This paper considers recovering a continuous angular power spectrum (APS) from the channel covariance. Building on the projection-onto-linear-variety (PLV) algorithm, an affine-projection approach introduced by Miretti \emph{et. al.}, we analyze PLV in a well-defined \emph{weighted} Fourier-domain to emphasize its geometric interpretability. This yields an explicit fixed-dimensional trigonometric-polynomial representation and a closed-form solution via a positive-definite matrix, which directly implies uniqueness. We further establish an exact energy identity that yields the APS reconstruction error and leads to a sharp identifiability/resolution characterization: PLV achieves perfect recovery if and only if the ground-truth APS lies in the identified trigonometric-polynomial subspace; otherwise it returns the minimum-energy APS among all covariance-consistent spectra.

Decentralized federated learning (DFL) is an emerging paradigm to enable edge devices collaboratively training a learning model using a device-to-device (D2D) communication manner without the coordination of a parameter server (PS). Aggregation weights, also known as mixing weights, are crucial in DFL process, and impact the learning efficiency and accuracy. Conventional design relies on a so-called central entity to collect all local information and conduct system optimization to obtain appropriate weights. In this paper, we develop a distributed aggregation weight optimization algorithm to align with the decentralized nature of DFL. We analyze convergence by quantitatively capturing the impact of the aggregation weights over decentralized communication networks. Based on the analysis, we then formulate a learning performance optimization problem by designing the aggregation weights to minimize the derived convergence bound. The optimization problem is further transformed as an eigenvalue optimization problem and solved by our proposed subgradient-based algorithm in a distributed fashion. In our algorithm, edge devices only need local information to obtain the optimal aggregation weights through local (D2D) communications, just like the learning itself. Therefore, the optimization, communication, and learning process can be all conducted in a distributed fashion, which leads to a genuinely distributed DFL system. Numerical results demonstrate the superiority of the proposed algorithm in practical DFL deployment.

Coherent states have been increasingly considered in optical quantum communications (OQCs).With the inherent non-orthogonality of coherent states, non-orthogonal multiple-access (NOMA) naturally lends itself to the implementation of multi-user OQC. However, this remains unexplored in the literature. This paper proposes a novel successive interference cancellation (SIC)-based photon-number-resolving detection (PNRD)-Kennedy receiver for uplink NOMA-OQC systems, along with a new approach for power allocation of the coherent states emitted by users. The key idea is to rigorously derive the asymptotic sum-rate of the considered systems, taking into account the impact of atmospheric turbulence, background noise, and lossy photon channel. With the asymptotic sum-rate, we optimize the average number of photons (or powers) of the coherent states emitted by the users. Variable substitution and successive convex approximation (SCA) are employed to convexify and maximize the asymptotic sum-rate iteratively. A new coherent-state power allocation algorithm is developed for a small-to-medium number of users. We further develop its low-complexity variant using adaptive importance sampling, which is suitable for scenarios with a medium-to-large number of users. Simulations demonstrate that our algorithms significantly enhance the sum-rate of uplink NOMA-OQC systems using coherent states by over 20%, compared to their alternatives.

Photon counting (PhC) is an effective detection technology for underwater optical wireless communication (OWC) systems. The presence of signal-dependent Poisson shot noise and asynchronous multi-user interference (MUI) complicates the processing of received data signals, hindering the effective signal detection of PhC OWC systems. This paper proposes a novel iterative signal detection method in grant-free, multi-user, underwater PhC OWC systems with signal-dependent Poisson shot noise. We first introduce a new synchronization algorithm with a unique frame structure design. The algorithm performs active user identification and transmission delay estimation. Specifically, the estimation is performed first on a user group basis and then at the individual user level with reduced complexity and latency.We also develop a nonlinear iterative multi-user detection (MUD) algorithm that utilizes a detection window for each user to identify interfering symbols and estimate MUI on a slot-by-slot basis, followed by maximuma-posterioriprobability detection of user signals. Simulations demonstrate that our scheme achieves bit error rates comparable to scenarios with transmission delays known and signal detection perfectly synchronized.

Decentralized machine learning (DML) supports collaborative training in large-scale networks with no central server. It is sensitive to the quality and reliability of inter-device communications that result in time-varying and stochastic topologies. This paper studies the impact of unreliable communication on the convergence of DML and establishes a direct connection between the spectral properties of the mixing process and the global performance. We provide rigorous convergence guarantees under random topologies and derive bounds that characterize the impact of the expected mixing matrix's spectral properties on learning. We formulate a spectral optimization problem that minimizes the nontrivial spectral radius of the expected second-order mixing matrix to enhance the convergence rate under probabilistic link failures. To solve this non-smooth spectral problem in a fully decentralized manner, we design an efficient subgradient-based algorithm that integrates Chebyshev-accelerated eigenvector estimation with local update and aggregation weight adjustment, while ensuring symmetry and stochasticity constraints without central coordination. Experiments on a realistic low Earth orbit satellite constellation with time-varying inter-satellite link models and real-world remote sensing data demonstrate the feasibility and effectiveness of our method. The method significantly improves classification accuracy and convergence efficiency compared to existing baselines, validating its applicability in satellite and other decentralized systems.

Integrated sensing, communication, and computation (ISC2) has been increasingly studied to support high-precision sensing and low-latency computing. Fairness in ISC2remains unaddressed due to the non-convexity introduced by local computing, task offloading, result delivery, and sensing. This paper develops a new approach to max-min fair task computing while ensuring target sensing demands in ISC2systems, by holistically optimizing the beamformers of the base station (BS), the transmit powers and CPU frequencies of users (UEs), and the uplink and downlink durations. To solve this non-convex problem, we develop a new algorithm that judiciously decouples the problem into manageable steps, leveraging Rayleigh quotient maximization, semidefinite relaxation (SDR), and successive convex approximation. We rigorously prove the (local) optimality of the algorithm by proving the tightness of the SDR, i.e., a rank-one solution for the dual-purpose beamforming of the BS for both communication and sensing, and the corresponding sensing-only beamforming, can always be constructed from a solution without rank constraint. Moreover, we reveal the optimal structure of the transmit powers of the BS and UEs, the CPU frequencies of the UEs, and the attainability of absolute fairness. As corroborated numerically, our algorithm substantially outperforms its alternatives in task completion and fairness.

Collaborative artificial intelligence (AI) inference has effectively deployed well-trained AI models at the network edge to empower immersive intelligent services such as autonomous driving and smart cities. This paper proposes an integrated sensing-computation-communication (ISCC) scheme for decentralized multi-task collaborative inference systems. The proposed scheme connects multiple devices via device-to-device (D2D) links. Each device first extracts a homogeneous feature vector from the raw sensory data obtained from the same wide view of the source target and then aggregates all local feature vectors using the over-the-air computation (AirComp) technique to complete a specific inference task. To enhance spectrum efficiency, the full-duplex communication technique is adopted, which allows all devices to transmit and receive in the same frequency band. To suppress the self-interference caused by full duplex communications and simultaneously enhance all tasks’ performance, a multi-objective optimization problem is formulated, where discriminant gain is adopted as the inference performance metric. The challenges to solve this problem arise from three aspects: The impact of the self-interference (SI) channel incurred by full-duplex communication, the precoding design of each device, and the coupling among subcarrier allocation, sensing, computation, and communication processes. To tackle this problem, aquadratic transformandweighted bipartite matchingbased alternating maximization approach is proposed. Numerical results based on jointly completing three tasks of human motion classification, human gender recognition, and human age group classification, verify the effectiveness of the proposed method by showing that the proposed method outperforms the state-of-the-art successive convex approximation (SCA) based algorithm.

Massive Multiple Input Multiple Output (MIMO) is a cornerstone technology for achieving high capacity and spectral efficiency. A key challenge in frequency division duplex (FDD) massive MIMO systems lies in obtaining accurate downlink (DL) channel state information (CSI), as the absence of uplink (UL)DL reciprocity hinders conventional estimation methods, creating a bottleneck in system performance. To address this issue, various approaches have been developed, broadly categorized into Conversion and Feedback approaches. However, partial reciprocity and quantization loss in feedback codebook design make accurate DL CSI acquisition a persistent challenge. In this paper, we propose a novel CGAN (Conditional Generative Adversarial Network)-based CSI-fusion framework that integrates both the UL channel statistics obtained from sounding reference signals (SRS) and the DL feedback from user equipments (UEs). These two sources of DL CSI-related information are fused and utilized as conditional inputs to CGAN to map the UL channel statistics to DL CSI. The proposed CGAN-based CSI-Fusion framework significantly enhances DL CSI acquisition accuracy, offering a practical solution to overcoming DL CSI acquisition challenges.

The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

Integrated sensing and communication (ISAC) can enhance spectrum and hardware efficiency, but commonly considered phased arrays suffer from high power consumption and hardware cost. This paper presents a novel holographic ISAC system, introducing a reconfigurable holographic surface (RHS) beamforming framework to maximize the sensing mutual information (MI) while satisfying user data rate requirements. The key idea is that we derive an asymptotic lower bound for the MI, and transform it, involving Kronecker products in both the numerator and denominator, into a quadratic objective using the generalized Charnes-Cooper transformation. This enables a tight semidefinite relaxation (SDR) with the guaranteed existence of a rank-one solution, leading to the optimal full-digital (FD) beamformer. Another important aspect is that we prove the optimal FD beamformer directly translates into the globally optimal hybrid RHS beamforming design, when the number of radio frequency (RF) chains matches the RHS elements. When the RF chains are fewer, we develop an alternating optimization algorithm, which employs a tight SDR for digital beamforming design and a Gaussian randomization-based SDR for RHS amplitude control solution in each iteration. Simulations demonstrate that holographic ISAC surpasses phased arrays by 12.4% in sensing MI. Holographic ISAC is highly power-efficient, requiring only 33.3% of the transmit power needed by phased arrays to achieve the same radar detection probability.

Mounted on an uncrewed aerial vehicle (UAV), UAV-borne reconfigurable intelligent surfaces (RISs) can enjoy flexibility with a considerable probability of line-of-sight (LoS) channels, and assist communications between terrestrial nodes. Challenges arise in designing UAV-borne RIS (U-RIS) assisted communications, such as the influence of non-LoS, dynamic RIS configuration, and energy consumption of the UAV. To tackle these challenges, we put forth a new U-RIS assisted uplink transmission framework, in which the direct links from the ground users to the corresponding ground base station are blocked. We optimize the energy efficiency of the network by collectively devising the RIS configuration, user scheduling, power allocation, and UAV trajectory, under a probabilistic LoS channel model. Through alternating optimization and successive convex approximation, an efficient approach is established. Simulations validate that our approach is able to significantly enhance energy efficiency by 78% in comparison with its benchmarks.

The attitude of an unmanned aerial vehicle (UAV) is highly related to its motion status, such as velocity and acceleration, and thus needs to be taken into consideration in UAV localization and tracking. In this paper, we study a short-range UAV localization and tracking system, where a UAV flies in the near-field region of an extremely large antenna array (ELAA). The ELAA is arranged to track the UAV by continuously estimating its position and attitude. To accomplish this task, we leverage an array partitioning approach to establish the signal model between the UAV and the ELAA based on the subarray-wise far-field assumption. Then, we characterize the relationship between UAV’s attitude and motion status based on force analysis. Building on the analysis, we formulate a probabilistic UAV tracking problem that jointly estimates the UAV position and attitude in an online fashion. A new message-passing-based algorithm is proposed to solve this problem, which combines attitude and motion status information to enhance tracking performance. We also derive the Bayesian Cramér Rao bound (BCRB) of the problem as a performance benchmark. Numerical results show that the proposed algorithm outperforms other alternatives, and demonstrate that the information fusion of the UAV attitude and motion status can effectively improve the accuracy of the UAV localization and tracking.

This paper develops a novel framework for differentially private (DP) wireless federated learning (FL) with integrated sensing and communication (ISAC). In this framework, which is referred to as DP-ISAC-FL, wireless devices sense data and upload the trained local models using ISAC technique. The local training can take place concurrently with sensing at each device. We analyze the convergence upper bound of DP-ISAC-FL and rigorously capture the impact of device selection (for model training), time allocation between sensing/training and model uploading for the selected devices, and the allocations of channels, modulations, and transmit powers. We also develop an algorithm that enforces the convergence of DP-ISAC-FL by minimizing the convergence upper bound in an OFDMA system with discrete modulations. The beamforming for sensing, device selection, and the allocations of time, subchannels, modulations, and transmit powers are jointly optimized using successive convex approximation (SCA), adapting to the channels and computing capabilities of the devices. Experiments on multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) show that DP-ISAC-FL with optimal allocations can significantly improve the learning convergence and accuracy under different privacy levels, e.g., by 7% and 18%, compared with its benchmarks. This is attributed to 68% more sensing data that DP-ISAC-FL can admit for model training.

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adjust their individual learning rates and local training iterations, adapting to their instantaneous computing powers. The convergence upper bound of FLARE is established rigorously under a general setting with non-convex models in the presence of non-i.i.d. datasets and imbalanced computing powers. By minimizing the upper bound, we further optimize the scheduling of FLARE to exploit the channel heterogeneity. A nested problem structure is revealed to facilitate iteratively allocating the bandwidth with binary search and selecting devices with a new greedy method. A linear problem structure is also identified and a low-complexity linear programming scheduling policy is designed when training models have large Lipschitz constants. Experiments demonstrate that FLARE consistently outperforms the baselines in test accuracy, and converges much faster with the proposed scheduling policy.

To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). By collectively training a unified learning model on edge devices, FL bypasses the need of direct data transmission, thereby addressing problems such as latency issues and privacy concerns inherent in centralized ML. However, in practical deployment FL suffers from low learning efficiency due to the involved straggler issue and huge uplink overhead. In this paper, we develop a UAV-enabled over-the-air asynchronous FL (UAV-AFL) framework to address this problem. This framework significantly enhance the learning efficiency by supporting the UAV as the parameter server (UAV-PS) in collecting data over-the-air and updating model continuously. We conduct a convergence analysis to quantitatively capture the impact of model asynchrony, device selection and communication errors on the UAV-AFL learning efficiency. Based on this analysis, a unified communication-learning problem is formulated to maximize asymptotical learning accuracy by optimizing the UAV-PS trajectory, device selection and over-the-air transceiver design. Simulation results reveal valuable insights for the system design and demonstrate that the proposed UAV-AFL scheme achieves substantially improvement in learning efficiency compared with the state-of-the-art approaches.

