Recent advances in LLM-based multi-agent systems have demonstrated remarkable capabilities in complex decision-making scenarios such as financial trading and software engineering. However, evaluating each individual agent’s effectiveness and online optimization of underperforming agents remain open challenges. To address these issues, we present HiveMind, a self-adaptive framework designed to optimize LLM multi-agent collaboration through contribution analysis. At its core, HiveMind introduces Contribution-Guided Online Prompt Optimization (CG-OPO), which autonomously refines agent prompts based on their quantified contributions. We first propose the Shapley value as a grounded metric to quantify each agent's contribution, thereby identifying underperforming agents in a principled manner for automated prompt refinement. To overcome the computational complexity of the classical Shapley value, we present DAG-Shapley, a novel and efficient attribution algorithm for Directed Acyclic Graph (DAG)-structured multi-agent workflows that leverages the inherent DAG structure of the agent workflow to axiomatically prune non-viable coalitions. By hierarchically reusing intermediate outputs of agents in the DAG, our method further reduces redundant computations, and achieving substantial cost savings without compromising the theoretical guarantees of Shapley values. Evaluated in a multi-agent stock-trading scenario, HiveMind achieves superior performance compared to static baselines. Notably, DAG-Shapley reduces LLM calls by over 80 percent while maintaining attribution accuracy comparable to full Shapley values, establishing a new standard for efficient credit assignment and enabling scalable, real-world optimization of multi-agent collaboration.

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.

Leveraging the spatiotemporal continuous aware ness of low Earth orbit satellites, space-based spectrum monitoring systems can construct a spectrum situation map (SSM) to facilitate spectrum surveillance and management in mobile cognitive communication networks. However, due to limited on orbit processing capabilities and unfavorable ground-to-satellite transmission environments, spectrum measurements will inevitably be incomplete and corrupted by anomalies. Existing SSM construction methods assume stationary spectral environments and do not consider the dynamic changes in spectrum situation. Considering spectrum state is evolving constantly over time, this paper proposes a robust time-aware online streaming tensor (R TAST) completion algorithm by exploiting the time-frequency space correlation and temporal properties in real-world spectral measurements. Based on compressed wideband sampling, the proposed R-TAST algorithm integrates rank estimation, anomaly removal, and dynamic spectral tensor completion to achieve spectrum situation completion and evolutionary prediction. Numerical analyses conducted on simulated and realistic ray tracing based datasets demonstrate the effectiveness and efficiency of the proposed R-TAST in comparison with state-of-the-art streaming tensor completion and prediction algorithms.

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.

Data privacy protection and secure sharing are the main issues faced by smart healthcare IoT systems. In medical uses, patient health information is frequently kept in the cloud, which limits the user’s ability to entirely control their data. Additionally, standard encryption keys do not sufficiently mitigate the risks posed by malicious entities like compromised cloud service providers. To address these issues, blockchain technology, combined with Internet of Medical Things (IoMT) can securely safeguard patient medical records through a peer-to-peer, secure, and collective ledger. Therefore, we propose a novel IoT-driven architecture that leverages blockchain technology to protect patient medical files from tampering and unauthorized access. This architecture integrates patient medical files with blockchain and is enhanced by a combination of Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNN). Utilizing blockchain for the transmission of encrypted data significantly strengthens data security and minimizes the risk of data breaches. The process of generating encryption and decryption keys through a coupled CNN and BiLSTM ensures the robustness and uniqueness of these keys. Additionally, the selection of the best key is performed using the Gradient Descent Optimization Algorithm (GDOA), which demonstrates the effectiveness and efficiency of the encryption and decryption process. We also compare the implementation of our model with existing technologies, assessing its performance based on various metrics, including restoration efficiency, response time, record time, key generation time, encryption time, decryption time, turnaround time, and overall running time. Our proposed method is confirmed to be more effective than current techniques in terms of these performance metrics.

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.

Photovoltaic (PV) power forecasting is important for promoting the integration of renewable energy sources. However, neural network-based methods, particularly deep learning for PV power forecasting, face challenges with computational and memory requirements when dealing with industry-scale datasets. To address this, we introduce Rec-PF, a robust computational framework employing the tensor-train (TT) technique. This framework aims to streamline the training process of massive deep learning recommendation models (DLRMs) on constrained resources. Rec-PF employs a high-performance compressed embedding table, enhancing TT decomposition using key computing primitives. It serves as a drop-in replacement for the PyTorch API. Additionally, Rec-PF utilizes an index reordering technique to leverage local and global information from training inputs, thereby enhancing performance. Furthermore, Rec-PF adopts a pipeline training model, eliminating the need for communication between training workers and host memory. We are pioneers in applying DLRM to PV power prediction to reduce training time without compromising accuracy. Our approach demonstrates a twofold improvement in training time compared to methods that do not incorporate our approach. To better demonstrate the enhanced performance of the algorithm, we specifically compare its efficiency with other frameworks using datasets commonly employed in recommender systems. Comprehensive experiments indicate that Rec-PF is capable of processing the largest publicly accessible DLRM and PV datasets on a single GPU, offering a threefold acceleration compared to state-of-the-art DLRM and PV frameworks.

In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple distributed probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum network revealing that there exist more than 50% low-degree Ethereum nodes that weaken the network robustness.

The widespread adoption of Network Address Translation (NAT) technology has led to a significant number of network end nodes being located in private networks behind NAT devices, impeding direct communication between these nodes. To solve this problem, a technique known as "hole punching" has been devised for NAT traversal to facilitate peer-to-peer communication among end nodes located in distinct private networks. However, as the increasing demands for speed and security in networks, TCP-based hole punching schemes gradually show performance drawbacks. Therefore, we present a QUIC-based hole punching scheme for NAT traversal. Through a comparative analysis of the hole punching time between QUIC-based and TCP-based protocols, we find that the QUIC-based scheme effectively reduces the hole punching time, exhibiting a pronounced advantage in weak network environments. Furthermore, in scenarios where the hole punched connection is disrupted due to factors such as network transitions or NAT timeouts, this paper evaluates two schemes for restoring the connection: QUIC connection migration and re-punching. Our results show that QUIC connection migration for connection restoration saves 2 RTTs compared to QUIC re-punching, and 3 RTTs compared to TCP re-punching, effectively reducing the computational resources consumption for re-punching.

This work presents Reputation-Based Streamlet (RBStreamlet), an improved version of the Streamlet consensus algorithm, designed to address limitations such as high bandwidth load and high communication complexity during the Propose and Vote phases. RBStreamlet introduces a reputation mechanism for leader election and fork selection, along with erasure coding and Merkle trees to reduce data transmission volume in the Propose phase. Furthermore, RBStreamlet leverages threshold signature technology to decrease the message complexity in the Vote phase. We implement the proposed RBStreamlet algorithm in a prototype blockchain system and build a network emulator to evaluate its performance under conditions resembling a practical wide-area network. Through extensive experiments, RBStreamlet demonstrates superior performance, scalability, and resilience to Byzantine attacks compared to the original Streamlet, especially in bandwidth-limited scenarios. These findings suggest its potential for practical applications in blockchain systems.

