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.

A compact broadband four-port multiple-input multipleoutput (MIMO) antenna for UAV to assist automotive communication is proposed. By utilizing a 3D ground dielectric layer, the microstrip patch elements are vertically placed on each side of the cube to achieve omnidirectional coverage. Grounding branches are introduced on both sides of the radiating patch to realize short circuit, significantly improves impedance matching, resulting in a broadband of 4.16 GHz. 8.84 GHz and a compact size of 70 mm × 70 mm × 10 mm. Additionally, parasitic ground structures are incorporated inside the 3D ground dielectric layer, effectively enhancing port isolation. Measurements show that the port coupling coefficient is better than.18 dB in the entire frequency band, and it is even superior to.25 dB in the V2X frequency band. The envelope correlation coefficient remains below 0.0048 throughout the operating band. Furthermore, the diversity gain achieves 9.992 dB, while the channel capacity loss is maintained below 0.4 bits/s/Hz. This design provides an efficient and stable signal transmission solution for drone-assisted automotive communication systems

Integrated sensing and communication (ISAC) emerges as a transformative paradigm for enabling future wireless networks by jointly supporting high-rate communication and precise environmental perception. In this paper, we investigate a robust beamforming framework tailored for monostatic ISAC systems in near-field channels, where the impact of spherical wavefront effects is significant and cannot be neglected. In particular, we formulate a minimum mean squared error (MMSE)-driven beamforming optimization problem that strikes an effective balance between sensing accuracy and communication quality, while considering both power and outage constraints and explicitly accounting for channel estimation errors. To address the inherent non-convexity arising from coupled sensing-communication constraints and uncertainties due to channel errors, a semidefinite relaxation (SDR)-based algorithm leveraging sphere bounding techniques is developed to acquire a favorable suboptimal solution, with polynomial-time complexity. Furthermore, extensive simulations are conducted to rigorously validate the proposed methodology under a variety of practical conditions. Our results demonstrate that the proposed design reduces sensing mean squared error (MSE) by up to 3.8 dB and improves achievable communication rate by 33.4% over non-robust baselines under imperfect channel state information (CSI). Furthermore, compared to conventional far-field schemes, our design achieves up to 1.6 dB lower MSE and 28.0% higher rate in millimeter-wave (mmWave) scenarios, and 2.5 dB lower MSE and 31.7% rate gain in terahertz (THz) scenarios at 10 dB communication signal-to-interference-plus-noise ratio (SINR). To further underscore the practical significance of our approach, the experimental results reveal critical insights into the delicate balance between communication and sensing performance in ISAC systems under real-world conditions. Specifically, our findings emphasize the importance of robust beamforming techniques that optimize resource allocation to mitigate the adverse effects of channel estimation errors, particularly in high-frequency regimes such as mmWave and THz. These insights are vital for the design of adaptive ISAC systems, where trade-offs between sensing precision and communication reliability must be dynamically managed to meet stringent performance requirements in next-generation wireless networks.

Integrated Sensing and Communication (ISAC) systems represent a transformative paradigm for next-generation wireless networks by enabling dual-functional efficiency through simultaneous information transmission and environmental sensing. This paper investigates the critical challenge of robust beamforming design for monostatic ISAC systems operating in the near-field (NF) regime, where conventional far-field channel assumptions become fundamentally invalid. We develop a novel robust beamforming framework that optimizes minimum mean squared error (MMSE) estimation for sensing performance while guaranteeing stringent communication quality-of-service requirements. A distinctive feature of our approach lies in the proposed spherical wavefront-based channel model that incorporates both distance and angular response, providing superior accuracy compared to conventional planar wavefront approximations in NF scenarios. To resolve the inherent non-convex optimization problem with coupled sensing-communication constraints, we devise an efficient semidefinite relaxation (SDR)-based algorithm with guaranteed convergence properties. Comprehensive simulations demonstrate significant improvements in both sensing and communication performance, even under imperfect channel state information.

Next-generation communications demand high-speed data transmission, making millimeter-wave (mmWave) and terahertz (THz) technologies essential. Reducing beam-forming overhead has led to the integration of sensing-assisted predictive beamforming with deep learning. However, in high-mobility scenarios, real-time collection of complete time-series data is often challenging due to network instability and device limitations. Motivated by this, we propose an LSTM-based method for completing incomplete time-series sensing data and develop a multimodal sensing-assisted predictive beamforming model. Experimental results show that the position data imputation error remains within meters, and training with the completed data improves model performance, achieving over 50% Top-1 and 93.53% Top-5 accuracy. This approach enhances prediction accuracy while reducing beamforming overhead

A compact antenna array is developed for W-band vehicle mounted millimeter-wave radar, also suitable for Internet of Things (IoT) and intelligent vehicular transportation systems (IVTSs). The proposed array provides a flat shoulder shaped (FSS) radiation pattern, obtained using a beamforming algorithm, enabling medium- and long-range radar detection and information transmission between wireless devices. The beamforming algorithm determines the excitation amplitudes and phases of each subarray. Power dividers are designed using a scheme of equal/unequal power division to meet the requirements of amplitude. And phase shifters are used to create required phases. To obtain a compact design, a substrate integrated waveguide (SIW) feeding structure are employed to integrate power dividers, phase shifters, and waveguide transitions into a single part. Quadratic recursive exhaustive search in combination with full-wave simulation is used to optimize the design. Further improvements are made to achieve a more compact feeding structure. The realized array provides a stable FSS radiation pattern in the H-plane. In addition, its maximal gain appears in the boresight direction, which is usually a challenge for patch element array. The realized gain is 22 dBi, and the ripple in the shoulder range is less than 2.3 dB, which is better than other designs. The feeding structure is only 14 mm$\times 16$mm large, showing a minimized design. The demonstrated antenna presents itself to be an excellent hardware candidate for IoT and IVTS.