This paper studies the large-scale cell-free networks where dense distributed access points (APs) serve many users. As a promising next-generation network architecture, cell-free networks enable ultra-reliable connections and minimal fading/blockage, which are much favorable to the millimeter wave and terahertz transmissions. However, conventional beam management with large phased arrays in a cell is very time-consuming in the higher-frequencies, and could be worsened when deploying a large number of coordinated APs in the cell-free systems. To tackle this challenge, the spatial-spectral cell-free networks with the leaky-wave antennas are established by coupling the propagation angles with frequencies. The beam training overhead in this direction can be significantly reduced through exploiting such spatial-spectral coupling effects. In the considered large-scale spatial-spectral cell-free networks, a novel subchannel allocation solution at sub-terahertz bands is proposed by leveraging the relationship between cross-entropy method and mixture model. Since initial access and AP clustering play a key role in achieving scalable large-scale cell-free networks, a hierarchical AP clustering solution is proposed to make the joint initial access and cluster formation, which is adaptive and has no need to initialize the number of AP clusters. After AP clustering, a subchannel allocation solution is devised to manage the interference between AP clusters. Numerical results are presented to confirm the efficiency of the proposed solutions and indicate that besides subchannel allocation, AP clustering can also have a big impact on the large-scale cell-free network performance at sub-terahertz bands.

Decentralized federated learning (DFL) is an emerging paradigm for leveraging the rapidly growing data from wireless devices in a fully distributed manner. However, the deployment of DFL is facing some pivotal challenges, including communication bottlenecks due to extensive inter-device message exchanges and the difficulty for edge devices to achieve consensus. To address these challenges, this paper proposes to employ the over-the-air computation (Aircomp) technique to improve communication efficiency and introduces a mixing matrix mechanism to guarantee consensus. Specifically, we present a novel multiple-input multiple-output over-the-air DFL (MIMO OA-DFL) framework for addressing the DFL design problem in general ad hoc networks. A rigorous convergence bound is derived to quantitatively capture the impact of mixing matrix and communication error on the system performance. The results show that the communication errors, the spectral gap of the mixing matrix, and the mixing matrix itself have a significant impact on the learning performance. Building on this result, we formulate a joint communication-learning optimization problem to optimize transceiver beamformers and mixing matrix. Numerical experiments demonstrate the substantial performance enhancement achieved by our proposed scheme.

Federated Learning, as an emerging edge artificial intelligence paradigm, enables a group of clients to collaboratively train a global model without revealing their local data. The conventional FL algorithms usually depend on the access to exact gradient or Hessian matrix, which may be inaccessible due to resource limitation or application restriction. Meanwhile, Federated Learning intrinsically suffers from data heterogeneity, which restricts the global model from performing well on each clients’ task. To simultaneously tackle these two challenges, we propose a gradient free personalized federated learning framework, namely pFedZO. We utilize infimal convolution to bridging the gap between personal and global knowledge, and exploit zeroth-order gradient estimator to solve the problem. We theoretically show the local approximation can converge sublinearly and the global problem converge to the neighbourhood of the optimal with a same speed. We further propose pFedZO-Heur to accelerate training procedure. Experimentally, we verify that pFedZO excels at test accuracy with the vanilla Zeroth-Order Optimization (ZOO) based FL by <?TeX $5\%$?> Math 1 . We also show pFedZO-Heur can achieve the same performance level with lower time consumption.

Fine-tuning pre-trained models with task-specific data can produce customized models effective for downstream tasks. However, operating large-scale such fine-tuning tasks in real time in the data center faces non-trivial challenges, including unpredictable task arrival and system environment dynamics, complex deadline-driven fine-tuning scheduling, and intertwined task pricing and cost management. In this paper, targeting the popular Low-Rank Adaptation (LoRA) fine-tuning technique, we present the design and study of a novel auction-based mechanism to jointly schedule and price LoRA tasks in an online manner. We first model the social welfare maximization problem as an integer program for the fine-tuning service provider, capturing all the aforementioned challenges. Then, to solve this NP-hard problem online, we equivalently reformulate this original problem into a schedule selection problem, where each schedule corresponds to a concrete pre-specified operation plan over time for a task. We can thus design a polynomial-time online approximation algorithm via the online primal-dual method to determine the schedule, and with the dual variables, also determine the pricing for each admitted task. We rigorously prove the competitiveness of our online approach against the offline optimum, and prove the economic properties of truthfulness and individual rationality regarding pricing. Finally, we conduct extensive experiments and have validated the substantial advantages of our approach compared to existing methods.

Scheduling generative-AI jobs in the edge computing environment faces multiple non-trivial challenges, including the Directed Acyclic Graph (DAG) dependency among tasks, the intrinsic intertwinement between task scheduling and model selection, and the dynamic unpredictable arrival of job DAGs. In this work, we capture all such challenges and formulate a non-linear integer program to optimize the long-term profit of the generative-AI service provider, i.e., service revenue of the admitted jobs minus system costs of executing the tasks contained in such job DAGs. This problem is NP-hard even in the offline setting. To solve it, we first reformulate it into an equivalent schedule selection problem using generated schedules to tackle complex constraints. Then, we design a new online scheduling method through the online primal-dual technique. Experimental results confirm that our approach can increase the total service profit by up to 41.2% compared to existing algorithms.

This paper presents a novel two-stage deep reinforcement learning (DRL) algorithm built on a Transformer Encoder-based Deep Deterministic Policy Gradient (TEDDPG) framework, named TS-TEDDPG, which jointly optimizes the learning latency, energy consumption and model accuracy of Asynchronous Federated Learning (AFL) systems with prescribed security. The CPU configuration of local training and the transmit power of model uploading are learnt by the TEDDPG in the first stage. A linear programming-based device scheduling and cooperative jamming strategy is designed to efficiently optimize the rest of the decisions in the second stage and evaluates the immediate reward to train the TEDDPG. Experimental results based on a CNN model and the MNIST dataset demonstrate that the proposed TS-TEDDPG can reduce the training latency and energy consumption by 68.6% compared to its benchmarks, when the required test accuracy is 0.9.

In order to provide design guidelines for energy efficient 6G networks, we propose a novel radial basis function (RBF) based optimization framework to maximize the integrated relative energy efficiency (IREE) metric. Different from the conventional energy efficient optimization schemes, we maximize the transformed utility for any given IREE using spectrum efficiency oriented RBF network and gradually update the IREE metric using proposed Dinkelbach’s algorithm. The existence and uniqueness properties of RBF networks are provided, and the convergence conditions of the entire framework are discussed as well. Through some numerical experiments, we show that the proposed IREE outperforms many existing SE or EE oriented designs and find a new Jensen-Shannon (JS) divergence constrained region, which behaves differently from the conventional EE-SE region. Meanwhile, by studying IREE-SE trade-offs under different traffic requirements, we suggest that network operators shall spend more efforts to balance the distributions of traffic demands and network capacities in order to improve the IREE performance, especially when the spatial variations of the traffic distribution are significant.

We consider the identifiability issue of maximum-likelihood based activity detection in massive MIMO-based grant-free random access. An intriguing observation by (Chen et al., 2022) indicates that the identifiability undergoes a phase transition for commonly-used random user signatures as$L^{2}$,$N$and$K$tend to infinity with fixed ratios, where$L$,$N$and$K$denote the user signature length, the total number of users, and the number of active users, respectively. In this letter, we provide a precise analytical characterization of the phase transition based on a spectral universality conjecture. Numerical results demonstrate excellent agreement between our theoretical predictions and the empirical phase transitions.

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This paper presents a new two-phase deep deterministic policy gradient (DDPG) framework, referred to as “TP-DDPG”, to balance online the learning delay and model accuracy of an FL process in an energy harvesting-powered HFL system. The key idea is that we divide optimization decisions into two groups, and employ DDPG to learn one group in the first phase, while interpreting the other group as part of the environment to provide rewards for training the DDPG in the second phase. Specifically, the DDPG learns the selection of participating clients, and their CPU configurations and the transmission powers. A new straggler-aware client association and bandwidth allocation (SCABA) algorithm efficiently optimizes the other decisions and evaluates the reward for the DDPG. Experiments demonstrate that with substantially reduced number of learnable parameters, the TP-DDPG can quickly converge to effective polices that can shorten the training time of HFL by 39.4% compared to its benchmarks, when the required test accuracy of HFL is 0.9.

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. Specifically, the detection success rate is improved by up to 65%, 55%, and 51% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.

The use of legitimate unmanned aerial vehicles (UAVs) to surveil and track misbehaved UAVs can serve a crucial role in public safety and security. This paper proposes a new deep reinforcement learning (DRL)-based online control scheme for visual-based UAV-on-UAV tracking and monitoring, where a solar-powered, fixed-wing UAV tracks a suspicious UAV target by having the target inside its effective visual range. The key idea is a new deep deterministic policy gradient (DDPG)-based model, which can cope with the continuous state and action spaces of the monitor and learn the optimal acceleration control policy adapting to the solar power availability and the target’s movement. The state space is designed to be the relative position of the monitor to the target, thereby preventing model infeasibility. Experiments show that the new algorithm can maintain a desired distance from the target, and outperform control-and optimization-based alternatives in terms of energy efficiency and tracking accuracy. An interesting finding is that our algorithm learns faster and better with a constraint of a minimum allowed battery energy reserve. The reason is that, without the constraint, the monitor is more likely to deplete its battery before the end of a surveillance mission.

Federated learning (FL) can suffer from communication bottlenecks when deployed in mobile networks, limiting participating clients and deterring FL convergence. In this context, the impact of practical air interfaces with discrete modulation schemes on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F2L) over an orthogonal frequency division multiple-access (OFDMA) air interface, termed as “OFDMA-F2L”, allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulation scheme, adapting to channel conditions and computing power. Specifically, we derive an upper bound on the optimality gap of OFDMA-F2L capturing the impact of these selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a “winner-takes-all” policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F2L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18% and 5%, compared to potential alternatives.

Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner. With the cooperation of edge devices, DFL enables joint training of machine learning model in a device to device (D2D) communication fashion without the coordination of a parameter server. However, the deployment of wireless DFL is facing some pivotal challenges. Communication is a critical bottleneck due to the required extensive message exchanges between neighbor devices to share the learned model. Besides, model consensus becomes increasingly difficult as the number of devices grows because there is no available central server for coordination. To overcome these difficulties, this paper proposes the use of over-the-air computation (Aircomp) to improve communication efficiency by exploiting the superposition property of analog waveforms in multi-access channels, and introduce the mixing matrix mechanism to promote consensus using the spectral property of symmetric doubly stochastic matrix. Specifically, we develop a novel multiple-input multiple-output (MIMO) over-the-air DFL (OA-DFL) framework to study over-the-air DFL problem over MIMO multiple access channels. We conduct a general convergence analysis to quantitatively capture the impact of aggregation weights and communication error on the MIMO OA-DFL performance inad hocD2D networks. The result shows that the communication error together with the spectral gap of the mixing matrix has a significant impact on the learning performance. Based on this, a joint communication-learning optimization problem is formulated to optimize the transceiver beamformers and the mixing matrix. Extensive numerical experiments are performed to reveal the characteristics of different topologies and demonstrate the substantial learning performance enhancement of our proposed algorithm.

Integrated sensing and massive connectivity (MC) can potentially provide not only low-cost full-coverage sensing by utilizing the transmit signals of the huge number of devices in the wide areas, but also efficient user activity detection and channel estimation by utilizing the sensed environment/channel information. However, due to the huge number of users, burst traffic, and short packets, random and non-orthogonal multiple access is a natural choice for MC. In this case, efficient receiver design for integrated sensing and massive connectivity is clearly a challenging problem. In this paper, by introducing auxiliary variables properly, we reconstruct the probability model of the system, and then propose an iterative Bayesian receiver structure, consisting of four modules, namely multi-user decomposition (MUD), multipath decomposition (MPD), parameter fusion (PF), and communications and sensing output (CSO). These modules are designed based on message passing, variational inference, sum-product rules, and Bayesian inference, respectively, to compose a novel variational message passing receiver. Numerical results show that the proposed design can obtain high-accuracy positioning and sensing performance as well as improved performance of channel estimation and data communication.

This article presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet of Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance of a reconfigurable intelligent surface (RIS), we propose to augment the radio environment, suppress jamming signals, and enhance the desired signals. The UAV is designed to learn its trajectory and the RIS configuration based solely on changes in its received data rate, using the latest deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) models. Simulations show that the proposed DRL algorithms give the UAV with strong resistance against jamming and that the TD3 algorithm exhibits faster and smoother convergence than the DDPG algorithm, and suits better for larger RISs. This DRL-based approach eliminates the need for knowledge of the channels involving the RIS and jammer, thereby offering significant practical value.

Internet of Things (IoT) time-series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security. Nevertheless, the complex spatial–temporal dynamics and high dimensionality of IoT time series make the analysis increasingly challenging. In recent years, the powerful feature extraction and representation learning capabilities of deep learning (DL) have provided an effective means for IoT time-series analysis. However, few existing surveys on time series have systematically discussed unsupervised DL-based methods. To fill this void, we investigate unsupervised DL for IoT time series, i.e., unsupervised anomaly detection and clustering, under a unified framework. We also discuss the application scenarios, public data sets, existing challenges, and future research directions in this area.

As a core performance metric for green communications, the conventional energy efficiency (EE) definition has successfully resolved many issues in the energy-efficient wireless network design. In the past several generations of wireless communication networks, the traditional EE measure plays an important role to guide many energy-saving techniques for slow varying traffic profiles. However, for the next-generation wireless networks, the traditional EE fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultrascale multiple antennas and space–air–ground integrated network (SAGIN). In this article, we present a novel EE metric named integrated relative EE (IREE), which is able to jointly measure the traffic profiles and the network capacities from the EE perspective. On top of that, the IREE-based green tradeoffs have been investigated and compared with the conventional energy-efficient design. Moreover, we apply the IREE-based green tradeoffs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and SAGIN. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional EE metric. Since the IREE-oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy-efficient network design.

Mobile edge computing (MEC) offers an opportunity for devices relying on wireless power transfer (WPT), to accomplish computationally demanding tasks. Such WPT-powered MEC systems have yet to be optimized for long-term efficiency, due to random and changing task demands and wireless channel states of the devices. This paper presents an augmented two-staged deep Q-network (DQN), referred to as “TS-DQN,” for online optimization of WPT-powered MEC systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN for learning the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. Another important aspect is that a new action generation method is developed to expand and diversify the actions of the DQN, further accelerating its convergence. As validated by simulations, the proposed TS-DQN is much more energy efficient and converges much faster, than its potential alternative directly using the state-of-the-art Deep Deterministic Policy Gradient algorithm to learn all decision variables.

Photon counting is an effective technique to detect low-power optical signals in underwater optical wireless communications (UOWC), but undergoes signal-dependent Poisson shot noises that lead to intractable data rate expressions and hinder effective power allocation of photon-counting systems. This paper presents a new approach to the optimal power allocation of a multiuser photon-counting UOWC system, where we first derive the asymptotic achievable rate as the background radiation is large under the signal-dependent Poisson shot noises. With the tractability of the asymptotic achievable rate, we formulate a new power allocation problem to maximize the weighted sum-rate of the multiuser photon-counting UOWC system. A new algorithm is developed to decompose the problem into subproblems with deterministic convexity or concavity and accordingly convexified and solved using successive convex approximation. We also propose to pre-select the subproblems, thereby reducing the complexity significantly with negligible loss of the weighted sum-rate. Simulations validate our asymptotic achievable rate, and show that the proposed algorithms can improve the weighted sum-rates of the UOWC systems by orders of magnitude, compared to the existing approaches.

