Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propose the Sensing Dataset Protocol (SDP), a protocol-level specification and benchmark framework for large-scale wireless sensing. SDP defines how heterogeneous wireless signals are mapped into a unified perception data-block schema through lightweight synchronization, frequency-time alignment, and resampling, while a Canonical Polyadic-Alternating Least Squares (CP-ALS) pooling stage provides a task-agnostic representation that preserves multipath, spectral, and temporal structures. Built upon this protocol, a unified benchmark is established for detection, recognition, and vital-sign estimation with consistent preprocessing, training, and evaluation. Experiments under the cross-user split demonstrate that SDP significantly reduces variance (approximately 88%) across seeds while maintaining competitive accuracy and latency, confirming its value as a reproducible foundation for multi-modal and multitask sensing research.

To support the development of low altitude economy, the air-ground integrated sensing and communication (ISAC) networks need to be constructed to provide reliable and robust communication and sensing services. In this paper, the sensing capabilities in the cooperative air-ground ISAC networks are evaluated in terms of area radar detection coverage probability under a constant false alarm rate, where the distribution of aggregated sensing interferences is analyzed as a key intermediate result. Compared with the analysis based on the strongest interferer approximation, taking the aggregated sensing interference into consideration is better suited for pico-cell scenarios with high base station density. Simulations are conducted to validate the analysis.

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.

Wi-Fi sensing—especially human activity recognition (HAR)—is emerging as a device-free means of understanding human–space interactions for smart healthcare and elder care. However, most learning-centric HAR systems focus on improving feature-extraction efficiency or advancing neural-network architectures while overlooking physical information—such as location—which is crucial for accurate activity recognition and interpretation. To close this gap, we introduceDWSen, a Dual-Path Wavelet-Attention Kolmogorov-Arnold Network (KAN) framework for joint activity and indoor location sensing. Specifically, DWSen is a compact multi-task model that combines two complementary encoders with a lightweight decoder. First, an Attention-Masked ResNet-1D (AMRN) suppresses noisy subcarriers and thus highlights discriminative temporal patterns. Meanwhile, a Dual Laplace Wavelet Convolution and bidirectional GRU (Bi-GRU) branch (DLWCB) injects physics-aware, band-pass time–frequency cues and captures long-range temporal dependencies in CSI sequences, thereby enhancing the accuracy and robustness of joint activity and indoor location recognition. Thereafter, the dual features are fused at a late stage and decoded by Wavelet-based Kolmogorov-Arnold Network (WavKAN) heads, which incorporate analytical wavelet kernels as priors and combine them with a nonlinear path and batch normalization to generate activity and location logits. Moreover, training employs an uncertainty-based Adaptive Weighted Loss (AWL) that dynamically balances the objectives, thus mitigating negative transfer without manual tuning. Additionally, we collected an activity- and location-sensing dataset, ElderAL-CSI, covering six activities at nine locations performed by three participants. Extensive experiments on ARIL, CSIDA, and ElderAL-CSI demonstrate that our method attains 94.24%/98.20%, 94.37%/99.65%, and 99.56%/85.20% accuracy (activity/location), respectively, and offers competitive or superior performance compared with recent baselines across most settings. These results point to a low-cost, privacy-friendly path toward reliable ambient monitoring in real homes.

Adaptive video streaming has become a core technology for modern video delivery, particularly in cellular networks. However, the growing dynamics of mobile environments and the diversity of user preferences present major challenges for adaptive bitrate (ABR) algorithms. Existing approaches often struggle to maintain a balance between high in-distribution performance and strong generalization across heterogeneous network conditions and personalized Quality of Experience (QoE) demands. To address these challenges, we propose NMoEABR, a unified ABR decision-making framework that integrates a nonlinear Mixtureof- Experts (NMoE) architecture with preference-aware metareinforcement learning. Specifically, we design an NMoE-based actor network that adaptively aggregates expert policies through dynamic convolution conditioned on real-time network states, thereby enhancing robustness and cross-network generalization in a zero-hot manner. Furthermore, to mitigate convergence difficulties arising from the joint optimization of expert policies and expert-weight prediction, we introduce a preference-aware meta-RL strategy that incorporates user preference embeddings and virtual preference synthesis to stabilize meta-policy updates. Comprehensive evaluations on real-world traces and wireless testbed demonstrate that NMoEABR consistently outperforms mainstream ABR benchmarks in terms of average QoE, stability, and adaptability, particularly under unseen network conditions and diverse user preference distributions.