Vehicle recognition constitutes a foundational technology within intelligent transport systems (ITS), enabling real-time recognition, classification, and tracking of vehicles. With the characteristics of low construction cost, flexible deployment and strong environment adaptability, unmanned aerial vehicle (UAV) is increasingly leveraged for vehicle target recognition, acts as the air part of future intelligent transport systems (ITS) for traffic management, accident handling and vehicle order management, and provides a more efficient, safe and sustainable transport mobility solutions in future ITS. Promoted by the massive number of intelligent vehicles and growing demands of connected vehicles in ITS, continuous and high-fidelity spatio-temporal monitoring of vehicle movement is expected in future ITS, raising the pursuit of higher vehicle recognition performance. As a typical distributed training framework, federated learning (FL) is a desired paradigm to improve sensing performance with the communication of sensing parameters for UAV-enabled ITS. Due to the heterogeneity of sensing data in the cooperative UAV-enabled ITS, the non-independent identically distribution (Non-IID) issue is inevitable. The existing data augmentation works aimed at Non-IID issue in FL utilize single-agent reinforcement learning (SARL), where the local model parameters are input into a central network, resulting in the model privacy leakage problem. To deal with the above issue, a multi-agent reinforcement learning (MARL) algorithm is applied to optimize the training accuracy and data augmentation efficiency for UAV in ITS. Moreover, a decentralized blockchain-based FL (BFL) framework is proposed to avoid the single-point failure in UAV-enabled ITS. The experiments are conducted on the generated vehicle dataset (VRID) and the simulation results indicate that our proposed algorithm exhibits a superior performance than the benchmark algorithms, especially in terms of higher vehicle target recognition accuracy and lower communication overhead, which provides a significant technology support for vehicle identification and tracking in future ITS.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, playing a crucial role in enhancing the rate and spectral efficiency of wireless networks. As ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region where the spherical wavefront propagates. Near-field beam training requires information on both angle and distance, which inevitably leads to a significant increase in the beam training overhead. To address this challenge, we propose a near-field beam training method based on deep learning. Specifically, we employ a convolutional neural network (CNN) to efficiently extract channel characteristics from historical data by strategically selecting padding and kernel sizes. The negative value of the user average achievable rate is utilized as the loss function to optimize the beamformer, maximizing the achievable rate in multi-user networks without relying on predefined beam codebooks. Once deployed, the model requires only pre-estimated channel state information (CSI) to compute the optimal beamforming vector. Simulation results demonstrate that the proposed scheme achieves more stable beamforming gains and substantially outperforms traditional beam training approaches. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the near-field beam training overhead.

Deep-learning-based approaches have achieved remarkable progress for complex real scenario denoising, yet their accuracy-efficiency tradeoff is still understudied, particularly critical for mobile devices. As real noise is unevenly distributed relative to underlay signals in different frequency bands, we introduce a frequency-aware divide-and-conquer strategy to develop a frequency-aware denoising network (FADN). FADN is materialized by stacking frequency-aware denoising blocks (FADBs), in which a denoised image is progressively predicted by a series of frequency-aware noise dividing and conquering operations. For noise dividing, FADBs decompose the noisy and clean image pairs into low- and high-frequency representations via a wavelet transform (WT) followed by an invertible network and recover the final denoised image by integrating the denoised information from different frequency bands. For noise conquering, the separated low-frequency representation of the noisy image is kept as clean as possible by the supervision of the clean counterpart, while the high-frequency representation combining the estimated residual from the successive FADB is purified under the corresponding accompanied supervision for residual compensation. Since our FADN progressively and pertinently denoises from frequency bands, the accuracy-efficiency tradeoff can be controlled as a requirement by the number of FADBs. Experimental results on the SIDD, DND, and NAM datasets show that our FADN outperforms the state-of-the-art methods by improving the peak signal-to-noise ratio (PSNR) and decreasing the model parameters. The code is released at https://github.com/NekoDaiSiki/FADN.

In this paper, we investigate the robust waveform design problem for integrated sensing and communications (ISAC) in the presence of imperfect communcation channel state information (CSI). Specifically, the estimation error obtained through the minimum mean squared error (MMSE) criterion is used to represent sensing performance. Subsequently, under the premise of minimizing the estimation error, constraints on signal-to-interference-plus-noise ratio (SINR) outage probability and power budget are introduced. The non-convex optimization problem is then addressed using the semidefinite relaxation (SDR) and sphere bounding method. Simulation results demonstrate a enhancement in both sensing and communication performance with the proposed robust waveform design, validating the effectiveness and robustness of the proposed approach.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, pivotal in improving wireless systems’ rate and spectral efficiency. However, as ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region. This inevitably leads to a significant increase in the overhead of beam training, requiring two-dimensional beam searching in both the angle and the distance domain. To address this problem, we propose a learning-based codebook-free near-field beamforming method. We strategically select padding and kernel size of convolutional neural network to efficiently extract complex channel state information features. We optimize the beamformers to maximize achievable rates in a multi-user network without predefined beam codebooks. Our solution requires only pre-estimated channel state information for optimal beamforming vector derivation during deployment. Simulation results demonstrate stable beamforming gain compared to baseline schemes, and the deep learning approach substantially reduces near-field beam training overhead.

In this paper, we study the parameter estimation performance for monostatic downlink integrated sensing and communications (ISAC) systems. In particular, we analyze the mean squared error (MSE) lower bound for target sensing in the downlink ISAC system that reveals the suboptimality in re-using the conventional communication waveform for sensing. To realize a practical dual-functional waveform, we propose a waveform augmentation strategy that imposes an extra signal structure, namely the degrees-of-freedom (DoF) completion method. The proposed approach is capable of improving the parameter estimation performance of the ISAC system and achieving the derived MSE lower bound. To improve the performance of the proposed strategy, we formulate an MSE minimization problem to design the ISAC precoder, subject to the communication users' signal-interference-plus-noise-ratio (SINR) constraints. Despite the non-convexity of the waveform design problem, we obtain its globally optimal solution via semi-definite relaxation (SDR) and the proposed constructive method. Simulation results validate the proposed DoF completion technology could achieve the derived MSE lower bound and the effectiveness of the MSE-based ISAC waveform design.