Photon counting is an effective detection technique for weak optical signals in underwater optical wireless communications (UOWC). This paper proposes a new approach for power allocation in an uplink$M$-ary pulse position modulation (PPM), photo-counting non-orthogonal multiple-access (PhC-NOMA) system. Different from existing techniques in photon-counting systems, the new approach supports consistent duty cycles across underwater devices and adjusts the transmit rates of the devices through their transmit powers, thereby avoiding the delays of duty cycle adjustments and supporting high-speed transmissions. Power allocation is non-trivial in photon-counting systems due to signal-dependent Poisson shot noises. As a key contribution, we derive the exact and asymptotic expressions for the achievable rate of the$M$-ary PPM PhC-NOMA system with the signal-dependent Poisson shot noise and multiuser interference considered. With the expressions, we reveal the received power at the base station (BS) is minimized when their minimum data rate requirements are delivered and can be solved using an incremental algorithm. We also asymptotically maximize the photon efficiency of the devices while preventing the saturation of the receiving photon detector, using Karush-Kuhn-Tucker (KKT) conditions. Simulations show that our approach can reduce the received power at the BS by up to 25% and double the photon efficiency, as compared to the existing techniques.

Channel fading can have a strong impact on the convergence of over-the-air federated edge learning (OTA-FEEL). This paper develops a new and optimal power control policy to minimize the optimality gap of OTA-FEEL under any independently and identically distributed fading. Specifically, we reveal that the optimal power control policy takes a structure where the variance of the effective channel from a device to the server should be minimized when its mean is given. Following this structure, a novel nested optimization algorithm is developed to iteratively minimize the variance using the Lagrange-dual method and then optimize the mean of the effective channel using one-dimensional search. A quasi-closed-form expression of the optimal power control policy is derived. It is shown that the optimal adaptive power control for OTA-FEEL performs an integration of the “channel inverting” strategy and the opposite “channel-proportional” strategy to balance the mean and variance of the effective channel. We also generalize the new policy when the channel statistics are unknown a-priori, and show that the optimal policy can be asymptotically approached over time. Simulations confirm the superiority of the policy to its existing alternatives in the convergence speed and learning accuracy of OTA-FEEL.

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming. It is non-trivial to perform holistic selections of users, data streams, and modulation-coding modes for all subchannels, and RIS configuration in a downlink multiuser OFDMA system under jamming attacks, because of a mixed-integer program nature and difficulties in acquiring the channel state information (CSI) of the channels to and from the RIS and from an uncooperative jammer. We propose a new deep reinforcement learning (DRL)-based approach that learns through changes in the data rates of the users to reject jamming and maximize the sum rate. The key idea is to decouple the continuous RIS configuration from the discrete selections of users, data streams, subchannels, and modulation-coding modes. Another critical aspect is that we show the optimal selections almost surely follow a winner-takes-all strategy. Accordingly, the new DRL framework learns the RIS configuration with a twin-delayed deep deterministic policy gradient and takes the winner-takes-all strategy to evaluate the reward, thereby reducing the action space and accelerating learning. Simulations show the framework converges fast and fulfills the benefit of the RIS. With no need for the CSI of the channels to and from the RIS and from the jammer, the framework offers practical value.

The present paper considers the model-based and data-driven control of unknown discrete-time linear systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging precollected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as practicality of the proposed co-design methods.

Latent graph structure and stimulus of graph-structured data contain critical private information, such as brain disorders in functional magnetic resonance imaging data, and can be exploited to identify individuals. It is critical to perturb the latent information while maintaining the utility of the data, which, unfortunately, has never been addressed. This paper presents a novel approach to obfuscating the latent information and maximizing the utility. Specifically, we first analyze the graph Fourier transform (GFT) basis that captures the latent graph structures, and the latent stimuli that are the spectral-domain inputs to the latent graphs. Then, we formulate and decouple a new multi-objective problem to alternately obfuscate the GFT basis and stimuli. The difference-of-convex (DC) programming and Stiefel manifold gradient descent are orchestrated to obfuscate the GFT basis. The DC programming and gradient descent are employed to perturb the spectral-domain stimuli. Experiments conducted on an attention-deficit hyperactivity disorder dataset demonstrate that our approach can substantially outperform its differential privacy-based benchmark in the face of the latest graph inference attacks.

Unmanned aerial vehicles (UAVs) play a critical role in radio surveillance to decipher malicious messages, thanks to their flexibility, mobility, and likely line-of-sight (LoS) to ground targets. Reconfigurable intelligent surfaces (RISs) can potentially create radio surveillance channels towards the UAVs by passively configuring the radio environments without raising suspicion. This paper presents a new deep reinforcement learning (DRL)-driven framework for radio surveillance, where a fixed-wing UAV is employed to acquire the radio fingerprint of a suspicious transmitter (Tx) with the aid of a benign RIS. A new Twin Delayed Deep Deterministic policy gradient (TD3) model is designed to allow the UAV to learn its trajectory and the RIS configuration based on its observed transmit rate of the suspicious Tx, eliminating the need for channel state information to and from the RIS. The novel contributions include the consideration of the fixed-wing UAV, and the action and reward designed to capture the mobility constraint of the UAV. Simulations demonstrate that the new approach offers the UAV monitor an exceptional and reliable radio surveillance capability, while keeping a desired distance from the UAV to the Tx. The use of the RIS allows for significant improvements of over 37% and 59% in the eavesdropping success probability and average eavesdropping rate, respectively.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

In this paper, we consider asynchronous massive connectivity, where massive low-power and low-rate devices with sporadic activity patterns connect to a multi-antenna access point (AP) in an asynchronous manner. Asynchronous transmission minimizes the amount of coordination between the devices and the AP, and thereby simplifies the transmitter design, yet at the cost of a more challenging receiver design. Specifically, asynchronous transmission results in inter-symbol interference (ISI) since the sampling at AP generally cannot match with the symbol intervals of uncoordinated devices. To enable reliable reception, we develop a turbo approximate message passing (TAMP) algorithm that consists of a channel-signal decomposition (CSD) module and a delay learning (DL) module. The CSD carries out sparse matrix factorization to estimate the channels and the ISI corrupted signals of active devices, and the DL is designed to estimate the delay of each active user and resolve the corresponding ISI based on the Bayesian principle. To refine the delay estimation, we further divide the DL module into symbol-level delay learning (SDL) and sub-symbol-level delay learning (sub-SDL) submodules. In particular, the sub-SDL estimates the residue delays (obtained by taking modulo of the symbol interval) and then finely compensates the ISI. Due to the continuity and randomness of time delay, the receive signal constellation consists of lines and curves instead of discrete points, even if the transmit signal constellation is discrete. To reduce the complexity of soft demodulation, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed TAMP algorithm. Particularly, the TAMP algorithm is able to approach the single-user bound with known user delay.

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.

This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.

In this paper, we develop an asynchronous massive access framework. Besides the multiple-access interference issue, asynchronous transmission induces inter-symbol interference effect of the same user that is determined by the unknown user delay. To solve this problem, we propose an Bayesian receiver where the whole receiver is divided into two modules: the channel-signal decomposition (CSD) module and the delay learning (DL) module. The CSD module demixes the transmit signals of different users by leveraging the bilinear generalized approximate message passing (BiGAMP) algorithm, and the DL module is designed to estimate the time delay of each user based on the Bayesian principle. Additionally, due to the continuity of time delay, the constellation of all possible received user signals consists of lines and curves instead of discrete points, even if the original transmit signals are discrete. To reduce complexity, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed scheme. Particularly, the proposed scheme is able to approach the single-user interference-free bound with known user delay.

In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). Specifically, the model updates for all the tasks are transmitted and superimposed concurrently over a non-orthogonal uplink fading channel, and the model aggregations of all the tasks are reconstructed at the ES through a modified version of the turbo compressed sensing algorithm (Turbo-CS) that overcomes inter-task interference. Both convergence analysis and numerical results show that the OA-FMTL framework can significantly improve the system efficiency in terms of reducing the number of channel uses without causing substantial learning performance degradation.

While fixed-wing unmanned aerial vehicles (UAVs) provide an effective means to collect data from sensor nodes (SNs), the design of energy-efficient three-dimensional (3D) trajectories has been deterred by the lack of adequate propulsion energy models. This paper develops a new UAV-assisted data collection scheme to save the energy of the UAV and SNs by holistically designing the 3D trajectory and data collection schedule of a fixed-wing UAV. The key aspect is that a new propulsion energy model is established for a fixed-wing UAV flying a 3D trajectory. Another important aspect is that a new approach is developed to save the energy of UAV-based data collection by jointly optimizing the 3D trajectory and the data collection schedule. Difference-of-convex functions and successive convex approximation are employed to tackle the non-convexity pertaining to the new model. Simulations show the validity of the new model and considerable energy saving by optimizing the 3D trajectories for the UAV, as compared to two-dimensional (2D) trajectories.

Wall-clock convergence time and communication load are key performance metrics for the distributed implementation of stochastic gradient descent (SGD) in parameter server settings. Communication-adaptive distributed Adam (CADA) has been recently proposed as a way to reduce communication load via the adaptive selection of workers. CADA is subject to performance degradation in terms of wall-clock convergence time in the presence of stragglers. This paper proposes a novel scheme named grouping-based CADA (G-CADA) that retains the advantages of CADA in reducing the communication load, while increasing the robustness to stragglers at the cost of additional storage at the workers. G-CADA partitions the workers into groups of workers that are assigned the same data shards. Groups are scheduled adaptively at each iteration, and the server only waits for the fastest worker in each selected group. We provide analysis and experimental results to elaborate the significant gains on the wall-clock time, as well as communication load and computation load, of G-CADA over other benchmark schemes.

A large number of Internet of Things (IoT) devices have been interconnected for information collection and exchange. The data are only meaningful if it is captured at the expected location (i.e., the IoT devices or sensors are not removed accidentally or intentionally). This article presents a new algorithm, which cooperatively locates multiple IoT devices deployed in a 3-D space based on pairwise Euclidean distance measurements. When the distance measurement noises are negligible, a new feasibility problem of rank-3 variables is formulated. We solve the problem using the difference-of-convex (DC) programming to preserve the rank-3 constraints, rather than relaxing the constraints, using semidefinite relaxation (SDR). When the distance measurements are corrupted by additive noises and nonlight-of-sight (NLOS) propagation, a maximum-likelihood estimation (MLE) problem is formulated and transformed to a DC program solved with the rank-3 constraints preserved. Simulation results indicate that the proposed approach can achieve satisfactory accuracy results with a low complexity and strong robustness to the irregular topology, poor connectivity, and measurement errors, as compared to existing SDR-based alternatives.

Edge infrastructures, including edge computing servers, are increasingly powered by renewable energy and smart grid combined. Two-way energy trading allows the surplus or shortfall of renewable energy to be traded between a server and the smart grid, but is non-trivial due to randomly varying computation demands and renewables. This paper proposes a new online policy, namely, distributed online resource allocation and load management (DORL), which enables such an edge server and its serving devices to minimize their energy cost and energy consumption, respectively, in a fully distributed manner. The key idea is that we employ the stochastic dual-subgradient method to interpret the battery of the server as a virtual queue. Based on the virtual queue and task queues, the CPU frequencies of the devices and the edge server, the offloading transmit rates of the devices (to the server) and the energy trading decisions of the server (with the smart grid) are decoupled over time and among devices, and optimized on an ongoing basis. Furthermore, we prove that the DORL yields a feasible and asymptotically optimal solution with a cost-backlog tradeoff of$[\eta, 1/\eta]$. Simulations show that the DORL reduces the system cost by nearly 50%, as compared to existing benchmarks.

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%.

Ethereum is one of the most popular blockchain systems that support more than half a million transactions every day and foster miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this article, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytic platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract, and user-contract graphs, are constructed according to trading relationships and are segmented with an appropriate time window. We observe a strong correlation between the size of the user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights, and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions are validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, “the rich is already very rich.”

Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.

Optical wireless communication (OWC) meets the demands of the future six-generation mobile network (6G) as it operates at several hundreds of Terahertz and has the potential to enable data rate in the order of Tbps. However, most beam-steering OWC technologies require high-accuracy positioning and high-speed control. Resonant beam communication (RBCom), as one kind of non-positioning OWC technologies, has been proposed for high-rate mobile communications. The mobility of RBCom relies on its self-alignment characteristic where no positioning is required. In a previous study, an external-cavity second-harmonic-generation (SHG) RBCom system has been proposed for eliminating the echo interference inside the resonator. However, its energy conversion efficiency and complexity are of concern. In this paper, we propose an intra-cavity SHG RBCom system to simplify the system design and improve the energy conversion efficiency. We elaborate the system structure and establish an analytical model. Numerical results show that the energy consumption of the proposed intra-cavity design is reduced to reach the same level of channel capacity at the receiver compared with the external-cavity one.

By orchestrating resources of edge and core network, the delays of edge-assisted computing can decrease. Offloading scheduling is challenging though, especially in the presence of many edge devices with randomly varying link and computing conditions. This paper presents a new online learning-based approach to the offloading scheduling, where multi-agent multi-armed bandit (MA-MAB) learning is designed to exploit the randomly varying conditions and asymptotically minimize the computing delay. We first propose a combinatorial bandit upper confidence bound (CB-UCB) algorithm, where users collectively feed back the observed delays of all edge devices and links. The optimistic bound of the delay is derived to facilitate centralized offloading scheduling for all users. In addition, we put forth a distributed bandit upper confidence bound (DB-UCB) algorithm, where users take random turns to make conflict-free, distributed selections of edge devices. The optimistic confidence bound of each user is developed to allow the user’s selection only based on its own observations and decisions. Furthermore, we establish the asymptotic optimality of the proposed algorithms by proving the sublinearity of their regrets, and that the random turns the users take to make decisions do not compromise the asymptotic optimality of the DB-UCB algorithm, as corroborated by numerical simulations.

Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by legitimate monitoring UAVs (or monitors) can be crucial to public safety and security. It is non-trivial to optimize the trajectory of a monitor while conceiving its monitoring intention, due to typically non-convex propulsion and thrust power functions. This article presents a novel framework to jointly optimize the propulsion and thrust powers, as well as the 3D trajectory of a solar-powered monitor which conducts covert, video-based, UAV-on-UAV tracking and surveillance. A multi-objective problem is formulated to minimize the energy consumption of the monitor and maximize a weighted sum of distance keeping and altitude changing, which measures the disguising of the monitor. Based on the practical power models of the UAV propulsion, thrust and hovering, and the model of the harvested solar power, the problem is non-convex and intangible for existing solvers. We convexify the propulsion power by variable substitution, and linearize the solar power. With successive convex approximation, the resultant problem is then transformed with tightened constraints and efficiently solved by the proximal difference-of-convex algorithm with extrapolation in polynomial time. The proposed scheme can be also applied online. Extensive simulations corroborate the merits of the scheme, as compared to baseline schemes with partial or no disguising.

