Non-Intrusive Load Monitoring (NILM) is widely employed to disaggregate a building’s total electrical load and estimate the energy consumption of individual devices. Recently, image-based NILM has garnered interest for its ability to capture temporal patterns in time series data. However, existing methods often convert time series data into a single image, resulting in information loss and inferior disaggregation performance. To address this issue, we propose a novel tensor-based image encoding approach for NILM. Our method leverages a proposed diagonal projection method, which enables nearly 100% recovery of raw time series values through inverse normalization. It only performs linear scaling without altering inherent data characteristics. We also integrate three additional image conversion techniques-Recurrence Plot, Gramian Angular Field and Markov Transition Field-to construct a four-dimensional image set that preserves data integrity. This set is then encoded into a multi-dimensional tensor, providing rich geometric features for model training. Additionally, we introduce an involution model to expand the convolutional receptive field and reduce parameter redundancy. Experimental results on the REDD and AMPds datasets demonstrate that our approach outperforms existing NILM techniques, highlighting its significant potential for building energy disaggregation.

Frequency synchronization and reciprocity calibration across base stations (BSs) are crucial for coherent transmission in distributed massive multiple-input multiple-output (MIMO) networks, yet are often disrupted by carrier frequency offsets (CFOs) from distinct BS oscillators and phase offsets caused by radio frequency hardware impairments. Traditional approaches typically employ maximum likelihood or least squares estimation for CFOs and phase offsets, requiring additional estimation of inter-BS channels and resulting in high computational costs. This paper introduces an over-the-air protocol, named Sync4CT, which estimates and compensates CFOs and phase offsets to enable coherent transmission. Sync4CT estimates CFOs utilizing the autocorrelation matrices of the received pilot signals and assesses phase offsets in the frequency domain. Both procedures offer closed-form estimators without the need of inter-BS channel estimation, and are executed in parallel across BSs, thereby significantly reducing computational complexity. Theoretical analysis shows that, for Sync4CT, the mean square error (MSE) of CFO estimation approaches the Cramér-Rao bound (CRB), while the MSE of phase offset estimation achieves the CRB in narrowband systems. Simulations further validate the substantial performance improvements of Sync4CT for coherent transmission in distributed MIMO networks.

This paper investigates detection orientated integrated sensing and communication (ISAC) system aided by hybrid reconfigurable intelligent surface (RIS). Target detection is conducted through communication signal echoes under the practical condition of unknown attenuation coefficient and sensing noise covariance, which makes our study more challenging than existing pertinent works. Firstly, we develop a closedform based generalized likelihood ratio test (GLRT) detector, which first effectively extrapolates unknown parameters through maximum likelihood estimation and then conducts hypothesis testing. Besides, we derive the asymptotic detection probability of the proposed GLRT detector in an analytic form, which is highly accurate for moderate sample size. Based on the above analysis, we propose robust beamforming design to maximize the worst-case detection probability while ensuring ergodic communication rate in awareness of channel state information (CSI) uncertainties. We provide a semidefinite programming (SDP) formulation to solve the robust beamforming problem. Additionally, by converting the variational and ergodic forms in robust formulation into explicit approximations, we further develop an efficient second order cone programming (SOCP) based solution, which is highly reliable when the CSI uncertainty becomes low. Numerical results validate the efficacy of the proposed GLRT detector, the correctness of the detection performance analysis, and the benefit of robust beamforming against CSI uncertainty.

This paper considers an active reconfigurable intelligent surface (RIS) aided device-based uplink integrated sensing and communication (ISAC) system. In this context, base station (BS) receives pilot and communication signals transmitted concurrently from mobile users to provision sensing and communication services. For the considered setup, we investigate beamforming design by jointly optimizing RIS configuration, mobile users’ transmit power and linear combiner at the BS to minimize Cramér-Rao bound (CRB) of angle-of-arrival (AoA) estimation for the sensing users while ensuring spectral efficiency of communication users. The considered device-based sensing paradigm raises unique challenge since communication signals contribute to noise covariance in AoA measurements, which leads to a highly complicated CRB expression. To resolve this challenge, we transfer the problem into a quartic form, equivalently represent covariance matrix inverse into an equation condition, decouple the intractable covariance equality constraint by introducing splitting variables followed by penalty dual-decomposition (PDD) methodology, which develops an iterative process updating all variable blocks alternatively. Extensive numerical results verify the effectiveness of our proposed algorithm and demonstrate the significant advantage of device-based sensing scheme over the device-free counterpart when the sensing targets can get connected in the ISAC network.

In recent years, joint radar and communication (JRC) systems have garnered significant attention due to their enhanced equipment utilization and high spectrum efficiency. This paper investigates a JRC-enabled multi-UAV cooperative system, where multiple UAVs concurrently execute communication tasks for communication users (CUs) and perception tasks for sensed targets (STs) distributed across a specified region. To strike the trade-off between the communication performance and sensing accuracy, we formulate a weighted performance optimization problem aimed at simultaneously maximizing the data transmission for CUs and minimizing the squared position error bound (SPEB) for STs, by jointly optimizing user association and channel assignment, power allocation, as well as UAV deployment. To effectively address this challenging problem, we initially recast the non-differentiable objective function into a more tractable and interpretable form with the aid of smooth approximation techniques. Subsequently, by virtue of the specific problem structure, we decompose the original joint optimization problem and develop an iterative method to optimize each subproblem sequentially. Extensive simulations demonstrate the significant performance gains of the proposed design compared to other benchmark schemes.

Cloud-based Large Language Model (LLM) services often face challenges in achieving low inference latency and meeting Service Level Objectives (SLOs) under dynamic request patterns. Speculative decoding, which exploits lightweight models for drafting and LLMs for verification, has emerged as a compelling technique to accelerate LLM inference. However, existing speculative decoding solutions often fail to adapt to fluctuating workloads and dynamic system environments, resulting in impaired performance and SLO violations. In this paper, we introduce AdaSpec, an efficient LLM inference system that dynamically adjusts speculative strategies according to real-time request loads and system configurations. AdaSpec proposes a theoretical model to analyze and predict the efficiency of speculative strategies across diverse scenarios. Additionally, it implements intelligent drafting and verification algorithms to maximize performance while ensuring high SLO attainment. Experimental results on real-world LLM service traces demonstrate that AdaSpec consistently meets SLOs and achieves substantial performance improvements, delivering up to 66% speedup compared to state-of-the-art speculative inference systems. The source code is publicly available at https://github.com/cerebellumking/AdaSpec

This paper investigates hybrid reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario that utilizes the echoes of communication signals to accomplish target detection without prior knowledge on signal attenuation coefficient and sensing noise covariance. To realize effective detection, we develop an analytic based generalized likelihood ratio test (GLRT) detector and theoretically analyze its detection performance. Based on that, we further develop an efficient iterative optimization process to conduct robust beamforming design that improves detection performance against channel state information (CSI) uncertainty while guaranteeing achievable ergodic communication rates. Numerical results verify the effectiveness of our proposed GLRT detector, the correctness of our performance analysis, and the benefit of the developed robust beamforming design.

In cellular networks, cell handover refers to the process where a device switches from one base station to another, and this mechanism is crucial for balancing the load among different cells. Traditionally, engineers would manually adjust parameters based on experience. However, the explosive growth in the number of cells has rendered manual tuning impractical. Existing research tends to overlook critical engineering details in order to simplify handover problems. In this paper, we classify cell handover into three types, and jointly model their mutual influence. To achieve load balancing, we propose a multi-agent-reinforcement-learning (MARL)-based scheme to automatically optimize the parameters. Experimental results show that our proposed scheme outperforms existing benchmarks in balancing load and improving network performance.

In this paper, we introduce a novel user association (UA) and resource block group (RBG) allocation (RA) method utilizing multi-agent reinforcement learning (MARL) for a multi-user multiple-input multiple-output (MU-MIMO) downlink system. Unlike traditional MARL radio resource management (RRM) approaches, which utilize user equipment (UE) as learning agents, base stations (BS) are deployed as agents for practical consideration. However, this will significantly enlarge the action space and bring about action constraint violation problems. We employ dual-actor neural networks to separate UA and RBG allocation actions, thereby effectively reducing the joint action space and accelerating exploration. In addition, a Q-value-rank-based action projection algorithm is proposed to address the cross-agent coupling constraints. The simulation results demonstrate that the proposed MARL framework with action projection outperforms other baselines in terms of RRM performance.

A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emerged as a promising approach for solving ILPs. However, a significant challenge arises when applying GNNs to ILPs with symmetry: classic GNN architectures struggle to differentiate between symmetric variables, which limits their predictive accuracy. In this work, we investigate the properties of permutation equivalence and invariance in GNNs, particularly in relation to the inherent symmetry of ILP formulations. We reveal that the interaction between these two factors contributes to the difficulty of distinguishing between symmetric variables. To address this challenge, we explore the potential of feature augmentation and propose several guiding principles for constructing augmented features. Building on these principles, we develop an orbit-based augmentation scheme that first groups symmetric variables and then samples augmented features for each group from a discrete uniform distribution. Empirical results demonstrate that our proposed approach significantly enhances both training efficiency and predictive performance.

Graph neural networks (GNNs) have recently emerged as powerful tools for solving complex optimization problems, often being employed to approximate solution mappings. Empirical evidence shows that even shallow GNNs (with fewer than ten layers) can achieve strong performance in predicting optimal solutions to linear programming (LP) problems. This finding is somewhat counter-intuitive, as LPs are global optimization problems, while shallow GNNs predict based on local information. Although previous theoretical results suggest that GNNs have the expressive power to solve LPs, they require deep architectures whose depth grows at least polynomially with the problem size, and thus leave the underlying principle of this empirical phenomenon still unclear. In this paper, we examine this phenomenon through the lens of distributed computing and average-case analysis. We establish that the expressive power of GNNs for LPs is closely related to well-studied distributed algorithms for LPs. Specifically, we show that any $d$-round distributed LP algorithm can be simulated by a $d$-depth GNN, and vice versa. In particular, by designing a new distributed LP algorithm and then unrolling it, we prove that constant-depth, constant-width GNNs suffice to solve sparse binary LPs effectively. Here, in contrast with previous analyses focusing on worst-case scenarios, in which we show that GNN depth must increase with problem size by leveraging an impossibility result about distributed LP algorithms, our analysis shifts the focus to the average-case performance, and shows that constant GNN depth then becomes sufficient no matter how large the problem size is. Our theory is validated by numerical results.

The Max-$k$-Cut problem is a fundamental combinatorial optimization challenge that generalizes the classic $\mathcal{NP}$-complete Max-Cut problem. While relaxation techniques are commonly employed to tackle Max-$k$-Cut, they often lack guarantees of equivalence between the solutions of the original problem and its relaxation. To address this issue, we introduce the Relax-Optimize-and-Sample (ROS) framework. In particular, we begin by relaxing the discrete constraints to the continuous probability simplex form. Next, we pre-train and fine-tune a graph neural network model to efficiently optimize the relaxed problem. Subsequently, we propose a sampling-based construction algorithm to map the continuous solution back to a high-quality Max-$k$-Cut solution. By integrating geometric landscape analysis with statistical theory, we establish the consistency of function values between the continuous solution and its mapped counterpart. Extensive experimental results on random regular graphs and the Gset benchmark demonstrate that the proposed ROS framework effectively scales to large instances with up to $20,000$ nodes in just a few seconds, outperforming state-of-the-art algorithms. Furthermore, ROS exhibits strong generalization capabilities across both in-distribution and out-of-distribution instances, underscoring its effectiveness for large-scale optimization tasks.

Federated learning (FL) enables the training of a global machine learning model among multiple local clients in a collaborative fashion without directly sharing the details of their data. Due to this advantage, it has been utilized in a wide range of applications where privacy is a critical concern and has attracted great attention for graph representation learning (GRL). Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. Unlike existing techniques, it utilizes generative models as bridges to enable information sharing among clients, thereby facilitating the collaborative training of GRL models. Instead of directly using the generative model trained on each client individually, we aggregate them into the globally generative model to gain a global view of the entire graph, which effectively alleviates the heterogeneity and complementarity issues simultaneously. We formulate the training of the generative and GRL models as a min-max adversarial learning problem and theoretically prove the convergence. Furthermore, the effectiveness of the method is demonstrated using experimental results on six real-world datasets.

Traditional ground-based sensor deployment for time difference of arrival (TDOA) localization usually assumes distance-independent measurement error between the target and the sensors. With the growing popularity of using unmanned aerial vehicles (UAVs) for radio localization, existing TDOA sensor deployment solutions could be no longer applicable due to the high mobility of UAVs. This paper considers a robust deployment problem of the sensing UAVs for TDOA localization with the target located in a circular uncertainty area. The objective is to minimize the Cramér-Rao Bound (CRB), in which the covariance matrix of the TDOA measurement noise is formulated as a function of the distance between the target and the sensing UAVs. To tackle the highly non-convex problem, we reformulate it in a master-slave manner as two subproblems. Inspired by the prevailing Gibbs sampling method, we propose a grid-based dual-space stepwise optimization (GDSSO) algorithm to solve the master problem, while the slave problem for finding the worst-case point in the uncertainty area could be efficiently solved through gradient projection. Simulation results demonstrate the rationality of our problem formulation as well as the effectiveness of the proposed algorithm.

Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to consider a semantic-bit coexisting communication system, where a base station (BS) serves SemCom users (sem-users) and BitCom users (bit-users) simultaneously. Such a system faces severe and heterogeneous inter-user interference. In this context, this paper provides a new semantic-bit coexisting communication framework and proposes a spatial beamforming scheme to accommodate both types of users. Specifically, we consider maximizing the semantic rate for semantic users while ensuring the quality-of-service (QoS) requirements for bit-users. Due to the intractability of obtaining the exact closed-form expression of the semantic rate, a data driven method is first applied to attain an approximated expression via data fitting. With the resulting complex transcendental function, majorization minimization (MM) is adopted to convert the original formulated problem into a multiple-ratio problem, which allows fractional programming (FP) to be used to further transform the problem into an inhomogeneous quadratically constrained quadratic programs (QCQP) problem. Solving the problem leads to a semi-closed form solution with undetermined Lagrangian factors that can be updated by a fixed point algorithm. This method is referred to as the MM-FP algorithm. Additionally, inspired by the semi-closed form solution, we also propose a low-complexity version of the MM-FP algorithm, called the low-complexity MM-FP (LP-MM-FP), which alleviates the need for iterative optimization of beamforming vectors. Extensive simulation results demonstrate that the proposed MM-FP algorithm outperforms conventional beamforming algorithms such as zero-forcing (ZF), maximum ratio transmission (MRT), and weighted minimum mean-square error (WMMSE). Moreover, the proposed LP-MMFP algorithm achieves comparable performance with the WMMSE algorithm but with lower computational complexity.

Recently, the decentralized baseband processing (DBP) paradigm and relevant uplink detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-output technology. Under the DBP architecture, base station antennas are divided into several independent clusters, each connected to a local computing fabric. However, current detection methods tailored to DBP only consider ideal white Gaussian noise scenarios, while in practice, the noise is often colored due to interference from neighboring cells. Moreover, in the DBP architecture, linear minimum mean-square error (LMMSE) detection methods require the knowledge of noise covariance matrix which must be estimated using distributedly stored noise samples. This presents a significant challenge for decentralized LMMSE-based equalizer design. To address this issue, this paper proposes decentralized LMMSE equalization methods under colored noise scenarios for both star and daisy chain DBP architectures. Specifically, we first propose two decentralized equalizers for the star DBP architecture based on dimensionality reduction techniques. Then, we derive an optimal decentralized equalizer using the block coordinate descent method for the daisy chain DBP architecture with a bandwidth reduction enhancement scheme based on decentralized low-rank decomposition. Finally, simulation results demonstrate that our proposed methods can achieve excellent detection performance while requiring much less communication bandwidth.

In this letter, we look into the emitter localization task within the Direct Position Determination (DPD) paradigm. This paradigm is by essence a largest eigenvalue problem which treats the channel attenuation variables as free parameters. We consider the channel fading physical rule on electromagnetic signal propagation and reformulate the traditional DPD problem with a channel contextual prior. Thereafter, we develop iterative optimization algorithms based on the majorization-minimization (MM) framework. Numerical results show that the proposed algorithms outperform the traditional DPD estimators with better localization performance.

This paper addresses the joint beamforming and data stream allocation (DSA) optimization problem for non-coherent joint transmission (NCJT). A critical yet neglected issue in NCJT beamforming is the tightly related DSA, which involves determining the number of streams transmitted from access points (APs) to their serving user equipments (UEs) according to the channel quality, so that the weighted sum-rate (WSR) can be maximized. However, since the integer number of streams directly determines the dimensions of beamformers, the joint optimization problem is mixed-integer and nonconvex with tightly coupled decision variables, making it NP-hard. To solve this problem, we first fix the DSA variables and propose a distributed and reduced weighted minimum mean square error (WMMSE) beamforming algorithm, called distributed RWMMSE, by leveraging the low-dimensional subspace property of the beamformer obtained by the traditional WMMSE. The distributed RWMMSE achieves the same WSR as the traditional WMMSE but with significantly lower computational complexity (scaling linearly with the number of AP antennas) and reduced interaction cost. Building on this, a joint beamforming and DSA optimization algorithm, named RWMMSE-LSA, is proposed by decoupling the decision variables through introduced stream indicator matrices. The RWMMSE-LSA optimizes beamformers and DSA via the distributed RWMMSE and linear programming, respectively, both of which have closed-form solutions. Simulations validate substantial performance gain of our proposed algorithms over existing alternatives in computational and interaction costs.

Conventional multi-user scheduling in cellular networks are required to make a decision every transmission time interval (TTI) of at most several milliseconds. Only quite simple schemes can be implemented under the stringent time constraint, resulting in far-from-optimum performance. In this paper, we focus on the case of scheduling multiple users in a fixed wireless access (FWA) network with stable channel characteristics. We propose a scheduling approach by which a high-quality scheduling decision based on statistical channel state information (CSI) is made across all TTIs instead of making simple TTI-level decisions. The proposed design is essentially a mixed- integer non-smooth non-convex stochastic problem. We first replace the indicator functions in the formulation by smooth sigmoid functions to tackle nonsmoothness. By leveraging deterministic equivalents (D.E.), we then convert the original stochastic problem into an approximated deterministic one, followed by linear relaxation of the integer constraints. However, the converted problem is still nonconvex due to implicit equation constraints formerly introduced by D.E. Therefore, we employ implicit optimization technique to compute the gradient explicitly, with which we further propose an algorithm design based on a modified version of Frank-Wolfe method. Numerical results verify the effectiveness of our proposed scheme.

The rapid development of wireless communications has driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model’s generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model’s applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks.

As a promising distributed learning paradigm, federated learning (FL) involves training deep neural network (DNN) models at the network edge while protecting the privacy of the edge clients. To train a large-scale DNN model, batch normalization (BN) has been regarded as a simple and effective means to accelerate the training and improve the generalization capability. However, recent findings indicate that BN can significantly impair the performance of FL in the presence of non-i.i.d. data. While several FL algorithms have been proposed to address this issue, their performance still falls significantly when compared to the centralized scheme. Furthermore, none of them have provided a theoretical explanation of how the BN damages the FL convergence. In this article, we present the first convergence analysis to show that under the non-i.i.d. data, the mismatch between the local and global statistical parameters in BN causes the gradient deviation between the local and global models, which, as a result, slows down and biases the FL convergence. In view of this, we develop a new FL algorithm that is tailored to BN, called FedTAN, which is capable of achieving robust FL performance under a variety of data distributions via iterative layer-wise parameter aggregation. Comprehensive experimental results demonstrate the superiority of the proposed FedTAN over existing baselines for training BN-based DNN models.

This paper investigates group-level multi-user precoding schemes for multi-antenna multi-subcarrier systems. Existing literature often presupposes an individual precoder for each tone, uniformly applying standard narrowband precoding techniques to each subcarrier. Such solutions, however, are unfeasible in practice due to unrealistic hardware cost and computational complexity. Motivated by realistic industrial protocol that allocates one common precoder for multiple subcarriers, this paper investigates a more viable sum-rate maximization paradigm employing group-level precoding. We first introduce two innovative schemes for assessing spectral efficiency of subcarrier blocks, that takes into account the fluctuating signal-to-interference-and-noise-ratios (SINRs) across all tones. Our formulation shows that these group-level precoding strategies present highly nonconvex challenges. To address these difficulties, we adopt successive convex approximation (SCA) methodology, which resolves the original nonconvex challenge via iteratively convexifying subproblems. Moreover, we devise low-complexity methods utilizing gradient projection, obviating the necessity for numerical solvers. Numerical experiments affirm the convergence and efficacy of our proposed algorithms. Notably, our 5G link-level simulations reveal that, compared to traditional methods, group-level precoding not only ensures a more uniform distribution of SINRs across subcarriers but also significantly improves throughput performance.

To support the development of air-ground integrated sensing and communication (ISAC), network-level performance analysis is needed for providing an essential guide on the network design. Following the widely adopted orthogonal frequency-division multiplexing (OFDM) technology in existing wireless systems, a cooperative air-ground wireless network based on OFDM-ISAC is introduced in this paper, where the ISAC-enabled base stations (BSs) following the two-dimensional homogeneous Poisson point process (HPPP) distribution serve the terrestrial communication users while sensing the aerial targets. In particular, cooperative beamforming schemes are designed for mitigating the interference among ISAC BSs. First, we analyze the communication as well as sensing performances in terms of different metrics including area communication coverage probability, area communication spectral efficiency, area radar detection coverage probability, and average Cramér-Rao Bound. Simulation results are then presented to validate the theoretical analysis and illustrate the effects of key system parameters on the network performance. It is observed that both the communication and sensing (C&S) performances depend on the BS density and height, while the sensing performance also depends on the height of sensing target together with the numbers of OFDM subcarriers and symbols. Moreover, there exists a tradeoff between the C&S performances with respect to the BS density and height. The results of this paper provide useful guidance to the design and implementation of air-ground wireless network for harnessing the dual benefits of ISAC.

Wireless network optimization is essential for improving the network performance in mobile communications. However, due to the stochastic nature of wireless networks, existing schemes based on analytical models and deterministic optimization are less reliable. To this end, we design a framework for robust network optimization based on deep generative models and stochastic optimization. Inspired by the powerful diffusion process, we propose a deep generative simulator to capture the statistical distribution of the network performance. By sampling from the deep generative simulator, we can alleviate the inherent uncertainty related to the network performance and devise an innovative expectation-quantile-based stochastic objective function. The inner expectation is designed for the temporal statistics, while the outer quantile is developed for the spatial statistics. This designated two-tier objective function is capable of mitigating temporal fluctuations and ensuring satisfactory network performance across most geographical grids, thereby achieving robustness. To solve this stochastic optimization problem, a smooth zeroth-order approach is introduced by taking advantage of the unique structure of quantile functions. Through theoretical performance analysis and simulation experiments with real-world datasets, we demonstrate the superiority of our approach over other baseline schemes, highlighting its practical utility in robust network optimization.

We are interested in multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication-based vehicle state detection (VSD) (including vehicle location, velocity and pose angle) in multipath interference scenarios, towards 6G integrated sensing and communications. Yet, communication-based VSD is challenging, since its signals undergo multipath interference and random fading, while channel state and reflector locations are even unknown, in addition to vehicle state. To address these challenges, a novel multipath information fusion-assisted VSD scheme is devised to smartly aggregate geometric knowledge from both direct and reflection paths, thus yielding a robust VSD solution against multipath interference. In addition, we propose to divide the complex VSD problem into four subproblems: (i) angle-of-arrival detection, (ii) time-of-flight estimation, (iii) joint reconstruction of angle-of-departure, radial speed and channel state, and (iv) vehicle-and-reflector state detection. An efficient four-step cascaded VSD method is devised by exploiting linearity, quadratic, orthogonality and space-time-domain correlation of MIMO OFDM signals, which finally achieves simultaneous VSD, reflector positioning and channel estimate. It is verified by simulations that our VSD scheme outperforms state-of-the-art baselines due to our specially-tailored problem decoupling and multipath information fusion, which builds a technical foundation for designing environment sensing-assisted communication strategies.

Accurate classification of medical images is essential for modern diagnostics.Deep learning advancements led clinicians to increasingly use sophisticated models to make faster and more accurate decisions, sometimes replacing human judgment.However, model development is costly and repetitive.Neural Architecture Search (NAS) provides solutions by automating the design of deep learning architectures.This paper presents ZO-DARTS+, a differentiable NAS algorithm that improves search efficiency through a novel method of generating sparse probabilities by bilevel optimization.Experiments on five public medical datasets show that ZO-DARTS+ matches the accuracy of state-of-the-art solutions while reducing search times by up to three times.

Cooperative sensing, a technique employed in cognitive radio (CR) networks for spectrum sensing, exhibits promising potential in bolstering spectrum utilization and enhancing network performance. This approach leverages the information captured by distributed CR users, which is subsequently aggregated at a fusion center. However, the challenges arise when the data are transmitted with low-quality, resulting in the consequential issue of missing data. These factors introduce complexity in detecting primary signals and undermine the reliability of cooperative sensing. In this study, we present a significant advancement in cooperative sensing methodologies by introducing a novel approach: a generalized likelihood ratio test (GLRT) type detector specifically designed to be robust to missing data. More specifically, our proposed robust GLRT detector modifies the computation of the classical GLRT test statistic to accommodate the inherent incompleteness of the data and effectively estimates the desired unknown parameters. Through numerical experiments, we demonstrate the resilience and robustness of our proposed cooperative signal detection method.

Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structures of signals. However, this power of most deep learning techniques heavily relies on an abundant amount of training data, so the performance of classic neural nets decreases sharply when the number of training data samples is small or unseen data are presented in the testing phase. This calls for an advanced strategy, i.e., model-agnostic meta-learning (MAML), which can capture the invariant representation of the data samples or signals. In this paper, inspired by the special structure of the signal, i.e., real and imaginary parts consisted in practical time-series signals, we propose a Complex-valued Attentional MEta Learner (CAMEL) for few-shot signal recognition in the complex domain by leveraging attention and meta-learning. Experimental results showcase the superiority of the proposed CAMEL compared with the state-of-the-art methods.

We consider ergodic weighted sum rate (WSR) maximization in a massive multi-user multiple-input multiple-output system. Existing solutions iteratively minimize the average WSR based on all the historical information, and use bisection search to satisfy the power constraint at each iteration, resulting in both high storage burden and high computational complexity. In contrast, we propose an efficient stochastic proximal weighted minimum mean-square error (SPWMMSE) algorithm, which updates the precoder only based on the current single channel realization, without checking the power constraint at each iteration. Furthermore, we propose a novel proximal term to incorporate all the previous channel and surrogate function information in precoder updates. Our analysis shows that SPWMMSE converges to the stationary point of the original ergodic WSR maximization problem almost surely. Simulation results demonstrate the effectiveness of SPWMMSE over the current best alternatives.

A fundamental problem in cognitive radio is spectrum sensing, which detects the presence of the primary users in a licensed spectrum. To boost the detection performance and robustness, the multiantenna detector has been investigated and various related methods have been developed, e.g., the energy detector, the eigenvalue arithmetic-to-geometric mean detector, and the generalized likelihood ratio test detector. Cooperative sensing, which makes use of multiple receivers distributed in different locations, has the advantage of being able to make full use of the distributed antennas and enjoy a high spatial diversity gain. However, the successful employment of cooperative sensing depends on the reliable information exchange among the cooperating receivers over a long range, which may be impractical for real-world scenarios. In this paper, we consider the scenario where each receiving node can only broadcast its received raw data in a short-range communication fashion. We propose a novel cooperative sensing scheme by allowing each node to send to the fusion center only local correlation coefficients, computed within a neighborhood. A detection algorithm, based on matrix factorization of the partially received sample covariance matrix, i.e., with missing entries, is proposed. The performance of our proposed cooperative scheme is verified via numerical experiments.