The urgent need for efficient utilization of spectral and hardware resources has led to the emergence of integrated sensing and communications (ISAC). Some research is trying to introduce ISAC into low earth orbit (LEO) satellite systems, which adopt multi-beam techniques and a full frequency reuse (FFR) scheme. To ensure efficient communication and sensing quality during the forward link, we propose a two-stage beam hopping method based on multi-agent actor-critic. The beam pattern and power allocation are optimized separately. Agents representing directions and allocated powers generate dispersed beam patterns to avoid interference between beams, and optimally allocate the limited onboard resources. Experimental results verified the effectiveness of our proposed method in avoiding inter-beam interference, improving system throughput, reducing latency, and allocating resources on demand.

Abstract With the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

With the rapid development of cloud-edge–terminal (CET) technology, ubiquitous sensing devices are able to collaborate with edge terminals, enabling real-time, intelligent environmental awareness. For device-free sensing systems, the number of each human gesture category may vary (class imbalance), which makes previously distributed device-free sensing algorithms ineffective. In this article, we propose a novel monitoring scheme for device-free human action sensing for CET collaboration under class-imbalance conditions. Specifically, the body-coordinated velocity profile (BVP) features of wireless fidelity (WiFi) signals are used to detect human actions. To recognize human gestures, we develop a convolutional neural network (CNN) using a monitor to detect gradient changes under class imbalance. To mitigate the effects of class imbalance, a corresponding correction is applied to the loss function. To validate the effectiveness of the proposed method, we conduct numerical experiments under class-imbalance conditions. Different parameter settings and proportions of participating nodes are explored for their effects on experimental results. Additionally, numerical experiment results demonstrate that the proposed method improves recognition accuracy by 3.85%–34.1% compared to baseline algorithms. Overall, the proposed method addresses the challenge of distributed device-free sensing under class-imbalance conditions and achieves superior recognition accuracy performance.

Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink wideband channel estimation for IRS aided multiuser multiple-input single-output (MISO) systems with hybrid architectures. Combining the structure of model driven and data driven deep learning approaches, a hybrid driven learning architecture is devised for joint estimation and learning the properties of the channels. For a passive IRS aided system, we propose a residual learned approximate message passing as a model driven network. A denoising and attention network in the data driven network is used to jointly learn spatial and frequency features. Furthermore, we design a flexible hybrid driven network in a hybrid passive and active IRS aided system. Specifically, the depthwise separable convolution is applied to the data driven network, leading to less network complexity and fewer parameters at the IRS side. Numerical results indicate that in both systems, the proposed hybrid driven channel estimation methods significantly outperform existing deep learning-based schemes and effectively reduce the pilot overhead by about 60% in IRS aided systems.

Integrated sensing, computing and communications (ISC2) is a promising technology to enable both physical-digital spatial sensing, intelligent communication and computing. This paper studies a federated learning-assisted ISC2system, in which edge nodes coordinate edge computing resource for model training based on their local integrated sensing and communications (ISAC) data. In the process of a completely distributed collaborative training, sharing and transmission of local parameters may lead to a serious Byzantine attack. To improve the system's anti-attack capability, we design a blockchain-federated edge learning framework, which utilizes the non-tampering and traceability features of the blockchain, and design a verification algorithm for federated aggregation. Particularly, an aggregation algorithm is designed to improve the fitting efficiency and accuracy of our model. Experiments based on the measured ISAC data show that the proposed scheme can effectively resist up to 30% of data tampering and up to 30% of model tampering attacks.

To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated learning. The architecture considers an integrated sensing and communication (ISAC) approach, employs knowledge distillation techniques to compress and accelerate data processing models, and implements data communication technology based on airborne computing aggregation to reduce data transmission delays and improve the efficiency of data communication and computation among nodes. To address the challenge of data sharing for largescale heterogeneous network nodes in the integrated scenario, this paper adopts a sample expansion technology of distributed remote sensing data based on WGAN-GP to address the issue of insufficient data, and considers blockchain encryption technology to protect data privacy, thus promoting progress in data privacy sharing under distributed ISAC conditions and facilitating the construction of the 6G communication network.

As an important direction in the field of information hiding, steganography is a significant means to realize secret communication. With the development of artificial intelligence, researchers have tried to use Deep Learning (DL) to design automated information hiding schemes, but the existing schemes still have shortcomings in security, hiding capacity, and robustness. To solve this problem, this paper designs a set of safe and robust image information hiding schemes by using a DL network, Quick Response (QR) coding, and introducing a noise layer mechanism. In addition, this scheme makes information hiding technology get rid of the dependence on human operation and prior professional knowledge, breaks the dilemma that information hiding needs to choose the appropriate hidden carrier and modify the carrier to embed information, and also proves that it has great potential in the field of information security.

Integrated sensing and communication (ISAC) has received increasing attention as a potential technology to alleviate spectrum shortage. This paper studies the power-saving problem based on power assignment and subcarrier allocation in ISAC. Specifically, we propose a joint design method to jointly optimize the subcarrier and transmit power allocated to sensing services and communication services respectively. To ensure the quality of sensing service (SS) and communication service (CS), the minimum total power required by the system is obtained by optimizing the allocation of subcarriers and transmission power. Since the formulated problem is nonconvex, which is generally difficult to solve effectively. Inspired by the classical subcarrier allocation algorithm based on channel gain information, we decompose the formulated problem into two convex optimization subproblems that are easy to solve. Numerical simulation results show that the proposed algorithm can effectively improve the power-saving performance of the system compared with the existing algorithms.

Software Defined Networking (SDN) simplifies network control and management by decoupling the control plane from the data plane. However, the actual packet behaviors, conforming to the rules in the data plane flow tables, may violate the original policies in the controller due to the inconsistency between the data plane and control plane. To address this problem, we propose 2MVeri, a framework for measuring the consistency between the Data and Control plane, defined as the consistency between the control plane policies and data plane rules. 2MVeri uses a modules, a Bloom filter and a two-dimensional vector as a tag which is inserted in the packet header and is updated in each switch that the packet traverses. By exploiting path information compressed in the tag, 2MVeri can verify the consistency between the data and control plane. Moreover, when verification fails, 2MVeri is able to localize the faulty switch. Experimental results show that in the k = 4 fat tree topology, the verification accuracy of 2MVeri is as high as 100%. In addition, when the actual path is inconsistent with the expected path, 2MVeri can locate the wrong switch with an accuracy of 99.8%.