Thanks to their quick placement and high flexibility, unmanned aerial vehicles (UAVs) can be very useful in the current and future wireless communication systems. With a growing number of smart devices and infrastructure-free communication networks, it is necessary to legitimately monitor these networks to prevent crimes. In this paper, a novel framework is proposed to exploit the flexibility of the UAV for legitimate monitoring via joint trajectory design and energy management. The system includes a suspicious transmission link with a terrestrial transmitter and a terrestrial receiver, and a UAV to monitor the suspicious link. The UAV can adjust its positions and send jamming signal to the suspicious receiver to ensure successful eavesdropping. Based on this model, we first develop an approach to minimize the overall jamming energy consumption of the UAV. Building on a judicious (re-)formulation, an alternating optimization approach is developed to compute a locally optimal solution in polynomial time. Furthermore, we model and include the propulsion power to minimize the overall energy consumption of the UAV. Leveraging the successive convex approximation method, an effective iterative approach is developed to find a feasible solution fulfilling the Karush-Kuhn-Tucker (KKT) conditions. Extensive numerical results are provided to verify the merits of the proposed schemes.

With excellent flexibility, unmanned aerial vehicles (UAVs) can act as airborne computing servers to assist smart terminals (STs) with their computationally-intense and delay-sensitive tasks. This paper presents a new UAV-assisted edge computing framework, which jointly optimizes the trajectory and CPU frequency of a fixed-wing UAV, and the offloading schedule to minimize the energy consumption of the UAV. The key idea is that we reveal the condition for the convexity of the optimization, when the UAV flies a linear trajectory. Under the condition, alternating optimization- and successive convex approximation (SCA)-based algorithms are developed to efficiently achieve the globally optimal linear trajectory, CPU configuration, and offloading schedule. Another important aspect is that we prove the SCA-based algorithm can achieve a local optimum satisfying the Karush-Kuhn-Tucker (KKT) conditions, when the revealed condition is unmet or the UAV flies horizontally in two dimensions. By analyzing the KKT conditions, we also unveil the underlying patterns for the optimal CPU frequency and offloading schedule. Extensive simulations validate the patterns and corroborate the merits of our schemes.

In this paper, we jointly design resource allocation and load management in a mobile-edge computing (MEC) system with wireless power transfer (WPT), to minimize the total energy consumption of the BS, while meeting computation latency requirements. For the first time, the cooling energy, which is non-negligible, is considered to minimize the energy consumption of the MEC system. By orchestrating the alternative optimization technique, Lagrange duality method and subgradient method, we decompose the original optimization problem and obtain the optimal solution in a semi-closed form. Extensive numerical tests corroborate the merits of the proposed algorithm over existing benchmarks in terms of energy saving.

The exponential growth of data traffic over mobile internet leads to a need of heterogeneous networks (HetNets) which integrate multiple radio access technologies (multi-RATs) to allocate task-offloading with quick coordination. In this paper, we present a novel mobile edge computing (MEC) architecture for multi-RAT HetNets, and propose an MEC-centric offloading decision mechanism. By formulating the intended task as a multi-armed bandit (MAB) problem, we leverage an online learning approach to develop a fronthaul aware upper confidence bound (FA-UCB) algorithm that is capable of dealing with uncertainty and asymmetry of network state information. It is rigorously established that the proposed FA-UCB algorithm has a sublinear regret bound against the optimal scheme with full a-priori knowledge. In addition, numerical results demonstrate that the proposed FA-UCB scheme can significantly outperform the existing alternatives in terms of learning regret.

The vision and requirements of the sixth generation (6G) mobile communication systems are expected to adopt freespace optical communication (FSO) and wireless power transfer (WPT). The laser-based WPT or wireless information transfer (WIT) usually faces the challenges of mobility and safety. We present a mobile and safe resonant beam communication (RBCom) system, which can realize high-rate simultaneous wireless information and power transfer (SWIPT). We propose an analytical model to depict its carrier beam and information transfer procedures. The numerical results show that RBCom can achieve more than 40 mW charging power and 1.6 Gbit/s channel capacity with orthogonal frequency division multiplexing (OFDM) scheme, which can be applied in future scenario where power and high-rate data are simultaneously desired.

For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. We divide the detection scheme into two modules: slot-wise multi-user detection (SMD) and combined signal and channel estimation (CSCE). SMD is designed to decouple the transmissions of different users by leveraging the approximate message passing (AMP) algorithms, and CSCE is designed to deal with the nonlinear coupling of activity state, channel coefficient and transmit signal of each user separately. To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm. JUICESD-RIGM achieves a performance close to JUICESD with a much lower complexity. Capitalizing on the feature of RIGM, we further analyze the performance of JUICESD-RIGM with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and the derived state evolution method predicts the system performance accurately.

Massive machine-type communication is one of the most important scenarios for future wireless communications. Due to its huge number of potential users and its bursty packet arrivals, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides a possible solution, and at the same time poses a challenge for efficient receiver design. In this paper, we propose a joint user identification, channel estimation, and signal detection (JUICESD) scheme based on message passing principles to solve the problem. By introducing two types of auxiliary variables, we divide the whole iterative receiver into two modules: one is a linear module leveraging the existing approximate message passing (AMP) algorithm for its low complexity and asymptotic optimality; the other is a non-linear module decoupled for different users. We further discuss the message passing in the non-linear module and between the two modules. Instead of using the conventional Gaussian approximation, we propose to use a structured Gaussian mixture approximation in message updates. With these ideas, an efficient iterative algorithm is developed and analyzed. Numerical results show that the proposed scheme achieves a significant performance improvement over the existing alternatives. Moreover, the complexity of our scheme is linear with the number of users, which is especially suitable for machine type communication with massive devices.

This paper studies a fundamental problem regarding the security of blockchain on how the existence of multiple misbehaving pools influences the profitability of selfish mining. Each selfish miner maintains a private chain and makes it public opportunistically for the purpose of acquiring more rewards incommensurate to his Hashrate. We establish a novel Markov chain model to characterize all the state transitions of public and private chains. The minimum requirement of Hashrate together with the minimum delay of being profitable is derived in close-form. The former reduces to 21.48% with the symmetric selfish miners, while their competition with asymmetric Hashrate puts forward a higher requirement of the profitable threshold. The profitable delay increases with the decrease of the Hashrate of selfish miners, making the mining pools more cautious on performing selfish mining.

For the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid beamforming has been proposed to reap the great gain of the large number of antennas with a limited number of radio frequency (RF) chains. The hybrid beamforming relies on a phase shifter network (PSN) in the RF domain to steer the signal power along the desired direction (or subspace). However, the RF circuits connecting the antennas and the RF chains can introduce distinct phase deviations, which need to be calibrated for efficient hybrid beamforming designs. This paper develops a novel approach to the estimation and calibration of the PSN in mmWave massive MIMO communication systems. To this end, we formulate the calibration problem as an optimization program with the constant modulus constraint. An efficient iterative algorithm is then proposed to estimate the phase deviations to be calibrated. We also derive the Cramer-Rao lower bound (CRLB) of the phase estimates. The numerical results validate the efficiency of our approach by showing that the algorithm yields estimates whose mean squared errors (MSE) are close to the CRLB.

Multiuser MIMO (MU-MIMO) empowers access points (APs) with multiple antennas to transmit multiple data streams concurrently to users by exploiting spatial multiplexing. In MU-MIMO, users need to estimate channel state information (CSI) and report it to APs, thus opening a backdoor to attackers who may forge CSI to eavesdrop the content of victims. In this paper, we explore the eavesdropping attack in a novel and practical context in which CSI forgery entangles MU-MIMO user selection in a many-users regime. The attacker hopes to optimize both the eavesdropping opportunity of being selected with the victim and the corresponding decoding quality. We propose new attack and defense mechanisms: (1) USE Attack that enables attackers to achieve near optimal eavesdropping opportunity and high decoding quality through constructing orthogonal CSI against victims followed by stepwise refinements; (2) AngleSec that exploits channel reciprocity for attacker detection without any modification to legacy CSI feedback in which CSI forgery induces a mismatching of downlink and uplink angular spectra at the AP. We implement and evaluate USE Attack and AngleSec in a software defined radio platform WARPv3. Extensive experiments manifest that USE Attack significantly improves the overall eaves-dropping quality compared with state-of-the-art counterparts and AngleSec is able to detect CSI forgery attackers almost for sure.

Internet of Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to a massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.

This paper proposes a new fully decentralized approach to online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is of practical value, as network services comprising a chain of virtual network functions (VNFs) are proposed to be queued on the basis of leading unexecuted VNFs at every server, rather than on the typical basis of services, reducing queues per server and facilitating queue management and signaling. It is also non-trivial because the VNFs of network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or offloading. Exploiting Lyapunov optimization techniques, we decouple the optimal decisions by deriving and minimizing the instantaneous upper bound of the NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, achieving stable redeployment of VNFs, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared with existing approaches.

Network Function Virtualization (NFV) can cost-efficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the real-time decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of [ε, 1/ε], for any ε > 0. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learn-and-adapt strategy is further designed to speed the placement up with an improved tradeoff [ε, log2(ε)/ε]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 23 percent and reduce the queue length (or delay) by 74 percent, as compared to existing benchmarks.

This paper proposes a new fully decentralized approach for online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is non-trivial as the virtual network functions (VNFs) constituting network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or forwarding. Leveraging Lyapunov optimization techniques, we decouple the optimal decisions by minimizing the instantaneous NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared to existing approaches.

In public channels, common signals are required to transmit omnidirectionally to ensure cell-wide coverage. In this paper, we focus on omnidirectional beamforming for common signal transmission in a massive multiple-input multiple-output (MIMO) system equipped with a uniform rectangular array (URA). We first model the omnidirectional transmission as a feasibility optimization problem in a matrix form. By counting the number of equality constraints and the degrees of freedom of the matrix variable, we conjecture that it takes at least three beamforming vectors to achieve constant-power signal transmission at any direction for a URA with more than two rows/columns. We then propose an efficient iterative rank-reduction algorithm, which can obtain three beamforming vectors that can generate a perfectly flat radiation beampattern, but cannot reduce further for a URA with more than two rows/columns. That is, the algorithm numerically verifies the conjecture. The numerical results validate the superior performance of the proposed scheme over the existing alternatives.

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance creating bottlenecks. To minimize flow entry update time, a dependency graph, a kind of DAG (directed acyclic graph), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about 100x faster than state-of-the-art solutions with a flow table of 1k line size.

By performing fog computing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the challenges of fog computing include lack of central coordination, selfish behaviors of devices, and multi-hop signaling delays, which can result in outdated network knowledge and prevent effective cooperations beyond one hop. This paper presents a new approach to enable cooperations of N selfish devices over multiple hops, where selfish behaviors are discouraged by a tit-for-tat mechanism. The titfor-tat incentive of a device is designed to be the gap between the helps (in terms of energy) the device has received and offered; and indicates how much help the device can offer at the next time slot. The tit-for-tat incentives can be evaluated at every device by having all devices broadcast how much help they offered in the past time slot, and used by all devices to schedule task offloading and processing. The approach achieves asymptotic optimality in a fully distributed fashion with a timecomplexity of less than O(N2). The optimality loss resulting from multi-hop signaling delays and consequently outdated titfor-tat incentives is proved to asymptotically diminish. Simulation results show that our approach substantially reduces the timeaverage energy consumption of the state of the art by 50% and accommodates more tasks, by engaging devices hops away under multi-hop delays.

Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multiantenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time-division multiple access protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamforming at the AP, the central processing unit frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP's total energy consumption subject to the users' individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semiclosed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

Future fifth-generation (5G) cellular networks, equipped with energy harvesting devices, are uniquely positioned to closely interoperate with smart grid. New interoperable functionalities are discussed in stochastic two-way energy trading and online planning to improve efficiency and productivity. Challenges lie in the unavailability of a-priori knowledge on future wireless channels, energy pricing and harvesting. Lyapunov optimization techniques are utilized to address the challenges and stochastically optimize energy trading and planning. Particularly, it is able to decouple the optimization of energy trading and planning during individual time slots, hence eliminating the need for joint optimization across a large number of slots.

Decoupling network services from underlying hardware, network function virtualization (NFV) is expected to significantly improve agility and reduce network cost. However, network services, sequences of network functions, need to be processed in specific orders at specific types of virtual machines (VMs), which couples decisions of VMs on processing or routing network services. Built on a new stochastic dual gradient method, our approach suppresses the couplings, minimizes the time-average cost of NFV, stabilizes queues at VMs, and reduces the backlogs of unprocessed services through online learning and adaptation. Asymptotically optimal decisions are instantly generated at individual VMs, with a cost-delay tradeoff [ε,log2(ε)/√ε]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 30% and reduce the queue length (or delay) by 83%, as compared to existing non-stochastic approaches.

In energy harvesting (EH) network, the energy storage devices (i.e., batteries) are usually not perfect. In this paper, we consider a practical battery model with finite battery capacity, energy (dis-)charging loss, and energy dissipation. Taking into account such battery imperfections, we rely on the Lyapunov optimization technique to develop a stochastic online control scheme that aims to maximize the utility of data rates for EH multi-hop wireless networks. It is established that the proposed algorithm can provide a feasible and efficient data admission, power allocation, routing and scheduling solution, without requiring any statistical knowledge of the stochastic channel, data-traffic, and EH processes.

Integrating mobile-edge computing (MEC) and wireless power transfer (WPT) is a promising technique in the Internet of Things (IoT) era. It can provide massive low-power mobile devices with enhanced computation capability and sustainable energy supply. In this paper, we consider a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute latency-sensitive computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of the tasks to the AP based on a time division multiple access (TDMA) protocol. Under this setup, we pursue an energy-efficient MEC-WPT system design by jointly optimizing the transmit energy beamformer at the AP, the central processing unit (CPU) frequencies and the offloaded bits at each user, as well as the time allocation among different users. In particular, we minimize the energy consumption at the AP over a particular time block subject to the computation latency and energy harvesting constraints per user. By formulating this problem into a convex framework and employing the Lagrange duality method, we obtain its optimal solution in a semi-closed form. Numerical results demonstrate the merits of the proposed joint design over alternative benchmark schemes.

