Mobile edge computing (MEC), integrated with directed acyclic graph (DAG) blockchain technology, has emerged as a promising paradigm for ensuring secure and efficient resource trading between IoT user equipment (UEs) and edge service providers (ESPs). However, due to the open and heterogeneous nature of MEC networks, ESPs are susceptible to malicious attacks, rendering resource trading information potentially unreliable. While DAG blockchains ensure the reliability of on-chain data, they fail to guarantee the trustworthiness of ESPs and cannot effectively incentivize their participation in resource trading and blockchain consensus. To address these challenges, we develop a trust-driven resource trading framework for DAG blockchain-aided MEC networks. In this framework, we first design an off-chain trust-driven resource pricing mechanism, in which the resource price set by each ESP is positively correlated with its trust value. In addition, we design an on-chain trust-driven consensus mechanism, wherein the on-chain security of transactions published by each ESP is positively associated with its trust level. To enable UEs to better evaluate the on-chain transaction security, we design a novel metric termed transaction security satisfaction and incorporate it into the utility function of UEs. Furthermore, we model the resource trading between UEs and ESPs as a multi-leader multi-follower Stackelberg game, and verify the existence and uniqueness of its equilibrium. To maximize the utilities of both ESPs and UEs, we propose a backward induction-based iterative algorithm to jointly optimize resource pricing, resource demand, and offloading strategy. Numerical simulations validate the effectiveness of our proposed scheme, demonstrating its superior performance compared with baseline schemes.

Satellite communications have been considered a key part of global connectivity, effectively supporting diverse applications such as the Internet of Things (IoT) and real-time communication services. However, security-sensitive devices face significant challenges due to the threat of eavesdropping satellites, which compromise data confidentiality. Existing approaches often rely on deterministic models and fail to account for the stochastic nature of eavesdropping threats and the dynamic demands of satellite networks, limiting their applicability in practical scenarios. To address these challenges, this work proposes a novel secure access strategy for satellite mobile edge computing (SatMEC) systems, integrating a stochastic risk assessment model and an evolutionary game-theoretic framework. The proposed solution leverages a probabilistic model to evaluate the spatial distribution of eavesdropping satellites, quantifies the eavesdropping risk via the concept of eavesdropping capacity, and incorporates a dynamic service selection strategy that balances secrecy capacity and queuing delay.Furthermore, a distributed algorithm is developed to enable IoT devices to select service satellites based on real-time utility optimization adaptively. Extensive simulation experiments validate the effectiveness of the proposed strategy, demonstrating its ability to improve system security, balance the network load, and enhance overall performance in large-scale and dynamic satellite network environments. The results highlight the reliability and scalability of the proposed solution, making it a practical approach for secure and efficient access in LEO satellite networks.

For ultra-dense vehicular networks, dynamic resource optimization among a large number of heterogeneous agents is rather challenging. This paper proposes a learning-based resource allocation scheme in a vehicle-assisted mobile edge computing (MEC) network, where the uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs) are equipped with the MEC servers to provide the computational offloading services to the ground users with time-varying computing demands. Each self-interested vehicle jointly optimizes its trajectory planning and data offloading policies to maximize the expectation of its locally cumulative energy efficiency under the collision and energy constraints. We model the non-cooperative interactions among the massive co-channel vehicles as a multi-population mean-field game (MPMFG), where each vehicle constructs two types of mean-field terms to model the UAVs’ and UGVs’ population distributions, respectively. We propose a multi-population mean-field parameterized deep Q network (MPMF-PDQN) algorithm to solve the equilibrium among the vehicular servers in a discrete-continuous hybrid action space. The simulation results demonstrate that the proposed algorithm significantly enhances the average energy efficiency of the vehicles compared with the baseline algorithms.

The rapid growth of Web3.0 is transforming the Internet from a centralized structure to decentralized, which empowers users with unprecedented self-sovereignty over their own data. However, in the context of decentralized data access within Web3.0, it is imperative to cope with efficiency concerns caused by the replication of redundant data, as well as security vulnerabilities caused by data inconsistency. To address these challenges, we develop a Trustworthy Decentralized Cooperative Caching (TDC-Cache) framework for Web3.0 to ensure efficient caching and enhance system resilience against adversarial threats. This framework features a two-layer architecture, wherein the Decentralized Oracle Network (DON) layer serves as a trusted intermediary platform for decentralized caching, bridging the contents from decentralized storage and the content requests from users. In light of the complexity of Web3.0 network topologies and data flows, we propose a Deep Reinforcement Learning-Based Decentralized Caching (DRL-DC) for TDC-Cache to dynamically optimize caching strategies of distributed oracles. Furthermore, we develop a Proof of Cooperative Learning (PoCL) consensus to maintain the consistency of decentralized caching decisions within DON. Experimental results show that, compared with existing approaches, the proposed framework reduces average access latency by 20%, increases the cache hit rate by at most 18%, and improves the average success consensus rate by 10%. Overall, this paper serves as a first foray into the investigation of decentralized caching framework and strategy for Web3.0.

Unmanned aerial vehicles (UAVs) are widely deployed as aerial base stations to provide flexible communication coverage for ground users (GUs), yet the air-ground communications remain highly vulnerable to the jamming attacks. Unlike conventional fixed-policy jammers, the intelligent jammers dynamically adapt their jamming strategies based on the observed UAV communication policies, creating significant anti-jamming challenges particularly under asymmetric information. In this paper, we formulate the strategic interactions between a UAV-mounted server and a jammer as a partially observable pursuit-evasion game, where the UAV aims to maximize the GUs' uplink rates through dynamic evasion while the jammer strategically pursues to maximize the jamming effect. The information asymmetry is explicitly modeled by considering both the jammer's hidden location from the UAV and the jammer's inability to observe the UAV's remaining energy state. To optimize the UAV's anti-jamming policy under these challenges, we propose a novel opponent-modeling based reinforcement learning algorithm, named neural fictitious self-play with dueling double deep recurrent Q network (NFSP-D3RN). This algorithm optimizes the UAV's anti-jamming policy through reinforcement learning, while maintaining robustness against non-stationarity induced by the jammer's adaptive behavior through implicit opponent modeling. Extensive simulations demonstrate that our proposed algorithm achieves superior anti-jamming performance compared with the benchmarks under unknown jammer locations, with results approaching the upper bound of perfect location knowledge.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

In the resource constrained unmanned aerial vehicle (UAV) assisted communication networks, a major challenge is how to achieve efficient multi-UAV cooperation with minimal communication overhead. This paper proposes a teammate modeling based resource allocation scheme for a multi-UAV network, where multiple co-channel UAVs cooperatively provide the downlink communication services to the ground users (GUs) with temporal-correlated task demands. We formulate the cooperative resource allocation as a decentralized partially observable Markov decision process (Dec-POMDP), in which the UAVs jointly optimize their trajectory planning, power allocation, and user association policies to maximize the system's cumulative achievable sum rate. To address the non-stationarity introduced by parallel decision-making among the partially observable UAV agents, we propose a teammate modeling based multi-agent reinforcement learning algorithm, named teammate-advisory advantage actor-critic (TAA2C). This algorithm facilitates the exchange of low-dimensional advisory information among the UAV agents to enhance the inter-Uavcollaboration while maintaining low communication overhead. Simulation results demonstrate that, our proposed TAA2C algorithm achieves a better trade-off between communication overhead and cooperation efficiency, compared with the baseline algorithms of Independent A2C (IA2C), Federated A2C (FA2C), and Multi-Agent A2C (MAA2C). As the network scale increases, TAA2C even surpasses the centralized training baseline MAA2C in terms of cumulative sum rate, at only 10% of the communication overhead.