In this manuscript, we present an energy-efficient alternating optimization framework based on the multiantenna ambient backscatter communication (AmBSC)-assisted cooperative nonorthogonal multiple access (NOMA) for next-generation (NG) Internet of Things (IoT)-enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multicluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PACs) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multiantenna-assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in intercluster interference (ICI) (among different clusters) and intracluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF)-based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intracluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the Quality-of-Service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered nonconvex passive-beamforming problem is transformed into a standard semidefinite programming (SDP) problem by exploiting the successive-convex approximation (SCA), as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.

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.

Recent studies have shown that side-information estimation (SIE) via JPEG image deblocking/restoration is effective in enhancing steganographic security when the side-information of JPEG rounding errors is unavailable. The polarity map of deblocking errors can work well in modulating handcrafted embedding costs. However, it may not be easy to design an optimal deblocking method that is universal to enhance security for all images, and it is unclear how to better utilize the polarity map in modulating statistical model-based embedding costs. To circumvent the difficulty of deblocking method design, we propose an optimal polarity map selection (OPMS) method leveraging existing well-performed deblocking methods. OPMS is designed based on polarity-oriented synthetic stego and minimum feature distance, so that the selected optimal polarity map (OPM) ensures a high security performance to each image. Besides, we propose a statistical model-based modulation method to better exploiting OPM in a quaternary quantized Gaussian embedding (QQGE) model. Through shifting the mean of the distribution, QQGE can derive effective modulated embedding probabilities and reduce the number of modified coefficients without increasing coding complexity. Experimental results demonstrate that the proposed overall steganographic method, called OPMS-QQGE, greatly surpasses existing state-of-the-art SIE-based methods in resisting both CNN-based and feature-based steganalyzers.

Occlusion poses a major challenge for person re-identification (ReID). Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy. In particular, they may fail when facing complex occlusions, such as those between pedestrians. Accordingly, in this paper, we propose a novel method named Quality-aware Part Models (QPM) for occlusion-robust ReID. First, we propose to jointly learn part features and predict part quality scores. As no quality annotation is available, we introduce a strategy that automatically assigns low scores to occluded body parts, thereby weakening the impact of occluded body parts on ReID results. Second, based on the predicted part quality scores, we propose a novel identity-aware spatial attention (ISA) module. In this module, a coarse identity-aware feature is utilized to highlight pixels of the target pedestrian, so as to handle the occlusion between pedestrians. Third, we design an adaptive and efficient approach for generating global features from common non-occluded regions with respect to each image pair. This design is crucial, but is often ignored by existing methods. QPM has three key advantages: 1) it does not rely on any outside tools in either the training or inference stages; 2) it handles occlusions caused by both objects and other pedestrians; 3) it is highly computationally efficient. Experimental results on four popular databases for occluded ReID demonstrate that QPM consistently outperforms state-of-the-art methods by significant margins. The code of QPM is available athttps://github.com/Wang-pengfei/QPM.

The existing medium access control (MAC) protocol of Wi-Fi networks (i.e., carrier-sense multiple access with collision avoidance (CSMA/CA)) suffers from poor performance in dense deployments due to the increasing number of collisions and long average backoff time in such scenarios. To tackle this issue, we propose an intelligent wireless MAC protocol based on deep learning (DL), referred to as DL-MAC, which significantly improves the spectrum efficiency of Wi-Fi networks. The goal of DL-MAC is to enable not only intelligent channel access but also intelligent rate adaptation. To achieve this goal, we design a deep neural network (DNN) that takes the historical received signal strength indications (RSSIs) as inputs and outputs joint channel access and rate adaptation decision. Notably, the proposed DLMAC takes the constraints of practical applications into account and the DL-MAC is evaluated using the experimental wireless data sampled from the actual environments on the 2. 4GHz frequency band. The experimental results show that our DLMAC can achieve around 86% performance of the global optimal MAC, and about twice the performance of the traditional Wi-Fi MAC in the environments of our lab and the Shenzhen Baoan International Airport departure hall.

To support more Internet-of-Things devices, we present a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, devices store block data to the cloud service provider, offload the proof-of-work mining tasks to base stations (BSs), and associate with the macrocell BS or small-cell BSs. Then, we analyze the user association problem, computation offloading problem, and block storage problem in the blockchain-enabled HetNet. We also design corresponding algorithms to solve these problems. Furthermore, to tackle the challenge of data congestion of BSs, based on the obtained computation offloading and block storage strategies, we propose a modified user association algorithm. The analysis shows that the proposed blockchain-enabled HetNet can effectively attain computing offloading, block storage, and user association strategies, and more devices can access the blockchain network. Analytical results show that the proposed modified user association algorithm can greatly avoid data congestion of BSs. Numerical results demonstrate the effectiveness of our proposed algorithms for computation offloading and block storage, and the proposed modified user association algorithm has a significantly great advantage compared with the traditional nearest BS association algorithm in terms of avoiding data congestion.

This paper investigates the application of physical-layer network coding (PNC) to Industrial Internet of Things (IIoT) in which a controller and a robot are out of each other’s transmission range, and they exchange messages with the assistance of a relay. We particularly focus on a scenario where 1) the controller has more information to transmit than the robot; 2) the channel of the controller is stronger than that of the robot, and both users have nearly the same transmit power. To reduce the communication latency, we put forth an asymmetric PNC transmission scheme in which the controller transmits more information than the robot by exploiting its stronger channel gain in the uplink of PNC. However, the current channel-coded PNC requires the two users to transmit the same amount of source information in order to preserve the linearity of the two users’ channel codes at the relay for successful decoding. Therefore, a challenge in the asymmetric PNC transmission scheme is how to construct a channel decoder at the relay, considering that a superimposed symbol at the relay contains different amounts of source information from the controller and robot. To fill this gap, we propose a lattice-based encoding and decoding scheme in which the robot and controller encode and modulate their information in lattices with different lattice construction levels. The network-coded messages are decoded level-by-level in the lattice. Our design is versatile on that the controller and the robot can freely choose their modulation orders based on their channel power, and the design is applicable for arbitrary channel codes, not just for one particular channel code. The simulation results demonstrate the effectiveness of the proposed channel-coded asymmetric PNC transmission scheme.

In this paper, we investigate a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, mobile users associate with the serving base stations (BSs) and offload their computation-intensive proof-of-work mining tasks to the mobile edge computing server of the macro-cell BS. We initialize the user association strategy by using the traditional nearest BS association algorithm in the blockchain-enabled HetNet. Then, we formulate the computation offloading problem and design an alternating iterative algorithm to attain the computing demand strategies for all users. Based on obtained computing demand strategies, we propose a modified user association algorithm in order to improve the data congestion of BSs. Analytical results show that the blockchain-enabled HetNet can serve more users, and the proposed algorithms can effectively obtain strategies of user association and computation offloading. Numerical results demonstrate that the proposed alternating iterative algorithm for computation offloading has fast convergence and good stability, and the proposed modified user association algorithm can avoid data congestion better than the traditional nearest BS association algorithm.

