, we propose an approximation for the intended PDF by modeling a correlation parameter in the covariance matrix of the received signals. Based on the covariance-based formulation, a learning approach is further proposed to automatically learn the correlation parameter. Simulation results demonstrate the performance of the covariance-based activity detection, and the superiority of the proposed covariance-based learning approach.

Device activity detection in the emerging cell-free massive multiple-input multiple-output systems has been recognized as a crucial task in machine-type communications, in which multiple access points jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a distributed algorithm that satisfies the highly nonconvex constraints in a gentle fashion as the iteration number increases. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed two distributed algorithms outperform state-of-the-art approaches. Moreover, the accelerated distributed algorithm requires a very small number of quantization bits to approach the ideal detection performance.

A great amount of endeavour has recently been devoted to device activity detection in massive machine-type communications. This paper targets at a practical issue: communication-efficient joint signal compression and activity detection in cell-free massive MIMO with capacity-limited fronthauls. To this end, we propose a novel deep learning framework which jointly optimizes the compression modules, quantization modules at the access points, and the decompression module and detection module at the central processing unit. Specifically, deep unfolding is leveraged for designing the detection module in order to inherit the domain knowledge derived from the optimization algorithm, and the other modules are constructed by generic layers for increasing the learning capability. A joint training strategy is proposed to optimize all the modules in an end-to-end manner. Numerical results demonstrate the superiority of the proposed end-to-end learning framework compared with classical optimization methods.

Cooperative beamforming has been recognized as an effective approach to meet the dramatically increasing demand of various wireless data traffics. Conventionally, the beamforming design problem is posed as an optimization problem and solved through iterative algorithms, which are difficult for real-time implementations. Recent advances in the field have witnessed the emergence of learning-based methods for beamforming design in real-time. Graph Neural Networks (GNNs) have been demonstrated to leverage the graph topology in wireless networks and generalize to unseen problem sizes. However, the current implementations of GNNs suffer from a limitation in modeling more complex cooperative beamforming, where the beamformers are on the graph edges. To address this shortcoming, this paper presents a novel Edge-Graph-Neural-Network (Edge-GNN) which incorporates an edge-update mechanism, thus allowing for the learning of cooperative beamforming on graph edges. Simulation results affirm the superiority of the proposed Edge-GNN over state-of-the-art approaches. The Edge-GNN achieves a higher sum rate with reduced computation time and exhibits excellent generalization to different numbers of base stations and user equipments.

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. In order to improve the system spectral efficiency (SE) while guaranteeing the quality-of-service (QoS) of each user, we formulate an optimization problem to maximize the sum SE by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we propose an iterative algorithm based on successive convex approximation (SCA). Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. In addition, RSMA has better flexibility than other benchmark schemes in meeting the increasing QoS requirements of users.

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%.

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains. The fundamental objective is to understand the underlying "invariance" behind these observational distributions and such invariance has been shown to have a close connection to causality. While many existing approaches make use of the property that causal features are invariant across domains, we consider the invariance of the average causal effect of the features to the labels. This invariance regularizes our training approach in which interventions are performed on features to enforce stability of the causal prediction by the classifier across domains. Our work thus sheds some light on the domain generalization problem by introducing invariance of the mechanisms into the learning process. Experiments on several benchmark datasets demonstrate the performance of the proposed method against SOTAs. The codes are available at: https://github.com/lithostark/Contrastive-ACE.

To support modern machine-type communications, a crucial task during the random access phase is device activity detection, which is to identify the active devices from a large number of potential devices based on the received signal at the access point. By utilizing the statistical properties of the channel, state-of-the-art covariance based methods have been demonstrated to achieve better activity detection performance than compressed sensing based methods. However, covariance based methods require to solve a high dimensional nonconvex optimization problem by updating the estimate of the activity status of each device sequentially. Since the number of updates is proportional to the device number, the computational complexity and delay make the iterative updates difficult for real-time implementation especially when the device number scales up. Inspired by the success of deep learning for real-time inference, this paper proposes a learning based method with a customized heterogeneous transformer architecture for device activity detection. By adopting an attention mechanism in the architecture design, the proposed method is able to extract features reflecting relevance among device pilots and received signal, permutation equivariant with respect to devices, and its training parameter number is independent of the device number. Simulation results demonstrate that the proposed method achieves better activity detection performance with much shorter computation time than state-of-the-art covariance approach, and generalizes well to different numbers of devices and BS-antennas, different pilot lengths, transmit powers, and cell radii.

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links.

Grant-free random access is a promising mechanism to support modern massive machine-type communications in which devices are sporadically active with small payloads. Under this random access, a unique challenge is the detection of device activity without the cooperation from devices. Furthermore, for only a few bits of data, it is more efficient to embed the data to the signature sequences so that the activity and data detection can be jointly carried out. However, compared with the vanilla device activity detection, joint activity and data detection has an extra discontinuous sparsity constraint, which makes the detection problem more challenging. In contrast to the prevalent way of first neglecting the discontinuous sparsity constraint and reinforcing it at the end, this paper proposes a novel approach to incorporate the discontinuous sparsity constraint into the optimization procedure. In particular, we first establish the equivalence between the discontinuous sparsity constraint and a continuous difference-of-norms (DN) form. Then, by introducing a DN penalty term in the objective function, an iterative DN penalty method with an increasing penalty weight is adopted. We prove theoretically that by solving each penalized problem to a stationary solution, the discontinuous sparsity constraint can be exactly satisfied when the penalty weight is sufficiently large, and the resulting solution is guaranteed to be at least a stationary point of the original problem. Due to the superior theoretical guarantee, simulation results demonstrate that the proposed method achieves around 10 times better detection performance than state-of-the-art approaches.

Reconfigurable intelligent surface (RIS) has the revolutionary ability to customize the radio propagation environment for enhancing the secure transmission performance. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the channel state information (CSI) is imperfectly obtained at the base station, leading to uncertain CSI. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for secure transmission is currently unknown due to the difficulty of handling the probabilistic constraint with coupled variables. To fill this gap, this paper investigates the energy efficient secure transmission while incorporating the probabilistic constraint. By transforming the probabilistic constraint and decoupling the variables, the secure energy-efficiency maximization problem can be solved via alternatively executing the concave-convex procedure and penalty-based method. Simulation results show that the proposed RIS-aided secure transmission scheme significantly improves the energy-efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and time-critical applications in future Internet of Things (IoT) era. However, the uplink transmission performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Recently, intelligent reflecting surface (IRS) has drawn much attention because of its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies of wireless communications. In this article, we consider an IRS-aided multidevice MEC system where each IoT device follows the binary offloading policy, i.e., a task has to be computed as a whole either locally or remotely at the edge server. We aim to minimize the total energy consumption of devices by jointly optimizing the binary offloading modes, the CPU frequencies, the offloading powers, the offloading times, and the IRS phase shifts for all devices. Two algorithms, which are greedy based and penalty based, are proposed to solve the challenging nonconvex and discontinuous problem. It is found that the penalty-based method has only linear complexity with respect to the number of devices, but it performs close to the greedy-based method with cubic complexity with respect to the number of devices. Furthermore, binary offloading via IRS indeed saves more energy compared to the case without IRS.

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters.

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency.

Belief propagation (BP) is a popular message passing algorithm for distributed cooperative localization. However, due to the nonlinearity of measurement functions, BP implementation has no closed-form expression and requires message approximations. While nonparametric BP can be used, it suffers from a high computational complexity, thus being impractical in energy-constrained networks. In this paper, a parametric Bayesian method with Gaussian BP implementation is proposed for distributed cooperative localization. With linearization of the Euclidean norm in ranging measurements, the joint posterior distribution of agents’ locations is successively approximated with a sequence of high-dimensional Gaussian distributions. At each iteration of the successive Gaussian approximation, vector-valued Gaussian BP is further adopted to compute the marginal distributions of agents’ locations in a distributed way. It is proved by the principle of majorization-minimization that the proposed successive Gaussian approximation is guaranteed to converge, and the sequence of the estimated agents’ locations converges to a stationary point of the objective function of the maximum a posteriori estimation. Furthermore, although cooperative localization involves loopy network topologies, in which convergence property of Gaussian BP is generally unknown, it is proved in this paper that vector-valued Gaussian BP converges, making the proposed parametric BP-based method being the first one achieving convergence guarantee. Compared to the nonparametric BP counterpart, the proposed method has a much lower computational complexity and communication overhead. Simulation results demonstrate that the proposed method achieves a superior performance in localization accuracy compared to existing cooperative localization methods.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the eavesdroppers’ channel state information is imperfect at the base station. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for massive antennas and reflecting elements secure transmission are challenging to solve due to the outage probabilistic constraint with coupled variables. To fill this gap, this paper formulates a problem of energy-efficient secure transmission design with the probabilistic outage constraint. By leveraging the exponential distribution property of the received signal power, the stochastic resource allocation is equivalently transformed into a deterministic one, and the secure energy efficiency maximization problem can be iteratively solved via low complexity first-order algorithms under the alternating maximization (AM) framework. However, due to the nonsmooth problem, the convergence of the objective function value and nature of the converged solution under AM iteration are uncertain. Therefore, the convergence properties with respect to the objective function value and sequence of solutions are further established. Simulation results corroborate the convergence results of the first-order algorithms and show that the proposed algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant RIS aided secure transmission significantly improves the energy efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimize the nonconvex nonsmooth loss bound. Experimental results in the Car Learning to Act (CARLA) platform show that the proposed UMWFL framework with PAM algorithm achieves smaller training losses and testing errors than those of the benchmark scheme.