Nowadays, a possible approach to designing a commercial-attractive sensing solution is integrating sensing capability into widely deployed communication systems, e.g., the force coming fifth-generation (5G) new radio (NR), by slightly modifying the standard. To this end, in this paper, we firstly investigate the possibility of re-using the NR waveform for sensing by reviewing current NR frame structure. Then, the self-ambiguity and cross-ambiguity functions are analyzed to exploit the NR waveform performance limitations. Several synchronizations and reference NR signal structures are considered for both downlink and uplink NR transmissions. Finally, the simulated NR frame and its self- and cross-ambiguity simulation results demonstrate the performance limitations.

This paper investigates the problem of fairness in Dual-Function radar-communications system (DFRC) which exploits orthogonal frequency division multiplexing (OFDM) waveforms for performing radar and communication operations simultaneously. A novel iterative algorithm are proposed to maximize the fairness among different communication users (CUs) with the constraint on radar user’s (RU) perfermance. In our considered system, the optimization problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. In order to make this thorny problem easy to deal with, we propose an approximate algorithm based on BSUM theory to obtain the feasible solution of the original problem in polynomial time. It is shown, using simulation results, that the proposed optimization strategy outperform other strategies in term of fairness.

Device-free gesture recognition (DFGR) is a critical technology for human-computer interaction and can be used for applications such as smart homes, and virtual reality in Wi-Fi sensing. Existing deep learning-based DFGR techniques typically require a large amount of labeled sensing data and are sensitive to the scene, which limits the development of ubiquitous sensing. In this study, in order to achieve device-free cross-scene gesture recognition from limited sensing samples, we consider the use of a small amount of Wi-Fi channel status information (CSI) data that are continuously obtained from low-cost commercial Wi-Fi devices. To this end, a few-shot learning-based cross-scene DFGR model is proposed for capturing highly discriminative information from dynamic CSI sequences. This information is then used to distinguish different gestures in the limited samples. Our experimental results using Wi-Fi signal collected at real world show that our model is able to realize 99.52% accuracy and can work well even with only one-piece data of new scene.

Abstract Recently, deep learning (DL) based spectrum sensing (SS) has drawn much attention due to its better capacity of feature extraction and superb performance. However, the model robustness of the DL based scheme is limited by reason of the dynamic radio environment, leading to the floating of sensing performance. Motivated by this, adversarial transfer learning is applied to SS here, where the model is pre‐trained at the central node firstly and fine‐tuned at the local nodes. More specifically, a 2D dataset of the observed signal is constructed under various signal‐to‐noise‐ratio (SNRs) and a convolution neural network (CNN) model is designed. Then a part of samples with various SNRs in the constructed dataset are employed to pre‐train the proposed CNN model. After that, the pre‐trained CNN model is distributed to local nodes with different SNRs and the pre‐trained CNN model is fine‐tuned. The proposed CNN model is pre‐trained based on the samples under various SNRs, resulting in its stronger adaptability at the local node. The simulation experiments validate the effectiveness of the proposed scheme.

Gesture recognition is the central enabler of human-computer interaction (HCI). In addition to the semantic information contained in gestures, gesture-based user identification can effortlessly enhance HCI system security. Recently, the Wi-Fi-integrated sensing and communication (ISAC) technology has shown great potential in a field hitherto occupied by computer vision and radar sensing. In this work, leveraging Wi-Fi sensing, we propose a system called WiGesID that achieves joint gesture recognition and human identification (JGRHI). The basic idea behind WiGesID is to identify personalized spatiotemporal dynamic patterns from the gestures of different users. Moreover, we develop an effective approach to recognize new categories of gestures and users by computing relation scores between the features of the new category samples and the support samples. To evaluate the performance, we implemented WiGesID and conducted extensive experiments. The results demonstrate that our system outperforms the state-of-the-art method for cross-domain sensing and accurately recognizes new categories, which promotes the use of this application of Wi-Fi sensing in HCI.

Human activity recognition (HAR) plays a vital role in many applications, such as surveillance, in-home monitoring, and health care. Portable radar sensor has been increasingly used in HAR systems in combination with deep learning (DL). However, it is both difficult and time-consuming to obtain a large-scale radar dataset with reliable labels. Insufficient labeled data often limit the generalization of DL models. As a result, the performance of DL models will drop when being applied to a new scenario. In this sense, only labeling a small portion of data in the large-scale radar dataset is more feasible. In this article, we propose a semisupervised transfer learning (TL) algorithm, “joint domain and semantic transfer learning(JDS-TL),” for radar-based HAR, which is composed of two modules: unsupervised domain adaptation (DA) and supervised semantic transfer. By employing a sparsely labeled dataset to train the HAR model, the proposed method alleviates the need of labeling a significantly large number of radar signals. We adopt a public radar micro-Doppler spectrogram dataset including six human activities to evaluateJDS-TL. Experiments show that the proposedJDS-TLis able to recognize the six activities with an average accuracy of 87.6% when there are only 10% instances labeled in the training dataset. Ablation analysis also demonstrates the efficiency of the DA and the semantic transfer modules.

The facial expression contains abundant emotional information, mirrors the psychological state of people and plays an important role in daily communication. Facial emotion recognition (FER) has been a popular research field with great significance. However, FER has been regarded as a challenging issue due to individual variation, illumination, occlusion, etc. In this paper, a novel network SferNet is proposed for Static Facial Emotion Recognition (SFER), where data enhancement, Batch Normalization (BN) and Dropout modules are considered. SferNet improves the generalization capacity of FER and applies to non-peak expression recognition. Extensive comparisons on two popular datasets, CK+ and JAFFE, demonstrate the proposed framework makes a trade-off between recognition rate and model complexity.

In this paper, an Unmanned Aerial Vehicles (UAV) control signal detection scheme is proposed with Convolutional Neural Network (CNN). More specifically, the sampled signal images of UAV control signal are considered to train the classical LeNet network under various signal-to-noise ratios (SNR). The simulation experiments state that the detection performance of UAV control signal is greatly improved. In addition, the conclusion is drawn that the increase in signal image size helps to improve the detection performance.

