The rapid proliferation of emerging video services has substantially increased the demand for real-time transmission of high-definition video content. However, optimizing the trade-off between video transmission quality and communication bandwidth remains a significant challenge. To address this issue, we propose RAJSCC, a rate-adaptive video deep joint source-channel coding (DeepJSCC) framework. RAJSCC incorporates a spatio-temporal token merging mechanism to aggregate semantically similar tokens, effectively reducing redundancy both within and across frames. Additionally, an adaptive token merging predictor, designed based on simple statistical features of the input videos, enables dynamic rate control at the group of pictures (GoP) level, ensuring smooth and continuous variation in the overall video coding rate. Extensive experiments demonstrate that RAJSCC significantly outperforms traditional video transmission schemes, such as H.264 and H.265 with low-density parity-check (LDPC), as well as existing DeepJSCC methods, in terms of reconstruction quality. More importantly, the proposed adaptive spatio-temporal token merging mechanism reduces bandwidth consumption by 63.5% and computational cost by 13.0%, while incurring only a marginal 1–2 dB degradation in reconstruction quality. These findings highlight the effectiveness of RAJSCC in achieving a superior balance between transmission efficiency and video quality, making it a promising solution for real-time high-definition video communication in bandwidth-constrained environments.

The development of satellite communication is driving the demand for device identification that can respond to security threats from illegal uplink terminals. Specific emitter identification (SEI) is a promising technology of physical layer device identification. Deep learning (DL)-based SEI can effectively learn features for distinguishing emitters through a large number of samples. In the satellite communication scenario, due to the high cost of labeling, wireless signals often face the dilemma of few labeled samples, leading to a decrease in recognition accuracy of existing methods. Therefore, we introduce an innovative FS-SEI method leveraging multi-domain fusion and metric learning (MDF-ML), eliminating the reliance on an auxiliary dataset. Specifically, MDF-ML is proposed to explore additional implicit samples within the sample space, enhancing generalization performance through multi-domain representation and phase rotation. It then uses metric learning to constrain feature distances in the feature space, thereby boosting discriminability. Our simulation results demonstrate that the accuracy of our proposed SEI method exceeds 90%, which outperforms the existing method by 24.21% under 5 sample shots.

In space-air-ground integrated systems, radar signal analysis is crucial for effective spectrum management. In recent years, time-frequency transforms (TFT) have gained significant attention for radar signal detection and identification. However, challenges such as cross-term interference and low signal-to-noise ratio (SNR) limit the effectiveness in multicomponent signal analysis. Therefore, this paper proposes a novel multitask learning-based TFT framework, named One-Stage TFT (OSTFT), which directly generates high-quality time-frequency representations (TFR) from raw in-phase and quadrature signals. OSTFT incorporates a generative network combined with classification and localization tasks to enhance feature extraction and image clarity. Experimental results demonstrate that OSTFT achieves superior performance in TFR quality and radar signal recognition, with an 81.4% detection rate using the You Only Look Once (YOLO) frame-work, outperforming existing TFT methods under various noise conditions. Compared to the best-performing TFR-denoising method, OSTFT improves the detection rate by 2.7%.

The frequency-dependent beamforming technology has demonstrated outstanding performance in two-dimensional beam training for wideband THz systems, owing to its synchronized and high-resolution scanning capabilities. However, extending this technique to three-dimensional beam training poses challenges, as linearly split beams cannot provide comprehensive angular coverage in the candidate area. To this end, we propose a two-stage 3D beam training scheme, consisting of an initial stage and a refinement stage. In the initial stage, the angular coverage of frequency-dependent beamforming is modeled as a controllable rectangular region, and the codebook is designed by arranging this rectangular region within the candidate area. In the refinement stage, frequency-dependent beamforming is densely arranged around the candidate positions estimated in the initial stage to achieve precise localization. Additionally, we design a true-time delay network to facilitate frequencydependent beamforming for uniform planar arrays. Simulation results show that the proposed method approaches near-optimal performance in terms of achievable sum-rate while maintaining acceptable pilot overhead.

