Using solar power to drive ground sensors in a UAV-IoT MEC system deployed in inaccessible or hazardous areas provides a sustainable solution to battery replacement for IoT sensors. Nevertheless, this approach faces two critical challenges. Firstly, terrain variations and landscape shadowing cause uneven light distribution, leading to significant disparities in solar energy harvesting among nodes, which subsequently affects system throughput fairness due to unequal energy availability for data computation and task offloading; Secondly, atmospheric attenuation dynamics introduce stochastic variations in solar panel output, resulting in energy conversion instability and potential temporal battery outages. These challenges are further aggravated by the randomness of data arrival, which can destabilize the data queue. To address these difficulties, in this paper, we first design an α-fairness utility function to tackle the throughput fairness issue. After that, to handle the randomness of energy and data arrivals, we employ a Lyapunov-based optimization approach to maximize the long-term system utility function, formulating the problem as a multi-stage online stochastic optimization, with time average constraints on solar energy supply, data queue stability, and energy consumption of the sensor. We then decompose the original problem into a series of deterministic per-slot optimization problems to decouple control solutions across slots. Afterward, we iteratively optimize the data admission control, communication and computation resource allocations, and the UAV’s trajectory in each slot. The proposed scheme has low computation complexity for online execution. Extensive simulations demonstrate its effectiveness in achieving application-specific throughput fairness while maintaining energy and data queue stability under fluctuating working conditions. In addition, compared with benchmark algorithms, our scheme achieves higher system throughput through more judicious resource management and trajectory control strategies.

Combining wireless sensing and edge intelligence, edge perception networks enable intelligent data collection and processing at the network edge. However, traditional sample partition based horizontal federated edge learning (HFEEL) struggles to effectively fuse complementary multi-view information from distributed devices. To address this limitation, we propose a vertical federated edge learning (VFEEL) framework tailored for feature-partitioned sensing data. In this paper, we consider an integrated sensing, communication, and computation (ISCC)-enabled edge perception network, where multiple edge devices utilize wireless signals to sense environmental information for updating their local models, and the edge server aggregates feature embeddings via over-the-air computation (AirComp) for global model training. First, we analyze the convergence behavior of the ISCC-enabled VFEEL in terms of the loss function degradation in the presence of wireless sensing noise and aggregation distortions during AirComp. Then, to accelerate convergence, we aim to optimize the batch size, sensing power, and transmission power control at edge devices as well as the denoising factors at the edge server under limited network constraints on overall energy consumption and per-round latency. Due to the tight coupling of variables, the problem is non-convex. To address this problem, we design an alternating optimization-based algorithm to efficiently obtain a high-quality solution. Numerical results are conducted based on a human motion recognition task to verify that the proposed ISCC-enabled VFEEL algorithm achieves higher accuracy compared with other benchmarking schemes including ISCC-enabled HFEEL approach.

Edge intelligence (EI) allows resource-constrained edge devices (EDs) to offload computation-intensive AI tasks (e.g., visual object detection) to edge servers (ESs) for fast execution. However, transmitting high-volume raw task data (e.g., 4K video) over bandwidth-limited wireless networks incurs significant latency. While EDs can reduce transmission latency by degrading data before transmission (e.g., reducing resolution from 4K to 720p or 480p), it often deteriorates inference accuracy, creating a critical accuracy-latency tradeoff. The difficulty in balancing this tradeoff stems from the absence of closed-form models capturing content-dependent accuracy-latency relationships. Besides, under bandwidth sharing constraints, the discrete degradation decisions among the EDs demonstrate inherent combinatorial complexity. Mathematically, it requires solving a challengingblack-boxmixed-integer nonlinear programming (MINLP). To address this problem, we propose LAB, a novel learning framework that seamlessly integrates deep reinforcement learning (DRL) and Bayesian optimization (BO). Specifically, LAB employs: (a) a DNN-based actor that maps input system state to degradation actions, directly addressing the combinatorial complexity of the MINLP; and (b) a BO-based critic with an explicit model built from fitting a Gaussian process surrogate with historical observations, enabling model-based evaluation of degradation actions. For each selected action, optimal bandwidth allocation is then efficiently derived via convex optimization. Numerical evaluations on real-world self-driving datasets demonstrate that LAB achieves near-optimal accuracy-latency tradeoff, exhibiting only 1.22% accuracy degradation and 0.07s added latency compared to exhaustive search. Notably, it outperforms conventional DRL with 3.29% higher accuracy and 42.60% lower latency, demonstrating its advantageous performance in handling black-box optimization problems. The complete source code for LAB will be published on GitHub upon acceptance.

Semantic communication (SemComm) has emerged as new paradigm shifts. Most existing SemComm systems transmit continuously distributed signals in analog fashion. However, the analog paradigm is not compatible with current digital communication frameworks. In this paper, we propose an alternating multi-phase training strategy (AMP) to enable the joint training of the networks in the encoder and decoder through non-differentiable digital processes. AMP contains three training phases, aiming at feature extraction (FE), robustness enhancement (RE), and training-testing alignment (TTA), respectively. In particular, in the FE stage, we learn the representation ability of semantic information by jointly training the encoder and decoder in an analog manner. When we take digital communication into consideration, the domain shift between digital and analog demands the fine-tuning for encoder and decoder. To cope with joint training process within the non-differentiable digital processes, we propose the alternation between updating the decoder individually and jointly training the codec in RE phase. To boost robustness further, we investigate a mask-attack (MATK) in RE to simulate an evident and severe bit-flipping effect in a differentiable manner. To address the training-testing inconsistency introduced by MATK, we employ an additional TTA phase, fine-tuning the decoder without MATK. Combining with AMP and an information restoration network, we propose a digital joint source-channel coding system for image transmission, named AMP-SC1. Comparing with the representative benchmark, AMP-SC achieves 0.82 ~ 1.65dB higher average reconstruction performance among several representative datasets at different scales and a wide range of signal-to-noise ratios.

In mountainous environments, terrain occlusion causes non-line-of-sight (NLoS) transmission, significantly reducing the signal propagation range. To improve emergency rescue efficiency, a mobile unmanned aerial vehicle (UAV)-based integrated localization and communication (ILAC) network should be deployed to achieve optimal performance through adaptive trajectory planning and resource allocation. However, irregular and unpredictable terrain occlusions, coupled with dynamic users, make traditional optimization ineffective and reinforcement learning (RL) inefficient. To address these challenges, this paper proposes a hybrid action space soft Actor-Critic with geographic information-based state aggregation (GeoAgg-HSAC) decision-making scheme. First, an RL state aggregation method based on graph contrastive learning is designed. Through a pre-trained graph neural network (GNN), the UAV network states experiencing the same occlusion are mapped to similar low-dimensional representations. This method reduces the state dimension and allows similar states to share policy experience, thereby improving sample efficiency and accelerating convergence. A hybrid action space SAC network is then designed, which simultaneously makes decisions for continuous UAV trajectories and discrete resource allocation. Finally, a simulation environment based on real mountain terrain and wireless data is built for the experiment. The experimental results show that the proposed scheme has significant advantages for optimizing communication and localization performance.

The distortion-perception (DP) tradeoff reveals a fundamental conflict between distortion metrics (e.g., MSE and PSNR) and perceptual quality. Recent research has increasingly concentrated on evaluating denoising algorithms within the DP framework. However, existing algorithms either prioritize perceptual quality by sacrificing acceptable distortion, or focus on minimizing MSE for faithful restoration. When the goal shifts or noisy measurements vary, adapting to different points on the DP plane needs retraining or even re-designing the model. Inspired by recent advances in solving inverse problems using score-based generative models, we explore the potential of flexibly and optimally traversing DP tradeoffs using a single pre-trained score-based model. Specifically, we introduce a variance-scaled reverse diffusion process and theoretically characterize the marginal distribution. We then prove that the proposed sample process is an optimal solution to the DP tradeoff for conditional Gaussian distribution. Experimental results on two-dimensional and image datasets illustrate that a single score network can effectively and flexibly traverse the DP tradeoff for general denoising problems.

This paper studies UAV detection by multiple collaborative base stations in an integrated sensing and communication (ISAC) manner. In particular, we propose a computationally efficient UAV 3D localization and velocity estimation approach based on matched filtering (MF) to process the orthogonal frequency division multiplexing (OFDM) sensing signals. The proposed method consists of two main steps: a MF-based preprocessing step at each single base station to efficiently estimate distance, velocity, and angle parameters, and a symbol-level fusion step using a grid searching approach to integrate results from multiple base stations. Compared with traditional multiple signal classification (MUSIC)-based fusion techniques, our approach reduces the overall computational complexity by more than 98.5%. Meanwhile, it demonstrates significantly higher robustness in low SNR conditions (SNR ≤ 0 dB), as evidenced by a reduction in localization error from meter-level to centimeter-level accuracy. In positive SNR conditions (SNR > 0 dB), it also improves the localization and velocity estimation accuracy by approximately 33.5% and 26.3%, respectively. These results demonstrate the practical advantage of the proposed method in real-time UAV sensing application.

Using solar power to drive ground sensors in a UAV-assisted IoT MEC system deployed in inaccessible or hazardous areas offers a sustainable solution to battery replacement for IoT sensors. However, the uneven distribution of solar power leads to unbalanced throughput among sensors. Additionally, fluctuations in solar energy and the stochastic nature of data arrivals destabilize the energy and data queues. To address these issues, we first design an α-fairness utility function to ensure throughput fairness. Then, to stabilize the system queues, we employ Lyapunov optimization to maximizing the utility by formulating it as a multi-stage online stochastic optimization problem. We decompose the original problem into a series of deterministic per-slot optimizations and iteratively optimize data admission control, resource allocation, and the UAV’s trajectory in each time slot. The proposed scheme achieves the desired level of throughput fairness in time-varying environments. Moreover, compared to benchmark algorithms, it attains higher system throughput and energy efficiency.

In this paper, we consider reusing a rotary-wing UAV as both an airborne base station (BS) and radar to provide integrated sensing and communication (ISAC) wideband service to a ground mobile user. Specifically, the UAV transmits orthogonal frequency-division multiplexing (OFDM) signals where a part of the sub-carriers are assigned for communication purposes. We formulate an online optimization problem that jointly optimizes the UAV trajectory and power allocation of the OFDM sub-carriers to provide a balanced communication and localization service to the ground user. The problem is very challenging because of the non-convex localization accuracy metric with respect to the trajectory and transmit power. For this, we decouple the original problem into a sub-carrier power allocation sub-problem and a trajectory design sub-problem, and propose efficient algorithms to solve them respectively. Simulation results show that the proposed algorithm reduces the localization error by more than 66% at the cost of affordable decrease of communication rate compared to the representative benchmark method considered.