Intelligent reflecting surface (IRS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of the eavesdropper and the cascaded channel brought by the IRS, the eavesdropper’s channel state information is imperfectly obtained at the base station. Under the channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for secure transmission is currently unknown due to the difficulty of handling the probabilistic constraint with coupled variables. To fill this gap, this paper formulates a secrecy rate maximization problem while incorporating the probabilistic constraint. By transforming the probabilistic constraint and decoupling variables, the secrecy rate maximization problem can be solved via alternatively executing difference-of-convex programming and semidefinite relaxation method. The simulation results validate the strength of this newly established transmission scheme when compared to baseline schemes of random phase-shift, fixed phase-shift, and IRS ignoring CSI uncertainty.

Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many machine learning tasks. However, existing methods for TT decomposition either suffer from noise overfitting, or require extensive fine-tuning of the balance between model complexity and representation accuracy. In this paper, a fully Bayesian treatment of TT decomposition is employed to avoid noise overfitting without parameter tuning. In particular, theoretical evidence is established for adopting a Gaussian-product-Gamma prior to induce sparsity on the slices of the TT cores. Furthermore, based on the proposed probabilistic model, an efficient learning algorithm is derived under the variational inference framework. Experiments on real-world data demonstrate the proposed algorithm performs better in image completion and image classification, compared to other existing TT decomposition algorithms.

Edge learning (EL), which uses edge computing as a platform to execute machine learning algorithms, is able to fully exploit the massive sensing data generated by Internet of Things (IoT). However, due to the limited transmit power at IoT devices, collecting the sensing data in EL systems is a challenging task. To address this challenge, this article proposes to integrate unmanned ground vehicle (UGV) with EL. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs? This article further proposes a graph-based path planning model, a network energy consumption model, and a sample size planning model that characterizes F-measure as a function of the minority class sample size. With these models, the joint path, energy and sample size planning (JPESP) problem is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is nontrivial to solve due to the high-dimensional discontinuous variables related to UGV movement. To this end, it is proved that each IoT device should be served only once along the path, thus the problem dimension is significantly reduced. Furthermore, to handle the discontinuous variables, a tabu search (TS)-based algorithm is derived, which converges in expectation to the optimal solution to the JPESP problem. Simulation results under different task scenarios show that our optimization schemes outperform the fixed EL and the full path EL schemes.

As a typical usage scenario for the next-generation mobile communication network, massive Internet of Things (mIoT) is requested to provide high machine-type communication device (MTCD) density service. Nonorthogonal multiple access (NOMA) and mobile-edge computing (MEC) can further enhance the performance of mIoT. Furthermore, to cope with the energy consumption constraint of MTCD, energy harvesting (EH) can be leveraged. In this article, considering the social trusts of MTCDs, we propose an MEC offloading scheme for cellular Internet of Things networks with massive NOMA-aided EH MTCD and several road side units with edge servers randomly distributed in a macrocell. We aim to maximize the total sum rate of the network by jointly considering the processing mode selection, device clustering, subchannel allocation, and power allocation while satisfying the power, energy, latency, and quality of service requirements. To this end, we prove the NP-hardness of the considered optimization problem and decompose it into three subproblems, which can be solved by an iterative algorithm. Numerical results demonstrate the superior performance of the proposed scheme.

Non-orthogonal multiple access (NOMA) is a promising solution for secure transmission under massive access. However, in addition to the uncertain channel state information (CSI) of the eavesdroppers due to their passive nature, the CSI of the legitimate users may also be imperfect at the base station due to the limited feedback. Under both channel uncertainties, the optimal power allocation and transmission rate design for a secure NOMA scheme is currently not known due to the difficulty of handling the probabilistic constraints. This article fills this gap by proposing novel transformation of the probabilistic constraints and variable decoupling so that the security guaranteed sum-rate maximization problem can be solved by alternatively executing branch-and-bound method and difference of convex programming. To scale the solution to a truly massive access scenario, a first-order algorithm with very low complexity is further proposed. Simulation results show that the proposed first-order algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant transmission scheme significantly improves the security guaranteed sum-rate compared to the orthogonal multiple access transmission and NOMA ignoring CSI uncertainty.

This work investigates the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. To begin with, the achievable data rates for multicast and unicast services are derived in closed form, as well as the received radio frequency (RF) power at each UE. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem with complex constraints. To suit the massive access setting, a first-order algorithm is developed to find both initial feasible point and the nearly optimal solution. Moreover, an accelerated algorithm is designed to improve the convergence speed. Numerical results demonstrate that the proposed first-order algorithms can achieve almost the same EE as that of second-order approaches yet with much lower computational complexity, which provides insight into the superiority of the proposed algorithms for massive access in cell-free massive MIMO systems.

Nowadays, intelligent transportation industry is becoming a hot spot in Internet of vehicles (IoV). However, owing to the existence of numerous intelligent terminals, communication security becomes a pressing problem. On the other hand, pervasive edge computing (PEC), as a pivotal technology, can significantly improve the performance of the system compared to the traditional cloud computing. In this article, we propose a nonorthogonal multiple access (NOMA)-based PEC power allocation framework in IoV, aiming at minimizing the system latency in the presence of eavesdroppers. Besides, queuing models, imperfect channel state information, and vehicles' speeds are all considered. Since the formulated problem is complicated, we consider its lower bound and derive the suboptimal closed-form expressions of the power allocation coefficients. Furthermore, a Frank-and-Wold algorithm is proposed to achieve the optimum total power. Simulation results illustrate the superior performance of the proposed NOMA scheme.

Edge caching can effectively reduce backhaul burden at core network and increase quality-of-service at wireless edge nodes. However, the beneficial role of edge caching cannot be fully realized when the offloading link is in deep fade. Fortunately, the impairments induced by wireless propagation environments could be renovated by a reconfigurable intelligent surface (RIS). In this paper, a new RIS-aided edge caching system is proposed, where a network cost minimization problem is formulated to optimize content placement at cache units, active beamforming at base station and passive phase shifting at RIS. After decoupling the content placement subproblem with the hybrid beamforming design, we propose an alternating optimization algorithm to tackle the active beamforming and passive phase shifting. For active beamforming, we transfer the problem into a semidefinite programming (SDP) and prove that the optimal solution of SDP is always rank-one. For passive phase shifting, we introduce the block coordinate descent method to alternately optimize the auxiliary variables and the RIS phase shifts. Further, a conjugate gradient algorithm based on manifold optimization is proposed to deal with the non-convex unit-modulus constraints. Numerical results show that our RIS-aided edge caching design can effectively decrease the network cost by improving the quality of offloading links.

Gaussian belief propagation (BP) finds extensive applications in signal processing but it is not guaranteed to converge in loopy graphs. In order to determine whether Gaussian BP would converge, one could directly use the classical convergence conditions of Gaussian BP, such as diagonal dominance, walk-summability, and convex decomposition. These classical conditions assume that the convergence conditions for Gaussian BP precisions and means are the same, which has been proved to be unnecessary. Generally, the condition for guaranteeing the convergence of Gaussian BP precisions is looser than that of Gaussian BP means. Moreover, the convergence of Gaussian BP means could be improved by damping when Gaussian BP precisions converge. Therefore, the convergence of Gaussian BP precisions is a prerequisite for guaranteeing the convergence of Gaussian BP means. This paper derives a simple convergence condition for Gaussian BP precisions, which can be verified in a distributed way. Through numerical examples, it is found that there exists scenarios where the new condition is satisfied but the classical conditions are not.

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.

In multiple access systems, physical layer security is degraded since more attacking targets are available for the eavesdropper. Fortunately, it has been recently demonstrated that non-orthogonal multiple access (NOMA) could improve secure transmission performance. However, it is still unknown how to design a transmission scheme for maximizing the sum rate when the channel state information is imperfectly known at the transmitter. To fill this gap, we formulate a maximization problem of sum rate while incorporating versatile metrics such as outage probability, quality of service, and transmit power. By leveraging the first-order and log-concavity properties of the Marcum Q-function, the maximum sum rate of the secure NOMA transmission scheme is efficiently obtained. Simulation results validate the strength of this newly established scheme when compared with conventional orthogonal multiple access scheme.

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper’s angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms.