Respiration serves as a critical indicator of human health, reflecting the well-being of various bodily organs. The potential of device-free WiFi signals for respiration detection has been shown in recent investigations. Nevertheless, there are several disadvantages to the current WiFi signal-based respiration detection methods. These include inadequate utilization of the respiratory component within the signal, and the high-quality subcarriers cannot be screened out effectively. In response to these issues, we propose a novel respiration detection system named Wi-Mapping. Firstly, to enhance the utilization of the respiratory component within the signal, we introduce a novel complex plane mapping approach. This method reconstructs a signal with a more noticeable respiratory component by integrating the original amplitude and phase of Channel State Information (CSI). Wi-Mapping then concentrates on utilizing features in the frequency domain and subcarrier dimension. Additionally, a subcarrier screening method is proposed, which combines Respiration Energy Ratio (RER) and correlation between subcarriers. This method can effectively screen out subcarriers with higher quality. Finally, a dataset containing various scenes was established to confirm Wi-Mapping’s respiration detecting capabilities. The detection error achieved by Wi-Mapping is 0.382, with a detection rate of 86.91%, surpassing that of state-of-the-art methods.

Current WiFi-based respiration detection algorithms may experience performance degradation due to variations in user location, as the relationship between user location and patterns of respiration has not been adequately considered. To overcome this limitation, this paper proposes a spatially directional respiration detection approach, named Wi-locind. Wi-locind employs antenna arrays on commercial WiFi receivers to achieve directional enhancement of respiration signals. Combined with post-filtering techniques, Wi-locind is capable of extracting respiration patterns that are independent of changes in the user's location. Specifically, the Minimum Variance Distortionless Response algorithm is used to identify the arrival angle of the target user and directionally enhance the received signal in the corresponding direction. The Empirical Mode Decomposition algorithm is subsequently utilized to suppress the environmental noise and time domain artifacts caused by the enhancement method, enabling the extraction of the target's respiration pat-tern. Our results show that the proposed approach consistently achieves an average absolute error of less than 0.3 breaths per minute across all positions, significantly outperforming the baseline approaches.

WiFi-based sensing technology has become a very popular research area. However, the current major research focuses on single-task sensing, and the very few studies on WiFi multi-task sensing expose the problems of unbalanced sharing of information between multiple WiFi sensing tasks and unclear task relevance. To address these issues, this paper propose Wimuss, a WiFi-based multi-task sensing system that can perform three tasks simultaneously: activity recognition, identity recognition, and indoor localisation. The system first uses Convolutional Neural Networks and Bi-directional Long and Short Term Memory (CNN-BiLSTM) as the base model to simultaneously extract temporal and spatial features of WiFi data. Subnetworks are then constructed using a selective sharing module, and multiple subnetworks are combined with each other and trained together in conjunction with task-specific layers to achieve reasonable sharing of partial parameters of each task. We collect a large amount of data in real scenarios to fully verify the feasibility of the constructed multi-task sensing system. The evaluation results show that Wimuss achieves accuracy rates of 92.5%, 96.5% and 98.3% for activity recognition, identity recognition and indoor localisation respectively. More importantly, we also demonstrate that the application of selective sharing module in WiFi-sensing models outperforms other sharing mechanisms in multi-task learning, and effectively improves task accuracy and model generalisation performance.

Gesture recognition based on WiFi has recently attracted wide attention from academia and industry. However, the position-independent sensing is still a challenging problem. Existing work has made a breakthrough by extracting position-independent features through multiple transceiver pairs. We explore the position-independent gesture recognition methods that maintain accuracy and robustness while providing only one transceiver pair. Due to the limited information access and spatial resolution in that scenarios, noise cannot be effectively eliminated and gesture features are easily confused. Therefore, we propose a subcarrier selection method to select the subcarrier with less interference by noise. We extract dynamic phase as features for gesture recognition, which is position-independent. In addition, we split the dynamic phase variations of different gestures into a series of segments code based on the actions (traverse, approach and away). The easily confused gesture features are transformed into distinguishable gesture code. We developed a prototype on a Commercial Off-The-Shelf WiFi device. Extensive experimental results show that our system achieves position-independent gesture recognition using only one transceiver pair within an acceptable error range, achieving a maximum recognition accuracy of 94.33% and an average recognition accuracy of 87.25% in different positions.

