In this paper, we present a novel small-scale fading model, named the extended k-u model, which incorporates the imbalance of multipath clusters by adding a new parameter based on the original k-u model. The extended k-u model has more accurate modeling capability than the extended η-u model in scenarios with line-of-sight (LoS) paths. Additionally, it is mathematically more tractable than the a-k-η-u model. The extended k-u model provides an effective channel modeling tool for millimeter (mmWave) LoS scenarios. Through theoretical derivations, we obtain closed-form expressions for the key statistical characteristics of this model, including the probability density function, the cumulative distribution function, moments of arbitrary order, and the moment generating function. Based on these statistics, this study further derives and analyzes the expressions for some performance metrics of the communication system, including the amount of fading, the probability of outage, the average bit error rate, and the effective rate. Using the measured fading data extracted from literature, which cover communication scenarios at 28 GHz, 65 GHz, and 92.5645 GHz with LoS paths, we apply the proposed model in mmWave scenarios and compare it with the k-u model and the extended η-u model. The results show that the extended k-u model has better capability in characterizing such fading than the other two models, verifying that this extension enhances its ability to model LoS mmWave scenarios.

Overcoming class imbalance is a critical challenge for graph-based semi-supervised classification methods. In this letter, we address this issue from the perspective of graph filtering and propose a novel adaptive graph filter. By introducing learnable thresholds into the adjacency matrix, the filter enables dynamic suppression of majority-class bias during label propagation through the incorporation of discontinuities. Additionally, we develop a modified Fruit Fly Optimization Algorithm (m-FOA) to optimize the filter's coefficients, which achieves lower loss and faster convergence compared to other heuristic algorithms. To evaluate the effectiveness of our approach, we conduct a Monte Carlo simulation on a real-world dataset. The results demonstrate that our method outperforms the baseline methods in both classification accuracy and efficiency when handling class imbalance. We note that the model's scalability to very large graphs is limited and the solving procedure can be time-consuming due to the dense construction of the filter.

In this paper, we propose an innovative relay-aided physical layer security scheme leveraging cooperative transmission and jamming that operates effectively in time-division-multiple-access-based multi-user visible light communication (MuVLC) systems with obstructed communication links. First, to circumvent obstacles between the light source (LS) and legal receivers, we utilize a transmission relay (TR) to forward information signals from the LS to the receiver plane. Subsequently, to ensure communication security, we select a jamming relay to enlarge the channel capacity gap between eavesdroppers and legal receivers through cooperative jamming. Using stochastic geometry, we derive closed-form expressions for key performance metrics, including the cumulative distribution function of secrecy capacity, security outage probability, and their lower and upper bounds, both with and without relay cooperation. To further enhance system security, we introduce a disk-shaped security-protected zone around the TR. All analytical expressions are numerically verified through Monte Carlo simulations, serving as benchmarks to evaluate security performance. Numerical results confirm that our proposed scheme can ensure continuous signal reception and high-level security. Furthermore, while relay cooperation modestly enhances system security, the introduction of a security-protected zone around the TR yields substantial security improvement, which offers practical insights for designing secure time-division-multiple-access-based MuVLC where communication links are obstructed.

Multi-view 3D human pose estimation (MHPE) is an important research task in computer vision. To maintain consistency during the data collection, hardware synchronization devices are commonly used to connect cameras, ensuring that images from different views are captured simultaneously. However, synchronizing with extra devices has two apparent limitations: the hardware is i) usually expensive and ii) less flexible for deployment in outdoor open scenarios. Suppose the model can improve its tolerance for the time differences in multi-view image capture. In that case, the difficulty and cost of deployment will be greatly reduced, and MHPE will become more widespread. In this paper, we try to answer how to build a model that performs pose estimation directly using ''weakly synchronized images" from multiple views, where the captured images shift from each other within a frame. To this end, we introduce a new multi-view 3D human pose estimation task given weakly synchronized image inputs. Apart from existing well-synchronized datasets, we present the first weakly synchronized dataset comprising 800k images. Thereon, we propose SyncDiffPose, a novel model based on the diffusion method for pose estimation to denoise the error in such data. By combining simple synchronization strategies, e.g., the timer method, our approach can perform pose estimation without hardware calibration.

