With the widespread adoption of Internet of Things (IoT) devices, device identification has emerged as a critical challenge for ensuring the security of wireless communications. Radio Frequency (RF) fingerprinting, a physical-layer security approach that requires no hardware modifications, has attracted considerable attention for its cost-effectiveness and robust security properties. Conventional RF fingerprinting models insufficiently exploit signal characteristics, thereby constraining recognition accuracy. To address this limitation, we propose a Multi-Channel Attention Residual Fusion (MCARF) method. It integrates five heterogeneous signal representations, including raw IQ data, individual in-phase (I) and quadrature (Q) components, and features derived from the Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT), to comprehensively capture device-specific characteristics. To effectively manage the heterogeneity across these channels, a Dual Attention Residual Block (DARB) is introduced to amplify critical feature responses while mitigating the vanishing gradient issue in deep neural networks. Additionally, a Multi-Channel Feature Fusion (MCFF) module is developed to adaptively learn inter-channel feature weights through attention mechanisms, thereby enhancing relevant information and suppressing redundant or noisy data. Extensive experiments conducted on two representative datasets, namely a simulated dataset under varying SNR conditions and the real-world ORACLE dataset, demonstrate that MCARF consistently outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score. Moreover, the MCARF method exhibits superior discriminative capability, particularly in challenging low-SNR environments.

With the increasing demands for computational efficiency and service quality in Internet of Things (IoT) applications, task offloading in edge computing faces many key challenges, such as complex task dependencies, limited service caches, and conflicting optimization objectives. To address these issues, this paper constructs a comprehensive optimization model that comprehensively considers multiple performance metrics and deployment constraints. Although reinforcement learning (RL) methods have global optimization capabilities, they often suffer from reward sparsity problems in large decision spaces. In contrast, heuristic algorithms are efficient and practical, but lack adaptability and global coordination. Therefore, we propose BiRLH, a Bi-level optimization framework that integrates reinforcement learning (RL) and heuristic algorithms, which decomposes the offloading problem into dependency graph structure enhancement and task scheduling plan optimization. In the upper layer, a structured action space and a dynamic masking mechanism are designed to guide the insertion of admissible edges to enhance the dependency graph structure. In the lower layer, a critical path-based heuristic algorithm is used to generate an efficient scheduling plan. Experimental results show that BiRLH outperforms the baseline models under various task topologies and edge resource conditions, achieving higher hit rates, lower average performance, and strong generalization capabilities.

Text-to-image generation applications based on diffusion models face substantial challenges in computational efficiency and latency, particularly in time-sensitive scenarios, due to the inherently iterative denoising process. Although approximate caching techniques can reduce denoising iterations by reusing intermediate states of diffusion models, existing approaches fail to adequately capture user request behaviors. It is observed that users tend to issue a large number of prompt requests within short time intervals (bursty patterns), and that prompts from the same user often exhibit high similarity over short time periods (temporal locality). In this work, we define and formalize the Intermediate State Selection (ISS) problem to minimize denoising iterations. We further prove the NP-hardness of the ISS problem via a polynomial-time reduction from the Dominating Set problem. We then exploit both characteristics of prompt requests and present EdgeDiffusion, a novel edge-cloud cooperative framework in which the cloud retains image generation, while prompts caching and intermediate state selection are offloaded to edge servers. Specifically, we design an ISS algorithm that optimizes state reuse by leveraging temporal locality and an adaptive caching strategy tailored to bursty patterns. Experimental results on real-world datasets demonstrate that EdgeDiffusion achieves 18.3%-77.3% computational savings over baseline strategies (NIRVANA, qLRU-AC, LRU and LFU), while maintaining 98% quality of images.