This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation (ISCC), in which one edge server coordinates multiple edge devices to wirelessly sense the objects and use the sensing data to collaboratively train a machine learning model for recognition tasks. In this system, over-the-air computation (AirComp) is employed to enable one-shot model aggregation from edge devices. Under this setup, we analyze the convergence behavior of the ISCC-enabled Air-FEEL in terms of the loss function degradation, by particularly taking into account the wireless sensing noise during the training data acquisition and the AirComp distortions during the over-the-air model aggregation. The result theoretically shows that sensing, communication, and computation compete for network resources to jointly decide the convergence rate. Based on the analysis, we design the ISCC parameters under the target of maximizing the loss function degradation while ensuring the latency and energy budgets in each round. The challenge lies on the tightly coupled processes of sensing, communication, and computation among different devices. To tackle the challenge, we derive a low-complexity ISCC algorithm by alternately optimizing the batch size control and the network resource allocation. It is found that for each device, less sensing power should be consumed if a larger batch of data samples is obtained and vice versa. Besides, with a given batch size, the optimal computation speed of one device is the minimum one that satisfies the latency constraint. Numerical results based on a human motion recognition task verify the theoretical convergence analysis and show that the proposed ISCC algorithm well coordinates the batch size control and resource allocation among sensing, communication, and computation to enhance the learning performance.

Over-the-air computation (AirComp) exploits the waveform superposition of wireless channels for fast data aggregation from multiple devices. The implementation of AirComp requires amplitude alignment among devices, which requires better channel conditions. Meanwhile, movable antenna (MA) is an emerging method to create better channel states via local antenna movement. To fully utilize the channel gain obtained by adjusting the positions of MA, we consider a MA-enabled AirComp system equipped with one-dimensional MA at transmitter to aggregate wireless data from a large number of devices. We aim to minimize the mean squared error (MSE) by jointly optimizing the antenna position vectors (APV), transmit power, and denoising factor at devices. To address this highly non-convex problem, an alternating optimization (AO) based algorithm is adopted by decomposing it into two sub-problems for power control and APV optimization, respectively. Specifically, we obtain a semi-closed form solution of transmit power control and denoising factor under any given antenna position, and then use second-order Taylor expansion to derive a more tractable MSE counterpart for APV optimization under successive convex approximation (SCA) technique. Experimental results show that, compared with other benchmark schemes, our proposed scheme demonstrates better performance.

Movable antennas (MAs) have emerged as a promising enhancement for integrated sensing and communication (ISAC) by dynamically adjusting antenna positions to significantly improve channel quality and overall ISAC system performance. In this paper, we investigate an MA-enabled ISAC system, where an ISAC base station (BS) equipped with a one-dimensional MA array needs to communicate with devices and detect potential targets at the same time. In particular, we aim to maximize the downlink common (minimum) throughput among all devices by jointly optimizing the transmit beamforming and antenna position vector (APV) at BS, while ensuring beampattern gain constraints in specified directions for sensing tasks and maximum transmit power constraint. Note that the formulated problem is highly non-convex and hard to be solved. To tackle with this problem, we adopt an alternating optimization (AO) based algorithm by decomposing the original problem into two subproblems. In the first subproblem, we construct a surrogate function via second-order polynomial expansion to optimize APV under successive convex approximation (SCA) technique. In the second subproblem, we use the bisection method to obtain feasible transmit beamforming under any given antenna position. The numerical results demonstrate that our proposed scheme not only improves the communication performance of communication devices but also ensures the required sensing performance.

The rapid advancement of 6G technology has driven extensive research on integrated sensing, communication, and computation (ISCC), enabling applications in smart transportation, digital twins, and edge intelligence. ISCC aims to integrate communication, sensing, and computation functions to enhance system performance (e.g., energy efficiency, spectrum efficiency, and reduced latency) by designing an integrated architecture that comprehensively considers system resources and energy consumption. This paper provides an overview of key ISCC technologies, research contents, challenges, and prospects. It starts by analyzing key technical points, introducing their development history and metrics, and then discusses the reasons for integrating these key technologies to lay the groundwork for ISCC research. Subsequently, this paper categorizes and discusses existing ISCC research, ranging from different computational paradigms to emerging communication paradigms, highlighting the current research trends in ISCC. Additionally, it explores the interaction between ISCC and AI and how they can be mutually beneficial in research. Finally, we propose challenges for future ISCC research based on existing studies and suggest potential directions and ideas for research combining new technologies. The integration of ISCC and AI is expected to offer strong support for intelligent 6G by enabling intelligent resource management, reducing AI task handling latency, and improving AI inference accuracy.