This paper introduces a new task: skeleton-based hand motion sequence prediction, which can be applied to VR/AR systems and human-computer interaction systems to enhance user experience. To tackle this task, we performed a comprehensive analysis of hand movement patterns and propose a Global-Local Spatio-Temporal Graph Convolutional Network (GLST-GCN). Based on the local and global correlation characteristics of hand motions, the GLS block of GLST-GCN is proposed to extract spatial features through local branches and global modules. Additionally, we considered the variation in hand movement speeds, which leads to inconsistencies in temporal features across different time scales, and thus proposed the MGLT block to model both global and local temporal dependencies across multiple scales. We also conduct extensive experiments based on the remaked Bighand2.2M and FPHA datasets, and numerical results show that our proposed method offers state-of-the-art performance.

Minimum storage regenerating (MSR) codes are extensively studied in the literature to reduce the network bandwidth consumed during node repair. In this paper, we focus on repairing multiple node failures and construct binary cooperative MSR codes with optimal access bandwidth. Specifically, we present explicit constructions of the codes by designing the parity-check matrices over a special polynomial ring ${\mathcal{R}} = {{\mathbb{F}}_2}[x]/\left({1 + x + \cdots + {x^{p - 1}}}\right)$ where p is a prime. The obtained codes with length n and dimension k achieve the lower bound on repair bandwidth under cooperative repair model with optimal access property for any 2 ≤ h ≤ r and k+1 ≤ d ≤ n – h where h and d are the number of failure nodes and helper nodes, respectively. Moreover, the computation operations involved in encoding, decoding and nodes repair are only exclusive ORs and cyclic shifts, resulting in less CPU overhead compared with the complex multiplication operations over finite fields.

Harnessing the benefits of blockchain, such as decentralization, immutability, and transparency, to bolster the credibility and security attributes of federated learning (FL) has garnered increasing attention. However, blockchain-enabled FL (BFL) still faces several challenges. The primary and most significant issue arises from its essential but slow validation procedure, which selects high-quality local models by recruiting distributed validators. The second issue stems from its incentive mechanism under the transparent nature of blockchain, increasing the risk of privacy breaches regarding workers’ cost information. The final challenge involves data eavesdropping from shared local models. To address these significant obstacles, this paper proposes a Blockchain-enabled Incentivized and Secure Federated Learning (BIT-FL) framework. BIT-FL leverages a novel loop-based sharded consensus algorithm to accelerate the validation procedure, ensuring the same security as non-sharded consensus protocols. It consistently outputs the correct local model selection when the fraction of adversaries among validators is less than$1/2$with synchronous communication. Furthermore, BIT-FL integrates a randomized incentive procedure, attracting more participants while guaranteeing the privacy of their cost information through meticulous worker selection probability design. Finally, by adding artificial Gaussian noise to local models, it ensures the privacy of trainers’ local models. With the careful design of Gaussian noise, the excess empirical risk of BIT-FL is upper-bounded by$\mathcal {O}(\frac{\ln n_{\min}}{ n_{\min}^{3/2}}+\frac{\ln n}{n})$, where$n$represents the size of the union dataset, and$n_{{\min}}$represents the size of the smallest dataset. Our extensive experiments demonstrate that BIT-FL exhibits efficiency, robustness, and high accuracy for both classification and regression tasks.