Large-scale MU-MIMO is a promising technology to scale network capacity and the capacity gain grows linearly with the numbers of antennas and users in theory. However, its practical deployment faces three critical challenges in the state-of-the-art WLANs: i) the demand of a large number of expensive RF chains; ii) the linear growth of feedback overheads with the number of antennas; iii) the lack of scalable user selection scheme for a large user population. In this paper, we design BUSH, a large-scale MU-MIMO prototype that performs scalable beam user selection with hybrid beamforming for phased-array antennas in legacy WLANs. The architecture of BUSH consists of three components. Firstly, a low complexity algorithm assigns each pair of RF chain and analog beam to the users to effectively reduce channel correlation and cross-talk interference without instantaneous CSI feedbacks. Secondly, as a prerequisite of user selection, BUSH presents a low-overhead probing scheme in multi-carrier WLANs, and designs a highly accurate blind Power Azimuth Spectrum (PAS) estimation algorithm using a single RF chain. Thirdly, the phased-array antennas use analog beamforming to steer spatial beams toward each selected downlink user, and the finite number of RF chains use beamforming to further mitigate the interference among users. We implement BUSH on the WARPv3 boards and evaluate its performance in more than 30 indoor scenarios. The experimental results show that in terms of total throughput BUSH outperforms the legacy 802.11ac by 2.08×, and an alternative benchmark system by 1.22× on average.

This paper develops a generalized channel-aware power control scheme to enhance the multi-packet reception (MPR) capability for random access. In the proposed scheme, each user randomly selects its transmit power with a probability in accordance with a distribution function based on its own channel state. For two-user systems, we show that the optimal power distribution function has a simple discrete structure, based on which the optimal random access strategy can be efficiently computed. Leveraging the relevant insights, we further propose a decentralized binary power control scheme for general K-user systems. It is established that such a simple scheme can asymptotically approach the performance of the optimal centralized scheme, as the system load grows large. Numerical results show that the proposed schemes obtain noticeable performance improvement over the existing alternatives.

This study addresses the problem of power allocation and mode selection in a hybrid coordinated multi‐cell transmission system with limited backhaul capacity. The power consumption is considered as the extra expense due to data exchange via backhaul links. A novel hybrid multi‐cell scheme is presented, in which each mobile station (MS) can dynamically select either interference channel or multi‐cell multi‐input and multi‐output cooperation mode. To maximise energy efficiency (EE), the optimal power allocation scheme with low complexity is proposed. Moreover, the authors propose an energy‐efficient mode selection metric for each MS, which reflects both benefits and extra expense brought by the selected cooperation mode. Based on the proposed metric, the optimal mode selection strategy maximising EE is obtained. Numerical results show that the hybrid multi‐cell scheme outperforms the traditional single cooperation mode scheme in terms of EE.

Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.

In this paper, a novel two-scale stochastic control framework is put forth for smart-grid powered coordinated multi-point (CoMP) systems. Taking into account renewable energy sources (RES), dynamic pricing, two-way energy trading facilities and imperfect energy storage devices, the energy management task is formulated as an infinite-horizon optimization problem minimizing the time-averaged energy transaction cost, subject to the users' quality of service (QoS) requirements. Leveraging the Lyapunov optimization approach and the stochastic subgradient method, a two-scale online control (TS-OC) approach is developed to make online control decisions at two timescales. It is analytically established that the TS-OC is capable of yielding a feasible and asymptotically near-optimal solution.

This paper considers the beamforming design for a multiuser multiple-input single-output (MU-MISO) downlink with an arbitrary number of (context-specific) shaping constraints. In this setup, the state-of-the- art beamforming schemes cannot attain the well-known performance bound promised by the semidefinite program (SDP) relaxation technique. To close the gap, we propose a linear space-time beamforming (LiST-BF) scheme, consisting of a circulant space-time symbol mapper followed by the beamforming design with orthogonality constraints. It is shown that the proposed LiST-BF scheme can perform general rank-$K$ beamforming for user symbols in a low-complexity and structured manner. Sufficient conditions are derived to guarantee that the LiST-BF scheme always achieves the SDP bound for linear beamforming schemes. Based on such conditions, an efficient algorithm is then developed to obtain the optimal LiST-BF solution in polynomial time. Numerical results demonstrate that the proposed scheme enjoys substantial performance gains over the existing alternatives.

An infinite time-horizon resource allocation problem is formulated to maximize the time-averaged multi-input multi-output (MIMO) downlink throughput, subject to a time-averaged energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient based online control (SGOC) approach is developed for the resultant smart-grid powered communication system. It is analytically established that even without a-priori knowledge of the underlying random processes, the proposed online algorithm is capable of yielding a feasible and asymptotically optimal solution.

A systematic framework is put forth in this paper to integrate renewable energy sources (RES), distributed storage units, cooling facilities, as well as dynamic pricing into the workload and energy management tasks for a data center network. To cope with RES uncertainty, the resource allocation task is formulated as a robust optimization problem minimizing the worst-case net cost. The resulting problem is reformulated as a convex program, and then solved in a distributed fashion using the dual decomposition approach. Numerical tests demonstrate the performance gain of the proposed approach over the existing alternative.

Multi-view video streaming is essential for various mobile 3D and immersive applications that can capture the same scene from multiple angles. However, the large traffic volume of multi-view streaming will drain the battery power quickly. This paper studies the power efficient delivery of 3D content with multi-view video coding (MVC) in the emerging 802.11-like MIMO wireless systems, and the purpose is to minimize the power consumption with the video quality guarantee. We propose an efficient algorithm to perform antenna assignment and transmission power allocation, by exploiting both the source coding characteristics of MVC and channel diversity of multiple antennas. A proof-of-concept system, namely PoM, is designed on the software radio platform and is evaluated in realistic indoor environments. To the best of our knowledge, this is the first practical system for energy efficient multi-view video streaming. Experimental results show that PoM can significantly save energy in the transmission by 12% ~ 65% on average when the required PSNR decreases from 45dB to 35dB.

Shortest-path queries on weighted graphs are an essential operation in computer networks. The performance of such algorithms has become a critical challenge in emerging software-defined networks (SDN), since SDN controllers need to perform a shortest-path query for every flow. Unlike classic solutions (e.g., Dijkstra's algorithm), high-performance shortest-path query algorithms include two stages: preprocessing and query answering. Despite the improved query answering time, existing two-stage algorithms are still extremely time-consuming in preprocessing large-scale graphs. In this paper, we propose an efficient shortest-path query algorithm, called BBQ, which reduces the running time of both stages via tree decomposition. BBQ constructs a distance oracle in a bottom-top-bottom manner, which significantly reduces preprocessing time over existing algorithms. In addition, BBQ can answer batch queries in bulk by traversing the decomposed tree instead of executing separate queries. Our experimental results show that BBQ outperforms state-of-the-art approaches by orders of magnitude for the running time of the preprocessing stage. Meanwhile, BBQ also offers remarkable acceleration for answering batches of queries. As a result, SDN controllers that use BBQ can sustain 1.1-27.9 times higher connection request rates.

Cloud-of-clouds storage is a recent approach to improve the security and reliability of data storage for online applications. It encrypts and encodes the user data, and disperses the results to multiple clouds. Thus, the data can tolerate cloud failures, while cannot be inferred even when some clouds are compromised. However, efficiency is a well-known challenge to such a paradigm, since its data storing process (also known as the dispersal process) is time-consuming involving encryptions, encoding, and transmissions, posing a barrier to its wide application. How to speed up the dispersal process is yet to be well addressed. We observe that the dispersal process consists of two types of operations: calculation and transmission. We find that they can execute simultaneously. Hence, the process can be optimized with a pipelined architecture. To this end, we propose the pipelined versions of two state-of-the-art cloud-of-clouds storage approaches, i.e., AONT-RS and CAONT-RS. We implement both proposals and release them open-source online. To verify their effectiveness, extensive experiments are conducted on a prototype storage system with real-world traces. The results show that the pipelined architecture can improve the performance of the dispersal process.

Software-Defined Networking (SDN) has great potentials in changing the fragile and complex enterprise networks. One operational challenge to SDN deployment is the settlement of legacy switches. A hybrid SDN consisting of both SDN and legacy switches may be a tradeoff. Nevertheless most of the current SDN control planes can not handle legacy switches. To overcome this problem, we present HybridFlow, a lightweight control plane for hybrid SDN. HybridFlow can abstract a hybrid network into a logical SDN network and existing SDN control applications can run on it transparently.

In data storage systems, data are typically stored in redundant storage nodes to ensure storage reliability. When storage nodes fail, with the help of the redundant nodes, the lost data can be restored in new storage nodes. Such a regeneration process may be aborted, since storage nodes may fail during the process. Therefore, reducing the time of regeneration process is a well-known challenge to improve the reliability of storage systems. Delayed repair is a typical repair scheme in real-world storage systems. It reduces the overhead of the regeneration process by recovering multiple node failures simultaneously. How to reduce the regeneration time of delayed repair is yet to be well addressed. Since available bandwidth is flowing in storage systems and the regeneration time is seriously affected by the available bandwidth, we find the key to solve this problem is determining the start time of the regeneration process. Via modeling this problem with Lyaponuv optimization framework, we propose an OMFR scheme to reduce the regeneration time. The experimental results show that OMFR scheme can reduce cumulative regeneration time by up to 78% compared with traditional delayed repair schemes.

Lattice reduction (LR) aided multiple-input multiple-output (MIMO) detectors have been considered as an option to obtain near-maximum likelihood (ML) performance. We first give the analysis to show that large signal-to-noise ratio (SNR) corresponds to the short length of the dual basis vectors. Then, in order to further alleviate the complexity of LR aided MIMO detectors while maintaining acceptable performance, we study the dual-lattice reduction methods and propose a fast dual-lattice reduction (FDLR) algorithm which minimizes the orthogonality deficiency of dual-basis. And a tree search method is presented to implement the FDLR algorithm, which enables a flexible trade-off between performance and complexity. Compared to the existing dual Lenstra-Lenstra-Lovasz (DLLL) algorithm, out proposed FDLR algorithm requires less iteration time and yields more orthogonal basis vectors. Simulation results show that FDLR aided detectors achieve better performance and lower complexity than DLLL aided detectors, especially for large MIMO system.

In this paper, we develop the robust transceiver optimization for the multiple-input single-output (MISO) interference channels where each transmitter (Tx) is equipped with multiple antennas and each single-antenna receiver performs simultaneous wireless information and power transfer (SWIPT) based on a power-splitting architecture. Assuming imperfect channel state information (CSI) at the Txs, we design jointly optimal transmit beamforming and receive power-splitting scheme that minimizes the total transmission power under the worst-case signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) constraints. When the channel uncertainties are bounded by ellipsoidal regions, we show that the worst-case SINR and EH constraints can be recast into quadratic matrix inequality forms, and the intended problem can be relaxed as a tractable semi-definite program. Furthermore, relying on the alternating direction method of multipliers (ADMM), we propose a decentralized algorithm capable of computing the optimal beamforming and power- splitting schemes with local CSI and limited information exchange among the Txs.

Recently massive multi-input multi-output (MIMO) techniques have attracted growing research interest. However, most existing works assume centralized control, which may involve a heavy overhead as the number of users also becomes massive. In this paper, we focus on random-access massive MIMO systems. We first analyze the performances of two conventional schemes widely used in massive MIMO, and derive their closed-form expressions in the asymptotic case. Building on the latter, we maximize the system throughput through transmission control. To further exploit the potential benefit of massive MIMO techniques, we propose a novel multi-level transmission and grouped inter-ference cancellation (MLT-GIC) scheme, which can obtain a higher system throughput and provide a flexible tradeoff between throughput and complexity. Numerical results demonstrate the merits of the proposed scheme.

A large number of geo-distributed data centers begin to surge in the era of data deluge and information explosion. To meet the growing demand in massive data processing, the infrastructure of future data centers must be energy-efficient and sustainable. Facing this challenge, a systematic framework is put forth in this paper to integrate renewable energy sources (RES), distributed storage units, cooling facilities, as well as dynamic pricing into the workload and energy management tasks of a data center network. To cope with RES uncertainty, the resource allocation task is formulated as a robust optimization problem minimizing the worst-case net cost. Compared with existing stochastic optimization methods, the proposed approach entails a deterministic uncertainty set where generated RES reside, thus can be readily obtained in practice. It is further shown that the problem can be cast as a convex program, and then solved in a distributed fashion using the dual decomposition method. By exploiting the spatio-temporal diversity of local temperature, workload demand, energy prices, and renewable availability, the proposed approach outperforms existing alternatives, as corroborated by extensive numerical tests performed using real data.

Benefiting from technological advances in the smart grid era, next-generation multi-input multi-output (MIMO) communication systems are expected to be powered by renewable energy sources (RES) integrated in the distribution grid, thus realizing the vision of “green communications.” However, penetration of renewables introduces variabilities in the traditional power system, making RES benefits achievable only after appropriately mitigating their inherently high variability, which challenges existing resource allocation strategies. Aligned with this goal, an infinite time-horizon resource allocation problem is formulated to maximize the time-average MIMO downlink throughput, subject to a time-average energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient-based online control approach is developed for the resultant smart-grid powered communication system. It is established analytically that even without a priori knowledge of the independently and identically distributed (i.i.d.) processes involved such as channel coefficients, renewables, and electricity prices, the proposed online control algorithm is still able to yield a feasible and asymptotically optimal solution. Numerical results further demonstrate that the proposed algorithm also works well in non-i.i.d. scenarios, where the underlying randomness is highly correlated over time.

Due to increasing threats of global warming and climate change concerns, green wireless communications have recently drawn intense attention toward reducing carbon emissions. Aligned with this goal, the present paper deals with dynamic energy management for smart-grid powered coordinated multipoint (CoMP) transmissions. To address the intrinsic variability of renewable energy sources, a novel energy transaction mechanism is introduced for grid-connected base stations that are also equipped with an energy storage unit. Aiming to minimize the expected energy transaction cost while guaranteeing the worst-case users’ quality of service, an infinite-horizon optimization problem is formulated to obtain the optimal downlink transmit beamformers that are robust to channel uncertainties. Capitalizing on the virtual-queue-based relaxation technique and the stochastic dual-subgradient method, an efficient online algorithm is developed yielding a feasible and asymptotically optimal solution. Numerical tests with synthetic and real data corroborate the analytical performance claims and highlight the merits of the novel approach.

Airborne relaying can extend wireless sensor networks (WSNs) to remote human-unfriendly terrains. However, lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs) are critical issues, adversely affecting success rate and network lifetime, especially in real-time applications. We propose an energy-efficient cooperative relaying scheme which extends network lifetime while guaranteeing the success rate. The optimal transmission schedule of the UAVs is formulated to minimize the maximum (min-max) energy consumption under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimisation techniques. We also propose a computationally efficient suboptimal algorithm to reduce the scheduling complexity, where energy balancing and rate adaptation are decoupled and carried out in a recursive alternating manner. Simulation results confirm that the suboptimal algorithm cuts off the complexity by orders of magnitude with marginal loss of the optimal network yield (throughput) and lifetime. The proposed suboptimal algorithm can also save energy by 50 percent, increase network yield by 15 percent, and extend network lifetime by 33 percent, compared to the prior art.

