Sparse code multiple access (SCMA) is a competitive candidate multiple access technology for future wireless communication systems. In recent years, SCMA systems are modeled as autoencoders to design high performance codebooks, which reduces the suboptimal problem caused by multi-stage optimization in traditional codebook design schemes. However, the current SCMA autoencoder only considers the AWGN channel, so the designed codebook can achieve excellent performance in AWGN channels but poor performance in Rayleigh fading channels. In this article, we propose a novel SCMA autoencoder scheme that incorporates minimum Euclidean Distance (MED) constraints for designing uplink SCMA codebooks. The core design of the proposed scheme is to introduce a constraint network, which aims to maximizing the MED between superimposed codewords while ensuring the distance between each user’s one-dimensional codewords. First, the framework of the proposed SCMA autoencoder model is introduced. Then, the network structures that comprise the proposed model are presented separately. Finally, the details of the proposed loss function are discussed. Simulation results show that the BER performance for the proposed codebooks is better than that of the existing codebook.

Sparse code multiple access (SCMA) is a competitive candidate multiple access technology for future wireless communication systems. In this paper, a transfer learning (TL)-based SCMA detection scheme is proposed to improve the performance of the deep neural network (DNN) detector for the downlink SCMA system. First, we propose a detection framework that preserves model parameters trained on datasets with varying signal-to-noise ratios (SNRs) and adaptively selects them during detection based on the estimated channel SNR. Then, we analyze the reason why the DNN detector, which can achieve the same BER performance as the message passing algorithm (MPA) in the low SNR region, fails to achieve MPA performance in the high SNR region. Later, a TL-based SCMA detection scheme is proposed, which consists of pre-training, fine-tuning and online detection. Simulation results show the proposed TL-based SCMA detection scheme can achieve improved BER performance compared to the deep learning (DL)-based scheme trained from scratch. Moreover, to alleviate the gap between the source domain and the target domain, a successive transfer learning strategy is proposed, which makes the transfer process smoother and further improves the performance by introducing intermediate states.

Summary The objective of makeup transfer is to apply the makeup style of, thereby creating the similar appearance as if it was professionally done. This technique has significant practical applications in fashion, beauty, and video special effects industries. However, there are several challenges faced by current makeup transfer models: (1) Low‐resolution images can only achieve partial makeup transfer in mainstream models. (2) Difficulty arises in obtaining paired data consisting of both makeup and non‐makeup images. (3) Spatial displacement occurs due to differences in subject and pose between reference and source images, affecting corresponding feature regions. (4) Mainstream models primarily focus on local feature characteristics while lacking global feature perception. To address these challenges, this paper proposes a nonpaired data makeup transfer model based on swin transformer generative adversarial networks. Additionally, an improved progressive generative adversarial network model (PSC‐GAN), incorporating semantic perception and channel attention mechanisms, is proposed to enhance the effectiveness of makeup transfer.

By separating huge dimensional matrix-matrix multiplication at a single computing node into parallel small matrix multiplications (with appropriate encoding) at parallel worker nodes, coded distributed computing (CDC) tackles the straggler problem and hence speeds up the computation significantly. Existing CDC encoding schemes are based on linear combination (LC), which have two drawbacks: First, heavy computational burden is introduced to both encoding and decoding phases. Second, large numerical error occurs in the decoding phase. To relieve these two effects, a fresh new 2D-SAZD-CDC framework that non-trivially generalizes 1D-SAZD-CDC is proposed, where D is short for dimension, the operation for encoding and decoding is implemented by shift-and-add (SA) and zigzag decoding (ZD) that replaces LC and matrix inversion, respectively. The non-trivial generalization lies in joint design of the operations in 2D are needed in both the encoding and the decoding phases, so as to ensure possesion of combination property (CP) and ZD from 2D viewpoint. More specifically, 2D-SA encoding is designed, 2D-ZD decoding (alternates intermittently between 2D) is proposed, and a proof for satisfying CP and ZD from 2D viewpoint is also given. Numerical studies show that 2D-SAZD-CDC significantly improves the numerical stability and computational load performance over existing LC based schemes.

Under the outbreak of the COVID-19 pandemic, respiration state monitoring plays an important role in assisting respiratory disease diagnosis and treatment. Thanks to the nonintrusive nature and low deployment cost, Wi-Fi-based wireless respiration state monitoring methods have gained increasing popularity. By analyzing the variation of channel state information (CSI) of Wi-Fi signals, the respiration states of a target person under the wireless coverage, such as cough, sneeze, and yawn, can be accurately detected. A major problem of the current wireless respiration state monitoring methods is being overly domain-dependent. That is, a sensing algorithm fine-tuned to a specific device placement and background setting (i.e., a domain) can result in drastic drop in detection accuracy when applied to a dissimilar new domain. To enhance the robustness of wireless sensing and reduce the sensing cost across different domains, we propose in this article a domain-adaptive respiration state monitoring system (ResMon) that achieves highly accurate cross-domain detection performance while requiring very limited labeled samples in the new domain. In a nutshell, the proposed ResMon consists of a source domain meta-training stage and a target domain meta-testing stage. In the meta-training stage, we leverage the rich source domain labeled data set to train an embedding model as a feature extractor of high-dimensional CSI data measurements. In particular, we apply the statistical Bayesian deep learning technique to improve the generalization performance of the embedding model in cross-domain applications. In the meta-testing stage, we combine the embedding model with a few-shot learning technique to train a domain-specific classifier using very limited labeled samples in the target domain. Experiment results show that the proposed ResMon can achieve on average 87.26% cross-domain detection accuracy in a 4-class respiration state classification task using only five labeled samples per class, which significantly outperforms the considered benchmark methods.

