Ensuring safe and reliable data sharing is crucial for the development of Internet of Vehicles (IoV) technology. To provide a trusted data environment for IoV and enable traditional consensus algorithms to meet the high dynamic requirements of the IoV. In this article, we propose a blockchain-based data sharing scheme for IoV (BLDTS) to achieve secure and trusted sharing. First, we design a false information identification strategy that utilizes a bayesian inference model to determine the authenticity of shared data with the assistance of reputation value. Second, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to construct a novel lightweight consensus mechanism based on vehicle reputation values and traffic environment factors, and the nodes with high scores were selected as participants in the consensus, which can reduce the computational overhead. Finally, experimental results show that our scheme has advantages in improving the accuracy of false message identification and consensus efficiency.

The Internet of Things (IoT) connects numerous de-vices and sensors, generating data with significant informational and economic value. However, data silos hinder effective data utilization and trading, leading to the dispersion of data across various devices and systems. Additionally, traditional third-party trading models face challenges related to data security and trust. To address these issues, this paper proposes a secure data trading framework based on a consortium blockchain and designs a corresponding solution. Specifically, it introduces the integration of zero-knowledge proofs into the smart contract scheme for authenticity and integrity verification of transaction data. From the perspective of IoT device users, this paper aims to enable secure data trading through a decentralized platform, using off-chain storage methods to reduce the blockchain's data burden while ensuring security and privacy. Off-chain storage encrypts and securely stores sensitive data, recording only necessary information on the blockchain, effectively protecting user privacy. To validate the practicality of the proposed solution, experiments were conducted using Hyperledger Fabric, demonstrating its feasibility in facilitating secure storage and trustworthy trading of IoT data. Finally, this study analyzes the experimental results and offers valuable insights for future research.

The awareness of data privacy preservation in the Internet of Things (IoT) environment and the amount of IoT data production, are growing almost in parallel with each other. As a privacy-preserving framework, Federated Learning (FL) allows many participants to collaboratively build machine learning models while ensuring that their raw data remains local and undisclosed. However, as the devices charged in data collection are deployed in different IoT environments, we also face a significant challenge i.e., dealing with non-independently and identically distributed (non-IID) data. If the data is not distributed uniformly among the participants, it may lead to a significant performance degradation of the generated global model, which is far from the case when the data is distributed uniformly. To address this challenge, this study innovatively designs the Enhancing Federated Learning algorithm with Client-Specific Data Augmentation (FedSDA). The FedSDA matches clients by servers, and clients train local models using augmented datasets to overcome the negative influence mainly caused by non-IID, which consequently enhances the model accuracy. Our simulation experiments on the datasets Fashion-MNIST and CIFAR-10 ultimately demonstrate that FedSDA outperforms contemporary state-of-the-art FL strategies with similar design characteristics.

Cross-chain payment, serving as critical infrastructure for multi-chain ecosystem interoperability, confronts the fundamental challenge of simultaneously ensuring privacy preservation, regulatory compliance, and quantum-resistant security—objectives that are inherently difficult to reconcile. This paper proposes a Lattice-based Dynamic Privacy-preserving Cross-chain Payment Scheme (LDPCPS) that innovatively integrates advanced cryptographic primitives. Specifically, LDPCPS employs a privacy-preserving scalar product (PPSP) protocol enabling ciphertext-domain aggregation and verification, constructs a dynamic regulatory framework using signatures of knowledge (SoK) for zero-knowledge compliance proofs and risk-triggered traceability, and implements proxy re-encryption to facilitate seamless quantum-resistant key migration. Experimental results demonstrate that LDPCPS has significant superiority over state-of-the-art alternatives in quantum resistance, computational efficiency, and regulatory adaptability, thereby establishing a robust foundation for secure and compliant cross-chain transactions.

