The emerging 6G-IoT paradigm is driving communication toward intelligent services, semantic communication enables efficient semantic sharing via artificial intelligence (AI), significantly boosting communication efficiency. However, current semantic systems suffer from black-box decision-making, while existing explainable artificial intelligence (XAI) methods face two key challenges: explicability granularity mismatch and closed-loop optimization gap. To address these, we propose a semantic communication framework integrated with XAI (XAI-SCS). Specifically, we first design an explainable semantic codec architecture enhanced by Kolmogorov–Arnold Networks (KAN), where traditional fixed activation functions are replaced with learnable and parameterized ones, enabling function-level visualization to improve model explainability. Second, we develop an explainable semantic transmission module driven by contrastive learning that enhances the robustness of semantic transmission, and incorporating a semantic separability metric to quantify channel impacts on semantic integrity. Third, we introduced a KAN-enhanced causal semantic decoder, which integrates counterfactual interventions to generate pixel-level difference maps. We also propose a contrastive explanation consistency metric to evaluate the sensitivity of key features, enhancing the quality of the reconstruction. The experimental results show that our approaches enhance explainability across the entire decision process, achieve a significant accuracy improvement of up to 55% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/25, and also obtain competitive image reconstruction quality in increasing compression levels. The source code is publicly available at: https://github.com/guyuangui/XAI-SCS.git.

With the development of surveillance cameras, more bandwidth is required to transmit surveillance videos. Since surveillance videos contain a large amount of redundant information, it causes a waste of bandwidth. Meanwhile, previous video compression methods with the fixed compression standards are unable to handle asymmetric information effectively. To address these problems, we propose Task-driven Semantic Communication with Unsupervised Semantic Segmentation (TSCUSS) for surveillance video assisted by Generative Artificial Intelligence (GAI), to improve efficiency. First, at the transmitter, we segment the videos into the foreground semantic and background models. Second, in the transmission side, we transmit the extracted semantic information in two-stage semantic communication, which greatly reduces redundant information. Third, at the receiver, we merge the foreground and background semantic models through the diffusion model to recover the original semantic content. Finally, our experiment shows that our method not only achieves 78.34% average video compression rate and improves bandwidth utilization, but also dominates in both semantic segmentation accuracy and generative foreground background merge similarity.

With the rapid development of artificial intelligence (AI) and ubiquitous connectivity, speech communication is becoming increasingly important in achieving intelligent interactions between humans, machines, and objects in computational social systems. However, current neural network-based semantic communication frameworks primarily focus on transmitting semantic information while largely overlooking the emotion features in speech communication, which is vital for naturalness and effective interaction in computational social systems. In this article, we propose an emotion-enhanced speech semantic communication system, which effectively enhances the expressiveness and robustness of emotion AI for speech communication. First, we propose an emotion fusion encoding module at the transmitter, where features are dynamically fused via attention mechanisms and subsequently encoded through a channel encoder. Then, we introduce an emotion orthogonal decoding module at the receiver, which reconstructs the fused features via a channel decoder followed by an orthogonally constrained disentanglement network. In addition, a conditional diffusion model guided by emotion features reconstructs high-fidelity speech with enriched emotional expressiveness. Finally, experimental evaluations demonstrate that the proposed framework significantly improves both the bit error rate and the mean opinion score over state-of-the-art models. Furthermore, the system achieves notable reductions in transmission dimensionality.