Linear combination (LC) based coded distributed computing (CDC) suffers from the problem of poor numerical stability. Therefore, LC-CDC based model parallel (MP) training for a deep nueral network (DNN) may have poor accuracy. To enhance accuracy, we propose to replace LC by shift-and-addition (SA) and replace matrix inversion by zigzag decoding (ZD) in the encoding and decoding process of each layer, respectively, and call the scheme Naive SAZD-CDC based MP training (N-SAZD-CDC-MP-T). However, N-SAZD-CDC-MP-T encounters the problem of bottleneck at the master node, which is caused by frequent encoding/decoding at the master node and frequent huge volume of data delivery between master and worker node. This bottleneck problem may pull down the training speed significantly. To alleviate this bottleneck problem, we further design an enhanced version, by offloading certain processing from master node to distributed encoding and updating (DEU) at the worker nodes and call it DEU-SAZD-CDC-MP-T. A proof that DEU-SAZD-CDC-MP-T automatically maitains the code structure during each iteration is provided. Extensive numerical studies show that the prediction accuracy of SAZD-CDC-MP-T improves significantly over that of Poly (which is representative of LC) based scheme. In addition, the training speed of DEU-SAZD-CDC-MP-T over N-SAZD-CDC-MP-T is improved significantly.

This paper considers a wireless powered mobile edge computing (MEC) system consisting of multiple wireless devices (WDs) and one hybrid access point (HAP) broadcasting radio frequency (RF) energy to the WDs. Relying on the harvested energy, the WDs senses data from the monitored environment and execute the task data locally or offload the task to the HAP for edge processing. Given an average power constraint at the HAP, we aim to design an energy-efficient online algorithm under random fading channels to maximize the long-term average data sensing rate of WDs while meeting the system data queue stability. We formulate the target problem as a multi-stage stochastic optimization, where the major difficulty lies in the uncertainty of future channel state and the tight couplings among control decisions over different time slots. To solve this problem, we propose a Lyapunov optimization-based online algorithm named LEESE. Specifically, LEESE equivalently transforms the multi-stage stochastic optimization into per-slot deterministic problems. For each per-slot problem, we derive the optimal closed-form solution. We show that the optimal control on WPT and data processing follows an interesting threshold-based manner decided by the battery state and data queue backlog. Numerical simulations show that the proposed LEESE algorithm can achieve more than 21.9% performance improvement over the considered benchmark methods.

The coupling of unmanned aerial vehicle (UAV) and Internet of Things (IoT) systems can provide an efficient method to collect ground data for the Sixth Generation (6G) networks. Under this UAV-IoT scenario, an intelligent energy balancing strategy should be designed to achieve tunable energy fairness level among all the IoT devices, such that sensors can differ in their lifespans to meet specific application requirements. In this paper, we propose an intelligent $\alpha-$fairness strategy to balance the energy consumption among IoT sensors. Specifically, the heterogeneities among the sensor nodes, i.e., different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an $\alpha-$utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to judiciously tune the $\alpha$ value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

Abstract Network densification is recognized as the key technology to meet the ever growing demand of data traffic in next generation wireless networks. However the proliferation of small cell base stations (SCBSs) introduces vulnerabilities to information security, as they are prone to eavesdropping attacks. In this paper, it is studied how user association strategies can be specifically tailored to meet this security challenge in a ultra dense heterogeneous cellular network(UDHCN) with a group of colluding eavesdroppers. In particular, the situation is considered where eavesdroppers may have limited capabilities in intercepting and decoding data traffic. In this setting each eavesdropper has to make its own decision on eavesdropping targets, and they are able to take concerted actions to sabotage information security. To address this challenge, a zero sum game framework to reflect the conflicting interests of users and eavesdroppers is proposed and a user association scheme is devised that aims to maximize system sum secrecy rate against these adversaries whose actions intend to minimize sum secrecy rate. The case that all adversaries have limited eavesdropping capabilities is first considered, and it is shown that the corresponding zero sum game is indeed a bilinear game, and its Nash equilibrium solution can be readily approximated using a saddle point Frank Wolfe(SP‐FW)algorithm. Then the framework is extended to the case where adversaries with a diverse configuration of eavesdropping capabilities exist. In this case, it is shown that the underlying minimax optimization problem is indeed a nonsmooth convex‐nonconcave one. A two stage method is proposed by first smoothing the objective function with a Hermite cubic polynomial approximation, and then obtaining a nearly stationary solution via a recently proposed proximal point gradient descent method. Simulation results show that the proposed framework leads to significant increases in sum secrecy rate and secrecy probability against both the capability‐limited adversaries and a hybrid type of adversaries.