This paper introduces the concept of UAV (Unmanned Aerial Vehicle) corridors as structured, wirelessoptimized flight paths to reduce collision risks and enhance safety in urban environments. To address challenges such as signal interference and collisions at shared altitudes, we formulate the UAV trajectory planning and task offloading problem as a continuous-time integer nonlinear programming problem, considering communication constraints, time delay, task offloading, and energy consumption. A novel Signal-Interference Proximal Policy Optimization (SI-PPO) algorithm is proposed, incorporating a reward mechanism based on signal interference to improve UAV coordination in dense urban areas. Simulation results demonstrate that SI-PPO significantly outperforms traditional methods such as Deep Q-Learning (DQN), Simulated Annealing (SA), Greedy Algorithm, and Deep Deterministic Policy Gradient (DDPG) in terms of trajectory optimization, delay reduction, and energy efficiency.

This paper investigates deploying semantic edge inference systems for performing a common image clarification task. In particular, each system consists of multiple Internet of Things (IoT) devices that first locally encode the sensing data into semantic features and then transmit them to an edge server for subsequent data fusion and task inference. The inference accuracy is determined by efficient training of the feature encoder/decoder using labeled data samples. Due to the difference in sensing data and communication channel distributions, deploying the system in a new environment may induce high costs in annotating data labels and re-training the encoder/decoder models. To achieve cost-effective transferable system deployment, we propose an efficient Domain Adaptation method for Semantic Edge INference systems (DASEIN) that can maintain high inference accuracy in a new environment without the need for labeled samples. Specifically, DASEIN exploits the task-relevant data correlation between different deployment scenarios by leveraging the techniques of unsupervised domain adaptation and knowledge distillation. It devises an efficient two-step adaptation procedure that sequentially aligns the data distributions and adapts to the channel variations. Numerical results show that, under a substantial change in sensing data distributions, the proposed DASEIN outperforms the best-performing benchmark method by 7.09% and 21.33% in inference accuracy when the new environment has similar or 25 dB lower channel signal to noise power ratios (SNRs), respectively. This verifies the effectiveness of the proposed method in adapting both data and channel distributions in practical transfer deployment applications.

The recent advance of edge computing technology enables significant sensing performance improvement of Internet of Things (IoT) networks. In particular, an edge server (ES) is responsible for gathering sensing data from distributed sensing devices, and immediately executing different sensing tasks to accommodate the heterogeneous service demands of mobile users. However, as the number of users surges and the sensing tasks become increasingly compute-intensive, the huge amount of computation workloads and data transmissions may overwhelm the edge system of limited resources. Accordingly, we propose in this paper a scalable edge sensing framework for multi-task execution, in the sense that the computation workload and communication overhead of the ES do not increase with the number of downstream users or tasks. By exploiting the task-relevant correlations, the proposed scheme implements a unified encoder at the ES, which produces a common low-dimensional message from the sensing data and broadcasts it to all users to execute their individual tasks. To achieve high sensing accuracy, we extend the well-known information bottleneck theory to a multi-task scenario to jointly optimize the information gathering and broadcast processes. We also develop an efficient two-step training procedure to optimize the parameters of the neural network-based codecs deployed in the edge sensing system. Experiment results show that the proposed scheme significantly outperforms the considered representative benchmark methods in multi-task inference accuracy. Besides, the proposed scheme is scalable to the network size, which maintains almost constant computation delay with less than 1% degradation of inference performance when the user number increases by four times.

The performance of modern communication and radar systems can suffer severe performance degradation from oscillator phase noise. Existing receivers commonly assume that the carrier phase is a constant over a window of a few symbol intervals and average the received signals over these intervals to obtain an estimate of the carrier phase for data demodulation. For mmWave/THz wireless and optical communications with fast time-varying phase noise, this quasi-static carrier phase assumption will no longer be applicable to ensure optimum estimation and compensation for the unknown carrier phase. We present here an Mth-power receiver for$M$-ary phase-shift keying ($M\text{PSK}$) modulation and a Wiener process carrier phase model that is applicable in many situations, especially in optical communications. The receiver can eliminate the$M$-ary phase modulation by raising the received signal samples (one sample per symbol interval) with noise to the power of$M$. The resulting unmodulated phase samples then enable the receiver to perform joint maximum likelihood estimation and maximum a posteriori probability estimation of the unknown initial carrier phase and Wiener carrier phase noise process. The estimation performance improves with a relatively small data block length for any given signal-to-noise ratio and Wiener phase noise variance, and this leads to better error probability performance in data detection. Simulation results are obtained for the estimation mean square error of the noisy carrier phase and the error probability of the detected$M\text{PSK}$symbols.

This article considers deploying a dual-functional unmanned aerial vehicle (UAV) as both an aerial data collector and aerial anchor node (AN) to assist the ground base stations in providing integrated localization and communication (ILAC) service in urban areas. A major challenge to the urban ILAC service quality lies in the severe blockage of ground-to-air links by densely located buildings. To improve the service quality, we leverage the recent advance in urban physical environment map (PEM), also known as the three-dimensional (3-D) city map, to aid in optimizing the 3-D deployment of UAV. This allows a UAV to avoid blockages and establish strong acrlong LoS links to all target ground users. We propose a PEM-aided ILAC service model and formulate a UAV 3-D deployment optimization problem. The aim is to maximize the sum communication rate of ground users while satisfying individual localization accuracy and communication rate constraints. The problem is very challenging to solve mainly because the localization accuracy and blockage-avoiding constraints are both nonconvex with respect to UAV position. To tackle the problem, we first adopt a new localization accuracy metric and subsequently derive a convex expression of the localization constraint. Then, we convert the blockage-avoiding constraints into an equivalent and analytically tractable form and propose an efficient iterative algorithm to solve the UAV deployment optimization problem. Simulation results show that the proposed method achieves close-to-optimal performance under dense urban blockage setups, while significantly reducing the computational complexity.

As a nonverbal form of communication, gestures convey information through bodily movements and postures. Gesture recognition provides a more intuitive and natural human-computer interaction (HCI) experience, making it an integral component of the field of HCI. Recently, Wi-Fi-based gesture recognition has become a popular direction in research and applications due to its low cost, privacy-friendly nature, and convenience. However, due to the differences in the distribution of gesture data between the known and target environments, deploying the gesture recognition model in a new environment may induce high costs in annotating data labels and retraining the model. To achieve a cost-effective transferable gesture recognition model, we propose an efficient Wi-Fi-based gesture recognition domain-adaptive method [label-free domain-adaptive wireless gesture recognition (LAGER)] that can maintain high recognition accuracy in a new environment without the need for labeled samples. Specifically, LAGER divides the cross-domain Wi-Fi gesture recognition problem into two interrelated subproblems, where we iteratively apply a pseudo-label-guided feature alignment and feature augmentation method in a latent space by leveraging the wisdom of unsupervised domain adaptation. To minimize the negative impact of erroneous pseudo-labels in the early training stage, we introduce a preheated training technique that separates the training process into two parts associated with different training strategies. We evaluate our method on the Widar3.0 data set and compare the performance under various cross-domain settings with several representative benchmark methods. The proposed LAGER evidently outperforms all the benchmark methods. In particular, compared to the cross-domain recognition method used in Widar3.0, the proposed LAGER achieves 6.89%–7.83% higher average accuracy in different cross-domain experiments considered.

Due to its agility, reusability, and programmability, the unmanned aerial vehicle (UAV) can be utilized as a flying base station in mobile edge computing (MEC) systems, providing cost-effective computation services to distributed ground devices in the absence of terrestrial infrastructure. A defect of traditional UAVs is that they rely heavily on the onboard limited battery for the power supply, severely restricting UAVs’ operating endurance and flying range. To tackle this problem, we consider using a solar-powered UAV as the edge server for sensing data collection and processing. However, owing to the atmospheric absorption, the amount of harvested solar energy increases with the flying altitude, resulting in a non-trivial tradeoff between energy harvesting and communication performance. In addition, the dynamics of the moving clouds also make energy harvesting exhibit stochastic variations in the solar panel’s output, rendering the instability of the energy conversion. In this paper, given the randomness of energy and data arrivals, we propose a Lyapunov-based method to maximize the long-term system throughput, subject to the time average constraints on the solar power supply, the data queue stability, and the energy consumption of the devices. Specifically, without knowing the future system knowledge, we formulate the problem as a multi-stage online stochastic optimization and decompose the original problem into per-slot deterministic optimization problems. In each slot, we iteratively optimize the data sensing rate, the computation offloading, the communication resource allocation, and the 3D trajectory of the UAV. The proposed algorithm can adaptively adjust the UAV’s altitude according to its residual energy, thus striking a balance between energy harvesting and system throughput. Furthermore, it has low complexity which makes it suitable for online implementation. Extensive simulations have demonstrated the effectiveness of the algorithm, in that, it significantly outperforms the benchmark schemes in the system throughput, while satisfying the prescribed time average constraints at the same time.

The emerging multi-access edge computing (MEC) technology effectively enhances the wireless streaming performance of 360-degree videos. By connecting a user's head-mounted device (HMD) to a smart MEC platform, the edge server (ES) can efficiently perform adaptive tile-based video streaming to improve the user's viewing experience. Under constrained wireless channel capacity, the ES can predict the user's field of view (FoV) and transmit to the HMD high-resolution video tiles only within the predicted FoV. In practice, the video streaming performance is challenged by the random FoV prediction error and wireless channel fading effects. For this, we propose in this paper a novel two-stage adaptive 360-degree video streaming scheme that maximizes the user's quality of experience (QoE) to attain stable and high-resolution video playback. Specifically, we divide the video file into groups of pictures (GOPs) of fixed playback interval, where each GOP consists of a number of video frames. At the beginning of each GOP (i.e., the inter-GOP stage), the ES predicts the FoV of the next GOP and allocates an encoding bitrate for transmitting (precaching) the video tiles within the predicted FoV. Then, during the real-time video playback of the current GOP (i.e., the intra-GOP stage), the ES observes the user's true FoV of each frame and transmits the missing tiles to compensate for the FoV prediction errors. To maximize the user's QoE under random variations of FoV and wireless channel, we propose a double-agent deep reinforcement learning framework, where the two agents operate in different time scales to decide the bitrates of inter- and intra-GOP stages, respectively. Experiments based on real-world measurements show that the proposed scheme can effectively mitigate FoV prediction errors and maintain stable QoE performance under different scenarios, achieving over 22.1% higher QoE than some representative benchmark methods.