In a wiretap channel, due to the passive nature of eavesdropper and the inevitable errors during channel estimation or feedback, the channel state information is usually imperfectly known at the transmitter. While probabilistic constrained secure transmission provides an elegant formulation to tackle these uncertainties, current works mostly focus on the fixed-rate secure transmission design. To exploit the dynamic channel state information for performance enhancement, this paper investigates a variable-rate transmission scheme with adjustable rate and power, under the outage probabilistic constraints and upper bounding rate constraint. By leveraging the first-order and log-concavity properties of the Marcum Q-function, closed-form optimal secure transmission design is obtained. Furthermore, the optimality of the proposed method empowers us to concisely quantify the performance gain brought by rate variation. Numerical results show that the proposed scheme achieves significantly lower average outage probability and higher throughput than the fixed-rate scheme no matter with or without upper bound rate limitation.

Cloud radio access network (C-RAN) has been recognized as a promising architecture for next-generation wireless systems to support the rapidly increasing demand for higher data rate. However, the performance of C-RAN is limited by the backhaul capacities, especially for the wireless deployment. While C-RAN with fixed BS caching has been demonstrated to reduce backhaul consumption, it is more challenging to further optimize the cache allocation at BSs with multi-cluster multicast backhaul, where the inter-cluster interference induces additional non-convexity to the cache optimization problem. Despite the challenges, we propose an accelerated first-order algorithm, which achieves much higher content downloading sum-rate than a second-order algorithm running for the same amount of time. Simulation results demonstrate that, by simultaneously delivering the required contents to different multicast clusters, the proposed algorithm achieves significantly higher downloading sum-rate than those of time-division single-cluster transmission schemes. Moreover, it is found that the proposed algorithm allocates larger cache sizes to the farther BSs within the nearer clusters, which provides insight to the superiority of the proposed cache allocation.

While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.

One key feature of massive multiple-input multiple-output systems is the large number of antennas and users. As a result, reducing the computational complexity of beamforming design becomes imperative. To this end, the goal of this paper is to achieve a lower complexity order than that of existing beamforming methods, via the parallel accelerated random coordinate descent (ARCD). However, it is known that ARCD is only applicable when the problem is convex, smooth, and separable. In contrast, the beamforming design problem is nonconvex, nonsmooth, and nonseparable. Despite these challenges, this paper shows that it is possible to incorporate ARCD for multicast beamforming by leveraging majorization minimization and strong duality. Numerical results show that the proposed method reduces the execution time by one order of magnitude compared to state-of-the-art methods.

While backscatter communication emerges as a promising solution to reduce power consumption at IoT devices, the transmission range of backscatter communication is short. To this end, this work integrates unmanned ground vehicles (UGVs) into the backscatter system. With such a scheme, the UGV could facilitate the communication by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper proposes a joint graph mobility and backscatter communication model. With the proposed model, the total energy minimization at UGV is formulated as a mixed integer nonlinear programming (MINLP) problem. Furthermore, an efficient algorithm that achieves a local optimal solution is derived, and it leads to automatic trade-off between spending energy on moving versus on communication. Numerical results are provided to validate the performance of the proposed algorithm.

Gaussian belief propagation (BP) is a low-complexity and distributed method for computing the marginal distributions of a high-dimensional joint Gaussian distribution. However, Gaussian BP is only guaranteed to converge in singly connected graphs and may fail to converge in loopy graphs. Therefore, convergence analysis is a core topic in Gaussian BP. Existing conditions for verifying the convergence of Gaussian BP are all tailored for one particular pairwise factorization of the distribution in Gaussian Markov random field (MRF) and may not be valid for another pairwise factorization. On the other hand, convergence conditions of Gaussian BP in pairwise linear Gaussian model are developed independently from those in Gaussian MRF, making the convergence results highly scattered with diverse settings. In this paper, the convergence condition of Gaussian BP is investigated under a general pairwise factorization, which includes Gaussian MRF and pairwise linear Gaussian model as special cases. Upon this, existing convergence conditions in Gaussian MRF are extended to any pairwise factorization. Moreover, the newly established link between Gaussian MRF and pairwise linear Gaussian model reveals an easily verifiable sufficient convergence condition in pairwise linear Gaussian model, which provides a unified criterion for assessing the convergence of Gaussian BP in multiple applications. Numerical examples are presented to corroborate the theoretical results of this paper.

There is a recent surge of research interest in the study of performance-enhancing techniques for orthogonal frequency division multiplexing (OFDM)-based relay systems. Among those, subcarrier mapping has been verified to be an effective one for boosting the system capacity and improving the error performance. However, it has to be performed at the relay, which subsequently conveys the subcarrier permutation information to the destination. The existing signaling scheme occupies a portion of subcarriers to this end, leading to a loss of spectral efficiency. In this paper, we propose a novel signaling scheme to eliminate this overhead by transferring the subcarrier permutation to the mode permutation that can be implicitly conveyed without consuming additional spectrum resources. We adopt phase rotation for mode design considering both non-adaptive and adaptive modulation, and illustrate the proposed scheme by taking the dual-hop OFDM relaying with semi-blind amplify-and-forward protocol as an example. An asymptotically tight upper bound on the bit error rate (BER) of the proposed scheme is derived in closed-form over Rayleigh fading channels. BER simulation results validate the analysis and show that the proposed scheme asymptotically approaches the ideal case that assumes perfect knowledge of subcarrier permutation information at the destination and significantly outperforms the existing scheme in the asymptotic signal-to-noise ratio region at the same spectral efficiency.

Activity detection in machine-type communication (MTC) has been recognized as an effective way to support massive connectivity of the Internet-of-Things (IoT) devices. However, due to the sporadic traffic pattern of the MTC, only a small portion of the massive potential devices are active, making the activity detection a challenging large-scale sparsity-constrained problem. On the other hand, since the low-cost IoT devices are commonly equipped with cheap crystal oscillators, the resulting frequency offsets would intensify the multi-user interference during the activity detection and invalidate existing detection methods that are designed under ideal frequency synchronization. To fill this gap, this paper proposes two methods for activity detection under unknown frequency offsets: a Lasso-based method and a sparsity-constrained method. Both the methods are first-order algorithms, making them suitable for large-scale IoT systems. Furthermore, the sparsity-constrained method can be executed in parallel and is proved to converge to a set of critical points. The simulation results show that both the proposed methods achieve much better detection performance than a two-stage approach that separately performs frequency synchronization and activity detection. Moreover, the proposed sparsity-constrained method is shown to perform better than two competing algorithms exploiting hierarchical sparsity.

As the demand for supporting hybrid multicast and unicast services is rapidly increasing, a non-orthogonal multiplexing transmission scheme called layered-division multiplexing (LDM) has been recognized as an effective way to provide high spectrum efficiency (SE). However, high SE is not necessarily equivalent to high energy efficiency (EE). In fact, it is still unclear how much benefit LDM would provide for hybrid multicast and unicast services under EE maximization, which belongs to the more challenging class of fractional programs. To fill this gap, we formulate the problem of energy-efficient precoding design for the LDM-based multi-user multi-input-multi-output downlink system, under both multicast and unicast multi-stream data rate constraints of each user. Although the problem is nonsmooth and nonconvex, we propose a first-order algorithm for finding both the initial point and the final solution. Since the proposed first-order algorithm involves only gradient information, it achieves very low complexity. The simulation results demonstrate that, compared with the orthogonal transmission schemes, the LDM transmission under the proposed precoding can provide a much higher EE. Moreover, the proposed first-order algorithm achieves the same EE as that of a second-order based approach, but requires much shorter computation time.

Collecting data from the massive Internet of Things (IoT) devices is a challenging task since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in the IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this paper proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem, unfortunately, involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. The simulation results show that if the noise power is small (e.g., ≤-100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., -60 dBm), the UGV should spend more motion energy to get closer to the IoT users.

In multimedia-rich communication scenarios, popular contents are requested by many users. This calls for the communication system design perspective transferring from user-centric to content-centric. To realize the content-centric paradigm, one of the dominant approaches is the multi-group multicast transmission. However, different content groups may cause interference with each other, and the quality of service is difficult to be guaranteed without coordination. Fortunately, a cloud radio access network (C-RAN) perfectly fills this gap as all the computations in the network are off-loaded to the computation center, making the central coordination possible. But a major challenge that C-RAN faces is that the resultant problem size could be extremely large, invalidating many existing second-order algorithms. In this paper, content-centric sparse multicast beamforming in a large-scale C-RAN is studied. In addition to the large-scale nature, this problem is further complicated by the discontinuity and non-convexity of the cost function and constraints. Despite the challenges, a first-order algorithm is proposed. Not only is the proposed algorithm guaranteed to converge to a critical point, but its complexity order is only linear with respect to the problem size. This is in sharp contrast to the cubic order of an existing solution, making the proposed algorithm indispensable for large-scale C-RAN with hundreds or thousands of users.