The rise of Internet of Things (IoT) systems has enabled us to access real-time information about our surrounding environments. However, IoT data collection in hostile and inaccessible areas without infrastructure supports is a challenging issue due to the inherent physical constraints associated with the tiny sensors. A viable solution to this problem is to use agile and controllable unmanned aerial vehicles (UAVs) to collect the ground data and relay it to the remote cloud for further processing. Under this UAV–IoT scenario, the limited battery supply carried by the sensor must be efficiently utilized so as to prolong the lifetime of the IoT system. Nevertheless, lifetime extension does not merely entail the reduction of the sum energy expenditure of sensors. In this article, we first show that minimizing the sum energy consumption cannot effectively extend the system lifetime due to the imbalance in energy expenditure among sensors, which, in fact, can render early energy depletion for some overburdened sensors. We also reveal a tradeoff between energy efficiency and energy fairness. To tackle this imbalance issue, we then propose an$\alpha $-fairness approach to balance the energy consumption among IoT sensors. Specifically, in our study, the heterogeneities among the sensor nodes—different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an$\alpha $-utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to properly set the$\alpha $value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

Wireless powered multi-access edge computing (MEC) has emerged as a promising paradigm to enable high-performance computation of energy-constrained wireless devices (WDs) in internet of things (IoT) systems. However, to overcome the severe path loss of both energy transfer and data communications, wireless powered MEC suffers from high operating power consumption. To achieve sustainable and economic system operation, this paper focuses on developing energy-efficient online data processing strategy for wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we target at maximizing the long-term average data sensing rate of the WDs while maintaining task data queue stability. To this end, we formulate a multi-stage stochastic optimization problem to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose a Lyapunov optimization-based online algorithm named LEESE, which decomposes the multi-stage stochastic problem into per-slot deterministic optimization problems. We show that each per-slot problem can be equivalently transformed into a convex optimization problem. To facilitate online implementation in large-scale MEC systems, instead of solving the per-slot problem with off-the-shelf convex algorithms, we propose a block coordinate descent (BCD)-based method that produces a close-to-optimal solution in less than 0.04% of the computation delay. Simulation results demonstrate that the proposed LEESE algorithm can provide 18% higher data sensing rate than the representative benchmark methods considered, while incurring sub-millisecond computation delay suitable for real-time control under fading channel.

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

Portfolio is a financial concept to combine several stocks to reduce the risks and improve the profits. To choose the basic members of portfolio, we can group similar stocks into one cluster and then choose representative stock from each cluster. In this paper, we focus on the method of choosing representative stocks in clusters. The ordinary representative of a cluster is often the center of that cluster. We propose a new cluster representative method MDR (maximum distance representatives). In our method MDR, we choose the stocks which has maximum distance with other representatives. MDR can construct a more diverse portfolio than center method. The effectiveness of cluster representative selection methods can be evaluated by an index IBEF based on the concept of efficient frontier. Our experiments show that MDR can effectively improve the efficient frontier, which means MDR can bring more profits than center representative method at the same risk level.

In this paper, we consider a multi-user mobile-edge computing (MEC) network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing future channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems of much smaller size. Then, it integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that the proposed LyDROO achieves optimal computation performance while satisfying all the long-term constraints. Besides, it induces very low execution latency that is particularly suitable for real-time implementation in fast fading environments.

Abstract Cognitive radio based cooperative spectrum sensing (CSS) is severely affected when some secondary users maliciously attack it. Two attacks regarded as key adversaries to the success of CSS are spectrum sensing data falsification (SSDF) and primary user emulation attack (PUEA). Defending SSDF and PUEAs has received significant attention in research in the past decade globally. This paper performs a state‐of‐the‐art comprehensive survey of the researches on defending SSDF and PUEAs. First, the preliminaries like Hypothesis testing for detecting the primary user and different models of CSS are discussed briefly. Then a categorization of the defence mechanisms for defending both the attacks has been proposed as active and passive. Active mechanisms are suitable for an immediate defence in a limited time span, while passive mechanisms are suitable for flexible CSS systems that are ready to detect the attacks over a period of time and suppress them permanently by bringing changes in their underlying operations. An in‐depth tutorial on both the defence mechanisms is provided from the perspectives of the secondary users throughput and the interference to the primary user. Finally, a detailed survey on the open research problems in this area and some possible solutions has been performed.

Intelligent reflecting surface (IRS) is an emerging technology to enhance the energy efficiency and spectrum efficiency of wireless-powered communication networks (WPCNs). In this article, we investigate an IRS-assisted multiuser multiple-input single-output (MISO) WPCN, where the single-antenna wireless devices (WDs) harvest wireless energy in the downlink (DL) and transmit their information simultaneously in the uplink (UL) to a common hybrid access point (HAP) equipped with multiple antennas. Our goal is to maximize the weighted sum rate (WSR) of all the energy-harvesting users. To make full use of the beamforming gain provided by both the HAP and the IRS, we jointly optimize the active beamforming of the HAP and the reflecting coefficients (passive beamforming) of the IRS in both DL and UL transmissions, as well as the transmit power of the WDs to mitigate the interuser interference at the HAP. To tackle the challenging optimization problem, we first consider fixing the passive beamforming, and converting the remaining joint active beamforming and user transmit power control problem into an equivalent weighted minimum mean-square error problem, where we solve it using an efficient block-coordinate descent method. Then, we fix the active beamforming and user transmit power, and optimize the passive beamforming coefficients of the IRS in both the DL and UL using a semidefinite relaxation method. Accordingly, we apply a block-structured optimization method to update the two sets of variables alternately. The numerical results show that the proposed joint optimization achieves significant performance gain over other representative benchmark methods and effectively improves the throughput performance in multiuser MISO WPCNs.

Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing the future realizations of random channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems. By doing so, it guarantees to satisfy all the long-term constraints by solving the per-frame subproblems that are much smaller in size. Then, LyDROO integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that under various network setups, the proposed LyDROO achieves optimal computation performance while stabilizing all queues in the system. Besides, it induces very low computation time that is particularly suitable for real-time implementation in fast fading environments.

Mobile edge computing (MEC) platform allows its subscribers to utilize computational resource in close proximity to reduce the computation latency. In this paper, we consider two users each has a set of computation tasks to execute. In particular, one user is a registered subscriber that can access the computation service of MEC platform, while the other unregistered user cannot directly access the MEC service. In this case, we allow the registered user to receive computation offloading from the unregistered user, compute the received task(s) locally or further offload to the MEC platform, and charge a fee that is proportional to the computation workload. We study from the registered user's perspective to maximize its total utility that balances the monetary income and the cost on execution delay and energy consumption. We formulate a mixed integer non-linear programming (MINLP) problem that jointly decides the execution scheduling of the computation tasks (i.e., the device where each task is executed) and the computation/communication resource allocation. To tackle the problem, we first derive the closed-form solution of the optimal resource allocation given the integer task scheduling decisions. We then propose a reduced-complexity approximate algorithm to optimize the combinatorial computation scheduling decisions. Simulation results show that the proposed collaborative computation scheme effectively improves the utility of the helper user compared with other benchmark methods, and the proposed solution method approaches the optimal solution within 0.1% average performance gap with significantly reduced complexity.

Intelligent reflecting surface (IRS) can effectively enhance the energy and spectral efficiency of wireless communication system through the use of a large number of low-cost passive reflecting elements. In this paper, we investigate throughput optimization of IRS-assisted user cooperation in a wireless powered communication network (WPCN), where the two WDs harvest wireless energy and transmit information to a common hybrid access point (HAP). In particular, the two WDs first exchange their independent information with each other and then form a virtual antenna array to transmit jointly to the HAP. We aim to maximize the common (minimum) throughput performance by jointly optimizing the transmit time and power allocations of the two WDs on wireless energy and information transmissions and the passive array coefficients on reflecting the wireless energy and information signals. By comparing with some existing benchmark schemes, our results show that the proposed IRS-assisted user cooperation method can effectively improve the throughput performance of cooperative transmission in WPCNs.

Coded distributed computing (CDC) can overcome the problem that the computation of matrix multiplication with an extremely huge dimension cannot be executed in a single Internet-of-Things (IoT) node. All the encoding of existing CDC schemes are based on the linear combination (LC) to generate independent computation tasks, which introduces a heavy computational load, including a significant volume of expensive multiplications (compared with inexpensive additions) and even more expensive divisions to the encoding and decoding phases. Note that the number of elementwise multiplications of the LC operation during the encoding phase is N times that of the original computation task, where N denotes the number of worker nodes. In this article, to avoid expensive multiplications introduced by LC, a fresh new CDC framework based on shift-and-addition (SA) over the real field is proposed. In addition, to avoid the expensive matrix inverse operation (divisions) in the decoding phase, zigzag decoding (ZD) is incorporated. The proposed scheme, which combines SA and ZD and is hence named SAZD-based CDC, avoids expensive multiplications and divisions in both the encoding and decoding phases. It targets the following simultaneous objectives: an arbitrary K out of N generated computation tasks is independent and can recover the original computation tasks with the ZD algorithm, and the shift distance is small so as to cause a light additional computational load in the computation phase. Both analysis and practical study show that compared to the LC-based CDC, the SAZD-based CDC significantly reduces the computational load.

For one moving Internet of Things (IoT) collector to download files from nearby industrial IoT devices, storing network coded files into multiple IoT devices can achieve good reliability performance if the following (n, k) erasure property (EP) is fulfilled: k source packets are encoded into n packets, and k packets chosen from these n packets in an arbitrary manner can reconstruct all the source packets. Besides, low decoding complexity is desired for time-sensitive applications and energy-limited moving IoT collectors, and binary zigzag decoding (BZD) achieves significantly low decoding complexity. The objective of previous EP-BZD designs is unilateral, which limits its application scenarios. In this article, a novel EP-BZD code is designed, which achieves good tradeoff among largest storage room overhead (SRO), SRO variance, and wide range of (n, k). To implement such a code, the source packets are shifted by several bits, respectively, and then Xored together. The numbers of bits shifted are represented by a matrix, which is obtained from a specially constructed triangle by taking a certain maximal submatrix. The triangle is obtained by a series of steps, including the construction of base vector, parallelogram, trapezoid, etc. The proof that the proposed code possesses EP and BZD simultaneously is also provided. A series of comparisons verify that the proposed code achieves significantly better tradeoff among triple objectives than existing EP-BZD codes.