Detecting tiny objects in remote sensing images has always been a challenging and intensive research area. This problem has not been well solved due to the fact that object detection in remote sensing images is characterized by large scale variations and complex backgrounds. On this basis, we propose the EMS-Net constructed based on YOLOv8s for tiny object detection network in remote sensing images. First, a new module multi-branch context aggregation(MCA) is proposed to improve deep feature extraction and deep feature fusion of the model. In addition, we use our self-designed multi-scale feature communication module (MFCM) aimed at reducing the loss of semantic information of object and mitigating the obstruction of foreground object by complex background. Finally, Wise IoU-Normalized Wasserstein distance (WIoU-NWD) is used as the bounding box regression loss to adapt the model to different object scale while improving the ability to localize tiny object. Comprehensive experiments on three popular datasets demonstrate that our method outperforms existing detectors, particularly in detecting tiny objects. Specifically, our approach achieves the mean average precision (mAP) of 77.2% on the DIOR dataset, 96.7% on the RSOD dataset and 75.1% on the DOTA-v1.5 dataset.

With the advancement of the industrial Internet of Things, minimizing delay has become a critical performance metric for many industrial applications. Edge computing effectively addresses this requirement by offloading tasks to nearby edge servers, significantly reducing task processing delay, including both transmission and computation delays at local or edge servers. However, current researches often focus on isolated aspects of the task processing delay and typically handle multiple simultaneous tasks by dividing computing capacity for concurrent processing, which can lead to increased delays. In this article, we propose a comprehensive delay model that captures the entire process from task generation to completion, termed the overall delay of task processing (ODTP), along with a computing resource allocation strategy that sequentially allocates computing resource based on an optimized scheduling order (SAOS). To minimize the ODTP in resource-constrained, container-based industrial edge computing environments, we introduce an optimization problem termed ODTP-M and a corresponding solution, ODTP-O, which optimizes task offloading, computing resource scheduling order, container caching, and image caching. Due to the nonlinear coupling of variables, which makes solving ODTP-M directly challenging, we transform it as an equivalent linear problem, termed l-ODTP-M, using a series of mathematical techniques. Numerical simulation results demonstrate that this transformation quickly achieve the global optimal solution of ODTP-M. Furthermore, we developed an environment to simulate the task process in resource-constrained container-based industrial edge computing. Compared to concurrent task processing and random order sequential processing, SAOS achieves the lowest task processing delay. In addition, ODTP-M consistently minimizes task processing delay across various scenarios, including resource-constrained conditions, outperforming recent studies that focus on specific aspects of task delay.

5G-WiFi heterogeneous network (HetNet) is considered to be an effective solution to the edge computing capability crisis, the introduction of WiFi networks can well share the burden of the 5G network. In this paper, based on 5G-WiFi HetNet, we formulate the optimization problem of joint task offloading and resource allocation to maximize the utility of uplink transmission rate and business cost while ensuring high reliability. In order to address the high nonlinearity caused by the binary variables and the fractional form. We utilize a fractional transform method and propose a two-layer iterative algorithm based on the penalty dual decomposition (PDD) approach to effectively solve this mixed-integer nonlinear programming (MINLP) problem. Simulation results verify the convergence of the proposed algorithm and show that it improves the utility of the system while ensuring high reliability.

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) holds significant promise, providing a robust solution to address the limitations imposed by the computing and energy resources in wireless devices (WDs) operating within various low-power networks. In this context, intelligent reflecting surface (IRS) technology emerges as a noteworthy communication innovation that not only conserves energy but also optimizes spectrum resources. With the aid of IRS, wireless-powered MEC systems can considerably enhance the efficiency of radio frequency (RF) energy transmission while simultaneously improving wireless information transmission performance. This article delves into the long-term energy efficiency of IRS-aided multiuser wireless-powered MEC systems. Recognizing the stochastic nature of task arrivals, we introduce a stochastic optimization problem aimed at addressing the energy efficiency challenge. This problem’s objective is to optimize the system’s long-term energy efficiency while adhering to constraints related to network stability, energy stability, task offloading policies, IRS phase shift vectors, device central processing unit (CPU) frequencies, transmission power, and temporal causality. Subsequently, we transform this long-term optimization problem into a short-term deterministic problem using Lyapunov optimization theory. However, the transformed problem remains highly coupled and involves discrete variables. Consequently, we have developed an algorithm based on the penalty dual decomposition (PDD) method to effectively address this challenge. Simulation results prove conclusively that with the assistance of IRS, system energy efficiency is effectively improved.