With the development of real-time video conferences, interactive multimedia services have proliferated, leading to a surge in traffic. Interactivity becomes one of the main features on future multimedia services, which brings a new challenge to Computer Vision (CV) for communications. In addition, many directions for CV in video, like recognition, understanding, saliency segmentation, coding, and so on, do not satisfy the demands of the multiple tasks of interactivity without integration. Meanwhile, with the rapid development of the foundation models, we apply task-oriented semantic communications to handle them. Therefore, we propose a novel framework, called Real-Time Video Conference with Foundation Model (RTVCFM), to satisfy the requirement of interactivity in the multimedia service. Firstly, at the transmitter, we perform the causal understanding and spatiotemporal decoupling on interactive videos, with the Video Time-Aware Large Language Model (VTimeLLM), Iterated Integrated Attributions (IIA) and Segment Anything Model 2 (SAM2), to accomplish the video semantic segmentation. Secondly, in the transmission, we propose a two-stage semantic transmission optimization driven by Channel State Information (CSI), which is also suitable for the weights of asymmetric semantic information in real-time video, so that we achieve a low bit rate and high semantic fidelity in the video transmission. Thirdly, at the receiver, RTVCFM provides multidimensional fusion with the whole semantic segmentation by using the Diffusion Model for Foreground Background Fusion (DMFBF), and then we reconstruct the video streams. Finally, the simulation result demonstrates that RTVCFM can achieve a compression ratio as high as 95.6%, while it guarantees high semantic similarity of 98.73% in Multi-Scale Structural Similarity Index Measure (MS-SSIM) and 98.35% in Structural Similarity (SSIM), which shows that the reconstructed video is relatively similar to the original video.

Self-taught learning (STL) is a promising solution that reduces the performance gap between weakly supervised and fully supervised learning for easily accessible, label-free images. The success of traditional STL solutions relies on the assumption that the target appearance is completely visible and well-defined. In real-world facial expression recognition scenarios, however, saliency regions are often partially occluded, which significantly hampers the generalization capability of STL methods. Nevertheless, few studies have investigated the impact of occlusion on STL. In this paper, we propose an interweaved autoencoder network for weakly supervised facial expression recognition in occlusion scenarios. The key innovation of our network lies in the Residual Connection Union (RCU) blocks that can integrate the Convolutional Neural Network (CNN) and Transformer layers into a multi-scale structure. The RCU enables a progressive masking strategy to accurately identify and focus on contributive yet often overlooked image patches by analyzing the relationships among region-level target representations. In addition, we introduce a self-knowledge distillation module for the effective training of the proposed autoencoder network. Extensive experiments are conducted on four public datasets to demonstrate the superiority of our method over related works.

The transition from 5G to beyond 5G (B5G) heralds a shift towards more pervasive and intelligent communication trends. This evolution necessitates a departure from traditional information theory towards semantic communication (SemCom), propelled by artificial intelligence (AI), aimed at enhancing capacity and optimizing resources. Concurrently, image SemCom (ISC) emerges to empower image applications. However, ISC demands suitable devices and ample computing resources to support complex neural network models and their training, presenting a significant challenge. In response, we propose a federated semantic feature distillation (FedSFD) architecture to enhance the overall performance of ISC. Combining federated learning (FL) and feature distillation (FD), FedSFD facilitates the transfer of group feature knowledge. Specifically, the powerful server model refines itself by approximating the distance between its middle layer features and those of the devices via FD. Subsequently, lightweight device ISC models leverage FD to incorporate the server model’s knowledge during training. This iterative process is bolstered by the information bottleneck (IB)-based loss function, enhancing image compression and reconstruction capabilities. Notably, this architecture operates without necessitating a unified model, thereby offering improved privacy protection. Simulation experiments demonstrate that compared to the baseline, our approach can achieve superior integration capability for ISC at the noisy edge.

The evolution from fifth-generation mobile communications (5G) to beyond 5G (B5G) will lead to more ubiquitous and smarter paradigms in the Internet of Things (IoT). Communication will shift from the classical information theory to a semantic communication (SemCom) paradigm driven by artificial intelligence (AI) to enhance the capacity and optimize resources. Image SemCom (ISC) will empower IoT applications, such as drone image acquisition. However, ISC requires suitable devices and sufficient computing resources to support complex neural network models, posing a significant challenge. To address this, we propose a federated semantic feature distillation (FedSFD) architecture to improve the global performance of ISC by combining federated learning (FL) and feature distillation (FD) for the feature knowledge transfer. First, lightweight IoT device models in the edge group and the powerful server model alternately minimize losses to update parameters. After learning middle-layer features of all the edge models, the server can guide the individual device models. Second, we incorporate the information bottleneck (IB) concept into the design of loss functions to balance compression and reconstruction. Third, focusing on the tradeoff between the local training and knowledge interaction, FedSFD achieves image semantic reconstruction without sharing the private data, ensuring personalization and privacy protection in the FL framework. Finally, compared to the baseline, the simulation experiments show that the proposed approach achieves better ISC reconstruction and noise robustness during group ISC.