As the basis of cognitive radio technology, Spectrum sensing (SS) has received widespread attention because it is very important to improve spectrum efficiency. However, the limited sensing time makes it difficult to obtain sufficient sample data, which will seriously affect the performance of the spectrum sensing model. In this paper, SS is considered to be a binary classification problem, in which Deep Convolutional Generative Adversarial Networks(DCGAN) is improved and used to expand the training set to cope with the shortage of sample data. More specifically, the sampling covariance matrix of the received signal is firstly transformed into the true color picture which is divided into a training set and a test set. After that, the obtained training set is expanded with the improved DCGAN. Finally, the LeNet network is trained based on the extended data. Simulation results show that the proposed scheme greatly improve the sensing accuracy. The probability of detection(PD) and the probability of false alarm(PFA) fluctuate less after expanding the dataset with DCGAN. Especially when SNR= -4dB, the minimum value of PD increases by 0.2.

To improve detection performance, this paper proposes a non-cooperative unmanned aerial vehicle (UAV) detection strategy based on multichannel detection of remote signal with energy detector (ED). More specifically, the sampling point of remote signal on each subchannel adaptively varies with environmental signal-to-noise (SNR) within constrained scope. The decision on the presence or absence of remote signal is made at the fusion center (FC) based on the majority voting rule. Both theoretical derivation and simulation experiments validate the effectiveness of the proposed scheme.

The signal-to-noise ratio (SNR) is an effective evaluation index for channel status and communication quality, and plays an important role in signal analysis. Under the gradual complexity of the unmanned aerial vehicle (UAV) remote control signal environment and the rapid development of neural network models in deep learning, this paper proposes a convolutional neural network (CNN) model-based SNR estimation method of UAV remote control signal environment. We construct a simulation dataset of UAV remote control signal with different SNRs, then train the model and its parameters, save the model with better performance and use the test set to verify the performance of the algorithm finally. The experimental result shows that the performance of the algorithm is improved compared to the two known algorithm.

The dual-function radar communication system develops rapidly with the integration of sensing function and communication function, the combination of vehicle tracking and positioning and vehicle communication leads to a more efficient vehicle networking system in the future. This paper proposes a beam tracking prediction scheme for intergraded sensing and communications (ISAC) aided vehicle to infrastructure communications. In detail, we focus on the beam misalignment problem between roadside units (RSU) and high dynamic passing vehicles. To solve this problem, we propose a particle filter-based predictive beamforming method that can predict the motion parameters of vehicles by using transmitted ISAC signals and received the vehicle echoes. The simulation results show that the proposed particle filter algorithm can reduce the overhead and predict the vehicle motion parameters and the vehicle's angle relative to the RSU when the vehicle is moving.

Wireless sensing can enable human identification by quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric system, WirelessID, that explores the human fine-grained behavior and body physical signatures embedded in channel state information by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to identification performance. Thus, to extract robust features, we introduce an attention mechanism into our system. Particularly, commercial Wi-Fi devices are used for prototyping WirelessID in a laboratory with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.

Currently, developments in wireless sensing technologies have shown that wireless signals can be employed to transmit information between wireless communication devices and are also able to realize passive target wireless sensing. Wireless sensing has diverse Internet-of-Things applications in indoor human detection, such as in device-free localization, activity recognition and fall detection, respiration detection, gait recognition, user identification, and so forth. Deep learning (DL), with the latest breakthroughs in machine learning (ML) and artificial intelligence (AI), seems to be a feasible technique for device-free wireless sensing (DFWS) and human detection in a more intelligent and autonomous manner. Although DL has attracted wide spread attention in computer vision (CV), AI games, speech recognition, automated vehicles, and other fields, its application in wireless sensing systems (WSSs) is relatively new, and little attention has been paid to it. Motivated by these developments, this article clarifies the motivation and mechanism of the DL-aided WSSs for human detection. First, we survey the most advanced architecture of DL that may be powerful for WSSs. We also review conventional ML and DL approaches to human detection based on red green blue (RGB)/depth camera and radar: one reason is to introduce the successful experience in these areas to the field of wireless sensing and another reason is that the possibility of combining and fusing information from the heterogeneous types of sensors is expected to improve the overall performance of practical human detection systems. We provide a comprehensive survey of the state-of-the-art research on wireless sensing for human detection with a focus on WSSs. Furthermore, a general structure of the DL-based WSS is introduced in detail for hitherto unexplored applications and future wireless sensing scenarios. We also discuss some open research issues in wireless sensing for human detection, including data acquisition for DL model training, calibration of signals from commercial devices, multimodal sensing, simultaneous user identification and activity recognition, multiuser human detection, and generalization ability of DL models, to indicate future research directions.

The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating the burden of collecting and annotating a large number of radar samples. ITL is a unique algorithm that consists of three interconnected parts, including DL model pretraining, correlated source data selection, and adaptive collaborative fine-tuning (FT). Any of the three components cannot be excluded; otherwise, the performance of the entire algorithm decreases. The experiments with a radar data set of six human motions show that ITL achieves state-of-the-art performance for HMR with limited training samples, outperforming several existing transfer learning approaches. Especially, when there are only 100 samples per person per class, ITL yields an F1 score of 96.7%. Last but not least, ITL is more generalized to human motion differences. Though adapted to recognize the persons’ motions in a small-scale target data set, ITL can also classify the persons’ motion data used for pretraining, achieving up to 11.0% F1 score enhancement over the conventional FT method.

Concerns have been raised about our recently published article on the fixed region beamforming using frequency diverse subarray for secure mmWave wireless communications. In a comment, the authors thought our precoding vector normalization method of the sidelobe randomization scheme has a flaw and proposed a non-physical-layer-security-oriented (non-PLS-oriented) normalization method by keeping the norm of the steering vector as a unit. However, we believe our PLS-oriented normalization method of the transmit beamforming vector is correct and reasonable from the PLS perspective, i.e., we hope to keep the target use's beampattern gain unit. In this rebuttal, we further clarify and justify our scheme to show its correctness. In addition, we also present a generalized normalization method to compare our proposed PLS-oriented scheme and the non-PLS-oriented scheme in the comment to offer useful insights.