This work tackles the asymptotics of the maximal throughput of covert communications over AWGN channels when the covert metric is Kullback-Leibler divergence (KL divergence). It is shown that the first and second order asymptotics of the maximal throughput are$\sqrt{n\delta\log e}$and (2)${ }^{\frac{1}{2}}(n \delta)^{\frac{1}{4}}(\log e)^{\frac{3}{4}} \cdot Q^{-1}(\epsilon)$, respectively by$n$channel uses, where$\delta$and$\epsilon$are constraints imposed on covertness and channel decoding error probabilities, respectively. The technique we use in the achievability is quasi-$\varepsilon$-neighborhood notion from information geometry. For finite blocklength$n$, the generating distributions are chosen to be a family of truncated Gaussian distributions with decreasing variances. The law of decreasing is carefully designed so that it maximizes the throughput at the main channel in the asymptotic sense under the condition that the output distributions satisfy the covert constraint. For the converse, the optimality of Gaussian distribution for minimizing KL divergence under second order moment constraint is extended from dimension 1 to dimension$n$, which further leads to the direct converse bound in terms of covert metric.

Heatmap regression using deep neural networks has become the dominant approach in 2D pose estimation. Nonetheless, the intrinsic variability in the flexibility of distinct keypoints engenders discernible momentum deviation among them, leading to training biases. Moreover, previous methods indiscriminately treat all pixels in a heatmap, further exacerbating biases. Regrettably, conventional loss functions, including the Mean Squared Error (MSE) loss, fail to rectify this issue adequately. Consequently, the need arises to recalibrate weights to concentrate the loss’s impact on specific regions. To this end, we introduce a novel Deviation Wing (DW) loss function for high-performance 2D pose estimation, incorporating two improvement aspects. Firstly, we use Gaussian Momentum Deviation encoding craft deviation maps, leveraging momentum deviation as a source of prior knowledge to enhance supervision over keypoints characterized by substantial momentum deviation. Subsequently, we harness the Cosine Wing function to amplify the loss concerning minor errors residing within the keypoint region and supervise errors across diverse scales. Our comprehensive empirical exploration spans multiple datasets encompassing 2D human pose and hand pose estimation. The experimental results demonstrate the efficacy of our proposed loss function in enhancing heatmap regression performances.

Erasure codes are widely implemented in distributed storage systems to provide high fault tolerance with small storage overhead. Maximum distance separable codes are an common choice as they achieve the optimal tradeoff between fault tolerance and storage overhead. In this paper, we focus on the repair of multiple erasures of binary MDS array codes. Specifically, we present constructions of binary MDS array codes with optimal cooperative repair bandwidth by stacking multiple Blaum-Roth code instances whose “evaluation points” are judiciously designed. The constructed array codes with length$n$and dimension$k$can achieve the optimal cooperative repair bandwidth for$2\leq h\leq n-k$and$k+1\leq d\leq n-h$where$h$and$d$are the numbers of failed nodes and helper nodes, respectively. As the codes are constructed on a special polynomial ring over binary field, computation operations involved in nodes repair and file reconstruction for these codes are only XORs and cyclic shifts. Moreover, due to the inherent parallel structure of the codes, both the encoding and decoding procedures can be finished in parallel, speeding up the computing process.

Maximum distance separable (MDS) codes are commonly deployed in distributed storage systems as they provide the maximum failure tolerance for some given redundancy. The repair problem of MDS codes has drawn much attention and various constructions of MDS array codes with optimal repair bandwidth have been proposed in the last decade. However, few of the existing codes are constructed over the binary field. In this paper, we propose new constructions of binary MDS array codes with optimal repair (or access) bandwidth for single-node failure. Specifically, by stacking multiple Blaum-Roth code instances of which the parity-check matrices are judiciously designed, we obtain three families of binary MDS array codes with optimal repair bandwidth; using the permutation matrices as building blocks, we also construct two families of binary MDS array codes with optimal access bandwidth. Moreover, error-resilient capability while achieving the lower bound on repair (or access) bandwidth is obtained when the number of helper nodes d < n - 1. All the codes in this paper are constructed over a particular ring of binary polynomials. Consequently, computation operations involved in the encoding, decoding and node repair procedures for these codes are only XORs and cyclic shifts, avoiding complex multiplications and divisions over large finite fields.