This study applies reinforcement learning (RL) from the AI machine learning field to derive an optimal Bitcoin-like blockchain mining strategy. A salient feature of the RL learning framework is that an optimal (or near-optimal) strategy can be obtained without knowing the details of the blockchain network model. Previously, the most profitable mining strategy was believed to be honest mining encoded in the default blockchain protocol. It was shown later that it is possible to gain more mining rewards by deviating from honest mining. In particular, the mining problem can be formulated as a Markov Decision Process (MDP) which can be solved to give the optimal mining strategy. However, solving the mining MDP requires knowing the values of various parameters that characterize the blockchain network model. In real blockchain networks, these parameter values are not easy to obtain and may change over time. This hinders the use of the MDP model-based solution. In this study, we employ RL to dynamically learn a mining strategy with performance approaching that of the optimal mining strategy. Since the mining MDP problem has a nonlinear objective function (rather than linear functions of standard MDP problems), we design a new multidimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multidimensional RL mining algorithm can still achieve optimal performance over time-varying blockchain networks.

Mobile-edge computing (MEC) is considered as a promising technology to reduce the energy consumption (EC) and task accomplishment latency of smart mobile user equipments (UEs) by offloading computation-intensive tasks to the nearby MEC servers. However, the Quality of Experience (QoE) for computation highly depends on the wireless channel conditions when computation tasks are offloaded to MEC servers. In this article, by considering the imperfect channel-state information (CSI), we study the joint offloading decision, transmit power, and computation resources to minimize the weighted sum of EC of all UEs while guaranteeing the probabilistic constraint in multiuser MEC-enabled Internet-of-Things (IoT) networks. This formulated optimization problem is a stochastic mixed-integer nonconvex problem and challenging to solve. To deal with it, we develop a low-complexity two-stage algorithm. In the first stage, we solve the relaxed version of the original problem to obtain offloading priorities of all UEs. In the second stage, we solve an iterative optimization problem to obtain a suboptimal offloading decision. As both stages include solving a series of nonconvex stochastic problems, we present a constrained stochastic successive convex approximation-based algorithm to obtain a near-optimal solution with low complexity. The numerical results demonstrate that the proposed algorithm provides comparable performance to existing approaches.

Real-world traffic sign recognition is an important step toward building autonomous vehicles, most of which highly dependent on deep neural networks (DNNs). Recent studies demonstrated that DNNs are surprisingly susceptible to adversarial examples. Many attack methods have been proposed to understand and generate adversarial examples, such as gradient-based attack, score-based attack, decision-based attack, and transfer-based attacks. However, most of these algorithms are ineffective in real-world road sign attack, because 1) iteratively learning perturbations for each frame is not realistic for a fast moving car and 2) most optimization algorithms traverse all pixels equally without considering their diverse contribution. To alleviate these problems, this article proposes the targeted attention attack (TAA) method for real-world road sign attack. Specifically, we have made the following contributions: 1) we leverage the soft attention map to highlight those important pixels and skip those zero-contributed areas-this also helps to generate natural perturbations; 2) we design an efficient universal attack that optimizes a single perturbation/noise based on a set of training images under the guidance of the pretrained attention map; 3) we design a simple objective function that can be easily optimized; and 4) we evaluate the effectiveness of TAA on real-world data sets. Experimental results validate that the TAA method improves the attack successful rate (nearly 10%) and reduces the perturbation loss (about a quarter) compared with the popular RP2 method. Additionally, our TAA also provides good properties, e.g., transferability and generalization capability. We provide code and data to ensure the reproducibility: https://github.com/AdvAttack/RoadSignAttack.

To enable more Internet-of-Things (IoT) devices for participating in the Proof-of-Work (PoW) mining process of public blockchains, we propose a cooperative mobile-edge computing (MEC)-aided blockchain network. In the network, devices can offload computation-intensive PoW mining tasks to base stations and store their block data to the cloud service provider. Then, we study the joint computation offloading, block storage, and resource service pricing problem as a three-stage Stackelberg game. We analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Furthermore, we derive the upper bound of the ergodic throughput of the cooperative scheme and the maximum number of devices connected to the network. The analysis shows that the proposed cooperative MEC-aided blockchain network can significantly improve the system throughput, and more devices can access the blockchain network. Analytical results show that the proposed backward induction-based iterative algorithm can efficiently attain the Nash equilibrium of the game. Numerical results show that our proposed backward induction-based iterative algorithm has fast convergence and good stability, and the proposed cooperative scheme can serve more devices in comparison with other noncooperative schemes.

The proof-of-work (PoW) mining process requires a large amount of intensive computing, which leads to some plights such as heavy equipment and fixed access nodes in traditional blockchain networks. A novel mobile blockchain network with the help of a mobile edge computing (MEC) server is presented, where all mobile users participate in the PoW mining process. The traditional Bitcoin network adjusts the target difficulty value to ensure a stable block time. However, for MEC-assisted mobile blockchain networks, the adjusted difficulty value needs to be broadcast to all mobile users, which results in expensive communication costs. To maintain a stable block time of mobile blockchain networks, we formulate the delay-limited computation offloading strategy of the PoW-based mining task as a non-cooperative game that maximizes an individual revenue in the MEC-assisted mobile blockchain network. Specifically, the non-cooperative game problem can be divided into multiple sub-game optimization problems to obtain final solutions for all users. We analyze the sub-game optimization problem and prove the existence of Nash equilibrium (NE) of the non-cooperative game. Moreover, we design an alternating iterative algorithm based on the continuous relaxation and greedy rounding (CRGR) to achieve the NE of this game. Given the sub-optimal delay-limited computation offloading results, we also derive the optimal transmit power for an individual user within the maximum mining delay range. From the analytical results, we can see that the proposed CRGR-based alternating iterative algorithm can efficiently attain the sub-optimal delay-limited computation offloading strategies of all mobile users in the polynomial time. The individual transmit power increases accordingly with the delay-limited computation offloading strategies of all users. Numerical results demonstrate that the proposed CRGR-based alternating iterative algorithm has fast convergence and good stability.

Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user’s nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain’s difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain’s difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attacks because each agent locally estimates its (sub)gradient without any supervision. This work leverages the convolutional neural network (CNN) to perform the detection and localization of the insider attackers. Compared to the previous work, CNN can learn appropriate decision functions from the original state information without preprocessing through artificially designed rules, thereby alleviating the dependence on complex pre-designed models. Simulation results demonstrate that the proposed CNN-based approach can effectively improve the performance of detecting and localizing malicious agents, as compared with the conventional pre-designed score-based model.

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.

We design and implement Publication Chain (PubChain), a decentralized open-access publication platform built on decentralized and distributed technologies of blockchain and IPFS peer-to-peer file sharing systems. The existing publication platforms have some severe drawbacks. First, instead of promoting widespread knowledge sharing, access to publications on the platforms owned by publishers is often on a fee basis. This drawback of pay wall prevents researchers from "standing on the shoulders of giants". Moreover, the peer review process on most all existing publication platforms (including both openaccess and publisher platforms) is prone to be ineffective, since there is no proper incentive to reviewers for performing high-qualified reviews. PubChain is an alternative platform to the existing publication venues aiming to address their drawbacks. No central third-party owns the contents (i.e., papers and reviews) of PubChain. Exploiting blockchain technology, we devise an elaborate incentive scheme on PubChain to incentivize key stakeholders (i.e., authors, readers and reviewers) to participate publication activities on PubChain in a substantive manner by earning credits and rewards through self-motivated interactions. We have performed simulations to investigate the robustness of our proposed incentive scheme against fraudulent publications and reviews. We also have implemented a prototype of PubChain to demonstrate its key concepts.