Optimizing antenna parameters like azimuth, down-tilt, and power is crucial for coverage and capacity optimization (CCO) in next-generation wireless networks. However, traditional expert knowledge-based methods struggle to maintain optimal results when faced with changing environments. To address this, we propose a guided deep reinforcement learning (DRL) algorithm that learns a policy to dynamically adjust antenna parameters based on the evolving environment. Our approach employs proximal policy optimization-based DRL and integrates a problem-specific pretraining process using zero-order gradient descent. The pretrain policy serves as a guiding policy, enabling the agent to explore and discover high-reward regions, thus accel-erating the learning process. The performance of our solution is validated by numerical experiments conducted on a 5G simulation platform with real-world topological properties. The results show that our approach achieves significantly faster convergence and outperforms baseline methods in terms of CCO performance.

This paper proposes an innovative graph neural network (GNN)-based approach to address the challenge of recovering ill-conditioned sparse signals within the task of multi-grid localized statistical channel modeling (LSCM). Our proposed GNN architecture captures the structural sparsity inherent in the channel angular power spectrum (APS) by leveraging reference signal receiving power (RSRP) measured from multiple grids. It can effectively mitigate the severe coherence in the measurement matrix. Furthermore, we present a novel online unsupervised training scheme that enables real-time adaptability for multi-grid LSCM applications. Through extensive simulations, we demonstrate the superior performance of our GNN-based method in the context of multi-grid LSCM, showcasing its advantages over existing sparse recovery techniques.

Solving constrained nonlinear programs (NLPs) is of great importance in various domains such as power systems, robotics, and wireless communication networks. One widely used approach for addressing NLPs is the interior point method (IPM). The most computationally expensive procedure in IPMs is to solve systems of linear equations via matrix factorization. Recently, machine learning techniques have been adopted to expedite classic optimization algorithms. In this work, we propose using Long Short-Term Memory (LSTM) neural networks to approximate the solution of linear systems and integrate this approximating step into an IPM. The resulting approximate NLP solution is then utilized to warm-start an interior point solver. Experiments on various types of NLPs, including Quadratic Programs and Quadratically Constrained Quadratic Programs, show that our approach can significantly accelerate NLP solving, reducing iterations by up to 60% and solution time by up to 70% compared to the default solver.

Graph neural networks (GNNs) have recently emerged as powerful tools for addressing complex optimization problems. It has been theoretically demonstrated that GNNs can universally approximate the solution mapping functions of linear programming (LP) problems. However, these theoretical results typically require GNNs to have large parameter sizes. Conversely, empirical experiments have shown that relatively small GNNs can solve LPs effectively, revealing a significant discrepancy between theoretical predictions and practical observations. In this work, we aim to bridge this gap by providing a theoretical foundation for the effectiveness of small-size GNNs. We prove that polylogarithmic-depth, constant-width GNNs are sufficient to solve packing and covering LPs, two widely used classes of LPs. Our proof leverages the capability of GNNs to simulate a variant of the gradient descent algorithm on a carefully selected potential function. Additionally, we introduce a new GNN architecture, termed GD-Net. Experimental results demonstrate that GD-Net significantly outperforms conventional GNN structures while using fewer parameters.

In this paper, we study the classical Logistic Regression (LR) problem in machine learning. Traditionally, the solving algorithms are based on either the first- or second-order approximation of the objective. For instance, the Fixed-Hessian Newton (FHN) method approximates the true Hessian with a constant estimate. In contrast, our design additionally exploits the third-order information. Applying the majorization–minimization (MM) framework, we construct a novel majorizing function based on the third-order Taylor expansion and the minimization solution is in closed-form with perseverance of the true gradient and Hessian structures. In analysis, we prove the convergence rate of the proposed algorithm. The enhanced numerical performance can be verified through simulation results.

In this paper, we consider an intelligent reflecting surface (IRS) assisted downlink multiuser communication system with improper Gaussian signaling (IGS) that serves as generalized Gaussian signaling and can effectively combat multiuser interference. We focus on the max-min achievable rate optimization problem by jointly optimizing the transmit beamforming vectors and reflecting phase shifts, subject to the transmit power budget constraint at the access point (AP). We propose a low-complexity iterative algorithm based on a two-layer iterative procedure, which differs from these existing algorithms that rely on inefficient alternating optimization framework and high computational complexity convex optimization tools. Specifically, in the outer layer procedure, we employ a tractable lower bound of user communication rate to reformulate the original problem and repeatedly update the lower bound in each iteration. In the inner layer procedure, based on the alternating direction method of multipliers (ADMM), we decompose the reformulated problem into several convex sub-problems, which can be alternately solved by closed-form solutions. Furthermore, we study the initialization, convergence, and computational complexity of the proposed algorithm. Additionally, we simplify the algorithm to make it applicable for the cases of conventional proper Gaussian signaling (PGS) and without IRS. Finally, numerical results validate the advantages of the proposed algorithm over benchmarking algorithm in terms of rate performance and average execution time.

The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, leading to increased overhead and reduced communication throughput. To address this challenge, integrated sensing and communications (ISAC) techniques have been employed at the base station (gNB) within vehicle-to-infrastructure (V2I) networks, aiming to minimize overhead and improve spectral efficiency. In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery. These new frame structures employ 75% fewer pilots and reduce reference signals by 43.24%, capitalizing on the sensing capability of ISAC signals. Through extensive link-level simulations, we demonstrate that our proposed approach enables faster beam establishment during initial access, higher throughput and more precise beam tracking in connected mode with reduced overhead, and expedited detection and recovery from beam failures. Furthermore, the numerical results obtained from our simulations showcase enhanced spectrum efficiency, improved communication performance and minimal overhead, validating the effectiveness of the proposed ISAC-based techniques in NR V2I networks.

Integrated sensing and communication (ISAC) capability is envisioned as one key feature for future cellular networks. Classical half-duplex (HD) radar sensing is conducted in a “first-emit-then-listen” manner. One challenge to realize HD ISAC lies in the discrepancy of the two systems’ time scheduling for transmitting and receiving. This difficulty can be overcome by full-duplex (FD) transceivers. Besides, ISAC generally has to comprise its communication rate due to realizing sensing functionality. This loss can be compensated by the emerging reconfigurable intelligent surface (RIS) technology. This paper considers the joint design of beamforming, power allocation and signal processing in a FD uplink communication system aided by RIS, which is a highly nonconvex problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Besides, by wisely exploiting alternative direction method of multipliers (ADMM) and optimality analysis, we further develop a low complexity solution that updates all variables analytically and runs highly efficiently. Numerical results are provided to verify the effectiveness and efficiency of our proposed algorithms and demonstrate the significant performance boosting by employing RIS in the FD ISAC system.

Network optimization plays a crucial role in wireless communications. However, the optimization of contemporary 5G networks is challenging due to its black-box nature and huge searching space. To address these challenges, this paper introduces efficient zeroth-order (ZO) algorithms and a parallel framework for optimizing large-scale networks. By leveraging the gradient-based searching strategy, the proposed algorithms, namely ZO projected gradient descent (ZO-PGD) and ZO block coordinate projected gradient descent (ZO-BCPGD), can significantly improve optimization quality and computational efficiency. Both algorithms guarantee a convergence towards stationary points under mild conditions, eliminating inherent errors in traditional ZO methods. We further propose a parallel framework for optimizing the network parameters in a simultaneous manner. By partitioning the entire network into manageable subnetworks, the original network optimization problem is reformulated as a consensus optimization problem and tackled in parallel using the penalty dual decomposition (PDD) method. We have also designed a tailored size-constrained grid clustering algorithm for network partitioning to ensure load balance among parallel working nodes. The efficacy of our proposed schemes is verified by extensive numerical results. Our ZO algorithms outperform existing methods in both computational efficiency and solution quality. Moreover, the parallel framework significantly reduces execution time without sacrificing network performance.

This paper investigates a novel deep learning framework for the general nonconvex quality-of-service (QoS)-constrained beamforming design problems in multi-user systems. While existing deep learning-based approaches have shown great success for various power allocation and beamforming design problems, most of the considered problems are equipped with simple constraints (e.g., power budget constraints), which can be satisfied by a simple projection operation. However, it is still a challenge to tackle the more complicated QoS constraints, in which the beamformers and the wireless channels are commonly coupled. To fill this gap, this paper proposes an augmented Lagrangian based penalty-dual training algorithm, which trains two individual neural networks for inferring the beamformers and the corresponding Lagrange multipliers alternatingly. Furthermore, we apply the proposed penalty-dual learning framework to optimize the energy-efficient unicast beamformers and the power-minimized multicast beamformers, respectively. The neural network architectures are judiciously designed based on the solution structures of the two problems. Simulation results on the two applications demonstrate that the proposed penalty-dual approach outperforms state-of-the-art learning approaches and optimization-based algorithms in terms of the constraint violation and the computational time, respectively.

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

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.

Localized channel modeling is crucial for offline performance optimization of wireless networks, but existing channel models are not well suited for wireless network optimization. In this paper, we propose a physics-based and data-driven localized statistical channel model for wireless network optimization. The proposed channel modeling solely relies on the reference signal receiving power (RSRP). The key is to build the statistical relationship between the RSRP and the angular power spectrum (APS). Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the APS indicate the channel paths’ powers and angles of departure. Although such problem typically can be handled by orthogonal matching pursuit (OMP)-type algorithms, our problem is more challenging due to the non-uniform and closely parallel columns of the coefficient matrix. To address these issues, we propose the weighted non-negative OMP (WNOMP) and the second-order-statistics-based WNOMP (SWOMP) algorithms. The WNOMP algorithm can alleviate the effect of non-uniform columns, while the SWOMP algorithm can further identify the closely parallel columns correctly. Finally, comprehensive experiments based on synthetic and real-world RSRP are presented to demonstrate that the proposed methods outperform classic methods in terms of accuracy and mean absolute error (MAE).

To reduce the downlink beam sweep overhead of mmWave systems, we propose a deep reinforcement learning based dynamic beam selection (DRL-DBS) method. A new learning motivation is presented by analyzing the dynamic change laws of high- and low-frequency channels in the spatial domain: to learn the index offset between the optimal beam of mmWave and sub-6 GHz spatial spectrum. In the DRL-DBS method, we propose a novel action space where actions can dynamically adjust the size of the beam sweep subset according to the high-and low-frequency channel propagation laws. Hence, the DRL-DBS method can predict a mmWave downlink beam sweep subset with dynamic size, and the optimal beamforming index is from beam sweep results on the subset. A dual-input dueling Q-network with noisy networks and prioritized experience replay is designed to select the optimal action. The DRL-DBS method can achieve a dynamic trade-off between mmWave beam selection quality and beam sweep overhead based on the reward function. Simulation results demonstrate the superior performance of the DRL-DBS method compared with the existing strategies. Especially, the DRL-DBS method outperforms the exhaustive search algorithm in achievable rate because the overhead of mmWave beam sweep is considered.

Unmanned aerial vehicle (UAV) swarm-based sensing technology has become increasingly important due to its exceptional maneuverability, versatile coverage capabilities, and reliable line-of-sight (LoS) connectivity. However, the sensing accuracy improvement by exploiting resource coordination strategy poses a new challenge on multi-UAV sensing system. In this paper, we consider the problem of cooperative sensing via a system of multi-UAV, where each UAV is equipped with a directional antenna to cooperatively conduct energy detection for several targets of interest. To measure the perception ability of the system, we choose the energy detection probability as the metric, aiming to maximize the sum detection probability of the network by jointly optimizing UAVs’ deployment, as well as the directional antenna orientations. By virtue of the specific problem structure, we recast the formulation into an equivalent yet more tractable form with the aid of auxiliary vectors. Subsequently, we propose an efficient iterative algorithm for the solution based on the alternating direction penalty method (ADPM), which decomposes the formulated non-convex problem into multiple subproblems and solves them alternately. Extensive simulations validate the efficacy of the proposed algorithm and provide valuable insights for practical system design.

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Channel estimation (CE) of RIS device is a critical but also challenging issue for its development. The mainstream of existing CE methods is confined to the so-called cascaded channel (CscdChn) estimation scheme, which treats the multiplicative two-hop RIS channels as an effective one and measures it as a whole. This CscdChn training method suffers from severe double-fading attenuation loss, which significantly degrades the CE accuracy. In this paper, we propose a novel RIS-transmitting (RIS-TX) based CE scheme, which has lower pilot overhead than CscdChn scheme and effectively overcomes the double-fading curse via incorporating only one single transmit radio frequency (RF)-chain into RIS. We develop highly efficient gradient descent (GD) and penalty duality decomposition (PDD)-based solutions to resolve the pilot design task for the RIS-TX CE scheme, which is a difficult quartic optimization problem. Our designed pilot signal outperforms the discrete Fourier transform (DFT) sequence, which is reported to be optimal for CscdChn scheme. Besides, both theoretical analysis and numerical results demonstrate that our proposed RIS-TX scheme exhibits distinct performance characteristics as opposed to its CscdChn counterpart and yields superior accuracy when RIS device is not extremely large.

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks.

We consider downlink beamforming design to maximize the weighted sum rate (WSR) in a user-centric cell-free network, where distributed access points (APs) are organized in a cluster to jointly serve each user equipment (UE). This architecture ensures uniform service quality even at the cell edge, but synchronization between APs can be problematic, necessitating the utilization of non-coherent joint transmission (NCJT) that eliminates the need for strict synchronization. Most existing works on beamforming design for NCJT assume that the classic weighted minimum mean square error (WMMSE) approach is not applicable, and design their beamforming algorithms based on the successive convex approximation (SCA) method with high computational complexity. In this work, we for the first time demonstrate the applicability of the WMMSE approach for NCJT in cell-free networks. Based on the unique observations on the structures of the WSR maximization problem for NCJT, we propose an efficient WMMSE based beamforming algorithm. Our proposed algorithm is guaranteed to converge to a stationary point of the WSR maximization problem. Furthermore, our beamforming updates are in closed form with low computational complexity. Simulation results demonstrate substantial performance gain of our proposed algorithm over the current best SCA based alternatives in both computational complexity and convergence time.