Reconfigurable intelligent surfaces (RIS) play a crucial role in enhancing the security of integrated sensing and communication (ISAC) systems. In this paper, RIS is explored to assist the secure transmission of user data in ISAC system. Through the joint design of the transmit beamforming and RIS discrete phase shifter, we aim to maximize user's secure rates while ensuring target sensing performance. Due to the coupling of optimization variables, conventional optimization methods are hard to address this formulated problem. Therefore, a deep reinforcement learning (DRL) scheme by utilizing the soft actor-critic (SAC) and alternating optimization (AO) algorithms is employed to design the transmit beamforming and the RIS discrete phase shifter, respectively. Simulation results indicate that the problem scheme could obtain a significant improvement in enhancing user secure rates compared to other benching scheme.

Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their advantages on the flexible mobility and reliable line-of-sight links. However, in the scenarios with multiple UAV-MECs (UMs) operating concurrently, potential conflicts in their trajectories need to be mitigated. Thus, the 3-D trajectory needs to be properly designed in a highly reliable manner. Besides, such an infrastructure-free communication paradigm also exposes a potential risk of misuse by malicious parties, which allows them to eavesdrop on private communications, posing a threat to the security and privacy. Therefore, we consider a multi-UAV-assisted MEC communication system, where a UAV maliciously eavesdrops on the data transmission from the user devices (UDs) while a jammer is deployed on the ground to interfere with the eavesdropping channel. In specific, our objective is to minimize the energy consumption and latency while incorporating fairness metrics by optimizing the 3-D trajectories of UMs, transmission power of UDs, and the offloading strategies under the constraints of ensuring communication security and load fairness. Given the complexity of this mixed-integer nonconvex programming problem, we decompose the formulated problem into three subproblems. Specifically, at each time slot, we optimize the transmit power and offloading strategies using theoretical derivation and mathematical analysis, respectively. Additionally, a multiagent deep deterministic policy gradient (MADDPG) algorithm is employed to optimize the trajectories of UMs. Simulation results demonstrate that our proposed joint optimization algorithm successfully minimizes the system energy consumption and delay as compared to benchmarking schemes.

Over-the-air computation(AirComp) has emerged as a new analog power-domainnon-orthogonal multiple access(NOMA) technique for low-latency model/gradient-updatesaggregation in federated edge learning(FEEL). By integrating communication and computation into a joint design, AirComp can significantly enhance the communication efficiency, but at the cost of aggregation errors caused by channel fading and noise. This paper studies a particular type of FEEL with federated averaging (FedAvg) and AirComp-based model-update aggregation, namelyover-the-airFedAvg (Air-FedAvg). We investigate the transmission power control in Air-FedAvg to combat against the AirComp aggregation errors for enhancing the training accuracy and accelerating the training speed. Towards this end, we first analyze the convergence behavior (in terms of the optimality gap) of Air-FedAvg with aggregation errors at different outer iterations. Then, to enhance the training accuracy, we minimize the optimality gap by jointly optimizing the transmission power control at edge devices and the denoising factors at edge server, subject to a series of power constraints at individual edge devices. Furthermore, to accelerate the training speed, we also minimize the training latency of Air-FedAvg with a given targeted optimality gap, in which learning hyper-parameters including the numbers of outer iterations and local training epochs are optimized jointly with the power control. Finally, numerical results show that the proposed transmission power control policy achieves significantly faster convergence speed for Air-FedAvg, as compared with benchmark policies with fixed power transmission or per-iterationmean squared error(MSE) minimization. It is also shown that the Air-FedAvg achieves an order-of-magnitude shorter training latency than the conventional FedAvg with digitalorthogonal multiple access(OMA-FedAvg).

Over-the-air federated edge learning(Air-FEEL) has emerged as a communication-efficient solution to enable distributed machine learning over edge devices by using their data locally to preserve the privacy. By exploiting the waveform superposition property of wireless channels, Air-FEEL allows the “one-shot” over-the-air aggregation of gradient-updates to enhance the communication efficiency, but at the cost of a compromised learning performance due to the aggregation errors caused by channel fading and noise. This paper investigates the transmission power control to combat against such aggregation errors in Air-FEEL. Different from conventional power control designs (e.g., to minimize the individualmean squared error(MSE) of the over-the-air aggregation at each round), we consider a new power control design aiming at directly maximizing the convergence speed. Towards this end, we first analyze the convergence behavior of Air-FEEL (in terms of the optimality gap) subject to aggregation errors at different communication rounds. It is revealed that if the aggregation estimates are unbiased, then the training algorithm would converge exactly to the optimal point with mild conditions; while if they are biased, then the algorithm would converge with an error floor determined by the accumulated estimate bias over communication rounds. Next, building upon the convergence results, we optimize the power control to directly minimize the derived optimality gaps under the cases without and with unbiased aggregation constraints, subject to a set of average and maximum power constraints at individual edge devices. We transform both problems into convex forms, and obtain their structured optimal solutions, both appearing in a form of regularized channel inversion, by using the Lagrangian duality method. Finally, numerical results show that the proposed power control policies achieve significantly faster convergence for Air-FEEL, as compared with benchmark policies with fixed power transmission or conventional MSE minimization.