With the advancement of multimedia technology and wireless networks, there is a growing demand for high-quality video streaming. Delivering stable video streaming in extremely dynamic wireless networks, nevertheless, is still an open problem. Recent developments in client computing and mobile edge computing (MEC) technologies have both shown promise in enhancing the adaptive bitrate (ABR) streaming services. In this paper, we consider a video streaming system in multi-tier computing networks, enabled by joint edge-side video transcoding and client-side video enhancement. By “enhancement,” we mean that the client improves the video chunk quality via client-side image processing modules. In particular, we aim to design a joint bitrate adaptation, edge transcoding, and client image-processing algorithm, maximizing the quality of experience (QoE) of streaming services. The majority of the prior art has concentrated on super-resolution-enabled video streaming. Contrarily, we show that the video enhancement method outperforms the super-resolution approach in terms of signal-to-noise ratio and frames per second, implying a superior alternative for client-side processing in ABR streaming. We formulate the problem as an event-triggered Markov decision process (E-MDP), and propose a deep reinforcement learning (DRL)-based framework, named EDTEA. To deal with the delayed feedback induced by multi-tier computing, the entropy and the expected re-buffering terms are introduced to the objective and the reward, respectively. Extensive simulations based on real-world videos and bandwidth traces manifest that compared with state-of-the-art approaches, EDTEA provides$10.4\%\sim 78.4\%$extra QoE while reducing re-buffering time by$85.5\%\sim 91.7\%$.

Semantic communication has emerged as new paradigm shifts in 6G from the conventional syntax-oriented communications. Recently, the wireless broadcast technology has been introduced to support semantic communication system toward higher communication efficiency. Nevertheless, existing broadcast semantic communication systems target on general representation within one stage and fail to balance the inference accuracy among users. In this paper, the broadcast encoding process is decomposed into compression and fusion to improve communication efficiency with adaptation to tasks and channels. Particularly, we propose multiple task-channel-aware sub-encoders (TCEs) and a channel-aware feature fusion sub-encoder (CFE) towards compression and fusion, respectively. In TCEs, multiple local-channel-aware attention blocks are employed to extract and compress task-relevant information for each user. In GFE, we introduce a global-channel-aware fine-tuning block to merge these compressed task-relevant signals into a compact broadcast signal. Notably, we retrieve the bottleneck in DeepBroadcast and leverage information bottleneck theory to further optimize the parameter tuning of TCEs and CFE. We substantiate our approach through experiments on a range of heterogeneous tasks across various channels with additive white Gaussian noise (AWGN) channel, Rayleigh fading channel, and Rician fading channel. Simulation results evidence that the proposed DeepBroadcast outperforms the state-of-the-art methods.

In this paper, we consider a massive ultrareliable and low-latency communication (mURLLC) network with multiple antennas at each transmitter. We formulate a distributed beamforming and power control problem by minimizing the logarithm-based average utility of decoding error probability for the worst link over different network topologies and channels, where the policy is represented by a graph neural network (GNN). To reduce signaling overhead and computation delay for distributed inference, we first develop a GNN for mURLLC (G4U) framework, where the graph embedding of each node is updated according to its previous graph embedding. In addition, we represent the local message of each node by the amplitude and phase of its pilot signal, such that the graph convolution can be accomplished efficiently by broadcasting the pilot signals. To further reduce the overall latency, we propose the pipeline G4U (PG4U), where each node determines its policy solely based on the channel state information acquired in the previous frames. The feedforward neural networks in PG4U for graph convolution can be executed efficiently during data transmission. To train the GNNs in mURLLC where the decoding error probability is small, we develop a novel loss function based on the asymptotic expression of the GaussianQ-function. Simulation results show that G4U and PG4U are scalable to a different number of links. They can outperform the existing GNN and other policies significantly in terms of the QoS outage probability. Moreover, PG4U is suitable for mURLLC networks with short frame durations and highly correlated channels, while G4U is suitable for moderate frame durations with low channel correlation coefficients.

With recent advancements in artificial intelligence (AI), wireless sensing has recently been accepted as an attractive solution to enable accurate detection of human activities by analyzing the radio signal variations of sensor devices (SDs) using a well-trained AI model. However, due to the limited communication and computation resources at SDs, it is impractical to support energy- and delay-sensitive sensing services by solely processing the massive computation workload at local or offloading it to the edge server (ES) for edge inference. To address this problem, we consider in this paper device-edge co-inference in a wireless sensing system where multiple users collaboratively perform a common inference task. In particular, the AI model deployed at each SD can be split into two sequential parts. Each SD executes the former part of AI model at local, and leaves the remaining part computed at the ES. We aim to minimize the energy consumption of SDs subject to a prescribed inference latency requirement. To this end, we formulate a mixed integer non-linear programming (MINLP) to jointly optimize the model splitting point and system resource allocation, where the major difficulty lies in the tight couplings among splitting decisions of collaborative SDs. To solve the problem, we propose an integrated learning and optimization algorithm named LOP, which tackles the combinatorial model splitting by using a deep reinforcement learning (DRL)-based method, and deals with the remaining resource allocation problem via convex optimization. To gain some engineering insights, we study the optimal model splitting design in a practical wireless indoor crowd counting system, where the optimal splitting point exhibits a threshold-based structure related to the user channel gain. Simulation results demonstrate that the proposed LOP algorithm can achieve a near-optimal energy performance with on average 0.8% optimality gap while enjoying a hundredfold reduction in computation delay.

In future B5G/6G broadband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance, especially when uplink users share the wireless medium using non-orthogonal multiple access (NOMA) schemes. This is because the successive interference cancellation (SIC) decoding technique, used in NOMA, is incapable of eliminating the interference caused by PA distortion. Consequently, each user’s decoding process suffers from the cumulative distortion noise of all uplink users. In this paper, we establish a new and tractable DPD-PA distortion signal model based on real-world measurements, where the distortion noise power is a polynomial function of PA transmit power diverging from the oversimplified linear function commonly employed in existing studies. Applying the proposed signal model, we characterize the capacity rate region of multi-user uplink NOMA by optimizing the user transmit power. Our findings reveal a significant contraction in the capacity region of NOMA, attributable to polynomial distortion noise power. For practical engineering applications, we formulate a general weighted sum rate maximization (WSRMax) problem under individual user rate constraints. We further propose an efficient power control algorithm to attain the optimal performance. Numerical results show that the optimal power control policy under the proposed non-linear PA model achieves on average 13% higher throughput compared to the policies assuming an ideal linear PA model. Overall, our findings demonstrate the importance of accurate PA distortion modeling to the performance of NOMA and provide efficient optimal power control method accordingly.

Motivated by the success of deep learning, semantic communication has emerged as new paradigm shifts in 6G from the conventional data-oriented communications. However, the semantic communication systems suffer performance degradation at the receiver side or computation latency accumulation at the transmitter side, when serves multiple task execution. To address the issue, we develop a VisionTransformer based multi-device semantic communication system called MDSC to effectively perform multiple tasks. In particular, we introduce the shared semantic encoder to the transmitter to extract global semantic information, preventing for computation accumulation at the transmitter side. To cope with the fact that the semantic information for different task may differ from each other, we propose multiple-encoder-multiple-decoder channel codec architecture, improving the compression efficiency with task-specific codec. The compression efficiency leads to higher noise robustness and downstream task execution accuracy at the receiver side. In the experiments, we validate the proposed system with two tasks in NYUD-v2 and four tasks in PASCAL-Context, respectively. Compared with the state-of-the-art multi-task semantic communication system, MDSC achieves higher performance simultaneously for all tasks in both datasets.

In future B5G/6G wideband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance. The performance impact is especially significant when uplink users share the wireless medium using Non-orthogonal Multiple Access (NOMA) scheme. This is because the successive interference cancellation (SIC) information decoding technique of NOMA cannot eliminate the interference caused by the PA non-linear distortion, such that the decoding of each user will suffer from the aggregate distortion noise of all the uplink users. In this paper, we study the impact of PA non-linear distortion on the performance of uplink NOMA. In particular, we first establish a new PA distortion signal model based on real-world measurements, where the distortion noise power is a polynomial function of PA transmit power, instead of a simplified linear function in most existing studies. Under the proposed signal model, we then accurately characterize the capacity region of a two-user uplink NOMA by optimizing the user transmit power. We show that the polynomial distortion noise power significantly shrinks the achievable capacity region of NOMA. This indicates that existing studies may have overestimated the communication performance of NOMA in practical wideband systems. Besides, the non-linear noise power also leads to a rather different optimal power allocation strategy to attain maximum throughput. Simulation results show that, for a PA following the polynomial distortion noise power model, the proposed optimal power allocation method achieves on average 12.2% higher sum throughput than that obtained from ideal PA model. Overall, our results demonstrate the importance of accurate PA distortion modeling to the performance of NOMA and provide an efficient power allocation method to attain the optimal performance.

Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. Recent development of the WiFi-based ICC technology relies on detecting the variation of wireless channel state information (CSI) caused by human motions and has gained increasing popularity due to its low hardware cost, reliability under all lighting conditions, and privacy preservation in sensing data processing. To attain high estimation accuracy, existing WiFi-based ICC methods often require a large amount of labeled CSI training data samples for each application domain, i.e., a particular WiFi transceiver or background deployment. This makes large-scale deployment of the WiFi-based ICC technology across dissimilar domains extremely difficult and costly. In this article, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of the few-shot learning (FSL) paradigm consisting of two major stages: 1) source domain meta training and 2) target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain data set to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few amounts of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68%, and on average 96.37% detection accuracy in a 0–8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.

Under the outbreak of the COVID-19 pandemic, respiration state monitoring plays an important role in assisting respiratory disease diagnosis and treatment. Thanks to the nonintrusive nature and low deployment cost, Wi-Fi-based wireless respiration state monitoring methods have gained increasing popularity. By analyzing the variation of channel state information (CSI) of Wi-Fi signals, the respiration states of a target person under the wireless coverage, such as cough, sneeze, and yawn, can be accurately detected. A major problem of the current wireless respiration state monitoring methods is being overly domain-dependent. That is, a sensing algorithm fine-tuned to a specific device placement and background setting (i.e., a domain) can result in drastic drop in detection accuracy when applied to a dissimilar new domain. To enhance the robustness of wireless sensing and reduce the sensing cost across different domains, we propose in this article a domain-adaptive respiration state monitoring system (ResMon) that achieves highly accurate cross-domain detection performance while requiring very limited labeled samples in the new domain. In a nutshell, the proposed ResMon consists of a source domain meta-training stage and a target domain meta-testing stage. In the meta-training stage, we leverage the rich source domain labeled data set to train an embedding model as a feature extractor of high-dimensional CSI data measurements. In particular, we apply the statistical Bayesian deep learning technique to improve the generalization performance of the embedding model in cross-domain applications. In the meta-testing stage, we combine the embedding model with a few-shot learning technique to train a domain-specific classifier using very limited labeled samples in the target domain. Experiment results show that the proposed ResMon can achieve on average 87.26% cross-domain detection accuracy in a 4-class respiration state classification task using only five labeled samples per class, which significantly outperforms the considered benchmark methods.