Deriving the spatial features of user equipment (UE) and traffic distribution is crucial for optimizing real-world wireless network performance, such as load balancing and improving cell-edge user experience. In this paper, we creatively construct the beam-space grid with high-dimensional beam reference signal receiving power (RSRP), serving as spatial reference system for 5G mobile networks. However, this is a challenging task due to i) access to 3D UE geographic information is limited by privacy protection and device settings, ii) beam-RSRP with up to 75% loss in measurement reports (MRs), iii) large computation and storage requirements of grid information caused by the exploding grid number in high-dimensional beam-space. A novel approach is designed to tackle these challenges, that coherently integrate low-rank matrix completion and dimension reduction techniques. Using telco big data, specifically MRs from live networks, we recover the beam-RSRP with high-precision and construct the beam-space grid even without geographic information. This research ensures stable derivation of spatial distribution features in the beam-space grid while efficiently processing grid information.

Batch normalization (BN) has been widely used for accelerating the training of deep neural networks. However, recent findings show that, in the federated learning (FL) scenarios, BN can damage the learning performance when the clients have non-i.i.d. data. While several FL schemes have been proposed to address this issue, they still suffer a significant performance loss compared to the centralized scheme. In addition, none of them have explained how the BN impacts the FL convergence analytically. In this paper, we present the first convergence analysis to show that the mismatched local and global statistical parameters due to non-i.i.d data cause gradient deviation and it leads the algorithm to converge to a biased solution with a slower rate. To remedy this, we further present a new FL algorithm, called FedTAN, based on an iterative layer-wise parameter aggregation procedure. Experiment results are presented to show the superiority of FedTAN.

Neural Architecture Search (NAS) is a silver bullet in alleviating time consumption and human effort for deep neural network design. It is however challenging to search for good architectures with low consumption. In this paper, we propose a novel NAS framework to address the differentiable neural architecture search problem by inspecting the bi-level problem formulation from scratch. Combined with the Zeroth-Order (ZO) gradient descent technique and implicit gradients, the proposed algorithm can not only reduce search time for suitable architectures than existing works but maintain the final accuracy simultaneously. Experimental results show the efficacy of our proposed ZO-based NAS approach.

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm.

This paper studies integrated sensing and communication (ISAC) technology in a full-duplex (FD) uplink communication system. As opposed to the half-duplex system, where sensing is conducted in a first-emit-then-listen manner, FD ISAC system emits and listens simultaneously and hence conducts uninterrupted target sensing. Besides, impressed by the recently emerging reconfigurable intelligent surface (RIS) technology, we also employ RIS to improve the self-interference (SI) suppression and signal processing gain. As will be seen, the joint beamforming, RIS configuration and mobile users' power allocation is a difficult optimization problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Numerical results demonstrate the effectiveness of our proposed solution and the great benefit of employing RIS in the FD ISAC system.

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 massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detection (AD) and channel estimation (CE) problems in massive-connected communication systems with low-resolution analog-to-digital converters. To further exploit the sparse structure in transmission, we propose a maximum posterior probability (MAP) estimation problem based on both sporadic activity and sparse channels for joint AD and CE. Moreover, a majorization-minimization-based method is proposed for solving the MAP problem. Finally, various numerical experiments verify that the proposed scheme outperforms state-of-the-art methods.

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

The rising reconfigurable intelligent surface (RIS) is a promising technology and a multitude of literature focuses on its channel estimation (CE), which is a critical and challenging task. Most existing works adopt a type of “cascaded channel” training scheme, where the “two-hop” channel cascaded by RIS is treated as one and measured by one shot. As unveiled by the latest researches, however, the concatenated channel suffers from severe fading loss and hence seriously degrades the CE precision. To resolve this difficulty, this paper proposes a novel training scheme. Specifically, being equipped with one transmit RF chain, the RIS broadcasts pilot signals to all other devices during the training period. This novel scheme can overcome double fading loss at a low hardware cost. The pilot design of the newly proposed training scheme is a difficult quartic optimization problem and we develop a gradient descent (GD) based solution to resolve it. Numerical results verify the effectiveness of our solution and demonstrate our training scheme can significantly outperform the traditional cascaded channel training method.

In military and civilian scenarios, many signal recognition tasks are often accompanied by the growth of signal classes due to signal camouflage or increased communication devices. However, conventional approaches based on deep learning cannot effectively cope with the growth of classes. To address this problem, in this paper, we propose a complex-valued class-incremental learning (CIL) framework for signal recognition (C-SRCIL), which can extract the latent complex-valued features of modulation signals and complete the CIL for complex-valued signal recognition. In our C-SRCIL, We first decouple the feature representation from the classifier to combat catastrophic forgetting, and introduce complex-valued neural networks and an integrated loss function in the feature representation. Then a complex-valued CIL adapter is designed based on the nodalization idea for updating the decoupled classifier in the incremental stage. Experiments on datasets RADIOML2016.10A and SIGNAL2022 show that C-SRCIL brings about a 1.32% performance improvement with a 39.96% reduction in training time compared to the state-of-the-art. It suggests that C-SRCIL extends the classification boundaries of existing models while preserving previous knowledge.

The direction of arrival (DOA) estimation of the signal is an important task in radio signal positioning. Various methods have been investigated to cope with the DOA task. However, since the imperfect interference factors are often present in practical antenna arrays, the performance of DOA estimation is often significantly degraded. Besides, few methods deal with the DOA estimation for signals of multiple frequencies. In this paper, we consider the problem of two-dimensional DOA estimation in the presence of imperfect factors, and propose a novel approach where the convolutional attention network is used for DOA estimation. The frequency information is introduced as a token added to the network, which improves the network robustness while taking into account the case of the signal of multiple frequencies. Besides, we extend the mean square error (MSE) to the design of a new loss function for training to improve the accuracy of the model. The advantages of the proposed DOA estimation scheme are demonstrated through numerical experiments.

In this paper, we consider the problem of cooperative sensing via a system of multi-unmanned aerial vehicles (UAVs), where each UAV is equipped with a directional antenna to cooperatively perform detection tasks for several targets of interest. To measure the perception ability of the system, we choose the detection probability as the metric, aiming to maximize the sum detection probability of targets by jointly optimizing UAVs’ deployment and directional antenna orientations. To tackle the inherent nonconvexity of the formulated problem, we first decompose it into two sub-problems, i.e., a slave problem for optimizing the antenna orientations with a given UAVs’ deployment, and a master problem for optimizing the UAVs’ deployment. By virtue of the slave problem structure, an efficient block coordinate descent (BCD) algorithm is developed. Meanwhile, to deal with the lack of the closed expression of the sum detection probability with respect to the UAVs’ deployment, we further develop an iterative algorithm to acquire an efficient solution with the aid of Gibbs Sampling (GS) approach. Extensive simulations demonstrate the efficacy of the proposed algorithm.

Although the reconfigurable intelligent surface (RIS) technology is envisioned promising to enhance communication from all aspects, including physical-layer security, increasing concerns have lately been cast onto its defects—the severe “double-fading” loss and its confined-to-half-space coverage. Diverse novel RIS architectures have recently emerged to partially overcome these shortcomings, yet perfect solution is still absent. This paper proposes a novel double-faced active (DFA)-RIS structure to surmount the above two prominent defects simultaneously. Furthermore, we utilize the DFA-RIS to promote secrecy performance via jointly designing access point (AP)’s beamforming and DFA-RIS configuration towards maximizing sum secrecy rate (SR). The optimization problem is highly challenging due to the constraints deriving from the DFA-RIS architecture, especially the presence of power splitting parameters. By leveraging majorization–minimization (MM) and penalty dual decomposition (PDD) methods, we develop an efficient solution that updates all variables via convex optimization techniques. Our proposed solution is significant and general as it is applicable to all other cutting-the-edge RIS architectures to maximize sum SR, which has not yet been thoroughly worked out. Numerical results verify the convergence and effectiveness of our proposed algorithm and demonstrate that our proposed DFA-RIS architecture outperforms all other state-of-the-art RIS techniques to enhance communication security.

In this paper, a new randomized iterative detection algorithm (NRIDA) is proposed for uplink large-scale MIMO systems, where the random iterations in it are designed to work for the detection model (denoted by$\mathbf {y}=\mathbf {Hx}+\mathbf {n}$) directly. Different from those traditional iterations designed for the linear system (denoted by$\mathbf {Ax}=\mathbf {b}$with$\mathbf {A}=\mathbf {H}^{H}\mathbf {H}$and$\mathbf {b}=\mathbf {H}^{H}\mathbf {y}$), we show that besides the complexity reduction about the matrix inversion, in the proposed NRIDA the computational complexity of matrix multiplication for the linear detection is also greatly reduced without any performance loss, thus leading to a much lower detection complexity. Meanwhile, according to convergence analysis, we demonstrate that the proposed NRIDA enjoys a globally exponential convergence performance, enabling it well suited to the various detection cases of interest. Besides, further complexity reduction and the choices of the sampling distribution in NRIDA are studied as well in full details. Moreover, in order to achieve a better detection trade-off between performance and complexity, we introduce the concept of the conditional sampling into NRIDA, which brings significant gains in both iteration convergence and efficiency. Finally, simulations with respect to the uplink large-scale MIMO detection are presented to illustrate the remarkable gains of the proposed NRIDA in both performance and complexity.

Although reconfigurable intelligent surface (RIS) technology has manifested great potentials in improving wireless network’s power saving, most existing literature restricts to pure physical (PHY) layer beamforming design and neglects the impact of media access control (MAC) layer’s data traffic flows. Simultaneously, current RIS technology suffers from defects — the severe fading loss and the limitation of half-space coverage. This paper aims to perform a cross-layer design via jointly optimizing MAC layer scheduling and PHY layer RIS beamforming to reduce power consumption. Besides, we propose a novel double-faced-active (DFA)-RIS architecture to promote RIS’ capability. The proposed design task leads to a highly challenging stochastic problem to minimize long-term power consumption while stabilizing queues. Inspired by Lyapunov control theory, we propose an online optimization strategy to resolve this challenge. Via exploiting alternative directional method of multipliers (ADMM), we develop an analytic-based solution to solve the online sub-problems highly efficiently without resorting to any numerical solvers. Our strategy theoretically guarantees all queues’ stability and achieves a tunable trade-off between the power expenditure and queue lengths. Extensive numerical results are presented to demonstrate the effectiveness of our proposed cross-layer design and the DFA-RIS’ advantage over other cutting-the-edge RIS architectures.

This article considers the 3-D collaborative trajectory optimization (CTO) of multiple unmanned aerial vehicles to improve multitarget tracking performance with an asynchronous angle of arrival measurements. The predicted conditional Cramér–Rao lower bound is adopted as a performance measure to predict and subsequently control tracking error online. Then, the CTO problem is cast as a time-varying nonconvex problem subjected to constraints arising from dynamic and security (height, collision, and obstacle/target/threat avoidance). Finally, a comprehensive solution method (CSM) is presented to tackle the resulting problem, according to its unique structures. Specifically, if all security constraints are inactive, the CTO can be simplified as a nonconvex problem with convex dynamic constraints, which can be solved by the nonmonotone spectral projected gradient (NSPG) method. Oppositely, an alternating direction penalty method (ADPM) is presented to solve the CTO problem with some positive security constraints. The ADPM introduces auxiliary vectors to decouple the complex constraints and separates the CTO into several subproblems and tackles them alternately, while locally adjusting the penalty factor at each iteration. We show the subproblem w.r.t. the position vector is nonconvex but with convex constraints, which can be efficiently solved by the NSPG method. The subproblems w.r.t. the auxiliary vectors are separable and have closed-form solutions. Simulation results demonstrate that the CSM outperforms the unoptimized method in terms of tracking performance. Besides, the CSM achieves the near-optimal performance provided by the genetic algorithm with much lower computational complexity.

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the traditional Pareto and Selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided Tabu Search (PGG-TS) algorithm is developed to obtain a stable coalitional structure. Numerical results verify that the utility of the proposed OCF game scheme based on the PGG-TS algorithm increases by 18% against that of the non-overlapping CF game scheme, and the utility of the proposed BMBT order increases by 25%, compared with other orders.

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

Reconfigurable intelligent surface (RIS) is envisioned as a promising technology to enhance future wireless communication systems. Very recently, a novel active RIS architecture has been proposed via introducing amplifiers into the reflecting elements. Although these embedded amplifiers can effectively extend the RIS coverage, they also bring non-negligible energy expenditure. To overcome this drawback, this paper proposes a novel sub-array based structure, which divides the entire RIS array into multiple sub-arrays with each being flexibly turned on/off. We aim to minimize the power consumption of the whole system via jointly activating sub-arrays and designing beamforming, which is highly challenging due to its combinatorial nature. Via inducing the group sparsity and leveraging the majorization-minimization (MM) approach, we develop an efficient solution to resolve this challenge. Numerical results demonstrate that our proposed sub-array structure can significantly reduce the power consumption compared to the conventional “all-on” scheme.

Localizing the root cause of network faults is crucial to network operation and maintenance (O&M). Significant operational expenses will be saved if the root cause can be identified agilely and accurately. However, this is challenging for human beings due to the complicated wireless environments and network architectures. Resorting to data analysis and machine learning is promising but remains difficult due to various practical issues, such as the lack of well-labeled samples, hybrid fault behaviors, missing data, and so on. In this paper, we introduce a novel real-world dataset for wireless communication network fault diagnosis. The goal is to infer the root cause timely when we observe certain symptoms in a network. Several baseline methods are provided.