Spectrum sensing (SS) has attracted much attention in the field of Internet of things (IoT) due to its capacity of discovering the available spectrum holes and improving the spectrum efficiency. However, the limited sensing time leads to insufficient sampling data due to the tradeoff between sensing time and communication time. In this paper, deep learning (DL) is applied to SS to achieve a better balance between sensing performance and sensing complexity. More specifically, the two‐dimensional dataset of the received signal is established under the various signal‐to‐noise ratio (SNR) conditions firstly. Then, an improved deep convolutional generative adversarial network (DCGAN) is proposed to expand the training set so as to address the issue of data shortage. Moreover, the LeNet, AlexNet, VGG‐16, and the proposed CNN‐1 network are trained on the expanded dataset. Finally, the false alarm probability and detection probability are obtained under the various SNR scenarios to validate the effectiveness of the proposed schemes. Simulation results state that the sensing accuracy of the proposed scheme is greatly improved.

As a key technology to discover idle spectrum in cognitive radio (CR) networks, classical spectrum sensing schemes mainly focus on the improvement of sensing accuracy and available throughput. However, both sensing performance and sensing complexity are significant elements that influence the performance of a CR in computation‐constrained scenarios. Motivated by this, theproposed study is devoted to computation‐constrained spectrum sensing and a tradeoff is considered between sensing performance and sensing complexity. First, the authors give two functions to evaluate the detection efficiency and communication efficiency of a CR. On this basis, two specific models are provided to obtain the optimal sensing operations in computation‐constrained conditions, respectively. Then they analyse these two models and conclude their advantages and disadvantages. Finally, simulation experiments validate the effectiveness of the proposed schemes. Note that computation‐constrained spectrum sensing works as a significant issue in the internet of things (IoT)‐based applications and the proposed schemes provide effective solutions for it.

Automatic modulation classification facilitates many important signal processing applications. Recently, deep learning models have been adopted in modulation recognition, which outperform traditional machine learning techniques based on hand-crafted features. However, automatic modulation classification is still challenging due to the following reasons. Existing deep learning methods are only applicable to the data of the same distribution. In practical scenarios, data distribution is varying with sampling frequency, thus domains with different sampling rates are formed. Besides, it is difficult to construct large-scale well-annotated datasets for all domains of interest. We define the domain with sufficient data as the source domain, while the domain with insufficient data as the target domain. Obviously, the classification model performs weakly in the target domain. To address these challenges, we propose an adversarial transfer learning architecture (ATLA), incorporating adversarial training and knowledge transfer in a unified way. Adversarial training performs an asymmetric mapping between domains and reduces the domain shift. Knowledge transfer is used to mine prior knowledge from the source domain. Experimental results demonstrate that the proposed ATLA substantially boosts the performance of the target model, which outperforms the existing parameter-transfer approach. With half of the training data reduced, the target model achieves competitive recognition accuracy to supervised learning. With one-tenth of training data, the promoted accuracy is up to 17.3% points.

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack. In this paper, we propose a novel anti-intelligent UAV jamming strategy, in which the ground users can learn the optimal trajectory to elude such jamming. The problem is formulated as a stackelberg dynamic game, where the UAV jammer acts as a leader and the ground users act as followers. First, as the UAV jammer is only aware of the incomplete channel state information (CSI) of the ground users, for the first attempt, we model such leader sub-game as a partially observable Markov decision process (POMDP). Then, we obtain the optimal jamming trajectory via the developed deep recurrent Q-networks (DRQN) in the three-dimension space. Next, for the followers sub-game, we use the Markov decision process (MDP) to model it. Then we obtain the optimal communication trajectory via the developed deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium and derive the closed-form expression for the stackelberg equilibrium in a special case. Moreover, some insightful remarks are obtained and the time complexity of the proposed defense strategy is analyzed. The simulations show that the proposed defense strategy outperforms the benchmark strategies.

Millimeter-wave (mmWave) using conventional phased array (CPA) enables highly directional and fixed angular beamforming (FAB), therefore enhancing physical layer security (PLS) in the angular domain. However, as the eavesdropper is located in the direction pointed by the mainlobe of the information-carrying beam, information leakage is inevitable and FAB cannot guarantee PLS performance. To address this threat, we propose a novel fixed region beamforming (FRB) by employing a frequency diverse subarray (FDSA) architecture to enhance the PLS performance for mmWave communications. In particular, we carefully introduce multiple frequency offset increments (FOIs) across subarrays to achieve a sophisticated beampattern synthesis that ensures a confined information transmission only within the desired angle-range region (DARR) in close vicinity of the target user. More specifically, we formulate the secrecy rate maximization problem with FRB over possible subarray FOIs, and consider two cases of interests, i.e., without/with the location information of eavesdropping, both turn out to be NP-hard. For the unknown eavesdropping location case, we propose a seeker optimization algorithm to minimize the maximum sidelobe peak of the beampattern outside the DARR. As for the known eavesdropping location case, a block coordinate descend linear approximation algorithm is proposed to minimize the sidelobe level in the eavesdropping region. Moreover, we propose an inverted subarray subset technique to further randomize the sidelobes against sensitive eavesdropping. By using the proposed FRB, the mainlobes of all subarrays are constructively superimposed in the DARR while the sidelobes are destructively overlayed outside the DARR. Therefore, FRB exhibits prominent effect on confining information transmission within the DARR. Numerical simulations demonstrate that the proposed FDSA-based FRB can provide superior PLS performance over the CPA-based FAB.

Wearable activity recognition is an important area for healthcare applications, especially for children and elderly people. Current research proves that deep learning techniques are capable of learning deep features from the sensor raw signal. However, this task faces two challenges. First, it is hard to capture the dynamic and non-linear interactions between different sensor modalities and time slots. Besides, the spatial relationship between sensors convey significant information for wearable activity recognition, thus how to model the sensor relationship is one of the problems that need to be solved. In this work, we propose GraphConvLSTM, a scalable framework that model spatial relationship, local interaction, and long-term temporal dependency simultaneously. We evaluate our model on two real-world activity recognition datasets, and it achieves 1.75%, 2.68% accuracy improvements over existing methods (accuracy: 90.83%, 93.08%) for wearable activity recognition task, which demonstrates the ability of the proposed approach to learn spatial, local and global temporal features.

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies.