Cross-modal communications that incorporate audio-visual and tactile signals will bring a more holistic immersive experience to people. However, due to the different transmission requirements of these signals, it is a challenging task to rationalize the allocation of transmission resources. Therefore, this work proposes a joint transmission scheme to deal with the resource allocation problem of diverse signals. Network slicing and puncturing architecture are introduced in the scheme to achieve flexible resource allocation and reduce the wasting of resources. To reduce the negative impact of puncturing transmission on users, we construct the optimization problem related to transmission rate and reliability. This problem can realize the reasonable allocation of radio resources and meet the transmission requirements of the two types of signals. Next, we divide the optimization problem into two parts: video traffic resources allocation and tactile traffic puncturing resources allocation. To solve both of the problems, we leverage the channel matching (CM) algorithm and puncturing resource allocation (PRA) algorithm. In addition, we discuss the advantages and disadvantages of both ways to occupy puncturing resources, namely, occupy resources proportionally (ORP) and occupy resources blocks for transmission (ORB). Finally, the effectiveness of the proposed scheme is verified by comparing the excepted rate and resources loss ratio of system users with different schemes.

Simultaneous wireless information and power transfer (SWIPT) is a promising solution for future wireless networks as it provides convenient and perpetual energy supplies to wireless users. This study proposes three new transceiver design schemes in two‐way relay networks with SWIPT. Firstly, a transmit power minimisation solution is proposed with an aim to minimise the transmit power while meeting an energy harvesting requirement. Then, a harvested energy maximisation solution that has the same complexity as the first solution but provides a much better sum harvested energy is proposed. Finally, a signal‐to‐interference‐plus‐noise ratio maximisation solution is developed, which gives the best sum‐rate performance for the two‐way relay networks while ensuring a desired level of energy harvesting. Furthermore, the complexity of the three new transceiver solutions is discussed. Simulation results have demonstrated the effectiveness of the proposed transceiver design schemes in two‐way relay networks with SWIPT.

By offloading video streaming from the centralised cloud to the edge, mobile edge computing (MEC) servers offer new opportunities for real‐time video transmission. Deploying on the edge of users can ensure low latency transmission, however, the limited storage and computing ability cannot adapt to the currently used video transmission technologies such as video transcoding or simulcast. To solve this problem, a more flexible video transmission architecture needs to be considered. Under this motivation, the authors propose a device‐to‐device (D2D) assisted video streaming scheme, which fuses the technical advantages of MEC and scalable video coding. Specifically, they first construct a novel architecture for delay‐sensitive live video streaming services in edge‐enabled wireless heterogeneous networks named MEC‐enabled goodput‐aware (MEGA) model. Then they present a mathematical formulation for optimising the aggregation goodput performance of video traffic including both cellular and D2D links. Finally, they derive a three‐step solution based on a distributed heuristic algorithm. Numerical simulation results show that MEGA outperforms existing models in terms of goodput, end‐to‐end delay, effective loss rate, and users' quality‐of‐experience.

Mobile crowdsensing (MCS) has become a promising solution to support the location-based content sharing applications. To meet users' demand on personalized content sharing, a general region of interest (RoI) distribution model that allows each user to have its specific RoI needs to be considered. In this context, how to deal with the asymmetry of cooperation caused by different RoI distribution is of significance for achieving the full benefits of personalized content sharing. Thus motivated, we propose an intelligent content sharing scheme based on cooperative crowdsensing, which ensures both efficiency and fairness. Specifically, users' decision-making of whether to participate in MCS is cast as a MCS participation game (MPG). The game captures the impact of different RoI distributions on the collective cooperation of MCS. By computing the Nash equilibrium of MPG with desirable properties, we develop a cooperation scheme that maximizes the overall system utility and is acceptable to all users. The system efficiency of the proposed scheme is further quantified by numerical simulations over various parameters.