Recent research has demonstrated the great potential of leveraging existing WiFi infrastructure for ubiquitous non-invasive respiration monitoring. Although this WiFi-based approach opens up a new direction for respiratory rate detection, existing studies are limited as only some simple scenarios have been considered. Consequently, the feasibility of using this technology in realistic scenarios needs to be further verified, especially for ensuring the detection performance in the following two cases: (1) long-distance and (2) different body postures. To address above two complex case studies, this paper presents several selection mechanisms to enable a robust WiFi-based respiration detection system. Firstly, a double-variance antenna links selection strategy is proposed to select the most sensitive link for breathing movements. Moreover, three subcarrier selection combining solutions are developed, where secondary selection is conducted to obtain the optimal respiration pattern in diverse situations. We conduct extensive experiments in two typical scenes. The evaluation results demonstrate that the detection error of our system is less than 0.7 bpm in each scene. More importantly, it outperforms compared with state-of-the-art systems.

Accurate and efficient foliage penetration (FOPEN) target recognition plays a vital role in many mission-critical applications, ranging from civilian to surveillance and military. Recently, device-free sensing (DFS), as an emerging technique, has gained great popularity because it requires no dedicated equipment other than wireless transceivers. Although some DFS-based approaches have been successfully applied in foliage environments, they are vulnerable to climate dynamics and heavily rely on re-labeling large amounts of new data when the weather is altered. To address this issue, a CNN-based weather adaptive target recognition network (WATRNet) is proposed in this paper. Specifically, a lightweight weather conditional normalization (WCN) module is embedded atop each convolutional block to encode inputs under different weather conditions into a shared latent feature space. Under an end-to-end learning manner, the proposed WATRNet first learns knowledge from sufficient labeled data under a certain weather condition to achieve a precise classifier. When applying this model under another weather condition, only the WCN module needs to be retrained using limited new labeled samples to learn weather-invariant features, while the rest convolutional parameters in WATRNet are frozen. Consequently, the domain discrepancy caused by climate variations can be adaptively mitigated with as few relabeled data as possible. Comprehensive evaluations are carried out on a real FOPEN dataset collected under four different weather conditions. Experimental results verify that the presented method can achieve over 90% accuracy, even when it implements from a normal weather condition to another severe weather condition with only small amounts of training samples.

WiFi-based human activity recognition technology has attracted widespread attention for its prominent application value and theoretical significance. Existing approaches have made great achievements in the same domain sensing, which means the activity samples applied for training the model have a similar distribution with the testing data. However, in practical application, we hope that the same activity of different people with various states and habits in different locations can be accurately recognized and produce the same reaction. Therefore, cross-domain sensing technology is pretty important. Some studies explore the location-independent and environment-independent methods, but few attempts consider the influence of the initial states of the users, such as standing and sitting, which actually have very different effects on the transmission of the wireless signal. This paper presents a human activity recognition method adapted to different initial states. Meanwhile, we solve the accompanying issue of the small sample size sensing, obviating the need for the cumbersome wok resulting from the massive data collection. We take advantage of the idea of metric learning and few-shot learning to realize cross-domain sensing with very few samples. The experiments demonstrate the feasibility and excellent performance of our method, which could recognize human activities with different initial states as the training data.

Device-free human activity recognition based on WiFi signals has become a very popular research field. However, it still has one major problem that is activities of “unseen” humans cannot be accurately classified, which makes it infeasible in real-world application. To tackle this issue, in this paper, we present a human activity recognition (HAR) system based on identity (ID) transfer mechanism named CrossID, which can cross the boundaries of identity by taking the high-level personal characteristics of the source domain and target domain as IDs for training and transferring. Specifically, we employ the margin-based loss function to improve the training speed and accuracy. To fully evaluate the feasibility of the proposed approach for human activity recognition, a variety of the data samples have been taken at 16 locations conducted by six people performing four different types of activities. Through extensive experiments on our dataset, we verify the effectiveness, robustness, and generalization ability of proposed system. Our average recognition rate in the target domain is 95%, which is slightly lower than 98% in the source domain.

Device-free sensing (DFS) is an emerging technology that empowers wireless communication systems with the ability for not only data communication but also smart sensing. By taking advantage of machine-learning technologies, DFS transforms traditional wireless communication networks into intelligent context-aware networks and will open the doors for a myriad of promising 6G-enabled Internet of Things (IoT) applications, ranging from smart home to smart buildings. Although significant progress has been made for human activity recognition at a single location by leveraging this technology, performance at multiple locations has not been fully explored. As far as multilocation activity sensing is concerned, the performance is compromised along with the change of locations and labor-intensive annotation works caused by multilocation. To tackle this issue, an activity decomposition network (ActNet) is presented to decompose the activity information directly from input samples by using the training data from different locations together. Instead of dealing with different locations separately, our ActNet can assemble data from different locations together for training to mitigate the data limitation issue caused by a single location. To achieve this, a multiple-input–multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) technology-based prototype system is utilized to collect data samples at 24 different locations in a cluttered office environment. Especially, for each location, only ten samples of each activity are used for training. Experiments demonstrate that the average classification accuracy is 94.6% across all locations with ensured robustness produced by our method.