In drive-thru internet, road side units (RSUs) are deployed along the road, which facilitate content dissemination to the vehicles on the go through vehicle to infrastructure (V2I) communications. Due to many factors including short and intermittent connections, the fading nature of wireless channel, and relatively fast speed of the vehicle, the vehicles might not be able to receive the complete content file successfully. Therefore, caching a file at multiple RSUs along the road in a collaborative manner is needed. To this end, the combination property (CP) is desired for caching the content: if k source packets are mapped into n ≥ k packets and with any k out of these n packets are able to recover all the information. Reed-Solomon (RS) codes possess CP and have been widely adopted in distributed storage (DS) systems. RS codes operating within a large size finite field have high encoding/decoding complexity, which dramatically increase the computation burden and prolong the processing delay. By introducing several overhead bits and by smart design, binary zigzag decoding (BZD) can significantly reduce the decoding complexity, and CP-BZD codes that possess both CP and BZD have been proposed recently. For CP-BZD structured drive-thru internet system, packets delivered over the air might encounter errors in certain bits. In this work, without adding extra checking bits, existing overhead bits in CP-BZD is used instead, and a novel decoding method that reaps error correction ability is proposed. In other words, our method has self-error correction ability. Complexity analysis of this proposed method is performed and a low complexity algorithm is designed. This error correction module is completely optional, adaptable, and flexible to be deployed to various environments. Numerical studies show that the proposed method can achieve CP with both low decoding complexity and self-error correction.

The proliferation of Internet of Things (IoT) systems provides us a formidable way to monitor a multitude of things by recording field data and delivering it to the faraway controlling center. However, in hostile or inaccessible areas without infrastructure supports, transmission of IoT data is a daunting task due to the limited physical constraints associated with the weak communication unit and tiny battery supply at the ground sensors. A feasible solution to this problem is to use flexible and programmable unmanned aerial vehicles (UAVs) to gather the ground IoT data and then relay it to the end user, forming a UAV-IoT data collection system. Nevertheless, as we reveal in this article, there is a tradeoff between the two performance metrics-system throughput and sensor energy efficiency. Therefore, the data collection for UAV-IoT system should be power-aware, i.e., expending just enough energy to achieve the required system performance. To this end, in this article, by locating the optimal system parameters-the UAV flying speed and altitude, as well as the frame length at the MAC layer, we can strike a balance between the two conflicting metrics, in that, we can maximize the energy efficiency at the ground sensors, while satisfying the required system performance at the same time. In addition, with a cross-layer design, we can adaptively tune the frame length at MAC layer according to the varying UAV flying speed at the PHY layer, thus promptly switching the system between “system-efficient mode” and “energy-efficient mode”.

This paper proposes a Gaussian mixture message passing (GMMP) scheme to implement the blind known-interference cancellation (BKIC). Being aware of interference data as a priori information, the BKIC aims at canceling the interference without estimating the interference channel. Since the target signals are represented by continuous real-valued variables, the previous BKIC scheme is constructed as a real-valued belief propagation (RBP) for implementing message passing on the factor graph that represents the corresponding signal model. To implement the RBP-BKIC, the real-valued variables are actually quantized into vectors of discrete values. As such, the quantized RBP-BKIC has some drawbacks: 1) its performance is determined by the quantization step size and 2) it can only be applied to real signaling with 1-D PAM modulations. To overcome these drawbacks, we propose a GMMP scheme for the BKIC. First, we reveal that all messages passing over the factor graph of BKIC systems can be exactly represented by the mixtures of weighted Gaussian probability density functions. Superior to the quantized RBP-BKIC, we further show that the proposed GMMP scheme is an exact and efficient solution to the BKIC. In particular, it can approach performances of point-to-point communication systems with complex QAM modulations at the cost of affordable computational complexities. Moreover, we put forth a message passing framework that combines the GMMP-BKIC and the channel decoding into an iterative message passing scheme.

Clustering stocks by their time series data is a significant but challenging task in computer supported financial decision systems. In this paper, we propose an effective stocks clustering method based on hybrid correlation coefficient called SLU correlation coefficient which is a weighted combination of Spearman rank correlation, upper tail correlation and lower tail correlation. The upper and lower tail correlation is defined by Copula function and estimates parameters by EM algorithm. The similarity matrix is defined by SLU and inputs into Affinity Propagation algorithm for clustering. The experiment shows the effectiveness of the SLU, compared to Pearson correlation and DTW distance.

This study derives in the closed‐form the probability density function and characteristic function of the ratio of two products of arbitrary number of Nakagami‐ m random variables. Then, the resulting mathematical framework is applied to analyse and gain insights into the bit error rate performance of the dual‐hop channel state information‐assisted amplify‐and‐forward relaying system in the presence of co‐channel interference. All analytical results are corroborated by Monte–Carlo simulation results and they are shown to be efficient tools to evaluate system performance.

A code is said to possess the combination property if k source packets are mapped into n k packets and any k out of these n packets are able to recover the information of the original k packets. While the class of maximum-distance-separable codes are well known to have this property, its decoding complexity is generally high. For this reason, a new class of codes which can be decoded by the zigzag-decoding algorithm is considered. It has a lower decoding complexity at the expense of extra storage overhead in each parity packet. In this work, a new construction of a zigzag decodable code is proposed. The novelty of this new construction lies in the careful selection of the amount of bit-shift of each source packet in obtaining each parity packet. Besides, an efficient on-the-air repair scheme based on physical-layer network coding is designed.