Deep Joint Source-Channel Coding (JSCC) has gained increased attention, asserting its significance in the communication field. However, existing Deep JSCC techniques struggle to mitigate time-varying noise due to the deep neural networks being trained beforehand and fixed. To address this issue, we propose a robust deep JSCC scheme. Firstly, a multi-network parallel structure, as well as error-correcting codes, is introduced to effectively exploit label information. Secondly, a closed-form linear encoder and decoder pair is employed at the input and output ends of the channel to deal with the varying noise, which releases the neural network from dealing with a large range of varying noise levels. Thirdly, a transfer learning algorithm is utilized for estimating real-time noise statistics, which outperforms conventional estimation methods when noise statistics are time-dependent. These three components are effectively integrated as a comprehensive transmission system. Experimental results demonstrate that our optimized scheme outperforms existing approaches in the literature.

For signal detection tasks in wireless communications, most of the existing algorithms either ignore the co-channel interference or treat it as Gaussian noise, which may result in unsatisfactory accuracy when the interference is non-negligible and complex-distributed. When neural networks are motivated in the design of the detectors, difficulty arises in the training due to the fact that there are few accessible pilots in each packet. In this paper, we consider a data-driven detector based on multi-source transfer learning (MSTL) for signal detection in a fading channel with interference. The MSTL detector transfers channel knowledge of previous packets into the latest detection. In particular, we consider a linear combination of the pilots and historical symbols in the distribution space, and design the optimal combination coefficients based on the number of those symbols as well as distributions similarity. Numerical simulations on a Gauss-Markov flat Rayleigh fading channel with co-channel interference validate the advantages of our algorithms, compared with several existing training schemes including directly applying the fully connected deep neural network (FCDNN) and conventional linear minimum mean square error (LMMSE) detector.

It is well known that finite blocklength analysis plays an important role in evaluating performances of communication systems in practical settings. This paper considers the achievability and converse bounds on the maximal channel coding rate (throughput) at a given blocklength and error probability in covert communication over AWGN channels. The covert constraint is given in terms of an upper bound on total variation distance (TVD) between the distributions of eavesdropped signals at an adversary with and without presence of active and legitimate communication, respectively. For the achievability, Gaussian random coding scheme is adopted for convenience in the analysis of TVD. The classical results of finite blocklength regime are not applicable in this case. By exploiting and extending canonical approaches, we first present new and more general achievability bounds for random coding schemes under maximal or average probability of error requirements. The general bounds are then applied to covert communication in AWGN channels where codewords are generated from Gaussian distribution while meeting the maximal power constraint. We further show an interesting connection between attaining tight achievability and converse bounds and solving two total variation distance based minimax and maxmin problems. The TVD constraint is analyzed under the given random coding scheme, which induces bounds on the transmission power through divergence inequalities. Further comparison is made between the new achievability bounds and existing ones derived under deterministic codebooks. Our thorough analysis thus leads us to a comprehensive characterization of the attainable throughput in covert communication over AWGN channels.

Currently, 5G control/user (C/U) plane split heterogeneous network is a potential technology to provide high-speed rail (HSR) with higher capacity, better reliability and less co-channel interference communication. However, the Quality of Service (QoS) of the wireless HSR communication is greatly degraded due to frequent handover. In addition, a complete Inter- macrocell handover in C/U plane split network requires both the C-plane and U-plane service succeed during their individual handovers, which results in more handover problems. Some efforts have been made to design a handover scheme for C/U plane network. Motivated by this idea, we redesign a seamless dual- link handover scheme with optimized threshold for C/U plane split network. According to our proposed three principles, the optimized handover trigger algorithm depends not only on the hysteresis exceeding level, but also on the Received Signal Strength (RRS) of both antennas. Furthermore, the front antenna has more chances to execute handover while the rear antenna still keeps communicating with an Evolved Node B (eNB) in the new handover procedure. The simulation results demonstrate that the proposed scheme reduces the handover failure probability remarkably.