Device-free activity recognition is an indispensable technology in Human-Computer Interaction (HCI). The activity recognition system based on WiFi signals relying on the wide coverage of WiFi makes HCI more convenient. The previous research on WiFi-based activity recognition system has achieved high recognition accuracy. While the challenge that activity recognition is limited to fixed location and complex background, remains unresolved. In this paper, we propose a location-free activity recognition system which leverages fine-grained channel state information (CSI) to recognize same activities regardless of different locations and background. With CSI recorded in the Network Interface Card (NIC), Angle Difference of Arrival (ADoA) is reckoned to eliminate the location and background information, which is only consistent with the activity tendency. Then the Principal Component Analysis (PCA) method is utilized to reduce the dimension and followed by curve smoothing to make the signal more smoother. Furthermore, Bidirectional Long Short-Term Memory (BiLSTM) network is selected as ideal training machine to deal with issues that are highly correlated with time series. We use two commercial wireless network cards in the typical life scene, and finally achieve 93.7 % of recognition accuracy.

Standard blind interference alignment (sBIA) suffers from noise accumulation which severely deteriorates received signal-to-noise ratio (SNR) and significantly reduces transmission rate. A noise accumulation factor is proposed to describe the loss between the user's received SNR, and the final post processing SNR which determines the performance of the decoding of the encoded data streams (EDSs). A heterogeneous semi-BIA (H-SBIA) framework where users with different noise accumulation factors can be flexibly allocated effective EDSs (E-EDSs) is constructed. Relying on the H-SBIA framework, a heuristic H-SBIA algorithm is designed for maximizing the overall E-EDSs considering both fairness and coherence time constraints. Extensive simulations demonstrate that H-SBIA produces great fairness performance improvement at a limited cost in the achievable sum rate. The Jain's fairness index is about 2.2 times greater than that for SNR-SBIA proposed in previous work, at the cost of sacrificing 10% of the achievable sum rate.

Physical (PHY)-layer security has received considerable interest as a way to safeguard data confidentiality and achieve security and privacy in wireless networks. Authentication between two devices is a challenging problem. In this paper, a machine learning algorithm is proposed to detect and identify rogue transmitters relying on a low-dimensional channel feature vector that is obtained by the Karhunen-Loeve transform (KLT). Specifically, a Linde-Buzo-Gray algorithm is designed for improving the reliability and robustness of the proposed scheme, where a Gaussian Mixture Model (GMM) is employed to learn and track the changes of physical layer properties. Simulation results demonstrate that the proposed authentication scheme achieves a higher spoofing detection rate compared to other existing methods.

Military surveillance events and rescue activities are vital missions for the Internet-of-Things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot perform promisingly under severe weather conditions, such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an auto-encoder/decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding additive white Gaussian noise to the original target signals with different signal to noise ratios. Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.

In this study, the authors consider secure communications in multiuser wireless networks where full‐duplex (FD) jamming operates to enhance physical layer security. The considered multiuser system is equipped with FD legitimate receivers in contrast to conventional frameworks where a half‐duplex (HD) receiver is at hand. This study investigates an alternative solution in which the authors take advantage of FD capability of the receivers to send jamming signals against the eavesdropper. Under these assumptions, the impact of self‐interference and channel interference on physical layer security is investigated. They derive the expressions of secrecy outage probability and ergodic secrecy rate in the proposed system. Two FD jammer selection schemes are proposed to further improve the security. In addition, they apply a reinforcement learning technology, called Q‐learning, to model the interaction between the source and multiple jamming users. The preliminary results show that the application of FD jamming and user selection scheme leads to a significant improvement in the wireless network security.