Multistage detection has inspired a heated debate due to its capacity to take full advantage of each detector. Motivated by this, an investigation into multistage spectrum sensing is conducted and a two‐stage spectrum detector is proposed based on energy detector and covariance absolute value (CAV) detector here. The two‐stage spectrum detector periodically determines which detector is appropriate for current radio environment according to the comparison between estimated SNR and SNR threshold provided here. The total blindness of CAV detector and available noise variance by SNR estimation scheme results in the blindness of the proposed detector to the characteristics of observed signal and noise. Simultaneously, the proposed scheme balances the sensing complexity and detection accuracy and provides higher practicability in consequence. Simulations validate the performance of the proposed method.

As an issue under heated discussion, spectrum sensing (SS) exhibits its particular significance in various application scenarios due to its limited spectrum resource and low spectrum utilisation. For a given SS scheme, detection probability, false alarm probability and available throughput jointly determine its performance. To consider these three factors as a whole, the possible performance improvement of an SS scheme will make a great difference in active demand for idle spectrum, especially in Internet of things‐based domains. Motivated by this, this study proposes a new strategy on SS to further improve its sensing performance, where the optimal sensing performance is demonstrated to be better than traditional schemes when signal‐to‐noise ratio is higher than 1.76 dB. Additionally, the authors extend the proposed scheme to multiple secondary user case and give optimal N for N ‐out‐of‐ K fusion rule. Both theoretical derivation and simulation experiments validate the effectiveness of the proposed scheme.

In the channel training phase, the attacker launches a pilot contamination attack by sending a synchronized and identical pilot signal with the legitimate transmitter. Such an attack can contaminate the channel estimation and alter the legitimate beamformer design. In this letter, first, we propose a pilot contamination attack detection scheme for wireless communications. By considering the prior uncertainty of the attack, we find that the decision-maker will conservatively decide the state of the attack, which is a subjective choice. In this case, we derive the subjective detection probability, the subjective false alarm probability, and the threshold. We analyze the tradeoff problem between ergodic wiretap channel rate and subjective detection probability. Next, based on the worst case that the attacker adopts the optimal power allocation to launch the optimal attack, we discuss the defense strategy of the optimal attack. Simulations show that the proposed scheme has a better performance than the benchmark method.

Physical-layer security has drawn ever-increasing attention in the next generation wireless communications. In this paper, we focus on studying the secure communication in an HPN-to-devices (HTD) network, in which a new type of MAC spoofing attack is considered. To detect the malicious attack, we propose a novel algorithm, namely, eigenvalue test using random matrix theory (ETRMT) algorithm, which needs no prior information about the channel. In particular, when the number of samples is finite at the receiver or the number of devices is large, the sampled signal is the biased estimation of the actual signal, which inspires us to use the random matrix theory to analyze the spoofing attack detection. The closed-form expressions of the detection probability, the false alarm probability, and the Neyman-Pearson threshold are derived based on eigenvalue distribution of the spiked population model. In addition, taking the channel time-varying into consideration, we provide an adaptive threshold tracking method by using Bayesian forecasting. Finally, the simulations are conducted to validate our proposed method and some insightful conclusions are obtained.

Cooperative localizer is a potential positioning technique for vehicular ad-hoc networks (VANETs). However, it would suffer from the non-line-of-sight (NLOS) problems widely existing in VANETs. This letter proposes a geographical information enhanced cooperative localizer (GIE-CL) for VANET with time-of-arrival (TOA) measurements. It iterates between NLOS identification and extended generalized approximate message passing (EGAMP) motivated cooperative positioning. A region sampling method is developed to identify NLOS measurements based on geographical information and current vehicle position estimations. Subsequently, the detected NLOS measurements are removed and the EGAMP localizer is activated to re-estimate the vehicle positions. The above-mentioned iteration will be terminated until convergence is reached. Initial positions are provided by Global Navigation Satellite System (GNSS). Simulation results show that GIE-CL can handle the NLOS problem, and approach to its performance upper bound provided by the case with known NLOS/LOS link-type information. Compared to EGAMP localizer, the positioning accuracy of GIE-CL is improved by eight times when the allowed localization error is less than 5 m.

Random numbers are widely used in 5G communication security. In this paper, we propose a wireless physical layer (PHY-layer) characteristics based random number generator in vehicular networks. Firstly, the closed form expression of random transmission success probability is derived under the presence of multiple jamming attackers in a Nakagami-m fading channel. Secondly, a novel Random Transmission Success Probability based Physical Random Number Generator (RTSP-PhRNG) is presented. Finally, numerical results are conducted and a Universal Software Radio Peripheral (USRP) based prototype is implemented to validate our proposed method. Furthermore, the standard randomness test suite from NIST shows that our proposed PhRNG reveals good randomness.

This paper proposes a multiuser magnitude-only (MO-)MIMO, whose base station acquires quantized magnitudes of the complex baseband signals through envelop detectors and low-resolution ADCs. Consequently, MO-MIMO enjoys much lower circuit power and cost in comparison with the conventional MIMO. Because the phase information is unavailable, all the existing MIMO baseband algorithms cannot be applied into MO-MIMO. Therefore, two types of channel estimators and multiuser detectors are constructed by first categorizing the channel estimation and multiuser detection problems as a quantized phase retrieval (PR) problem, and then solving the latter by developing two methods under the framework of generalized approximate message passing (GAMP). The first method directly applies GAMP to solve the quantized PR problem by exploiting the probability relationships between the quantized magnitude measurements and unknown complex signals. The second method iterates between the missing phase estimation and signal recovery, where the latter calls for GAMP to handle a linear mixing problem with quantized observations. The developed estimators and detectors call for matrix-vector multiplications and nonlinear function calculations as the most complex operations, handle the nonlinear quantization loss specially, and exploit the signal prior probability distributions. Finally, their effectiveness is validated experimentally.

Image recognition, especially traffic sign recognition is an important task for autonomous driving and driver assistance systems. A new Convolutional Neural Network model with the ability of feature selection in frequency domain is presented in this paper, called Frequency Selective Filter Aided (FSFA) CNN model. The new model can integrate low-pass and high-pass filters into both forward and backward propagations in order to place special emphases on feature components in different frequency bands. The theoretical basis, as well as forward and backward propagations are also formulated. Experiments on CIFAR and GTSRB traffic sign recognition datasets show that the proposed model yields better performance for the task of image recognition compared with classic methods.