To prolong the lifetime of energy constrained devices in Internet of Things, devices can harvest wireless energy from the control signal multicast from the access point. Unfortunately, hampered by the path-loss, the efficiency of such multicast wirelessly powered network is low. While large-scale antennas at access point can be used to improve the efficiency, the beamforming design problem in multicast wirelessly powered network is known to be NP-hard, and the traditional difference of convex programming becomes prohibitively time consuming in large-scale settings. On the other extreme, by using the assumption of infinite number of antennas and applying the law of large numbers, simple beamforming solution is possible. However, when applied to scenarios with finite number of antennas, the performance of such asymptotic solution is far from that of difference of convex programming. To resolve this apparent complexity-performance dilemma, this paper develops an algorithm which reduces the computation time by orders of magnitude, while still guaranteeing the same performance compared with the difference of convex programming. In particular, the proposed algorithm consists of two fast-convergent iterative procedures and is guaranteed to obtain a Karush-Kuhn-Tucker solution. Furthermore, in each iteration, the algorithm only requires the computation of inner products between channel vectors and can be run in parallel for all the users. Thus, the complexity scales linearly with the number of antennas at access point. Finally, numerical results validate the performance and the speed of the proposed scheme.

Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local meansurements/observations are scattered over a wide geographical area. However, the convergence of Gaussian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular on the convergence of the information matrix. We show analytically that the exchanged message information matrix converges for arbitrary positive semidefinite initial value, and its distance to the unique positive definite limit matrix decreases exponentially fast.

We design novel transmission strategies to maximize the energy efficiency (EE) of the uplink multi-user multipleinput and multiple-output relay channel. In this channel, K multi-antenna users communicate with a multi-antenna base station (BS) through a multi-antenna relay. To achieve the goal of EE maximization, we propose new iterative algorithms to jointly optimize the multi-user precoder and the relay precoder under transmit power constraints for two cases. In the first case, the perfect global channel state information (CSI) is available, while in the second case, the CSI between the relay and the BS is imperfect. To surmount the non-convexity of our formulated EE optimization problems in both cases, we introduce the parameter subtractive function into the proposed algorithms. Then, the EE parameter in the parameter subtractive function is updated by Dinkelbach's algorithm in the perfect CSI case, and by the bisection method in the imperfect CSI case. Moreover, in the perfect CSI case, the relay precoder is optimized by the diagonalization operation and the multi-user precoder is optimized based on the weighted minimum mean square error method. Differently, in the imperfect CSI case, we apply the sign-definiteness lemma to promote the semidefinite programming formulation of the EE optimization problem. Furthermore, we present the numerical results to demonstrate that our proposed iterative algorithms have a good convergence rate in both cases. In addition, we show that our proposed iterative algorithms achieve a higher EE performance than the existing algorithms in both CSI cases.

While two-way communication can improve the spectral efficiency of wireless networks, distances from the relay to the two users are usually asymmetric, leading to excessive wireless energy at the nearby user. To exploit the excessive energy, energy harvesting at user terminals is a viable option. Unfortunately, the exact gain brought by wireless power transfer (WPT) in two-way communication is currently unknown. To fill this gap, in this paper, the achievable rate region of wirelessly powered two-way communication with a fixed relay is derived. Not only this newly established result is shown to enclose the existing achievable rate region of two-way relay channel without energy harvesting but also the gain is precisely quantified. On the other hand, it is well-known that a major obstacle to WPT is the path-loss. By endowing the relay with mobility, the distances between the relay and users can be varied, thus providing a potential solution to combat pathloss at the expense of energy for transmission. To characterize the consequence brought by such a scheme, a pair of inner and outer bounds to the achievable rate region of wirelessly powered two-way communication under a mobile relay is further derived. By comparing the exact achievable rate region for the fixed relay case and the achievable rate bounds for the mobile relay case, it is possible to quantify the relative advantage of spending energy on moving versus on transmission in wirelessly powered two-way communication.

In this paper, we consider the joint optimization of relay transmit-receive beamformers, users' transmit powers, and users' power splitting ratios in wirelessly-powered two-way relay channel under data-rate quality-of-service constraints. In order to solve the problem, we first establish that the uplink data-rate constraints would be active at the global optimum. Then we transform it into an equivalent problem by introducing slack variables and applying the linear matrix inequalities. Based on the transformed problem, the global optimal solution is derived. Numerical results on network power consumption versus circuit power and data-rate QoS show that the proposed algorithm outperforms existing algorithms.

With the promise of meeting future capacity demands, 3-D massive-MIMO/full dimension multiple-input-multiple-output (FD-MIMO) systems have gained much interest in recent years. Apart from the huge spectral efficiency gain, 3-D massive-MIMO/FD-MIMO systems can also lead to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of the system, accurate channel state information is critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for 3-D millimeter wave massive-MIMO orthogonal frequency division multiplexing (OFDM) systems. To be specific, the DoA is estimated using estimation of signal parameter via rotational invariance technique method, and the root mean square error of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system. An ergodic capacity analysis of the system in the presence of DoA estimation error is also conducted, and an optimum power allocation algorithm is derived. Furthermore, it is shown that the DoA-based channel estimation achieves a better performance than the traditional linear minimum mean squared error estimation in terms of ergodic throughput and minimum chordal distance between the subspaces of the downlink precoders obtained from the underlying channel and the estimated channel.

In this paper, direction-of-arrival (DOA) estimation problem for massive multiple-input multiple-output (MIMO) systems with a two dimensional (2D) array is investigated, assuming no knowledge of path number, noise power, path gain correlations and bad data statistics. A novel iterative algorithm operating on tensor represented data is proposed, with integrated features of effective bad data mitigation and automatic source enumeration. Simulation results are presented to illustrate the excellent performance of the proposed algorithm in term of accuracy and robustness.

A novel optimization method is proposed to solve the probabilistic mean square error (MSE) constrained multiuser multiple-input single-output (MU-MISO) transceiver design problem. Since the probabilistic MSE constraints cannot be expressed in closed-form under Gaussian channel uncertainty, existing probabilistic transceiver design methods rely on probability inequality approximations, resulting in conservative MSE outage realizations. In this paper, based on local structure of the feasible set in the probabilistic MSE constrained transceiver design problem, a set squeezing procedure is proposed to realize tight MSE outage control. Simulation results show that the MSE outage can be realized tightly, which results in significantly reduced transmit power compared to the existing inequality based probabilistic transceiver design.

In OFDM-based spectrum sharing networks, due to inefficient coordination or imperfect spectrum sensing, the signals from femtocells or secondary users appear as interference in a subset of subcarriers of the primary systems. Together with the inter-carrier interference (ICI) introduced by high mobility, equalizing one subcarrier now depends not only on whether interference exists, but also the neighboring subcarrier data. In this letter, we propose a novel approach to iteratively learn the statistics of noise plus interference across different subcarriers, and refine the soft data estimates of each subcarrier based on the variational inference. Simulation results show that the proposed method avoids the error floor effect, which is exhibited by existing algorithms without considering interference mitigation, and performs close to the ideal case with perfect ICI cancelation and knowledge of noise plus interference powers for optimal maximum a posteriori probability (MAP) equalizer.

This paper presents a distributed hybrid power state estimator, with measurements from both the traditional supervisory control and data acquisition (SCADA) system and the newly invented phasor measurement units (PMUs). The proposed distributed algorithm, which jointly estimates the power states and PMU phase errors, only involves local computations and limited information exchange between neighboring areas, thus alleviating the heavy communication burden compared to the centralized approach. Simulation results show that the performance of the proposed algorithm is very close to that of centralized optimal hybrid state estimates without sampling phase error.

In this paper, equalization of multihop relaying orthogonal frequency division multiplexing (OFDM) signal is investigated under time-varying channel with unknown noise powers, channel orders and Doppler frequencies. An iterative algorithm is developed under variational expectation maximization (EM) framework. The proposed algorithm iteratively estimates the channel, learns the channel and noise statistical information, and recovers the unknown data, using only limited number of pilot subcarrier in one OFDM symbol. Simulation results show that, without any statistical information, the performance of the proposed algorithm is very close to that of the optimal channel estimation and data detection algorithm, which requires specific information on system structure, channel tap positions, channel lengths, Doppler shifts as well as noise powers.

In order to compute the marginal distribution from a high dimensional distribution with loopy Gaussian belief propagation (BP), it is important to determine whether Gaussian BP would converge. In general, the convergence condition for Gaussian BP variance and mean are not necessarily the same, and this paper focuses on the convergence condition of Gaussian BP variance. In particular, by describing the message-passing process of Gaussian BP as a set of updating functions, the necessary and sufficient convergence condition of Gaussian BP variance is derived, with the converged variance proved to be independent of the initialization as long as it is greater or equal to zero. It is further proved that the convergence condition can be verified efficiently by solving a semi-definite programming (SDP) optimization problem. Numerical examples are presented to corroborate the established theories.

