The quest for higher wireless carrier frequencies spanning the millimeter-wave (mmWave) and Terahertz (THz) bands heralds substantial enhancements in data throughput and spectral efficiency for next-generation wireless networks. However, these gains come at the cost of severe path loss and a heightened risk of beam misalignment due to user mobility, especially pronounced in near-field communication. Traditional solutions rely on extremely directional beamforming and frequent beam updates via beam management, but such techniques impose formidable computational and signaling overhead. In response, we propose a novel approach termed trajectory-adaptive beam shaping (TABS) that eliminates the need for real-time beam management by shaping the electromagnetic wavefront to follow the user's predefined trajectory. Drawing inspiration from self-accelerating beams in optics, TABS concentrates energy along pre-defined curved paths corresponding to the user's motion without requiring real-time beam reconfiguration. We further introduce a dedicated quantitative metric to characterize performance under the TABS framework. Comprehensive simulations substantiate the superiority of TABS in terms of link performance, overhead reduction, and implementation complexity.

Constant envelope orthogonal frequency-division multiplexing (CE-OFDM) has attracted increasing attention as a power-efficient modulation scheme for Internet of Things (IoT) networks, particularly in energy-constrained scenarios such as space–air–ground integrated networks (SAGINs). Despite its inherently low peak-to-average power ratio (PAPR), CE-OFDM suffers from significant bit error rate (BER) degradation due to nonlinear phase wrapping caused by phase modulation. To address this limitation, we propose a selective mapping (SLM)-aided CE-OFDM scheme, in which the transmit sequence with the minimum number of phase jumps is selected from a pre-defined set of candidates. This strategy effectively mitigates the impact of phase wrapping and significantly improves BER performance. Furthermore, to eliminate the spectral overhead caused by conventional side information transmission, we propose an embedded side information (ESI) mechanism that seamlessly incorporates index data into the transmitted signal without requiring additional bandwidth. Simulation results verify that the proposed SLM-aided CE-OFDM scheme, equipped with ESI, achieves substantial BER improvements over conventional CE-OFDM systems, while maintaining the low-PAPR property essential for energy-constrained IoT devices.

Vehicular metaverses, blending traditional vehicular networks with metaverse technology, are expected to revolutionize fields such as autonomous driving. As virtual intelligent assistants in vehicular metaverses, Artificial Intelligence (AI) agents empowered by large language models can create immersive 3D virtual spaces for passengers to enjoy on-board vehicular applications and services. To provide users with seamless and engaging virtual interactions, resource-limited vehicles offload AI agents to RoadSide Units (RSUs) with adequate communication and computational capabilities. Due to the mobility of vehicles and the limited coverage of RSUs, AI agents need to migrate from one RSU to another. However, potential network attacks pose significant challenges to ensuring reliable and efficient AI agent migration. In this paper, we first explore specific network attacks, including traffic-based attacks (i.e., DDoS attacks) and infrastructure-based attacks (i.e., malicious RSU attacks). Then, we model the AI agent migration process as a Partially Observable Markov Decision Process (POMDP) and apply multi-agent proximal policy optimization algorithms to mitigate DDoS attacks. In addition, we propose a trust assessment mechanism to counter malicious RSU attacks. Numerical results demonstrate that the proposed solutions effectively defend against these network attacks and reduce the total latency of AI agent migration by approximately 12.8%.

The space–air–ground integrated network (SAGIN) has emerged as a promising architecture for future wireless communication systems. However, its open and heterogeneous nature introduces significant physical-layer security risks. To overcome the security concerns in SAGIN, we propose a constant-envelope orthogonal frequency division multiplexing with index modulation (CE-OFDM-IM) based transmission framework, where the constant-envelope property ensures compatibility with hardware-constrained SAGIN environments, while the index modulation mechanism inherently supports covert signaling via implicit subcarrier activation patterns. To enable reliable detection at legitimate receivers, we design both optimal and low-complexity receiver architectures, and further introduce a clipping-based technique to suppress phase wrapping and enhance demodulation robustness. Additionally, the average bit error probability (ABEP) is analytically characterized through performance analysis. Finally, simulation results demonstrate that the proposed CE-OFDM-IM system achieves robust transmission with zero peak-to-average power ratio (PAPR), offering a practical and energy-efficient solution for secure communications in SAGIN environments.

The growing demand for large artificial intelligence model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latency, privacy-preserving applications. In particular, edge-device co-inference, which jointly executes LAIM inference across edge devices and servers, has emerged as a promising strategy for resource-efficient LAIM execution in wireless networks. In this paper, we investigate a pruning-aware LAIM co-inference scheme, where a pre-trained LAIM is pruned and partitioned into on-device and on-server sub-models for deployment. For analysis, we first prove that the LAIM output distortion is upper bounded by its parameter distortion. Then, we derive a lower bound on the parameter distortion via rate-distortion theory, analytically capturing the relationship between pruning ratio and co-inference performance. Next, based on the analytical results, we formulate an LAIM co-inference distortion bound minimization problem by jointly optimizing the pruning ratio, split point, transmit power, and computation frequency under system latency, energy, and available resource constraints. Moreover, we propose an efficient algorithm to tackle the considered highly non-convex problem. Finally, extensive experimental results demonstrate the effectiveness of the proposed design. In particular, model parameter distortion is shown to provide a reliable bound on output distortion. Also, the proposed joint design achieves superior performance in balancing trade-offs among inference performance, system latency, and energy consumption compared with various benchmark schemes.

Traffic status monitoring is essential to support intelligent transportation, and demands for highly efficient and secure processing and transmission of massive data. Inspired by recent information-theoretic study, the traffic status monitoring is formulated as a privacy-preserving semantic coding problem in this work to characterize the fundamental trade-off among data compression, traffic image reconstruction, segmentation of traffic participants, and preservation of pixel regions of traffic participants. Grounded on the theoretic results, a novel generative semantic coding scheme is further developed for data-driven semantic coding design with the aid of attention mechanisms to capture correlations between sparse information. Experiments on the RCooper dataset demonstrate the effectiveness of the proposed generative semantic coding scheme and its advantages over the benchmark compression methods.

The advance of vehicle-to-everything (V2X) networks has led to many emerging data-intensive applications at the network edge. To meet the soaring data rate requirements of these applications, numerous coding schemes has been developed. However, the high heterogeneity of edge users bring challenges to these methods, including adaptation to performance requirements, coping with unknown or varying channels, as well as inefficient multicasting. In this paper, we address those problems by developing aratelessdeep joint source-channel coding scheme featuring fine-grained control over rate and informativeness at the user. Towards this end, we first design a novel class of variational information bottleneck (VIB) by employing the multinomial-Gaussian (MG) distribution, to achieve rateless transmission over an erasure channel. We derived important results on the statistical properties of this latent distribution to facilitate efficient training of MG-VIB. Then, we apply this framework to multicasting, proposing MG-VIB-M to enhance adaptability and scalability. Simulations show that our proposed method is more flexible regarding rate-relevance tradeoffs, has greater robustness against channel imperfections, and reduces bandwidth requirements for task-oriented multicasting.

Multi orbit low earth orbit (LEO) satellites communication is envisioned as a key infrastructure to deliver global coverage, enabling future services from space air ground integrated networks.However, the optimized design of LEO which jointly addresses satellite selection, association control, and resource scheduling while accounting for dynamic visibility in multi orbit constellations still remains open. Satellites moving along distinct orbital planes yield phase shifted ground tracks and heterogeneous, time varying coverage patterns that significantly complicate the optimization.To bridge the gap, we propose a dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers. The framework is built upon Markov approximation and matching game theory. Specifically, we formulate a combinatorial optimization problem that maximizes the sum rate under per satellite power budgets. The problem is NP hard , combining discrete user association (UA) decisions with continuous power allocation, and an inherently non convex sum rate maximization objective. We address it through a problem specific Markov approximation. Moreover, we alternately solve UA or bandwidth allocation via a matching game and power allocation via a Lagrangian dual program, which together form a block coordinate descent method tailored to this problem. Simulation results show that the proposed algorithm converges to a suboptimal solution across all scenarios. Extensive experiments against four state of the art baselines further demonstrate that our algorithm achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.

Over-the-air computation (AirComp) enables swift wireless data aggregation by leveraging the superposition property of multiple-access channels (MAC), making it essential for the seamless integration of communication and computing in future networks. Meanwhile, fluid antenna systems (FAS) offer dynamic spatial degrees of freedom (DoF) by reconfiguring antenna positions, thus enhancing adaptability under varying channel conditions. This paper investigates the integration of FAS into a communication and AirComp coexistence framework. We aim to jointly optimize the transceiver beamforming vectors and the antenna positioning vector (APV) to minimize the computation distortion while ensuring reliable cellular communication performance. To tackle this highly non-convex problem, we develop an efficient joint learning-optimization framework. Specifically, we propose a neural network (NN) framework with a dedicated surrogate loss function design to infer optimal APV based on multi-path channel conditions, while an alternating optimization (AO) method is developed to find a locally optimal solution of transceivers by iteratively optimizing each variables with the others being fixed. Besides, to provide analytical tractability and benchmark insight, the APV design problem is relaxed and transformed into a tractable quadratically constrained quadratic program (QCQP) by successive convex approximation (SCA) as a special case under line-of-sight (LoS) channels, which reveals the performance bounds and convergence properties of the system. Numerical results show that proposed method significantly improves the system performance compared with traditional fixed-position antenna (FPA) as well as various benchmark schemes with remarkable generalization capabilities across diverse channel conditions.