Quantum networks are an actively researched and promising field, aiming to achieve efficient quantum information transmission by interconnecting quantum nodes. In large-scale quantum networks, end-to-end throughput is a critical factor that affects the overall performance of the network. The maximum flow problem, extensively studied in classical network theory, identifies a set of paths between the source and destination nodes that maximizes the total flow. This study extends the maximum flow problem to quantum networks, focusing on coordinating multiple paths for multi-path quantum communication. We propose a Quantum Multi-Path Communication Protocol (QMCP) that employs maximum flow theory to allocate transmission resources across multiple nodes efficiently, thus maximizing the total transmission capacity from the source to the destination. Our evaluation demonstrates that QMCP significantly enhances end-to-end throughput in quantum networks.

Resistive random-access memory (ReRAM) is a promising non-volatile memory technology. However, its crossbar array structure leads to a severe problem known as sneak path interference (SPI), which is correlated and data-dependent. From an information-theoretic perspective, memory systems like ReRAM can be considered as special types of communication channels. Inspired by deep learning applications in communication systems, the detection of ReRAM channels with SPI was formulated as a learning problem recently, and a multi-layer perceptron (MLP) network was employed to mitigate SPI. However, it requires a large amount of training data to achieve satisfactory performance. In this paper, we first propose a bidirectional long short-term memory (BiLSTM) based detector for ReRAM to exploit the correlation between memory cells introduced by SPI. Moreover, a few-shot learning algorithm based on meta-transfer learning (MTL) is proposed to further improve the generalization ability of the detector. The bit error rate (BER) bound and generalization bound are also derived to verify the effectiveness of our proposed schemes. Simulation results demonstrate that the BiLSTM-based detector with MTL can dramatically reduce the required training samples by four to five orders of magnitude while improving the BER performance compared to the existing MLP-based detection scheme.

A decentralized identity system that can provide users with selfsovereign digital identities to facilitate complete control over their own data is paramount to Web 3.0.The account system on blockchain is an ideal archetype for realizing Web 3.0 decentralized identity.However, a disadvantage of such completely anonymous identity system is that users can create multiple accounts without authentication to obfuscate their activities on the blockchain.In particular, the current anonymous blockchain account system cannot accurately register the social relationships and interactions between real human users, given the amorphous mappings between users and blockchain identities.This work proposes zkBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems.zkBID links souls (blockchain accounts) to humans (users' personhood credentials) in a one-to-one manner to truly reflect the social relationships and interactions between humans on the blockchain.zkBID conceals the one-to-one relationships between blockchain accounts and users' personhood credentials for privacy protection using zero-knowledge proofs and linkable ring signatures.Thus, with zkBID, the users' blockchain accounts are credibly anonymous.Importantly, zkBID is fully decentralized: all user-related data are generated by users and verified by smart contracts on the blockchain.We implemented zkBID and built a blockchain test network for evaluation purposes.Our tests demonstrate the effectiveness of zkBID and suggest proper ways to configure zkBID system parameters.

Blockchain-assisted federated learning (BFL) can achieve decentralized storage and management of model data without relying on a central server. However, security issues caused by deliberate attacks in distributed systems and efficiency issues induced by heterogeneous computing consumption in resource-limited systems need to be urgently addressed in BFL. To address these issues, we propose a decentralized reputation management (DRM) mechanism for a trustworthy BFL (T-BFL) network, that explores, stores, and utilizes the endogenous reputation of distributed nodes to promote system security and efficiency. The proposed DRM includes three core modules, i.e., decentralized reputation evaluation, reputation-based model aggregation, and reputation-based blockchain consensus. Specifically, in the off-chain phase of T-BFL, the reputation value of each node is evaluated based on model quality, which other peer nodes can verify. This reputation value further determines the weight of global aggregation at each node. In the on-chain phase, the reputation of each node serves as the stake to dynamically adjust its consensus difficulty. Furthermore, we investigate the convergence rate of the T-BFL network under the poisoning attack, and dynamically optimize the energy allocation of local training, consensus, and communications by minimizing the upper bound of the global loss function. Extensive experiments are conducted to evaluate the performance of T-BFL on MNIST, Fashion-MNIST, and Cifar-10 datasets. The experimental results demonstrate that, compared with traditional BFL, T-BFL can achieve up to 56.12% accuracy improvement and$8.6\times $acceleration for reaching the target learning accuracy under the poisoning attack.

Nowadays, covert communication is envisioned as a promising and secure method of delivering private information. However, higher bit error rates, limited data rates, and vulnerability to advanced machine learning detection methods significantly challenge the application of covert communication. In this paper, we propose a multiple carrier index keying orthogonal frequency division multiplexing (MCIK-OFDM) based covert communication system aided by a chaotic modulation scheme to improve covert data rate and covertness. First, we propose a covert information embedding method by dynamically selecting the activation or deactivation of a subcarrier to embed covert bits according to a previously negotiated covert key between the transmitter and receiver. Then, the chaotic modulation scheme is developed to mask transmitted signals with generated chaotic signals. Moreover, we propose an adversarial machine learning-based (AML) perturbation algorithm to resist the eavesdropper’s detection of covert signals. Furthermore, the closed-form bit error rate (BER) and the achievable covert rate of the proposed covert communication system are derived. Numerical and simulation results demonstrate that the BER of the proposed MCIK-OFDM-based hybrid chaotic covert communication system is much lower than that of conventional chaotic communication systems. In addition, the proposed AML perturbation algorithm can more effectively protect covert communication from being detected by supervised and unsupervised machine learning methods compared to traditional algorithms.