In the Internet of Things (IoT), a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network. However, devices face the challenge of a single point of failure, a malicious user may forge device identity to gain access and jeopardize system security. In addition, devices collect and transmit sensitive data, and the data can be accessed or stolen by unauthorized user, leading to privacy breaches, which posed a significant risk to both the confidentiality of user information and the protection of device integrity. Therefore, in order to solve the above problems and realize the secure transmission of data, this paper proposed EBIAS, a secure and efficient blockchain-based identity authentication scheme designed for IoT devices. First, EBIAS combined the Elliptic Curve Cryptography (ECC) algorithm and the SHA-256 algorithm to achieve encrypted communication of the sensitive data. Second, EBIAS integrated blockchain to tackle the single point of failure and ensure the integrity of the sensitive data. Finally, we performed security analysis and conducted sufficient experiment. The analysis and experimental results demonstrate that EBIAS has certain improvements on security and performance compared with the previous schemes, which further proves the feasibility and effectiveness of EBIAS.

With the growth of the Internet of Things (IoT), millions of users, devices, and applications compose a complex and heterogeneous network, which increases the complexity of digital identity management. Traditional centralized digital identity management systems (DIMS) confront single points of failure and privacy leakages. The emergence of blockchain technology presents an opportunity for DIMS to handle the single point of failure problem associated with centralized architectures. However, the transparency inherent in blockchain technology still exposes DIMS to privacy leakages. In this paper, we propose the privacy-protected IoT DIMS (PPID), a novel blockchain-based distributed identity system to protect the privacy of on-chain identity data. The PPID achieves the unlinkability of identity-credential-verification. Specifically, the PPID adopts the Zero Knowledge Proof (ZKP) algorithm and Shamir secret sharing (SSS) to safeguard privacy security, resist replay attacks, and ensure data integrity. Finally, we evaluate the performance of ZKP computation in PPID, as well as the transaction fees of smart contract on the Ethereum blockchain.

This paper investigates the joint active transceiver and passive beamforming design to maximize the weighted sum-rate (WSR) of an IRS-aided multi-streams multiuser multiple-input multiple-output broadcast channel (MIMO-BC) downlink transmission system. Due to the coupling of the transceiver parameters, the considered WSR optimization problem is highly non-convex and thus challenging to solve. Different from the normally used methods, such as the weighted minimum mean-square error (WMMSE), we rely on the matrix fractional programming (MFP) theory to derive an effective algorithm to the WSR problem. Specifically, we reformulate the original problem into a tractable one by exploiting the special structure of the objective function, i.e., a MFP which involves a matrix ratio inside a logarithm in the objective function. An alternating optimization (AO) framework is then devised to decompose the reformulated problem into four subproblems, which optimize the introduced auxiliary variable, the transmit beamforming matrix, the receive matrix, and the reflecting beamforming matrix by fixing other variables respectively. Through the matrix quadratic transform, we reformulate the MFP problem as a convex one, and thus obtain the optimal transmit beamforming matrix. By leveraging the optimality conditions for unconstrained optimization problems, the optimal receive beamforming matrix and the introduced auxiliary variable are derived in closed form. For solving the passive beamforming subproblem, we propose an iterative algorithm based on successive convex approximation (SCA). Since the computational complexity of SCA is relatively high, we propose a computationally efficient method based on manifold optimization (MO) to optimize the passive beamforming matrix. Finally, we also consider the robust beamforming design when the system suffers from imperfect CSI. Simulation results demonstrate the effectiveness of the proposed methods.

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

With the rapid development of information technologies such as cloud computing, big data, artificial intelligence, and edge computing, data centers have become essential infrastructure supporting the modern information society. When constructing data center networks, as the network scale increases, both latency and cost also grow. Therefore, it is essential not only to consider network scalability but also to focus on link overhead and communication latency. The hypercube is an excellent base topology for constructing data center networks. The Möbius cube, a version of the hypercube, not only retains the hypercube’s favorable properties, but also outperforms it in terms of link overhead and network diameter. In this paper, we propose a new server-centric data center network architecture, called MBDC, which is based on the Möbius cube. For networks of the same scale, MBDC achieves a smaller diameter than most existing server-centric networks. Additionally, we present an adaptive fault-tolerant routing scheme for MBDC, which is based on an improved local security information model. Extensive evaluations demonstrate that MBDC is an attractive data center network for constructing low-latency and cost-effective data centers.