In recent years, uncrewed aerial vehicles (UAVs) have become increasingly prevalent for collecting environmental data from various wireless sensors. However, existing research on employing UAVs to collect data from wireless sensors has often ignored the heterogeneous requirements of sensors. In this paper, we investigate joint deployment, user association, and power allocation for data collection in the UAV-assisted wireless sensor network to accommodate the heterogeneous requirements of sensors, where a novel satisfaction function is designed for three types of sensors, including sensors with delay requirements, sensors with energy consumption requirements, and sensors with both delay and energy consumption requirements. Leveraging the satisfaction function, we formulate the optimization problem aimed at jointly optimizing the positions of UAVs, the association between sensors and UAVs, and the power allocation of sensors to maximize overall satisfaction of sensors. In order to effectively address the considered problem, we decompose it into two subproblems, i.e., joint UAV deployment and user association subproblem, and transmission power allocation subproblem. An enhanced human evolutionary algorithm is developed to tackle the joint UAV deployment and user association subproblem, and the Lagrange dual method and gradient descent method are employed to solve the transmission power allocation subproblem. The suboptimal solution is achieved by iteratively addressing the two subproblems until convergence of the proposed enhanced Lagrange and gradient descent-based human evolutionary optimization algorithm is attained. Extensive simulations demonstrate the effectiveness of the proposed algorithm in enhancing overall satisfaction of sensors, underscoring its significant advantages in managing heterogeneous network environments.

The joint design of sensing, communication, computing, and control (SC3) is crucial for supporting environment-aware Industrial Internet of Things (IIoT) applications. Considering the uncontrollable wireless propagation environments and limited spectrum resources, wireless communication performance often becomes the primary design bottleneck for such an integrated system. To address this challenge, this paper presents a design framework for reconfigurable intelligent surface (RIS)-enhanced semantic-aware SC3networks, where RIS and semantic communication technologies are employed to improve wireless communication efficiency. To facilitate real-time closed-loop control, we further formulate a weighted sum execution latency minimization problem, while imposing constraints on maximum execution latency and energy consumption of individual IoT device, as well as minimum information entropy to meet specific control requirements measured by linear quadratic regulator cost. In addition, the design framework aims at optimizing bandwidth allocation, RIS phase shift matrix, time scheduling, transmit power, and CPU-cycle frequency for IoT devices and the base station (BS). To handle the coupled multi-dimensional optimization variables, the block coordinate descent method is utilized to decompose the formulated problem into more tractable subproblems, which are then solved using a penalty-function-based approach and geometric programming technique. Simulation results demonstrate the performance advantages achieved by our proposed method compared to several benchmark approaches. Additionally, we explore the impact of various parameters on SC3systems, offering deeper insights and meaningful research observations.

Timeseries regression from multimodal data is a difficult task when modalities have missing data or different sampling frequencies. Current approaches such as feature fusion often rely on interpolation and might result in either too simplistic or overly computationally expensive algorithms. We propose ITNet, an end-to-end trainable multihead causal cross-attention model adapted for irregularly sampled multimodal timeseries data. We show the performances of the model on synthetic data from 2d linear state-space models with a varying number of modalities, varying data missingness and varying signal-to-measurement-noise ratio (SMNR). We compare with Kalman filters exploiting either one or all available modalities. Provided a sufficient number of modalities and high enough SMNR, ITNet outperforms the closed form Kalman Filter. Importantly, our model achieves these results without assuming knowledge of the state transition matrix. This is of particular importance towards the use of ITNet for practical cases.

We investigate the performance of primitive rateless (PR) codes and introduce an enhanced ordered statistics decoder (OSD) designed for decoding them. We demonstrate that constructing PR codes using linear Boolean functions simplifies decoding to identifying dual bases over$\text{G F}\left(2^{k}\right)$. By leveraging self-dual bases over$\text{G F}\left(2^{k}\right)$, the decoding process for high-rate PR codes is simplified, contributing to a reduced complexity OSD algorithm. Through simulations, we establish that high-rate PR codes can achieve block error rates comparable to their BCH counterparts across various signal-to-noise ratios (SNRs) and code rates. The PR code can be tailored to any rate and block length, and the proposed OSD algorithm makes it well-suited for low-latency applications.

LEO satellites are key to global coverage in 6G wireless communications. However, efficiently selecting service satellites in multi-orbit systems remains a challenge due to dynamic topologies and limited resources. To address this challenge, this paper proposes a joint optimization framework for satellite selection, user association, and resource allocation in Space-AirGround Integrated Networks (SAGINs). We design a computationally efficient algorithm that leverages Markov approximation for satellite selection and employs matching game theory for user association and resource allocation. Our simulation results show that the proposed algorithms outperform benchmark methods.

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for 6G networks. This paper addresses the joint beamforming design challenge in ISAC systems to prevent sensing information leakage to legitimate communication users. We propose a novel optimization framework that leverages semidefinite relaxation (SDR) and alternating optimization (AO) techniques to jointly design the base station beamforming vectors and the phase shift matrix of the RIS deployed at the radar target. The proposed approach ensures the satisfaction of communication SINR requirements while effectively suppressing the eavesdropping capability of sensing eavesdroppers. Simulation results demonstrate that our method achieves near-complete eavesdropping elimination compared to RIS-free and random-phase RIS configurations, enabling secure decoupling of sensing and communication functionalities.

With the rapid development of the Internet of Things (IoT), the number of connected devices has increased exponentially, bringing significant convenience to various aspects of daily life and business operations. However, communication between IoT devices requires a significant amount of bandwidth, putting a strain on the communication system. To address this challenge, we introduce a classification-oriented semantic communication approach that transmits only essential information. We present a novel end-to-end task-oriented semantic communication model, which efficiently serves the classification task at the receiver. In particular, the proposed model first utilizes a neural network-based semantic encoder to extract classification-related semantic features. A transformer-based semantic decoder is used at the receiver to retrieve semantic features and generate classification results. We further introduce a channel encoder and decoder module to improve the ability of a single model to deal with various channel conditions. Simulation results show that, compared with the traditional method, the proposed scheme achieves higher classification accuracy on the ESC-50 dataset and UrbanSound8K dataset and has better performance for various channel conditions.

Vehicle-Infrastructure cooperative perception (VICP) serves as a promising solution to enhance the environmental sensing capability of autonomous driving systems. However, most existing VICP algorithms assume ideal data exchange between the road-side and on-board sensors. In practice, channel distortion and limited bandwidth in vehicle-to-infrastructure (V2I) communication would cause significant performance degradation. To address this issue, we propose VICP-SC, a solution that integrates VICP with semantic communication (SC) for the LiDAR-based 3D object detection function. Specifically, a task-driven joint source-channel coding (JSCC) module and a multi-scale feature fusion (FF) module are developed to efficiently transmit and effectively fuse the infrastructure’s LiDAR point clouds with those attained by vehicles. Extensive experiments on the real-world DAIR-V2X dataset demonstrate that the proposed VICP-SC method achieves excellent performance in terms of perception accuracy, bandwidth efficiency, and system robustness against channel variations compared with existing baselines, particularly in the low signal-to-noise (SNR) regimes.

Integrated Sensing and Communication (ISAC) systems promise to revolutionize wireless networks by concurrently supporting high-resolution sensing and high-performance communication. This paper presents a novel radio access technology (RAT) selection framework that capitalizes on vision sensing from base station (BS) cameras to optimize both communication and perception capabilities within the ISAC system. Our framework strategically employs two distinct RATs, LTE and millimeter wave (mmWave), to enhance system performance. We propose a vision-based user localization method that employs a 3D detection technique to capture the spatial distribution of users within the surrounding environment. This is followed by geometric calculations to accurately determine the state of mmWave communication links between the BS and individual users. Additionally, we integrate the SlowFast model to recognize user activities, facilitating adaptive transmission rate allocation based on observed behaviors. We develop a Deep Deterministic Policy Gradient (DDPG)-based algorithm, utilizing the joint distribution of users and their activities, designed to maximize the total transmission rate for all users through joint RAT selection and precoding optimization, while adhering to constraints on sensing mutual information and minimum transmission rates. Numerical simulation results demonstrate the effectiveness of the proposed framework in dynamically adjusting resource allocation, ensuring high-quality communication under challenging conditions.

Reconstructing accurate radio maps is crucial for optimizing wireless network performance and managing spectrum efficiently. In real-world scenarios, radio map data, often sparse and incompletely labelled, poses significant challenges to traditional learning techniques. Graph Neural Networks (GNNs) have become instrumental in efficiently reconstructing radio maps (RMR) in such environments by effectively encoding correlations in unstructured data. Existing GNN-based methods, however, are limited as they typically consider only single factors like location, environment, or transmitter characteristics during correlation encoding. To overcome this limitation, we introduce RadioGAT, a propagation model-based approach that comprehensively integrates these factors. We further utilize Graph Attention Networks to enable semi-supervised learning, enhancing the accuracy of radio map reconstruction. Our experimental results demonstrate the superiority and robustness of RadioGAT, particularly at low sampling rates, and highlight the importance of selecting appropriate correlation encoding methods based on the data availability for RMR.