Traditional federated learning (FL) frameworks rely on a central server for model coordination among distributed mobile terminals (MTs). The centralization faces two critical challenges, i.e., single point of failure and potential privacy leakage. Differentially private decentralized FL (DP-DFL) has been proposed to address these challenges, wherein the MTs exchange models in a decentralized manner and maintain the differential privacy (DP) guarantee by adding noise to local models before model interaction. However, existing DP-DFL frameworks confront difficulty in achieving the expected privacy and convergence performance, simultaneously. To address this issue, we propose a novel DP-DFL framework (called randomized DP-DFL) that employs a randomized model interaction scheme to lower the model exposure frequency and hence reduce privacy budget consumption. Specifically, the scheme includes two sequential steps, i.e., randomized terminal assignment and randomized model transmission. In Step 1), the model interaction phase of DFL is further divided into several sequential substages. MTs are randomly assigned to each sub-stage. In Step 2), each MT sequentially transmits either a model previously received from its neighbors or its own local model according to the assigned sub-stage order. The proposed scheme enhances the MTs' privacy of DFL since the exposure probabilities of the MTs' local models are significantly reduced via these two randomized steps. Besides, we theoretically analyze the convergence and privacy performance of randomized DP-DFL. In particular, properly tuning the number of sub-stages in randomized DP-DFL can achieve an optimal balance between privacy and convergence. Experimental results show that randomized DP-DFL consistently outperforms traditional frameworks. Compared with baselines, randomized DP-DFL reduces 40.9% privacy loss under the same target accuracy while improving 9.5% learning accuracy under the same privacy loss on EMNIST and CIFAR-10, respectively

In ultra-dense autonomous aerial vehicle (AAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale AAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense AAV communication network, where the GUs’ service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel AAVs as a stochastic game, where each AAV jointly optimizes its trajectory, user association, and downlink power control to maximize the expectation of its locally cumulative energy efficiency under the interference and energy constraints. To cope with the scalability issue in a large-scale network, we further formulate the problem as a mean-field game (MFG), which simplifies the interactions among the AAVs into a two-player game between a representative AAV and a mean-field. We prove the existence and uniqueness of the equilibrium for the MFG, and propose a model-free mean-field reinforcement learning algorithm named maximum entropy mean-field deep Q network (ME-MFDQN) to solve the mean-field equilibrium in both fully and partially observable scenarios. The simulation results reveal that the proposed algorithm improves the energy efficiency compared with the benchmark algorithms. Moreover, the performance can be further enhanced if the GUs’ service demands exhibit higher temporal correlation or if the AAVs have wider observation capabilities over their nearby GUs.

In this paper, we develop a non-coherent differential chaos shift keying based index modulation by using initial value index (IVI-DCSK) to convey additional information for wireless communications. In the proposed scheme,mcmapped bits are carried by 2mcchaotic sequences by exploiting the quasi-orthogonality of different chaotic signals, while the modulated bit is carried by DCSK. To diminish the multiuser interference, the references allocated to different users are sent in individual time slots, while the information-bearing sequences for the mapped bits of users are sent simultaneously. We then derive the bit error rate (BER) expression of multi-user IVI-DCSK over multipath Rayleigh fading channels. The theoretical and consistent simulation results show that the proposed IVI-DCSK achieves significant gains over the conventional chaotic-based index modulations, i.e., permutation index DCSK (PI-DCSK) and code index modulation DCSK (CIM-DCSK). This gain can be more than 4 dB in fading channels with high multipath delay. In addition, it achieves higher energy and spectral efficiencies over the latter ones. The superiority of the proposed scheme is further verified in practical ultra-wideband (UWB) communications. Thus, this proposed scheme is efficient and promising for chaotic-based low-complexity communications, such as in wireless local area network (WLAN) and indoor applications.

The NAND flash memory channel is corrupted by different types of noises, such as the data retention noise and the wear-out noise, which lead to unknown channel offset and make the flash memory channel non-stationary. In the literature, machine learning-based methods have been proposed for data detection for flash memory channels. However, these methods require a large number of training samples and labels to achieve a satisfactory performance, which is costly. Furthermore, with a large unknown channel offset, it may be impossible to obtain enough correct labels. In this paper, we reformulate the data detection for the flash memory channel as a transfer learning (TL) problem. We then propose a model-based deep TL (DTL) algorithm for flash memory channel detection. It can effectively reduce the training data size from 106samples to less than 104samples. Moreover, we propose an unsupervised domain adaptation (UDA)-based DTL algorithm using moment alignment, which can detect data without any labels. Hence, it is suitable for scenarios where the decoding of error-correcting code fails and no labels can be obtained. Finally, a UDA-based threshold detector is proposed to eliminate the need for a neural network. Both the channel raw error rate analysis and simulation results demonstrate that the proposed DTL-based detection schemes can achieve near-optimal bit error rate (BER) performance with much less training data and/or without using any labels.

Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods mostly rely on centralized deep training, that is, the sensitive data obtained by edge devices needs to be uploaded to the server, which raises latency and privacy issues. To tackle these issues, we propose to accomplish object detection through sparse federated training with dynamic model aggregation, namely FedWeg, to reduce the communication cost and privacy leakage induced by data transmission. Specifically, FedWeg performs sparse training in edge devices and uploads the lightweight models to the server. To reduce the unnecessary transmission overhead, we propose a dynamic sparsity adjustment scheme that gradually increases the sparsity ratios. Then, we propose to utilize the inverse ratio of sparsity ratios from different edge devices to calculate aggregate weights to diminish the negative impact of sparse training on learning performance. Moreover, we theoretically analyze the convergence rate of FedWeg, which reveals that the impact of network sparsity on model performance, and higher average sparsity rates result in greater errors. Finally, we conduct extensive experiments on four real-life datasets using YOLOv3 and VGG-16. The results show that our FedWeg algorithm outperforms baselines in terms of communication costs and test accuracy.

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions.

In decentralized federated learning (DFL), clients exchange their models with each other for global aggregation. Due to a lack of centralized supervision, a client may easily duplicate shared models to save its computing resources. Generally, this plagiarism behavior is hard to detect, while it is harmful to model training performance. To address this issue, we propose an anti-plagiarism DFL framework to efficiently detect plagiarism misconduct. Specifically, we first design a method for detecting plagiarism by adding a time-shift pseudo-noise (PN) sequence to each client's local model before broadcasting. Second, we develop an upper bound of the loss function of DFL with the proposed PN sequence detection method, which is proved to be the convex function of both the amplitude of PN sequences ($\alpha$) and the detection threshold ($\lambda$). Next, we propose an adaptive plagiarism detection (APD) algorithm by jointly optimizing$\alpha$and$\lambda$to enhance the learning performance. Finally, we conduct extensive experiments on MNIST, Adult, Cifar-10, and SVHN datasets to demonstrate that our analytical bounds are consistent with the experimental results. Remarkably, the proposed framework can recover up to a 10% classification accuracy loss in the presence of 40% plagiaristic clients.