YOLOv5s is a classic deep learning target detection framework with balanced speed and performance in recognition, which is widely used in industrial defect inspections. YOLOv5s relies on the residual structure in the C3 module for feature extraction, but due to its simple structure and the small, fixed convolution kernel, it is difficult to accurately and completely extract target features when applied to the detection of defects with inconspicuous features and irregular shapes, which results in a decrease in recognition accuracy. To solve this problem, this paper proposes an improved YOLO architecture based on receptive field enhancement and redundant feature reuse. Firstly, the method designs a redundant feature reuse module (RFRM) to replace the residual structure in the C3 module for feature extraction. This module can deepen the network with a smaller cost and enhance the network’s ability to extract features. Furthermore, the feature reuse process allows the network to deepen while retaining detailed information, enabling the network to classify and localize more accurately. Secondly, a simplified convolutional block attention module (SCBAM) is designed and embedded into the neck of the detection network. This module directs the detection network to focus more on inconspicuous defect features and reduce interference from background noise in the feature map, thereby improving the accuracy of defect classification and localization. Finally, a receptive field enhancement module (RFEM) is designed and inserted between the neck and head of the detection network. This module adds only a small computational cost to expand the receptive field of the network, enabling it to further extract and comb irregular features from the neck in a comprehensive manner, making the features reaching the detection head more complete. Applying the above improvements to defect detection, the experimental results show that the improved YOLO architecture based on receptive field enhancement and redundant feature reuse proposed in this paper has better detection performance in defect image detection scenarios compared to other mainstream target detection methods.

Federated learning (FL) is a distributed machine learning framework enabling multiple clients to collaboratively train a shared ML model without sharing raw data. Despite its aim to safeguard data security and privacy, FL faces risks from advanced adversarial attacks like membership inference attack (MIA), potentially leaking sensitive information. To counter these threats, differential privacy (DP) methods add noise to shared model parameters. However, traditional DP methods struggle with balancing model accuracy, training efficiency, and privacy protection, often compromising one for the other. Additionally, uniform DP mechanisms may not cater to varying client privacy and performance needs. To tackle these challenges, we propose a federated learning framework based on localized random differential privacy (FedLRDP). This approach empowers each client to control noise levels based on their privacy requirements, enhancing model performance while preserving data privacy. We further optimize client-side loss functions to enhance model performance. Theoretical analysis establishes the convergence bounds for FedLRDP, demonstrating its superior convergence performance. Experimental results on MNIST reveal that FedLRDP improves model accuracy by 3.28% compared to traditional DP methods while mitigating MIA with a 72.48% lower attack success rate than FedAvg. Moreover, on EMNIST, FedLRDP boosts model accuracy by 7.31% compared to traditional DP methods, maintaining a 59.4% lower attack success rate than FedAvg. These findings underscore FedLRDP’s efficacy in meeting client privacy needs while enhancing model performance.

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

Surface electromyography (sEMG) signal is a physiological electrical signal produced by muscle contraction. Different gestures can be effectively recognized from the characteristics of the sEMG signal. Currently, convolutional neural networks (CNNs) have been widely used in sEMG gesture recognition systems due to their capabilities in acquiring spatial features of sEMG signals. However, these classical CNNs are inefficient in extracting temporal correlation that resides in the time serials of sEMG signals, which is definitely important for gesture recognition. To overcome such a drawback of traditional CNN-based gesture recognition methods, we propose a multichannel hybrid deep learning model for gesture recognition by combining the multichannel CNNs with a gated recurrent unit (GRU). Specifically, we use multiple CNNs to preprocess the original multichannel EMG signals in a one-by-one manner to obtain the spatial features in the current observing window. The outputs of the multiple CNNs are concatenated and fed to a temporal-feature extracting module, which is designed by cascading a GRU with an attention mechanism. Through the GRU, the temporal features of successive signal frames can be established, while the attention mechanism is introduced to further focus on the key information in recognizing the gestures, which is beneficial to improve the robustness and accuracy of the model. Experiments show that the recognition accuracy of the proposed method reaches 97.6% and 96.7% on the Ninapro DB2 and Ninapro DB5 datasets, respectively. Compared with the classical CNN method, the performance improvement is 2.9% and xx% higher than that of the traditona CNN model, respectively.