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

The challenges associated with channel state information (CSI) acquisition in the frequency division duplexing (FDD) extremely large-scale MIMO (XL-MIMO) system significantly impede its application in 6G communications. In such context, this contribution introduces a CSI feedback framework with pilot precoding capitalizing on the partial FDD reciprocity towards significantly enhanced efficiency. Specifically, by exploiting the near-field channel sparsity in the polar-delay domain, the proposed polar-delay sparsity (PDS) codebook remarkably reduces pilot and CSI feedback overhead while maintaining high spectral efficiency (SE). Furthermore, a class of innovative compression and decompression methods are also developed to enable further reduction of training overhead without compromising system performance. Notably, the proposed design ensures consistently low computational complexity and feedback overhead at the user equipment (UE), making it well-suited for the massive connectivity demands of heterogeneous devices in 6G Internet of Everything (IoE) applications. Finally, simulation results demonstrate the satisfactory SE performance enhancement achieved by the proposed schemes.

In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators—forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system’s behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3loops.

In the era of smart cyber-physical grid, dynamic insecurity risk has become a significant concern due to the increasing integration of renewable energy sources and the inherent uncertainties in smart grid. Dynamic security assessment (DSA) has been adopted to hedge against such risks by estimating the stability of large-scale smart grids. Existing DSA approaches often involve complex high dimensional models which incur high communication and computational costs, hindering their practical adoption. In this paper, we address these limitations with the Quantum Federated Ensembled Variational Adaptive Learning (QFEVAL) approach for smart grid DSA. QFEVAL is designed to combine quantum machine learning and federated learning to handle the differential-algebraic equations that describe smart grid stability, providing an efficient way to deal with high-dimensional data and uncertainties. QFEVAL enables the training of the hybrid quantum-classical neural networks on distributed DSA datasets located at different nodes in smart grids, without requiring large numbers of parameters to be transmitted. QFEVAL accurately predicts the stability of the smart grid under various conditions, enabling the implementation of preventive stability control measures. Through extensive experiments, we demonstrate that QFEVAL achieves comparable performance to 9 state-of-the-art DSA approaches with more than 2 orders of magnitude fewer model parameter transmissions. QFEVAL paves the way for reliable, secure, and continuous electricity supply, offering a robust solution to the challenges of DSA in smart grids.

To achieve stringent performance requirements in next generation wireless networks, such as ultra-high data rates, ubiquitous connectivity, and extremely high reliability, this paper proposes a radically novel rate splitting assisted dual-polarized stacked metasurface (RS-DPSM) transceiver architecture. In this architecture, a multi-layer dual-polarized metasurface is stacked at the active antennas and its two inherent polarizations are implemented to enable RS’s common and private messages in parallel. In sharp contrast to the conventional multiple-input multiple-output (MIMO) and metasurface-based transceiver designs, our proposed transceiver is capable of enhancing the channel capacity and introducing multi-dimensional degrees of freedom (DoFs) in the power, spatial, and polarization domains, thus enabling multi-functional, broad-spectrum, and all-time/domain/space communications without requiring massive radio-frequency (RF) chains. In addition, we derive new analytical expressions for the upper bounds of RS-DPSM transceiver’s channel capacity and ergodic sum rate, and provide some key insights. To highlight its potential benefits, we apply the proposed RS-DPSM transceiver to anti-jamming communications, and formulate a generalized sum rate maximization problem under the jammer’s imperfect angular channel state information and unknown cross-polarization discrimination. To enable an efficient resource management under the above practical conditions, we present a low-complexity optimization framework by leveraging the discretization method, properties of the quadratic function, reduced-majorization-minimization algorithm, and block successive upper-bound minimization, which admit the semi-closed-form solutions. Finally, our numerical simulations verify the superiority of our proposed transceiver architecture and optimization framework over key benchmarks.

The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demands, particularly in resource-constrained systems e.g., mobile phones and UAVs, make TOC challenging for many tasks. To address the problem, we propose a novel TOC method with two models: a static and a dynamic model. In the static model, we apply a neural network (NN) as a task-oriented encoder (TOE) when there is no computation budget constraint. The dynamic model is used when device computation resources are limited, and it uses dynamic NNs with multiple exits as the TOE. The dynamic model sorts input data by complexity with thresholds, allowing the efficient allocation of computation resources. Furthermore, we analyze the convergence of the proposed TOC methods and show that the model converges at rate$O\left ({{\frac {1}{\sqrt {T}}}}\right)$with an epoch of lengthT. Experimental results demonstrate that the static model outperforms baseline models in terms of transmitted dimensions, floating-point operations (FLOPs), and accuracy simultaneously. The dynamic model can further improve accuracy and computational demand, providing an improved solution for resource-constrained systems.

In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to mitigate the negative impact of the stragglers. However, when aggregating these gradients, fixed weights are consistently applied across iterations, neglecting the generation of the global coded dataset and the dynamic nature of the trained model over iterations. This oversight may result in diminished learning performance. To overcome this drawback, we propose a new method named adaptive coded federated learning (ACFL). In ACFL, before the training, each device uploads a local coded dataset with additive noise to the central server to generate a global coded dataset under privacy-preservation requirements. During each iteration of the training, the central server aggregates the gradients received from the non-stragglers and the gradient computed from the global coded dataset, where an adaptive policy for varying the aggregation weights is designed. Under this policy, we optimize the performance in terms of privacy and learning, where the learning performance is analyzed through convergence analysis and the privacy performance in sharing local coded datasets with the server is characterized via mutual information differential privacy. Finally, we perform simulations to demonstrate the superiority of ACFL compared with the baseline methods.

In this paper, we consider the problem of federated learning (FL) with devices that have intermittent connectivity to the central server. For this problem, the concept of semi-decentralized FL has been proposed in the literature. This paradigm allows non-straggler devices to relay the gradients computed by the stragglers to the server, and enables realization of gradient coding (GC) to mitigate the negative impact of the stragglers that fail to communicate directly to the central server. However, for GC in semi-decentralized FL, the communication overhead caused by information transmission among the devices is significant. To overcome this shortcoming, inspired by the existing communication-optimal exact consensus algorithm (CECA), we propose a new communication-efficient semi-decentralized FL method (COFFEE). In each round, the devices exchange information by taking a certain number of steps towards communication-optimal exact consensus, ensuring that each device obtains the average of the gradients computed by both its previous neighbors and itself. Afterwards, the non-stragglers transmit the local average result to the server for global aggregation to update the global model. We analyze the convergence performance and the communication overhead of COFFEE analytically. Building on this, to further enhance learning performance under a specific communication overhead, we propose an enhanced version of COFFEE with an adaptive aggregation rule at the central server, referred to as A-COFFEE, which adjusts to the straggler pattern of the devices over training rounds. Experiments are conducted to verify that the proposed methods outperform the baseline methods.

Designing short Luby transformation (LT) codes with low overhead and good error performance is crucial and challenging for the deployment of vehicle-to-everything networks, which require high reliability, high spectral efficiency, and low latency. In this paper, we investigate the design of globally optimal transmission strategies that consider interactions between feedback for short LT codes using reinforcement learning (RL), where traditional asymptotic analysis based on random graph theory is known to be inaccurate in this context. First, in order to reduce the decoding overhead of short LT codes, we derive the gradient expression for optimizing the degree distribution of LT codes, and propose a RL-based distribution optimization (RL-DO) algorithm for designing short LT codes. Then, to improve the reliability and overhead of LT codes under limited feedback, we model the feedback optimization problem as a Markov decision process, and propose the RL-based joint feedback and distribution optimization (RL-JFDO) algorithm, which aims to design globally-optimal feedback schemes. Simulations show that our methods have lower decoding overhead, error rate, and decoding complexity compared to existing feedback fountain codes.

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results show the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

This work studies gradient coding (GC) in the context of distributed training problems with unreliable communication. We propose cooperative GC (CoGC), a novel gradient-sharing-based GC framework that leverages cooperative communication among clients. This approach eliminates the need for dataset replication, making it communication- and computation-efficient and suitable for federated learning (FL). By employing the standard GC decoding mechanism, CoGC yields strictly binary outcomes: the global model is either recovered exactly or the recovery is meaningless, with no intermediate outcomes. This characteristic ensures the optimality of the training and demonstrates strong resilience to client-to-server communication failures. However, due to the limited flexibility of the recovery outcomes, the decoding mechanism may also result in communication inefficiency and hinder convergence, especially when communication channels among clients are in poor condition. To overcome this limitation and further exploit the potential of GC matrices, we propose a complementary decoding mechanism, termed GC+, which leverages information that would otherwise be discarded during GC decoding failures. This approach significantly improves system reliability against unreliable communication, as the full recovery1of the global model dominates in GC+. To conclude, this work establishes solid theoretical frameworks for both CoGC and GC+. We assess the system reliability by outage analyses and convergence analyses for each decoding mechanism, along with a rigorous investigation of how outages affect the structure and performance of GC matrices. Finally, the effectiveness of CoGC and GC+is validated through extensive simulations.

To cope with the unendurable delay of maritime wireless networks (MWNs), this paper proposes a collaborative transmission framework utilizing a multi-antenna uncrewed surface vessel (USV) and multiple cache-aided buoys to satisfy the on-demand file requirements for remote users (RUs). Specifically, a direct transmission scheme is adopted for hit-requested files and a multi-hop transmission scheme is devised to handle cache misses. To fully exploit the local cache and signal processing capabilities, we integrate two schemes into a collaborative transmission framework, where the USV dynamically supports buoys in uncached file fetching, and buoys collaborate to forward both cached and fetched files to RUs through a cooperative beamforming policy. We aim to minimize the overall transmission completion time by jointly optimizing the USV trajectory, cooperative beamforming, and transmission duration under the constraints of USV kinetic, transmit power, and file requirements. By leveraging the completion condition analysis, the original problem is transformed into a sequence of one-slot problems and a finite-horizon problem, where the closed-form solution for the local caching beamforming at each buoy is derived. Due to the complexity of the multivariable coupling, we propose an equivalent rate transformation method for transmission strategy design. Numerical results validate the effectiveness of the proposed scheme and algorithm.