The similarity measurements of business processes have important applications in business process management, such as process model search indexing, facilitate reuse, processes merge, etc. Existing researches are mostly based on the syntactic or semantic of text labels of activities or tasks. This paper presents a method for similarity measurement based on the internal structure of business processes without considering the text labels of process activities. Petri nets are used to define the process models. The nodes (transitions and places) are mapped by an iterative mapping strategy to identify the correspondence of two Petri nets. After getting a stable best mapping of places and transitions, we can compute the similarity mesurement of two processes. Experiments on the real data sets show that our algorithm is reliable and effective to the actual demand.

Energy balancing is an effective technique in enhancing the lifetime of a wireless sensor network WSN. Specifically, balancing the energy consumption among sensors can prevent losing some critical sensors prematurely due to energy exhaustion so that the WSN's coverage can be maintained. However, the heterogeneous hostile operating conditions-different transmission distances, varying fading environments, and distinct residual energy levels-have made energy balancing a highly challenging task. A key issue in energy balancing is to maintain a certain level of energy fairness in the whole WSN. To achieve energy fairness, the transmission load should be allocated among sensors such that, regardless of a sensor's working conditions, no sensor node should be unfairly overburdened. In this paper, we model the transmission load assignment in WSN as a game. With our novel utility function that can capture realistic sensors' behaviors, we have derived the Nash equilibrium NE of the energy balancing game. Most importantly, under the NE, while each sensor can maximize its own payoff, the global objective of energy balancing can also be achieved. Moreover, by incorporating a penalty mechanism, the delivery rate and delay constraints imposed by the WSN application can be satisfied. Through extensive simulations, our game theoretic approach is shown to be effective in that adequate energy balancing is achieved and, consequently, network lifetime is significantly enhanced. Copyright © 2012 John Wiley & Sons, Ltd.

Two-hop security communication with an eavesdropper in wireless environment is a hot research direction. The basic idea is that the destination, simultaneously with the source, sends a jamming signal to interfere the eavesdropper near to or co-located with the relay. Similar as physical layer network coding, the friendly jamming signal will prevent the eavesdropper from detecting the useful information originated from the source and will not affect the destination on detecting the source information with the presence of the known jamming signal. However, existing investigations are confined to Gaussian distributed signals, which are seldom used in real systems. When finite constellation signals are applied, the behavior of the secrecy rate becomes very different. For example, the secrecy rate depends on phase difference between the input signals with finite constellations, which is not observed with Gaussian signals. In this paper, we investigate the secrecy capacity and derive its upper bound for the two-hop relay model, by assuming an eavesdropper near the relay and the widely used M-PSK modulation. With our upper bound, the best and worst phase differences in high SNR region are then given. Numerical studies verify our analysis and show that the derived upper bound is relatively tight.

A pair of terminals exchanging information via a layer of parallel relay nodes under slow fading is considered. Two protocols are proposed based on the combination of opportunistic relaying (OR) with analogue network coding (ANC), named ORANC, or with digital network coding (DNC), named ORDNC, respectively. Two schemes/versions of ORDNC, including 2‐phase ORDNC (2P‐ORDNC) and 3‐phase ORDNC (3P‐ORDNC) are proposed. Their outage performances are investigated. ORANC and 2P‐ORDNC are proved to achieve optimal diversity‐multiplexing tradeoff (DMT), whereas 3P‐ORDNC is proved to be suboptimal. However, from diversity viewpoint only, all the above schemes are proven to achieve full diversity order. Simulation results verify the analysis, and show that 3P‐ORDNC and ORANC shows advantage at low‐ and high‐data rate regions, respectively.

ABSTRACT In this paper, we propose a new diversity combining scheme to save power, which is called as the generalized selection combining with double threshold (DT‐GSC). It selects the branch whose SNR is above an input threshold to combine, and this process will keep running until the combined output SNR is larger than an output threshold or until all paths are examined. The values of both thresholds are required to be predetermined on the basis of the practical communication conditions. For comparing the complexity of various combining schemes, we will show the mathematical formulas of the average number of path estimation and the average number of combined branches. Moreover, we will also compare the average bit‐error‐ratio performance of the proposed DT‐GSC with absolute threshold GSC (AT‐GSC) and output threshold MRC (OT‐MRC). Numerical examples and simulation results show that the proposed DT‐GSC leads to a lower complexity than the conventional AT‐GSC and OT‐MRC while it has a satisfactory performance.Copyright © 2012 John Wiley & Sons, Ltd.

This paper considers the problem of multiple distributed nodes in a wireless network wishing to join forces and cooperatively beamform data to a destination. Since the nodes are not physically connected and have independent oscillators, there are carrier frequency and phase errors that degrade the beamforming performance. This paper proposes a novel open-loop synchronization protocol, which exploits the broadcast nature of the wireless channel to achieve synchronization much faster than prior methods, with the difference increasing as the number of nodes increases.