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.

This paper concerns the problem of authenticating the transmitter device in non-orthogonal multiple access (NOMA) systems. This problem is important because of high vulnerabilities in wireless communications and an additional security vulnerability when some users collude with the adversary. In this paper, we consider two attacking scenarios. In the first scenario, there is no user that colludes with the adversary. In the second scenario, at least one user colludes with the adversary. In this paper, we propose an authentication approach in the down-link scheme of a NOMA system using physical-layer authentication mechanism because of its advantages: provide information theoretic security, reduce complexity, save power and allow us to construct a two-factor authentication system. Based on the aforementioned two attacking scenarios, we propose three physical-layer authentication schemes for a NOMA system: Physical-Layer Authentication with Shared Authentication Tag (PLA-SAT), Physical-Layer Authentication with Superimposed Independent authentication Tags (PLA-SIT), and Physical-Layer Authentication with TDM authentication tags (PLA-TDM). We analyze the theoretical performance of our schemes over fading channels and derive their closed-form expressions. Then, we optimize the parameters of our schemes to achieve both the reliability fairness and the authentication-accuracy fairness. We implemented our schemes and conducted extensive performance comparisons. Our experimental results show that for the first attacking scenario, the PLA-SAT scheme offers more than 97% authentication accuracy when the received SNR of the served user with poor channel condition is at least 10 dB. For the second attacking scenario, both the PLA-SIT scheme and the PLA-TDM scheme offer more than 97% authentication accuracy when the received SNR of the served user with poor channel condition is at least 12 dB.

Consensus is one of the key problems in blockchains. There are many articles analyzing the performance of threat models for blockchains. But the network stability seems lack of attention, which in fact affects the blockchain performance. This paper studies the performance of a well adopted consensus algorithm, Raft, in networks with non-negligible packet loss rate. In particular, we propose a simple but accurate analytical model to analyze the distributed network split probability. At a given time, we explicitly present the network split probability as a function of the network size, the packet loss rate, and the election timeout period. To validate our analysis, we implement a Raft simulator and the simulation results coincide with the analytical results. With the proposed model, one can predict the network split time and probability in theory and optimize the parameters in Raft consensus algorithm.

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.

Due to the underlying sparse structure of the mmWave channels, which indeed makes the exact closed-form capacity expressions inherently hard to derive, there has been less research on the ergodic capacity of mmWave systems. To overcome this problem, by means of the majorization theory, this paper analyzes the ergodic capacity of point-to-point mmWave communication systems under finite-dimensional channel model. In particular, we derive several closed-form ergodic capacity approximations, which exhibit excellent tightness in spite of whether the steering matrices are singular or not. Then, several Jensen's approximations and bounds of the ergodic capacity are also derived. The results indicate that the ergodic capacity seems to increase logarithmically with the number of antennas, the transmit SNR per antenna, and the eigenvalues of the steering matrix products. Besides, the DFT matrices can effectively characterize the spatial directions of mmWave channels when the number of antennas grows large. After that, high-SNR ergodic capacity, high-SNR slope, and power offset are also analyzed. It indicates that for a finite-dimensional channel, the maximum multiplexing gain increases with the number of paths instead of the number of antennas in Rayleigh channels. Numerical simulations are performed to validate the results.

In this paper, we propose a trellis coded modulation (TCM) based non-orthogonal multiple access (NOMA) scheme. Different from those in the traditional code-domain NOMA, the incoming bit streams of multiple layers are jointly coded and mapped to the codewords so as to improve the coding gain of the system. Based on the multi- dimensional TCM techniques, additional coding gain from the error control coding can be achieved without any bandwidth extension. New design criteria are provided and a novel set partitioning algorithm is proposed for multi-dimensional signal set labeling. To achieve the trade-off between the BER performance and complexity, a suboptimal two- layer Viterbi algorithm is proposed for joint decoding. Simulation results show that our proposed TCM-based NOMA scheme performs significantly better than the traditional code- domain NOMA in terms of the BER performance.

This paper investigates multi-antenna beamforming and time allocation to maximize the network energy-efficiency (EE) in a wireless powered communication network (WPCN). Since the EE optimization problem has an inherent fractional form, it is difficult to obtain the optimal value directly due to the lack of convexity in the objective function. To overcome this challenge, we first convert the original problem into a more tractable one by the fractional programming. Then, two schemes are proposed to find the optimal value. In the first scheme, the iterative value is updated according to the EE based on energy beamforming and time allocation derived in the current iteration. In the second one, the optimal value is obtained by consecutively shrinking the region in which it is located. Simulation results show that the proposed two schemes can improve the network EE significantly compared with the algorithm that only pursues high throughput. In addition, it is shown that the two schemes have simlilar performance in the network EE. However, the computation complexity of the first one is lower than that of the second one.

Authentication is a key requirement for secure communications in modern wireless systems. Compared with the conventional authentication at the upper layer using a cryptographic tool, authentication at the physical layer has many advantages, including enhanced security through the introduction of uncertainty to adversaries and increased efficiency and compatibility through the avoidance of operations at the upper layer, particularly in heterogeneous coexistence environments, e.g., 5G wireless systems. In this paper, we investigate authentication at the physical layer under time-varying fading channels. Conventional authentication schemes at the physical layer operate poorly under fast fading or frequency selective fading channels. Furthermore, conventional schemes require additional complicated preprocessing such as channel estimation and message symbol recovery through demodulation and decoding. This paper proposes a new blind authentication scheme at the physical layer that combines the techniques of blind known interference cancellation (BKIC) and differential processing to implement authentication without requiring any of the above-described preprocessing. The proposed scheme utilizes both the smoothing (SM) technique and belief propagation (BP) technique to achieve BKIC through the distinct blind authentication schemes with a superimposed tag over pilots (BSUPs) referred to, respectively, as BSUP-SM and BSUP-BP. The proposed scheme not only effectively suppresses the deteriorate effect of fading channels without any additional preprocessing but is also covert to unaware users, robust to interference, and secure for identity verification. The tradeoffs of using the proposed system with respect to various goals are discussed and analyzed. The performance of the BSUP-SM scheme depends on the channel fading and the length of the pilot cluster, while the performance of the BSUP-BP scheme is not sensitive to the above factors but depends instead on the quantization step used. The BSUP-BP scheme works well in fast fading channels, even in frequency selective fading channels.