In this paper, a novel system utilizing a hybrid relay-intelligent reflecting surface (HR-IRS) to boost the security performance of directional modulation (DM) is established. In particular, the malicious attacker works in full-duplex (FD) mode and it will eavesdrop on confidential message (CM) as well as send malicious jamming. To maximize the secrecy rate (SR), a joint problem of optimizing the receive beamforming, transmit beamforming, power allocation (PA) factor, and phase shift matrix (PSM) of HR-IRS is formulated. Since the optimization problem is un-convex and the variables are coupled with each other, we address this problem by iteratively optimizing these variables. First, the receive beamforming is designed based on the generalized Rayleigh-Ritz theorem. Then, the transmit beamforming and PA factor are optimized via Dinkelbach’s Transform and successive convex approximation methods. And for PSM, two strategies, called separate optimization of PSM (SO-PSM) and joint optimization of PSM (JO-PSM), are proposed. Thus, two iterative schemes are proposed accordingly, namely maximizing SR based on SO-PSM (Max-SR-SOP) and maximizing SR based on JO-PSM (Max-SR-JOP). The former has a better performance and the latter has a lower complexity. Simulation results show that given a sufficient power budget of HR-IRS, the proposed Max-SR-SOP and Max-SR-JOP can enable HR-IRS-aided DM network to obtain a higher SR than that aided by passive IRS.

The integration of directed acyclic graph (DAG) blockchain and mobile edge computing (MEC) has emerged as a promising means to enable computation-intensive, delay-sensitive, and secure task execution in Internet of Things (IoT) applications. However, off-chain task execution results are not credible even if the results have been recorded on the chain, since blockchain cannot extend the trust of on-chain data to off-chain. To make the off-chain and on-chain trust consistent, we first develop a trusted MEC (T-MEC) framework by employing a DAG blockchain-aided decentralized trust management (DAG-DTM) mechanism. Specifically, DAG-DTM evaluates the off-chain trust of edge nodes according to the quality of task execution results, and the trust can be further verified off the chain by any edge node under the same trust management rule. Moreover, the approval time for recording the execution result of the edge node on the chain is positively correlated with the verified off-chain trust, which can further promote on-chain transaction security of trusted edge node. Second, we jointly optimize the bandwidth and computation resource allocation to minimize the system latency that consists of off-chain task execution delay and on-chain transaction confirmation delay. Numerical results compare system latency and security performance between the optimized T-MEC and the benchmark schemes. In particular, the optimized T-MEC can achieve a 33.12% gain of computation delay and a 10.19% gain of system latency at an affordable cost of transaction confirmation delay (i.e., 3.21%) over T-MEC, while meeting the requirements of off-chain task execution latency and on-chain transaction security simultaneously.

A critical security issue in the blockchain-aided cooperative spectrum sensing (B-CSS) network is that, blockchain cannot guarantee the reliability of off-chain data source, even though the data has been recorded on the chain. Furthermore, the performance optimization of the B-CSS networks is constrained by an underlying tradeoff between throughput and security. Driven by these issues, we first develop a novel B-CSS framework with a decentralized reputation management (DRM) mechanism, wherein nodes not only collaborate to detect the availability of target spectrum off the chain, but also act as the blockchain nodes to maintain global decisions on the chain. In the off-chain phase, the DRM mechanism can enhance the trustworthiness of CSS by evaluating each node's reputation according to its contribution to the global detection. Furthermore, in light of the on-chain throughput-and-security tradeoff, verifiable reputation can be utilized as the consensus stake to adjust the difficulty level of block generation. Then, given the on-chain reputation consensus, we maximize the average throughput of the proposed framework by jointly optimizing the block size, sensing time, and block generation time. Simulation results demonstrate the optimized performance of the proposed framework. Moreover, compared with the baseline schemes, our proposal is more robust to the threat of malicious attacks such as data-tampering attack and collusion attack.

Non-volatile memory (NVM) channels suffer from unknown offsets due to the presence of various impairments of the memory devices. Machine learning based methods have been proposed for data detection for NVMs under unknown channel offsets. However, the existing methods require a large number of training samples and labels to achieve a satisfactory data detection performance, which will result in large read latency and more power consumption. In this paper, we formulate a deep learning based data detection framework as a transfer learning problem. A deep transfer learning (DTL) based data detection scheme is proposed to reduce the number of required training samples and labels. The optimal symbol error rate is also derived as the performance benchmark by assuming that the perfect channel knowledge is known to the detector. Our experiment results demonstrate that the proposed DTL-based data detection scheme can achieve near-optimal performance with the training data size being reduced by two orders of magnitude compared with the original deep learning-based detector.

This paper proposes a dynamic resource trading scheme in unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network. A UAV-assisted MEC server adaptively adjusts its trajectory to sell the computation offloading services to the mobile users (MUs), where the MUs have stochastic task arrivals. In this context, we formulate the sequential resource trading problem as a stochastic Stackelberg game, which is composed of two stages for each trading round. In the first stage, the self-interested UAV jointly optimizes its trajectory and service price to maximize its long-term profits. In the second stage, the non-cooperative MUs optimize their binary offloading decisions to minimize the average task processing delay and service payment. However, it is challenging to obtain the equilibrium across the fully decentralized agents with constantly evolving and tightly coupled policies, where each agent is confronted with a non-stationary environment. To solve this problem, we propose an opponent modeling based double deep Q learning (OM-DDQN) algorithm, where each agent adopts opponent modeling to effectively predict the trading strategies of other agents in the network. Simulation results demonstrate that, compared with the baseline algorithms, the proposed algorithm can achieve a win-win resource trading outcome that not only enhances the UAV's profit but also reduces the MUs' costs.

The covert communication technology has emerged as a novel method for network authentication, copyright protection, and providing the evidence of cybercrimes. However, how to design the covert communication scheme in the physical layer of wireless networks and how to optimize the data rate for the covert communication channels are very challenging. In this article, we propose a wireless covert communication system (CCS), where the transmit antennas are selected and coded to generate a covert codebook. According to the covert codebook, the antennas can be dynamically combined to transmit different covert messages. In addition, we adopt a modulation scheme, named covert quadrature amplitude modulation (QAM), to modulate the covert messages, where the precoding method is designed to deviate the constellations for covert information bits from those for the public information bits. Furthermore, we derive the closed-form expressions of capacity and bit error ratio (BER) for the proposed CCS. To maximize the covert data rate of the CCS, we formulate an optimization problem of the covert data rate and solve the problem to find the optimal precoding matrix. To reduce the covert information leakage, artificial noise is introduced to the system to jam the communication between the transmitting and watching nodes. We design a beamforming scheme to maximize the secure rate for the CCS, where the leakage of covert information can be minimized while the covert communication is not influenced. Simulation results show that the proposed CCS can significantly improve the covert data rate and reduce the covert BER in comparison with the traditional CCSs.