This paper investigates interference-cancellation schemes at the receiver, in which the interference data, which is valid data intended for another receiver, is known a priori. The interference channel, however, is unknown (the blind part). Such a priori knowledge is common in wireless relay networks. For example, a relay could be relaying data that was previously transmitted by a node A. If node A is now receiving a signal from another node B, the interference from the relay is actually self-information known to node A. Besides the case of self-information, the node could also have overheard or received the interference data in a prior transmission by another node. Directly removing the known interference requires accurate estimate of the interference channel, which may be difficult in many situations. In this paper, we propose a novel scheme, Blind Known-Interference Cancellation (BKIC), to cancel known interference without interference channel information. BKIC consists of two steps. The first step combines adjacent symbols to cancel the interference, exploiting the fact that the channel coefficients are almost the same between successive symbols. After such interference cancellation, however, the signal of interest is distorted. The second step recovers the signal of interest amidst the distortion. We propose two algorithms for the critical second steps. The first algorithm (BKIC-S) is based on the principle of smoothing. It is simple and has near optimal performance in the slow fading scenario. The second algorithm (BKIC-RBP) is based on the principle of real-valued belief propagation. Since there is no loop in the Tanner graph, BKIC-RBP can achieve MAP-optimal performance with fast convergence, and has near interference-free performance even in the fast fading scenario. Both BKIC schemes outperform the traditional self-interference cancellation schemes that have perfect initial channel information by a large margin, while having lower complexities.

This paper investigates interference-cancellation schemes at the receiver, in which the original data of the interference is known a priori. Such a priori knowledge is common in wireless relay networks. Directly removing the known interference requires accurate estimate of the interference channel, which may be difficult in many situations. In [1], we proposed a novel scheme, Blind Known Interference Cancellation (BKIC), for blind cancellation of known interference without interference channel information. BKIC consists of two steps. The first step combines adjacent symbols to cancel the interference, exploiting the fact that the channel coefficients are almost the same between successive symbols. After such interference cancellation, however, the signal of interest is also distorted. The second step recovers the signal of interest amidst the distortion. Two schemes for the second step, BKIC-S and successive BKIC-RBP, were proposed in [1]. BKIC-S removes distortion by smoothing while BKIC-RBP does so using a real-value belief propagation algorithm. Although successive BKIC-RBP performs well and is superior to BKIC-S, it requires a long processing time proportional to the packet length. To overcome this problem, this paper proposes a parallel BKIC-RBP algorithm. Parallel BKIC-RBP has similar performance as successive BKIC-RBP. It has the advantage of being amenable to parallel implementation with a much shorter processing time.

In this paper, we consider energy fairness problem in wireless sensor networks. However, the heterogeneous hostile operating conditions - different transmission distances, varying fading environments and distinct remained energy levels, have made energy balancing a highly challenging design issue. To tackle this problem, we model the packet transmission of sensor nodes as a game. By properly designing the utility function, we get the Nash equilibrium, in which, while each node can optimize its own payoff, the global objective - energy balancing can also be achieved. In addition, by imposing penalty mechanism on sensors to punish selfish behaviors, the delivery rate and delay constraints are also satisfied. Through extensive simulations, the proposed game theoretical approach is proved to be effective in that the energy consumption is balanced and the energy resources are efficiently utilized, which can significantly improve the network lifetime.

In this paper, we propose a spatial multi-user physical layer network coding (SM-PNC) scheme by jointly utilizing PNC and beamforming techniques in a multi-user one relay communication system. The relay is equipped with multiple antennas and each user node is only equipped with a single antenna. The two way relay transmission consists of two phases. In multiple access phase, the summation and difference of the user data are combined with log likelihood ratio combination, which has a superior robustness to the traditional separation detection under any channel condition; In broadcast phase, optimal adaptive select-group broadcasting scheme is proposed, which can efficiently overcome the problem of near interference in beamforming. Simulation results validate the ability of the proposed algorithms.

Abstract Ultra‐wideband (UWB) system is one of the possible solutions to future short‐range indoor data communications with large frequency bandwidth. However, it must coexist with other narrowband wireless systems that may cause interference to each other, and furthermore a large bandwidth will inevitably result in multi‐path fading. The Rake receiver is applicable to combat multi‐path fading but its performance degrades greatly when the narrowband interference (NBI) is present. Although some optimized Rake receivers were proposed to suppress the NBI, such as the minimum mean square error (MMSE) one, their computational complexities are usually too high to be practically implemented. In this paper, we present a new adaptive Rake receiver which can effectively suppress the NBI, based on the nonlinear Masreliez‐type approximate conditional mean (ACM) technique. Simulation results show that it outperforms the previous schemes and even it achieves almost the same performance as that of a MMSE Rake receiver but with much lower complexity. Copyright © 2010 John Wiley & Sons, Ltd.