Over-the-air computation (AirComp) is emerging as a promising technology for wireless data aggregation. However, its performance is hampered by users with poor channel conditions. To mitigate such a performance bottleneck, this paper introduces an active reconfigurable intelligence surface (RIS) into the AirComp system. We begin by exploring the ideal active RIS model and propose a joint optimization of the transceiver and RIS configuration to minimize the mean squared error (MSE) between the target and estimated function values. To manage the resulting tri-convex optimization problem, we employ the alternating optimization (AO) framework to decompose it into three convex subproblems, each of which can be solved optimally. We then investigate two specific cases and analyze their respective asymptotic performance to reveal the superiority of the active RIS in mitigating the MSE relative to its passive counterpart. Lastly, we adapt our transceiver and RIS configuration optimization approach to account for the self-interference of the active RIS. To handle the resulting highly non-convex problem, we further develop a two-layer AO framework. Simulation results confirm the superiority of the active RIS in enhancing AirComp performance compared to its passive counterpart.

In this paper, we study the problem of distributed learning (DL) with devices transmitting sign information of the local gradients to the server under communication constraints, where the devices are susceptible to Byzantine attacks. For this problem, a sign-based gradient descent method with majority vote and stochastic 1-bit quantization (Sign-M-stochastic) has been proposed very recently. However, the Byzantine resilience of Sign-M-stochastic is inherently limited, based on the fact that all Byzantine devices and honest devices participate equally in the training process. To overcome this drawback and enhance the resilience to Byzantine attacks, inspired by the audit-based distributed detection systems, we propose a novel DL method with an audit mechanism (DL-AM). In each iteration, the sign information of the local gradients are obtained by the devices from stochastic 1-bit quantization. All devices, partitioned into groups, send the sign information to the server through multiple paths, both directly and via other devices in the same group. This approach provides the server with additional information about the identities of the devices, which enables the server to form the global model update by aggregating the sign information of different devices with varying weights. We analyze the convergence performance of the proposed method from a theoretical perspective. Finally, numerical results demonstrate the superiority of DL-AM over the baseline methods.

Benefiting from the outstanding advantages in speeding up task processing and saving energy consumption, vehicular edge computing has entered a period of rapid development. Given the sharp increase in application services, it is vital to fully utilize all available computation resources to guarantee personalized requirements from different users. Specially, a lot of idle vehicle resources can be exploited for task execution to improve the service experience. On the other hand, most works focus on the system performance and fail to guarantee diversified user demands. To this end, we propose a novel distributed collaborative computing scheme for task completion rate maximization (TCRM) in vehicular networks by taking into account both vertical and horizontal collaboration. The novelty of horizontal collaboration lies in the full use of available one-hop vehicle resources for task computing. In order to simultaneously guarantee the system-level performance and the user-level performance, TCRM aims to maximize the task completion rate while minimizing the energy consumption by intelligent resource optimization and task allocation. A TD3-based algorithm combined with the Dirichlet distribution is proposed to obtain the optimization decisions. Extensive simulations demonstrate that TCRM significantly improves performance compared to baseline algorithms.

This paper studies the joint optimization of multi-objective dependent task scheduling, resource allocation, and service caching in an aerial-ground integrated mobile edge computing system that includes multiple uncrewed aerial vehicles (UAVs). These UAVs, in coordination with a high-altitude platform, work together to process numerous dependent tasks collected by the UAVs. The optimization problem involves two conflicting objectives that need to be minimized simultaneously: the average execution delay of all dependent tasks and the average energy consumption of all UAVs. The conflict between the two objectives makes the problem quite challenging. Recently, some multi-objective approaches, such as multi-objective evolutionary algorithms (MOEAs), have been introduced to address dependent task scheduling. However, these approaches often suffer from premature convergence and tend to fall into local optima. To address these issues, we propose a modified MOEA based on decomposition that incorporates two performance-improving strategies. The first one is a probability-based neighborhood search strategy that selects two individuals to update neighborhood individuals based on the neighborhoods and external population, thereby improving population updating efficiency. The second one is a dynamic voltage and frequency scaling-based energy reduction strategy that further enhances the quality of solutions by adjusting the computing frequencies. Experimental results verify that the proposed algorithm obtains a number of outstanding nondominated solutions and achieves a better balance between objectives compared with several algorithms.

Quantum federated learning (QFL) is an emerging interdisciplinary field that merges the principles of quantum computing (QC) and federated learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform framework by capitalizing on the benefits of maximum distance separable (MDS) code and the reconfigurable intelligent surface (RIS). The proposed scheme is referred to as MDS-OFDM-RIS. The proposed design scheme consists of (i) an MDS code based amplitude and phase modulation scheme for OFDM transmission, which helps increase the minimum Hamming distance among symbols and improve on the error detection capabilities, (ii) a RIS that is placed near the radio frequency (RF) source, (iii) as well as a reduced-complexity maximum likelihood (RC-ML) detection algorithm at the receiver by utilizing the error detection ability of the MDS codes. We derive an upper bound for the bit error rate (BER) and a closed-form expression of the mutual information. Using the obtained analytical expressions, we formulate two optimization problems and derive the corresponding optimal solutions for RIS phase shifts. It is found that the two optimization problems share the same optimal solution, which indicates that the obtained RIS phase shifts optimize the system BER and channel capacity simultaneously. Simulation results show that compared with conventional OFDM systems, the proposed system can better combat multipath fading and provide higher channel capacity, especially when the RIS phase shifts are optimal. Moreover, the accuracy and low complexity of the proposed RC-ML detection scheme are demonstrated by numerical results.

As mobile networks progress towards a unified framework for integrated sensing and communication (ISAC), it is foreseeable to introduce new privacy concerns, particularly the potential exposure of position information to unintended receivers. In other words, the scope of physical-layer security (PLS) needs to be expanded to encompass both communication and sensing privacy. Therefore, in contrast to conventional PLS schemes that focus predominantly on preventing eavesdropping, this paper proposes a novel physical-layer privacy (PLP) design within ISAC frameworks, in order to guarantee the secrecy of data transmission while obscuring transmitter’s directional information. Specifically, we introduce a metric termed angular-domain peak-to-average ratio (ADPAR) to assess sensing resistance (SR) performance. Subsequently, three fundamental optimization problems are formulated under such ADPAR constraints to enhance communication secrecy, depending upon the integrity of illegitimate channel state information. These problems are then tackled using advanced strategies such as null-space projection and the cooperation with artificial noise. Additionally, closed-form solutions are further derived in a few specific cases by leveraging singular value decomposition (SVD) and generalized SVD. Finally, simulation results affirm the effectiveness of our design in safeguarding the twofold privacy within ISAC networks.

In this paper, aimed to against spoofing attack, we propose a novel physical layer authentication (PLA) framework for unmanned aerial vehicle (UAV) communication networks under Rayleigh and Rician channels. A new PLA metric, called authentication distance (AD), is defined by jointly considering the geographical locations, elevation angles, and channel randomness between a legitimate sensor and a malicious spoofer. For Rayleigh channel in dense urban environment, the closed-form expressions for the false alarm probability (FAP) and miss detection probability (MDP) are obtained by adopting method of convolution and integration by parts. Next, the PLA hypothesis test model with Rician channel is established in suburban environment where both the Rician factor and the path loss exponent are functions of UAV altitude. To proceed, the expressions for the FAP and MDP are derived based on the doubly non-centralFdistribution. In addition, MDP minimization solutions subject to certain FAP requirement are developed in both Rayleigh and Rician channels by optimizing the detection threshold and UAV altitude jointly. Simulation results show that our derived analytical expressions of FAP and MDP match the Monte Carlo simulations well. Moreover, simulation results also imply the effectiveness of the proposed PLA framework for UAV communication networks.

Stragglers’ effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained clients collaboratively train a global model by iteratively optimizing a local objective function with their local datasets and transmitting local model updates to the central parameter server (PS) through fading channels. To tackle communication stragglers without dataset sharing or prior information about the network at PS, we propose cooperative gradient coding (CoGC) for semi-decentralized FL to enable the exact global model recovery at PS. Furthermore, we conduct a thorough theoretical analysis of the proposed approach. Namely, an outage analysis of the proposed approach is provided, followed by a convergence analysis based on the failure probability of the global model recovery at PS. Nevertheless, simulation results reveal the superiority of the proposed approach in the presence of stragglers under imbalanced data distribution.

In this paper, a novel design of amplitude phase shift keying-aided space-time block coded differential spatial modu-lation (APSK-STBC-DSM) is proposed. This design conceived not only maintains the diversity benefits of the space-time block coded differential spatial modulation (STBC-DSM) system, but also conveys extra information bits with the aid of amplitude phase shift keying (APSK), so as to enhance the transmission rate. Moreover, an improved low-complexity detector is also proposed, in order to achieve near-optimal detection performance. Ultimately, simulation results exhibit that the proposed APSK-STBC-DSM system is capable of providing significant bit error rate (BER) performance gains over conventional STBC-DSM and other differential spatial modulation (DSM) counterparts.