Device-to-Device (D2D) multicast content sharing is becoming a promising technology to alleviate video traffic overload and can improve the quality of local area services. Whereas existing studies mainly focus on the delay or throughput performance. However, for delay sensitive real-time video traffic, throughput as an indicator of the network-layer cannot properly indicate the benefits of upper-layer applications. Thus, in this paper we propose a video multicast scheme for D2D cooperative scalable video coding (SVC) streaming distribution to cope with the difference between multicast channels firstly. Then, we have provided analytical expressions of the goodput in heterogeneous multicast networks based on D2D collaboration, and a distributed heuristic algorithm is proposed to solve this NP-hard optimization problem. Our results show that the proposed scheme can effectively reduce end-to-end delay, effective loss rate and improve the goodput in the system.

The abundant spectrum resources in millimeterwave (mmWave) frequency band enable high throughput for indoor communications. However, the huge path loss and high blockage probability result in vulnerability during transmission. The performance of propagation is severely influenced by blocked odds according to the reflections off smooth surfaces. In this paper, we propose a random blockage model to characterize the reflections off the walls and ceiling. To adjust the variant transmission which is suffering different reflections, we first propose a combinational temperature random algorithm (CTRA) to arrange resource blocks with plausible availability. The CTAR includes two phases: partition and matching, which highly enhance the average throughput. The main conclusions are that the modified algorithm brings about higher system throughput with appropriate temperature factors and performances much better.

Compared with the conventional degrees of freedom, the orbital angular momentum (OAM), which describes the helical phase structure of electromagnetic wave, provides a new degree of freedom. As a new multiple access scheme, mode division multiple access (MDMA) is constructed in millimetre wave frequency band utilising the orthogonality and high dimensionality in this study. Various traditional resources such as frequency, time and code pattern have been shared. Therefore, addresses of signals from different terminal users can be distinguished by OAM mode to realise multi‐address connection. In this study, the theoretical analysis of the number of terminals in MDMA scheme is carried out. According to the analysis results, infinite terminals can be connected together in the ideal case. Moreover, the simulation results show that compared with the conventional multi‐input multi‐output millimetre wave communication systems, the performance indicators of MDMA millimetre wave communication systems are improved remarkably.

Device‐to‐Device communication underlaying cellular network can increase the spectrum efficiency due to direct proximity communication and frequency reuse. However, such performance improvement is influenced by the power interference caused by spectrum sharing and social characteristics in each social community jointly. In this investigation, we present a dynamic game theory with complete information based D2D resource allocation scheme for D2D communication underlaying cellular network. In this resource allocation method, we quantify both the rate influence from the power interference caused by the D2D transmitter to cellular users and rate enhancement brought by the social relationships between mobile users. Then, the utility function maximization game is formulated to optimize the overall transmission rate performance of the network, which synthetically measures the final influence from both power interference and sociality enhancement. Simultaneously, we discuss the Nash Equilibrium of the proposed utility function maximization game from a theoretical point of view and further put forward a utility priority searching algorithm based resource allocation scheme. Simulation results show that our proposed scheme attains better performance compared with the other two advanced proposals.