In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of O(N2) in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.

Optimal resource management for smart grid powered multi-input multi-output (MIMO) systems is of great importance for future green wireless communications. A novel framework is put forth to account for the stochastic renewable energy sources (RES), dynamic energy prices, as well as random wireless channels. Based on practical models, the resource allocation task is formulated as an optimization problem that aims at maximizing the weighted sum-rate of the MIMO broadcast channels. A two-way transaction mechanism and storage units are introduced to accommodate the RES variability. In addition to system operating constraints, a budget threshold is imposed on the worst-case energy transaction cost due to the possibly adversarial nature. Capitalizing on the uplink-downlink duality and the Lagrangian relaxation-based subgradient method, an efficient algorithm is developed to obtain the optimal strategy. Generalizations to the setups of time-varying channels and ON-OFF transmissions are also discussed. Numerical results are provided to corroborate the merits of the novel approaches.

In energy harvesting (EH) networks, the energy storage devices (i.e., batteries) are usually not perfect. In this paper, we consider a practical battery model with finite battery capacity, energy (dis-)charging loss, and energy dissipation. Taking into account such battery imperfections, we rely on the Lyapunov optimization technique to develop a stochastic online control scheme that aims to maximize the utility of data rates for EH multi-hop wireless networks. It is established that the proposed algorithm can provide a feasible and efficient data admission, power allocation, routing and scheduling solution, without requiring any statistical knowledge of the stochastic channel, data-traffic, and EH processes. Numerical results demonstrate the merit of the proposed scheme.

In this paper, we investigate joint base station (BS) activation and beamforming design for coordinated downlink transmissions in a cellular heterogeneous network (HetNet). We formulate the total power minimization problem as a mixed integer program. Using the Benders' partitioning method and the convex duality theory, we show that the global optimal solution can then be computed by solving a sequence of relaxed master programs and the associated subproblems with affordable complexity.

The paper deals with dynamic energy management for smart-grid powered coordinated multi-point (CoMP) transmissions. Aiming to minimize the expected energy transaction cost while guaranteeing the worst-case users' quality of service (QoS), an infinite-horizon optimization problem is formulated to obtain the optimal downlink transmit beamformers that are robust to channel uncertainties. Capitalizing on the virtual-queue based relaxation technique and the stochastic dual-subgradient method, an efficient online algorithm is developed in this context. Without a-priori knowledge of any statistics of the underlying random processes, it is rigorously established that the proposed algorithm is able to yield a feasible and asymptotically optimal solution.

Airborne relaying is of potential to extend wireless sensor networks (WSN) to human-unfriendly terrains. Challenges arise due to lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs). We propose an energy-efficient relaying scheme to overcome the challenges. A swarm of UAVs are deployed to listen to remote sensors from distributed locations, improving packet reception over lossy channels. UAVs report their reception qualities to the base station where the optimal schedule with guaranteed success rates and balanced energy consumption can be generated. Such scheduling is an NP-hard binary integer programming. We develop a suboptimal solution by decoupling the processes of energy balancing and data rate adjustment. Simulations confirm that, in terms of network yield, our method is indistinguishable to the NP-hard optimal solution, 15% higher than greedy algorithms. Our method can reduce the complexity by orders of magnitude, and extend network lifetime by 33%.

OpenFlow has been widely used in Software Defined Networking (SDN) to customize data plane behaviors through policies given by a logically centralized controller. The centralized control plane design brings the potential of simplifying network management, but it also raises scalability concern for large-scale networks. In this paper, we aim to build a highly scalable and flexible OpenFlow control plane. We propose the design and implementation of cCluster, which leverage the parallelism of cluster to balance the control plane load. Compared with existing solutions, cCluster can achieve better scalability, and can enable elastic management. We evaluated the scalability and response time of cCluster using Mininet, and our results show that cCluster can serve thousands of flow entries simultaneously, with only slightly latency penalty.

Present day requirements of high quality audio and video surveillance has instigated research interests in wireless multimedia sensor networks (WMSN). In order for the WMSN to achieve trademark performance in audio and video surveillance applications, certain design requirements must be met. In this work, we identify vital design issues affecting diversity gain in conditions especially, where channel fading characteristics fluctuate rapidly. We apply the cooperative communication technique in WMSN to create a framework that optimizes diversity gain in an environment where channel state information (CSI) is unknown. We then discuss promising research directions for optimizing diversity gain and noncoherent communication efficiency in cooperative WMSN.

Energy harvesting is an emerging solution to prolong the lifetime of energy-constrained nodes in wireless networks. This letter develops a novel distributed energy beamforming scheme for realizing simultaneous wireless information and power transfer in the two-way relay channel (TWRC), where two source nodes exchange information via an energy-harvesting relay node. We investigate the optimal transceiver design to maximize the achievable sum-rate of the TWRC. We also propose a low-complexity power-splitting based suboptimal scheme with closed-form solution. Numerical results demonstrate significant performance improvement of the proposed schemes over the conventional power-splitting based scheme.

This letter considers a downlink multi-input single-out (MISO) system where each user performs simultaneous wireless information and power transfer (SWIPT) based on a power splitting receiver architecture. Assuming imperfect channel state information (CSI) at the base station, we develop two robust joint beamforming and power splitting (BFPS) designs that minimize the transmission power under both the signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) constraints per user. In the first design, we consider the worst-case (WC) SINR and EH constraints, and show that the WC-BFPS problem can be relaxed as a semidefinite program (SDP) through a linear matrix inequality representation for (infinitely many) robust quadratic matrix inequality constraints. In the second design, we consider the chance constraints (CCs) for SINR and EH, and resort to both semidefinite relaxation and Bernstein-type inequality restriction to transform the CC-BFPS problem into another convex SDP. Based on these convex reformulations, the (near-)optimal robust BFPS designs can be efficiently solved. Numerical results are provided to demonstrate the merit of the proposed robust designs.

To maximize power transfer for inductively charging electric vehicles (EVs), charger and battery coils must be aligned. Wireless sensors can be installed to estimate misalignments; however, existing ranging techniques cannot satisfy the precision requirements of the misalignment estimation. We propose a high-precision wireless ranging and misalignment estimation scheme, where high precision is achieved by iteratively measuring, estimating, and aligning the coils. Another key aspect is to convert the nonconvex misalignment estimation to a more tractable problem with a convex objective. We develop a conditional gradient descent method to solve the problem, which performs gradient descent (or conditional gradient descent on the boundary of the search space) and projects out-of-boundary points back into the space. Employing experimentally validated models, we show that our scheme can achieve 92% of the efficiency of perfectly aligned coils in 90% of operations, and tolerate correlated distance measurement errors. In contrast, the prior art is susceptible to correlation, undergoing a significant efficiency degradation of 18.5%.

Recently, hybrid cloud computing paradigm has be widely advocated as a promising solution for Software-as-a-Service (SaaS) providers to effectively handle the dynamic user requests. With such a paradigm, the SaaS providers can extend their local services into the public clouds seamlessly so that the dynamic user request workload to a SaaS can be elegantly processed with both the local servers and the rented computing capacity in the public cloud. However, although it is suggested that a hybrid cloud may save cost compared with building a powerful private cloud, considerable renting cost and communication cost are still introduced in such a paradigm. How to optimize such operational cost becomes one major concern for the SaaS providers to adopt the hybrid cloud computing paradigm. However, this critical problem remains unanswered in the current state of the art. In this paper, we focus on optimizing the operational cost for the hybrid cloud paradigm by theoretically analyzing the problem with a Lyapunov optimization framework. This allows us to design an online dynamic provision algorithm. In this way, our approach can address the real-world challenges where no a priori information of public cloud renting prices is available and the future probability distribution of user requests is unknown. We then conduct extensive experimental study based on a set of real-world data, and the results confirm that our algorithm can work effectively in reducing the operational cost.

A framework is introduced to integrate renewable energy sources (RES) and dynamic pricing capabilities of the smart grid into beamforming designs for coordinated multipoint (CoMP) downlink communication systems. To this end, novel models are put forth to account for harvesting, storage of nondispatchable RES, time-varying energy pricing, and stochastic wireless channels. Building on these models, robust energy management and transmit-beamforming designs are developed to minimize the worst-case energy cost subject to the worst-case user QoS guarantees for the CoMP downlink. Leveraging pertinent tools, this task is formulated as a convex problem. A Lagrange dual-based subgradient iteration is then employed to find the desired optimal energy-management strategy and transmit-beamforming vectors. Numerical results are provided to demonstrate the merits of the proposed robust designs.

This paper develops a novel approach to optimal broadcast scheduling for an energy-harvesting powered transmitter with non-ideal circuit power consumption. Relying on the uplink-downlink duality and convex optimization tools, the proposed approach provides low-complexity algorithms to obtain the optimal transmission policies that maximize the weighted sum-throughput for multi-input multi-output (MIMO) broadcast channels. For both time-invariant and time-varying channels, it is revealed that the optimal transmission between any two consecutive channel or energy state changing instants, termed epoch, can only take one of the three strategies: 1) no transmission; 2) transmission with an energy-efficiency (EE) maximizing sum-power over part of the epoch; or 3) transmission with a sum-power greater than the EE-maximizing power over the whole epoch. The proposed approach can provide the optimal benchmarks for practical schemes in energy-harvesting MIMO broadcast transmissions, and can be employed to develop efficient online scheduling schemes which require only causal knowledge of energy arrival realizations.

This paper develops novel approaches for energy-harvesting powered transmissions of delay-limited busty data packets under both ideal and non-ideal circuit power consumption. It is shown that the problems can be formulated as convex programs. Relying on the specific structure of the optimality conditions, we put forth efficient algorithms to find the optimal transmission strategies with a low computational complexity. It is revealed that the optimal energy departure curve for the ideal circuit power case can be yielded by a vivid calculus method. On the other hand, the optimal energy departure for the general non-ideal circuit power case can be obtained by simply adjusting the ideal-case energy departure in accordance to an energy efficient (EE) maximizing power value.

This paper considers transceiver design for the cellular two-way relay channel (cTWRC), where a multi-antenna base station (BS) exchanges information with multiple single-antenna mobile stations via a multi-antenna relay station. We propose a novel network coding scheme to approach the sum capacity of the cTWRC. Specifically, a new non-linear lattice-based precoding technique is proposed at the BS to pre-compensate the inter-stream interference, in order to allow efficient interference-free lattice decoding at the relay. We derive sufficient conditions for the proposed scheme to asymptotically achieve the sum capacity of the cTWRC in the high signal-to-noise ratio (SNR) regime. Numerical results show that the proposed scheme outperforms the existing schemes and is able to asymptotically achieve the cut-set bound of the cellular relay networks.

We develop a novel approach to optimal broadcast scheduling over time-varying channels for an energy harvesting transmitter with finite-capacity battery and non-ideal circuit power consumption. Relying on the convex optimization tools, a low-complexity algorithm is proposed to obtain the optimal transmission policy that maximizes the weighted sum-throughput for multi-input multi-output (MIMO) broadcast channels. Our approach provides the optimal benchmark to all the practical schemes for energy harvesting powered broadcasting with non-ideal circuit power.

Energy harvesting has been widely considered in many wireless applications, especially the wireless sensor networks. This paper develops a novel approach to energy-harvesting powered transmissions under arbitrary packet arrival process and strict deadline constraints over time-varying channels. It is shown that the problem can be formulated as a convex program. Relying on the specific structure of the optimality conditions, we put forth an efficient algorithm with a low computational complexity to find the optimal rate control strategy. An insightful visualization is also provided to depict the construction of the optimal policy. Numerical results are presented to demonstrate the merit of the proposed scheme.

This paper develops a novel approach to energy-efficient transmission schedule for delay-limited bursty data arrivals under non-ideal circuit power consumption. Assuming aprior knowledge of packet arrivals and deadlines, an efficient algorithm is proposed to find the optimal policy that minimizes the total energy consumption, with a low computational complexity. It is revealed that the optimal data departure for the general nonideal circuit-power case can be obtained by simply adjusting the ideal-case data departure in accordance to an energy efficiency (EE) maximizing rate value. The proposed approach provides optimal benchmark for all the practical schemes, and can be employed to develop efficient online scheduling schemes which assume only causal knowledge of the data arrival and deadline realizations.

We consider the transmit beamforming design for a multi-user downlink with multiple transmit antennas at the base station. Different from the conventional sum-power constraint across the transmit antennas, we assume individual power constraints per antenna. Assuming that perfect channel state information (CSI) is available at the base station, we develop an efficient algorithm to find the optimal beamforming scheme for the classic max-min signal-to-interference-plus-noise ratio (SINR) problem based on solving a sequence of “dual” per-antenna power balancing problems as second-order cone programs. It is proven that the proposed algorithm can find the max-min SINR beamforming solution with guaranteed global optimality and fast convergence speed. Relying on robust optimization techniques, the approach is also generalized to obtain the robust beamforming design that maximizes the worst-case user SINR when the channel uncertainty is bounded by a spherical region. Numerical results are provided to demonstrate the merits of the proposed transmit beamforming schemes.

Hybrid cloud computing paradigm has recently be widely advocated, where Software-as-a-Service (SaaS) providers can extend their local services into the public clouds seamlessly. In this way, dynamic user request workload to a SaaS can be elegantly handled with the rented computing capacity in public cloud. However, although a hybrid cloud may save cost compared with the private cloud, it still introduces considerable renting cost and communication cost. How to optimize such an operational cost becomes one major concern for the SaaS providers to adopt such a hybrid cloud computing paradigm. However, this critical problem remains unanswered in the current state of the art. In this paper, we focus on optimizing the operational cost for the hybrid cloud model by theoretically analyzing the problem with a Lyapunov optimization framework, and accordingly providing an online dynamic provision algorithm. In this way, our approach can address the real-world challenges where no a priori information of public cloud renting prices is available and the future probability distribution of user requests is unknown. We then conduct experimental study based on a set of real-world data, and the results confirm that our algorithm can work well in reducing the cost.

This paper develops a unified approach to obtain optimal transmission schedule for an energy-harvesting node with non-ideal circuit power consumption. For both time-invariant and time-varying fading channels, we show that the optimal transmission between any two consecutive channel or energy state changing time, termed epoch, can only take one of the three strategies: (i) no transmission, (ii) transmission with an energy-efficiency (EE) maximizing power over part of the epoch, or (iii) transmission with a power greater than the EE-maximizing power over the whole epoch. Taking into account this structure, we develop efficient algorithms capable of computing the optimal scheduling schemes with a low complexity. The proposed approach can provide the optimal benchmarks for practical schemes in energy-harvesting powered transmissions, and can be employed to develop efficient online scheduling schemes.