Beam alignment is essential for high-quality data transmission in millimeter wave (mmWave) communication systems. Recent studies have revealed that the beam alignment method can train a small-scale probing codebook that is customized to specific sites, and leverage the codebook measurements to determine the optimal transmit beam. However, existing approaches still necessitate a certain number of probing beams in order to achieve high-accuracy alignment. In this work, we propose a multi-task learning-based beam alignment method, leveraging channel reconstruction and contrastive representation as auxiliary tasks, to improve the primary task of joint probing codebook design and optimal beam selection. Specifically, we offer a channel reconstruction module that estimates the wireless channel with the probing measurements, to improve the channel sensing efficiency of the probing codebook. Likewise, we also expose a contrastive representation module to improve the beam selector's representation robustness against the noisy channel. Results obtained from simulations using realistic public datasets indicate that the proposed method surpasses current state-of-the-art beam alignment techniques. The proposed method demonstrates superior performance in terms of alignment accuracy, achieved throughput, and beam sweeping complexity.

With growing popularity of enormous public safety and transportation infrastructure cameras, there are increasing demands for automatic mobile video analytics. The emerging multi-access edge computing (MEC) technology has been recently applied to improve the accuracy-latency tradeoff of mobile video analytics. In this paper, we study an MEC-enabled multi-device video analytics system and formulate the problem as a Markov decision process (MDP) to meet two practical challenges: i) the absence of ground truth in real-time and ii) the content-varying degradation-accuracy relation. In particular, we aim to design an online joint frame degradation and bandwidth allocation algorithm with the time-varying function and limited feedback from each device. Thanks to the MDP formulation and$n$-step return technique, the long-term goal offers adaptive information gathering and thus improves the average accuracy and latency. For sample efficiency, we decompose the MDP problem into discrete degradation adaptation subproblems and continuous bandwidth allocation subproblems. Based on the decomposition, we propose a deep reinforcement learning (DRL) based framework, referred to as DBAG, to solve the decomposed subproblems. DBAG integrates model-based optimization and model-free DRL to solve the MDP problem with a discrete-continuous hybrid action space. Under various network setups and public datasets, DBAG greatly improves the accuracy-latency tradeoff.

In emergency scenarios, unmanned aerial vehicles (UAVs) can be deployed to assist localization and communication services for ground terminals. In this paper, we propose a new integrated air-ground networking paradigm that uses dual-functional UAVs to assist the ground networks for improving both communication and localization performance. We investigate the optimization problem of deploying the minimal number of UAVs to satisfy the communication and localization requirements of ground users. The problem has several technical difficulties including the cardinality minimization, the non-convexity of localization performance metric regarding UAV location, and the association between user and communication terminal. To tackle the difficulties, we adopt$D$-optimality as the localization performance metric, and derive the geometric characteristics of the feasible UAV hovering regions in 2D and 3D based on accurate approximation values. We solve the simplified 2D projection deployment problem by transforming the problem into a minimum hitting set problem, and propose a low-complexity algorithm to solve it. Through numerical simulations, we compare our proposed algorithm with benchmark methods. The number of UAVs required by the proposed algorithm is close to the optimal solution, while other benchmark methods require much more UAVs to accomplish the same task.

This paper considers a wireless powered mobile edge computing (MEC) system consisting of multiple wireless devices (WDs) and one hybrid access point (HAP) broadcasting radio frequency (RF) energy to the WDs. Relying on the harvested energy, the WDs senses data from the monitored environment and execute the task data locally or offload the task to the HAP for edge processing. Given an average power constraint at the HAP, we aim to design an energy-efficient online algorithm under random fading channels to maximize the long-term average data sensing rate of WDs while meeting the system data queue stability. We formulate the target problem as a multi-stage stochastic optimization, where the major difficulty lies in the uncertainty of future channel state and the tight couplings among control decisions over different time slots. To solve this problem, we propose a Lyapunov optimization-based online algorithm named LEESE. Specifically, LEESE equivalently transforms the multi-stage stochastic optimization into per-slot deterministic problems. For each per-slot problem, we derive the optimal closed-form solution. We show that the optimal control on WPT and data processing follows an interesting threshold-based manner decided by the battery state and data queue backlog. Numerical simulations show that the proposed LEESE algorithm can achieve more than 21.9% performance improvement over the considered benchmark methods.

The coupling of unmanned aerial vehicle (UAV) and Internet of Things (IoT) systems can provide an efficient method to collect ground data for the Sixth Generation (6G) networks. Under this UAV-IoT scenario, an intelligent energy balancing strategy should be designed to achieve tunable energy fairness level among all the IoT devices, such that sensors can differ in their lifespans to meet specific application requirements. In this paper, we propose an intelligent $\alpha-$fairness strategy to balance the energy consumption among IoT sensors. Specifically, the heterogeneities among the sensor nodes, i.e., different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an $\alpha-$utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to judiciously tune the $\alpha$ value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile-edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multiuser cache-assisted MEC system, users’ preferences for different types of services, possibly dependent on their locations, play an important role in the joint design of communication, computation, and service caching. In this article, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users’ preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource, and the bandwidth (BW) allocation to minimize the expected sum-energy consumption, subject to the BW and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer nonconvex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization-based discrete-action generation method. The proposed hybrid learning framework advocates both benefits from the model-free DL approach and the model-based optimization. The simulations verify that the proposed DL-based scheme saves roughly 33% and 6.69% of energy consumption compared with the greedy caching and the popular caching, respectively, while achieving up to 99.01% of the optimal performance.

The rise of Internet of Things (IoT) systems has enabled us to access real-time information about our surrounding environments. However, IoT data collection in hostile and inaccessible areas without infrastructure supports is a challenging issue due to the inherent physical constraints associated with the tiny sensors. A viable solution to this problem is to use agile and controllable unmanned aerial vehicles (UAVs) to collect the ground data and relay it to the remote cloud for further processing. Under this UAV–IoT scenario, the limited battery supply carried by the sensor must be efficiently utilized so as to prolong the lifetime of the IoT system. Nevertheless, lifetime extension does not merely entail the reduction of the sum energy expenditure of sensors. In this article, we first show that minimizing the sum energy consumption cannot effectively extend the system lifetime due to the imbalance in energy expenditure among sensors, which, in fact, can render early energy depletion for some overburdened sensors. We also reveal a tradeoff between energy efficiency and energy fairness. To tackle this imbalance issue, we then propose an$\alpha $-fairness approach to balance the energy consumption among IoT sensors. Specifically, in our study, the heterogeneities among the sensor nodes—different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an$\alpha $-utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to properly set the$\alpha $value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an energy-aware adaptive live streaming scheme in wireless edge networks. In particular, we aim to design a joint uplink transmission and edge transcoding algorithm maximizing the video followers’ QoE, while minimizing the energy consumption of the video streamer. We formulate the problem as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based framework, named SACCT, to determine the streamer’s encoding bitrate, the uploading power as well as the edge transcoding bitrates and frequency. We decompose the MDP problem into inter-frame and intra-frame problems to address the key design challenges that arise from continuous-discrete hybrid action space, time-varying state and action spaces, and unknown network variation. By doing so, SACCT integrates model-based optimization and model-free DRL to determine the intra-frame continuous resource allocation decisions and the inter-frame discrete bitrate adaptation decisions, respectively. To integrate both the numerical features (e.g., channel gain) and the categorical features (e.g., bitrate), we propose a communication Transformer (CT) as a backbone of SACCT by representing network states as communication tokens and running Transformers to model multi-scale dependencies. Extensive simulations manifest that compared with state-of-the-art approaches, SACCT can provide 128.23% (on average) extra reward. As such, by leveraging joint uplink adaption and edge transcoding, the proposed scheme enables an intelligent wireless network edge with QoE-assured and energy-aware live streaming services.

Wireless powered multi-access edge computing (MEC) has emerged as a promising paradigm to enable high-performance computation of energy-constrained wireless devices (WDs) in internet of things (IoT) systems. However, to overcome the severe path loss of both energy transfer and data communications, wireless powered MEC suffers from high operating power consumption. To achieve sustainable and economic system operation, this paper focuses on developing energy-efficient online data processing strategy for wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we target at maximizing the long-term average data sensing rate of the WDs while maintaining task data queue stability. To this end, we formulate a multi-stage stochastic optimization problem to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose a Lyapunov optimization-based online algorithm named LEESE, which decomposes the multi-stage stochastic problem into per-slot deterministic optimization problems. We show that each per-slot problem can be equivalently transformed into a convex optimization problem. To facilitate online implementation in large-scale MEC systems, instead of solving the per-slot problem with off-the-shelf convex algorithms, we propose a block coordinate descent (BCD)-based method that produces a close-to-optimal solution in less than 0.04% of the computation delay. Simulation results demonstrate that the proposed LEESE algorithm can provide 18% higher data sensing rate than the representative benchmark methods considered, while incurring sub-millisecond computation delay suitable for real-time control under fading channel.

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

Symbol rate is one of the most important parameters in signal demodulation process. In real-time signal processing, traditional symbol rate estimation algorithms for the Multiple Phase Shift Keying (M-PSK) and the Multiple Quadrature Amplitude Modulation (M-QAM) are based on the Fourier transform of signal’s complex envelope. At the low signal-to-noise ratio (SNR), the accuracy of symbol rate estimation can be improved by increasing the number of symbols as much as possible. However, this improvement is infeasible in many applications such as the energy-limited Internet of Things devices and sporadic noncooperative transmissions. In this paper, we propose a data-driven bandpass filter (BPF) design scheme for accurate estimation of symbol rate under low SNR with only a small number of symbols available. The proposed scheme considerably improves the estimation performance by optimizing the BPF design using the equivalent dynamic linearization model with time-varying pseudo-partial derivatives. Specifically, the proposed scheme iteratively optimizes the upper and lower cut-off frequencies of the BPF based on the measured complex envelope spectrum until achieving the optimal BPF. Therefore, the peaks of the complex envelope spectrum are extracted as the estimate of the symbol rate by applying the optimal BPF. Experimental results indicate the promise of the proposed scheme as an efficient symbol rate estimator for sporadic signal at low SNR and with a small number of symbols.