In this paper, we consider the weighted sum of transferred power maximization under the secrecy rate (SR) constraints in a secure simultaneous wireless information and power transfer (SWIPT) communication network assisted by reconfigurable intelligent surfaces (RIS). To tackle this challenging problem, we combine the cutting-the-edge successive convex approximation (SCA) and penalty dual decomposition (PDD) methods and have successfully developed a novel iterative solution. Compared to the existing literature, our newly proposed algorithm can apply to the most generic system setting that has arbitrary number of information and/or energy receivers. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of RIS deployment.

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, namedFedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance ofFedTOEfor deep learning-based classification tasks with transmission latency constraints.

XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big data problems, it is crucial to deploy a secure and efficient federated XGBoost (FedXGB) model. Existing FedXGB models either have data leakage issues or are only applicable to the two-party setting with heavy communication and computation overheads. In this article, a lossless multi-party federated XGB learning framework is proposed with a security guarantee, which reshapes the XGBoost’s split criterion calculation process under a secret sharing setting and solves the leaf weight calculation problem by leveraging distributed optimization. Remarkably, a thorough analysis of model security is provided as well, and multiple numerical results showcase the superiority of the proposed FedXGB compared with the state-of-the-art models on benchmark datasets.

Power saving and battery-life extension have always been a critical concern for IoT network deployment. One effective solution is to switch wireless devices into sleep mode to save power. This paper considers the power control in an IoT network via jointly activating IoT sensors and designing their transmit beamforming. Besides, inspired by the great potential of reconfigurable intelligent surface (RIS) in energy saving, we additionally introduce RIS to further lower the sensors’ power consumption. The considered problem is highly challenging due to its combinatorial nature, the highly non-convex quality-of-service (QoS) constraint and the hardware restrictions from the RIS. By exploiting the cutting-the-edge majorization minimization (MM) and the penalty dual decomposition (PDD) frameworks, we have successfully developed highly efficient solutions to tackle this problem. Our proposed solutions can achieve nearly identical performance with that of the exhaustive search but with a much lower complexity. Besides, as revealed by the numerical experiments, our proposed sensor activation scheme can switch off a large portion of sensors under mild QoS requirements, which significantly reduces power expenditure. Moreover, the deployment of RIS can bring an additional 45% – 70% power saving compared to the no-RIS case.

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antenna increases, centralized baseband processing architectures encounter two bot-tlenecks, i.e., the tremendous data interconnection and the high-dimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear mini-mum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks.

Weighted sum-rate maximization (WSRM) is a fundamental problem for multiuser multiple-input-multiple-output (MU- MIMO) systems with finite-alphabet inputs. However, solving this problem is challenging because of the intractable expectation involved in rate functions. The state-of-art WSRM methods for the case of finite-alphabet inputs suffer from high computational complexity due to the issue of complicated numerical integrals for expectation calculation. Inspired by the stochastic successive upper-bound minimization (SSUM) method [1], this paper proposes a stochastic successive inexact lower-bound maximization (SSILM) algorithm for the WSRM problem with finite-alphabet inputs. Our algorithm significantly differs from SSUM in that we use an inexact lower bound of the objective function which is skillfully devised based on an exact but extremely loose lower bound of the objective function. Simulation results show that the proposed algorithm exhibits much faster convergence than state-of-art algorithms.

There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems. In particular, two types of codebooks (namely Type I and Type II codebooks) are introduced with different resolution and overhead. Although the Type I codebook based scheme requires lower feedback overhead, its channel state information (CSI) reconstruction and beamforming performance are not as good as those from the Type II codebook based scheme. However, since the Type I codebook based scheme has been widely used in 4G systems for many years, replacing it by the Type II codebook based scheme overnight is too costly to be an option. Therefore, in this paper, using Type I codebook, we leverage advances in cutting plane method to optimize the CSI reconstruction at the base station (BS), in order to close the gap between these two codebook based beamforming schemes. Numerical results based on channel samples from QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) are presented to show the excellent performance of the proposed algorithm in terms of beamforming vector acquisition.

Due to the emergence of deep learning, signal recognition has made great strides in performance improvement. The success of most deep learning methods relies on the accessibility of abundant labelled training data. However, the annotation of signals is quite expensive, making it challenging to train deep learning models substantially. This calls for the development of semi-supervised learning (SSL) method to fully utilize the unlabelled data to assist the training of deep learning models. To achieve this goal, three types of loss function tailored to the task of signal recognition are carefully designed in this paper. Together with the novel design of neural network structure, the proposed SSL method can effectively extract the information from unlabelled training data and thus overcome the difficulty of insufficient training. Extensive numerical results using real-world signal datasets are presented to show the remarkable performance of the proposed SSL method.

Clustering is a common technique for statistical data analysis and it has been widely used in many fields. This article investigates data clustering over the Internet-of-Things (IoT) network. Facing the IoT network challenges, including data volume, communication latency, and information security, we here propose a distributed soft clustering algorithm for the IoT environments where each IoT node may have data from multiple clusters. Considering that the main task of soft clustering is to compute each cluster center in a weighted averaging fashion, our distributed clustering method resorts to an efficient finite-time average-consensus algorithm. Moreover, to make the distributed clustering algorithm more stable and be able to escape from some bad local optimum, we propose a distributed deterministic initialization method based on data variance partitioning. Experiments show that the proposed distributed soft clustering algorithm can offer the same performance as its centralized counterpart in terms of both convergence and clustering quality. Besides, unlike most clustering methods relying on probabilistic initialization, our algorithm could provide stable clustering quality which makes it more suitable for IoT networks. A real-world case study about the clustering analysis for distributed data sets collected by environmental monitoring stations is offered, which shows the potential of our algorithms in practical applications.

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high-complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.

Developing resource allocation algorithms with strong real-time and high efficiency has been an imperative topic in wireless networks. Conventional optimization-based iterative resource allocation algorithms often suffer from slow convergence, especially for massive multiple-input-multiple-output (MIMO) beamforming problems. This paper studies learningbased efficient massive beamforming methods for multi-user MIMO networks. The considered massive beamforming problem is challenging in two aspects. First, the beamforming matrix to be learned is quite high-dimensional in case with a massive number of antennas. Second, the objective is often time-varying and the solution space is not fixed due to some communication requirements. All these challenges make learning representation for massive beamforming an extremely difficult task. In this paper, by exploiting the structure of the most popular WMMSE beamforming solution, we propose convolutional massive beamforming neural networks (CMBNN) using both supervised and unsupervised learning schemes with particular design of network structure and input/output. Numerical results demonstrate the efficacy of the proposed CMBNN in terms of running time and system throughput.

Clustering is a common technique for statistical data analysis and it has been widely used in many fields. When the data is collected via a distributed network or distributedly stored, data analysis algorithms have to be designed in a distributed fashion. This paper investigates data clustering with distributed data. Facing the distributed network challenges including data volume, communication latency, and information security, we here propose a distributed clustering algorithm where each IoT device may have data from multiple clusters. Considering that the main task of clustering is to compute each cluster center in a weighted averaging fashion, our distributed clustering method resorts to an efficient finite-time average-consensus algorithm. Experiments show that the proposed distributed clustering algorithm can offer the same convergence and clustering quality as its centralized counterpart but with less data traffic. Besides, experiments also show that our proposed algorithms outperforms the existing methods.

Beamforming is one of the most important transmission technologies to improve the quality of communication in cellular network systems. However, in massive multiple-input-multiple-output (MIMO) systems, conventional optimization-based iterative beamforming algorithms often suffer from high computational complexity and thus are not suitable for practical applications. This paper focuses on low complexity beamforming technique for multi-user massive MIMO systems. Specifically, a block-diagonal zero-forcing (BD-ZF) beamforming algorithm is proposed for achieving weighted sum-rate maximization. We show that the BD-ZF beamforming problems can be globally solved using water-filling algorithms. Extensive simulations demonstrate that the proposed BD-ZF beamforming method can offer better performance than the state-of-art low complexity beamforming technique ZF (even could sometimes coincide with the performance of the popular WMMSE algorithm) but with only an extra little bit computational overhead.

Source positioning is a key issue in a range of applications such as sensing, monitoring and tracking, etc. In this paper, we address the source localization problem with unknown clock skew and outliers in the TOA measurements. Considering the measurement outliers, we present a robust localization formulation by introducing Huber loss. Furthermore, we develop a lightweight iterative localization algorithm using majorization-minimization (MM) method with guaranteed convergence. In addition, we propose a novel semidefinite-relaxation-based algorithm for the proposed robust localization formulation. Simulations demonstrate that our proposed MM-based localization algorithms could achieve better performance than SDR-based localization algorithms in terms of both localization accuracy and computational time, for both cases with or without outliers.

In this work, the authors propose a transceiver design strategy based on switched preprocessing (SP) for interference management in K ‐pair MIMO interference channels. Each transmitter performs SP by using a small number of permutation matrices to allocate the entries of its precoder output vector on different transmit antennas. Each arrangement of permutation matrices among the K transmitters gives rise to a set of K parallel point‐to‐point transceivers, referred to as MIMO latent transceiver set (MLTS). Based on the given channel state information (CSI), the optimum MLTS among the available ones is chosen by minimising the squared Euclidean distance between the pre‐estimated noiseless received vector and the true transmit symbol vector. In addition, they consider two CSI error models, i.e. the stochastic error model and the norm‐bounded error model, and for each type they propose robust algorithms for the design of the MLTS associated to the different choices of permutation matrices, which are based on minimising various types of mean square error criteria. A detailed study of computational complexity for the proposed SP‐based MIMO transceiver design algorithms is carried out. Simulation results verify the effectiveness of the new SP‐based designs for MIMO interference channels.

In this article, relay-assisted computation offloading (RACO) is investigated, where user A wishes to share the results of computational tasks with another user B with the assistance of a mobile-edge relay server (MERS). To enable this computation offloading, we propose a hybrid relaying (HR) approach employing a pair of orthogonal frequency bands, which are, respectively, used for the amplify-forward relaying of computational results and the decode-forward relaying of the unprocessed raw tasks. The motivation here is to adapt the allocation of computing and communication resources both to dynamic user requirements and to diverse computational tasks. Using this framework, we seek to minimize the weighted sum of the execution delays and the energy consumption in the RACO system by jointly optimizing the computation offloading ratio, the bandwidth allocation, the processor speeds, as well as the transmit power levels of both user A and the MERS, under some practical constraints. By adopting a series of transformations, we first recast this problem into a form amenable to optimization and then develop an efficient iterative algorithm for its solution based on the concave-convex procedure (CCCP). By virtue of the particular problem structure in our case, we propose furthermore a simplified algorithm based on the inexact block coordinate descent (IBCD) method, which leads us to much lower computational complexity. Finally, our numerical results demonstrate the advantages of the proposed algorithms over the state-of-the-art benchmark schemes.

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users' confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption.

This work studies the joint problem of power and trajectory optimization in a rotary-wing unmanned aerial vehicle (UAV)-enabled mobile relaying system. In the considered system, in order to provide convenient and sustainable energy supply to the UAV relay, we consider the deployment of a power beacon (PB) which can wirelessly charge the UAV and it is realized by a properly designed laser charging system. To this end, we propose an efficiency (the weighted sum of the energy efficiency during information transmission and wireless power transmission efficiency) maximization problem by optimizing the source/UAV/PB transmit powers along with the UAV's trajectory. This optimization problem is also subject to practical mobility constraints, as well as the information-causality constraint and energy-causality constraint at the UAV. Different from the commonly used alternating optimization (AO) algorithm, two joint design algorithms, namely: the concave-convex procedure (CCCP) and penalty dual decomposition (PDD)-based algorithms, are presented to address the resulting non-convex problem, which features complex objective function with multiple-ratio terms and coupling constraints. These two very different algorithms are both able to achieve a stationary solution of the original efficiency maximization problem. Simulation results validate the effectiveness of the proposed algorithms.

The hardware implementation of large-scale multi-antenna systems requires power-efficient power amplifiers (PAs). However, the existing beamforming designs often cause a large peak-to-average power ratio and have to rely on power-inefficient PAs. In this paper, we propose a unified power-efficient beamforming design framework, which incorporates per-antenna constant envelope constraints to improve the power efficiency. We further propose an efficient algorithm named "sequential outer approximation procedure" (SOAP) to search a feasible point. Power-efficient design for beam pattern synthesis is developed based on SOAP.

In this paper, we investigate the joint design of hybrid beamforming and demanding computation tasks offloading in mmWave-based mobile edge computing (MEC) systems, in order to minimize the maximum latency. The resulting optimization problem is challenging, mainly due to the highly nonlinear objective function and the unit modulus constraints on the analog beamformers. By seeking the special structure of the problem, we divide it into two separate problems. An iterative weighted mean-square error minimization (WMMSE) approach is adopted to address the first optimization problem, and the second problem is solved in closed-form. We also investigate a more practical scheme when only finite resolution phase shifters are implemented. Simulation results are provided to confirm that the proposed strategy achieves significant better performance than recent reported beamforming algorithm with fixed offloading ratio, and it is effective when low-resolution phase shifters are used.

Broadband connectivity and mobile technology have been widely applied in the world. With these advanced technologies, the proliferation of smart devices and their applications by accessing mobile internet have come up with a giant leap forward, leading to the ever‐increasing scale and complexity of cellular networks. This presents imminent challenges to anomaly detection in cellular networks. In this study, the authors discuss challenges and current literature of anomaly detection for cellular networks to embrace the ‘big data’ era. First, they review the state‐of‐the‐art techniques in the area of anomaly detection in cellular networks. Then, the challenges are pinpointed for anomaly detection due to the cellular network big data. Finally, they introduce a big data analytic‐based anomaly detection method for cellular networks.