Scalable Video Coding (SVC) streaming multicast is considered as a promising solution to cope with video traffic overload and multicast channel differences. To solve the challenge of delivering high‐definition SVC streaming over burst‐loss prone channels, we propose a social‐aware cooperative SVC streaming multicast scheme. The proposed scheme is the first attempt to enable D2D cooperation for SVC streaming multicast to conquer the burst‐loss, and one salient feature of it is that it takes fully into account the hierarchical encoding structure of SVC in scheduling cooperation. By using our scheme, users form groups to share video packets among each other to restore incomplete enhancement layers. Specifically, a cooperative group formation method is designed to stimulate effective cooperation, based on coalitional game theory; and an optimal D2D links scheduling scheme is devised to maximize the total decoded enhancement layers, based on potential game theory. Extensive simulations using real video traces corroborate that the proposed scheme leads to a significant gain on the received video quality.

Vehicular ad-hoc networks (VANETs) are vulnerable to jamming attacks, and frequency hopping-based anti- jamming techniques are not always applicable in VANETs due to the high mobility of the onboard units (OBUs) especially under a large scale network topology. In this paper, we use unmanned aerial vehicles (UAVs) to deal with VANET jamming, especially smart jamming that changes the jamming policy based on the ongoing communication status of the VANET. More specifically, the UAV relays the data of OBUs to another roadside unit (RSU) with a better transmission condition if the serving RSU is located in a heavily jammed area. The interactions between the UAV and the jammer are formulated as an anti-jamming UAV relay game, in which the UAV decides whether or not to relay the data of the OBU to another RSU that is far away from the jammer, and the latter chooses the jamming power. The Nash equilibria (NE) of the game are derived to reveal how the best UAV relay strategy depends on the transmission cost and the radio channel model. A hotbooting policy hill climbing (PHC)-based UAV relay strategy is proposed to address jamming in the dynamic UAV-aided VANET game without the knowledge of network model and jamming model. Simulation results show that the proposed relay strategy can efficiently reduce the bit error rate (BER) of OBU data and thus increase the utility of VANET in comparison with a Q-learning based scheme.

With extensive promising applications of M2M (machine-to-machine) or MTC (machine type communication), while supporting multiple MTC device access networks has been considered essential for M2M communication. In a roaming environment, it has always been a great challenge to ensure safe and efficient access for MTC device groups. In this paper, in order to solve the real-time secure and efficient access problem of multiple MTCs, we proposed a group authentication protocol based on bilinear-pairing and aggregate signature. In proposed protocol, node key is generated jointly by KGC (Key Generation Center) and node simultaneously to resist camouflage attack, and the computational complexity in authentication process is significantly ameliorated as the session key is engendered by DLP (Discrete Logarithm Problem). Security analysis shows the strong security of proposed protocol, and performance evaluation proves that both transmission overhead and computational complexity decrease significantly compared with conventional schemes. In addition, it overcomes the weakness of key escrow in identity based aggregate signature protocol.

With the rapid advancement of technology, healthcare systems have been quickly transformed into a pervasive environment, where both challenges and opportunities abound. On the one hand, the proliferation of smart phones and advances in medical sensors and devices have driven the emergence of wireless body area networks for remote patient monitoring, also known as mobile-health (M-health), thereby providing a reliable and cost effective way to improving efficiency and quality of health care. On the other hand, the advances of M-health systems also generate extensive medical data, which could crowd today’s cellular networks. Device-to-device (D2D) communications have been proposed to address this challenge, but unfortunately, security threats are also emerging because of the open nature of D2D communications between medical sensors and highly privacy-sensitive nature of medical data. Even, more disconcerting is healthcare systems that have many characteristics that make them more vulnerable to privacy attacks than in other applications. In this paper, we propose a light-weight and robust security-aware D2D-assist data transmission protocol for M-health systems by using a certificateless generalized signcryption (CLGSC) technique. Specifically, we first propose a new efficient CLGSC scheme, which can adaptively work as one of the three cryptographic primitives: signcryption, signature, or encryption, but within one single algorithm. The scheme is proved to be secure, simultaneously achieving confidentiality and unforgeability. Based on the proposed CLGSC algorithm, we further design a D2D-assist data transmission protocol for M-health systems with security properties, including data confidentiality and integrity, mutual authentication, contextual privacy, anonymity, unlinkability, and forward security. Performance analysis demonstrates that the proposed protocol can achieve the design objectives and outperform existing schemes in terms of computational and communication overhead.