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

As the underlying consensus protocol of Bitcoin and Ethereum blockchains, Proof-of-Work (PoW) features a cryptographic mathematical puzzle whose solution is easy to verify but extremely hard to solve. Under PoW, miners maintain the security of blockchain by devoting computing powers to solve the puzzle; the miner who has solved the puzzle successfully generates a block, along with a reward (e.g., a set of cryptocurrency). The average waiting time to generate a block is inversely proportional to the computing power of the miner. To reduce the average block generation time, a group of individual miners can form a centralized mining pool to aggregate their computing power to solve the puzzle together and share the reward contained in the block. However, if the aggregated computing power of the pool forms a substantial portion of the total computing power in the network, the pooled mining undermines the core spirit of blockchain, i.e., the decentralization, and harms its security. To discourage the pooled mining, we develop a new consensus protocol called Proof-of-Age (PoA) that builds upon the native PoW protocol. The core idea of PoA lies in using Age-of-Work (AoW) to measure the effective mining periods that the miners have devoted to maintaining the security of blockchain. Unlike in the native PoW protocol, in our PoA protocol, miners benefit from its effective mining periods even if they have not successfully mined a block. We first employ a continuous time Markov chain (CTMC) to model the block generation process of the PoA based blockchain. Based on this CTMC model, we then analyze the block generation rates of the mining pool and solo miners respectively. Our analytical results verify that under PoA, the block generation rates of miners in the mining pool are reduced compared to that of solo miners, thereby disincentivizing the pooled mining. Finally, we simulate the mining process in the PoA blockchain to demonstrate the consistency of the analytical results.

Building upon the prevailing concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources. Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services. A trustworthiness model is introduced to evaluate the quality of each network entity throughout the transactions. We develop a two-level trading mechanism over smart contract to enable the automatic and efficient transactions among the network entities and provide high quality services. First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare. Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and determine the pricing of the winners. We show that the proposed double auction mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms. Moreover, we put forth a trustworthiness driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair allocation of block generation reward. Following the principle of PoS, we formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values. The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities compared with the conventional consensus mechanisms.

Deep neural network (DNN)-task enabled mobile edge computing (MEC) is gaining ubiquity due to outstanding performance of artificial intelligence. By virtue of characteristics of DNN, this paper develops a joint design of task partitioning and offloading for a DNN-task enabled MEC network that consists of a single server and multiple mobile devices (MDs), where the server and each MD employ the well-trained DNNs for task computation. The main contributions of this paper are as follows: First, we propose a layer-level computation partitioning strategy for DNN to partition each MD's task into the subtasks that are either locally computed at the MD or offloaded to the server. Second, we develop a delay prediction model for DNN to characterize the computation delay of each subtask at the MD and the server. Third, we design a slot model and a dynamic pricing strategy for the server to efficiently schedule the offloaded subtasks. Fourth, we jointly optimize the design of task partitioning and offloading to minimize each MD's cost that includes the computation delay, the energy consumption, and the price paid to the server. In particular, we propose two distributed algorithms based on the aggregative game theory to solve the optimization problem. Finally, numerical results demonstrate that the proposed scheme is scalable to different types of DNNs and shows the superiority over the baseline schemes in terms of processing delay and energy consumption.

In this work, we develop a pair of rate-diverse encoder and decoder for a two-user Gaussian multiple access channel (GMAC). The proposed scheme enables the users to transmit with the same codeword length but different coding rates under diverse user channel conditions. First, we propose the row-combining (RC) method and row-extending (RE) method to design practical low-density parity-check (LDPC) channel codes for rate-diverse GMAC. Second, we develop an iterative rate-diverse joint user messages decoding (RDJD) algorithm for GMAC, where all user messages are decoded with a single parity-check matrix. In contrast to the conventional network-coded multiple access (NCMA) and compute-forward multiple access (CFMA) schemes that first recover a linear combination of the transmitted codewords and then decode both user messages, this work can decode both the user messages simultaneously. Extrinsic information transfer (EXIT) chart analysis and simulation results indicate that RDJD can achieve gains up to 1.0 dB over NCMA and CFMA in the two-user GMAC. In particular, we show that there exists an optimal rate allocation for the two users to achieve the best decoding performance given the channel conditions and sum rate.

Blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear program based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}(\sqrt {V})$] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter$V$. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption.

Abstract The error‐correcting performance of multi‐level‐cell (MLC) NAND flash memory is closely related to the block length of error‐correcting codes (ECCs) and log‐likelihood‐ratios of the read‐voltage thresholds. Driven by this issue, this paper optimizes the read‐voltage thresholds for MLC flash memory to improve the decoding performance of ECCs with finite block length. First, through the analysis of channel coding rate and decoding error probability under finite block length, the optimization problem of read‐voltage thresholds to minimize the maximum decoding error probability is formulated. Second, a cross‐iterative search algorithm to optimize read‐voltage thresholds under the perfect knowledge of flash memory channel is developed. However, it is challenging to analytically characterize the voltage distribution under the effect of data retention noise. To address this problem, a deep neural network (DNN)‐aided optimization strategy to optimize the read‐voltage thresholds is developed, where a multi‐layer perception network is employed to learn the relationship between voltage distribution and read‐voltage thresholds. Simulation results show that, compared with the existing schemes, the proposed DNN‐aided read‐voltage threshold optimization strategy with a well‐designed Low Density Parity Check (LDPC) code can not only improve the program‐and‐erase endurance but also reduce the read latency.

Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}(V)$] to indicate that the system throughput goes up as the queue backlog increases, where$V$is a control parameter between the system throughput and the queue backlog. However, we find that, as$V$goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e.,$\mathcal {O}(V)$). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}((\log (V))^2)$] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only$\mathcal {O}((\log (V))^2)$queue backlog.

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy servers, and external attacks. To address these issues, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training on the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds$K$, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others’ trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal$K$, the learning parameters, and the proportion of lazy clients. Based on the MNIST and Fashion-MNIST datasets, we see that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than$5\%$, and the optimized$K$based on the upper bound can effectively minimize the loss function.