In this paper, we propose a fully distributed algorithm for joint clock skew and offset estimation in wireless sensor networks. With the proposed algorithm, each node can estimate its clock skew and offset by communicating only with its neighbors. Such algorithm does not require any centralized information processing or coordination. Simulation results show that estimation mean-square-error at each node converge to the centralized Cramér-Rao bound with only a few number of message exchanges.

In this paper, we propose a fully distributed algorithm for joint clock skew and offset estimation in wireless sensor networks based on belief propagation. In the proposed algorithm, each node can estimate its clock skew and offset in a completely distributed and asynchronous way: some nodes may update their estimates more frequently than others using outdated message from neighboring nodes. In addition, the proposed algorithm is robust to random packet loss. Such algorithm does not require any centralized information processing or coordination, and is scalable with network size. The proposed algorithm represents a unified framework that encompasses both classes of synchronous and asynchronous algorithms for network-wide clock synchronization. It is shown analytically that the proposed asynchronous algorithm converges to the optimal estimates with estimation mean-square-error at each node approaching the centralized Cramer-Rao bound under any network topology. Simulation results further show that {the convergence speed is faster than that corresponding to a synchronous algorithm.

We study the robust transceiver optimization in downlink multiuser multiple-input multiple-output (MU-MIMO) systems aiming at minimizing transmit power under probabilistic quality-of-service (QoS) requirements. Owing to the unknown distributed interference, the channel estimation error obtained from the linear minimum mean square error (LMMSE) estimator can be arbitrarily distributed. Under this situation, the QoS requirements should account for the worst-case channel estimation error distribution. While directly finding the worst-case distribution is challenging, two methods are proposed to solve the robust transceiver design problem. One is based on the Markov's inequality, while the other is based on a novel duality method. Two convergence-guaranteed iterative algorithms are proposed to solve the transceiver design problems. Furthermore, for the special case of MU multiple-input single-output (MISO) systems, the corresponding robust transceiver design problems are shown to be convex. Simulation results show that, compared to the non-robust method, the QoS requirement is satisfied by both proposed algorithms. Among the two proposed methods, the duality method shows a superior performance in transmit power, while the Markov method demonstrates a lower computational complexity. Furthermore, the proposed duality method results in less conservative QoS performance than the Gaussian approximated probabilistic robust method and bounded robust method.

Clock parameters (skew and offset) in sensor network are inherently time-varying due to imperfect oscillator circuits. This paper develops a distributed Kalman filter for clock parameters tracking. The proposed algorithm only requires each node to exchange limited information with its direct neighbors, thus is energy efficient, scalable with network size, and is robust to changes in network connectivity. A low-complexity distributed algorithm based on Coordinate-Descent with Bootstrap (CD-BS) is also proposed to provide rapid initialization to the tracking algorithm. Simulation results show that the performance of the proposed distributed tracking algorithm maintains long-term clock parameters accuracy close to the Bayesian Cramer-Rao Lower Bound.

In this paper, the challenging problem of signal detection under severe communication environment that plagued by unknown doubly selective channels (DSCs), multiple narrowband interferences (NBIs) and phase noises (PNs) is investigated for orthogonal frequency division multiplexing based virtual multiple-input multiple-output (OFDM-V-MIMO) systems. Based on the Variational Bayesian Inference framework, a novel iterative algorithm for joint signal detection, DSC, NBI and PN estimations is proposed. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the bit-error-rate performance of the proposed signal detection algorithm is very close to that of the ideal case which assumes perfect channel state information, no PN, and known positions and powers of NBIs plus additive white Gaussian noise. Furthermore, simulation results show that the proposed signal detection algorithm outperforms other state-of-the-art methods.

In this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables.

In this paper, we consider the robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems. THP is adopted at the source to mitigate the spatial inter-symbol interference and then a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. Based on the elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the optimization problem is greatly simplified and can be efficiently solved. Finally, the performance advantage of the proposed robust design is assessed by simulation results.

In this paper, robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems is investigated. At source node, THP is adopted to mitigate the spatial intersymbol interference. However, due to its nonlinear nature, THP is very sensitive to channel estimation errors. In order to reduce the effects of channel estimation errors, a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. With novel applications of elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the transceiver design problem reduces to a much simpler one with only scalar variables which can be efficiently solved. Finally, the performance advantage of the proposed robust design over non-robust design is demonstrated by simulation results.

Aiming to achieve the sum-rate capacity in multi-user multi-antenna systems where Ntantennas are implemented at the transmitter, opportunistic beamforming (OBF) generates Ntorthonormal beams and serves Ntusers during each channel use, which results in high scheduling delay over the users, especially in densely populated networks. Non-orthogonal OBF with more than Nttransmit beams can be exploited to serve more users simultaneously and further decrease scheduling delay. However, the inter-beam interference will inevitably deteriorate the sum-rate. Therefore, there is a tradeoff between sum-rate and scheduling delay for non-orthogonal OBF. In this context, system performance and implementation of non-orthogonal OBF with N >; Nt beams are investigated in this paper. Specifically, it is analytically shown that non-orthogonal OBF is an interference-limited system as the number of users K → ∞. When the inter-beam interference reaches its minimum for fixed Ntand N, the sum-rate scales as N In (N/(N-Nt)) and it degrades monotonically with the number of beams N for fixed Nt. On the contrary, the average scheduling delay is shown to scale as1/NK ln K channel uses and it improves monotonically with N. Furthermore, two practical non-orthogonal beamforming schemes are explicitly constructed and they are demonstrated to yield the minimum inter-beam interference for fixed Ntand N. This study reveals that, if user traffic is light and one user can be successfully served within a single transmission, non-orthogonal OBF can be applied to obtain lower worst-case delay among the users. On the other hand, if user traffic is heavy, non-orthogonal OBF is inferior to orthogonal OBF in terms of sum-rate and packet delay.

In this paper, joint transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors in both two hops is investigated. Due to the fact that various linear transceiver designs can be transformed to a weighted linear minimum mean-square-error (LMMSE) transceiver design with specific weighting matrices, weighted mean square error (MSE) is chosen as the performance metric. Precoder matrix at source, forwarding matrix at relay and equalizer matrix at destination are jointly designed with channel estimation errors taken care of by Bayesian philosophy. Several existing algorithms are found to be special cases of the proposed solution. The performance advantage of the proposed robust design is demonstrated by the simulation results.

In this paper, linear beamforming design for uplink amplify-and-forward relaying cellular networks, in which multiple mobile terminals rely on one relay station to communicate with the base station, is investigated. In particular, the base station, relay station and mobile terminals are all equipped with multiple antennas. Based on linear minimum mean-square-error (LMMSE) criterion and exploiting a hidden convexity in the problem, the precoder matrices at multiple mobile terminals, forwarding matrix at relay station and equalizer matrix at base station are jointly designed. Furthermore, several existing linear beamforming designs for multi-user (MU) MIMO systems and AF MIMO relaying systems can be considered as special cases of the proposed solution. Simulation results are presented to demonstrate the performance advantage of the proposed algorithm.

Aiming to achieve the sum-rate capacity in multi user multi-input multi-output (MIMO) channels with Ntantennas implemented at the transmitter, opportunistic beamforming (OBF) generates Ntorthonormal beams and serves Nt users during each transmission, which results in high scheduling delay over the users, especially in densely populated wireless networks. Non-orthogonal OBF with more than Nttransmit beams can be exploited to serve more users simultaneously and further decreases scheduling delay. However, the inter-beam interference will inevitably deteriorate the sum-rate. Therefore, there is a tradeoff between the sum-rate and the increasing number of transmit beams. In this context, the sum-rate of non-orthogonal OBF with N >; Ntbeams are studied, where the transmitter is based on the Grassmannian beamforming. Our results show that non-orthogonal OBF is an interference-limited system. Moreover, when the inter-beam interference reaches its minimum for fixed Nt and N, the sum-rate scales as N ln (N/N-Nt) and it decreases monotonically with N for fixed Nt. Numerical results corroborate the accuracy of our analyses.

Joint estimation of the carrier frequency offset (CFO) and the channel is developed for a two-way relay network (TWRN) that comprises two source terminals and an amplify-and-forward (AF) relay. The terminals use orthogonal frequency division multiplexing (OFDM). New zero-padding (ZP) and cyclic-prefix (CP) transmission protocols, which maintain the carrier orthogonality and ensure low estimation and detection complexity, are proposed. Both protocols lead to the same estimation problem which can be solved by the nulling-based least square (LS) algorithm and perform identically when the block length is large. We present detailed performance analysis by proving the unbiasedness of the LS estimators at high signal-to-noise ratio (SNR) and by deriving the closed-form expression of the mean-square-error (MSE). Simulation results are provided to corroborate our findings.