Device-to-device (D2D) communication has been widely studied to improve network performance and considered as a potential technological component for the next generation communication. Considering the diverse users’ demand, Quality of Experience (QoE) is recognized as a new degree of user’s satisfaction for media service transmissions in the wireless communication. Furthermore, we aim at promoting user’s Mean of Score (MOS) value to quantify and analyze user’s QoE in the dynamic cellular networks. In this paper, we explore the heterogeneous media service distribution in D2D communications underlaying cellular networks to improve the total users’ QoE. We propose a novel media service scheme based on different QoE models that jointly solve the massive media content dissemination issue for cellular networks. Moreover, we also investigate the so-called Media Service Adaptive Update Scheme (MSAUS) framework to maximize users’ QoE satisfaction and we derive the popularity and priority function of different media service QoE expression. Then, we further design Media Service Resource Allocation (MSRA) algorithm to schedule limited cellular networks resource, which is based on the popularity function to optimize the total users’ QoE satisfaction and avoid D2D interference. In addition, numerical simulation results indicate that the proposed scheme is more effective in cellular network content delivery, which makes it suitable for various media service propagation.

To achieve the flexible and efficient transmission of files in the video on demand system, this paper proposed a three-tier fragmentation strategy which provided a unfiled method of fragmentation for video of HTTP Live Streaming and large file. In order to prove the rationality of the three-tier fragmentation strategy, we compared it with the two layers fragmentation strategy and the fragmentation strategy of equal time. And the comparisons proved the superiority of three-tier fragmentation strategy.

On the basis of studying the principle and application technology of P2P protocol, we design the private protocol of P2P application layer to meet the needs of P2P communication. This application layer protocol splits up video into three types of blocks in order to improve transmission efficiency. Besides, the protocol can be expanded when needed since there are some invalid bits in the state list.

Abstract In low signal‐to‐noise ratio (SNR) cases, the performance of spectrum sensing algorithms cannot meet the practical needs, which is a major problem faced by spectrum sensing technology in current cognitive radio field. Now, existing algorithms based on random matrix theory (RMT) have high sensing performance, but they require a large number of samples, which are very difficult to satisfy in practice. Free probability theory (FPT) is a main branch of RMT. It describes the asymptotic behavior of large random matrices and portrays a strong link between two matrices and their sum or product matrices. FPT can also be utilized to the digital communication system that can be modeled by random matrices and has been applied to spectrum sensing in simplified ideal channels, for example, additive white Gaussian noise channel. The most pivotal issue and difficulty of the FPT‐based methods is to set up and solve the asymptotic freeness equation corresponding to a specific communication model. In this paper, FPT‐based spectrum sensing schemes are proposed for some typical wireless communication systems, such as multiple‐input multiple‐output system, Rayleigh multipath fading system, and orthogonal frequency division multiplexing system. It is shown that the asymptotic freeness behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for these typical systems with low SNR and very limited samples. Simulation results demonstrate that compared with the existing RMT‐based spectrum detection methods, for example, the maximum and minimum eigenvalue detectors, the proposed FPT‐based schemes offer superior detection performance and are more robust to low SNR cases, especially for a small sample of observations. Copyright © 2015 John Wiley & Sons, Ltd.