Mobile edge computing (MEC) enables computing services at the network edge closer to mobile users (MUs) to reduce network transmission latency and energy consumption. Deploying edge computing servers in small base stations (SBSs), operators make profit by offering MUs with computing services, while MUs purchase services to solve their own computation tasks quickly and energy-efficiently. In this context, it is of particular importance to optimize computing resource allocation and computing service pricing in each SBS, subject to its limited computing and communication resources. To address this issue, we formulate an optimization problem of computing resource management and trading in small-cell networks and tackle this problem using a two-tier matching. Specifically, the first tier targets at the association algorithm between MUs and SBSs to achieve maximum social welfare, and the second tier focuses on the collaboration algorithm among SBSs to make efficient usage of limited computing resources. We further show that the two proposed algorithms contribute to stable matchings and achieve weak Pareto optimality. In particular, we verify that the first algorithm arrives at a competitive equilibrium. Simulation results demonstrate that our proposed algorithms can achieve a better network social welfare than baseline algorithms while retaining a close-optimal performance.

This paper investigates a cache-aided cloud radio access network (C-RAN), comprising a central unit, K base stations (BSs) each with NTantennas, and M users each with NRantennas, where each BS and user have local caches to store some popular contents from the central unit. For this cache-aided network, we propose the linear network coded (NC) wireless caching that consists of linear wireless network coding assisted cache placement phase and signal-space alignment (SSA) enabled content delivery phase. In the cache placement phase, we design a joint NC caching function at the BSs to store linear combinations of messages from the central unit, as a form of linear wireless network coding. In the content delivery phase, we design the SSA pattern based on the NC caching to guide the precoding designs at BSs. Then, each user can reliably decode its requested messages by receiver shaping and reverse NC operation. The primary contribution of this work is to achieve the coding gain induced by the integration of linear wireless network coding and SSA, which has been not exploited in the field of wireless coded caching. In particular, to deal with high temporal variability of user requests, we show that the proposed cache placement is invariant to different user requests in the worst-case caching, without any shared caching messages at different BSs. Furthermore, we verify that the proposed scheme is also compatible with the insufficient caching scenario at the BSs. In addition, we analyze the achievable sum degrees of freedom (DoF) for the proposed caching network. Both analytical and numerical results verify that the proposed caching scheme achieves a higher sum DoF than the existing related works.

The energy-efficient task offloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computational tasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint task offloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computational task allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computational tasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes.

Mobile edge computing (MEC) enables feasible and scalable computation services for delay-sensitive and delay-tolerant tasks from mobile users. This paper considers an MEC network that consists of multiple users, multiple edge nodes (ENs), and a remote cloud computing server, where the ENs and the cloud server execute the delay-sensitive and delay-tolerant tasks respectively. First, we propose a unified linear network coded (NC) task offloading policy at the ENs to either execute the delay-sensitive tasks or assist the cloud server in the execution of delay-tolerant tasks. For the delay-sensitive task, the users jointly precode their task input messages according to a signal space alignment pattern, such that each EN can compute the linear NC messages for its intended user. For the delay-tolerant task, we put forth a compute-and-upload strategy for the ENs to upload their computed NC messages to the cloud server, such that the cloud server can first recover the input messages from all users and then execute the computation. Second, we develop the EN computation rules for different types of tasks. Finally, we characterize the computation load and normalized uploading time for the proposed task offloading. Our analytical results show that the proposed task offloading scheme is applicable to different NC computation with flexible requirements on computation load and uploading time.

As emerging memories such as spin-torque transfer magnetic random access memory (STT-MRAM) suffer from reliability issues caused by process variations and thermal fluctuations, the design of channel quantizer with the minimum number of quantization bits is critical to support effective error correction coding for ensuring high-density and high-speed memory data storage. In this paper, we first propose a quantized channel model for STT-MRAM. Based on the quantized channel model, we derive various information theoretic bounds, including the mutual information, cutoff rate, and the Polyanskiy-Poor-Verdú (PPV) finite-length performance bound. By using these bounds as design criteria, we optimize the quantizer design for the polar-coded STT-MRAM channel. Moreover, we also propose a polar-code-specific quantization design with the successive cancellation decoding algorithm, by using the block error probability bound obtained from density evolution (DE). Simulation results show that all our proposed quantizers generally outperform the prior art greedy merging quantizer. In addition, both the cutoff rate and PPV bound based quantizers outperform the most widely applied mutual information based quantizer for short-length polar codes with 2-bit quantization. Furthermore, the DE quantizer designed specifically for polar codes achieves the best performance among all the proposed quantizers.

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.

Channel output quantization plays a vital role in high-speed emerging memories such as the spin-torque transfer magnetic random access memory (STT-MRAM), where high-precision analog-to-digital converters (ADCs) are not applicable. In this paper, we investigate the design of the 1-bit quantizer which is highly suitable for practical applications. We first propose a quantized channel model for STT-MRAM. We then analyze various information theoretic bounds for the quantized channel, including the channel capacity, cutoff rate, and the Polyanskiy-Poor-Verdu ́(PPV) finite-length performance bound. By using these channel measurements as criteria, we design and optimize the 1-bit quantizer numerically for the STTMRAM channel. Simulation results show that the proposed quantizers significantly outperform the conventional minimum mean-squared error (MMSE) based Lloyd-Max quantizer, and can approach the performance of the 1-bit quantizer optimized by error rate simulations.

Coded modulation (CM) is a bandwidth efficient framework to approach the capacity limit of the differential chaos shift keying (DCSK) systems. In this paper, we propose an M-ary DCSK system operated with CM based on nonbinary protograph low-density parity-check (LDPC) codes over Rayleigh fading channels. First, we investigate the performance of the proposed nonbinary channel coded DCSK (CM-DCSK) and bit-interleaved coded DCSK (BICM-DCSK). In particular, we show that CM-DCSK outperforms BICM-DCSK over Rayleigh fading channels in terms of capacity limit. Compared with BICM-DCSK, CM-DCSK can simplify the receiver structure, since it does not require turbo iteration between the noncoherent detector and channel decoder. Second, guided by the modified extrinsic information transfer (EXIT) analysis, we put forth two new types of nonbinary protograph LDPC codes to approach the capacity limit of CM-DCSK. Both EXIT analysis and simulation results demonstrate that the proposed protograph-coded CM-DCSK achieves a better error performance than BICM-DCSK even with iterative decoding. Furthermore, we show the performance superiority of nonbinary protograph-coded CM-DCSK over a practical ultra-wideband channel. Hence, we conclude that this proposed scheme offers a good alternative for wireless local area network applications.