This paper proposes a Transformer based face swapping model, namely, TransFS. The proposed model mainly solves two current problems of face swapping: 1) the face swapping result does not fully preserve pose and expression of the target face as expected; 2) most of the existing models fail to accomplish high-quality face swapping on high-resolution images. To address these two challenges, we first propose a Cross- Window Face Encoder based on Swin Transformer that learns rich facial features including poses and expressions. Then, we devise an Identity Generator to reconstruct high-resolution images of specific identity with high quality while utilizing the Transformer attention mechanism to increase identity information retention. Finally, a Face Conversion Module is proposed to transform the source identity reconstructed image into the target face image to synthesize the final face swapping result while maintaining the details of pose and expression of the target face. Through extensive experiments, our method not only accomplishes face swapping for low-resolution images with arbitrary identities, but also accomplishes face swapping for high-resolution images. Furthermore, our method achieves the state-of-the-art performance in pose and expression controls compared to other methods.

Trajectory tracking of zebrafish is an important requirement in studying neurological disorders and developing new psychoactive drug. However, many challenges emerge for stable tracking, since zebrafish are similar in appearance, occlusion, agile, non-linear in moving and easy to swarm, all of which will lead to mistrack for multiple fish. And there is no precedent for tracking zebrafish through embedded edge artificial intelligence device. To overcome these difficulties, we present a tracking system for zebrafish based on RK3588-S. First, We construct an embedded edge AI hardware system consisting of two cameras driven by the RK3588-S, one for the front view and the other for top view of the fish. Then, we develop a 2D tracking algorithm based on YOLOv5 and the Observation-Centric Simple online and real-time tracking (OC-SORT) algorithm, which are transplanted to the RK3588-S for tracking the top and front views of the fish at the edge device. Compared with the previous methods, our method has fewer ID exchanges and highly real-time. Moreover, we apply the MQTT mechanism to establish communication links between the edge and the cloud to reliably transmit data to the cloud. The correspondence between cloud server and embedded AI is 1: N. Finally, we design a multi-view data fusion association algorithm to fuse the data of the two views in the cloud, which are further utilized to build the 3D tracklets of the zebrafish.

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time recognition.Experimental results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure.In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest.To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas.This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas.

Remote sensing image scene classification is to label remote sensing images as a specific scene category by understanding the semantic information of the images. It is an essential link in remote sensing image analysis and interpretation and has important research value. Convolutional neural networks (CNNs) have been dominant in remote sensing image scene classification due to their powerful feature extraction capabilities. The general trend has been to make deeper and wider CNN architectures to achieve higher classification accuracy. However, these advances to improve accuracy enlarge the network, creating too many parameters and high computational costs. Large models are difficult to deploy on resource-constrained edge devices for practical applications. Furthermore, CNNs can effectively capture local information but are weak in extracting global features. To overcome these drawbacks, we propose a novel knowledge distillation (KD) based method by employing Swin Transformer as a teacher network for guiding MobileNetV2 with Parameter-Free Attention (MobileNetV2-PFA). First, we modify MobileNetV2 by introducing PFA into the inverted bottleneck block; this improvement helps the model learn more latent and robust features without extra parameters. Second, Swin Transformer is an excellent architecture for capturing long-range dependencies via shifted window-based attention. So, we utilize the long-range dependency information from the Swin Transformer to assist MobileNetV2-PFA training through KD. Experimental results on the challenging NWPU-RESISC45 dataset show that the proposed method outperforms the original MobileNetV2 in classification accuracy with low computational consumption.