In topic modeling, identifiability of the topics is an essential issue. Many topic modeling approaches have been developed under the premise that each topic has a characteristic anchor word that only appears in that topic. The anchor-word assumption is fragile in practice, because words and terms have multiple uses; yet it is commonly adopted because it enables identifiability guarantees. Remedies in the literature include using three- or higher-order word co-occurence statistics to come up with tensor factorization models, but such statistics need many more samples to obtain reliable estimates, and identifiability still hinges on additional assumptions, such as consecutive words being persistently drawn from the same topic. In this work, we propose a new topic identification criterion using second order statistics of the words. The criterion is theoretically guaranteed to identify the underlying topics even when the anchor-word assumption is grossly violated. An algorithm based on alternating optimization, and an efficient primal-dual algorithm are proposed to handle the resulting identification problem. The former exhibits high performance and is completely parameter-free; the latter affords up to 200 times speedup relative to the former, but requires step-size tuning and a slight sacrifice in accuracy. A variety of real text copora are employed to showcase the effectiveness of the approach, where the proposed anchor-free method demonstrates substantial improvements compared to a number of anchor-word based approaches under various evaluation metrics.

In most existing robust array beam pattern synthesis studies, the bounded-sphere model is used to describe the steering vector (SV) uncertainties. In this letter, instead of bounding the norm of SV perturbations as a whole, we explore the amplitude and phase perturbations of each SV element separately, thereby obtaining a tighter SV uncertainty model. On the basis of this model, we formulate the robust pattern synthesis problem from the perspective of the min-max optimization, which aims to minimize the maximum side lobe response, while preserving the main lobe response. However, this problem is difficult due to the infinitely many nonconvex constraints. As a compromise, we employ the worst-case criterion and recast the problem as a convex second-order cone program (SOCP). To solve the SOCP, we further design an alternating direction method of multipliers based algorithm, which is computationally efficient by coming up with closed-form solutions in each step.

The joint design of hybrid beamforming matrices is conceived for multiuser mm-wave full-duplex (FD) multiple-input multiple-output (MIMO) relay-aided systems in the presence of realistic channel state information (CSI) errors. Specifically, considering a probabilistic CSI error model, we maximize the system's worst-case sum rate by jointly optimizing the base station's (BS's) analog and digital beamforming matrices, plus the analog receive and transmit beamforming matrices of the relay station (RS) as well as its digital amplify-and-forward beamforming matrix under practical constraints. Explicitly, the transmit power constraints of the BS and RS, the residual self-interference power constraint of the RS, the per-user quality of service constraints, and the unit-modulus constraints on the analog beamforming matrix elements are all taken into account. Since the resultant optimization problem is very challenging due to its highly nonlinear objective function and nonconvex coupling constraints, we first transform it into a more tractable form. We then develop a novel joint optimization algorithm based on the penalty dual decomposition (PDD) technique to solve the resultant problem. The proposed PDD-based algorithm performs double-loop iterations: the inner loop updates the optimization variables in a block coordinate descent fashion, while the outer loop adjusts the Lagrange multipliers and penalty parameter, hence ensuring convergence to the set of stationary solutions of the original problem. Our simulations show that the mm-wave FD hybrid MIMO relay systems relying on our new algorithm significantly outperform both their non-robust FD and conventional half-duplex counterparts.

Consider a network consisting of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipment's (UEs). For each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. We assume that the D2D transmission and cellular transmission are equally prioritized and share the same resources. To improve the network throughput, we maximize the sum rate by jointly optimizing the transmission mode of each UE pair and the associated transceivers. Due to the NP-hardness of this problem, we first perform some efficient approximation to it and then design an iterative algorithm, which is guaranteed to converge to a stationary solution by solving a series of weighted minimum mean square error (WMMSE) problems. The proposed algorithm has two distinguishing features. First, it only solves the WMMSE problem inexactly in each iteration, which thereby has a simplified algorithm structure and accelerated convergence behavior than the classical one. Second, we further fit the WMMSE problem into the alternating direction method of multipliers (ADMM) framework, making it amenable to parallel and distributed computation. Finally, the approximated problem is solved efficiently and distributively, with simple closed-form solutions in each step.

In this article, we address joint optimization of unmanned aerial vehicle (UAV) trajectories and user communication scheduling for a dual-UAV enabled secure communication system, where one UAV moves to serve multiple users on the ground in a time division multiple access (TDMA) mode while the other UAV in the area flies to jam the colluding eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum average secrecy rate among the users within each period by jointly optimizing UAV trajectories and user scheduling variables under the maximum UAV speed constraints, the UAV return constraints, and the discrete binary constraints on user scheduling variables. The resulting optimization problem is very challenging due to its highly nonconvex objective function and constraints. We then develop a novel algorithm based on the penalty concave-convex procedure (CCCP) technique to solve it. Based on our simulation results, the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

This paper investigates a multiuser mobile edge computing system under interference channels, where mobile users can offload their latency-sensitive (computation-intensive) tasks to the mobile edge server via a base station (BS). In this work, we seek to jointly optimize the user selection indicators for offloading and the computation resources, as well as the transmit power level of the offloading users in order to minimize the system energy consumption under latency-sensitive, computation and transmit power budget, transmission quality, and user selection constraints. The proposed optimization problem is nonconvex and highly coupled, which is difficult to solve. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then propose a concave-convex procedure (CCCP) based algorithm to obtain the resulting problem. Furthermore, a simplified algorithm is proposed to reduce the computational complexity. Simulation results are proposed to verify the proposed algorithms.

This paper considers a computational results sharing (CRS) system where user A wants to share its computational results with user B with the aid of a relay equipped with an mobile edge computing (MEC) server. The performance of the CRS systems can be greatly impacted by the relay forward protocol and resources allocation. To realize cooperative computation and communication in a relay aided mobile edge computing system, we develop a hybrid relay forward protocol and properly allocate the system computational and communication resources, where we seek to balance the execution delay and network energy consumption. The problem is formulated as a nondifferentialbe optimization problem which is nonconvex with highly coupled constraints. By exploiting the problem structure, we propose a lightweight algorithm based on inexact block coordinate descent method. Our results show that the proposed algorithm exhibits much faster convergence as compared with the popular concave-convex procedure based algorithm, while achieving good performance.

In this study, the authors consider the communication scenario where two sources communicate with the help of a single relay, modelled as a half‐duplex butterfly network. Closed‐form expressions of the outage probabilities are derived in the high signal‐to‐noise ratio (SNR) regime for the orthogonal amplify‐and‐forward and non‐orthogonal amplify‐and‐forward protocols. Then the expressions are approximated to enable power allocation. Closed‐form power allocation schemes are proposed for each protocol, where only statistical channel knowledge is required. It is shown that the authors analyses match well with simulation results and the approximated versions are very close to the simulation results. The proposed power allocation schemes achieve a large SNR gain over the equal‐power strategies and approach the optimal power allocation schemes.

In this paper, we investigate a novel unmanned aerial vehicle (UAV)-enabled secure communication system. Two UAVs are applied in this system where one UAV moves around to communicate with multiple users on the ground using orthogonal time-division multiple access while the other UAV in the area jams the eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum worst-case secrecy rate among the users within each period by jointly adjusting UAV trajectories and user scheduling under the maximum UAV speed constraints, the UAV return constraints, the UAV collision avoidance constraints, and the discrete binary constraints on user scheduling variables. Since the resulting optimization problem is very difficult to solve due to its highly nonlinear objective function and nonconvex constraints, we first equivalently transform it into a more tractable problem. In particular, the binary constraints are equivalently converted to a number of equality constraints. Then, we develop a novel joint optimization algorithm to handle the converted problem. In order to further improve the secrecy rate performance, we also extend the developed algorithm to the case with multiple jamming UAVs. The simulation results show that the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

This paper addresses the design of joint beamforming and jamming for a millimeter wave (mmWave) information surveillance system where a suspicious transmitter in the network sends messages to a suspicious receiver under the supervision of a surveillant controller (SC), which not only carries out the duty of a base station or other access point, but also legitimately monitor the suspicious link. Specifically, we seek to maximize the effective monitoring rate for information surveillance by jointly optimizing the analog transmit and receive beamforming vectors of the suspicious link, the analog jamming and monitoring beamforming vectors at the SC and the jamming signal's power level under transmit power, successful monitoring, and self-interference power constraints at the SC, along with unit modulus constraint on the elements of the radio frequency analog beamforming vectors. The resulting optimization problem is quite challenging due to the tight coupling of the design variables in the objective function and constraints. To solve it, we develop a novel algorithm based on the penalty dual decomposition (PDD) technique, where the exacting constraints are penalized and dualized into the objective function as augmented Lagrangian components. The proposed PDD-based algorithm performs double-loop iterations, i.e., the inner loop resorts to the concave-convex procedure to update the optimization variables; while the outer loop adjusts the Lagrange multipliers and penalty parameter of the augmented Lagrangian cost function. We show that the proposed PDD-based joint beamforming and jamming algorithm converges to a stationary solution of the original problem. Based on our simulation results, the proposed algorithm achieves significantly better performance than the conventional beamforming and jamming algorithms.

This paper considers the uplink of multiuser multiple-input multiple-output systems, where several mobile stations (MSs) cooperatively transmit hybrid messages, including common messages and private messages, to a single base station (BS). We aim to jointly design transmit precoding at the MSs' side and antenna selection at the BS side to maximize the achievable system throughput while reducing implementation complexity. The problem at hand is nonconvex and difficult to solve due to the antenna selection constraint. By exploiting the problem structure and linear relaxation, we propose using the Frank-Wolfe method and the well-known weighted mean-square error minimization approach to tackle the problem, leading to an efficient iterative algorithm. Moreover, due to the large number of antennas, the sparsity of antenna selection is also taken into account by introducing an l0-norm penalty function into the objective function. To tackle this nonconvex and discontinuous problem, we resort to quadratic approximation with smooth optimization and extend our proposed algorithm to the sparse optimization problem. The convergence of the proposed algorithms is analyzed and its effectiveness is verified by numerical examples in terms of the achieved system throughput.

A User Equipment (UE) beamforming method is proposed for massive MIMO based stratospheric communications with multiple High Altitude Platforms (HAPs). The method leverages multiple-antenna UEs to mitigate interference from neighboring HAPs. No additional channel information exchange between UEs and HAPs is needed. The beamforming vector is obtained at the UE side by using the semi-definite relaxation based method. Both the Rician channel model and the HAP- MIMO channel model based on the dynamic evolution of scatters are adopted. Simulations under various environmental configurations demonstrate the performance improvement of the proposed method.

This paper investigates multiuser full-duplex (FD) multiple-input multiple-output (MIMO) relay systems. We study joint design of the base station (BS) beamforming matrix and the relay station (RS) amplify-and-forward (AF) transformation matrix to maximize the system sum rate with only imperfect channel state information (CSI) at the RS. To deal with highly coupled design variables in the objective function and constraints in the optimization problem, we develop a novel algorithm based on the penalty dual decomposition (PDD) algorithmic framework. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

This paper studies the multiple-input multiple- output (MIMO) millimeter wave (mmWave) systems with non-linear power consumption model for 5G network. A new non-linear power consumption model is investigated, consisting of the power cost generated by the circuit and the non-linear power amplifiers. In this work, we aim to optimize the hybrid transceiver to maximize the system capacity subject to the resultant non-linear power constraint. In order to address this problem, we first transform the original optimization problem to a more tractable problem based on the weighted minimum mean squared error (WMMSE) approach. Then, we propose a novel transceiver design algorithm based on the penalty dual decomposition (PDD) optimization framework to address this problem. Moreover, a simplified algorithm is also proposed by using linear approximation. The effectiveness of the proposed algorithm is verified by simulation results.

Many problems of recent interest in signal processing, machine learning and wireless communications can be posed as nonconvex nonsmooth optimization problems. These problems are generally difficult to solve especially when the optimization variables are nonlinearly coupled in some nonconvex constraints. In this paper, we propose an algorithm named “penalty dual decomposition” (PDD) method, for the minimization of a nonconvex nonsmooth objective subject to nonconvex constraints. We show that the PDD converges to KKT solutions under certain constraint qualification condition. Simulations corroborate the excellent performance of the PDD method.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output (MU-MIMO) millimeter wave (mmWave) systems, where a number of mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation (5G) wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers (LNA). We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog) and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems which are solved via an alternating optimization (AO) method. Specifically, the antenna selection matrix is optimized via the concave-convex procedure (CCCP); the weighted mean-square error minimization (WMMSE) approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization (MO). The convergence of the proposed algorithm is analysed and its effectiveness is verified by simulation.

Wireless transmission with lens antenna arrays is becoming more and more attractive for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with limited radio frequency (RF) chains due to their energy-focusing capability. In this paper, we consider the joint design of beam selection and precoding to maximize the sum rate of a downlink single-sided lens MU-MIMO mmWave system under transmit power constraints. We first formulate the optimization problem into a tractable form using the popular weighted minimum mean squared error (WMMSE) approach. To solve this problem, we then propose an efficient joint beam selection and precoding algorithm based on the innovative penalty dual decomposition (PDD) method. Simulation results demonstrate that our proposed algorithm can achieve near-optimal performance when compared to the fully digital precoding scheme and thus outperform the competing methods.

This work studies the joint transceiver design for a full-duplex (FD) cloud radio access network (C- RAN) with simultaneous wireless information and power transfer (SWIPT). In the considered network, a number of FD remote radio heads (RRHs) receive information from uplink users (UUs), while transmitting both information and energy to a set of half-duplex (HD) downlink users (DUs) with power splitting receivers. Based on the particular problem structure, a block coordinate descent (BCD) method is proposed to minimize the total transmission power subject to both uplink-downlink quality of service (QoS) constraints and energy harvesting (EH) constraints. Although the problem has complicated constraints coupling a set of transceivers, uplink transmit power levels, and receive power splitting ratios, we prove that the proposed BCD algorithm converges to a Karush-Kuhn- Tucker (KKT) solution. Simulation results validate the effectiveness of the proposed algorithm as compared with the traditional HD scheme.