We study the problem of federated learning (FL) in the presence of stragglers, the devices that are intermittently connected to the central server. Although under the newly developed semi-decentralized federated learning (SFL) framework, gradient coding (GC) can be applied to evade the stragglers by letting them relay their locally computed gradients to the central server via non-stragglers, the communication burden of GC in SFL is very heavy. To overcome this drawback, motivated by the communication-optimal exact consensus algorithm (CECA) proposed in the literature, we propose a new communicationefficient semi-decentralized method (COFFEE) in SFL. In each round of COFFEE, the devices take a certain number of steps towards consensus in a decentralized manner with high communication efficiency, and each of them acquires the average of its own gradient and the gradients of its previous neighbors. After that, the non-straggler devices send the obtained average results to the server, which aggregates the received vectors to yield the global model update. The learning performance of the proposed method is analyzed through convergence analysis. Finally, we run simulations to show the superiority of COFFEE over the baseline method, i.e., GC in SFL.

Orthogonal Time-Frequency-Space (OTFS) modulation can effectively suppress the effects of Doppler shift in high-speed mobile scenarios. At the same time, the accuracy of OTFS channel estimation is an important factor that affects the performance of OTFS. In this paper, we propose a Sparse Bayesian Learning (SBL) algorithm to quickly and accurately estimate the OTFS channel by combining the pilot pattern and the sparsity of the OTFS channel. First, we propose a new pilot pattern to prevent the contamination of information symbols on pilot symbols. Since the pilot pattern uses only partial guard symbols, it can also improve the spectral efficiency. Then, we propose the Student-T prior SBL (STSBL) algorithm to improve the speed and accuracy of OTFS channel estimation by exploiting the sparsity of the OTFS channel. Simulation results show that the normalized mean squared error (NMSE), bit error rate (BER), and throughput of the proposed scheme outperform the benchmark schemes.

Abstract Intelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.

With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the concept of space-air-ground integrated network (SAGIN) has emerged as a promising architecture for future 6G wireless systems. In general, SAGIN aims to amalgamate terrestrial nodes, aerial platforms, and satellites to enhance global coverage and ensure seamless connectivity. Moreover, beyond mere communication functionality, computing capability is increasingly recognized as a critical attribute of sixth generation (6G) networks. To address this, integrated communication and computing have recently been advocated as a viable approach. Additionally, to overcome the technical challenges of complicated systems such as high mobility, unbalanced traffics, limited resources, and various demands in communication and computing among different network segments, various solutions have been introduced recently. Consequently, this paper offers a comprehensive survey of the technological advances in communication and computing within SAGIN for 6G, including system architecture, network characteristics, general communication, and computing technologies. Subsequently, we summarize the pivotal technologies of SAGIN-enabled 6G, including the physical layer, medium access control (MAC) layer, and network layer. Finally, we explore the technical challenges and future trends in this field.

Due to long-term reliability, wireless control systems (WCSs) have attracted significant interest recently. However, mission-critical control requires stringent ultra-reliability and low-latency communication (URLLC) with massive data delivery, which are major challenges for conventional wireless networks. This paper investigates downlink URLLC in WCS, where the semantic communication is adopted at the control center to extract task-oriented semantic information from original large-sized data. To efficiency, the control center utilizes the rate splitting policy to deliver semantic information through private messages, while the semantic knowledge is transmitted through one common message. We aim to maximize the weighted sum semantic information transmission rate by jointly optimizing the semantic information extraction, delivery duration, rate splitting, and transmit beamforming, subject to several practical constraints, including recovery accuracy, quality of service requirements, communication latency and computation delay. By the problem decomposition, two sub-problems are obtained, where the closed-form solution for the semantic information extraction is derived at each step. Due to the complexity of the multivariable coupling in the channel dispersion, we propose fractional transformation methods for rate splitting design. Numerical results confirm that the RSMA and semantic communication design can complement each other for multiplexing gains enhancement and latency reduction to achieve overloaded connections.

Cyber-physical systems (CPS) offer integrated resolutions for various applications by combining computer and physical components and enabling individual machines to work together for much more excellent benefits. The ad hoc –based CPS provides a promising architecture due to its decentralized nature and destructive-resistance. A growing number of information leakage events in CPSs and the following serious consequences have aroused ubiquitous concern about information security. In this article, we combine physical layer security solutions and millimeter-wave (mmWave) techniques to safeguard the ad hoc network and investigate the reliability-security tradeoff by taking user demands for the network into account, where eavesdroppers attempt to intercept messages. For the secrecy enhancements, we adopt an artificial noise (AN) assisted transmission scheme, in which AN is employed to create non-cancellable interference to eavesdroppers. The reliability and security are correspondingly characterized by the connection outage probability and secrecy outage probability, and their analytical expressions of them are attained through theoretical analysis for the purpose of the tradeoff issue discussion. Our results reveal that secrecy performance in mmWave ad hoc networks gains significant improvement through the use of AN. It also shows that given total transmit power, there exists a tradeoff between reliability and security to achieve optimal outage performance.

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MB-RMR within a single area, eliminating the need for multi-region datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multi-band data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework’s adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT’s enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT’s effectiveness and practicality for MB-RMR in environments with limited data availability.

In this paper, we investigate the joint unmanned aerial vehicle (UAV) deployment and resource allocation problem to minimize the latency of multi-UAV-assisted computation offloading in air-ground integrated millimeter-wave (mmWave) networks, in which UAVs have both computing and relaying capabilities, thereby providing more opportunities for ground user equipments (UEs) to access the moble edge computing (MEC) servers with rich computing resources. Moreover, the study also takes into account the dynamic interference experienced by UEs due to different uploading completion times during the computation offloading process. To efficiently address the considered non-convex problem, we split it into four subproblems, i.e., UAV deployment, MEC server selection, computation resource and task ratio allocation, and power allocation subproblems, and solve them iteratively. Specifically, the first one is solved by three-dimensional-strategy iterative weekly acyclic game, the second one is addressed by Markov Approximation approach in which the third one is solved by the interior point method at each iteration, and the last one is solved by whale optimization algorithm (WOA). Finally, extensive simulations are provided to demonstrate the effectiveness of the proposed approach, and results have shown the approach can effectively mitigate the effect of blockage on mmWave transmissions and reduce the total latency of all UEs, particularly in scenarios where the communication bandwidth is limited or data volumes of tasks are large.

This paper investigates the issue of resource allocation for unmanned aerial vehicle and reconfigurable intelligent surface (UAV-RIS) assisted vehicular communication systems. To adapt the high dynamics of vehicular networks, we conceive a digital twin-based system over RIS-embedded environment towards environmental-aware communications. Specifically, a digital twin system can leverage data-driven models to predict the large-scale fading of future stages, while RIS is capable of controlling the propagation environments in real time, which can be utilized to mitigate prediction errors imposed by the small-scale fading. Using the capabilities of “prediction" and “reconfiguration", we expect to comprehensively foresee the dynamic changes in vehicular networks. In particular, the above-mentioned issue is formulated as a multi-slot total power consumption minimization problem under the quality of service (QoS) and energy constraints. Considering the finite battery energy of the UAV and the circuit power of the RIS, the transmit power of the UAV and the number of active reflecting elements (REs) are jointly scheduled for a finite time horizon. To tackle this mixed integer non-linear programming (MINLP) problem, we transform the original model into a discrete-time dynamic system. According to whether the dynamics of radio environments are predictable or not, the optimal offline and online policies are derived by using the deterministic and stochastic dynamic programming algorithms, respectively. To reduce the computation complexity, we further propose a novel online policy based on the idea of double-strategy selection. Finally, numerical results demonstrate that the proposed online policy exhibits near-optimal performances and outperforms other benchmarks in terms of transmission failure probability and effective power consumption.

We consider a broadband over-the-air computation empowered model aggregation approach for wireless federated learning (FL) systems and propose to leverage an intelligent reflecting surface (IRS) to combat wireless fading and noise. We first investigate the conventional node-selection based framework, where a few edge nodes are dropped in model aggregation to control the aggregation error. We analyze the performance of this node-selection based framework and derive an upper bound on its performance loss, which is shown to be related to the selected edge nodes. Then, we seek to minimize the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones by optimizing the selected edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. By resorting to the matrix lifting technique and difference-of-convex programming, we successfully transform the formulated optimization problem into a convex one and solve it using off-the-shelf solvers. To improve learning performance, we further propose a weight-selection based FL framework. In such a framework, we assign each edge node a proper weight coefficient in model aggregation instead of discarding any of them to reduce the aggregation error, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required.We also analyze the performance of this weight-selection based framework and derive an upper bound on its performance loss, followed by minimizing the MSE via optimizing the weight coefficients of the edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. Furthermore, we use the MNIST dataset for simulations to evaluate the performance of both node-selection and weight-selection based FL frameworks.

Joint channel selection and power control (JCSPC) is important to manage the interference in a heterogeneous network (HetNet), which consists of multiple base station (BS) and user equipment (UE) pairs. The JCSPC problem involves in a mixed continuous-discrete resource allocation and is typically NP-hard. Conventional methods usually obtain a quasi-optimal solution of the JCSPC problem in a centralized manner by assuming that the instantaneous global channel state information (CSI) is available. However, it is demanding to collect the instantaneous global CSI in practical scenarios. In this paper, we develop an intelligent cloud-edge collaboration assisted JCSPC algorithm. With the new algorithm, each BS can independently optimize its JCSPC policy with only local information, meanwhile enhance the sum-rate of the whole HetNet. Simulation results show that the proposed algorithm can achieve comparable and even better average sum-rate performance to the quasi-optimal method with a much lower time complexity.