The well-known Internet of Things (IoT) is recently being considered for critical missions, such as search and rescue, surveillance, and border patrol. One of the most critical issues that these applications are currently facing is how to correctly distinguish between human and animal targets in a cost-effective way. In this paper, we present a relatively low-cost, but robust approach that uses a combination of device-free sensing (DFS) and machine-learning technologies to tackle this issue. In order to validate the feasibility of the presented approach, a variety of data is collected in a cornfield using impulse-radio ultra-wideband (IR-UWB) transceivers. These data are then used to investigate the influence of different statistical properties of the radio-frequency (RF) signal on the accuracy of human/animal target classification. Based on the probability density function of different statistical properties, two distinguishing features for target classification are found, namely, standard deviation and root mean spread delay spread. Using them, the impact on the classification accuracy due to different classifiers, number of training samples, and different values of signal-to-noise ratio is extensively verified. Even with the worst case, the classification accuracy of the system is still better than 91% in terms of distinguishing between human and animal targets (including goats and dogs), which indicates that the presented approach has a great potential to be deployed in the near future.

Foliage penetration (FOPEN) has been found to be a critical mission for a variety of applications, ranging from surveillance to military. Recently, an emerging technology, namely wireless sensor network (WSN)-based device-free sensing (DFS), has been introduced to the domain of FOPEN. This technology only utilizes radio-frequency signals for target detection and classification; thus, no additional hardware is required, just a wireless transceiver. Although the feasibility of using this technology for human detection indoors has been explored to some extent, it is questionable if the same technology can be transferred to outdoors. As far as FOPEN is concerned, the impact of seasonal variations on detection accuracy can be severe. To address this concern, in this paper, an experiment is conducted in four seasons, and how to ensure reasonable detection accuracy with seasonal variations is intensively investigated. To fully evaluate the potential of using the WSN-based DFS for FOPEN, an impulse-radio ultrawideband technology-based prototype is used to collect data samples in different seasons. Unlike the conventional approach based on a combination of statistical properties of received-signal strength and a support vector machine, this approach adopts two special measures for performance enhancement. One measure is to use a higher order cumulant (HOC) algorithm for feature extraction, so that the impact on detection accuracy due to unwanted clutters can be minimized. The other one is to determine the optimal parameters of the classifier by means of a flower pollination algorithm. Consequently, the adverse effects on detection accuracy due to variations of weather conditions in four seasons can be accommodated. According to the experimental result, it is shown that the average classification accuracy of the presented approach can be improved by at least 20% under all seasons with an ensured robustness.

Information security is a vital concern in Internet of Things (IoT). Traditional security method based on public or private key encryption scheme is limited by the trade‐off between low cost and high level of security. Among different security solutions, utilising compressed sensing (CS) in combination with the physical‐layer security to achieve the security is a remarkable method. However, in the current literatures, little attention has been given to the area of static environment, which will lead the risk of information leakage in the CS security model. In this study, the authors propose a new CS security model, in which circulant matrix is exploited to improve the generation efficiency of the measurement matrix, and binary resilient functions are utilised to enhance the security. Furthermore, considering the practical application, they present a feasible framework, named CS security scheme based on frequency‐selective, where the frequency‐selective feature of the wireless channel is applied to support the static environment. To verify the effectiveness of the proposed scheme, they conducted experiments and numerical simulations to evaluate the performance, and the results are satisfactory.

Spoofing attacks is one of the most critical attacks in wireless communication security. Traditional solutions are based on cryptology which is performed in the upper layers, and face many challenges especially in resource‐limited application. To overcome this hurdle, physical‐layer security has been received a lot of attention recently. In this study, the authors propose a physical‐layer spoofing detecting scheme, where signal processing and feature recognition are utilised to improve the detection performance. In this study, they present a pretreatment process based on sparse representation (SR) to reinforce the characteristic of the signal. Furthermore, they formulate the problem of spoofing detection as one of the feature extraction and recognition, and employ a developed fuzzy C‐mean algorithm to further increase the recognition accuracy. In addition, in order to verify the proposed method, they conduct experiments and use numerical simulation and analysis to evaluate the detection performance. Results showed that the proposed approach can improve the recognition accuracy significantly (increased by one order of magnitude) and the complexity is acceptable (polynomial complexity). Their findings showed that combining SR and feature extraction and recognition, the proposed method provided a good access to achieve a higher accuracy scheme of spoofing detection.