This paper considers a multiuser communication network, where a receiver is set to compute functions of the messages from K users. All user nodes and the receiver are equipped with multi-antenna. We propose a space-time (ST) coded multiple-input multiple-output (MIMO) linear physical-layer network coding (LPNC) scheme that promises full-rate and full-diversity, while achieving the maximum coding gain of LPNC. In the proposed framework, the users' messages are encoded by the same linear dispersion ST code and transmitted simultaneously. The receiver exploits the MIMO LPNC mapping in reconstructing an arbitrary number of linearly network-coded (NC) messages. We derive the NC generator matrix that leads to the greatest coding gain and minimized error probability at the receiver. On top of that, we analytically show that the proposed ST coded LPNC scheme guarantees the full-diversity and full-rate transmission. The proposed method applies to a wide range of network configurations. Two case studies on: (1) MIMO two-way relay network and (2) MIMO multiple-access relay network are presented in this paper. For both case studies, numerical results are shown to demonstrate the performance improvement of the proposed scheme over conventional schemes by more than 4 dB, while the full-rate and full-diversity behaviors are in line with our analysis.

In multiple-input multiple-output (MIMO) two-way relay networks (TWRN), linear physical-layer network coding (LPNC) was proposed to boost the throughput by using spatial multiplexing at source nodes. How to design optimal LPNC for full-rate full-diversity MIMO TWRN is still an open problem. In this paper, we propose a full-rate full-diversity (FRFD) LPNC scheme. In this scheme, two source nodes, each with two antennas, transmit full-rate universal space-time codes to a two-antenna relay simultaneously. Then, the relay applies LPNC to compute multiple network-coded (NC) messages. In particular, we explicitly solve the optimal LPNC mapping to minimize decoding errors of NC messages in the FRFD LPNC scheme. Our analytical results verify that the optimal FRFD LPNC scheme guarantees the full-diversity and full-rate transmission at the same time. Simulation results are consistent with the analytical results and further demonstrate that our optimal FRFD LPNC scheme outperforms the conventional MIMO LPNC scheme.

This paper presents several soft decision iterative decoding schemes for physical-layer network coding (PNC) operated with coded modulation (CM) and bit-interleaved coded modulation (BICM). With respect to PNC operated with CM, we consider network coding-based channel decoding (NC-CD) and multi-user complete decoding (MUD-NC) for PNC decoding at the relay. Their BICM counterparts are XOR-based channel decoding (XOR-CD) and MUD-XOR, respectively. First, we show that, when the decoding is non-iterative, there is a gap between the BICM capacities of both XOR-CD and MUD-XOR under Gray mapping and the capacities of their CM counterparts, NC-CD, and MUD-NC. This is in contrast to the conventional point-to-point communication system, for which the BICM capacity with Gray mapping is known to be very close to the CM capacity, without the need for iterative decoding. Second, we investigate the error performance of iteratively decoded BICM XOR-CD and MUD-XOR. Extrinsic information transfer chart analysis and simulation results indicate that for these Gray-mapped BICM PNC systems, iterative decoding can achieve considerable gains over non-iterative decoding. Again, this is in contrast to the Gray-mapped BICM point-to-point communication system, for which iterative decoding provides little gain over non-iterative decoding. We further show that Gray mapping gives rise to best PNC rate for MUD-XOR and XOR-CD systems among several bits-to-symbol mappings under study. Overall, our results indicate that BICM PNC systems exhibit different decoding behavior from conventional BICM point-to-point systems. This paper serves as a first foray into the investigation of this issue.

This paper proposes an encoding/decoding framework for achieving the optimal channel capacities of the two-user broadcast channel where each user (receiver) has the message targeted for the other user (receiver) as side information. Since the link qualities of the channels from the base station to the two users are different, their respective single-user non-broadcast channel capacities are also different. A goal is to simultaneously achieve/approach the single-user non-broadcast channel capacities of the two users with a single broadcast transmission by applying network coding. This is referred to as the rate-diverse wireless network coding problem. For this problem, this paper presents a capacity-achieving framework based on linear-structured nested lattice codes. The significance of the proposed framework, besides its theoretical optimality, is that it suggests a general design principle for linear rate-diverse wireless network coding going beyond the use of lattice codes. We refer to this design principle as the principle of virtual single-user channels. Guided by this design principle, we propose two implementations of our encoding/decoding framework using practical linear codes amenable to decoding with affordable complexities: the first implementation is based on Low Density Lattice Codes (LDLC) and the second implementation is based on Bit-interleaved Coded Modulation (BICM). These two implementations demonstrate the validity and performance advantage of our framework.

This paper presents the results of a comprehensive investigation of complex linear physicallayer network coding (PNC) in two-way relay channels. In this system, two nodes A and B communicate with each other via a relay R. Nodes A and B send complex symbols, wA and wB, simultaneously to relay R. Based on the simultaneously received signals, relay R computes a linear combination of the symbols, wN= αwA+ βwB, as a network-coded symbol and then broadcasts wN to nodes A and B. Node A then obtains wB from wN and its self-information wA by wB = β-1(wN -αwA). Node B obtains wB in a similar way. A critical question at relay R is as follows: “given channel gain ratio η = hA/hB, where hA and hB are the complex channel gains from nodes A and B to relay R, respectively, what is the optimal coefficients (α, β) that minimizes the symbol error rate (SER) of wN = αwA+ βwBwhen the relay attempts to detect wN in the presence of noise?” Our contributions with respect to this question are as follows: 1) we put forth a general Gaussian-integer formulation for complex linear PNC in which α, β, wA, wB, and wNare the elements of a finite field of Gaussian integers, that is, the field of 7G[i]/q, where q is a Gaussian prime. Previous vector formulation, in which wA, wB, and wN were represented by 2-D vectors and α and β were represented by 2 x 2 matrices, corresponds to a subcase of our Gaussian-integer formulation, where q is real prime only. Extension to the Gaussian prime q, where q can be complex, gives us a larger set of signal constellations to achieve different rates at different values of SNR; and 2) we show how to divide the complex plane of η into different Voronoi regions, such that the η within each Voronoi region shares the same optimal PNC mapping (αopt, βopt). We uncover the structure of the Voronoi regions that allows us to compute a minimum-distance metric that characterizes the SER of wN under optimal PNC mapping (αopt, βopt). Overall, the contributions in 1) and 2) yield a toolset for a comprehensive understanding of complex linear PNC in 7G[i]/q. We believe investigation of linear PNC beyond 7G[i]/q can follow the same approach.

We study a beamformed Alamouti coding scheme for a multigroup multicast downlink system. In this letter, we take into account imperfect channel state information (CSI) at the base station, where the CSI uncertainty region is formed by the intersection of multiple ellipsoids. This unified CSI uncertainty model leads to a challenging robust rank-two multicast beamforming problem. We first develop a conservative approximation method to handle the intractable constraints in the formulated robust beamforming problem under the considered CSI uncertainty region. Then, we apply the semidefinite relaxation (SDR) technique to approximately solve this problem. Our rank-profile analysis shows that the SDR is tight when there are at most two users in each multicast group and the CSI uncertainty region is not too large. Finally, we carry out simulations to validate the effectiveness of the proposed conservative approximation method. The simulation results coincide with the rank-profile analysis.