In cloud radio access network, a baseband unit (BBU) performs the baseband processing for a cluster of low‐power low‐cost remote radio heads (RRHs) that are connected to the BBU through low‐latency fronthaul links. In this study, the authors study the optimisation of two energy‐efficient compression and precoding strategies which take transmit power constraint, fronthaul capacity constraint and user specific rate constraint into account. To overcome the non‐convexity nature of the original problem, they first transform the objective of the original problem into a parameterised subtractive form and obtain an approximate convex problem via the successive convex approximation. Then, an effective optimisation algorithm with provable convergence is designed to solve the effective problem. Numerical results reveal that the proposed scheme outperforms the conventional maximum sum rate and minimum total power consumption schemes in terms of the energy‐efficiency criterion. In particular, compression after precoding strategy outperforms compression before precoding strategy when both of their RRHs perform the same user scheduling, while the opposite conclusion can be drawn otherwise.

In this study, the capacity problem in a two‐way fading channel is considered. The authors propose amplify‐and‐forward with lattice codes (AF&LC), exploiting a modulo operation at the relay. Without destroying the construct of codebook, the modulo operation reduces the power of the received signals, and thus achieves a larger power‐scaled gain than the amplified‐and‐forward with random codes (AF&RC). It is proved that AF&LC outperforms AF&RC in the high signal‐to‐noise ratio (SNR) regime by employing theoretical analyses. By simulating one‐dimension lattice codes, AF&LC achieves about 1.5 dB SNR gain over AF&RC with error probability 10 −2 . Compared with decode‐and‐forward with lattice codes (DF&LC) and fixed modulo‐and‐forward (FMF), AF&LC achieves larger rate region which is closer to upper bound in some scenarios. Moreover, AF&LC can work in fading two‐way channels without feedback schemes, which is easier to be implemented than DF&LC, FMF and compress‐and‐forward.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output millimeter wave systems, where several mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers. We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog), and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate under a set of constraints. The corresponding optimization problem is nonconvex and difficult to solve, mainly due to the receive antenna selection and constant modulus constraints on the analog receiving matrix. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems, which are solved via an alternating optimization method. The latter iteratively updates the antenna selection matrix, the transmit beamforming vectors, and the hybrid receiving matrices by sequentially addressing each subproblem while keeping the other variables fixed. Specifically, the antenna selection matrix is optimized via the concave-convex procedure; the weighted mean-square error minimization approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization. The convergence of the proposed algorithm is analyzed and its effectiveness is verified by simulation.

This letter studies a single-user power splitting-based multiple-input multiple-output system for simultaneous wireless information and power transfer. We aim to maximize the harvested power by joint design of transmit signal covariance matrix and receive power splitting factor under both a system rate constraint and a total power constraint. The harvested power maximization problem is difficult to solve due mainly to the nonconcave objective and the nonlinear coupling of design variables in the constraints. To tackle these challenges, we first derive a good approximation of the problem by ignoring some negligible noise terms and then further simplify it to a more tractable form by well exploiting the problem structure. Based on the Frank-Wolfe algorithm, we propose a simple yet efficient iterative algorithm to address the resulting problem. Numerical results validate the efficiency of the proposed algorithm.

This paper addresses joint transceiver design for secure downlink communications over a multiple-input multiple-output relay system in the presence of multiple legitimate users and malicious eavesdroppers. Specifically, we jointly optimize the base station (BS) beamforming matrix, the relay station (RS) amplify-and-forward transformation matrix, and the covariance matrix of artificial noise, so as to maximize the system worst-case secrecy rate in the presence of the colluding eavesdroppers under power constraints at the BS and the RS, as well as quality of service constraints for the legitimate users. This problem is very challenging due to the highly coupled design variables in the objective function and constraints. By adopting a series of transformation, we first derive an equivalent problem that is more tractable than the original one. Then, we propose and fully develop a novel algorithm based on the penalty concave-convex procedure (penalty-CCCP) to solve the equivalent problem, where the difficult coupled constraint is penalized into the objective and the resulting nonconvex problem is solved at each iteration by resorting to the CCCP method. It is shown that the proposed joint transceiver design algorithm converges to a stationary solution of the original problem. Finally, our simulation results reveal that the proposed algorithm achieves better performance than other recently proposed transceiver designs.

Owing to abundant spectrum resources, millimeter wave (mmwave) communication promises to provide Gbps data rates, which, however, may be restricted by large path-loss. Thus, antenna arrays are commonly used along with beam alignment (BA) as an important step to achieve the array gain. Efficient BA relies on the beam training codebook design. In this paper, we propose a new hierarchical codebook to achieve uniform BA performance with low overhead. To better elaborate on the design principle, a single-path channel model is considered first to frame the proposal. The codebook design is formulated as an optimization problem, where the ripple in the main/side lobes is constrained such that each training beam is close to the ideal one with a flat magnitude response and a narrow transition band. Then, we propose an efficient algorithm to find such a beam training codebook. Furthermore, we derive closed-form expressions of the BA misalignment probability or error rate of the proposed beam training codebook. Our results reveal that using the proposed codebook, the error rate of tree-search-based BA exponentially decreases with the SNR for a given channel, and linearly decreases in the log-log coordinate axis for a fading channel. We further propose a power allocation scheme used in different training stages to further improve the BA performance. Finally, the proposed framework is extended to the more complex case of multi-path channels. Numerical results confirm the effectiveness of the proposed training codebook and power allocation scheme as well as the accuracy of the performance analysis.

In this paper, we propose joint transceiver design algorithms for the full-duplex K -pair multiple-input multiple-output interference channel with simultaneous wireless information and power transfer. To mitigate and exploit the complex interference, we consider two important utility optimization problems, i.e., the sum power minimization problem and the sum-rate maximization problem. In the first problem, our aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization (AO) and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, where the concave-convex procedure (CCCP), the minimum mean-square error (MMSE) criterion, and the semidefinite relaxation technique are, respectively, employed to compute the vectors of power splitting ratios, the receiving matrices, and the transmitting beamforming vectors. Two simplified algorithms based on fixed beamformers, namely, the maximum ratio transmission and the maximum signal-to-interference-leakage beamformers are also proposed. In the second problem, our aim is to maximize the sum-rate under additional power and EH constraints. Due to the highly non-convex nature of this problem, we first reformulate it into an equivalent-weighted MMSE problem by introducing suitable weight factors, such that the global optima of the two problems are identical. Then, by utilizing the concept of AO and CCCP, we show that the equivalent problem can be efficiently solved. Again, with the aid of the fixed beamformers, two simplified algorithms are provided to reduce the computational complexity. Simulation results are presented to validate the effectiveness of the proposed algorithms.

This paper investigates a cellular network powered by a micro-grid (MG) in the context of green communications, which integrates the conventional generators, energy storage devices, and renewable energy generators, so as to supply electricity to base stations (BSs). Under this model, we study the efficiency aspect of the MG-powered cellular network from the economical perspective. Specifically, the concept of cost efficiency (CE) is employed to measure the sum rate delivered per dollar. Then, our goal is to maximize this CE subject to a series of constraints, including multi-variable coupling and time coupling constraints. Particularly, we assume the zero-forcing beamforming scheme employed by the BSs. To address this established fractional CE optimization problem, we first apply the Dinkelbach method, and then propose a low-complexity algorithm based on the alternating direction method of multipliers approach to jointly schedule power generation in the MG and optimize transmit power for BSs. We introduce a number of auxiliary variables to design a special variable splitting scheme so that the coupling inequality constraints can be separable among two variable sets. Consequently, the proposed algorithm only incorporates simple updates in each step and thus can be implemented in a parallel and completely distributed fashion. Simulation results demonstrate the convergence and energy scheduling performance of the proposed algorithm.

This paper studies joint transceiver design for a full-duplex (FD) cloud radio access network with simultaneous wireless information and power transfer. In the considered network, a number of FD remote radio heads receive information from uplink users, while transmitting both information and energy to a set of half-duplex (HD) downlink users with power splitting receivers. We aim to minimize the total power consumption with both uplink-downlink quality of service constraints and energy harvesting constraints. The resulting problem is challenging, because various design parameters, such as the transceiver beamformers, the uplink transmit power, and the receive power splitting ratios, are tightly coupled in the constraints. Four different solution approaches are proposed for the joint transceiver design problem, each one leading to a different numerical algorithm. In particular, a block coordinate descent method is proposed, and by exploiting the problem structure, we prove that the algorithm converges to a Karush-Kuhn-Tucker solution, despite the coupling of various design variables in the constraints. Simulation results validate the effectiveness of the proposed algorithms as compared with the traditional HD scheme.

This paper investigates a multi-cell system powered by a micro-grid (MG), where the conventional generators (CGs), energy storage devices (ESDs) and renewable energy generators (REGs) are scheduled to supply electricity to base stations (BSs) at different prices respectively. Under this energy schedule model, we study the efficiency aspect of the MG-powered multi-cell systems from the economical perspective, and propose a new concept of efficiency, which is referred to as cost efficiency (CE). Specifically, we consider the ratio of the sum rate for all BSs to the total energy cost that the system spends in providing electricity for BSs. Assuming that the zero-forcing (ZF) beamforming scheme is employed by the BSs, our goal is to maximize the CE by jointly scheduling energy in the MG and allocating the transmit power at the BSs. We apply the Lagrange duality decomposition technique and Dinkelbach method to address the established CE optimization problem. Simulations are provided to validate the effectiveness of the proposed algorithm.

This paper addresses the transceiver design problem for secure downlink communications over a multiple-input multiple-output (MIMO) relay system in the presence of multiple eavesdroppers. A new algorithm based on alternating optimization (AO) is first proposed to maximize the signal-to-noise ratio (SNR) of a legitimate receiver under power constraints at the base station (BS) and the relay station (RS) and a set of secrecy constraints, by using the semidefinite relaxation (SDR) technique. To reduce complexity, a simplified design algorithm based on switched relaying (SR) is also proposed, in which both the BS and the RS are equipped with a codebook of permutation matrices. Based on this codebook, we construct a number of latent transceivers, each consisting of a BS beamforming vector and an optimally scaled RS permutation matrix. We use the bisection search and second-order cone programming (SOCP) techniques to design each latent transceiver and choose the optimal one with the largest SNR. We also develop an efficient approach to construct the codebook of permutation matrices. Our results show that the SR based algorithm significantly reduces the computational complexity while maintaining a similar performance to the AO based algorithm.

The nonnegative matrix factorization (NMF) has been a popular model for a wide range of signal processing and machine learning problems. It is usually formulated as a nonconvex cost minimization problem. This work settles the convergence issue of a popular algorithm based on the alternating direction method of multipliers proposed in Boyd et al 2011. We show that the algorithm converges globally to the set of KKT solutions whenever certain penalty parameter ρ satisfies ρ > 1. We further extend the algorithm and its analysis to the problem where the observation matrix contains missing values. Numerical experiments on real and synthetic data sets demonstrate the effectiveness of the algorithms under investigation.

Consider a network that consists of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipments (UEs). In each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. All the D2D transmission and the uplink transmission of BS relaying share the same resources, while causing interference to each other. To improve the network throughput, we consider a sum-rate maximization problem by jointly optimizing the transmission mode of each UE pair and the corresponding transceivers. Due to the NP-hardness of the problem, we seek for some efficient approximate solutions to it. To this end, we first reformulate the problem by the weighted MMSE (WMMSE) approach, and then fit it into the alternating direction method of multipliers (ADMM) framework. Finally, an efficient distributed algorithm, which converges to a stationary solution, is developed. In particular, each step of the algorithm can be computed in closed form, thus giving it very low complexity.

This paper studies joint source transmit beamforming and relay amplification matrix design to achieve rate maximization for full-duplex (FD) MIMO amplify-and-forward (AF) relay systems with consideration of relay processing delay (RPD). The problem is difficult to solve due mainly to the self-interference constraint induced by the RPD. In this paper, we first propose a penalty-based algorithmic framework, called P-BSUM, for a class of constrained optimization problems with difficult equality constraints in addition to some convex constraints. We then apply the P-BSUM algorithm to the rate maximization problem and obtain a simple iterative algorithm. Finally, numerical results illustrate the efficiency of the proposed algorithm.

In this paper, we propose a joint transceiver design algorithm for the full-duplex (FD) K-pair multiple- input multiple-output (MIMO) interference channel with simultaneous wireless information and power transfer (SWIPT). The aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, aimed at successively optimizing: 1) the power splitting (PS) vectors of the EH nodes; 2) the receive beamforming vectors; 3) the transmit beamforming vectors.The first step is carried out based on concave-convex procedure (CCCP), the second step is based on the minimum mean square error (MMSE) criterion and the third step resorts to using semidefinite relaxation (SDR). Simulation results are presented to validate the effectiveness of the proposed algorithm.

In this paper, we investigate a multiuser multiple-input single-output relay system with simultaneous wireless information and power transfer, where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. Assuming that both base station (BS) and relay station (RS) are equipped with multiple antennas, we study the joint transceiver design problem for the BS beamforming vectors, the RS amplify-and-forward transformation matrix, and the power splitting (PS) ratios at the single-antenna receivers. The aim is to minimize the total transmission power of the BS and the RS under both signal-to-interference-plus-noise ratio and EH constraints. First, an iterative algorithm based on alternating optimization (AO) and with guaranteed convergence is proposed to successively optimize the transceiver coefficients. This AO-based approach is then extended into a robust transceiver design against norm bounded errors in channel state information (CSI), by using semidefinite relaxation and the S-procedure. Second, a novel design scheme based on switched relaying (SR) is proposed that can significantly reduce the computational complexity and overhead of the AO-based designs while maintaining a similar performance. In the proposed SR scheme, the RS is equipped with a codebook of permutation matrices. For each permutation matrix, a latent transceiver is designed, which consists of BS beamforming vectors, optimally scaled RS permutation matrix, and receiver PS ratios. For the given CSI, the optimal latent transceiver with the lowest total power consumption is selected for transmission. We propose concave-convex procedure-based and subgradient-type iterative algorithms, respectively, to design the latent transceivers under perfect and imperfect CSI. Simulation results are presented to validate the effectiveness of all the proposed algorithms.