In this letter, the possibility of using device-free sensing (DFS) technology for personnel detection in a foliage environment is investigated. Although the conventional algorithm that based on statistical properties of the received-signal strength (RSS) for target detection at indoor or open-field environment has come a long way in recent years, it is still questionable if this algorithm is fully functional at outdoor with the changing atmosphere and ground conditions, such as a foliage environment. To answer this question, a variety of the measured data have been taken using different targets in a foliage environment. Applying these data along with support vector machine, the impact on detection accuracy due to different classification algorithms is studied. An algorithm that based on the extraction of the high-order cumulant (HOC) of the signals is presented, while the conventional RSS-based one is used as a benchmark. The measurement results show that the classification accuracy of the HOC-based algorithm is better than the RSS-based one by at least 17%. Moreover, to ensure the reliability of the HOC-based approach, the impact on classification accuracy due to different numbers of training samples and different values of signal-to-noise ratio is extensively verified using experimentally recorded samples. To the best of our knowledge, this is the first time that a DFS-based sensing approach is demonstrated to have a potential to distinguish between human and small-animal targets in a foliage environment.

The standard Blind Interference Alignment (BIA) accumulates noise levels and causes great Signal Noise Ratio (SNR) deteriorations in the condition that the received SNR is not infinite. To address such problem, we take SNR feedback into consideration. We first define SNR reduced factor, reflecting SNR reduction between the received SNR and the final SNR for decoding data streams. A heterogeneous BIA (hBIA) is then presented where the users form different groups, and each group further consists of several sub-groups. Within one group, the users in one sub-group perform BIA with other sub-groups' users, while the users in different groups have different SNR reduced factors. To maximize the overall effective degree of freedom (DoF) with SNR guarantee, a greedy algorithm is proposed for allocating the users into different groups and subgroups. Evaluations demonstrate that hBIA achieves about 375% and 540% effective DoF improvement on average compared with the standard BIA and the grouping based BIA, respectively.

Blind interference alignment (BIA) can eliminate users’ interference without channel state information and greatly improve the channel capacity, which has been regarded as a promising technique in next generation wireless systems. However, current BIA mechanisms neglect noise accumulations, resulting in final signal‐to‐noise ratio (fSNR) deterioration and reduced transmission rates. Motivated by ameliorating such negative impact induced by noise accumulations, this study establishes an SNR aware semi‐BIA (SNR SBIA) framework in the multi‐input–single‐output broadcast channel, where the users form different groups performing BIA for different SNR reductions. An SNR SBIA algorithm is further proposed based on tabu search for maximising the overall performance ensuring all users’ fSNRs higher than an SNR threshold. Extensive evaluations demonstrate that SNR SBIA can adapt to the users’ diverse received SNRs and the corresponding achievable sum rate is about 1.38 times and 1.92 times that in standard BIA and grouped hierarchical BIA, respectively.

Compressed Sensing (CS) theory has recently received amount of attention in the image compression filed. The sparser the signal has, the better the performance recovery. Most wavelet-based reconstruction methods of CS are developed under the assumption that the small wavelet coefficients are close to zero. In other words, a part of image information has been lost before measurement sampling. As we know, most of images have many similar areas. In order to avoid the image information being lost as little as possible, in this paper, a new CS scheme based on the similarity of image blocks is proposed in wavelet domain. Instead of processing the image as a whole, the image is firstly divided into small image blocks. And a clustering algorithm is presented to gather the similar image blocks into a group. Experiments on images demonstrate favorable performances of the proposed method.

In many wireless communication systems, data can be divided into different importance levels. For these systems, unequal error protection (UEP) techniques are used to ensure lower bit error rate for the more important classes. Moreover, if the precise characteristics of the channel are known, UEP can be used to correctly recover the more important classes even under severe receiving conditions. In this study, a UEP scheme based on compressed sensing via a linear program is proposed. Discrete wavelet transform (DWT) is chosen as the sparsifying basis, and then DWT‐coded information is divided into two‐layered coded streams, each of which is transmitted differentially by applying an unequal number of information bits in linear codes according to the time‐varying characteristic of the corrupted channel. In this proposed transmission scheme, the more important information is to guarantee error‐free transmission. At the decoder, one can simply reconstruct the signal via the l 1 ‐minimisation algorithm. Simulation results show that the proposed scheme can achieve a higher peak signal‐to‐noise ratio (PSNR) and obviously improve the error resilience compared to the equal error protection scheme and other UEP methods. More importantly, with the increase of channel corrupted ratio, the drop rate of PSNR is much slower than other solutions. It indicates that the proposed method has better robustness for severe channel conditions.