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for privacy-preserving distributed learning over wireless networks. Air-FEEL allows "one-shot" over-the-air aggregation of gradient/model-updates by exploiting the waveform superposition property of wireless channels, and thus promises an extremely low aggregation latency that is independent of the network size. However, such communication efficiency may come at a cost of learning performance degradation due to the aggregation error caused by the non-uniform channel fading over devices and noise perturbation. Prior work adopted channel inversion power control (or its variants) to reduce the aggregation error by aligning the channel gains, which, however, could be highly suboptimal in deep fading scenarios due to the noise amplification. To overcome this issue, we investigate the power control optimization for enhancing the learning performance of Air-FEEL. Towards this end, we first analyze the convergence behavior of the Air-FEEL by deriving the optimality gap of the loss-function under any given power control policy. Then we optimize the power control to minimize the optimality gap for accelerating convergence, subject to a set of average and maximum power constraints at edge devices. The problem is generally non-convex and challenging to solve due to the coupling of power control variables over different devices and iterations. To tackle this challenge, we develop an efficient algorithm by jointly exploiting the successive convex approximation (SCA) and trust region methods. Numerical results show that the optimized power control policy achieves significantly faster convergence than the benchmark policies such as channel inversion and uniform power transmission.

Recently, over-the-air computation (AirComp) has emerged as an efficient solution for access points (APs) to aggregate distributed data from many edge devices (e.g., sensors) by exploiting the waveform superposition property of multiple access (uplink) channels. While prior work focuses on the single-cell setting where inter-cell interference is absent, this article considers a multi-cell AirComp network limited by such interference and investigates the optimal policies for controlling devices' transmit power to minimize the mean squared errors (MSEs) in aggregated signals received at different APs. First, we consider the scenario of centralized multi-cell power control. To quantify the fundamental AirComp performance tradeoff among different cells, we characterize the Pareto boundary of the multi-cell MSE region by minimizing the sum MSE subject to a set of constraints on individual MSEs. Though the sum-MSE minimization problem is non-convex and its direct solution intractable, we show that this problem can be optimally solved via equivalently solving a sequence of convex second-order cone program (SOCP) feasibility problems together with a bisection search. This results in an efficient algorithm for computing the optimal centralized multi-cell power control, which optimally balances the interference-and-noise-induced errors and the signal misalignment errors unique for AirComp. Next, we consider the other scenario of distributed power control, e.g., when there lacks a centralized controller. In this scenario, we introduce a set of interference temperature (IT) constraints, each of which constrains the maximum total inter-cell interference power between a specific pair of cells. Accordingly, each AP only needs to individually control the power of its associated devices for single-cell MSE minimization, but subject to a set of IT constraints on their interference to neighboring cells. By optimizing the IT levels, the distributed power control is shown to provide an alternative method for characterizing the same multi-cell MSE Pareto boundary as the centralized counterpart. Building on this result, we further propose an efficient algorithm for different APs to cooperate in iteratively updating the IT levels to achieve a Pareto-optimal MSE tuple, by pairwise information exchange. Last, simulation results demonstrate that cooperative power control using the proposed algorithms can substantially reduce the sum MSE of AirComp networks compared with the conventional single-cell approaches.