An efficient model-based bit allocation algorithm, in this paper, is proposed for quality scalability coding of H.264/scalable video coding (SVC). The conventional Rate-Distortion models are not available for quality scalability coding of H.264/SVC. To overcome this issue, the relationship between the percentage of header bits and quantization parameter is investigated to obtain an accurate single layer rate model. Moreover, MGS/CGS inter-layer MAD affine relationship is employed to extend the Rate-Distortion models from the base layer (BL) to the enhancement layer (EL). Finally the Lagrange solution of the bit allocation problem is worked out. Experimental results show that the proposed bit allocation algorithm outperforms Joint Scalable Video Model (JSVM) software algorithm.

This paper presents an improved decryption method aiming at covert Orthogonal Frequency Division Multiplexing (OFDM) transmission with cyclic delay diversity (CDD) featured multiple input multiple output (MIMO) technology. Particularly, we take advantage of the inherent sparse structure of multi-path wireless channel as well as Compressive Sensing (CS) technology, which has attracted a considerable attention for its breakthrough on Shannon's sampling theorem. Our work addresses the issues of bounded application range for conventional decryption methods, which is caused by the limitation of channel estimation with respect to uneven pilot arrangement that employed as a physical layer secret key. Besides, our proposed method relaxes minimum requirement for pilot number while the performance gain is improved instead. Simulation results show that our proposed method can achieve a considerable performance gain, with extra rewards of high spectral efficiency and much broader application prospect.

Gabor filters are one of the most successful methods for face recognition. However they dramatically increase the data volume for face representation. To extract compact and distinctive information, we propose the Variable Length Dominant Gabor Local Binary Pattern (VLD-GLBP) for face recognition. It significantly reduces the face representation data volume whereas the performance is comparable to that of the complex state-of-the-art techniques. Specifically, local binary pattern (LBP) features are first computed from the Gabor images. Then, the most frequently occurred patterns are extracted to form VLD-GLBP. Finally the distance between VLD-GLBPs is computed to realize the face image classification. The experiment results on FERET database verify the efficiency of the proposed VLD-GLBP method.

In this paper, an improved method for reconstruction of doubly selective wireless channels in piloted-aided OFDM systems based an existing estimation method is proposed. In this re-expansion channel estimation process, the first few Fourier coefficients of each channel tap are estimated from the pilot information and the received signal firstly. Then the channel taps are estimated in the framework of Basis Expansion Model (BEM) from their respective Fourier coefficients. In the process of recovering BEM coefficients, instead of using the inverse method which is a Least Square (LS) problem, this paper proposes an improved method of recovering BEM coefficients from the estimated Fourier coefficients based on the Minimum Mean Square Error (MMSE) criterion. The proposed method is validated by simulating a system conforming to the IEEE 802.16e standard. Numerical results illustrate the performance gains achieved by the improved method.

Video multicast over wireless networks suffers from both heterogeneous packet loss resulting from different channel conditions and user capacity heterogeneity in screen resolution and mobile device battery life. To solve resource scheduling problem in video multicasting in heterogeneous network, an Asymmetric Nash Bargaining Game model in layered hybrid FEC/ARQ for scalable video multicast is proposed in this paper. The scheme is applied in multicast server for each time slot, and the server will play the bargaining game for all users according to their real-time channel conditions and device capacities. By solving the bargaining problem, the server achieves to provide fair and efficient multicast utility for each user. Moreover, a formula of bargaining power is proposed in the asymmetric game model to adjust resource allocation according to system bias and user priority.

A new detection method based on sphere decoding (SD) is proposed in this paper to approach near-maximum likelihood (ML) performance for multi-input multi- output (MIMO) detection. The feature of the proposed method is that the complexity which means the electric power consumption in detection processing is stable for a wide range of signal-to-noise ratios (SNRs) and a number of antennas. We hold the complexity by a tree pruning mechanism which obtains detection radius through the close-form expression for the SD expected complexity R.Gowaikar and B.Hassibi, 2007. And the Increasing Radii Algorithm (IRA) mechanism is used in the method to reduce the SER at the stable expected complexity. The simulation results show that the method gets a low stable expected complexity without sacrificing much in terms of performance.

In this paper, we propose a pilot-aided channel estimation scheme for Orthogonal Frequency-Division Multiplexing (OFDM) systems where channels are assumed to be both time-varying and sparse. Basis Expansion Models (BEM) are often used to model and reconstruct time-varying channel taps. In this paper, the framework of BEM is applied to OFDM systems with time-varying sparse channels. A new method to detect the positions of significant taps is proposed based on the use of Constant Amplitude Zero Auto Correlation (CAZAC) sequence. Based on the results of detection, BEM based estimation is implemented to estimate the detected taps. The numerical simulations illustrate that the proposed two-step estimation scheme for significant channel taps outperforms direct estimation methods for all the channel taps and also this method can reduce the required pilots and thus reduce the computational load and improve the spectral efficiency.

Tree pruning can significantly reduce the complexity of sphere decoding (SD). How to determine the pruning rule is an open problem of tree pruning. In this paper, we propose a pruning strategy for SD based on path metric. Because only the nearest lattice point is concerned, if the ratio of the metric to the minimum metric is larger than a threshold, the path whose metric is large enough can be pruned. We analyze the influence of the choice of the thresholds on the performance and the complexity. Through analysis and the simulations, we can show that the complexity reduction is significant while maintaining the negligible performance degradation when proper thresholds are chosen. Besides, tradeoff between complexity and performance can be easily achieved by adjusting the thresholds.

A new edge detection method, based on decision-level information fusion, is proposed to classify image pixels into edge and non-edge categories. Traditional edge detection algorithms make the detection decision under a single criterion, which may perform inefficiently with a change of noise model. We use fusion entropy as a criterion to integrate decisions from different classifiers in order to improve the edge detection accuracy. The proposed decision fusion based edge detection method is applied to image filtering and leads to a weighted hybrid-filtering algorithm. Simulation results show that the new edge detection method has better performance than the single criterion edge detection methods.