While gaining significant attention for device-free applications, Wi-Fi sensing still faces challenges in differentiating multiple targets; this stems from the design priorities of Wi-Fi systems that prioritize coverage and stability over sensing diversity. Existing proposals that either expand bandwidth or increase antennas to enhance sensing diversity can be confined by the limited access to Wi-Fi firm/hardware. To this end, we propose Mirror-Fi, a novel Wi-Fi sensing system that improves sensing diversity without modifying Wi-Fi firm/hardware. Exploiting the reconfigurability of metasurfaces, Mirror-Fi augments beamforming with spatially significant features, facilitating the construction of exclusive sensing signal links for individual targets. We innovate in an encoding scheme that equips each metasurface with a distinct phase coding sequence to mark link uniqueness. We then train a deep neural model to leverage prior coding sequences for decomposing non-linearly superimposed channel samples into mutually independent channels; it removes the need for complex channel matrix parameter estimation and mitigates hardware-related offsets inherent to Wi-Fi. Extensive evaluations demonstrate that, with a sufficient number of auto-configured metasurfaces, Mirror-Fi successfully achieves multi-target sensing.

Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm for efficient and robust information transmission. However, its intrinsic characteristics also pose new security challenges, notably an increased vulnerability to eavesdropping attacks. Existing studies on defending against eavesdropping attacks in DeepJSCC, while demonstrating certain effectiveness, often incur considerable computational overhead or introduce performance trade-offs that may adversely affect legitimate users. In this paper, we present DeepGuard, to the best of our knowledge, the first physical-layer defense framework for DeepJSCC against eavesdropping attacks, validated through over-the-air experiments using software-defined radios (SDRs). Considering that existing eavesdropping attacks against DeepJSCC are limited to simulation under ideal channels, we take a step further by identifying and implementing four representative types of attacks under various configurations in orthogonal frequency-division multiplexing systems. These attacks are evaluated over-the-air under diverse scenarios, allowing us to comprehensively characterize the real-world threat landscape. To mitigate these threats, DeepGuard introduces a novel preamble perturbation mechanism that modifies the preamble shared only between legitimate transceivers. To realize it, we first conduct a theoretical analysis of the perturbation’s impact on the signals intercepted by the eavesdropper. Building upon this, we develop an end-to-end perturbation optimization algorithm that significantly degrades eavesdropping performance while preserving reliable communication for legitimate users. We prototype DeepGuard using SDRs and conduct extensive over-the-air experiments in practical scenarios. Extensive experiments demonstrate that DeepGuard effectively mitigates eavesdropping threats while preserving reliable communication for legitimate users. In particular, DeepGuard can reduce the eavesdropper’s reconstruction performance by as much as 29 dB in PSNR and decrease classification accuracy by up to 91% compared with the performance achieved by the legitimate user.

To promote the practicality of deep learning-based localization, existing studies aim to address the issue of scenario dependence through meta-learning. However, these studies primarily focus on variations in environmental layouts while overlooking the impact of changes in device configurations, such as bandwidth, the number of access points (APs), and the number of antennas used. Unlike environmental changes, variations in device configurations affect the dimensionality of channel state information (CSI), thereby compromising neural network usability. To address this issue, we propose Meta-SimGNN, a novelWiFi localization system that integrates graph neural networks with meta-learning to improve localization generalization and robustness. First, we introduce a fine-grained CSI graph construction scheme, where each AP is treated as a graph node, allowing for adaptability to changes in the number of APs. To structure the features of each node, we propose an amplitude-phase fusion method and a feature extraction method. The former utilizes both amplitude and phase to construct CSI images, enhancing data reliability, while the latter extracts dimension-consistent features to address variations in bandwidth and the number of antennas. Second, a similarity-guided meta-learning strategy is developed to enhance adaptability in diverse scenarios. The initial model parameters for the fine-tuning stage are determined by comparing the similarity between the new scenario and historical scenarios, facilitating rapid adaptation of the model to the new localization scenario. Extensive experimental results over commodity WiFi devices in different scenarios show that Meta-SimGNN outperforms the baseline methods in terms of localization generalization and accuracy.