This paper investigates various subtleties of applying linear physical-layer network coding (PNC) with q-level pulse amplitude modulation (q-PAM) in two-way relay channels. A critical issue is how the PNC system performs when the received powers from the two users at the relay are imbalanced. In particular, how would the PNC system perform under slight power imbalance that is inevitable in practice, even when power control is applied? To answer these questions, this paper presents a comprehensive analysis of q-PAM PNC. Our contributions are as follows. First, we give a systematic way to obtain the analytical relationship between the minimum distance of the signal constellation induced by the superimposed signals of the two users (a key performance determining factor) and the channel-gain ratio of the two users, for all q. In particular, we show how the minimum distance changes in a piecewise linear fashion as the channel-gain ratio varies. Second, we show that the performance of q-PAM PNC is highly sensitive to imbalanced received powers from the two users at the relay, even when the power imbalance is slight (e.g., the residual power imbalance in a power-controlled system). This sensitivity problem is exacerbated as q increases, calling into question the robustness of highorder modulated PNC. Third, we propose an asynchronized PNC system in which the symbol arrival times of the two users at the relay are deliberately made to be asynchronous. We show that such asynchronized PNC, when operated with a belief propagation decoder, can remove the sensitivity problem, allowing a robust high-order modulated PNC system to be built.

In this paper, we study space-time block code (STBC) designs for two-user MIMO X channels to achieve full diversity when the inter-user interference is cancelled by the group zero-forcing receiver. To do so, we first propose a design criterion and then propose a systematic design to satisfy the criterion so that it achieves the full diversity. The code rate approaches one when encoding length gets large. We prove that full diversity can be achieved by the proposed STBC without the channel information at the transmitters. Simulation results are provided to demonstrate the theory.

Partial interference cancellation (PIC) group decoding is an attractive decoding alternative for multiple-input multiple-output (MIMO) wireless communications. It can well deal with the tradeoff among rate, diversity and decoding complexity of space-time block codes. In this paper, a design criterion of full-diversity space-frequency codes (SFC) is proposed for MIMO-OFDM systems with the PIC group decoding. Based on the criterion, a systematic design of full-diversity SFC is proposed that can achieve full diversity under the PIC group decoding. Simulation results of the newly proposed codes demonstrate the theory.

In this paper, we study space-time block code (STBC) designs for two-user MIMO X channels to achieve full diversity when the inter-user interference is cancelled by the group zero-forcing receiver. To do so, we first propose a design criterion and then propose a systematic design to satisfy the criterion so that it achieves full diversity. The code rate approaches one when encoding length gets large. We prove that full diversity can be achieved by the proposed STBC without the channel information at the transmitters. Simulation results are provided to demonstrate the theory.

In this paper, a design criterion for space-time block codes (STBC) is proposed for two-user MIMO interference channels when a group zero-forcing (ZF) algorithm is applied at each receiver to eliminate the inter-user interference. Based on the design criterion, a design of STBC for two-user interference channels is proposed that can achieve full diversity for each user with the group ZF receiver. The code rate approaches one when the time delay in the encoding (or code block size) gets large. Performance results demonstrate that the full diversity can be guaranteed by our proposed STBC with the group ZF receiver.

Partial interference cancellation (PIC) group decoding proposed by Guo and Xia is an attractive decoding alternative for multiple-input multiple- output (MIMO) wireless communications. It can well deal with the tradeoff among rate, diversity and decoding complexity of space-time block codes. In this paper, two design criteria of full diversity space-frequency codes (SFC) are proposed for MIMO- OFDM systems with the PIC group decoding. Based on the criteria, a systematic design of full-diversity SFC with the PIC group decoding is proposed that can achieve full diversity and a rate of $2M_t/(M_t+1)$ for $M_t$ transmit antennas. Performance results demonstrate that the full diversity is offered by the newly proposed SFC with the PIC group decoding.

A fast-decodable distributed space-time block code (STBC) is proposed for a two-path successive relay network that can achieve both full diversity and full transmission rate. The proposed STBC employs a precoder to rotate the constellation of transmitted symbols at source. After decode-and-forward by relays, the rotated information symbols are decoded at destination. It is shown that single complex symbol decoding can be obtained at the destination. Simulation results demonstrate that the full diversity is offered by the newly proposed distributed STBC for the two-path successive relay network.

In this letter, we propose a systematic design of space-time block codes (STBC) which can achieve high rate and full diversity when the partial interference cancellation (PIC) group decoding is used at receivers. The proposed codes can be applied to any number of transmit antennas and admit a low decoding complexity while achieving full diversity. For M transmit antennas, in each codeword real and imaginary parts of PM complex information symbols are parsed into P diagonal layers and then encoded, respectively. With PIC group decoding, it is shown that the decoding complexity can be reduced to a joint decoding of M/2 real symbols. In particular, for 4 transmit antennas, the code has real symbol pairwise (i.e., single complex symbol) decoding that achieves full diversity and the code rate is 4/3. Simulation results demonstrate that the full diversity is offered by the newly proposed STBC with the PIC group decoding.

In this paper, we propose a systematic design of space-time block codes (STBC) which can achieve high rate and full diversity when the partial interference cancellation (PIC) group decoding is used at receivers. The proposed codes can be applied to any number of transmit antennas and admit a low decoding complexity while achieving full diversity. For $M$ transmit antennas, in each codeword real and imaginary parts of $PM$ complex information symbols are parsed into $P$ diagonal layers and then encoded, respectively. With PIC group decoding, it is shown that the decoding complexity can be reduced to a joint decoding of $M/2$ real symbols. In particular, for $4$ transmit antennas, the code has real symbol pairwise (i.e., single complex symbol) decoding that achieves full diversity and the code rate is $4/3$. Simulation results demonstrate that the full diversity is offered by the newly proposed STBC with the PIC group decoding.

Recently, space-time block codes (STBC) with a partial interference cancellation (PIC) group decoding was proposed to deal with the tradeoff among rate, diversity and complexity by Guo and Xia. In this paper, we propose a systematic design of STBC with reduced-complexity PIC group decoding. The proposed STBC is featured as Alamouti-type block code in which each entry of Alamouti code matrix is replaced by a block of multi-layer coded symbols. With the PIC group decoding and a particular grouping scheme, the proposed STBC can achieve full diversity, rate- 2M/M+2 and a low-complexity decoding for M transmit antennas. Simulation results show that the proposed codes can achieve the full diversity with PIC group decoding while requiring half decoding complexity of the existing codes.