Abstracdt Content delivery via device‐to‐device (D2D) communications is a promising technology for offloading the heavy traffic for future mobile communication networks. As security is a critical concern for the users, we focus on improving the secrecy capacity for content dissemination in D2D communications. In this work, we explore the inherent characteristics of wireless channels to prevent eavesdropping. Firstly, we propose a power control scheme to obtain the optimal transmission powers for the D2D links without violating secrecy requirement of cellular users. Then, we formulate the problem as a stochastic optimization problem, aiming at maximizing the secrecy capacity gain of D2D communications. By solving the expected value model for the stochastic optimization problem, the optimal D2D links are selected to realize maximal ergodic secrecy capacity gain. Specifically, a weighted conflict graph is formulated according to the protocol model. Thus, the optimization problem has been transformed to the maximum weighted independent set problem, which is solved by a greedy weighted minimum degree algorithm. Simulation results demonstrate that the content dissemination scheme with power control can bring high secrecy capacity gain to the network. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Device‐to‐device (D2D) communications have been viewed as a promising data offloading solution in cellular networks because of the explosive growth of multimedia applications. Because of the nature of distributed device location, distributed caching becomes an important function of D2D communications. By taking advantage of the caching capacity of the device, in this work, we explore the device storage and file frequent reuse to realize distributed content dissemination, that is, storing contents in mobile devices (namedhelpers). Specifically, we first investigate the average and lower bound of helper amount by dividing the network into small areas where the nodes are within each other's communication radius. Then, optimal helper amount is derived based on average helper amount and network topology. Subsequently, a location‐based distributed helper selection scheme for distributed caching is proposed based on the given optimal helper amount. In particular, nodes are selected as helpers according to their locations and degrees, and contents are placed in the manner for maximizing total user utility. Extensive simulation results demonstrate the factors that affect the optimal helper amount and the total user utility. Copyright © 2015 John Wiley & Sons, Ltd.

Device-to-Device (D2D) communication has been proposed to be a promising data offloading solution in the coming big data age, with multimedia dominating the digital contents. As quality of experience (QoE) is the major determining factor in the success of new multimedia applications, we novelly propose a QoE-driven cooperative content dissemination (QeCS) scheme in the paper. Specifically, all the users predict the QoE of the potential connections characterized by mean opinion score (MOS) and send the results to the content provider (CP). Then CP formulates a weighted oriented graph based on the network topology and MOS of each potential connection. By factorizing the graph, the content dissemination fashion is established through seeking 1-factor with the maximum weight thus achieving maximum total user MOS.

Free probability theory as a main branch of random matrix theory is a valuable tool for describing the asymptotic behavior of multiple systems, especially for those with large random matrices. But classical freeness results mainly focus on random matrices with independent and identically distributed (i.i.d.) entries. In this paper, by using the latest research results of random Vandermonde matrix (a class of matrix that does not satisfy the i.i.d. condition) in free probability theory, a new spectrum sensing scheme is proposed, which shows how the asymptotic behavior of random Vandermonde matrices can be used to assist spectrum sensing for cognitive radio. Simulation results show that the proposed scheme has a better detection performance than the energy detection technique and the maximum-minimum eigenvalue scheme even for the case of a small sample of observations.

Free probability theory, which has became a main branch of random matrix theory, is a valuable tool for describing the asymptotic behavior of multiple systems, especially for large random matrices. In this paper, using free probability theory, a new spectrum sensing scheme for cognitive OFDM system is proposed, which shows how asymptotic free behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for cognitive radios. Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance and lower power need compared with the energy detection technique even for the case of a small sample of observations.

In cognitive radio systems, cooperative spectrum sensing can detect the presence of the primary user accurately. However, with the increasing number of cooperative users, a larger dedicated control channel bandwidth is needed to transmit the local binary decisions of SUs to the fusion center. To further improve the sensing performance of cooperative spectrum sensing under bandwidth-limited constraints, we propose a new cooperative spectrum sensing. More specifically, only the secondary users with reliable information are allowed to send their local binary decisions to the fusion center while the others will send nothing. The sensing performance of the proposed method is studied and the analytical performance results are given. Our analysis and numerical results verify that the sensing performance is improved significantly compared to conventional cooperative spectrum sensing.

Free probability theory, which has became a main branch of random matrix theory, is a valuable tool for describing the asymptotic behavior of multiple systems, especially for large random matrices. In this paper, using free probability theory, a new cooperative scheme for spectrum sensing is proposed, which shows how asymptotic free behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for cognitive radios. Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance compared with the energy detection techniques even for the case of a small sample of observations.