This paper presents the Distributed Cooperative Access Points (DCAP) system that can simultaneously serve multiple clients using cooperative beamforming to increase the capacity of WiFi-type wireless networks. The distributed APs are connected by Ethernet and driven by independent low-cost local oscillators. To facilitate cooperative beamforming, we address three major challenges: the phase synchronization, the channel state information (CSI) measurement, and the user selection. Specifically, we develop 1) a cooperative tracking scheme to track signal phase drifts at symbol level without adding extra hardware complexity; 2) an incremental CSI estimation mechanism that removes the per-frame CSI measurement overhead of previous approaches; and 3) a simple random user selection algorithm that scales the network capacity linearly and delivers over 70 percent performance compared to the optimal but complex greedy algorithm. We implement DCAP on the Sora software radio platform and evaluate it in a wireless network with nine nodes. Experimental results show that the cooperative beamforming is feasible in practice, and our cooperative phase tracking can ensure strict phase alignment (≤ 0.03 radian) among APs during the entire beamforming period (1.2 ms). Otherwise, without tracking, phases may drift by 0.3 radian over merely 600 μs, causing that the symbol SNR decreases as large as 20 dB.

This paper proposes a device-to-device (D2D) communications based scarcity-aware two-stage multicast mechanism for delivering video streaming, in which the Base Station (BS) and D2D perform cooperative retransmissions to improve the mobile video quality and relieve the load of BS as well. In prior works, after BS's one-round multicast, the clients directly resort to D2D communications to obtain the missing frames by forming social groups. However, after the first-round multicast of BS, it is possible that some frames have not been correctly received by most of the clients, especially when the channel conditions are not good. That is, there are scarce frames in the network and the clients have very limited copies of them. In this case, it is inefficient for the clients to form social groups and directly exchange frames through D2D communications. Instead, this paper proposes an adaptive two-stage multicast mechanism: the BS first multicast video packets to the clients who request the same video services, after which the clients collect the information of missing frames and send it to the BS. Then the BS evaluates the scarcity of frames and re-multicast the scarce frames, if any, to the clients. After the BS's second-round multicast, the clients can then form groups to perform D2D communications so as to obtain the remaining missing packets from each other. Numerical studies show that the proposed simple scarcity-aware two-stage multicast mechanism can achieve about 5% video quality improvement than the existing one-stage multicast mechanism and have the potential to enhance the downlink capacity of the future 5G wireless network.

This paper considers the application of large circular array to Line-of-Sight (LoS) multi-user Communications for C-RAN networks using the millimeter wave (mmWave) spectrum. Based on the symmetric geometry of the arrays, a spatial-harmonic-based interference suppressor (SHBIS) is proposed and evaluated in closed-form. For this receiver to work, a 2-D direction-of-arrival (DoA) information estimation method based on synthesizing spatial harmonics to form a scanning function is put forward.

In this paper we propose a new analog beamforming structure by switching on or off each of multiple transmit antennas according to channel state information. The proposed analogue beamforming can significantly reduce the high cost, high power and bulky analogue phase-shifters which are employed in analog massive MIMO systems. On one hand, the high performance low cost commercial switch devices make our architecture easy to implement, saving both system cost and space. On the other hand, our on-off analog beamforming (OABF) can achieve good performance with low complexity algorithms. Specifically, we first propose two SNR-maximization algorithms, which determines the on-off state of each switch under per-antenna power constraint. After that, we theoretically prove our on-off analog beamforming scheme can achieve full system diversity gain and array gain with polynomial complexity. The simple structure of OABF makes the massive MIMO much easier to implement, at a cost of small constant rate loss.

This paper proposes a belief propagation (BP) message passing algorithm-based joint multiple symbol differential detection (MSDD) and channel decoding scheme for noncoherent differential ultra-wideband impulse radio (UWB-IR) systems. MSDD is an effective means to improving the performance of noncoherent differential UWB-IR systems. To optimize the overall detection and decoding performance, this paper proposes a novel soft-in soft-out (SISO) MSDD scheme for noncoherent differential UWB-IR. We first propose a new sampling mechanism for the noncoherent auto-correlation receiver to sample the received UWB-IR signal. The proposed sampling mechanism can exploit the dependences (imposed by the differential modulation) among data symbols throughout the whole packet. The signal probabilistic model has a hidden Markov chain structure. We use a factor graph to represent this hidden Markov chain. Then, we apply BP message passing algorithm on the factor graph to develop an SISO MSDD scheme, which is easy to integrate with SISO channel decoding to form a joint MSDD and channel decoding scheme. Performance results of bit error rate simulations and EXIT chart analyses indicate the performance advantages of our scheme over the previous MSDD scheme.

To support multi-hop device-to-device (D2D) transmission, this paper proposes a novel wireless architecture, device-to-device software defined networks (D2D-SDN). In D2D-SDN, mobile devices are not only terminals but also can act as wireless switches to forward packets for others based on the routing and scheduling instructions from controllers. Under the proposed D2D-SDN architecture, we test the routing and adaptive resource allocation by jointly exploiting network information collection, and network abstraction. We also developed a testbed based on USRP to conduct experiments and demonstrate the feasibility and superiority of our proposed D2D-SDN.

The data center network (DCN), wired or wireless, features large amounts of many-to-one (M2O) sessions. Each M2O session is currently established using point-to-point (P2P) communications and store-and-forward (SAF) relays, and is generally followed by a certain computation at the destination, typically a weighted summation of the received information digits. Fundamentally different from this separate P2P/SAF-based-transmission and computation framework, this paper proposes simultaneous transmission and air computation (STAC), a novel physical layer scheme that achieves STAC in wireless DCNs. In particular, STAC builds on a number of distinguishing characteristics of DCs to take advantage of the superposition nature of electromagnetic signals. With STAC, multiple sources transmit in the same time slot with appropriately chosen parameters, such that the superimposed signal can be directly transformed to the desired summation at the receiver. To enable STAC, we propose an enhanced software-defined network architecture, where a wired low-bandwidth backbone provides wireless transceivers with external reference signals. We also discuss some new challenges that STAC brings to scheduling and routing. Theoretical analysis and simulation results show that STAC can significantly improve both bandwidth and energy efficiency in DCNs.

Energy efficiency is a key issue in wireless body area networks (WBANs). A number of transmission power control (TPC) schemes have been developed to improve the efficiency of transmission, which is one of the most energy consuming operations in WBAN. To save energy, these schemes only probe the link quality from the received data packets. However, due to large intervals between data packets and fast dynamic on-body link characteristics in WBAN, the obtained link information is usually outdated. In this case, the performance of the current TPC scheme is poor. This paper proposes an accelerometer-assisted TPC (AA-TPC) scheme, which exploits the periodic fluctuations of link qualities to improve the transmission energy efficiency. Consider the relationship between link quality and body movement, AA-TPC makes transmissions at ideal channel points that are identified by using the local accelerometer. We first conduct experiments to investigate the correlation between periodic movements and link quality. Then, we propose an algorithm to locate the time point in each period with the best link quality to transmit packets. The specific transmission power is then determined by the feedback information from the receiver. Finally, we evaluate the energy efficiency of AA-TPC based on a CC2420 platform in both a periodic scenario (without any aperiodic movement to break the periodicity) and a realistic scenario (which has aperiodic movements 20% of the time). The results show that about 26.4% and 18% of total energy consumption can be saved on average in the periodic and realistic scenarios, respectively.