Over-the-air computation (AirComp) of a function (e.g., averaging) has recently emerged as an efficient multiple-access scheme for fast aggregation of distributed data at mobile devices (e.g., sensors) at a fusion center (FC) over wireless channels. To realize reliable AirComp in practice, it is crucial to adaptively control the devices' transmit power for coping with channel distortion to achieve the desired magnitude alignment of simultaneous signals. In this paper, we solve the power control problem. Our objective is to minimize the computation error by jointly optimizing the transmit power at devices and a signal scaling factor (called denoising factor) at the FC, subject to individual average power constraints at devices. The problem is generally non-convex due to the coupling of the transmit powers at devices and denoising factor at the FC. To tackle the challenge, we first consider the special case with static channels, for which we derive the optimal solution in closed form. The derived power control exhibits a threshold-based structure: if the product of the channel quality and power budget for each device, called quality indicator, exceeds an optimized threshold, this device applies channel-inversion power control; otherwise, it performs full power transmission. We proceed to consider the general case with time-varying channels. To solve the more challenging non-convex power control problem, we use the Lagrange-duality method via exploiting its “time-sharing” property. The derived power control exhibits a regularized channel inversion structure, where the regularization balances the tradeoff between the signal-magnitude alignment and noise suppression. Moreover, for the special case with only one device being power limited, we show that the power control for the power-limited device has an interesting channel-inversion water-filling structure, while those for other devices (with sufficiently large power budgets) reduce to channel-inversion power control. Numerical results show that the derived power control significantly reduces the computation error as compared with the conventional designs.

Over-the-air computation (AirComp) of a function (e.g., averaging) has recently emerged as an efficient multi-access scheme for fast aggregation of distributed data at devices (e.g., sensors) to fusion centers (FCs) over wireless channels. To realize reliable AirComp in practice, it is crucial to control the devices' transmit power for coping with channel distortion to achieve the desired magnitude alignment of simultaneous signals. % to strike a balance between enforcing signal-magnitude alignment for overcoming heterogenous channel fading and suppressing noise. In this paper, we study the power control problem for AirComp over fading channels. Our objective is to minimize the computation error by jointly optimizing the transmit power at devices and a signal scaling factor at the FC, called denoising factor, subject to the individual average power constraints at devices. The problem is generally non-convex due to the coupling of transmit power over devices and denoising factor. To optimally solve this problem, we apply the Lagrange duality method via exploiting its ''time-sharing'' property. The derived optimal power control exhibits a regularized channel inversion structure where the regularization has the function of balancing the tradeoff between the signal-magnitude alignment and noise suppression. Moreover, for the special case that only one device is power-limited, we show that the optimal power control for the power-limited device has an interesting channel-inversion water- filling structure, while those for other devices (with sufficiently large power budgets) reduce to channel-inversion power control over all fading states. Numerical results show that the optimal power control remarkably reduces the computation error as compared with other heuristic designs.

This paper proposes a novel user cooperation approach in both computation and communication for mobile edge computing (MEC) systems to improve the energy efficiency for latency-constrained computation. We consider a basic three-node MEC system consisting of a user node, a helper node, and an access point (AP) node attached with an MEC server, in which the user has latency-constrained and computation-intensive tasks to be executed. We consider two different computation offloading models, namely, the partial and binary offloading, respectively. For partial offloading, the tasks at the user are divided into three parts that are executed at the user, helper, and AP, respectively; while for binary offloading, the tasks are executed as a whole only at one of three nodes. Under this setup, we focus on a particular time block and develop an efficient four-slot transmission protocol to enable the joint computation and communication cooperation. Besides the local task computing over the whole block, the user can offload some computation tasks to the helper in the first slot, and the helper cooperatively computes these tasks in the remaining time; while in the second and third slots, the helper works as a cooperative relay to help the user offload some other tasks to the AP for remote execution in the fourth slot. For both cases with partial and binary offloading, we jointly optimize the computation and communication resources allocation at both the user and the helper (i.e., the time and transmit power allocations for offloading, and the central process unit frequencies for computing), so as to minimize their total energy consumption while satisfying the user's computation latency constraint. Although the two problems are nonconvex in general, we develop efficient algorithms to solve them optimally. Numerical results show that the proposed joint computation and communication cooperation approach significantly improves the computation capacity and energy efficiency at the user and helper, as compared to other benchmark schemes without such a joint design.

This paper proposes a joint computation and communication cooperation approach in mobile edge computing (MEC) systems for improving the energy efficiency in mobile computing. In particular, we consider a basic three-node MEC system that consists of a user node, a helper node, and an access point (AP) node attached with an MEC server. We focus on the user's latency-constrained computation over a finite-length block and develop a four-slot protocol for implementing the joint computation and communication cooperation. Under this setup, we jointly optimize the task partition and time allocation, and the transmit power for offloading and central processing unit (CPU) frequencies of local computing at the user and the helper, so as to minimize their total energy consumption subject to the user's computation latency constraint. This problem is optimally solved via convex optimization techniques. Numerical results show that the proposed approach significantly improves the computation capacity and the energy efficiency for the user, as compared to other benchmark schemes without such a joint design.