Sensing human activity via WiFi Channel State Information (CSI) has considerable application prospects in future intelligent interaction scenarios such as virtual reality, intelligent games, metaverse, etc. Recently, many Deep Learning-based WiFi sensing schemes have been proposed in the literature, which gained high accuracy for a wide range of simple activities such as standing, squatting, and bending. However, the performance will be suffered when existing approaches are used to recognize drastic activities, such as actions in vigorous sports. This is mainly due to the reason that the spatiotemporal information of these actions is not well utilized. To overcome this drawback, we propose a novel DL-based WiFi sensing method for drastic activity recognition by combining the Convolutional Neural Network (CNN) and the Bidirectional Long Short-Term Memory (BiLSTM) network. The designed CNN-BiLSTM architecture is in parallel with feature extraction, which can simultaneously extract sufficient spatiotemporal features of action data and establish the mapping relationship between actions and CSI streams, thereby improving the accuracy of activity recognition. The CNN is used to extract information on the spatial dimension, while the BiLSTM extracts information on the time dimension. To verify the performance of the proposed scheme, we build a hardware experiment platform and constrain a dataset with 1400 pieces of records for 7 classes of basketball actions. After training over the dataset, the proposed CNN-BiLSTM scheme achieves 96% experimental accuracy on the test set, which is better than the benchmark methods.

Generative Adversarial Networks (GANs) have been used in the field of speech enhancement due to their huge potentials in reducing the noise mixed in the signals. Most of existing GAN-based speech enhancement approaches either operate on time domain or exploit the magnitude spectra in time-frequency domain, but lack consideration of direct optimization of the phase. In this paper, we propose a GAN architecture for speech enhancement based on gated linear units (GLUs) and Dual-Path Transformers (DPTs), which simultaneously deals with the amplitude and phase information on the time-frequency domain. The generator of the proposed GAN architecture is designed following an autoencoder structure fed by the real and imaginary parts of the time-frequency frames. The encoder of the generator is constructed by multiple cascaded convolutional GLUs (ConvGLUs), while the decoder consists of two groups of cascaded deconvolutional GLUs (DeconvGLUs), one for the real part of the spectrogram and the other for the imaginary part. The GLUs are adopted since they are potential in avoiding the gradient vanishing issue dwelling in deep architectures by providing a linear path for the gradients while retaining non-linear capabilities. Aiming at capturing the long-range dependent features in speech, we place DPTs between the encoder and the decoder of the generator, which contains multi-head attention modules and Bi-directional Gated Recurrent Units (BiGRUs). Moreover, the DPT structure is also merged with multiple one-dimensional convolutional layers in the discriminator of the GAN. Such a design not only improves the speech enhancement performance of GAN by focusing on multiple features of speech, but also reducing the volume of model parameters of GAN. Experimental results suggest that the proposed GAN architecture outperforms the existing benchmark GANs in terms of both objective speech intelligibility and quality with less computational complexity.

Abstract Remote sensing scene classification aims to automatically assign a specific semantic label to each image. It is challenging to classify remote sensing scene images due to the images' diversity and rich spatial information. Recently, convolutional neural networks have been widely used to overcome these difficulties, such as the famous Visual Geometry Group (VGG) network. However, the VGG network with local receptive fields cannot model the global information of remote sensing images well. It also needs a large number of parameters and floating point operations to achieve satisfactory accuracy. To overcome these challenges, we introduce the self‐attention mechanism to the VGG network. Specifically, we replace the last four convolutional layers in the VGG‐19 network with two cascaded self‐attention blocks, each consisting of two multi‐head self‐attention (MHSA) layers with the residual network structure. The new structure can simultaneously explore the local and global information from remote sensing scenes. Such improvements not only reduce model parameters but also improve the classification performance. The effectiveness of the proposed method is validated through experiments on four public data sets, i.e., NaSC‐TG2, WHU‐RS19, AID and EuroSAT.