Video analytics plays a vital role in modern applications such as public safety and smart cities, yet transmitting high-resolution video over wireless networks is severely constrained by bandwidth and latency. Existing semantic communication approaches alleviate communication overhead by discarding irrelevant content, but they often impose prohibitive computational costs on resource-constrained surveillance devices. To overcome this limitation, we propose SenSem, a sensing-assisted semantic communication framework that uniquely leverages channel state information (CSI) to reduce both communication and computation overhead. SenSem first exploits location cues embedded in CSI to estimate the region of interest and crop frames before upload. On the cropped frames, a lightweight semantic evaluator scores blocks, and a joint block selection and transmit power control algorithm maximizes the analytics performance for multi-device uplink; at the edge, a sensing-assisted analytics network injects spatial cues to further boost inference. Extensive evaluations on the WARP platform demonstrate that SenSem consistently outperforms state-of-the-art baselines, achieving superior video analytics accuracy under strict latency constraints. By seamlessly reducing both transmission and device-side computation overhead, SenSem, offers a scalable and efficient solution for next-generation wireless video analytics systems.

Reusing Wi-Fi communication packets for sensing purpose has been regarded as one of the most cost-effective ways to realize integrated sensing and communication (ISAC) on commodity Wi-Fi. However, the channel state information (CSI) measured from these packets can be heavily compromised by modern Wi-Fi beamforming protocols tailored primarily to maximize communication throughput, hence inadvertently affecting Wi-Fi sensing performance. Existing approach attempts to mitigate this negative impact through passive signal processing in single-user sensing scenarios, but it fails to fundamentally resolve the problem. In contrast, we actively leverage beamforming, transforming its adverse effects into positive gains, and propose VersaBeam, a practical Wi-Fi ISAC system that simultaneously supports multiple sensing and communication users. Specifically, for multi-user scenarios, we design a correlation-based user pairing algorithm to ensure that the reused communication packets of each sensing receiver are transmitted with sufficiently high power along the sensing direction. Building on this, a novel ISAC-oriented beamforming strategy is proposed to balance the requirements of both sensing and communication. To further provide consistent inputs for sensing tasks, a CSI unification method is developed to remove inconsistencies resulting from diverse beamforming matrices when reusing packets from different communication users. Finally, a prototype of VersaBeam is implemented on commodity Wi-Fi devices, and its effective ness is validated through micro-benchmarking and real-world experiments across three representative sensing applications.

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on theclear-textbeamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, thefirstattack to manipulate traffic incommodityWi-Fi systems; it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it; the results reveal that BeamCraft effectively manipulates Wi- Fi traffic while maintaining a low exposure rate. Furthermore, we also introduce a defense strategy, namely BeamCrypt, that jointly leverages reciprocity and similarity of the channel within the coherence time to authenticate the legitimate user with low overhead. We implement it using WARP and evaluation results verify the effectiveness.

WiFi-based indoor localization, supported by comprehensive infrastructure, is considered a highly promising solution. However, practical applications of existing WiFi-based methods often struggle due to dynamic antenna configurations and potential influence from obstacles, which can undermine the reliability of channel state information and frustrate localization. Even worse, environmental changes may lead to a domain shift, further degrading localization accuracy and system robustness. To address these problems, this paper introduces GraphFi, a novel system that leverages graph neural networks (GNNs) to deliver accurate and robust localization using nearby access points (APs). GraphFi designs two types of graph structures: intra-AP graph and inter-AP graph, to maximize the use of information from all available APs. They aggregate the local features among antennas within each AP and global features across APs, effectively addressing the problem of dynamic antenna configurations. Two specialized GNNs are utilized to derive accurate user locations from these graphs. Additionally, we introduce an anomaly detection method to identify and exclude obstacle-affected APs. This method also employs a tailored GNN to mitigate influence from unpredictable obstacles. Furthermore, we integrate an unsupervised domain adaptation mechanism based on a gradient reversal layer into GNNs. This helps maintain localization performance in a cost-efficient manner and ensures sustained effectiveness in a cross-domain setting. We prototype GraphFi using commodity WiFi devices and conduct extensive experiments in various scenarios. The results demonstrate that GraphFi achieves average localization errors of 0.17 m in a single-domain setting and 0.2851 m in a cross-domain setting, surpassing existing solutions in both precision and robustness.

Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Recent deep learning-based localization methods leverage meta-learning to enhance adaptability across diverse environments. However, most existing approaches focus on variations in environmental layouts while overlooking the changes in device configurations—such as the number of access points, antennas, or bandwidth. Unlike environmental variations, changes in device configurations fundamentally alter the dimensionality of channel state information (CSI), which can significantly hinder the usability and generalizability of neural networks. To address this problem, we propose Meta-GLoc, an adaptive and robust WiFi localization system that combines meta-learning with graph neural networks to effectively handle variations in CSI dimensionality. Specifically, we introduce an amplitude-phase fusion method and a feature extraction method to construct fine-grained CSI graphs. The former fuses the cleaned amplitude and phase in a carefully determined ratio to construct robust CSI images, while the latter extracts dimension-consistent features to mitigate the impact of varying bandwidth and antenna configurations. Moreover, meta-learning is employed to realize adaptive localization in different environments. Experiment results on commodity WiFi devices across different configurations demonstrate that Meta-GLoc effectively improves localization accuracy and robustness.

Wi-Fi is deemed as a promising sensing media due to its ubiquity, yet Wi-Fi sensing is known to be confined by its limited bandwidth that leads to insufficient range resolution. Though sampling a wider spectrum multiple times can enable wideband sensing, its practicality is still hampered by the need for accessing Wi-Fi firmware. In this paper, we propose μCeiver-Fi to exploit spectrum resources for fine-granularity Wi-Fi sensing; it relies solely on a commodity multi-link receiver. Since the channel samples from multiple links under the same receiver can still be misaligned, we first innovate in a comprehensive calibration process to align these samples. This is followed by a novel optimization framework to extend effective sensing bandwidth to GHz-level using only a few channel samples. Finally, we specifically design a spectral representation for sensing information in order to bridge between wideband signals and diversified downstream applications. Through comprehensive evaluations in Wi-Fi pose estimation task, we demonstrate the promising performance of μCeiver-Fi in fine-granularity sensing.

With the widespread deployment of WiFi devices, utilizing beamforming feedback for sensing has become a popular trend in WiFi systems. However, the singular value decomposition (SVD)-based feedback method adopted in existing WiFi standards performs poorly in sensing performance since it only aims at maximizing the communication performance. To address this, we propose an integrated sensing and communication (ISAC)-oriented beamforming feedback protocol, which provides different sensing information based on the sensing indicator. Accordingly, we develop different ISAC-oriented CSI compression methods for different sensing applications requiring different feedback information. Taking the angle of departure (AoD) as an example, an optimization problem is formulated to maximize the data rate while preserving the complete AoD information. To resolve it, we propose an iterative algorithm to obtain the suboptimal solution and a heuristic algorithm to reduce the computational complexity. We further extend the proposed compression method for the AoD information to the compression of the angle of arrival (AoA) and time of flight (ToF). Test results show that our proposal achieves both excellent communication and sensing performance and can be applied to various sensing applications, such as localization and action recognition.

The millimeter wave (mmWave) communication becomes a key enabler for the future Internet of Things (IoT) due to its capability for supporting high rate and low-latency traffic. However, beamforming in the mmWave band faces issues of low efficiency since the narrow beam of mmWave devices would increase the search delay and overhead. Inspired by this, we utilize localization over sub-6 GHz band to assist the mmWave base station in performing fast and robust adaptive beamforming (RABF). Different from existing works, we focus on the indoor scenario and consider the effects of several practical issues, including localization errors and hardware defects. Specifically, a novel two-step access scheme is proposed. During the first step, we design a novel localization method customized for indoor scenarios, jointly considering the time of flight and angle of arrival. The localization error is further analyzed to determine the mmWave scanning angle and an optimal beamwidth expression is derived in closed-form to maximize system throughput with the considerations of the search delay. Moreover, considering the mismatch of the steering vector caused by the hardware defects, we propose an RABF method in closed-form. Simulation results demonstrate that the proposed scheme can effectively reduce the search delay and realize robust beamforming to enhance the mmWave communication performance.