This paper studies the global energy efficiency (GEE) optimization problem in a typical heterogeneous network (HetNet), where a macro base station (BS) and multiple micro BSs share the same spectrum band. Conventional optimization methods typically involve collecting global instantaneous channel state information (CSI) and utilizing centralized optimization algorithms to obtain the optimal transmit power and enhance the GEE. However, it is expensive to obtain the global instantaneous CSI in practical scenarios, and the optimization algorithms tend to be time-consuming. In this paper, we develop a lightweight cloud-edge collaboration framework based on the deep reinforcement learning (DRL) technique, such that the BSs in the edge do not need to exchange local instantaneous information with each other, and the core network in the cloud can collect only historical data rates and energy consumption information from each edge BS and feeds the calculated global reward back to them. Within the frame-work, we establish an independent actor-critic structure for each BS, and design a multi-agent independent actor-critic (MAIAC) power control algorithm, which enables each BS to determine its transmit power locally and enhance the GEE based on only local information. Simulation results indicate that the proposed MAIAC algorithm can achieve comparable GEE performance with conventional algorithms while requiring significantly less time complexity.

Human self-blockage can severely attenuate the mmWave signal and degrade the throughput, even in the absence of environmental blockages. Compared with environmental blockages, the human self-blockage is highly related to the direction of human movements, which has strong spatio-temporal correlations, and can be used to reduce beam training overheads meanwhile improve the throughput. In particular, we propose a convolutional long-short term memory (ConvLSTM) based deep spatio-temporal beam training algorithm, which can accurately infer the optimal beam by probing only a small portion of beams. Simulation results demonstrate that the proposed algorithm can provide a higher average throughput than the state of the arts.

In this paper, we investigate the communication designs of over-the-air computation (AirComp) empowered federated learning (FL) systems considering uplink model aggregation and downlink model dissemination jointly. We first derive an upper bound on the expected difference between the training loss and the optimal loss, which reveals that optimizing the FL performance is equivalent to minimizing the distortion in the received global gradient vector at each edge node. As such, we jointly optimize each edge node transmit and receive equalization coefficients along with the edge server forwarding matrix to minimize the maximum gradient distortion across all edge nodes. We further utilize the MNIST dataset to evaluate the performance of the considered FL system in the context of the handwritten digit recognition task. Experiment results show that deploying multiple antennas at the edge server significantly reduces the distortion in the received global gradient vector, leading to a notable improvement in recognition accuracy compared to the single antenna case.

As a revolutionary technology for future wireless communications, reconfigurable intelligent surface (RIS), characterized by an efficient way of manipulating wireless signals, has been widely investigated in recent years toward enhancing signal quality, energy efficiency, throughput, and so on. However, in RIS-assisted Internet of Things (IoT), a new issue as multipath separation emerges, especially, when deploying multiple RISs to assist communication, since the devices may have limited signal processing capabilities. For alleviating this problem, we conceive a novel RIS-enabled code-division multiple access (CDMA) structure, where each RIS holds a specified time-varying coefficient to tag the channel. Moreover, multipath extraction is further considered, including a practical channel estimation approach along with theoretical derivations in terms of Cramér–Rao lower bound, mean-square error, as well as ergodic channel capacity. Simulation results corroborate the feasibility of the conceived RIS-CDMA structure and the effectiveness of the proposed multipath extraction approach.

Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services, and applications combine to create a complex and heterogeneous network, which complicates the digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, the existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this article, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable, and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data, and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.

Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data to a centralized location from distinct ones. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices and the learning algorithm run on-device, with the aim of relaxing the burden at a central entity/server. Although gossip-based approaches have been used for this purpose in different use cases, they suffer from high communication costs, especially when the number of devices is large. To mitigate this, incremental-based methods are proposed. We first introduce incremental block-coordinate descent (I-BCD) for the decentralized ML, which can reduce communication costs at the expense of running time. To accelerate the convergence speed, an asynchronous parallel incremental BCD (API-BCD) method is proposed, where multiple devices/agents are active in an asynchronous fashion. We derive convergence properties for the proposed methods. Simulation results also show that our API-BCD method outperforms state of the art in terms of running time and communication costs.

We investigate the application of reinforcement learning (RL) on online fountain codes, and propose two schemes to reduce the full-recovery overhead with limited feedback. First, we use RL in determining the optimal degree of coded symbols for a given number of feedback, and propose the RL-based degree determination (RL-DD), with the help of theoretical analysis of the relationship between recovery rate and buffer occupancy. Then we propose online fountain codes with no build-up phase using sectioned distribution (OFCNB-SD), where the encoder sends symbols whose degrees are sampled from different sections of an overall distribution, and the decoder is improved to utilize coded symbols that are not immediately decodable. We present theoretical analysis of OFCNB-SD, and introduce RL-based sectioned distribution (RL-SD) scheme where the sectioning of the overall distribution is optimized with RL. Simulation results show that our proposed schemes could achieve lower full-recovery overhead with limited feedback compared to existing schemes.

The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner’s wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the traditional Pareto and Selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided Tabu Search (PGG-TS) algorithm is developed to obtain a stable coalitional structure. Numerical results verify that the utility of the proposed OCF game scheme based on the PGG-TS algorithm increases by 18% against that of the non-overlapping CF game scheme, and the utility of the proposed BMBT order increases by 25%, compared with other orders.

Recently, communications with the aid of reconfigurable intelligent surface (RIS), which operates with the aim of enhancing the system communication performance, have aroused extensive researches. Furthermore, the use of RIS for positioning has been considered. Therefore, we focus on a practical structure of indoor positioning assisted by distributed RISs through utilizing their ability to manipulate multipath signals, through the developed quasi-static and dynamic modes. Specifically, in the quasi-static mode, for reducing the implementation cost, the reflection coefficients for each RIS are preset and remain constant. In the dynamic mode, the reflection coefficients can be timely updated with a two-step positioning approach toward more accurate positioning performance. Furthermore, the Cramér-Rao lower bound of the developed positioning scheme is quantified through theoretical analysis. Both theoretical analysis and simulation results demonstrate that RIS has the potential to realize accurate positioning even with a single access point, due to its ability to mark the channel and replace traditional active positioning anchors. Meanwhile, we also show that the developed two-step positioning scheme can achieve considerable performance gain in accurate positioning.

The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.

We consider a typical heterogeneous network (HetNet), which consists of a macro base station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum sharing among different BS-user links may cause severe mutual interference and degrades the global energy efficiency (GEE), it is important to optimize the transmit power of each BS and enhance the GEE. Conventional methods first collect the global instantaneous channel state information (CSI) and then optimize the transmit power in a centralized manner. Nevertheless, it is demanding to obtain the global instantaneous CSI in practical situations and the centralized optimization may easily overwhelm the coherence time of wireless channels. To tackle these issues, we leverage the strong computing capability of the (cloud) core network and the fast configuration capability of (edge) BSs and propose an intelligent cloud-edge collaboration framework. By properly designing the cloud-edge collaboration, we develop a deep reinforcement learning (DRL) based energy efficient power control algorithm. With the proposed algorithm, each BS can configure its transmit power independently and enhance the GEE. Simulation results reveal that, in both static-user and mobile-user scenarios, the proposed algorithm can provide comparable GEE performance with the conventional methods while requiring a much lower time complexity.

In this paper, we propose a novel maximum distance separable (MDS) code based and reconfigurable intelligent surface (RIS) assisted wireless communication system with orthogonal frequency division multiplexing (OFDM). Specifically, input bits are firstly divided into groups and their MDS codes are utilized to decide the amplitudes and phases of subcarriers. The introduction of the MDS code helps to increase the minimum Hamming distance between symbols and improve on the capability of error detection. Besides, the RIS is adopted to create additional paths between the radio frequency (RF) and the receiver as well as alter the signal phases with derived optimal solution. Benefiting from the strength of the RIS, the proposed system can better overcome multipath fading compared with conventional systems. Simulation results are presented to demonstrate the efficacy of the proposed system in terms of reducing bit error rate (BER) through multipath channels.

Recently, reconfigurable intelligent surface (RIS) has arisen as an excellent technology for assisting wireless communications. In order to handle the intractable non-convex problem for jointly optimizing beamforming and PSs in multiple-input multiple-output (MIMO) transmission, we propose a novel alternating direction (AD) method by maximizing the achievable rate (AR) at the receiver. Specifically, the initial problem is divided into the following two processes: i) optimizing the beamforming vector with fixed PSs, ii) determining a specific PS based on a closed-form solution when the other PSs and beamforming are fixed. Simulation results corroborate that the proposed AD method provides robust attainable performance with reduced computational complexity compared to its traditional counterparts.

Highly directional beams in millimeter wave (mmWave) communications necessitate beam training or alignment between the access point (AP) and the user equipment (UE), and exhausted beam search (EBS) method is suggested in the current 3GPP standard. Nevertheless, EBS suffers from high overheads and inevitably lowers the throughput, especially when the beam space is large. In this paper, we utilize the spatial correlation among different beams as well as the strong feature extraction/representation capability of the deep convolutional neural network (CNN), and propose an intelligent beam training algorithm. With the proposed method, the AP can probe only a fixed subset of the whole beam space and identify the optimal beam intelligently. Simulation results show that, the proposed method can largely reduce the overheads for the beam training meanwhile boost the throughput performance compared with the state of the arts.