Relay transmission is promising for future wireless systems due to its significant cooperative diversity gain. The performance of dual-hop semi-blind amplify-and-forward (AF) relaying systems was extensively investigated, for transmissions over Rayleigh fading channels or Nakagami-m fading channels with integer fading parameter. For the general Nakagami-m fading with arbitrary m values, the exact closed-form system performance analysis is more challenging. In this paper, we explicitly derive the moment generation function (MGF), probability density function (PDF) and moments of the end-to-end signal-to-noise ratio (SNR) over arbitrary Nakagami-m fading channels with semi-blind AF relay. With these results, the system performance evaluation in terms of outage probability, average symbol error probability, ergodic capacity and diversity order, is conducted. The analysis developed in this paper applies to any semi-blind AF relaying systems with fixed relay gain, and two major strategies for computing the relay gain are compared in terms of system performance. All analytical results are corroborated by simulation results and they are shown to be efficient tools to evaluate system performance.

This paper addresses the problem of frequency synchronization in multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Different from existing work, a Bayesian approach is used in the parameter estimation problem. In this paper, the Bayes estimator for carrier frequency offset (CFO) estimation is proposed and the Bayesian Cram'er-Rao bound (BCRB) is also derived in closed form. Direct implementation of the resultant estimation scheme with conventional methods is challenging since a high degree of mathematical sophistication is always required. To solve this problem, the Gibbs sampler is exploited with an efficient sample generation method. Simulation results illustrate the effectiveness of the proposed estimation scheme.

In this paper, partial data-dependent superimposed training based channel estimation for OFDM systems over doubly selective channels (DSCs) is addressed. Due to the presence of unknown data as interference, we first derive a minimum mean square error (MMSE) channel estimator by treating the effect of unknown data as noise. To further improve the performance, a novel iterative algorithm which jointly estimates channel and suppresses interference from data is proposed via variational inference approach. Simulation results show that the proposed algorithm converges after a few iterations. Furthermore, after convergence, the performance of the proposed channel estimator is very close to that with full training at high SNRs.

Finding locations of the sensor nodes in Wireless Sensor Network has been an active research area in recent years. One important category of approaches uses distance measurements between anchors and sensors to localize the unknown node. However, most approaches assume that the anchor positions are perfectly known, while in practice the anchor positions may not be accurate due to estimation errors as well as observation errors. In this paper, we study the localization of wireless sensor node with erroneous anchors, and propose an EM estimator which iteratively refines the anchor positions and estimates the sensor location. Simulation results shows that the EM estimator converges in a few iterations and outperforms the existing robust least squares algorithm.

In this paper, robust transceiver design based on minimum-mean-square-error (MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is investigated. The channel estimation errors are modeled as Gaussian random variables, and then the effect are incorporated into the robust transceiver based on the Bayesian framework. An iterative algorithm is proposed to jointly design the precoder at the source, the forward matrix at the relay and the equalizer at the destination, and the joint design problem can be efficiently solved by quadratic matrix programming (QMP).

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for amplify-andforward (AF) two-way relay network (TWRN) that comprises two source terminals and one relay node. Both the system design and the estimation problem become more challenging when CFO is non-zero in a frequency-selective environment, as compared to the conventional point-to-point communication systems. By introducing some redundancy, we propose a cyclic prefix (CP) based OFDM modulation for TWRN that is capable of maintaining the advantage of using multi-carrier transmission and at the same time facilitates the system initialization, e.g., synchronization and channel estimation. We then apply a least square (LS) approach to solve the estimation problem. The approximated Cramér-Rao Bound (CRB) has been derived as the performance benchmark of the proposed estimator. Finally, simulations are provided to corroborate the theoretical studies. ©2010 IEEE.

Semi-blind joint CFO, channel estimation and data detection for OFDM systems over doubly selective channels (DSCs) is investigated in this work. A joint iterative algorithm is developed based on the maximum a posteriori expectation-maximization (MAP-EM) algorithm. In addition, a novel algorithm is also proposed to obtain the initial estimates of CFO and channels. Simulation results show that the performance of the proposed CFO and channel estimators approaches to that of the estimators with full training at high SNRs. Moreover, after convergence, the performance of data detection is close to the ideal case with perfect CFO and channel state information.

In this paper, the problem of clock synchronization for Wireless Sensor Network (WSN) under exponential delay is analyzed based on two-way message exchange mechanism. The Maximum Likelihood Estimator (MLE) for joint estimation of the clock offset and clock skew is derived, and an approximate Cramer-Rao Lower Bound (CRLB) is also developed. Simulation results verify that the proposed estimator gives improved performance compared to an existing algorithm.

In recent years, localization in a variety of Wireless Sensor Networks (WSNs) is a compelling but elusive goal. Several algorithms that use different methodologies have been proposed to achieve this goal. The performances of these algorithms depend on several factors, such as the sensor node placement, anchor deployment or network topology. In this paper, we propose a robust localization algorithm called Hybrid Efficient and Accurate Localization (HEA-Loc). HEA-Loc combines two techniques, Extended Kalman Filter (EKF) and Proximity-Distance Map (PDM) to improve localization accuracy. It is distributed in nature and works well in various scenarios as it is less susceptible to anchors deployment and the network topology. Furthermore, HEA-Loc has strong robustness and it can work well even the measurement errors are large. Simulation results show that HEA-Loc outperforms existing algorithms in both computational complexity and communication overhead.

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for two-way relay network (TWRN). We consider the frequency selective fading channels and adopt the zero padding (ZP) based orthogonal frequency division multiplexing (OFDM) as the modulation of the transmission. Due to the mixture of the first and the second transmission phases, the joint estimation problem becomes much challenging than that in the traditional point-to-point communication systems. By introducing some redundancy, we modify the structure of ZP-OFDM to cope with non-zero frequency synchronization errors. We then propose a nulling-based least square (NLS) method for joint CFO and channel estimation. A detailed performance analysis of NLS has been conducted, where we prove that the unbiasedness of NLS and derive the closed-form estimation mean-square-error (MSE) at high signal-to-noise ratio (SNR). Finally, simulations are provided to corroborate the proposed studies.

In this paper, robust joint design of linear relay precoders and destination equalizers for amplify-and-forward (AF) MIMO relay systems under Gaussian channel uncertainties is investigated. After incorporating the channel uncertainties into the robust design based on the Bayesian framework, a closedform solution is derived to minimize the mean-square-error (MSE) of the received signal at the destination. The effectiveness of the proposed robust transceiver is verified by simulations.

Data detection for OFDM systems over unknown doubly selective channels (DSCs) and carrier frequency offset (CFO) is investigated. A semiblind iterative detection algorithm is developed based on the expectation-maximization (EM) algorithm. It iteratively estimates the CFO, channel and recovers the unknown data using only limited number of pilot subcarriers in one OFDM symbol. In addition, efficient initial CFO and channel estimates are also derived based on approximated maximum likelihood (ML) and minimum mean square error (MMSE) criteria respectively. Simulation results show that the proposed data detection algorithm converges in a few iterations and moreover, its performance is close to the ideal case with perfect CFO and channel state information.

In this letter, we investigate the problem of CFO estimation in OFDM systems when the timing offset and channel length are not exactly known. Instead of explicitly estimating the timing offset and channel length, we employ a multi-model approach, where the timing offset and channel length can take multiple values with certain probabilities. The effect of multimodel is directly incorporated into the CFO estimator. Results show that the proposed estimator outperforms the estimator selecting only the most probable model and the method taking the maximal model.

The zero-padded block transmission with linear time-domain pre-equalizer is studied in this paper. A matched filter is exploited to guarantee the stability of the zero-forcing (ZF) and minimum mean square error (MMSE) pre-equalization. Then, in order to compute the pre-equalizers efficiently, an asymptotic decomposition is developed for the positive-definite Hermitian banded Toeplitz matrix. Compared to the direct matrix inverse methods or the Levinson-Durbin algorithm, the computational complexity of the proposed algorithm is significantly decreased and there is no bit error rate degradation when data block length is large.

In this paper, we address the robust linear transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems, where both transmitters and receivers have imperfect channel state information (CSI). With the statistics of channel estimation errors in the two hops being Gaussian, we formulate the robust linear-minimum-mean-square-error (LMMSE) transceiver design problem using the Bayesian framework, and derive a closed-form solution. Simulation results show that the proposed algorithm reduces the sensitivity of the relay system to channel estimation errors, and performs better than the algorithm using estimated channel only.

A unified framework to jointly solve the problems of localization and synchronization at the same time is presented in this paper. The joint approach is attractive because it can solve both localization and synchronization using the same set of message exchanges, which is extremely important for energy saving in wireless sensor networks. The inaccuracy of anchor locations and timings is taken into account to provide accurate joint localization and synchronization. The anchor uncertainties are assumed to be bounded, but knowledge of the statistics of anchor uncertainties is not required. The problem is formulated into a linear model with uncertainties on both sides of the equation. A robust joint estimator is then proposed based on minimizing the worst-case mean square error and the solution is obtained by solving a semidefinite programming problem. Simulation results show that the proposed estimator outperforms the traditional least squares estimator at the cost of higher computational complexity.