In this paper, we consider the power splitting technique for multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH) respectively. Specifically, assuming norm-bounded errors (NBE) in the channel state information (CSI), we study the robust joint beamforming and power splitting (JBPS) design problem, where the total transmission power is minimized subject to both signal-to-interference- plus-noise ratio (SINR) and EH constraints. We first propose an efficient approximation method based on semidefinite relaxation (SDR) for solving the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. Then, a low complexity algorithm is proposed using EH relaxation and cutting-set philosophy, which partitions the original problem into an alternating sequence of optimization and worst-case analysis subproblems with guaranteed convergence. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.

This paper considers a basic MIMO information-energy broadcast system, where a multi-antenna transmitter transmits information and energy simultaneously to a multi-antenna information receiver and a dual-functional multi-antenna energy receiver which is also capable of decoding information. Due to the open nature of wireless medium and the dual purpose of information and energy transmission, secure information transmission while ensuring efficient energy harvesting is a critical issue for such a broadcast system. Providing that physical layer security techniques are adopted for secure transmission, we study beamforming design to maximize the achievable secrecy rate subject to a total power constraint and an energy harvesting constraint. First, based on semidefinite relaxation, we propose global optimal solutions to the secrecy rate maximization (SRM) problem in the single-stream case and a specific full-stream case. Then, we propose inexact block coordinate descent (IBCD) algorithm to tackle the SRM problem of general case with arbitrary number of streams. We prove that the IBCD algorithm can monotonically converge to a Karush-Kuhn-Tucker (KKT) solution to the SRM problem. Furthermore, we extend the IBCD algorithm to the joint beamforming and artificial noise design problem. Finally, simulations are performed to validate the effectiveness of the proposed beamforming algorithms.

Recently, utilizing renewable energy for wireless system has attracted extensive attention. However, due to the instable energy supply and the limited battery capacity, renewable energy cannot guarantee to provide the perpetual operation for wireless sensor networks (WSN). The coexistence of renewable energy and electricity grid is expected as a promising energy supply manner to remain function of WSN for a potentially infinite lifetime. In this paper, we propose a new system model suitable for WSN, taking into account multiple energy consumptions due to sensing, transmission and reception, heterogeneous energy supplies from renewable energy, electricity grid and mixed energy, and multi-dimension stochastic natures due to energy harvesting profile, electricity price and channel condition. A discrete-time stochastic cross-layer optimization problem is formulated to achieve the optimal trade-off between the time-average rate utility and electricity cost subject to the data and energy queuing stability constraints. The Lyapunov drift-plus-penalty with perturbation technique and block coordinate descent method is applied to obtain a fully distributed and low-complexity cross-layer algorithm only requiring knowledge of the instantaneous system state. The explicit trade-off between the optimization objective and queue backlog is theoretically proven. Finally, through extensive simulations, the theoretic claims are verified, and the impacts of a variety of system parameters on overall objective, rate utility and electricity cost are investigated.

This paper considers a MISO interference channel with simultaneous wireless information and power transfer. We aim to jointly optimizing transmit beamformers and receive power splitting factors to minimize the total transmission power subject to both the signal-to-interference-plus-noise ratio constraints and energy harvesting constraints. We propose relaxation solution to the power minimization problem and provide an easily-checkable sufficient condition to confirm when the relaxation solution is optimum. Moreover, we propose a simple suboptimal solution to the power minimization problem when the sufficient optimality condition does not hold. Simulation results indicate that the proposed solution outperforms the existing suboptimal solution and reaches optimality.

This paper studies minimum mean square error (MMSE)-based training signal design for MIMO channel estimation with correlated disturbance (i.e., interference plus noise). First, we consider training signal design for Kronecker-structured MIMO channel estimation where both channel and disturbance are assumed in Kronecker structures. We prove the optimal training sequence structure for arbitrarily Kronecker-structured MIMO channel estimation. Using the optimal training sequence structure, we show that the MSE minimization problem can be globally solved. Second, we consider the training signal design problem in the case of general channel and disturbance model (i.e., without Kronecker structure assumption). We propose a simple iterative algorithm based on block coordinate descent method which can keep the MSE nonincreasing at each iteration. Finally, simulation results indicate good performance of the proposed iterative algorithm by comparing with the optimal training signal design method.

This paper studies a multi-user multiple-input single-output (MISO) downlink system for simultaneous wireless information and power transfer (SWIPT), in which a set of single-antenna mobile stations (MSs) receive information and energy simultaneously via power splitting (PS) from the signal sent by a multi-antenna base station (BS). We aim to minimize the total transmission power at BS by jointly designing transmit beamforming vectors and receive PS ratios for all MSs under their given signal-to-interference-plus-noise ratio (SINR) constraints for information decoding and harvested power constraints for energy harvesting. First, we derive the sufficient and necessary condition for the feasibility of our formulated problem. Next, we solve this non-convex problem by applying the technique of semidefinite relaxation (SDR). We prove that SDR is indeed tight for our problem and thus achieves its global optimum. Finally, we propose two suboptimal solutions of lower complexity than the optimal solution based on the principle of separating the optimization of transmit beamforming and receive PS, where the zero-forcing (ZF) and the SINR-optimal based transmit beamforming schemes are applied, respectively.

The transmission rate, delivery reliability, and network lifetime are three fundamental but conflicting design objectives in energy-constrained wireless sensor networks (WSNs). In this paper, based on stochastic network utility maximization framework, we address the optimal rate-reliability-lifetime tradeoff with time-varying channel capacity constraint, reliability constraint, and energy constraint. By introducing the weight parameters, we combine the optimization objectives of rate, reliability, and lifetime into a single objective to characterize the tradeoff among them. However, the optimization formulation of the rate-reliability-reliability tradeoff is neither separable nor convex. Through a series of transformations, a separable problem is derived, and an efficient distributed stochastic subgradient algorithm is proposed via dual decomposition and stochastic subgradient techniques. It is proved that the proposed algorithm can converge to the global optimum with probability one. Numerical examples confirm its convergence. In addition, numerical examples investigate the impact of weight parameters on the rate utility, reliability utility, and network lifetime, which provide guidance to properly set the value of weight parameters for a desired performance of WSNs according to the realistic application's requirements.

MIMO wiretap channel consists of a multiple antenna transmitter, a legitimate multiple antenna receiver and a multiple antenna eavesdropper. Signaling strategy optimization for MIMO wiretap channel, including secrecy capacity maximization problem and power minimization problem with a secrecy rate constraint, are studied. We identify when the two problems are globally solvable. Further, iterative algorithms based on concave-convex procedure are proposed for the two problems, which can monotonically converge to a Karush-Kuhn-Tucker (KKT) point of the two problems. Numerical examples validate the effectiveness of the proposed algorithms.

Using cyclic prefix (CP), the transmission schemes, orthogonal frequency-division multiplexing (OFDM), single carrier block transmission, and time reversal, can be unified as linear block precoding. Considering frequency-selective channels, this paper studies linear block precoding for CP-based multi-point transmission with per-antenna power constraints (PAPCs) under capacity maximization and mean-square-error (MSE) minimization criteria. We show that, the optimal precoders for both criteria could be, but not necessarily, in the form of OFDM transmission (i.e., an inverse discrete fourier transformation (IDFT) matrix multiplying a complex diagonal matrix). Based on the optimal precoder structure, the two problems are simplified to two matrix-free optimization problems for which we prove strong duality holds. Moreover, it is shown that the dual problems can be equivalent to two unconstrained convex optimization problems. Efficient optimum precoding algorithms are proposed for both problems. Simulation results show that the maximum capacity (or minimum MSE) in the PAPC case almost coincides with that in the sum power constraint (SPC) case when the power budgets for each antenna are equal, but a capacity (or MSE) gap exists between the two power constraint cases when the power budgets for each antenna are different.

Consider the MIMO interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to the users in other cells. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, we extend the algorithm to a general class of utility functions and establish its convergence. The resulting algorithm can be implemented in a distributed asynchronous manner. The effectiveness of the proposed algorithm is validated by numerical experiments.

We consider the robust beamforming problem under imperfect channel state information (CSI) subject to SINR constraints in a downlink multiuser MISO system. One popular approach to solve this nonconvex optimization problem is via semidefinite relaxation (SDR). In this paper, we prove that the SDR method is tight when the channel uncertainty bound is small or when the base station is equipped with two antennas.

Using cyclic prefix (CP), the transmission schemes, orthogonal frequency-division multiplexing (OFDM), single carrier block transmission, and time reversal, can be unified as linear block precoding. Considering frequency-selective channels, this paper studies linear block precoding for CP-based multi-point transmission with individual antenna power constraints under capacity maximization criterion. We prove that, the optimal precoder could be, but not necessarily, in the form of OFDM transmission (i.e., an inverse discrete fourier transformation (IDFT) matrix multiplying a complex diagonal matrix). Further, we show that the capacity maximization problem with individual power constraints can be simplified as an unconstrained problem, which allows us to solve the problem using the simple gradient decent method. Simulation results show that the proposed precoding method achieves near-optimal performance.

In this paper, a cooperative localization algorithm is proposed that considers the existence of obstacles in mobility-assisted wireless sensor networks (WSNs). An optimal movement scheduling method with mobile elements (MEs) is proposed to address limitations of static WSNs in node localization. In this scheme, a mobile anchor node cooperates with static sensor nodes and moves actively to refine location performance. It takes advantage of cooperation between MEs and static sensors while, at the same time, taking into account the relay node availability to make the best use of beacon signals. For achieving high localization accuracy and coverage, a novel convex position estimation algorithm is proposed, which can effectively solve the problem when infeasible points occur because of the effects of radio irregularity and obstacles. This method is the only rangefree based convex method to solve the localization problem when the feasible set of localization inequalities is empty. Simulation results demonstrate the effectiveness of this algorithm.

In this paper, we consider the range-based sensor network localization with inaccurate anchor position information. First, a novel optimization algorithm named sequential greedy optimization (SGO) algorithm is proposed, and then two distributed localization algorithms are obtained: the first is obtained by applying the SGO algorithm to a convex formulation of the localization problem, named CSGLA; while the second is obtained by applying the SGO algorithm to a nonconvex formulation of the localization problem, named NCSGLA. The CSGLA must converge globally while the NCSGLA may converge locally. Both algorithms are partially asynchronous and can be implemented in a distributed fashion in networks. We demonstrate the localization performance via simulations. Simulation results show that, 1) the CSGLA algorithm works faster than the synchronous algorithm with the same localization accuracy; 2) with a reasonably good initialization, the NCSGLA can work much better than the CSGLA.

The range-based localization problem often arises in TOA or RSSI based position estimation schemes. It is well-known that such a localization problem can be formulated as a nonlinear least-squares (NLS) estimation problem. In this paper, we formulate the problem as a constrained optimization problem, which is equivalent to the general NLS problem. By using a greedy optimization strategy, we derive a simple iterative algorithm with closed-form expressions for the NLS localization, which can be implemented in a distributed way. Simulation results show that the localization performance of the proposed localization algorithm is very close to the Cramer-Rao lower bound.

In this paper, a cooperative localization algorithm is proposed that considers the existence of obstacles in mobility-assisted wireless sensor networks (WSNs). In this scheme, a mobile anchor (MA) node cooperates with static sensor nodes and moves actively to refine location performance. The localization accuracy of the proposed algorithm can be improved further by changing the transmission range of mobile anchor node. The algorithm takes advantage of cooperation between MAs and static sensors while, at the same time, taking into account the relay node availability to make the best use of beacon signals. For achieving high localization accuracy and coverage, a novel convex position estimation algorithm is proposed, which can effectively solve the localization problem when infeasible points occur because of the effects of radio irregularity and obstacles. This method is the only range-free based convex method to solve the localization problem when the feasible set of localization inequalities is empty. Simulation results demonstrate the effectiveness of this algorithm.

Knowing the positions of nodes is essential to many wireless sensor network applications. In contrast to range-based localization methods, range-free methods are more appealing since they do not need additional expensive hardware for ranging. A range-free localization problem is a generalization of unit disk graph coordinates realization problem and in essence a feasibility problem with quadratic inequalities, which is NP- hard. In this paper, we formulate the range-free localization problem as a nondifferentiable optimization problem solved by a polynomial-time algorithm, normalized incremental subgradient (NIS) algorithm. Extensive simulations have been conducted. The simulation results show that the NIS-based localization algorithm significantly outperforms the MDS-MAP method and the SDP method, whether the network is regular or not.

When addressing the energy-based source localization problem using wireless sensor networks, distributed localization method is necessary to reduce the energy and bandwidth consumption. In this paper, a novel distributed source localization method called projection onto the nearest local minimum (PONLM) is proposed, which can be carried out at each of active nodes with quite lightweight computation, in contrast to most existing centralized method. Simulation results show our method can yield much better performance than the previous methods.

Location awareness is of great importance for many wireless sensor network applications. However, it is too expensive to equip all nodes with GPS receivers or configure the location for each node manually. Consequently, many localization methods have been proposed. While most of them are range-based, we propose a range-free localization method, which is based on semidefinite programming. The method can be not only used for relative localization but also for absolute localization. Extensive simulations have been conducted. The results show that 1) the method performs better with fewer anchors, especially for relatively uniform networks of high connectivity level, and 2) the method is more robust to anchor placement, as compared to the popular MDS-MAP method which is based on multidimensional scaling(MDS) technique. Moreover, the method can intrinsically keep the proximity of neighbor nodes, whether the network is regular or not.

A new incremental optimization algorithm called normalized incremental subgradient (NIS) algorithm is proposed in this letter, which can be used for distributed maximum likelihood estimation (MLE). Its convergence with a diminishing stepsize has been proved and analyzed theoretically. We then apply the NIS algorithm to the energy-based sensor network source localization problem where the decay factor of the energy decay model is unknown. Simulation results show it can achieve very high estimation performance, which is only somewhat lower than that of the centralized localization method based on global optimization techniques, but with hundreds of times lower computational complexity than the centralized method.