In a typical heterogeneous network (HetNet), in which a macro base station (BS) and multiple small BSs coexist on the same spectrum band, energy-efficiency (EE) performance is an important design metric and is highly related to the transmit power of BSs. Conventional methods optimize BSs’ transmit power to enhance the EE by assuming that the global channel state information (CSI) is available. However, it is challenging or expensive to collect the instantaneous global CSI in the HetNet. In this paper, we utilize deep reinforcement learning (DRL) technique to design an intelligent power control algorithm, with which each BS can independently determine the transmit power based on only local information. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in terms of both EE performance and time complexity.

More and more robot automation applications have changed to wireless communication, and network performance has a growing impact on robotic systems. This study proposes a hardware-in-the-loop (HiL) simulation methodology for connecting the simulated robot platform to real network devices. This project seeks to provide robotic engineers and researchers with the capability to experiment without heavily modifying the original controller and get more realistic test results that correlate with actual network conditions. We deployed this HiL simulation system in two common cases for wireless-network-controlled robotic applications: (1) safe multi-robot coordination for mobile robots, and (2) human-motion-based teleoperation for manipulators. The HiL simulation system is deployed and tested under various network conditions in all circumstances. The experiment results are analyzed and compared with the previous simulation methods, demonstrating that the proposed HiL simulation methodology can identify a more reliable communication impact on robot systems.

We consider a broadband over-the-air computation empowered model aggregation scheme for federated learning (FL) in Industrial Internet of Things systems. Due to fading and communication noise, the received global gradient parameters inevitably become inaccurate, leading to a notable decrease of the learning performance. Instead of discarding any edge nodes to reduce the aggregation error, we propose to assign each of them a proper weight coefficient in the model aggregation procedures, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required in this paper. We derive an upper bound on the performance loss of the proposed FL scheme, which is shown to be related to the weight coefficients of edge nodes and the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones. Then, we derive a closed-form expression for MSE and use it as the objective function to formulate an optimization problem with respect to the edge nodes’ transmit equalization coefficients, their weight coefficients, and the receive scalars of the cloud server. We transform the formulated optimization problem into a convex one and solve it optimally using CVX. Last, we leverage the popular MNIST dataset and conduct experiments to evaluate the prediction accuracy of the proposed FL scheme. Simulation results demonstrate its superior performances.

Artificial noise elimination (ANE) has arisen as a possible countermeasure for mitigating the influence of artificial noise (AN) at the eavesdropper (Eve). However, conventional ANE schemes require the attainable channel state information (CSI) between the transmitter (Alice) and legitimate receiver (Bob), which reduces the feasibility of this proposal. In this paper, we investigate the issue of ANE without the CSI of Alice-Bob link by minimizing the artificial-noise-to-signal ratio (ANSR). Moreover, the detailed minor component analysis (MCA) algorithm is presented, and the computational complexity is quantified. Simulation results demonstrate that MCA can effectively degrade the influence of AN without the knowledge of CSI.

In this paper, a batched sparse(BATS) code transmission scheme based on chunked outer code is proposed. At the source node, a source file is first segmented into K number packets, then all the packets are divided into N chunks, each of which contains the same number of packets except for the last one if K cannot be divided by N. The chunks are encoded with the chunked outer code of BATS codes before transmitted to the next node. At the destination node, only the decodable batches need to be decoded to recover the input packets, and undecodable batches are abandoned directly to save the storage resources. In the aspect of the decoding, a simplified decoder is proposed, which is based on Gaussian elimination decoding. The consumed number of batches with a source file successfully transmitted by using the proposed scheme is analyzed under both time-invariant and time-variant channels. The simulation results show that compared with conventional BATS codes, less number of batches are consumed by applying the proposed chunked outer code.

In this paper, a batched sparse(BATS) code transmission scheme based on Low-density Parity-check(LDPC) precoding over GF(2) is studied. At the transmitter, a source file is first segmented into several packets, which are then precoded with LDPC to generate more packets. The precoded packets are encoded with the outer coding of the conventional BATS codes before they are transmitted to the intermediate nodes, which apply inner coding to the received packets and forward them to the next node. At the sink node, only a portion of received packets need to be decoded successfully to recover the source file. Moreover a one-step BP decoder is then proposed which achieves the same performance as the two-step BP decoder. The simulation results show that compared with the conventional BATS codes, less transmission batches are required by using the LDPC precoding. Meanwhile, one step decoder achieves the same transmission performance as the two step decoder, which decodes BATS codes and LDPC codes successively.

In this paper, we develop an efficient training beam sequence design approach for millimeter wave MISO tracking systems. We impose a discrete state Markov process assumption on the evolution of the angle of departure and introduce the maximum a posteriori criterion to track it in each beam training period. Since it is infeasible to derive an explicit expression for the resultant tracking error probability, we turn to its upper bound, which possesses a closed-form expression and is therefore leveraged as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by analog phase shifters, we resort to the particle swarm algorithm to solve the formulated optimization problem. Numerical results validate the superiority of the proposed training beam sequence design approach.

To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique, especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of Things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two subproblems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables, including power variables, bandwidth variables, and transmission indicators. Then, a linear-search-based power and bandwidth allocation method is developed. Given appropriate hyperparameters, we show that the proposed communication-efficient FL (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.

Edge computing provides a promising paradigm to support the implementation of Internet of Things (IoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability, and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation and has shown faster convergence than gradient descent-based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IoT networks. We provide convergence properties for the proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has$\mathcal {O}(1/k) + \mathcal {O}(1/M)$convergence rate, where$k$and$M$are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experimental results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability and can well adapt to complex IoT environments.

Mobile operators collect and store the network generatedtraffic data for analysis. Time Series Prediction (TSP) has been used in mobile network traffic data analysis to produce predictive results for network planning and resource allocation. We propose a novel method of predicting mobile network traffic using neural networks based on conditional probability modeling between adjacent data windows. First, we develop a pre-processing method to aggregate the raw traffic log data and sample the aggregated time series to adjacent data windows, as training samples. Second, we use neural networks to parameterize the conditional probability between adjacent data windows and estimate the probability by training the neural networks with sampled data. The estimated conditional probability is then used to ensemble the prediction. Third, we show theoretically that the prediction based on all historical data is equivalent to the prediction based on just previous data window, given the estimation of conditional probability between adjacent data windows. We also analyze computation complexity and show that seasonality will reduce the computational complexity. In the experiment, we compare the prediction performance among the models with different seasonality, sample size and number of hidden layers, and show that the proposed schemes achieve better prediction accuracy than state-of-the-art.

Artificial noise (AN), aiming to disturb the eavesdropper while avoiding the influence on the legitimate receiver, has arisen as an excellent technology for improving the physical-layer security of wireless communications. In order to challenge AN, zero-forcing elimination (ZFE) has been introduced as a possible countermeasure to mitigate the AN for the eavesdropper at the cost of more available receive antennas. In this contribution, from the perspective of eavesdroppers, we further conceive a class of efficient null-space elimination (NSE) schemes in order to reduce the number of receive antennas while enhancing the detection quality compared to original ZFE. Furthermore, the performance of secrecy rate as well as bit-error rate (BER) is quantified for both ZFE and NSE schemes through theoretical derivation, while the influence of imperfect channel state information (CSI) is also evaluated. The performance comparison of the above-mentioned schemes illustrates that NSE can provide more robust performance for eavesdroppers over ZFE, with lower hardware requirements as well as moderate complexity increase.

A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications. In pursuit of the balance between energy efficiency and low-cost implementations, the cloud-management transmission protocol is adopted in the integrated multi-media system. Specifically, the messages of devices using heterogeneous propagation media, are firstly transmitted to the medium-matched AP, with the aid of the RIS-based dual-hop transmission. After the operation of intermediate frequency conversion, the access point (AP) uploads the received signals to the cloud for further demodulating and decoding process. Based on time division multiple access (TDMA), the cloud is able to distinguish the downlink data transmitted to different devices and transforms them into the input of the RIS controller via the dedicated control channel. Thereby, the RIS can passively reflect the incident carrier back into the original receiver with the exchanged information during the preallocated slots, following the idea of an index modulation-based transmitter. Moreover, the iterative optimization algorithm is utilized for optimizing the RIS phase, transmit rate and time allocation jointly in the delay-constrained cross-media communication model. Our simulation results demonstrate that the proposed RIS-based scheme can improve the end-to-end throughput than that of the AP-based transmission, the equal time allocation, the random and the discrete phase adjustment benchmarks.

The introduction of space-time block code (STBC) can efficiently increase the diversity of original spatial modulation (SM). However, the spectral efficiency of STBC will be reduced gradually with the increase of the transmit antennas. In this contribution, we conceive a novel quasi-orthogonal space-time block coded spatial modulation (QOSTBC-SM) design for multiple-input and multiple-out (MIMO) transmission by reaping their respective benefits, while improving the spectral efficiency compared to conventional space-time block coded spatial modulation (STBC-SM). More specifically, in the proposed QOSTBC-SM structure, the information bits are conveyed via the active antenna index, as well as the QOSTBC blocks at the transmitter, while at the receiver, a low-complexity detection scheme is proposed. Furthermore, a closed-form union bound of the bit error rate (BER) is also quantified by theoretical derivation. Finally, our simulation results demonstrate that QOSTBC-SM is capable of outperforming the conventional counterpart as STBC-SM with higher spectral efficiency.