This paper studies joint beamforming problems for an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system, and the goal is to maximize the sum rate by jointly optimizing the transmit beamforming vectors at BSs and the reflective beamforming vector at the IRS, subject to the individual maximum transmit power constraints at BSs, and the reflection constraints at the IRS. Due to the formulated optimization problem is highly non-convex, we propose an alternating optimization (AO) algorithm based on successive convex approximation (SCA) such that the transmit and reflective beamforming vectors can be optimized alternately. We further consider the SINR balancing beamforming design scheme by maximizing the minimum SINR among all users to enhance the fairness among users, in which the transmit and reflective beamforming vectors are optimized in an alternating manner. The transmit beamforming vectors are optimized by the second-order-cone programming (SOCP) based on bisection method and the reflective beamforming vector is updated based on the technique of semidefinite relaxation (SDR). Simulation results show that the two proposed algorithms considerably outperform the benchmark zero-forcing (ZF) scheme. Moreover, the AO algorithm based on SCA has good communication performance than the other two schemes. And the AO algorithm based on bisection search guarantees the fairness for all users.

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of$O(\ln n)$, where$n$is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of$\Theta (\ln n)$. Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule.

This paper studies the joint transceiver design and power splitting (PS) for a downlink multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. The objective of this work is to minimize transmit power by jointly optimizing the transmitter at a base station (BS), the PS factors and information decoding (ID) receivers at mobile stations (MSs) subject to both the mean-square-error (MSE) and energy harvesting (EH) constraints. To solve the formulated nonconvex optimization problem, a framework is proposed to iteratively solve a joint transmitter and PS factors optimization (JTxPS) sub-problem and a receiver side minimum mean-square error (MMSE) minimization subproblem. The nonconvex JTxPS sub-problem is reformulated as a convex semidefinite programming (SDP) and thus solved. Simulation results show the effectiveness of the proposed scheme.

Sums of exponentially distributed random variables (RVs) play important roles in performance analysis of various communication systems. Their logarithmic expectations can not only facilitate capacity analysis but also provide efficient analytical expressions of the system capacity. However, the analytical expressions for the logarithmic expectations have been rarely systematically provided in literature, resulting in inconvenience for related performance analysis. To overcome this issue, in this work, the analytical expressions for the logarithmic expectations of the sums of independent exponential RVs are summarized. Especially, for the case where the sum is composed of both independent non-identically distributed (i.n.i.d.) exponential RVs and independent identically distributed (i.i.d.) exponential RVs, a new closed-form probability density function (PDF) is derived. Compared to the previous PDF expressions, the derived PDF expression is much concise and easy to be determined. To demonstrate the effectiveness of the derived logarithmic expectations, case studies are performed by applying the derived logarithmic expectations to the analysis and derivations of the ergodic capacity of several multiple antenna systems. It is shown that the proposed approach can significantly facilitate the performance evaluation of multiple antenna communication systems.

Interference alignment (IA) is a promising interference management technique to efficiently eliminate multiuser interference in a K-user interference network. However, channel state information (CSI) at the transmitter is indispensable for the interference alignment precoding matrices design. In spite of the fact that the CSI can be obtained through feedback from receivers to transmitters in a frequency-division duplex (FDD) system or through the reciprocity in a time-division duplex (TDD) system, it may be imperfect for the reason such as estimation inaccuracy, feedback delay and time-varying of channel. In this paper, the impact of imperfect CSI on the achievable sum rate of interference alignment network is considered and a channel prediction technique based on Kalman filtering is proposed to verify the theoretical analysis. Through analysing, we find that the sum rate performance of an interference aligned network is affected by an integrated parameter, i.e., the product of user number, transmit power and channel error variance. Simulation results reveal that the performance of interference alignment is more sensitive to the uncertainty of CSI at high signal-to-noise ratio (SNR) regime than that at low SNR regime. It is also verified that the performance can be improved through channel prediction, comparing with interference alignment based on the delayed feedback CSI.