Integrated sensing, communication, and computation (ISCC) has been regarded as a prospective technology for the next-generation wireless network, supporting human-centric intelligent applications. However, the delay sensitivity of these computation-intensive applications, especially in a multi-device ISCC system with limited resources, highlights the urgent need for efficient sensing task execution frameworks. To address this, we propose a resource-efficient sensing framework in this paper. Different from existing solutions, it features a novel action detection module deployed at each device to detect the onset of an action. Only time windows filled with signals of interest are offloaded to the edge server and processed by the edge recognition module, thus reducing overhead. Furthermore, we quantitatively analyze the sensing performance of the proposed sensing framework and formulate a sensing accuracy maximization problem under power, delay, and resource limitations for the multi-device ISCC system. By decomposing it into two subproblems, we develop an alternating direction method of multipliers (ADMM)-based distributed algorithm. It alternatively solves a sensing accuracy maximization subproblem at each device and employs a closed-form computation resource allocation strategy at the edge server till convergence. Finally, a real-world test is conducted using commodity wireless devices to validate the sensing performance analysis. Extensive test results demonstrate that our proposal achieves higher sensing accuracy under the limited resource compared to two baselines.

To break through the transmission rate bottleneck of traditional communication, semantic communication is proposed to support emerging applications with extremely low latency requirements such as remote surgery and autonomous vehicle. Unlike the transmission of verbose symbols in traditional communication, mainstream semantic communications use deep learning technology to extract compact semantic information from data and convey it. However, the application of deep neural networks also poses security concerns, i.e., vulnerabilities to adversarial attacks. In this paper, we perform the first study on the security of semantic communication against both whitebox and black-box attacks by compromising the wireless channel between the transmitter and receiver. To launch practical and effective attacks, a systematic and universal attack framework is designed to craft content-agnostic, undetectable, robust whitebox perturbation signals as well as highly-transferable blackbox ones. Extensive experiments on two open-source datasets demonstrate that our attack framework can achieve over 87%, 99%, and 89% success rates in untargeted white-box, targeted white-box, and untargeted black-box attacks. This means that the proposed attack methods could severely threaten the quality of service of current semantic communications. We also propose two mitigation methods to resist such attacks.

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on the clear-text beamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, the first attack to manipulate traffic in commodity Wi-Fi systems; it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it; the results reveal that BeamCraft effectively manipulates Wi-Fi traffic while maintaining a low exposure rate.

The rise in renewable energy has driven the widespread use of large-scale energy storage batteries, which makes the risk of overheating more threatening. To ensure battery safety, it is essential to build a monitoring system with a comprehensive evaluation of large quantities of batteries. However, existing battery management systems exhibit significant limitations in terms of monitoring scope, analytical precision, and transmission efficiency. As an applicable solution, cloud–edge technology is an advanced integrated method that provides low-latency data access, accurate analysis capabilities, and adjustable monitoring ranges. In this work, the Kubernetes-orchestrated battery monitoring platform (KBMP), which integrates Kubernetes and cloud–edge technology, is proposed to provide comprehensive battery management. Specifically, Kubernetes is used to ensure low latency in data transmission and analysis, while the K-Means clustering algorithm is applied to provide accurate thermal runaway (TR) warnings. To validate the performance of KBMP, four sets of real battery TR data are fed to test its accuracy and latency. The experimental findings reveal that KBMP is capable of providing battery TR warnings in advance within 30 min. Additionally, the platform concurrently decreases data transmission latency by up to 20% and reduces replica scaling latency by 50% compared to the platform without integrating Kubernetes.