Physical-layer Network Coding (PNC) can significantly improve the throughput of two-way relay channels. An interesting variant of PNC is Analog Network Coding (ANC). Almost all ANC schemes proposed to date, however, operate in a symbol by symbol manner (memoryless) and cannot exploit the redundant information in channel-coded packets to enhance performance. This paper proposes a non-memoryless ANC scheme. In particular, we design a soft-input soft-output decoder for the relay node to process the superimposed packets from the two end nodes to yield an estimated MMSE packet for forwarding back to the end nodes. Our decoder takes into account the correlation among different symbols in the packets due to channel coding, and provides significantly improved MSE performance. Our analysis shows that the SNR improvement at the relay node is lower bounded by IIR (R is the code rate) with the simplest LDPC code (repeat code). The SNR improvement is also verified by numerical simulation with LDPC code. Our results indicate that LDPC codes of different degrees are preferred in different SNR regions. Generally speaking, smaller degrees are preferred for lower SNRs.

Energy conservation is a critical issue in the emerging standard IEEE 802.16e/m WiMAX supporting mobility. To guarantee QoS requirements in real-time services such as VoIP, traditional energy saving strategies adopt constant listen-sleep intervals, ignoring the On-Off characteristics of human speech. However, statistically the silence period can account for nearly 60% of the whole speech in time scale. Therefore, neglecting this fact can lead to unnecessary periodical listening in the silence duration, and, in turn, can result in excessive waste of battery energy. In this paper, we adopt a hybrid energy management for the downlink simplex VoIP. We also give an evaluation model to analyze the performance of the scheme. Guided by this model, we obtain the optimal window adjustment parameters. Extensive simulation results have validated the analytical model, and indicated that, compared with the traditional scheme, the hybrid scheme can achieve as much as 90% reduction in energy dissipation during silence period, while meeting the QoS requirements satisfactorily at the same time.

Abstract There is a plethora of recent research on high performance wireless communications using a cross‐layer approach in that adaptive modulation and coding (AMC) schemes at wireless physical layer are used for combating time varying channel fading and enhance link throughput. However, in a wireless sensor network, transmitting packets over deep fading channel can incur excessive energy consumption due to the usage of stronger forwarding error code (FEC) or more robust modulation mode. To avoid such energy inefficient transmission, a straightforward approach is to temporarily buffer packets when the channel is in deep fading, until the channel quality recovers. Unfortunately, packet buffering may lead to communication latency and buffer overflow, which, in turn, can result in severe degradation in communication performance. Specifically, to improve the buffering approach, we need to address two challenging issues: (1) how long should we buffer the packets? and (2) how to choose the optimum channel transmission threshold above which to transmit the buffered packets? In this paper, by using discrete‐time queuing model, we analyze the effects of Rayleigh fading over AMC‐based communications in a wireless sensor network. We then analytically derive the packet delivery rate and average delay. Guided by these numerical results, we can determine the most energy‐efficient operation modes under different transmission environments. Extensive simulation results have validated the analytical results, and indicates that under these modes, we can achieve as much as 40% reduction in energy dissipation. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Design of a nonlinear adaptive antenna array receiver is a challenging task in wireless communications due to the limited number of antenna elements and the presence of correlated signals, which directly affect the performance of an antenna array. More importantly, a conventional nonlinear array receiver is often associated with a high computational complexity that undermines its applicability in practice. In this paper, we present a new approach to adaptive beamforming receiver that provides superior performance in antenna array overloading and in the presence of correlated signals with a low complexity. In particular, the proposed receiver requires a small data‐record size to estimate the beamformer weights, which is beneficial in applications with fast fading channels. Simulation examples illustrate the performance improvement of the proposed array receiver when it is compared to the conventional beamformers. Copyright © 2008 John Wiley & Sons, Ltd.

The adaptive modulation and coding (AMC) schemes has long been adopted at physical layer to combat time-varying properties of the wireless channel. However, transmitting packet over deep fading channel can render extra energy expenditure, due to the incorporation of more error protection or usage of lower modulation mode, which is unaffordable for energy-limited wireless sensor device. To avoid such inefficient energy usage, a simple approach is to temporally buffer the packet when the channel is in deep fading, until the channel quality recovers. Nevertheless, buffering packet can lead to communication performance degradations - communication latency and packet overflow, which should be taken into consideration in sensing applications with QoS requirements. In this paper, by using previously proposed discrete time queuing model, we analyze the effects of Rayleigh fading on the sensor communication system, and propose a cross-layer design on power aware communication of sensor device. Specifically, in such channel adaptive system, each sensor can judiciously accesses the medium according to the channel condition, traffic load, and buffer variation. Simulation and analytical results indicate that, such cross-layer design can lead to energy conservation by as much as 30-40 per cent.

Abstract To enhance the lifetime of a sensor network which consists of hundreds or even thousands of resource‐limited devices, energy efficient communication is mandatory. Despite that a plethora of work has been done in designing energy efficient protocols for sensor networks, the time‐varying nature of wireless channel is largely unexplored. Indeed, we believe that a cross‐layer design on power aware communication is necessary to further optimize energy usage. In this paper, we propose a new channel adaptive power aware protocol, called CAEM, which works by dynamically adjusting the data throughput under different channel conditions with the help of an adaptive channel coding and modulation facility. Each sensor device judiciously accesses the wireless medium in that communication activity is reduced for devices under poor channel conditions. Simulation results indicate that the proposed CAEM protocol can lead to energy conservation by as much as 30 per cent. Furthermore, CAEM is also efficient in channel utilization as it generates a higher data throughput even under heavy traffic load. Copyright © 2007 John Wiley & Sons, Ltd.