In this paper, we study a two-phase underlay cognitive relay network, where there exists an eavesdropper who can overhear the message. The secure data transmission from the secondary source to secondary destination is assisted by two decode-and-forward (DF) relays. Although the traditional opportunistic relaying technique can choose one relay to provide the best secure performance, it needs to continuously have the channel state information (CSI) of both relays, and may result in a high relay switching rate. To overcome these limitations, a secure switch-and-stay combining (SSSC) protocol is proposed where only one out of the two relays is activated to assist the secure data transmission, and the secure relay switching occurs when the relay cannot support the secure communication any longer. This security switching is assisted by either instantaneous or statistical eavesdropping CSI. For these two cases, we study the system secure performance of SSSC protocol, by deriving the analytical secrecy outage probability as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER) region. We show that SSSC can substantially reduce the system complexity while achieving or approaching the full diversity order of opportunistic relaying in the presence of the instantaneous or statistical eavesdropping CSI.

This paper studies an untrusted relay channel, in which the destination sends artificial noise simultaneously with the source sending a message to the relay, in order to protect the source's confidential message. The traditional amplify-and-forward (AF) scheme shows poor performance in this situation because of the interference power dilemma. Providing better security by using stronger artificial noise will consume more power of the relay, impairing the confidential message's transmission. To solve this problem, this paper proposes a modulo-and-forward (MF) operation at the relay with nested lattice encoding at the source. For the proposed MF scheme with full channel state information at the transmitter (CSIT), theoretical analysis shows that the MF scheme approaches the secrecy capacity within 1/2 bit for all channel realizations, and, hence, achieves full generalized security degrees of freedom (G-SDoF). In contrast, the AF scheme can only achieve a small fraction of the G-SDoF. For the MF scheme without CSIT, the total outage event, defined as either connection outage or secrecy outage, is introduced. Based on this total outage definition, analysis shows that the proposed MF scheme achieves the full generalized secure diversity gain (G-SDG) of order one. On the other hand, the AF scheme can achieve a G-SDG of only 1/2 at most.

This paper aims at improving the distance-based location management scheme for mobile communication networks. In location management, a mobile terminal (MT) is tracked based on its location-update area (LA). The improvement is brought about by joint optimization of LA center and LA size. For LA center optimization (LCO), we determine the optimal center position of the LA given the initial position of the MT upon each location update. The investigation of optimal LA center has eluded research to date. Based on the popular continuous-time random walk (CTRW) mobility model, we propose an analytical framework that uses a diffusion equation to determine the optimal LA center that minimizes the total cost of location management, consisting of the location update cost and terminal paging cost. This framework allows us to easily model the non-Markovian movement of the MT and evaluate the impact of various measurable physical parameters (such as length of road section, angle between road sections, and road section crossing time) and LA center. In particular, we show that proper LA center can significantly reduce the total cost. For example, for the circular LA and low Poisson call-arrival rate, optimizing the LA center alone has the potential of reducing the cost by up to 37 percent. Joint optimization of the LA center and terminal paging scheme can reduce the cost even further. Simulations results match the theoretical analysis to a gap within 3 percent, indicating that our theoretical model is very accurate.

Motivated by applications such as battery-operated wireless sensor networks (WSN), we propose an easy-to-implement low-complexity two-way relaying scheme. In particular, we address the challenge of improving the standard two-way selective decode-and-forward protocol (TW-SDF) in terms of block-error-rate (BLER) with minor additional complexity and energy consumption. By following the principle of soft relaying, our solution is the two-way one-bit soft forwarding (TW-1bSF) protocol in which the relay forwards the one-bit quantization of a posterior information metric about the transmitted bits, associated with an appropriately designed reliability parameter. In WSN-related standards (such as IEEE802.15.6 and Bluetooth), block codes are adopted instead of convolutional and other sophisticated codes, due to their efficient decoder hardware implementation. As the second main contribution, we derive tight upper bounds on the BLER performance for both TW-SDF and TW-1bSF, when the two-way relaying network employs block codes and hard decoding. As a valuable tool for the BLER analysis, we introduce a new code-theoretic performance metric, named sphere partition function (SPF). The error probability analysis confirms the superiority of TW-1bSF. Moreover, we derive the asymptotic performance gain of TW-1bSF over TW-SDF, which further suggests that the proposed protocol is a good choice, especially when long block codes are used.

Opportunistic relaying scheme (ORS), where the best relay is selected for dual-hop communication, has been widely considered as the global optimum relaying technique. However, due to the requirement of acquiring the full channel state information (CSI) of all links, ORS has increased the system's complexity and might be harmful to the network stability, especially for the large-scale networks. In this paper, we therefore proposed an alternative scheme, namely, secure switch-and-stay combining (SSSC) protocol for providing the best secure performance. In particular, a two-phase underlay cognitive relay network, where one out of two decode-and-forward (DF) is activated to assist the secure data transmission. The secure relay switching occurs when the relay cannot support the secure communication any longer. We study the system secure performance of SSSC protocol by deriving an analytical secrecy outage probability as well as an asymptotic expression in the high main-to-eavesdropper ratio (MER) region. It is shown that SSSC can substantially reduce the switching rate with lower channel estimation complexity, and approach the full diversity meanwhile.

Characterizing user access methods in heterogeneous cloud radio access networks (H-CRANs)is critical for performance optimization. Different from the user access in cloud radio access networks, the inter-tier interference from macro base station has a great impact on user access in H-CRANs. In this paper, after considering the inter-tier interference, the ergodic rates of downlink H- CRANs for two proposed user access methods, namely distance based and cluster based, are analyzed. The corresponding mathematical expressions of ergodic rates have been derived. In particular, the closed-form expression for the upper bound of ergodic rate is proposed. Simulation results corroborate the accuracy of the derived expressions for these two methods. Furthermore, the cluster based user access method outperforms the distance based user access method when the intensity of remote radio heads is sufficiently high.

This work aims to design a novel multi-hop device-to-device (D2D) communication system across multiple cells, by applying the spirit of software-defined networking, where the controllers regulate the data flows between the device-level switches in a centralized way. We utilize a hierarchical control plane, where the global controller handles the cross-cell D2D transmission, and the local controller determines the intra-cell D2D routing and scheduling. Specifically, by collecting the network information periodically, the controllers can generate the topology of D2D network and provide it to the applications. Through the use of USRP hardware, we demonstrate the feasibility of our proposed system.

In this paper, we investigate the capacity of a known interference channel, where a transmitter sends information to a receiver in the presence of a block-fading interference link, and the receiver knows the interference data but not the channel gain of the interference link. An upper bound and a lower bound for the capacity of this known interference channel are derived. Specifically, the capacity lower bound is achieved by a blind known interference cancellation (BKIC) scheme, which can remove the interference without the knowledge of the interference channel gain. We further show that the achievable lower bound of BKIC can approach the upper bound in high SNR regime. Our results show that the lack of the knowledge of the channel gain of the interfering link causes only a small fractional loss of degrees of freedom (capacity prelog).