This article investigates an integrated sensing-communication-computation (ISCC)-based multiview-multitask (MVMT) edge artificial intelligence inference system. Each device senses a narrow view of a target area and processes the echo signal to generate real-time sensory data. An edge server receives and combines multiple views of data from multiple devices to complete several downstream inference tasks. Compared with existing designs where dedicated sensory data are obtained, transmitted, and processed for each task, this ISCC-based MVMT framework enjoys reduced costs of sensing, on-device computation, and communication overhead due to data sharing among different tasks. The challenges of improving all tasks’ inference accuracy lie in the tight coupling of sensing, communication, and computation among different devices and sensory view competition among different tasks. These two challenges intertwine, making the multitask optimization problem mixed-integer nonconvex programming. To tackle this problem, we propose a joint device scheduling and resource allocation (JDSRA) scheme, which alternatively solves a subproblem of joint device scheduling and time allocation and a subproblem of resource allocation till convergence. Particularly, in addition to a dynamic-programming-based optimal device scheduling algorithm, a low-complexity suboptimal algorithm is proposed based on sorting a derived closed-form indicator, which represents the increase of all tasks’ inference accuracy per time unit consumption. Besides, a low-complexity optimal resource allocation algorithm is proposed by parallelly solving multiple simple convex subproblems. Numerical results based on jointly completing three tasks of human motion recognition, human height recognition, and localization in smart home scenarios are conducted to verify the performance of our proposed schemes.

Given the fact that WiFi-based sensing can be realized through the reuse of WiFi communication facilities and frequency bands, integrated sensing and communication (ISAC) emerges as a pivotal direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from exclusive WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it demands high-quality and sufficient CSI measurements. In this paper, we propose SenCom as a step towards forward-compatible ISAC solution. SenCom extracts CSI from general WiFi packets, enabling CSI calibration across different WiFi communication modes and delivering quality CSI measurements for upper-layer sensing applications. A fitting-resampling scheme and an incentive strategy are also developed. The former one is to obtain evenly sampled CSI with consistent dimensionality and the latter one is to guarantee sufficient CSI measurements over time. We build a prototype of SenCom and conduct extensive experiments involving 15 participants. The results show that SenCom’s competence for a variety of sensing tasks while making minimal compromises to WiFi communication performance.

Behavior recognition plays an essential role in numerous behavior-driven applications (e.g., virtual reality and smart home) and even in the security-critical applications (e.g., security surveillance and elder healthcare). Recently, WiFi-based behavior recognition (WBR) technique stands out among many behavior recognition techniques due to its advantages of being non-intrusive, device-free, and ubiquitous. However, existing WBR research mainly focuses on improving the recognition precision, while rarely studying the security aspects. In this article, we reveal that WBR systems are vulnerable to manipulating physical signals. For instance, our observation shows that WiFi signals can be changed by jamming signals. By exploiting the vulnerability, we propose two approaches to generate physically online adversarial samples to perform untargeted attack and targeted attack, respectively. The effectiveness of these attacks are extensively evaluated over four real-world WBR systems. The experiment results show that our attack approaches can achieve 80% and 60% success rates for untargeted attack and targeted attack in physical world, respectively. We also show that our attack approaches can be generalized to other WiFi-based sensing applications, such as user authentication.

Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation mobile networks to embed sensing function on communication waveforms. A major challenge in ISAC is the effective integration of sensing and communication functions. Addressing this, this paper introduces a dual-functional waveform design that builds on the existing orthogonal frequency division multiplexing (OFDM) waveform. Unlike prior approaches that generally sacrifice communication performance to enhance sensing performance, our design contains a null-space sensing precoder that utilizes the null space of the communication channel to project additional sensing signals, thus improving the sensing functionality of the OFDM waveform without degrading any communication performance. We formulate a waveform optimization problem aimed at maximizing the sensing performance under the null-space sensing precoder and then propose a majorization-minimization (MM)-based waveform design algorithm. Additionally, to meet the real-time communication requirement in practice, we analyze the intrinsic characteristics of the high-performance sensing waveform and then develop a low-complexity waveform design algorithm. Simulation results show that the proposed MM-based algorithm can dramatically improve sensing performance without incurring any additional sensing power and degrading the communication performance. Furthermore, the low-complexity algorithm achieves substantial improvements in the sensing performance with much reduced computational complexity.