The emerging mobile edge computing (MEC) technology has been recently applied to improve adaptive bitrate (ABR) streaming service quality under time-varying wireless channels. In this paper, we consider a heterogeneous multi-user MEC-enabled video streaming network with time-varying wireless channels in sequential time frames. In particular, we aim to design an online joint transcoding and transmission resource allocation algorithm to maximize the ABR streaming user’s quality of experience (QoE) subject to the bandwidth and CPU constraints. The algorithm is “online” in the sense that the bitrate and resource allocation decisions made at each frame depend only on the observation of past events. We formulate the problem as a mixed integer non-linear programming (MINLP) that jointly determines bitrate adaptation, bandwidth allocation, and CPU cycle assignment. To cope with the challenge arising from the coupling decisions of adjacent frames, we propose a low-complexity online algorithm, named OCCA. Specifically, by introducing queueing model constraints, we transform the offline non-conex MINLP problem into a multi-frame problem. Then, we analytically decouple the multi-stage(frame) problem to multiple per-frame convex subproblems that can be solved with high robustness and low computational complexity. We perform simulations with realistic scenarios to evaluate the performance of the proposed algorithm. Results manifest that compared with state-of-the-art approaches, our proposed algorithm can provide 97.84% (on average) extra QoE.

Device-edge co-inference opens up new possibilities for resource-constrained wireless devices (WDs) to execute deep neural network (DNN)-based applications with heavy computation workloads. In particular, the WD executes the first few layers of the DNN and sends the intermediate features to the edge server that processes the remaining layers of the DNN. By adapting the model splitting decision, there exists a tradeoff between local computation cost and communication overhead. In practice, the DNN model is re-trained and updated periodically at the edge server. Once the DNN parameters are regenerated, part of the updated model must be placed at the WD to facilitate on-device inference. In this paper, we study the joint optimization of the model placement and online model splitting decisions to minimize the energy-and-time cost of device-edge co-inference in presence of wireless channel fading. The problem is challenging because the model placement and model splitting decisions are strongly coupled, while involving two different time scales. We first tackle online model splitting by formulating an optimal stopping problem, where the finite horizon of the problem is determined by the model placement decision. In addition to deriving the optimal model splitting rule based on backward induction, we further investigate a simple one-stage look-ahead rule, for which we are able to obtain analytical expressions of the model splitting decision. The analysis is useful for us to efficiently optimize the model placement decision in a larger time scale. In particular, we obtain a closed-form model placement solution for the fully-connected multilayer perceptron with equal neurons. Simulation results validate the superior performance of the joint optimal model placement and splitting with various DNN structures.

The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computation service to distributed devices in the absence of terrestrial infrastructure. In this paper, we consider a UAV-enabled MEC system serving multiple energy harvesting (EH) devices, where the energy and task data arrive at the users stochastically. Without any future knowledge of task data and energy arrivals, our objective is to design an online algorithm to jointly optimize the UAV energy and task processing rate, meanwhile satisfying the long-term data queue stability. We formulate the problem as a multi-stage stochastic programming and propose an online algorithm, named PLOT, based on perturbed Lyapunov optimization technique. In particular, PLOT resolves the coupling effect of sequential control actions, and converts the stochastic problem into per-slot deterministic optimization problem. For each per-slot problem, we design a low-complexity algorithm to solve it. We show that the PLOT algorithm can derive a feasible solution to the original problem and achieve an$[O(1/V),O(V)]$trade-off between the system cost and the data queue length. Simulation results justify our analysis and demonstrate that the PLOT algorithm achieves better performance in terms of system utility and maintains queue stability that is not achieved by other benchmark methods.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system with multiple energy harvesting (EH) devices. Considering the stochastic energy and data arrivals in sequential time slots, we formulate the UAV propulsion energy minimization problem with long-term data queue stability and battery causality constraints as a multi-stage stochastic optimization programming. To facilitate online control without any prior knowledge of future information, we adopt the perturbed Lyapunov optimization method that decouples the control decisions made in sequential time slots and determines the real-time control decisions by solving a deterministic problem in each time slot. For the per-slot deterministic problem, we decouple it into three sub-problems: the optimal energy harvesting, the computation resource allocation and the UAV trajectory control, and propose a reduced-complexity method to solve them sepa-rately. Simulation results demonstrate that the proposed algorithm guarantees the data queue stability that is not achievable by the benchmark method when the two methods consume identical UAV propulsion energy.

In this paper, we consider a multi-user mobile-edge computing (MEC) network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing future channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems of much smaller size. Then, it integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that the proposed LyDROO achieves optimal computation performance while satisfying all the long-term constraints. Besides, it induces very low execution latency that is particularly suitable for real-time implementation in fast fading environments.

Intelligent reflecting surface (IRS) is an emerging technology to enhance the energy efficiency and spectrum efficiency of wireless-powered communication networks (WPCNs). In this article, we investigate an IRS-assisted multiuser multiple-input single-output (MISO) WPCN, where the single-antenna wireless devices (WDs) harvest wireless energy in the downlink (DL) and transmit their information simultaneously in the uplink (UL) to a common hybrid access point (HAP) equipped with multiple antennas. Our goal is to maximize the weighted sum rate (WSR) of all the energy-harvesting users. To make full use of the beamforming gain provided by both the HAP and the IRS, we jointly optimize the active beamforming of the HAP and the reflecting coefficients (passive beamforming) of the IRS in both DL and UL transmissions, as well as the transmit power of the WDs to mitigate the interuser interference at the HAP. To tackle the challenging optimization problem, we first consider fixing the passive beamforming, and converting the remaining joint active beamforming and user transmit power control problem into an equivalent weighted minimum mean-square error problem, where we solve it using an efficient block-coordinate descent method. Then, we fix the active beamforming and user transmit power, and optimize the passive beamforming coefficients of the IRS in both the DL and UL using a semidefinite relaxation method. Accordingly, we apply a block-structured optimization method to update the two sets of variables alternately. The numerical results show that the proposed joint optimization achieves significant performance gain over other representative benchmark methods and effectively improves the throughput performance in multiuser MISO WPCNs.

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task.

This article proposes a reinforcement-learning (RL) approach for optimizing charging scheduling and pricing strategies that maximize the system objective of a public electric vehicle (EV) charging station. The proposed algorithm is “online” in the sense that the charging and pricing decisions made at each time depend only on the observation of past events, and is “model-free” in the sense that the algorithm does not rely on any assumed stochastic models of uncertain events. To cope with the challenge arising from the time-varying continuous state and action spaces in the RL problem, we first show that it suffices to optimize the total charging rates to fulfill the charging requests before departure times. Then, we propose a feature-based linear function approximator for the state-value function to further enhance the efficiency and generalization ability of the proposed algorithm. Through numerical simulations with real-world data, we show that the proposed RL algorithm achieves on average 138.5% higher charging-station profit than representative benchmark algorithms.

Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing the future realizations of random channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems. By doing so, it guarantees to satisfy all the long-term constraints by solving the per-frame subproblems that are much smaller in size. Then, LyDROO integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that under various network setups, the proposed LyDROO achieves optimal computation performance while stabilizing all queues in the system. Besides, it induces very low computation time that is particularly suitable for real-time implementation in fast fading environments.

Leveraging recent advances on mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support mobile artificial intelligence (AI) applications at the network edge. In this paper, we consider the AI service placement problem in a multi-user MEC system, where the access point (AP) places the most up-to-date AI program at user devices to enable local computing/task execution at the user side. To fully utilize the stringent wireless spectrum and edge computing resources, the AP sends the AI service program to a user only when enabling local computing at the user yields a better system performance. We formulate a mixed-integer non-linear programming (MINLP) problem to minimize the total computation time and energy consumption of all users by jointly optimizing the service placement (i.e., which users to receive the program) and resource allocation (on local CPU frequencies, uplink bandwidth, and edge CPU frequency). To tackle the MINLP problem, we derive analytical expressions to calculate the optimal resource allocation decisions with low complexity. This allows us to efficiently obtain the optimal service placement solution by search-based algorithms such as meta-heuristic or greedy search algorithms. To enhance the algorithm scalability in large-sized networks, we further propose an ADMM (alternating direction method of multipliers) based method to decompose the optimization problem into parallel tractable MINLP subproblems. The ADMM method eliminates the need of searching in a high-dimensional space for service placement decisions and thus has a low computational complexity that grows linearly with the number of users. Simulation results show that the proposed algorithms perform extremely close to the optimum and significantly outperform the other representative benchmark algorithms.

Provided with mobile edge computing (MEC) services, wireless devices (WDs) no longer have to experience long latency in running their desired programs locally, but can pay to offload computation tasks to the edge server. Given its limited storage space, it is important for the edge server at the base station (BS) to determine which service programs to cache by meeting and guiding WDs' offloading decisions. In this article, we propose an MEC service pricing scheme to coordinate with the service caching decisions and control WDs' task offloading behavior in a cellular network. We propose a two-stage dynamic game of incomplete information to model and analyze the two-stage interaction between the BS and multiple associated WDs. Specifically, in Stage I, the BS determines the MEC service caching and announces the service program prices to the WDs, with the objective to maximize its expected profit under both storage and computation resource constraints. In Stage II, given the prices of different service programs, each WD selfishly decides its offloading decision to minimize individual service delay and cost, without knowing the other WDs' desired program types or local execution delays. Despite the lack of WD's information and the coupling of all the WDs' offloading decisions, we derive the optimal threshold-based offloading policy that can be easily adopted by the WDs in Stage II at the Bayesian equilibrium. In particular, a WD is more likely to offload when there are fewer WDs competing for the edge server's computation resource, or when it perceives a good channel condition or low MEC service price. Then, by predicting the WDs' offloading equilibrium, we jointly optimize the BS' pricing and service caching in Stage I via a low-complexity algorithm. In particular, we first study the differentiated pricing scheme and prove that the same price should be charged to the cached programs of the same workload. Motivated by this analysis, we further propose a low-complexity uniform pricing heuristics.

In this paper, we consider the service placement problem in a multi-user MEC system, where the access point (AP) places the most up-to-date artificial intelligent (AI) program at user devices via a broadcast channel. In particular, a user that successfully receives the program can execute its tasks both locally and remotely at the AP via partial task offloading. Otherwise, all its computations must be offloaded to and executed at the AP. We formulate a mixed-integer non-linear programming (MINLP) problem to minimize the total computation time and energy consumption of all users. The problem is particularly challenging because the service placement solution (i.e., which users to receive the program) is combinatorial in nature and strongly coupled with the computation offloading decision of each user (how much task to be executed at the AP) and resource allocation (on local CPU frequencies and uplink bandwidth). We tackle the problem with an ADMM (alternating direction method of multipliers) based method that effectively decomposes the problem into parallel smaller and tractable MINLP subproblems. Simulation results show that the proposed method achieves a performance extremely close to the optimum and has a low computational complexity that grows linearly with the number of users.