Clock synchronization is an important issue for the design of a network composed of small sensor nodes. Based on the two-way timing message exchange mechanism and assuming an exponential network delay distribution, many analytical results have been presented in the literature by applying the techniques from statistical signal processing. This paper derives the minimum variance unbiased estimator for the clock offset for both symmetric and asymmetric exponential delay cases. For the asymmetric delays, it is shown to be a function of both the minimum and the mean link delays. This result is a very significant contribution since only the minimum link delay observations have been used to estimate the clock offset in the past. For the symmetric case, it is shown to coincide with the maximum likelihood estimator. In addition, the result is also applicable to clock synchronization problem in general computer networks.

In this paper, a joint channel estimation and data detection algorithm is proposed for OFDM systems under doubly selective channels (DSCs). After representing the DSC using Karhunen-Loe¿ve basis expansion model (K-L BEM), the proposed algorithm is developed based on the expectation-maximization (EM) algorithm. Basically, it is an iterative algorithm including two steps at each iteration. In the first step, the unknown coefficients in K-L BEM are first integrated out to obtain a function which only depends on data, and meanwhile, a maximum a posteriori (MAP) channel estimator is obtained. In the second step, data are directly detected by a novel approach based on the function obtained in the first step. Moreover, a Bayesian Cramer-Rao Lower Bound (BCRB) which is valid for any channel estimator is also derived to evaluate the performance of the proposed channel estimator. The effectiveness of the proposed algorithm is finally corroborated by simulation results.

This paper considers the problem of robust linear transceiver design for a dual-hop amplify-and-forward (AF) MIMO relay system, with Gaussian random channel uncertainties in both hops. By taking the channel uncertainties into account, an iterative algorithm is proposed to minimize the mean-square-error (MSE) of the output signal at the destination. Simulation results show that the proposed algorithm reduces the sensitivity of the AF MIMO relay systems to channel estimation errors, and performs better than the algorithm based on estimated channels only.

This paper addresses the problem of carrier frequency offset (CFO) estimation in orthogonal frequency division multiplexing (OFDM) systems using Bayesian method. Depending on the availability of the noise variance, two general CFO estimators are derived. Furthermore, the two general maximum a posteriori (MAP) estimators are developed into several special cases based on different degrees of prior information on parameters. The relationships between the proposed estimators and existing estimators are comprehensively investigated. Finally, numerical results demonstrate the effects of employing different prior information on the estimation performances.

In this paper, the problem of clock synchronization is analyzed based on the two-way message exchange mechanism. In order to estimate the clock offset and the clock skew with unknown fixed delay, the maximum likelihood estimator (MLE) and a low-complexity estimator are proposed. Furthermore, their corresponding performance limits and complexities are analyzed. It is found that the MLE achieves the best performance with the price of high complexity, while the newly proposed estimator achieves the same performance as the MLE with low complexity.

In timing synchronization, the Cramer Rao Bound has been used as performance bounds for timing estimation in AWGN channel. However, the instantaneous CRB for timing estimation in fading channels depends on the channel realizations and may fail to bound the mean square error (MSE) of the estimator because the equivalent signal-to-noise ratio (SNR) is too low. In this paper, we demonstrate that the conventional CRB for timing estimation is no longer valid in fading channels. Furthermore, a new performance bound called Weighted Bayesian CRB (WBCRB) is proposed for the estimation of both single and multiple timing offsets under fading channels. The relationship between the conventional CRB and WBCRB are discussed in details, where numerical results show that the WBCRB is a valid bound for all SNR even under fading channels.

Although localization and synchronization share many aspects in common, they are traditionally treated separately. In this paper, we present a unified framework to jointly solve these two problems at the same time. The joint approach is attractive because it can solve both localization and synchronization using the same set of message exchanges. This is extremely important for energy saving, especially for the energy constrained wireless sensor networks. Furthermore, since the accuracy of localization and synchronization is very sensitive to the accuracy of anchor locations and timings, the joint localization and synchronization problem with inaccurate anchor is considered in this paper. A novel generalized total least squares (GTLS) based method is proposed and the Cramer-Rao lower bound (CRLB) for the joint localization and time synchronization is derived. Simulation results show that the mean square error performances of the proposed estimator can attain the CRLB.

Recently, a time synchronization algorithm called pairwise broadcast synchronization (PBS) is proposed. With PBS, a sensor can be synchronized by overhearing synchronization packet exchange among its neighbouring sensors without sending out any packet itself. In an one-hop sensor network where every node is a neighbour of each other, a single PBS message exchange between two nodes would facilitate all nodes to synchronize. However, in a multi-hop sensor network, PBS message exchanges in several node pairs are needed in order to achieve network-wide synchronization. To reduce the number of message exchanges, these node pairs should be carefully chosen. In this paper, we investigate how to choose these ldquoappropriaterdquo sensors aiming at reducing the number of PBS message exchanges while allowing every node to synchronize. This selection problem is shown to be NP-complete, for which the greedy heuristic is a good polynomial-time approximation algorithm. Nevertheless, a centralized algorithm is not suitable for wireless sensor networks. Therefore, we develop a distributed heuristic algorithm allowing a sensor to determine how to synchronize itself based on its neighbourhood information only. The protocol is tested through extensive simulations. The simulation results reveal that the proposed protocol gives consistent performance under different conditions with its performance comparable to that of the centralized algorithm.

This paper addresses the problem of joint carrier frequency offset (CFO) and channel estimation in multiuser multiple-input multiple-output (MIMO) orthogonal frequency- division multiplexing (OFDM) systems. A joint CFO and channel estimator is derived based on the maximum likelihood (ML) criterion. A computationally efficient method using importance sampling technique is proposed to solve the highly demanding multi-dimensional exhaustive search required by the ML multi- CFO estimation. Simulation results verify the effectiveness of the proposed estimation scheme.

In this paper, a distributed network-wise synchronization protocol is presented. The protocol employs Pairwise Broadcast Synchronization (PBS) in which sensors can be synchronized by merely overhearing the exchange of synchronization packets. We investigate how to minimize the number of PBS required to synchronize all nodes in a network. We show that the problem of finding the minimum number of PBS required is NP-complete. A distributed greedy algorithm is proposed. The protocol is tested by extensive simulations. Although the algorithm behind is heuristic-based, the performance is closed to the centralized algorithm. The message overhead is compared with that of Timing-Sync Protocol for Sensor Networks (TPSN).

This letter addresses the problem of joint estimation of carrier frequency offset (CFO) and channel for OFDM systems in the presence of timing ambiguity. Based on two signal models for quasi-synchronized OFDM systems, two joint CFO and channel estimators are derived from the Maximum Likelihood (ML) criterion. The first estimator obtained is a joint estimator for all unknown parameters, which needs a multi-dimensional search. The second estimator has a reduced complexity, but its performance slightly degrades compared with the first one. Through MSE analyses, we find that when the number of subcarriers is large, the two estimators are equivalent.

This paper addresses the problem of optimal training for joint carrier frequency offset (CFO) and channel estimation in multiuser MIMO OFDM systems, where each user can utilize all available subcarriers. To choose the training sequence with the goal of providing the smallest estimation mean square error (MSE), the Cramer-Rao bound (CRB) is derived in close-form. However, little insight on the sequence selection can be obtained from the CRB directly due to its complicated dependence on training sequence and channel parameters. To proceed, asymptotic CRB, which has a much simpler dependence on the training, is derived. It is found that the optimal training sequences should be individually white and uncorrelated with each other. Simulation results illustrate the merits of the proposed training design.

This paper addresses the problem of joint estimation of carrier frequency offset (CFO) and frequency-selective channel in quasi-synchronized orthogonal frequency division multiplexing (OFDM) systems. Two equivalent signal models with embedded timing ambiguity are introduced first, and then two joint CFO and channel estimators are derived from the Maximum Likelihood (ML) criterion. The first estimator obtained is a joint estimator for timing, CFO and channel, which needs a two dimension search for parameter estimation. The second estimator has a reduced complexity, but its performance slightly degrades compared with the first estimator due to more unknown parameters required to be estimated. We analyze and compare the MSE performance of the two estimators and find that when the number of subcarriers is large, the two estimators are equivalent.

For orthogonal frequency division multiplexing (OFDM) systems, the frequency synchronization and channel estimation are always tightly coupled with timing synchronization. In this paper, we investigate the effects of timing offset on the performance of a recently proposed subspace based joint carrier frequency offset (CFO) and channel estimation scheme. It is found that this scheme is highly sensitive to timing offsets. An improved timing robust scheme is then proposed. In the proposed CFO estimation scheme, the orthogonality loss between two subspaces due to timing offset is restored by exploiting a reduced null-subspace. The missing taps in the original channel estimate are recovered by using an upper bound of the channel length instead of the actual value. The effectiveness of the proposed scheme is verified by simulations.