In integrated sensing, computation, and communication (ISCC) systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module that can detect whether the sensing target is static. Subsequently, we analyze the sensing performance of the proposed framework and formulate a sensing accuracy maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy and derive a threshold selection policy that demonstrates the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis, and extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

Given the fact that WiFi-based sensing can be realized by reusing WiFi communication facilities and communication frequency bands, integrated sensing and communication (ISAC) is considered a crucial development direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from customized WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it requires high-quality and sufficient CSI measurements. In this paper, we propose SenCom, which extracts CSI from general WiFi packets. SenCom enables CSI calibration across different WiFi communication modes and provides unified CSI measurements for upper-layer sensing applications. We also devise a fitting-resampling scheme to derive evenly sampled CSI with consistent dimensionality, and an incentive strategy to ensure sufficient CSI measurements over time. We build a prototype of SenCom and perform extensive experiments with 15 participants. The results show that SenCom is competent for a variety of sensing tasks, while incurring little compromise to the WiFi communication performance.

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations (BSs) which operate in different spectra. We propose a co-existence system of multiple-input multiple-output (MIMO) radar and multiple-input single-output (MISO) communication systems. We aim to minimize the system transmit power while maintaining the performance of both radar and communication. Due to the complexity of the original problem, we traverse the BS selection and transform the subproblem into a more tractable one by introducing auxiliary variables and propose a penalty dual decomposition (PDD)-based algorithm to solve it. In the inner loop, we propose a concave-convex procedure (CCCP)-based algorithm to deal with the optimization problem, and the block coordinate descent (BCD) algorithm is utilized to update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Finally, numerical simulations validate the superiority of our proposed algorithm over benchmark algorithms.

Integrated sensing and communication (ISAC) has recently been considered as a promising approach to save spectrum resources and reduce hardware cost. Meanwhile, as information security becomes increasingly more critical issue, government agencies urgently need to legitimately monitor suspicious communications via proactive eavesdropping. Thus, in this paper, we investigate a wireless legitimate surveillance system with radar function. We seek to jointly optimize the receive and transmit beamforming vectors to maximize the eavesdropping success probability which is transformed into the difference of signal-to-interference-plus-noise ratios (SINRs) subject to the performance requirements of radar and surveillance. The formulated problem is challenging to solve. By employing the Rayleigh quotient and fully exploiting the structure of the problem, we apply the divide-and-conquer principle to divide the formulated problem into two subproblems for two different cases. For the first case, we aim at minimizing the total transmit power, and for the second case we focus on maximizing the jamming power. For both subproblems, with the aid of orthogonal decomposition, we obtain the optimal solution of the receive and transmit beamforming vectors in closed-form. Performance analysis and discussion of some insightful results are also carried out. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithm in terms of eavesdropping success probability.

Behavior recognition plays an essential role in numerous behavior-driven applications (e.g., virtual reality and smart home) and even in the security-critical applications (e.g., security surveillance and elder healthcare). Recently, WiFi-based behavior recognition (WBR) technique stands out among many behavior recognition techniques due to its advantages of being non-intrusive, device-free, and ubiquitous. However, existing WBR research mainly focuses on improving the recognition precision, while neglecting the security aspects. In this paper, we reveal that WBR systems are vulnerable to manipulating physical signals. For instance, our observation shows that WiFi signals can be changed by jamming signals. By exploiting the vulnerability, we propose two approaches to generate physically online adversarial samples to perform untargeted attack and targeted attack, respectively. The effectiveness of these attacks are extensively evaluated over four real-world WBR systems. The experiment results show that our attack approaches can achieve 80% and 60% success rates for untargeted attack and targeted attack in physical world, respectively. We also propose three methods to mitigate the hazard of such attacks.

Integrated sensing and communication (ISAC) has been regarded as one of the most promising technologies for future wireless communications. However, the mutual interference in the communication radar coexistence system cannot be ignored. Inspired by the studies of reconfigurable intelligent surface (RIS), we propose a double-RIS-assisted coexistence system where two RISs are deployed for enhancing communication signals and suppressing mutual interference. We aim to jointly optimize the beamforming of RISs and radar to maximize communication performance while maintaining radar detection performance. The investigated problem is challenging, and thus we transform it into an equivalent but more tractable form by introducing auxiliary variables. Then, we propose a penalty dual decomposition (PDD)-based algorithm to solve the resultant problem. Moreover, we consider two special cases: the large radar transmit power scenario and the low radar transmit power scenario. For the former, we prove that the beamforming design is only determined by the communication channel and the corresponding optimal joint beamforming strategy can be obtained in closed-form. For the latter, we minimize the mutual interference via the block coordinate descent (BCD) method. By combining the solutions of these two cases, a low-complexity algorithm is also developed. Finally, simulation results show that both the PDD-based and low-complexity algorithms outperform benchmark algorithms.