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

Edge computing has become a new paradigm in response to the increasing demand for time-sensitive and computation-intensive tasks, offering advantages over traditional cloud computing due to its proximity to terminal devices and low transmission latency. Container-based edge computing provides a powerful way to deploy applications and manage resources at the edge of the network. However, optimizing the caching strategy is crucial due to the limited capacity of the edge server, and the startup time of services on edge servers is a crucial consideration when making decisions regarding computation offloading and service caching. In this paper, taking into account container startup time, we formulate an optimization model for the task offloading, container caching, and image caching in the container-based edge computing architectures, which is a nonlinear integer programming (NLIP) problem that is NP-hard. We then propose an algorithm that finds the global optimal solution to this NLIP problem by transforming it into an equivalent linear integer programming problem. Our simulation experiments demonstrate that our proposed algorithm can effectively and fast find a globally optimal solution to the underlying problem and that our model outperforms the existing model without considering the container start-up time.

In this paper, we present a novel sparse Bayesian learning (SBL) framework for large-scale image recovery. We formulate variational Bayesian (VB) and generalized approximate message passing (GAMP) into the SBL model (called VGAMP-SBL) to speed up image reconstruction. GAMP can be argued a scalar estimation function described by a set of simple state evolution (SE) equations. From the SE equations, one can accurately predict the values of SBL Params, while it can obtain better reconstruction results without matrix inversion. Moreover, the interaction between data fluctuations and parameter fluctuations is negligible in VB structure, so the maximum marginal likelihood function can be easily obtained, This improves the computation efficiency of our algorithm greatly. Experimental results corroborate these claims.

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

In this paper, a multi-hypothesis square-root cubature Kalman particle filter (MH-SRCKPF) is proposed for speaker tracking in noisy and reverberant environments with distributed microphone arrays. The conventional cubature Kalman particle filter (CKPF) uses the cubature Kalman filter (CKF) to generate its proposal for particle sampling. Such a proposal incorporates only one observation from a certain localization function for the state estimation, which is vulnerable to noise or reverberation, yielding the degraded tracking performance. To tackle the problem, by incorporating multiple possible observations into CKF for the proposal, a multi-hypothesis CKPF (MH-CKPF) algorithm is first developed. Furthermore, to improve the numerical stability, an MH-SRCKPF algorithm is developed, where the state estimate and the square root of the error covariance are propagated at each time. Finally, the MH-SRCKPF is applied to the speaker tracking problems in distributed microphone arrays. Experimental results demonstrate that the proposed MH-SRCKPF outperforms the competing methods in the presence of noise and reverberation. Meanwhile, by propagating the square root of the state covariance, the proposed method exhibits attractive numerical characteristics.

This paper investigates the achievable performance of cell-free massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) at both the access points (APs) and users. A closed-form expression for the achievable rate is derived, which enables the study of the effects of AP number, antenna number per AP, user number, and ADC resolution on the achievable rate. In addition, a simple asymptotic approximation for the individual user rate is presented, which shows that the user rate is mainly constrained by the ADC resolution at the user. Moreover, we propose an ADC resolution bits allocation scheme aiming at maximizing the sum rate subject to the total ADC resolution bits constraint, which substantially outperforms the equal ADC resolution bits allocation scheme. Furthermore, a max-min power control scheme is proposed, which not only ensures user fairness, but also improves the achievable rate. Finally, simulation results are provided to corroborate the analytical results.