With the proliferation of Internet of things (IoT) devices and their growing demand for computation-extensive and real-time services, fog computing or mobile edge computing (MEC) has become a promising solution to reduce wireless network costs. To further exploit the cloud-like functions at the network edge, a paradigm shift has taken place from pursuing solely computation-communication tradeoffs to joint design of computation, communication and service caching. In this paper, we consider a multi-user caching-enabled MEC system, where users with their task requests proactively cached and executed at the edge server can directly download the desired results without computation offloading under the assumption of reusable caching. In a frequency-division multiple access (FDMA) setup, cache placement and bandwidth (BW) are jointly optimized to minimize the weighted-sum energy of the edge server and the users subject to the limits of computation, communication and caching capacities as well as the computation latency constraints. To solve this mixed-integer non-convex problem, first, we solve a BW allocation problem given any (feasible) caching decisions leveraging Lagrangian duality and ellipsoid method. Next, we propose a heuristic algorithm to iteratively update the cache placement. To further reduce the complexity, a one-shot mixed-integer linear programming (MILP) is also designed leveraging the optimal solution to the BW allocation problem. The striking performance of task caching has been provided by simulations verifying the effectiveness of the suboptimal caching decisions as well.

In this paper, we consider a mobile-edge computing (MEC) system, where an access point (AP) assists a mobile device (MD) to execute an application consisting of multiple tasks following a general task call graph. The objective is to jointly determine the offloading decision of each task and the resource allocation (e.g., CPU computing power) under time-varying wireless fading channels and stochastic edge computing capability, so that the energy-time cost (ETC) of the MD is minimized. Solving the problem is particularly hard due to the combinatorial offloading decisions and the strong coupling among task executions under the general dependency model. To address the issue, we propose a deep reinforcement learning (DRL) framework based on the actor-critic learning structure. In particular, the actor network utilizes a deep neural network (DNN) to learn the optimal mapping from the input states (i.e., wireless channel gains and edge CPU frequency) to the binary offloading decision of each task. Meanwhile, for the critic network, we show that given the offloading decision, the remaining resource allocation problem becomes convex, where we can quickly evaluate the ETC performance of the offloading decisions output by the actor network. Accordingly, we select the best offloading action and store the state-action pair in an experience replay memory as the training dataset to continuously improve the action generation DNN. Numerical results show that for various types of task graphs, the proposed algorithm achieves up to 99.5% of the optimal performance while significantly reducing the computational complexity compared to the existing optimization methods.

Mobile edge computing (MEC) platform allows its subscribers to utilize computational resource in close proximity to reduce the computation latency. In this paper, we consider two users each has a set of computation tasks to execute. In particular, one user is a registered subscriber that can access the computation service of MEC platform, while the other unregistered user cannot directly access the MEC service. In this case, we allow the registered user to receive computation offloading from the unregistered user, compute the received task(s) locally or further offload to the MEC platform, and charge a fee that is proportional to the computation workload. We study from the registered user's perspective to maximize its total utility that balances the monetary income and the cost on execution delay and energy consumption. We formulate a mixed integer non-linear programming (MINLP) problem that jointly decides the execution scheduling of the computation tasks (i.e., the device where each task is executed) and the computation/communication resource allocation. To tackle the problem, we first derive the closed-form solution of the optimal resource allocation given the integer task scheduling decisions. We then propose a reduced-complexity approximate algorithm to optimize the combinatorial computation scheduling decisions. Simulation results show that the proposed collaborative computation scheme effectively improves the utility of the helper user compared with other benchmark methods, and the proposed solution method approaches the optimal solution within 0.1% average performance gap with significantly reduced complexity.

Intelligent reflecting surface (IRS) can effectively enhance the energy and spectral efficiency of wireless communication system through the use of a large number of low-cost passive reflecting elements. In this paper, we investigate throughput optimization of IRS-assisted user cooperation in a wireless powered communication network (WPCN), where the two WDs harvest wireless energy and transmit information to a common hybrid access point (HAP). In particular, the two WDs first exchange their independent information with each other and then form a virtual antenna array to transmit jointly to the HAP. We aim to maximize the common (minimum) throughput performance by jointly optimizing the transmit time and power allocations of the two WDs on wireless energy and information transmissions and the passive array coefficients on reflecting the wireless energy and information signals. By comparing with some existing benchmark schemes, our results show that the proposed IRS-assisted user cooperation method can effectively improve the throughput performance of cooperative transmission in WPCNs.

State estimation is critical to the monitoring and control of smart grids. Recently, the false data injection attack (FDIA) is emerging as a severe threat to state estimation. Conventional FDIA detection approaches are limited by their strong statistical knowledge assumptions, complexity, and hardware cost. Moreover, most of the current FDIA detection approaches focus on detecting the presence of FDIA, while the important information of the exact injection locations is not attainable. Inspired by the recent advances in deep learning, we propose a deep-learning-based locational detection architecture (DLLD) to detect the exact locations of FDIA in real time. The DLLD architecture concatenates a convolutional neural network (CNN) with a standard bad data detector (BDD). The BDD is used to remove the low-quality data. The followed CNN, as a multilabel classifier, is employed to capture the inconsistency and co-occurrence dependency in the power flow measurements due to the potential attacks. The proposed DLLD is “model-free” in the sense that it does not leverage any prior statistical assumptions. It is also “cost-friendly” in the sense that it does not alter the current BDD system and the runtime of the detection process is only hundreds of microseconds on a household computer. Through extensive experiments in the IEEE bus systems, we show that DLLD can perform locational detection precisely under various noise and attack conditions. In addition, we also demonstrate that the employed multilabel classification approach effectively enhances the presence-detection accuracy.

Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.

In mobile edge computing (MEC) systems, edge service caching refers to pre-storing the necessary programs for executing computation tasks at MEC servers. Service caching effectively reduces the real-time delay/bandwidth cost on acquiring and initializing service applications when computation tasks are offloaded to the MEC servers. The limited caching space at resource-constrained edge servers calls for careful design of caching placement to determine which programs to cache over time. This is in general a complicated problem that highly correlates to the computation offloading decisions of computation tasks, i.e., whether or not to offload a task for edge execution. In this paper, we consider a single edge server that assists a mobile user (MU) in executing a sequence of computation tasks. In particular, the MU can upload and run its customized programs at the edge server, while the server can selectively cache the previously generated programs for future reuse. To minimize the computation delay and energy consumption of the MU, we formulate a mixed integer non-linear programming (MINLP) that jointly optimizes the service caching placement, computation offloading decisions, and system resource allocation (e.g., CPU processing frequency and transmit power of MU). To tackle the problem, we first derive the closed-form expressions of the optimal resource allocation solutions, and subsequently transform the MINLP into an equivalent pure 0-1 integer linear programming (ILP) that is much simpler to solve. To further reduce the complexity in solving the ILP, we exploit the underlying structures of caching causality and task dependency models, and accordingly devise a reduced-complexity alternating minimization technique to update the caching placement and offloading decision alternately. Extensive simulations show that the proposed joint optimization techniques achieve substantial resource savings of the MU compared to other representative benchmark methods considered.

In this paper, we consider a mobile-edge computing (MEC) system, where an access point (AP) assists a mobile device (MD) to execute an application consisting of multiple tasks following a general task call graph. The objective is to jointly determine the offloading decision of each task and the resource allocation (e.g., CPU computing power) under time-varying wireless fading channels and stochastic edge computing capability, so that the energy-time cost (ETC) of the MD is minimized. Solving the problem is particularly hard due to the combinatorial offloading decisions and the strong coupling among task executions under the general dependency model. Conventional numerical optimization methods are inefficient to solve such a problem, especially when the problem size is large. To address the issue, we propose a deep reinforcement learning (DRL) framework based on the actor-critic learning structure. In particular, the actor network utilizes a DNN to learn the optimal mapping from the input states (i.e., wireless channel gains and edge CPU frequency) to the binary offloading decision of each task. Meanwhile, by analyzing the structure of the optimal solution, we derive a low-complexity algorithm for the critic network to quickly evaluate the ETC performance of the offloading decisions output by the actor network. With the low-complexity critic network, we can quickly select the best offloading action and subsequently store the state-action pair in an experience replay memory as the training dataset to continuously improve the action generation DNN. To further reduce the complexity, we show that the optimal offloading decision exhibits an one-climb structure, which can be utilized to significantly reduce the search space of action generation. Numerical results show that for various types of task graphs, the proposed algorithm achieves up to 99.1% of the optimal performance while significantly reducing the computational complexity compared to the existing optimization methods.

Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (e.g., on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and task execution time. The problem is challenging due to the combinatorial nature of the offloading decisions among all tasks and the strong coupling with resource allocation. To tackle this problem, we first assume that the offloading decisions are given and derive the closed-form expressions of the optimal offloading transmit power and local CPU frequencies. Then, an efficient bi-section search method is proposed to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimal offloading decisions. We then extend the investigation to a general multi-user scenario, where the input of a task at one WD requires the final task outputs from multiple other WDs. Numerical results show that the proposed method can significantly outperform the other representative benchmarks and efficiently achieve low complexity with respect to the call graph size.

In this paper, we consider an Internet of Electric Vehicles (IoEV) powered by heterogeneous charging facilities in the transportation network. In particular, we take into account the state-of-the-art vehicle-to-grid (V2G) charging and renewable power generation technologies implemented in the charging stations, such that the charging stations differ from each other in their energy capacities, electricity prices, and service types (i.e., with or without V2G capability). In this case, each electric vehicle (EV) user needs to decide which path to take (i.e., the routing problem) and where and how much to charge/discharge its battery at the charging stations in the chosen path (i.e., the charging scheduling problem) such that its journey can be accomplished with the minimum monetary cost and time delay. From the system operator's perspective, we formulate a joint routing and charging scheduling optimization problem for an IoEV network, and show that the problem is NP-hard in general. To tackle the NP-hardness, we propose an approximate algorithm that can achieve affordable computational complexity in large-size IoEV networks. The proposed algorithm allows the routing and charging solution to be calculated in a distributed manner by the system operator and EV users, which can effectively reduce the computational complexity at the system operator and protect the EV users' privacy and autonomy. Besides, a proximal method is introduced to improve the convergence rate of the proposed algorithm. Extensive simulations using real world data show that the proposed distributed algorithm can achieve near-optimal performance with relatively low computational complexity in different system set-ups.

In this paper, we consider a two-user mobile-edge computing (MEC) network, where each wireless device (WD) has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and execution time. The problem is challenging due to the combinatorial nature of the offloading decision among all tasks and the strong coupling with resource allocation among subsequent tasks. When the offloading decision is given, we obtain the closed-form expressions of the offloading transmit power and local CPU frequencies and propose an efficient method to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decision follows an one-climb policy, based on which a reduced-complexity algorithm is proposed to obtain the optimal offloading decision in polynomial time. Numerical results validate the effectiveness of our proposed methods.