Driven by the demand for massive and accurate sensing data to achieve wireless network intelligence under a limited available spectrum, the coexistence between radar and communication systems has attracted public attention. In this paper, we investigate a novel dual-functional full-duplex relay aided radar-communication system where the phased-array radar is employed at the amplify-and-forward (AF) relay. A joint transceiver design is proposed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all detection directions at the radar receiver under communication quality-of-service and total energy constraints. The formulated optimization problem is particularly challenging due to the highly nonconvex objective function and constraints. Based on the problem structure, we equivalently decompose it into the radar-energy and relay-energy minimization problems under SINR requirements. To solve the radar-energy minimization problem, we propose a low-complexity algorithm based on the alternating direction method of multipliers to optimize the radar transmit power and receiver. The relay-energy minimization problem can be simplified into an equivalent quadratic programming problem by introducing an insightful unitary matrix. Then, the closed-form expression for the AF relay beamforming matrix can be derived, which is jointly determined by the channel condition of relay communication and the detection direction of the radar. After that, we introduce the overall transceiver design algorithm to the original problem and discuss its optimality and computational complexity. Simulation results verify that the proposed algorithm significantly outperforms other benchmark algorithms.

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. Directly transmitting data for centralized learning will cause long communication latency and may incur severe privacy issue as well. To address these issues, we consider the importance-aware federated edge learning (FEEL) system in this paper. Based on the relation between loss decay and gradient norm, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Finally, the test results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms.

With the combination of deep learning and mobile edge computing, the accuracy of the computer vision task in mobile augmented reality (AR) applications can be significantly improved along with the enhancement on the end-to-end latency and energy efficiency. However, no architecture-based delay model for convolutional neural networks (CNNs) has been proposed in edge computing. In this paper, we first develop a new delay model to characterize the relation between the processing delay and the input image size of general CNN models. Then, we formulate a non-convex optimization problem to maximize the learning accuracy under the communication and computation resource constraints. By problem transformation, the optimal resource allocation policy is derived in closed-form and low-complexity search algorithm is also developed. Finally, test results validate the applicability of the delay model and demonstrate the learning accuracy improvement of the proposed algorithm.

It is an essential goal for future wireless networks to provide better artificial intelligent services. In this paper, we investigate the joint communication and computation resource optimization in the mobile edge learning system to support augmented reality applications, where the convolutional neural networks (CNNs) are deployed at the edge server. For such a system, we first develop a delay model to characterize the relation between the computation latency and the input image size of general CNN models. Then, we formulate a mixed integer nonlinear optimization problem to maximize the system computation capacity under the constraints of learning accuracy, end-to-end latency, and energy consumption. To solve this problem, we first investigate maximizing the system learning accuracy under the communication and computation resource constraints. The optimal resource allocation policy can be achieved by a low-complexity search algorithm. We further prove that the original problem is NP-hard and propose an efficient heuristic algorithm with a newly-developed offloading priority function. An upper bound for the proposed algorithm is also derived. Finally, test results validate the applicability of the delay model and demonstrate the performance improvement of the proposed algorithm as compared with the existing algorithms.

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. This paper focuses on FEEL with gradient averaging over participating devices in each round of communication. A novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the “importance” of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.