In spectrum underlay cognitive radio networks, secondary users (SUs) are allowed to reuse the spectrum allocated to a primary system. The interference between SUs actually carries information and can potentially be exploited to improve the network performance through information-theoretic interference processing. In this paper, we design an optimal joint power and rate control algorithm that maximizes the secondary system utility subject to the interference temperature constraints of primary users based on the capacity-approaching interference processing scheme called as the Han-Kobayashi scheme. The optimal solution is difficult to achieve because the optimization problem is in general non-convex. To make the optimization problem tractable, this paper first transforms the problem into a monotonic optimization problem through exploiting its hidden monotonicity. We then devise an effective algorithm to obtain the global optimal solution to the joint power and rate control problem in the Han-Kobayashi scheme. The key idea behind the proposed algorithm is to construct a sequence of shrinking polyblocks that approximate the upper boundary of the feasible region with increasing precision. Numerical results further show that the achieved utility of our scheme significantly outperforms the utility of conventional schemes which treat the interference between SUs as the noise.

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.

Currently, video-on-demand (VoD) streaming over Internet is a popular application. Because of the rapidly growing video population and user population, how to maintain high user quality of experience (QoE) with low cost is a challenging problem for Internet video streaming providers. A promising technique to potentially benefit VoD streaming system is network coding. A number of recent works studied how to use network coding to simplify chunk scheduling strategy to enhance VoD streaming performance. Most of these works only covered extreme cases of pure coding or pure chunk scheduling, emphasizing implementation, and experimentation in peer-to-peer (P2P) scenario without analytically evaluating the realizable performance gains explicitly. In this paper we discuss the strength and weakness of a family of coding strategies for CDN-based VoD streaming systems. The coding schemes are characterized by block sizes while the chunk scheduling strategy is characterized by the order to download chunks. Both pure coding strategy and pure chunk scheduling strategy are special cases of this family of strategies. We then propose a model to evaluate the benefits brought by each strategy. Basically, the coding scheme with larger block size gives more streaming and scheduling benefits with the cost of heavier overheads (e.g., encoding and decoding). System designers can take advantage of our model to balance the tradeoff between coding gain and coding overheads.

This paper studies a two-way relay channel where two single-antenna users exchange messages via a relay with$K$antennas. A new physical-layer network coding (PNC) method is proposed, referred to as channel-quantized PNC (CQ-PNC), that can achieve full diversity gain of$K$. The proposed method converts$K$received signals at the relay into two signals by a QR decomposition. The first one is a weighted summation of the two users' messages and the second one is a scaled version of one user's message. Then, the first signal is quantized at the relay by using the channel coefficient and the quantization error is cancelled by using the side information of the second signal. Finally, a Gaussian-integer weighted summation of the two users' messages is obtained, mapped to network codeword, and then broadcast to the two users. It is proved that the proposed CQ-PNC can achieve the full diversity gain of$K$with receiver side channel information, low computational complexity and symbol level synchronization. Simulation results demonstrate that the proposed method obtains optimum performance results with negligible gap.

This paper proposes an approximate method, equivalent access intensity (EAI), for the throughput analysis of CSMA wireless networks in which links have finite offered-load and their MAC-layer transmit buffers may be empty from time to time. Different from prior works that mainly considered the saturated network, we take into account in our analysis the impacts of empty transmit buffers on the interactions and dependencies among links in the network that is more common in practice. It is known that the empty transmit buffer incurs extra waiting time for a link to compete for the channel airtime usage, since when it has no packet waiting for transmission, the link will not perform channel competition. The basic idea behind EAI is that this extra waiting time can be mapped to an equivalent “longer” backoff countdown time for the unsaturated link, yielding a lower link access intensity that is defined as the mean packet transmission time divided by the mean backoff countdown time. That is, we can compute the “equivalent access intensity” of an unsaturated link to incorporate the effects of the empty transmit buffer on its behavior of channel competition. Then, prior saturated ideal CSMA network (ICN) model can be adopted for link throughput computation. Specifically, we propose an iterative algorithm, “Compute-and-Compare”, to identify which links are unsaturated under current offered-load and protocol settings, compute their “equivalent access intensities” and calculate link throughputs. Simulation shows that our algorithm has high accuracy under various offered-load and protocol settings. We believe the ability to identify unsaturated links and compute links throughputs as established in this paper will serve an important first step toward the design and optimization of general CSMA wireless networks with offered-load control.

This paper studies a MIMO Two-Way Relay Channel (TWRC) where two single-antenna nodes communicate with each other through a K-antenna relay. In the uplink phase, the two end nodes send their respective information simultaneously to the relay, and it is usually regarded as a virtual MIMO system. With traditional VBLAST MIMO detection, the maximum achievable diversity is K - 1. By noting that only the network coded packet, rather than the two individual packets, is needed at the relay node in TWRC, we propose a channel quantized physical-layer network coding (CQ-PNC) scheme based on VBLAST to achieve the full diversity K. Specifically, relay first uses QR decomposition to convert the K received signals into two valid signals, the first signal is a weighted summation of the two end nodes' symbols and the second signal is a scaled version of one end node's symbol. The relay first quantizes the first signal with the channel coefficient of one node and estimate the Gaussian integer summation of the two end nodes' information. At the same time, the quantization error is further canceled with the help of the second signal. After that, we adaptively map the Gaussian integer weighted summation to the network coding form. Moreover, we theoretically prove that our CQ-PNC can achieve the maximum diversity K. Finally, the numerical simulation shows that CQ-PNC performs within 2dB gap from the theoretical bound.

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.

The combination of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) is expected to improve the throughput of two-way relay network. In this paper, we propose a zero-forcing based MIMO two-way relay scheme in conjunction with a simple Max-Min relay antenna selection. This scheme solves the unpractical constraint encountered by many existing MIMO two-way relay schemes for application, which requires the relay to equip fewer antennas than the end node. Our scheme, on the other hand, benefits from the dedicated relay that has more antennas than the end node. A notable diversity advantage is obtained from judicious relay antenna selection. The reliability of the simple ZF based MIMO two-way relay is therefore improved. Of particular note, this paper extends our previous study to 1) support the more general application with non-binary PNC and 2) give a complete analysis on the attained end-to-end diversity with explicit theoretical result under i.i.d. Rayleigh fading channel.

This paper presents the first implementation of a two-way relay network based on the principle of physical-layer network coding. To date, only a simplified version of physical-layer network coding (PNC), called analog network coding (ANC), has been successfully implemented. The advantage of ANC is that it is simple to implement; the disadvantage, on the other hand, is that the relay amplifies the noise along with the signal before forwarding the signal. PNC systems in which the relay performs XOR or other denoising PNC mappings of the received signal have the potential for significantly better performance. However, their implementation also poses many challenges. For example, the relay must be able to deal with symbol and carrier-phase asynchronies of the simultaneous signals received from the two end nodes, and the relay must perform channel estimation before decoding. We investigate a PNC implementation in the frequency domain, referred to as FPNC, to tackle these challenges. FPNC is based on OFDM. In FPNC, XOR mapping is performed on the OFDM samples in each subcarrier rather than on the samples in the time domain. We implement FPNC on the universal soft radio peripheral (USRP) platform. Our implementation requires only moderate modifications of the packet preamble design of 802.11a/g OFDM PHY. With the help of the cyclic prefix (CP) in OFDM, symbol asynchrony and the multi-path fading effects can be dealt with simultaneously in a similar fashion. Our experimental results show that symbol-synchronous and symbol-asynchronous FPNC have essentially the same BER performance, for both channel-coded and unchannel-coded FPNC.