In this paper, we consider a wireless powered mobile edge computing (MEC) network, where the distributed energy-harvesting wireless devices (WDs) are powered by means of radio frequency (RF) wireless power transfer (WPT). In particular, the WDs follow a binary computation offloading policy, i.e., data set of a computing task has to be executed as a whole either locally or remotely at the MEC server via task offloading. We are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offloading). The major difficulty lies in the combinatorial nature of multi-user computing mode selection and its strong coupling with transmission time allocation. To tackle this problem, we propose a joint optimization method based on the ADMM (alternating direction method of multipliers) decomposition technique. Simulation results show that the proposed method can efficiently achieve near- optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered. Besides, using both theoretical analysis and numerical study, we show that the proposed method enjoys low computational complexity against the increase of networks size.

In this paper, we consider the profit-maximizing demand response of an energy customer in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy customer, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon continuous-state Markov decision process (MDP) problem. Through rigorous analysis, we show that the optimal actual demand policy exhibits a threshold structure, which can solve the MDP without the need of discretizing the state and action spaces. We demonstrate through extensive simulations that the proposed demand response strategy not only maximizes the profit of the energy customer, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed demand response strategy increases the energy customer's profit by 53.8% and saves the bills of other utilities by 80.4% comparing with the benchmark algorithms.

Wireless network virtualization enables mobile virtual network operators (MVNOs) to develop new services on a low-cost platform by leasing virtual resources from mobile network owners. In this paper, we investigate a two-stage spectrum leasing framework, where an MVNO acquires spectrum resources through both advance reservation and on-demand request. To maximize its surplus, the MVNO needs to jointly optimize the amount of spectrum resources to lease in the two stages by taking into account traffic intensity, random user locations, wireless channel statistics, quality-of-service requirements, and the price differences. Meanwhile, to maximize the utilization of the acquired resources, the MVNO dynamically allocates the spectrum resources to its mobile subscribers (users) according to fast wireless channel fading. We formulate the MVNO's surplus maximization problem as a tri-level nested optimization problem consisting of dynamic resource allocation (DRA), on-demand request, and advance reservation subproblems. To solve the problem efficiently, we first analyze the DRA problem, and then use the optimal solution to find the optimal leasing decisions in the two stages. In particular, we derive a closed-form expression of the optimal on-demand request, and develop a stochastic gradient descent algorithm to find the optimal advance reservation. For a special case when the proportional fairness utility function is adopted, we show that the optimal two-stage leasing scheme is related to the number of users and is irrelevant to user locations. Simulation results show that the two-stage spectrum leasing scheme can adapt to different levels of traffic and on-demand price variations, and achieve higher surplus than conventional one-stage leasing schemes.

This paper studies energy harvesting sensor networks in green cities that transmit a variety of data packets with different reward values. With the aim to maximize its long-term average transmission reward, almost all the existing optimal energy management strategies are based on the policy iteration algorithm, which suffers from the curse of dimensionality. By contrast, we focus on developing low-complexity optimal policies that can lead to practical implementation. Our main contribution is to propose a threshold-based scheduling policy for energy harvesting sensor networks achieving long-term average rewards. As a result, a sensor node only requires limited memory to store a few optimal value thresholds to perform energy management. Specifically, we propose an algorithm to compute the optimal thresholds, whose complexity is linear with the size of data and energy storage. Numerical results are studied based on real solar radiation data measured at Queensland and show that the optimal expected reward of our proposed scheduling policy approaches its theoretical offline upper bound.

Finite battery lifetime and low computing capability of size-constrained wireless devices (WDs) have been longstanding performance limitations of many low-power wireless networks, e.g., wireless sensor networks and Internet of Things. The recent development of radio frequency-based wireless power transfer (WPT) and mobile edge computing (MEC) technologies provide a promising solution to fully remove these limitations so as to achieve sustainable device operation and enhanced computational capability. In this paper, we consider a multi-user MEC network powered by the WPT, where each energy-harvesting WD follows a binary computation offloading policy, i.e., the data set of a task has to be executed as a whole either locally or remotely at the MEC server via task offloading. In particular, we are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offloading). The major difficulty lies in the combinatorial nature of the multi-user computing mode selection and its strong coupling with the transmission time allocation. To tackle this problem, we first consider a decoupled optimization, where we assume that the mode selection is given and propose a simple bi-section search algorithm to obtain the conditional optimal time allocation. On top of that, a coordinate descent method is devised to optimize the mode selection. The method is simple in implementation but may suffer from high computational complexity in a large-size network. To address this problem, we further propose a joint optimization method based on the alternating direction method of multipliers (ADMM) decomposition technique, which enjoys a much slower increase of computational complexity as the networks size increases. Extensive simulations show that both the proposed methods can efficiently achieve a near-optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered.

The massive integration of electric vehicles (EVs) will pose great challenges to the stability and efficiency of both the conventional power networks and transportation systems. The recently emerging smart grid technology, which integrates advanced communication, control, and charging infrastructures, provides promising solutions to tackle these challenges. In this paper, we consider a smart-grid enabled EV network with heterogeneous charging facilities of different charging costs and capabilities, e.g., allowing EV to sell energy back to the grid. In this case, an EV user needs to decide which path to take, and where and how much to charge/discharge its battery at charging stations in the chosen path such that its journey can be accomplished with the minimum monetary cost. From the system operator's perspective, we study a joint optimization of the routing selection and charging schedules to maximize the overall consumer surplus of a set of EVs. To reduce the computational complexity of the system operator and signaling exchange, we propose a distributed scheme such that each user can maximize its own profit, and the system operator can also achieve the maximum consumer surplus through limited signaling exchange with the EV users. Our simulation shows that the proposed algorithm could efficiently save energy cost of the users and improve the usage of renewable energy in the power network.

Wireless powered communication network (WPCN) is a novel networking paradigm that uses radio frequency (RF) wireless energy transfer (WET) technology to power the information transmissions of wireless devices (WDs). When energy and information are transferred in the same frequency band, a major design issue is transmission scheduling to avoid interference and achieve high communication performance. Commonly used centralized scheduling methods in WPCN may result in high control signaling overhead and thus are not suitable for wireless networks constituting a large number of WDs with random locations and dynamic operations. To tackle this issue, we propose in this paper a distributed scheduling protocol for energy and information transmissions in WPCN. Specifically, we allow a WD that is about to deplete its battery to broadcast an energy request buzz (ERB), which triggers WET from its associated hybrid access point (HAP) to recharge the battery. If no ERB is sent, the WDs contend to transmit data to the HAP using the conventional p-persistent CSMA (carrier sensing multiple access). In particular, we propose an energy queueing model based on an energy decoupling property to derive the throughput performance. Our analysis is verified through simulations under practical network parameters, which demonstrate good throughput performance of the distributed scheduling protocol and reveal some interesting design insights that are different from conventional contention-based communication network assuming the WDs are powered with unlimited energy supplies.

This paper considers an energy harvesting sensor node with finite data and energy storage, which transmits data packets with different reward values to its corresponding receiver node. In this regard, we propose an optimal threshold-based transmission scheduling policy for maximizing the long-term average transmission reward. In particular, we first analyze the performance of the proposed threshold-based transmission scheduling policy by studying the steady states of the energy harvesting sensor node and derive its expected transmission reward. We then propose a polynomial-time algorithm to compute these optimal reward value thresholds that maximize the expected reward. Numerical results show that the system expected reward increases with the increase of data and energy storage capacity. Our analysis further shows that the expected reward increases exponentially with the increase of data or energy storage capacity.

Wireless power transfer (WPT) technology provides a cost-effective solution to achieve a sustainable energy supply in wireless networks, where WPT-enabled energy nodes (ENs) can charge wireless devices (WDs) remotely without interruption to the use. However, in a heterogeneous WPT network with distributed ENs and WDs, some WDs may quickly deplete their batteries due to the lack of timely wireless power supply by the ENs, thus resulting in short network operating lifetime. In this paper, we exploit frequency diversity in a broadband WPT network and study the distributed charging control by ENs to maximize network lifetime. In particular, we propose a practical voting-based distributed charging control framework, where each WD simply estimates the broadband channel, casts its votes for some strong sub-channels, and sends to the ENs along with its battery state information, based on which the ENs independently allocate their transmit power over the sub-channels without the need of centralized control. Under this framework, we aim to design lifetime-maximizing power allocation and efficient voting-based feedback methods. Toward this end, we first derive the general expression of the expected lifetime of a WPT network and draw the general design principles for lifetime-maximizing charging control. Based on the analysis, we then propose a distributed charging control protocol with voting-based feedback, where the power allocated to sub-channels at each EN is a function of the weighted sum vote received from all WDs. Besides, the number of votes cast by a WD and the weight of each vote are related to its current battery state. Simulation results show that the proposed distributed charging control protocol could significantly increase the network lifetime under stringent transmit power constraint in a broadband WPT network. Reciprocally, it also consumes lower transmit power to achieve nearly perpetual network operation.

The applications of wireless power transfer technology to wireless communications can help build a wireless powered communication network (WPCN) with more reliable and sustainable power supply compared to the conventional battery-powered network. However, due to the fundamental differences in wireless information and power transmissions, many important aspects of conventional battery-powered wireless communication networks need to be redesigned for efficient operations of WPCNs. In this paper, we study the placement optimization of energy and information access points in WPCNs, where the wireless devices (WDs) harvest the radio frequency energy transferred by dedicated energy nodes (ENs) in the downlink, and use the harvested energy to transmit data to information access points (APs) in the uplink. In particular, we are interested in minimizing the network deployment cost with minimum number of ENs and APs by optimizing their locations, while satisfying the energy harvesting and communication performance requirements of the WDs. Specifically, we first study the minimum-cost placement problem when the ENs and APs are separately located, where an alternating optimization method is proposed to jointly optimize the locations of ENs and APs. Then, we study the placement optimization when each pair of EN and AP is colocated and integrated as a hybrid access point, and propose an efficient algorithm to solve this problem. Simulation results show that the proposed methods can effectively reduce the network deployment cost and yet guarantee the given performance requirements, which is a key consideration in future applications of WPCNs.

In this paper, we study the optimal node placement problem in wireless powered communication networks (WPCNs), where the wireless devices (WDs) harvest the radio frequency energy transferred by dedicated energy nodes (ENs) in the downlink, and use the harvested energy to transmit data to information access points (APs) in the uplink. In particular, we are interested in minimizing the deployment cost on ENs and APs, while satisfying the energy harvesting and communication performance requirements of the WDs. Specifically, we first study the optimal placement of ENs given fixed AP locations, where an efficient greedy algorithm is proposed to tackle the non-convexity of the problem. Based on the obtained results, we further propose an alternating optimization method that jointly optimizes the placements of ENs and APs. Simulation results show that the proposed methods can effectively reduce the network deployment cost to guarantee the given performance requirements, which is a key consideration in the future applications of WPCNs.