In this letter, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are casted into a unified framework. The finite sample mean-square error (MSE) expression for this class of estimators is established. Simulation results are also presented to corroborate the analytical results. It is found that the feedforward conditional maximum likelihood (CML) estimator and the square law nonlinearity (SLN) estimator with a properly designed prefilter perform the best and their performances coincide with the asymptotic conditional Cramer-Rao bound (CCRB), which is the performance lower bound for the class of estimators under consideration.

In this paper, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are cast into a unified framework. The finite sample mean-square error (MSE) expression for this class of estimators is established. Simulation results are also presented to corroborate the analytical results. It is found that the feedforward conditional maximum likelihood (CML) estimator and the square law nonlinearity (SLN) estimator with a properly designed prefilter perform the best and their performances coincide with the asymptotic conditional Cramer-Rao bound (CCRB), which is the performance lower bound for the class of estimators under consideration.

This comment corrects several errors found in the paper, "Class of Cyclic-Based Estimators for Frequency-Offset Estimation of OFDM Systems". In addition, we show that the minimum variance unbiased estimator for frequency offset derived in the above paper is the maximum-likelihood estimator when the timing delay is perfectly known.

Space-time coding has received considerable interest recently as a simple transmit diversity technique for improving the capacity and data rate of a channel without bandwidth expansion. Most research in space-time coding, however, assumes that the symbol timing at the receiver is perfectly known. In practice, this has to be estimated with high accuracy. In this paper, a new symbol-timing estimator for space-time coding systems is proposed. It improves the conventional algorithm of Naguib et al. such that accurate timing estimates can be obtained even if the oversampling ratio is small. Analytical mean-square error (MSE) expressions are derived for the proposed estimator. Simulation and analytical results show that for a modest oversampling ratio (such as Q equal to four), the MSE of the proposed estimator is significantly smaller than that of the conventional algorithm. The effects of the number of transmit and receive antennas, the oversampling ratio, and the length of training sequence on the MSE are also examined.

Based on the maximum-likelihood principle and the preamble structure of IEEE 802.11a wireless local area network (WLAN) standard, this paper proposes a new symbol synchronization algorithm for IEEE 802.11a WLANs over frequency-selective fading channels. In addition to the physical channel, the effects of filtering and unknown sampling phase offset are also considered. Loss in system performance due to synchronization error is used as a performance criterion. Computer simulations show that the proposed algorithm exhibits better performances than the simple correlation-based algorithms. When compared to the algorithm based on the generalized Akaike information criterion, the proposed algorithm presents comparable performance and exhibits reduced complexity.

In This work, the problem of training sequence design for symbol timing estimation in MIMO channels is addressed. In particular, we consider correlated fading between antennas. The optimal training sequences are derived by minimizing the modified Cramer-Rao bound (MCRB) with respect to the training data. It is found that when the transmit pulse is a root raised cosine pulse and there is no correlation among antennas, the optimal training sequences resemble the Walsh sequences. Furthermore, it is also found that the the impact of not knowing the antenna correlations when designing training sequences is very small.

In this paper, the maximum likelihood (ML) symbol timing estimator in a MIMO correlated channel, based on training data, is derived. It is shown that the approximated ML algorithm in (A. F. Naguib et al, IEEE J Select. Areas in Commun., vol.16, p.1459-1478, 1998) and (Y. C. Wu et al, IEEE Trans. on Wireless Comm., 2003) is just a special case of the proposed algorithm. Furthermore, the modified Cramer-Rao bound (MCRB) is also derived for comparison. Simulation results under different operating conditions (e.g., number of antennas and correlation between antennas) are given to assess the performances of the ML estimator and it is found that the mean square errors (MSE)s of the ML estimator: i) are close to the MCRBs; ii) are approximately independent of the number of transmit antennas; iii) are inversely proportional to the number of receive antennas; and iv) correlation between antennas has no effect on the MSE performance.

A new symbol-timing estimator for space-time coding systems is proposed. It improves the conventional algorithm of Naguib et al. such that accurate timing estimates can be obtained even if the oversampling ratio is small (such as oversampling ratio Q=4). The increase in implementation complexity with respect to that of the conventional algorithm is very small. The requirements and the design procedures for the training sequences are discussed. Analytical and simulation results show that the estimation mean square error of the proposed estimator is significantly smaller than that of the conventional algorithm.

This paper derives and computes the probability of synchronization failure P/sub fail/ for IEEE 802.11a wireless LANs on frequency-flat Rician fading channels. For a frequency offset within /spl plusmn/232 kHz, it is shown that its effect on the synchronization performance is minor. The E/sub ds//N/sub 0/ ratios required to achieve P/sub fail/=10/sup -3/ and 10/sup -4/ are computed, where E/sub ds/ is the data-symbol energy. We find that E/sub ds//N/sub 0/ ratios over 20 dB are generally required for channels with Rician factors K/spl les/6 dB. In particular, E/sub ds//N/sub 0/ ratios that yield P/sub fail/=10/sup -4/ exceed 30 dB for K/spl les/4 dB.

Abstract In this paper, the data aided (DA) and non‐data aided (NDA) maximum likelihood (ML) symbol timing estimators and their corresponding conditional Cramer–Rao bound (CCRB) and modified Cramer–Rao bound (MCRB) in multiple‐input‐multiple‐output (MIMO) correlated flat‐fading channels are derived. It is shown that the approximated ML algorithm in References [4,13] is just a special case of the DA ML estimator; while the extended squaring algorithm in Reference [14] is just a special case of the NDA ML estimator. For the DA case, the optimal orthogonal training sequences are also derived. It is found that the optimal orthogonal sequences resemble the Walsh sequences, but present different envelopes. Simulation results under different operating conditions (e.g. number of antennas and correlation between antennas) are given to assess and compare the performances of the DA and NDA ML estimators with respect to their corresponding CCRBs and MCRBs. It is found that (i) the mean square error (MSE) of the DA ML estimator is close to the CCRB and MCRB, (ii) the MSE of the NDA ML estimator is close to the CCRB but not to the MCRB, (iii) the MSEs of both DA and NDA ML estimators are approximately independent of the number of transmit antennas and are inversely proportional to the number of receive antennas, (iv) correlation between antennas has little effect on the MSEs of DA and NDA ML estimators and (v) DA ML estimator performs better than NDA ML estimator at the cost of lower transmission efficiency and higher implementation complexity. Copyright © 2004 John Wiley & Sons, Ltd.

Space-time coding has received considerable interest recently as a simple transmit diversity technique for improving the capacity and data rate of a channel without bandwidth expansion. Most research work in space-time coding, however, assumed that the symbol timing at the receiver is perfectly known. In practice, this has to be estimated with high accuracy. In this paper, two symbol timing recovery algorithms for space-time coding systems are proposed. The first one is based on orthogonal training sequences and approximated log likelihood function. It is an improvement of a previous symbol timing synchronization algorithm in that high estimation accuracy can be achieved even when the over sampling factor is small. The second one employs the squaring algorithm. It offers good performance and does not require training sequences.

This paper derives and computes the probability of synchronization failure, P/sub fail/, for IEEE 802.11a wireless LANs on frequency-flat Rician fading channels. For a frequency offset within /spl plusmn/232 kHz, it is shown that its effect on the synchronization performance is minor. The E/sub ds//N/sub 0/ ratios required to achieve P/sub fail/ = 10/sup -3/ and 10/sup -4/ are computed, where E/sub ds/ is the data symbol energy. We find that E/sub ds//N/sub 0/ ratio over 20dB are generally required for channels with Rician factors K /spl les/ 6 dB. In particular, E/sub ds//N/sub 0/ ratios that yields P/sub fail/ = 10/sup -4/ exceed 30 dB for K /spl les/ 4 dB.

This article proposes an efficient implementation of the Farrow (1988) structure using sum-of-powers-of-two (SOPOT) coefficients and multiplier-block (MB). In particular, a novel algorithm for designing the Farrow coefficients in SOPOT form is detailed. Using the SOPOT coefficient representation, coefficient multiplication can be implemented with limited number of shifts and additions. Using MB, the redundancy between multipliers can be fully exploited through the reuse of the intermediate results generated. Design examples show that the proposed method can greatly reduce the complexity of the Farrow structure while providing comparable phase and amplitude responses.

The timing-synchronization performance of IEEE 802.11a wireless LANs on multipath Rician fading channels is investigated. Simulation results yield the following observations. A higher Rician factor gives a better performance. At low signal-to-noise conditions, the multipath-diversity gain enables an improvement in the synchronization performance but at high signal-to-noise conditions, irreducible probabilities of synchronization failure, which cannot be reduced even if the signal power is increased, occur. A more dispersive channel gives a poorer synchronization performance, and the performance is poor for Rayleigh fading environments.

This paper proposes a practical linear software-radio architecture (dealing with linear modulations) that is suitable for multi-mode operation. In particular, it is shown how to integrate GMSK into the proposed architecture. Coherent and noncoherent detections of GMSK signals are detailed for the implementation of the proposed software radio.