Bandwidth-demanding mobile video applications are becoming increasingly popular in wireless networks, leading to a relentless growth in the demand for wireless throughput and quality of service (QoS). Multiuser Multiple-Input Multiple-Output (MIMO) has great potential to meet the growth of wireless throughput. However, this advancement in physical-layer technologies does not necessarily translate into better QoS for the applications, unless the design principles and operating protocols at the higher layers of the networking stack are adapted accordingly to fully capture this potential. We propose a new scheduling algorithm, which selects mobile users to form multiuser MIMO based on the priorities we carefully design to leverage the demands of wireless throughput and video quality. We also develop a new computationally efficient parallel technique to calculate the priorities precisely, which allows the users to be selected in a computationally effective way. Analyses and simulations show that the proposed scheme allows video applications to achieve close to the throughput upper bound of multiuser MIMO. Our scheme also improves the video quality by reducing the loss of video enhancement packets by an order of magnitude and by reducing the delay by 35%, compared to the state of the art.

We investigate the chunk-based resource allocation for OFDMA downlink, where data streams contain packets with diverse bit-error-rate (BER) requirements. Supposing adaptive transmissions based on a number of discrete modulation and coding modes, we derive the optimal scheme that maximizes the weighted sum of average user rates under the multiple BER and total power constraints. With the relevant optimization problem cast as an integer linear program, we show that the optimal strategy can be obtained through Lagrange dual-based gradient iterations with fast convergence and low computational complexity per iteration. Furthermore, a novel on-line algorithm is developed to approach the optimal strategy without knowledge of intended wireless channels a priori.

We develop a novel approach to energy-efficient transmissions with arbitrary packet arrival process and strict delay constraints over time-varying wireless channels. When the arrivals, deadlines, and channel realizations are known a priori, we formulate the problem as a convex program. Relying on the specific structure of the optimality conditions, we put forth an efficient algorithm with a linear computational complexity in the order of constraint number to find the (offline) optimal rate control strategy. It is revealed that the power usage under the optimal policy admits a multi-level water-filling form, where the determination of the multiple water-levels can be visualized by the trajectory of letting a string tie its two ends and then taut between what we call the "water" arrival and departure curves. Guided by the optimal strategy, development of energy-efficient online schedules in practical systems is discussed. Numerical results are provided to demonstrate the merits of the proposed novel scheme.

In this paper, we consider joint congestion control and wireless-link scheduling design for TCP applications over the Internet with ad-hoc wireless links. Differing from the existing methods, the main idea of our approach is to develop non-standard window-based implicit primal-dual solver for the intended optimization problem. Then queueing delays are employed to decompose this solver into local algorithms that can be deployed and operated asynchronously at different layers of network nodes. Capitalizing on this approach, we put forth the QUIC-TCP congestion control algorithm, and a new queueing-delay based carrier sense multiple access (CSMA) scheduling scheme. The proposed schemes can be implemented asynchronously without message passing among network nodes. Confined to the design space of TCP and CSMA, they are readily deployed for practical Internet applications. Moreover, global convergence of the proposed joint design to optimal network equilibrium can be established using the Lyapunov method in an idealized network fluid model. Simulation results are provided to evaluate the proposed schemes in practical networks.

Relying on a design-space oriented cross-layer optimization approach, we develop joint TCP congestion control and CSMA scheduling schemes for mobile applications over Internet with distributed multi-hop wireless links. Different from the existing solutions, the proposed schemes can be asynchronously implemented without message passing among network nodes. Confined to the design space of TCP and CSMA, they are readily deployed with current Internet infrastructure. Moreover, global convergence/stability of the proposed schemes to optimal equilibrium is established by using the Lyapunov method in an idealized network fluid model. Simulation results are provided to evaluate the proposed schemes in practical networks.

This paper considers the jointly optimal rate and power allocation that maximizes a utility function of average rates in OFDMA multiple access and broadcast channels. Derived under the assumption that superposition coding is allowed per subcarrier, our low-complexity (yet capacity-achieving) resource allocation scheme can yield higher achievable rates than the existing approaches based on exclusive subcarrier assignment. In addition, relying on the stochastic optimization tools, we develop a class of stochastic gradient iterations that are capable of converging to the optimal benchmarks without the knowledge of fading channel cdf a priori.

In this paper, we will illustrate a practice of pipeline process to maximize the parallelization of all possible interference-free relays in the broadcasting of wireless sensor networks (WSNs), in order to optimize the end-to-end delay performance in both the (synchronous) round-based systems and the (asynchronous) duty cycle systems. Broadcasting is one of the fundamental communications in WSNs. Existing delay-sensitive broadcasting schemes adopt an approximation approach that is based on counting the hop distance to the source. They require all relays in each 1-hop propagation to be synchronized together in order to avoid any interference, but this also incurs the block of the interference free relays from those 1-hop neighbors that have received the message. In the duty cycle system, such a block can cause the relay to miss the wake-up time of the successor node which incurs the extra delay. In our approach, a heuristic information model enumerating all the possible future sequences in terms of delay time is adopted first to achieve the optimal relay selection, initiating the study of global impact of local interference. Then, a lightweight model estimating the hop distance to the edge of network of the unaccomplished relay work is adopted with the well known greedy color scheme to achieve the close-to-optimal relay selection. The analytical and experimental results show the substantial improvement by our pipeline practice, compared with the best results known to date.

This paper considers the jointly optimal rate and power allocation that maximizes a utility function of average rates in fading OFDMA multiple access and broadcast channels. Derived under the assumption that superposition coding is allowed per subcarrier, the proposed low-complexity (yet capacity-achieving) resource allocation scheme can yield higher achievable rates than the existing approaches based on exclusive subcarrier assignment. In addition, relying on the stochastic optimization tools, we develop a class of stochastic gradient iterations that are capable of converging to the optimal benchmarks without the knowledge of fading channel statistics a priori.

This letter investigates masked beamforming schemes for multiuser multiple-input multiple-output (MIMO) downlink systems in the presence of an eavesdropper. With noisy and outdated channel state information (CSI) at the base station (BS), we aim to maximize the transmit power of an artificial noise, which is broadcast to jam any potential eavesdropper, while meeting individual minimum mean square error (MMSE) constraints of the desired user links. To this end, we adopt a Bayesian approach and derive an average MSE uplink-downlink duality with imperfect CSI. Using the duality, a robust beamforming algorithm is proposed. Simulation results show the effectiveness of the proposed scheme.

We consider a cognitive radio network where secondary cognitive users communicate with an access point relying on opportunistic transmissions over wireless fading channels. For such a network, it is important to maximize aggregate utility of average user rates under the constraints of average user power budgets and maximum allowable probabilities of collisions with the primary communications. To this end, jointly optimal sensing selection and power control scheme for the cognitive users is derived in a quasi-closed form through a Lagrange dual-based gradient approach. Moreover, we develop a stochastic optimization algorithm that can operate without a-priori knowledge of the fading channel statistics. It is established that the proposed stochastic scheme is capable of approaching the optimal sensing and power control strategy.

We consider joint congestion control and wireless link-scheduling design for mobile TCP applications. Adopting queueing delay as the congestion measure, we show that the optimal TCP congestion control and link scheduling scheme amounts to window-control oriented implicit primal-dual solvers for underlying network utility maximization. Based on this idea, we develop readily deployable, scalable yet optimal TCP congestion control schemes for the Internet with coupled wireless links, provided that the wireless access point performs a queueing-delay based link scheduling. Global convergence/stability of the proposed schemes to optimal network equilibrium is proved using a Lyapunov method. Simulation results are provided to evaluate the proposed schemes in practical network environments.

We consider adaptive subcarrier assignment and fair power control strategy that minimizes a cost function of average relay powers for multi-user wireless OFDM networks, where relays assist communication between sources and destinations with an amplify-and-forward strategy. Using a class of beta-fair cost functions to balance the tradeoff between energy efficiency and fairness, jointly optimal subcarrier and power allocation schemes at the relays are derived in the quasi-closed form. In addition, we develop novel stochastic optimization algorithms capable of dynamically learning the intended wireless channels on-the-fly to approach the optimal policy. Numerical results are provided to evaluate the proposed schemes for multi-user OFDM relay networks over block fading channels.

Resource allocation issues are investigated in this paper for multiuser wireless transmissions based on orthogonal frequency division multiplexing (OFDM). Relying on convex and stochastic optimization tools, the novel approach to resource allocation includes: i) development of jointly optimal subcarrier, power, and rate allocation for weighted sum-average-rate maximization; ii) judicious formulation and derivation of the optimal resource allocation for maximizing the utility of average user rates; and iii) development of the stochastic resource allocation schemes, and rigorous proof of their convergence and optimality. Simulations are also provided to demonstrate the merits of the novel schemes.

In practical orthogonal frequency division multiple-access (OFDMA) systems, subcarriers are grouped into chunks and a chunk of subcarriers is regarded as the minimum unit for subcarrier allocation. Given that the number of chunks and the number of subcarriers in each chunk are predefined, we consider the optimal chunk allocation that maximizes a utility function of average user rates for a wireless OFDMA system under different power control policies. The relevant optimization problems are formulated as non-convex mixed-integer programs; yet it is shown that the optimal schemes can be obtained through Lagrange dual-based gradient iterations with fast convergence and low computational complexity under conditions. Furthermore, novel low-complexity algorithms are developed to approach the optimal strategies without a-priori knowledge of statistics of the intended wireless channels. Numerical results are provided to gauge the performance of the proposed schemes.

We consider joint optimization for sensing-channel selection and ensuing power control problem with cognitive radios over time-varying fading channels. It is shown that this joint design can be judiciously formulated as a convex optimization problem. Optimal joint sensing-channel selection and power control scheme is then derived in closed-form under the constraints of average power budget and maximum allowable probability of collisions with the primary communications. In addition, we develop a stochastic optimization algorithm that can operate without a-priori knowledge of the fading channel statistics. It is rigourously established that the proposed stochastic scheme is capable of dynamically learning the intended wireless channels on-the-fly to approach the optimal strategy almost surely. Numerous results are also provided to evaluate the proposed schemes for cognitive transmissions over block fading channels.

In energy-constrained wireless networks such as sensor networks, it is important to maximize power efficiency by minimizing power consumption for a given quality of service such as the data rate; it is equally important to evenly or fairly distribute power consumption to all nodes to maximize the network life. In this paper, we develop optimal power control methods to balance the tradeoff between energy efficiency and fairness for wireless cooperative networks where several relays assist the communication of multiple source-destination pairs. Our optimal power control policy is derived in a quasi-closed form by solving a convex optimization problem with a properly chosen cost-function. We further develop novel stochastic optimization algorithms to dynamically learn the statistics of the wireless channels on-the-fly to approach the optimal power control policy. Moreover, our power control algorithm can be implemented distributedly at relays requiring each relay to know its local channel state information. Simulation results demonstrate the merits of our power control methods.

In practical orthogonal frequency division multiplexing (OFDM) systems, subcarriers are grouped into chunks and a chunk of subcarriers is regarded as the minimum unit for subcarrier allocation. We consider the optimal chunk assignment that maximizes a utility function of average user rates for a wireless OFDM system under different power control policies. It is shown that the optimal schemes adopt a greedy structure. Novel low-complexity algorithms are developed to approach the optimal strategies without a-priori knowledge of statistics of the intended wireless channels. Numerical results are provided to gauge the performance of the proposed schemes.

In this paper we develop the jointly optimal sensing-channel selection and power control scheme for cognitive communications over time-varying fading wireless channels. With judicious formulation, it is shown that this joint design can become a convex optimization problem. Optimal scheme is then derived in closed-form under the constraints of average power budget and maximum allowable probability of collisions with the primary communications. A stochastic optimization algorithm is further developed to dynamically learn the statistics of the wireless channels on-the-fly to approach the optimal policy. Simulation results demonstrate the merit of the proposed scheme.

We consider the energy-efficient network resource allocation that minimizes a cost function of average user powers for multi-hop wireless networks. A class of fair cost functions is derived to balance the tradeoff between efficiency and fairness in energy-efficient designs. Based on such cost functions, optimal routing, scheduling and power control schemes are developed. Relying on stochastic optimization tools, we further develop stochastic network resource allocation schemes which are capable of dynamically learning the traffic and channel statistics, and converging to the optimal strategy on-the-fly.

In this paper we consider the energy-efficient resource allocation that minimizes a general cost function of average user powers for small- or medium-scale wireless sensor networks, where the simple time-division multiple-access (TDMA) is adopted as the multiple access scheme. A class of so-called ß-fair cost functions is derived to balance the tradeoff between efficiency and fairness in energy-efficient designs. Based on such cost functions, optimal channel-adaptive resource allocation schemes are developed for both single-hop and multihop TDMA sensor networks. Relying on stochastic optimization tools, we further develop stochastic resource allocation schemes which are capable of dynamically learning the intended wireless channels and converging to the optimal benchmark without a priori knowledge of channel fading distribution function.

We consider the optimal rate and power allocation that maximizes a general utility function of average user rates in a fading multiple-access or broadcast channel. By exploiting the greedy structure of the capacity-achieving resource allocation for both multiple-access and broadcast channels, it is established that a utility-maximizing allocation policy can be obtained through dual-based gradient descent iterations with fast convergence and low complexity per iteration. Relying on stochastic averaging tools, we further develop a class of stochastic gradient iterations which are capable of asymptotically converging to the optimal benchmark with guarantees on the minimum average user rates, even when the fading channel distribution is unknowna priori.

In this paper, we consider the optimal rate and power allocation that maximizes a general utility function of average user rates in a fading multiple-access or broadcast channel. By exploiting the greedy structure of the capacity-achieving resource allocation for both multiple-access and broadcast channels, it is established that utility-maximizing allocation policy can be obtained through dual-based gradient descent iterations with fast convergence and low complexity per iteration. Relying on stochastic averaging tools, we further develop a class of stochastic gradient iterations which are capable of asymptotically converging to the optimal benchmark with guarantees on the minimum average user rates, even when the fading channel distribution is unknown a priori.

In this paper, we consider the energy-efficient resource allocation that minimizes a general cost function of average user powers in wireless networks. A class of so-called ß-fair cost functions is derived to balance the tradeoff between efficiency and fairness in energy-efficient designs. With these novel cost functions, optimal resource allocation schemes are developed for fading time-division multiple-access (TDMA) channels. Relying on stochastic approximation tools, we further develop the corresponding stochastic schemes which are capable of dynamically learning the intended wireless channels and converging to the optimal benchmark without a priori knowledge of fading distribution function.

Based on the variational Bayes expectation-maximization (VBEM) algorithm, a low complexity semi-blind Bayesian iterative receiver with joint signal detection and channel tracking is proposed in this paper for MIMO-OFDM systems over time-varying multi-path channels. Since the VBEM algorithm provides distribution estimation of all parameters, the detection performance can be improved by taking the channel estimation error into account. In addition, with the aid of the soft information provided by the signal detector, the recursive VBEM (RVBEM) algorithm is introduced to track the time-varying channels. Due to the high complexity of the RVBEM algorithm, a novel time-frequency domain recursive VBEM (TF-LCRVBEM) algorithm with low complexity is further proposed. The TFLCRVBEM algorithm simply predicts the channel impulse responses (CIRs) on time domain and recursively refines them on all subcarriers. The complexity analysis results demonstrate that the TF-LCRVBEM algorithm totally avoids computation of matrix inversion and obtains linear complexity. Moreover, the simulation results show that the proposed receiver not only dramatically outperforms the conventional receiver, but also provides performance close to the optimal receiver with perfect channel state information (PCSI).