The mobile edge computing (MEC) and device-to-device (D2D) communications take advantage of the proximity for supporting high-speed mobile computing and high-rate data communications, respectively. In this paper, we integrate both techniques to further improve the computation capacity of the cellular networks by proposing the D2D-MEC technique. We aim to maximize the number of supported devices and formulate a mixed integer non-linear problem. To solve it, we decouple it into two subproblems and prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. The first one minimizes the required edge computation resource for a given D2D pair while the second one maximizes the number of supported devices via optimal D2D pairing. Then, by solving two subproblems, the optimal algorithm is developed and some insightful results are also highlighted. Finally, numerical results show that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

In the paper, an inverse discrete Fourier transform-based Vandermonde-subspace frequency division multiplexing (IDFT-VFDM) waveform is proposed for the new radio (NR) supplementary uplink (SUL) to share the same time and frequency resources with the frequency division duplex (FDD) based long-term evolution (LTE) network. To avoid the co-channel interference to the LTE user equipment (UE) uplink transmission, the interference channel state information (CSI) is necessary for the NR UE to design the interference-free precoder. Since the operating band used for NR SUL corresponds to LTE FDD mode, the channel reciprocity condition in the time division duplex (TDD) mode is no longer held. To deal with it, the reciprocity on the channel related parameters for each path, i.e. amplitude, initial phase, propagation distance, angle of arrival, angle of departure, is exploited to estimate the uplink CSI from the NR UE to the LTE base station (BS) via the downlink CSI. Accordingly, the uplink waveform is designed for the NR UE to guarantee the absence of interference towards the LTE BS with the knowledge of uplink CSI. Numerical results are presented to validate the accuracy of the CSI estimation and the merit of the IDFT-VFDM as a potential waveform to achieve the LTE FDD-NR SUL co-existence.

In this paper, we investigate the latency minimization resource allocation problem in a hierarchical cloud-edge coexistence system by optimally splitting tasks for partial cloud computing and partial edge computing. A joint communication and computation resource allocation problem is first formulated and the structural characteristics are further analyzed. Next, by defining two novel parameters: the normalized backhaul communication capacity and the normalized cloud computation capacity, an optimal task splitting strategy is developed. With the help of these definitions, the joint communication and computation resource allocation policy can be devised in closed-form. Finally, numerical results demonstrate that the proposed collaborative cloud-edge computing scheme performs better than some baseline schemes in terms of minimizing the end-to-end latency of mobile devices.

Small-cell architecture is widely adopted by cellular network operators to increase spectral spatial efficiency. However, this approach suffers from low spectrum temporal efficiency. When a cell becomes smaller and covers fewer users, its total traffic fluctuates significantly due to insufficient traffic aggregation and exhibits a large “peak-to-mean” ratio. As operators customarily provision spectrum for peak traffic, large traffic temporal fluctuation inevitably leads to low spectrum temporal efficiency. To address this issue, in this paper, we advocate device-to-device (D2D) load-balancing as a useful mechanism. The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency. We provide theoretical modeling and analysis to characterize the benefit of D2D load balancing, in terms of total spectrum requirements and the corresponding cost, in terms of incurred D2D traffic overhead. We carry out empirical evaluations based on real-world 4G data traces and show that D2D load balancing can reduce the spectrum requirement by 25% as compared to the standard scenario without D2D load balancing, at the expense of negligible 0.7% D2D traffic overhead.

The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

In this paper, we investigate the latency minimization resource allocation for a multi-user augmented reality (AR) system based on mobile edge computing (MEC). First, we develop a novel data sharing model for the delay-sensitive AR tasks. Then, by integrating the partial offloading scheme into the task processing, we formulate a weighted-sum latency minimization problem to improve the quality of experience (QoE) for AR devices. Both the optimal task segmentation strategy and the optimal joint resource allocation are derived in closed-form. Finally, numerical results show that the proposed partial task offloading with data sharing scheme can achieve a better delay performance as compared against some benchmark schemes.

By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multi-user time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency.

In this paper, we consider latency-minimization resource allocation for a multi-user mobile edge computation offloading (MECO) system. First, we develop a novel partial computation offloading model and then formulate the weighted-sum latency-minimization problem by optimally allocating the communication and computation resources. After that, the closed-form expression for the optimal data segmentation strategy is derived. Based on this result, we transform the original problem into a piecewise convex optimization problem and propose a sub-gradient algorithm to find the optimal resource allocation solution. Moreover, we analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution is devised. Finally, numerical results show that the partial computation offloading model can achieve a better performance than other two baseline schemes.