This paper investigates the power allocation problems for non-orthogonal multiple access with coordinated direct and relay transmission (CDRT-NOMA), where a base station (BS) communicates with its nearby user directly, while communicating with its far user only through a dedicated relay node (RN). The RN is assumed to operate in either half-duplex relaying (HDR) mode or full-duplex relaying (FDR) mode. Based on instantaneous channel state information (CSI), the dynamic power allocation problems under HDR and FDR schemes are formulated respectively, with the objective of maximizing the minimum user achievable rate. After demonstrating the quasi-concavity of the considered problems, we derive the optimal closed-form power allocation policies under the HDR scheme and the FDR scheme. Then, a hybrid relaying scheme dynamically switching between HDR and FDR schemes is further designed. Moreover, we also study the fixed power allocation problems for the considered CDRT-NOMA systems based on statistical CSI so as to optimize the long-term system performance. The simulations show that our proposed power allocation policies can significantly enhance the performance of CDRT-NOMA systems.

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 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 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 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.

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.

This paper considers multiuser interference channels in which the transmitters have imperfect channel state information (CSI) where CSI perturbations are modeled stochastically. Transmitters are assumed to be equipped with multiple antennas serving single-antenna receivers. Transmitters use pre-designed discrete codebooks for beamforming directions and dynamically (based on the available CSI) select the best set of beamformers from the given codebook. The objective is to perform optimal power allocation to different users while certain quality of service (QoS) guarantees are ensured for the users. Imposed by stochastic CSI uncertainties, guarantees provided for the QoS measures have a stochastic nature too. The primary focus is placed on the interference channels for which two power allocation problems are considered. The first problem minimizes power consumption subject to serving users at certain data rates and the second problem considers max-min rate allocation subject to given power budgets for the transmitters. The core step in formalizing these problems in mathematically tractable forms relies on using Bernstein approximation, which approximates and convexifies the non-convex stochastic guarantees by conservative convex and deterministic counterparts. For solving this resulting convex and deterministic optimization problem, a specialized version of the long-step logarithmic barrier cutting plane (LLBCP) algorithm is used. Effectiveness of the proposed solutions and comparisons with other existing methods are assessed via extensive simulation results.

We consider SINR-constrained power minimization in MISO interference channel with imperfect channel state information (CSI). The problems is formulated as probability-constrained optimization problems. We make use of the Bernstein approximation to conservatively transform the probabilistic constraints into deterministic ones, and consequently convert the original stochastic optimization problems into convex optimization problems. Extensive simulation results are provided to demonstrate the effectiveness of the proposed method.

We address the problem of downlink beamforming for mitigating the co-channel interference in multi-cell OFDMA networks. Based on the network utility maximization framework, we formulate the problem as a non-convex optimization problem subject to the per-cell power constraints, in which a general utility function of SINR is used to characterize the network performance. To solve the problem in a distributed fashion, we devise an algorithm based on the non-cooperative game with pricing mechanism. We give a sufficient condition for the convergence of the algorithm to the Nash equilibrium (NE). Moreover, to speed up the optimization of the beam-vectors at each cell, we derive an efficient algorithm to solve the KKT conditions at each cell. We provide extensive simulation results to demonstrate that the proposed distributed multi-cell beamforming algorithm converges to an NE point in just a few iterations with low information exchange overhead. Moreover, it provides significant performance gains, especially under the strong interference scenario, in comparison with several existing multi-cell interference mitigation schemes, such as the distributed interference alignment method.

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.

Ad Hoc networks are characterized as fast time-varying. Thus, fast distributed algorithm to implement self-management is indispensable, especially for QoS support. In this paper, we propose rate control with QoS support in Ad Hoc networks based on primal-dual interior-point method. We apply Gaussian belief propagation algorithm to compute the Newton step. For implementing distributed computation in practical network, we design the mapping rules between GaBP-mapping network and the practical networks. Finally, the simulation results show that the proposed algorithm has favorable performance, including fast convergence, robustness and scalability.

In this paper, we formulate a flow control optimization problem for wireless sensor networks with lifetime constraint and link interference in an asynchronous setting. Our formulation is based on the network utility maximization framework, in which a general utility function is used to characterize the network performance such as throughput. To solve the problem, we propose a fully asynchronous distributed algorithm based on dual decomposition, and theoretically prove its convergence. The proposed algorithm can achieve the maximum utility. Extensive simulations are conducted to demonstrate the efficiency of our algorithm and validate the analytical results.