The performance of cloud radio access network (C-RAN) is constrained by the limited fronthaul link capacity under future heavy data traffic. To tackle this problem, extensive efforts have been devoted to design efficient signal quantization/compression techniques in the fronthaul to maximize the network throughput. However, most of the previous results are based on information-theoretical quantization methods, which are hard to implement practically due to the high complexity. In this paper, we propose using practical uniform scalar quantization in the uplink communication of an orthogonal frequency division multiple access (OFDMA) based C-RAN system, where the mobile users are assigned with orthogonal sub-carriers for transmission. In particular, we study the joint wireless power control and fronthaul quantization design over the sub-carriers to maximize the system throughput. Efficient algorithms are proposed to solve the joint optimization problem when either information-theoretical or practical fronthaul quantization method is applied. We show that the fronthaul capacity constraints have significant impact to the optimal wireless power control policy. As a result, the joint optimization shows significant performance gain compared with optimizing only wireless power control or fronthaul quantization. Besides, we also show that the proposed simple uniform quantization scheme performs very close to the throughput performance upper bound, and in fact overlaps with the upper bound when the fronthaul capacity is sufficiently large. Overall, our results reveal practically achievable throughput performance of C-RAN for its efficient deployment in the next-generation wireless communication systems.

In this paper, we provide an overview of the recent advances in microwave-enabled wireless energy transfer (WET) technologies and their applications in wireless communications. Specifically, we divide our discussions into three parts. First, we introduce the state-of-the-art WET technologies and the signal processing techniques to maximize the energy transfer efficiency. Then, we discuss an interesting paradigm named simultaneous wireless information and power transfer (SWIPT), where energy and information are jointly transmitted using the same radio waveform. At last, we review the recent progress in wireless powered communication networks (WPCN), where wireless devices communicate using the power harvested by means of WET. Extensions and future directions are also discussed in each of these areas.

Accurate state estimation is of paramount importance to maintain the power system operating in a secure and efficient state. The recently identified coordinated data injection attacks to meter measurements can bypass the current security system and introduce errors to the state estimates. The conventional wisdom to mitigate such attacks is by securing meter measurements to evade malicious injections. In this paper, we provide a novel alternative to defend against false data injection attacks using covert power network topological information. By keeping the exact reactance of a set of transmission lines from attackers, no false data injection attack can be launched to compromise any set of state variables. We first investigate from the attackers' perspective the necessary condition to perform an injection attack. Based on the arguments, we characterize the optimal protection problem, which protects the state variables with minimum cost, as a well-studied Steiner tree problem in a graph. In addition, we also propose a mixed defending strategy that jointly considers the use of covert topological information and secure meter measurements when either method alone is costly or unable to achieve the protection objective. A mixed-integer linear programming formulation is introduced to obtain the optimal mixed defending strategy. To tackle the NP-hardness of the problem, a tree-pruning-based heuristic is further presented to produce an approximate solution in polynomial time. The advantageous performance of the proposed defending mechanisms is verified in IEEE standard power system test cases.

A well-functioning power system relies on its accurate state estimation. However, recent research shows that well-structured false-data injection attacks can bypass the current security system and introduce arbitrary errors to state estimates. In this paper, we propose a novel defending mechanism against false-data injection attacks using covert power network topological information. By keeping the exact reactance of a set of transmission lines from attackers, no false data injection attack can be launched to compromise any set of critical state estimates. We first investigate from the attackers' perspective the necessary condition to perform injection attack. Based on the arguments, we characterize the optimal protection problem, which protects the state estimates with minimum number of covert transmission lines, into a minimum Steiner tree problem in a graph, where some off-the-shelf algorithms can be applied. Besides, we also propose a mixed defending strategy that jointly considers the use of covert topological information (CTI) and secure meter measurements when CTI protection alone fails to achieve the protection objective due to technical constraints. The advantageous performance of the proposed defending mechanisms is verified in IEEE standard power system testcase. Our results here will be useful in the security upgrade projects of large-scale electrical power system towards smart power grids.

The normal operation of electricity market requires accurate state estimation of the power grids. However, recent research shows that carefully synthesized false-data injection attacks can easily introduce errors to state estimates without being detected by the current security systems. In this paper, we analyze and formulate an effective false-data injection attack to control real-time electricity price at any tagged bus. It is observed that an adversary capable of false-data injection attack can induce false real-time electricity price by fabricating biased transmission congestion pattern. From the strategic level, we propose a simple algorithm that finds the effective congestion pattern with minor distortion to the normal system operation. For practical implementation, load redistribution attack, a special false-data injection attack which produces biased load estimates, is used to achieve the derived congestion pattern. We also propose a cost-aware neighborhood load redistribution attack, which only compromises limited measurements around the tagged bus. From theory to practice, our results here reveal the potential cyber vulnerabilities of current electricity market and would help to build more secure smart power grid in the future.

We present a novel physical layer encryption (PLE) scheme that randomizes the radio signals using a discrete Fourier transform (DFT) based encryption algorithm. For any baseband signaling method, we show that perfect secrecy is asymptotically achievable with the proposed DFT-based encryption method when the signal block length (N) approaches infinity. For practical systems with finite N, we also show that the proposed encryption method can transmit at a secrecy rate close to the main channel's achievable data rate. In this sense, transmission privacy is achieved without compromising the capability of the communication channel. Besides, the proposed encryption method can hide the transmission data rate and is immune to all existing upper-layer attacks. The performance advantages of the proposed DFT-based encryption method is verified through comparisons against other existing PLE methods.

Exponential backoff (EB) is a widely adopted collision resolution mechanism in many popular random-access networks including Ethernet and wireless LAN (WLAN). The prominence of EB is primarily attributed to its asymptotic throughput stability, which ensures a non-zero throughput even when the number of users in the network goes to infinity. Recent studies, however, show that EB is fundamentally unsuitable for applications that are sensitive to large delay and delay jitters, as it induces divergent second- and higher-order moments of medium access delay. Essentially, the medium access delay follows a power law distribution, a subclass of heavy-tailed distribution. To understand and alleviate the issue, this paper systematically analyzes the tail delay distribution of general backoff functions, with EB being a special case. In particular, we establish a tradeoff between the tail decaying rate of medium access delay distribution and the stability of throughput. To be more specific, convergent delay moments are attainable only when the backoff functions g(k) grow slower than exponential functions, i.e., when g(k)∈ o(r^k) for all r>1. On the other hand, non-zero asymptotic throughput is attainable only when backoff functions grow at least as fast as an exponential function, i.e., g(k)∈Ω(r^k) for some r>1. This implies that bounded delay moments and stable throughput cannot be achieved at the same time. For practical implementation, we show that polynomial backoff (PB), where g(k) is a polynomial that grows slower than exponential functions, obtains finite delay moments and good throughput performance at the same time within a practical range of user population. This makes PB a better alternative than EB for multimedia applications with stringent delay requirements.

The IEEE 802.11 wireless local area network (WLAN) standard was originally designed for best-effort services, targeting at providing high throughput and throughput fairness. However, high system throughput does not necessarily translate to good delay performance. Recent studies show that exponential backoff, the key collision avoidance mechanism in distributed coordination function (DCF) of 802.11, is fundamentally defected in the sense that it induces divergent moments of medium access delay. Essentially, the medium access delay follows a power law distribution, a subclass of heavy tailed distribution. With practical system configurations, the delay variance can easily approach infinity, which translates to service starvation of some users and eventually leads to severe unfairness among users. In this paper, we show that the power law delay distribution can be mitigated if exponential backoff is replaced by a polynomial backoff mechanism. Through rigorous analysis, we find that all delay moments are finite with polynomial backoff, thus fundamentally solving the problem of starvation and unfairness. In addition, polynomial backoff yields a similarly high throughput as exponential backoff with a practical network size when the order of the polynomial backoff function is set reasonably. In this sense, we argue that polynomial backoff is a better alternative than exponential backoff in IEEE 802.11 DCF, especially when there are increasingly more broadband multimedia traffics with stringent delay requirements in the network.

In multi-access wireless networks, transmission scheduling is a key component that determines the efficiency and fairness of wireless spectrum allocation. At one extreme, greedy opportunistic scheduling that allocates airtime to the user with the largest instantaneous channel gain achieves the optimal spectrum efficiency and transmission reliability but the poorest user-level fairness. At the other extreme, fixed TDMA scheduling achieves the fairest airtime allocation but the lowest spectrum efficiency and transmission reliability. To balance the two competing objectives, extensive research efforts have been spent on designing opportunistic scheduling schemes to reach certain tradeoff points between the two extremes by tuning the greediness in scheduling policy. In this paper and in contrast to the conventional wisdom, we find that in relay-assisted MIMO cellular networks, being greedy in user scheduling is unnecessary since it does not directly translate to larger diversity gain. When each mobile user has no less antennas than the base station, even fixed TDMA achieves the optimal diversity gain that is otherwise achievable by greedy opportunistic scheduling. In addition, by incorporating very limited opportunism, a simple TDMA-based scheme, named relaxed-TDMA, asymptotically achieves the same optimal system reliability in terms of outage probability as greedy opportunistic scheduling. This reveals a surprising fact: transmission reliability and user fairness are not necessarily contradicting each other in relay-assisted systems. They can be both achieved by the simple TDMA schemes. For practical implementations, we further propose a fully distributed algorithm to implement the relaxed-TDMA scheme. Our results here may find applications in the design of next-generation wireless communication systems with relay architectures such as LTE-advanced and WiMAX.

Opportunistic scheduling has been recognized as an effective method that significantly outperforms fixed TDMA scheduling in both channel capacity and communication reliability. Nevertheless the superior system performance comes at a price of high computational complexity, large signaling overhead, and unfairness among users. In this paper, we find that in relay-assisted next-generation wireless networks, fixed TDMA scheduling achieves the same diversity gain as opportunistic scheduling, as long as the best relay is carefully selected. Moreover, by introducing very limited opportunism, TDMA scheme yields the same optimal outage probability attained by full scale opportunistic scheduling at high SNR. In other words, we can safely enjoy the advantages of opportunistic scheduling without suffering its drawbacks. We also propose a simple distributed algorithm to implement our scheme. Our results here may find wide application in next-generation wireless communication systems, as relay-assisted cellular system is one of the major architectures in 4G wireless system included LTE or WiMAX.