The present paper deals with dynamic resource management based on quantized channel state information (CSI) for multi-carrier cognitive radio networks comprising primary and secondary wireless users. For each subcarrier, users rely on adaptive modulation, coding and power modes that they select in accordance with the limited-rate feedback they receive from the access point. The access point uses CSI to maximize the sum of generic concave utilities of the individual average rates in the network while respecting rate and power constraints on the primary and secondary users. Using a stochastic dual approach, optimum dual prices are found to optimally allocate resources across users per channel realization without requiring knowledge of the channel distribution.

The challenging task of scheduling multi-user orthogonal frequency-division multiplexed transmissions amounts to jointly optimum allocation of subcarriers, rate and power resources. The optimization problem for deterministic channels reduces to an integer program known to be exponentially complex. Interestingly, the present paper shows that almost surely optimal allocation is possible at low complexity in the wireless setup, provided that the random fading channel has continuous distribution function. Specifically, it is established that the ergodic capacity achieving allocation follows a greedy water-filling scheme with linear complexity in the number of users and subcarriers. The result extends to accommodate fairness through general utility functions and constraints on the minimum average user rates. When the channel distribution is known, the optimal on-line scheme relies on low-complexity provably convergent subgradient iterations to obtain pertinent dual variables off line. To accommodate channel uncertainties, stochastic subgradient iterations provide dual variables on line with guaranteed convergence to their off-line counterparts.

We investigate resource allocation policies for time-division multiple access (TDMA) over fading channels in the power-limited regime. For frequency-flat block-fading channels and transmitters having full channel state information (CSI), we first minimize power under a weighted sum average rate constraint and show that the optimal rate and time allocation policies can be obtained by a greedy water-filling approach with linear complexity in the number of users. Subsequently, we pursue power minimization under individual average rate constraints and establish that the optimal resource allocation also amounts to a greedy water-filling solution. Our approaches not only provide fundamental power limits when each user can support an infinite-size capacity-achieving codebook (continuous rates), but also yield guidelines for practical designs where users can only support a finite set of adaptive modulation and coding modes (discrete rates).

In this paper, we develop a random access scheme which combines the widely used binary exponential backoff (BEB) algorithm with a cross-layer tree algorithm (TA) that relies on successive interference cancellation (SIC) with first success (FS). BEB and SICTA/FS complement each other nicely in enabling the novel protocol to attain a maximum stable throughput (MST) as high as 0.6 without packet loss. Although BEB-SICTA/FS avoids the deadlock problem caused by the error propagation commonly present in successive interference cancellation (SIC) algorithms, it may still suffer from deadlock effects induced by the "level skipping" caused by harsh wireless fading effects. We further develop a novel BEB-SICTA/F1 protocol, which is a modified version of BEB-SICTA/FS. Analysis and simulations demonstrate that this simple modification leads to high-throughput random access while completely avoiding deadlock problems.

Space-frequency coding is an attractive approach to exploit the space and frequency diversity provided by an orthogonal frequency-division multiplexing (OFDM)-based frequency-selective multiple-input multiple-output (MIMO) fading channels. Due to the prohibitive complexity of acquiring knowledge of the fading coefficients, noncoherent space- frequency coding (NSFC) which dose not need the knowledge of channel is proposed and the design criteria is presented. However, the existing NSFCs adopt the scheme that one block occupies all the subcarriers of the OFDM system, and this large code size increases the coding and decoding complexity exponentially. In this paper, we address a new view of the transmission of space-frequency codes which transforms the transmission in frequency selective channels into flat fading channels. Then, a sub-block noncoherent space-frequency coding is proposed which divides the space-frequency plane into small sub-blocks and each one constitutes a space-frequency codeword. This reduces the code size and the coding-decoding complexity. By a modified signal model, an asymptotic analysis of the pairwise error probability (PEP) is derived and thus the design criteria for full diversity-achieving code is explicitly defined. We also propose a code construct that achieves the promised order of diversity and demonstrate our conclusion by computer simulation.

We deal with energy efficient time-division multiple access over fading channels with finite-rate feedback for use in the power-limited regime. Through FRP from the access point, users acquire quantized channel state information. The goal is to map channel quantization states to adaptive modulation and coding modes and allocate optimally time slots to users so that the average transmit-power is minimized. To this end, we develop a joint quantization and resource allocation approach, which decouples the complicated problem at hand into three minimization sub-problems and relies on a coordinate descent approach to iteratively effect energy efficiency. Numerical results are presented to evaluate the energy savings.

We put forth a unified framework for downlink and uplink scheduling of multiple connections with diverse quality-of-service requirements, where each connection transmits using adaptive modulation and coding over a wireless fading channel. Based on quantized channel state information at the transmitters (Q-CSIT), we derive the information-theoretic optimal downlink and uplink resource allocation/scheduling strategies using tools from convex/nonlinear optimization theory. When the fading statistics are not known a priori, we develop a class of stochastic primal-dual (SPD) algorithms which can dynamically adapt the scheduling policies online. We prove rigorously and confirm by simulations that with affordable complexity, these SPD algorithms asymptotically converge to the optimal scheduling strategies from any initial value.

We deal with energy-efficiency issues and resource allocation policies for time division multi-access (TDMA) over fading channels under average individual rate constraints. Supposing that the channels are frequency-flat block-fading and transmitters have full channel state information (CSI), we minimize power under average individual rate constraints and show that the optimal rate and time allocation policies can be obtained by a gready water-filling strategy. Our approach not only provides fundamental power limits when each user can support an infinite number of capacity-achieving codebooks, but also yields guidelines for practical designs where users can only support a finite number of adaptive modulation and coding (AMC) modes with prescribed symbol error probabilities.

Differential unitary space-frequency modulation reduces the complexity of multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems significantly. But the conventional single symbol differential detection (SSDD) results in a high error floor over a severe multipath spreading channel. To overcome this limitation, a multiple symbol differential stack algorithm is proposed by embedding a recursion of maximum-likelihood metric in the stack algorithm. The proposed algorithm is suitable for arbitrary nondiagonal unitary space-frequency constellations and enhances the flexibility to multipath spread compared with SSDD.

In this paper we derive a stochastic primal-dual (SPD) algorithm for downlink/uplink scheduling of multiple connections with rate requirements, where each connection transmits using adaptive modulation and coding over a wireless fading channel. Based on quantized channel state information at the transmitters, we derive the information-theoretic optimal downlink and uplink resource allocation/scheduling strategies. When the fading statistics are not known a priori, we develop an SPD algorithm which can dynamically adapt the scheduling policy online. We established analytically and confirm by simulations that with affordable complexity, this SPD algorithm asymptotically converges to the optimal scheduling strategies from any initial value.

Scheduling amounts to allocating optimally channel, rate and power resources to multiple connections with diverse quality-of-service (QoS) requirements. It constitutes a throughput-critical task at the medium access control layer of today's wireless networks that has been tackled by seemingly unrelated information-theoretic and protocol design approaches. Capitalizing on convex optimization and stochastic approximation tools, the present paper develops a unified framework for channel-aware QoS-guaranteed scheduling protocols for use in adaptive wireless networks whereby multiple terminals are linked through orthogonal fading channels to an access point, and transmissions are (opportunistically) adjusted to the intended channel. The unification encompasses downlink and uplink with time-division or frequency-division duplex operation; full and quantized channel state information comprising a few bits communicated over a limited-rate feedback channel; different types of traffic (best effort, non-real-time, real-time); uniform and optimal power loading; off-line optimal scheduling schemes benchmarking fundamentally achievable rate limits; as well as on-line scheduling algorithms capable of dynamically learning the intended channel statistics and converging to the optimal benchmarks from any initial value. The take-home message offers an important cross-layer design guideline: judiciously developed, yet surprisingly simple, channel-adaptive, on-line schedulers can approach information-theoretic rate limits with QoS guarantees.

A novel random access protocol combining a tree algorithm (TA) with successive interference cancellation (SIC) has been introduced recently. To mitigate the deadlock problem of SICTA arising in error-prone wireless networks, we put forth a SICTA with first success (SICTA/FS) protocol, which is capable of high throughput while requiring limited-sensing and gaining robustness to errors relative to SICTA.

A cross-layer design is developed for quality-of-service (QoS) guaranteed traffic. The proposed design jointly exploits the error-correcting capability of the truncated automatic repeat request (ARQ) protocol at the data link layer and the adaptation ability of the adaptive modulation and coding (AMC) scheme at the physical layer to optimize the system performance. The queuing behavior induced by both the truncated ARQ protocol and the AMC scheme is analyzed with an embedded Markov chain. Analytical expressions for performance metrics such as packet loss rate, throughput and average packet delay are derived. Using these expressions, a constrained optimization problem is solved numerically to jointly determine the retry limit for the truncated ARQ protocol as well as the prescribed packet error rate for the AMC scheme so that the overall system throughput is maximized under the specified QoS constraints.

We investigate energy-efficiency issues and resource allocation policies for time division multi-access (TDMA) over fading channels in the power-limited regime. Supposing that the channels are frequency-flat block-fading and transmitters have full channel state information (CSI), in this paper we minimize power under a weighted sum-rate constraint and show that the optimal rate and time allocation policies can be obtained by water-filling over realizations of convex envelopes of the minima for cost-reward functions. Our approach not only provides fundamental power limits when each user can support an infinite number of capacity-achieving codebooks, but also yields guidelines for practical designs where users can only support a finite number of adaptive modulation and coding (AMC) modes with prescribed symbol error probabilities

We investigate the potential for high throughput when combining random backoff schemes with a robust cross-layer tree algorithm (TA) for wireless random access. We first develop a BEB-SICTA/FS protocol which combines the binary exponential backoff (BEB) algorithm with a recently proposed SICTA/FS protocol saturation throughput analysis of BEB-SICTA/FS motivates the combined protocol herein because: 1) by using the practically feasible SICTA/FS to resolve collisions in a conventional BEB based protocol for wireless random access, we can achieve high throughput; and 2) BEB can sufficiently reduce the collision size and thus enhance the efficiency of SICTA/FS, since SICTA/FS is more efficient when the number of initially collided packets is small. Guided by our analysis, we further put forth a GBEB-SICTA/FS protocol which is capable of higher and more steadfast saturation throughput than BEB-SICTA/FS. Finally, we tailor our protocols for the IEEE 802.16 broadband wireless access (BWA) networks and test their performance through simulations

A novel random access protocol combining a tree algorithm (TA) with successive interference cancellation (SIC) has been introduced recently. By migrating physical layer benefits to the medium access control (MAC) through a cross-layer approach, SICTA can afford stable throughput as high as 0.693. However, SICTA may lead to deadlocks caused by channel fading and error propagation in error-prone wireless networks. To mitigate such effects, we put forth a truncated version of SICTA that we term SICTA/FS (SICTA with first success). We establish using analysis and simulations that while providing high throughput, SICTA/FS is robust to errors, it is easy to implement, and can be readily incorporated to existing standards.

Scheduling plays an important role in providing quality of service (QoS) support for multimedia networks. We propose a cross-layer scheduler at the medium access control (MAC) layer for multiple connections with diverse QoS requirements, where each connection employs adaptive modulation and coding (AMC) scheme at the physical (PHY) layer. Each connection is assigned a priority, which is updated dynamically based on its channel and service quality; and the connection with the highest priority is scheduled each time. Our scheduler provides diverse QoS guarantees, uses the wireless bandwidth efficiently and enjoys flexibility, scalability and low implementation complexity. The performance of our scheduler is evaluated via simulations in the IEEE 802.16 standard setting.

A modified bit-map-assisted dynamic queue (BMDQ) protocol is presented for wireless slotted networks with heterogeneous users and multiple packet reception (MPR) capability. As in our recently proposed BMDQ protocol, in the proposed protocol the traffic in the channel is viewed as a flow of transmission periods (TP). Each TP has a bit-map (BM) slot at the beginning followed by a data transmission period (DP). In the BMDQ protocol the BM slot is reserved for user detection so that accurate knowledge of the active user set (AUS) can be acquired. Then given the knowledge of the AUS and the channel MPR matrix, the number of users that can access the channel simultaneously in each packet slot in the DP is chosen to maximize the conditional throughput of every packet slot. In Wang et al. (2003), all users are assumed to have the same bit error probability, i.e. they were assumed to be homogeneous. In the proposed modified BMDQ protocol, we allow the users to have unequal bit error probability. In this case, given the AUS, the choice of users to transmit in a given slot to maximize the conditional throughput is no longer just the number of users, but also the specific choice of users. Simulation comparison of the performance of the modified BMDQ protocol with that of the original BMDQ protocol is presented.

A modified bit-map-assisted dynamic queue (BMDQ) protocol is presented for wireless slotted systems with multiple packet reception (MPR) capability and finite user-buffers. As in our BMDQ protocol (Xin Wang and Tugnait, J.K., Proc. Joint Intern. Conf. Wireless LANs & Home Networks and Networking, p.549-60, 2002), in the proposed protocol, the traffic in the channel is viewed as a flow of transmission periods (TP). Each TP has a bit-map (BM) slot at the beginning followed by a data transmission period (DP). In the BMDQ protocol the BM slot is reserved for user detection so that accurate knowledge of the active user set (AUS) can be acquired and in any given TP, each active user is allowed to transmit only one data packet. In the proposed modified BMDQ protocol, the active users are allowed to transmit all data packets in their finite buffer. An active user with more than one packet in its buffer is modeled as several different active pseudo-users. In the BM slot, each user transmits information about the number of data packets in its buffer. Then, according to the number of the active pseudo-users and the channel MPR capability, the protocol attempts to minimize the expected duration of the DP in the same way as the BMDQ protocol. Simulation comparison of the performance of the proposed modified BMDQ protocol with that of the original BMDQ protocol is presented.

An effective method for estimating the DOAs of incoming signals using three orthogonal sensors is proposed. First, the channel impulse response vectors of the three sensors are estimated, then all of the corresponding propagation delays and amplitude weights of the three sensors' channels can be obtained. By comparing the propagation delays of the three sensors' channels, we can identify the same signal replica, then its DOA can be estimated through the relationship of the three corresponding amplitude weights. Simulation results show this method can reach fairly high accuracy, and it needs only three sensors.

An effective location algorithm, which considers non-line-of-sight (NLOS) propagation, is presented. By using a new variable to replace the square term and adding the loose variable, the problem becomes a mathematical programming problem, and then the NLOS propagation's effect can be evaluated. This method is simple and does not add much computation time. Compared with other methods, it has high accuracy.