In this paper, we propose a joint design of MIMO-PNC (multiple-input multiple-output physical layer network coding) and channel decoding in two way relay channels. The paper shows that if we adopt the same Repeat Accumulate (RA) channel code at the two end nodes, there would be a compatible decoder at the relay which can transform the received superimposed packet to the network coding form of the two packets efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the MIMO PNC channel. The simulation results show that our new scheme outperforms the previously proposed schemes which combined MIMO NC with channel coding/decoding significantly in terms of BER with little added complexity.

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.

Combining of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) can significantly improve the performance of the wireless two-way relay network (TWRN). This paper proposes novel Max-Min optimization based relay antenna selection (RAS) schemes for zero-forcing (ZF) based MIMO-PNC transmission. RAS relaxes ZF's constraints on the number of antennas and extends the applications of ZF based MIMO-PNC to more practical scenarios, where the dedicated relay has more antennas than the end node. Moreover, RAS also brings diversity advantages to TWRN and the achievable diversity gains of the proposed schemes are theoretically analyzed. In particular, an equivalence relation is carefully built for the diversity gains obtained by 1) RAS for ZF based MIMO-PNC and 2) transmit antenna selection (TAS) for MIMO broadcasting (BC) with ZF receivers. This equivalence transforms the original problem to a more tractable form which eventually allows explicit analytical results. It is interesting to see that Max-Min RAS keeps the network diversity gain of ZF based MIMO-PNC to be the same as the diversity gain of the point-to-point link within the TWRN. This insight extends the understanding on the behaviors of ZF transceivers with antenna selection (AS) to relatively complicated MIMO-TWRN/BC scenarios.

A key issue in physical-layer network coding (PNC) is how to deal with the asynchrony between signals transmitted by multiple transmitters. That is, symbols transmitted by different transmitters could arrive at the receiver with symbol misalignment as well as relative carrier-phase offset. In this paper, 1) we propose and investigate a general framework based on belief propagation (BP) that can effectively deal with symbol and phase asynchronies; 2) we show that for BPSK and QPSK modulations, our BP method can significantly reduce the SNR penalty due to asynchrony compared with prior methods; 3) we find that symbol misalignment makes the system performance less sensitive and more robust against carrier-phase offset. Observation 3) has the following practical implication. It is relatively easier to control symbol timing than carrier-phase offset. Our results indicate that if we could control the symbol offset in PNC, it would actually be advantageous to deliberately introduce symbol misalignment to desensitize the system to phase offset.

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.

In random-access networks, such as the IEEE 802.11 network, different users may transmit their packets simultaneously, resulting in packet collisions. Traditionally, the collided packets are simply discarded. To improve performance, advanced signal processing techniques can be applied to extract the individual packets from the collided signals. Prior work of ours has shown that the symbol misalignment among the collided packets can be exploited to improve the likelihood of successfully extracting the individual packets. However, the failure rate is still unacceptably high. This paper investigates how channel coding can be used to reduce the failure rate. We propose and investigate a decoding scheme that incorporates the exploitation of the aforementioned symbol misalignment into the channel decoding process. This is a fine-grained integration at the symbol level. In particular, collision resolution and channel decoding are applied in an integrated manner. Simulation results indicate that our method outperforms other schemes, including the straightforward method in which collision resolution and channel coding are applied separately.

The two-phase MIMO NC (network coding) scheme can be used to boost the throughput in a two-way relay channel in which nodes are equipped with multiple antennas. The obvious strategy is for the relay node to extract the individual packets from the two end nodes and mix the two packets to form a network-coded packet. In this paper, we propose a new scheme called MIMO PNC (physical network coding), in which the relay extracts the summation and difference of the two end packets and then converts them to the network-coded form. MIMO PNC is a natural combination of the single-antenna PNC scheme and the linear MIMO detection scheme. The advantages of MIMO PNC are many. First, it removes the stringent carrier-phase requirement in single-antenna PNC. Second, it is linear in complexity with respect to the constellation size and the number of simultaneous data streams in MIMO. Simulation shows that MIMO PNC outperforms the straightforward MIMO NC significantly under random Rayleigh fading channel. Based on our analysis, we further conjecture that MIMO PNC outperforms MIMO NC under all possible realizations of the channel.

This paper investigates link-by-link channel-coded PNC (physical layer network coding), in which a critical process at the relay is to transform the superimposed channel-coded packets received from the two end nodes (plus noise), Y3= X1+ X2+W3, to the network-coded combination of the source packets, S1oplus S2. This is in contrast to the traditional multiple-access problem, in which the goal is to obtain both S1and S2explicitly at the relay node. Trying to obtain S1and S2explicitly is an overkill if we are only interested in S1oplusS2. In this paper, we refer to the transformation Y3rarr S1oplus S2as the channel-decoding- network-coding process (CNC) in that it involves both channel decoding and network coding operations. This paper shows that if we adopt the repeat accumulate (RA) channel code at the two end nodes, then there is a compatible decoder at the relay that can perform the transformation Y3rarr S1oplusS2efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the PNC multiple-access channel. Simulation results show that our new scheme outperforms the previously proposed schemes significantly in terms of BER without added complexity.

Direct application of network coding at the physical layer - physical layer network coding (PNC) - is a promising technique for two-way relay wireless networks. In a two-way relay network, relay nodes are used to relay two-way information flows between pairs of end nodes. This paper proposes a precise definition for PNC. Specifically, in PNC, a relay node does not decode the source information from the two ends separately, but rather directly maps the combined signals received simultaneously to a signal to be relayed. Based on this definition, PNC can be further sub-classed into two categories - PNCF (PNC over finite field) and PNCI (PNC over infinite field) - according to whether the network-code field (or groups, rings) adopted is finite or infinite. For each of PNCF and PNCI, we consider two specific estimation techniques for dealing with noise in the mapping process. The performance of the four schemes is investigated by means of analysis and simulation, assuming symbol-level time synchronization only.

Network coding has been receiving much attention recently for its ability to improve network throughput and enhance network robustness. In this paper, we investigate the design of network coding in wireless networks and propose a combined low complexity network coding and channel decoding scheme. We analyze the capacity of the proposed scheme for both the binary symmetric channel (BSC) and AWGN channel and show that it can achieve almost the same channel capacity as traditional network coding with a small degradation in the system bit error rate (BER) performance while achieving almost 50% complexity reduction. It is also shown that the proposed network coding design can be applied in wireless cooperative networks.

A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). The goal of this paper is to show how the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to "straightforward" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. PNC can yield higher capacity than straight-forward network coding when applied to wireless networks. We believe this is a first paper that ventures into EM-wave-based network coding at the physical layer and demonstrates its potential for boosting network capacity. PNC opens up a whole new research area because of its implications and new design requirements for the physical, MAC, and network layers of ad hoc wireless stations. The resolution of the many outstanding but interesting issues in PNC may lead to a revolutionary new paradigm for wireless ad hoc networking.