In this paper, we conceive the design of dynamic wireless networks assisted by multiple intelligent reflecting surfaces (IRSs), where the connection states between users and IRSs are capable of being updated timely. Taking into account the time-varying states of the system, we further construct a long-term dynamic process. Our goal is to maximize the time average sum-rate of the dynamic system under the time average rate and power constraints of users, via jointly optimizing the power allocation at users and the reflecting coefficients at IRSs. With the aid of Lyapunov concept-based drift-plus-penalty (DPP) algorithm, the long-term optimization problem is formulated as an infinite-horizon time-average one. Subsequently, the fractional programming method based on Lagrangian dual transform is applied to optimize power allocation and reflecting coefficients in an iterative manner, and the closed-form solutions of power and reflecting coefficients can be obtained at each iteration. Finally, simulation results demonstrate the convergence and effectiveness of the proposed algorithm. Further performance comparisons indicate that the proposed algorithm can maintain a balance between supply and demand for resource allocation and improve the fairness of users.

This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum band for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an$l$-norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches.

With emerging applications, e.g., factory automation, autonomous driving and augmented/virtual reality, there have been increasing technical challenges regarding reliability, latency and data rates for existing communication systems. Owing to abundant available bandwidth, millimeter Wave (mmWave) communications can potentially provide reliable communication with an order of magnitude capacity improvement relative to microwave, e.g., sub 6 GHz communications. Though there are many research results showing improved throughputs, the latency and reliability performance of mmWave communications is still not quite clear, especially for finite blocklength regimes. In this paper, we investigate achievable rates of mmWave channels using random coding error exponents. Under the assumption of perfect and imperfect channel state information at the receiver (CSIR), exact and approximate analytical expressions of achievable rates are derived to capture the relationships among rate, latency and reliability. Furthermore, we show that the achievable rate always increases as the bandwidth increases with perfect CSIR. However, there exists a critical bandwidth that maximizes the achievable rate for non-line-of-sight mmWave signals with imperfect CSIR, beyond which the achievable rate will decrease with increasing bandwidth. For imperfect CSIR, the training symbol length and power allocation factor for maximizing the achievable rate at the training phase are investigated and closed-form expressions for special cases are derived.

Orthogonal time frequency space (OTFS) system constitutes an effective structure conceived for efficiently utilizing the channel information, which is capable of achieving a promising transmission performance in high-mobility environment. To extract enough channel diversity, a two-dimensional Fourier transformation combined with a pulse shape is designed at the OTFS transmitter. Consequently, the amplitude of OTFS signals may fluctuate drastically, owing to the combined dependency of the OTFS transformation and the pulse shape. To quantify the amplitude fluctuation, we investigate the peak-to-average power ratio (PAPR) of OTFS signals, for a large amount of data in the delay-Doppler domain. We first reveal that when the number of data points approaches to infinity, based on central limit theorems for dependent variables, the complex-valued OTFS signals weakly converge to a Gaussian distribution. Then, according to the extremal theory of the Chi-squared process for stationary OTFS signals, an accurate expression of the PAPR distribution is derived, depending on the transmit pulse and the number of data points. It is also demonstrated that upon modifying the exponential factor, the analytical PAPR expression is applicable for the non-stationary Gaussian distribution caused by the bandlimited pulse with a large roll-off factor. Simulation results confirm the accuracy of the analytical PAPR probability for practical conditions.

Impulse interference is an important cause of transmission failure in the industry environments targeted by the Wireless High Performance (WirelessHP). As interleavers are commonly used to improve the reliability on the Orthogonal Frequency Division Multiplexing (OFDM) symbol level for long packet transmission, this paper considers the feasibility of applying short-packet bit interleaving to enhance the impulse/burst interference resisting capability on both OFDM symbol and frame level. Using the Universal Software Radio Peripherals (USRP) and PC hardware platform, the Packet Error Rate (PER) performance of interleaved coded short-packet transmission with Convolutional Codes (CC), Reed-Solomon (RS) codes, and RS+CC concatenated codes are tested and analyzed. The IEEE 1613 standard is applied for impulse interference generation, and extensive PER tests of CC$(1/2)$and RS$(31,21)+$CC$(1/2)$concatenated codes are conducted. We prove the effectiveness of bit interleaved coded short-packet transmission in real factory environments with practical experiments. Moreover, we investigate how PER performance depends on the interleavers, codes and impulse interference power and frequency.

In this paper, we consider a millimeter wave multiple-input single-output tracking system, where the time-varying angle of departure (AoD) is assumed to change following a discrete state Markov process. Depending on whether the associated AoD transition function is available or not, we propose two different training beam sequence design approaches. Specifically, in the case when the AoD transition function is available, we leverage the maximum a posteriori criterion to estimate the updated AoD in each beam tracking period. Since it is infeasible to derive an explicit expression for the resultant estimation error rate, we turn to its upper bound, which possesses a closed-form expression and is therefore used as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by the analog phase shifters, we resort to a particle swarm algorithm to solve the formulated optimization problem. In the case when the AoD transition function is unavailable, we turn to the maximum likelihood criterion for AoD estimation. To cope with the unknown AoD transition function, we reformulate the beam tracking problem as a partially observable Markov decision process problem and develop an actor-critic reinforcement learning framework to obtain an efficient training beam sequence design. Numerical results demonstrate superiorities of the proposed training beam sequence design approaches for both two cases.

The highly structured sequential data, such as from speech and handwriting, often contain complex relationships between the underlaying variational factors and the observed data. This paper will present different ways of using high level latent random variables in RNN to model the variability in the sequential data. We have developed the two-steps training algorithms of such RNN model under the VAE (Variational Autoencoder) principle. We proposed novel approach of using adversarial training to regularize the latent variable distributions in the variational RNN model. Contrary to competing approaches, our approach has theoretical optimum in the model training and provides better model training stability. Our approach also improves the posterior approximation in the variational inference network by a separated adversarial training step. Numerical results simulated from TIMIT speech data show that reconstruction loss and evidence lower bound converge to the same level and adversarial training loss converges to 0.

Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones, and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to analyze information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multiagent system with data locally processed via distributed edge nodes. A class of minibatch stochastic alternating direction method of multipliers (ADMMs) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed learning systems, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding-based stochastic incremental ADMM is investigated. Given an appropriate minibatch size, we show that the minibatch stochastic ADMM-based method converges in a rate of O(1/√k), where k denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication efficient, rapidly responding, and robust in the presence of straggler nodes compared with state-of-the-art algorithms.

In this paper, the idea of offset spatial modulation (OSM) is integrated with orthogonal frequency division multiplexing (OFDM), toward an efficient design to bridge their advantages. Compared to its conventional counterpart as spatial modulation (SM)-OFDM, the proposed OSM-OFDM scheme aims at providing a simplified implementation structure with less number of radio frequency (RF) chains, by introducing an offset between the RF chain and the index of the activated transmit antenna on each subcarrier. Specifically, three types of offset antenna selection (OAS) methods are developed to meet different scene requirements for different number of available RF chains. Furthermore, through theoretical analysis, we quantify the bit error rate upper bounds of OSM-OFDM with different types of OAS methods. Finally, extensive computer simulations demonstrate that OSM-OFDM provides a flexible tradeoff among implementation cost, computation complexity and error performance.

Online fountain codes (OFC) have attracted much attention for their good intermediate performance, which is important for receivers with low-complexity requirement. However, low-complexity receivers generally have limited buffer size to store coded symbols that have not been fully decoded yet, as well as limited power budget for feedback transmissions. In this paper, we propose improved transmission schemes for online fountain codes to reduce the buffer occupancy and feedback transmissions. Firstly, we analyze the relationship between buffer occupancy and overhead as well as the relationship between recovery rate and overhead for online fountain codes. Motivated by the analysis, we propose the weighted online fountain codes (WOFC) which can adapt to various buffer sizes by adjusting the weight to control the probability that a coded symbol can be fully processed immediately, and analyze its performance. Then we further propose weighted online fountain codes with low feedback (WOFC-LF), which utilize the proposed analysis to estimate the recovery rate, and reduce feedback transmissions. Simulation results verify the effectiveness of the analysis for both OFC and WOFC, and demonstrate the superior performance of WOFC-LF with limited buffer size and feedback transmissions.

This paper proposes a precoded optical space-domain index modulation scheme for indoor visible light communications, which is based on the optimization of the minimum Euclidean distance of optical spatial modulation (OSM) with real-valued modulation constellations. We find that the precoding matrix design can be formulated as a non-convex quadratically constrained quadratic program (QCQP), whose solution is generally intractable. To tackle this problem, we first consider the case of two optical transmit antennas ( Nt= 2) in the precoded OSM and derive a closed-form solution for arbitrary M-order pulse amplitude modulation (PAM). Based on the derived solutions and the error vector reduction method, we then propose a low-complexity iterative (LCI) algorithm to identify the precoding matrix for the setup Nt> 2. To strike a flexible complexity-BER (bit error rate) tradeoff, we propose a successive convex approximation (SCA)-assisted matrix-based optimization method to transform the non-convex QCQP problem into a series of linear convex subproblems, which can be solved by low-complexity solvers. Simulation results show that these proposed algorithms are capable of substantially improving the system error performance compared with conventional OSM systems. Besides, a symbol-based SCA algorithm is introduced and it is shown to outperform the matrix-based SCA and the suboptimal LCI algorithm in terms of the BER.

In adversarial scenarios, it is crucial to timely monitor what tactical messages that opponent transmitters are sending to intended receiver(s), and disrupt the transmissions immediately if in need. The issue becomes more challenging in face of an intelligent transmitter. To address the above-stated issue, a full-duplex (FD) technique is utilized to enable simultaneous jamming and eavesdropping (SJE) at a friendly jammer node. In particular, the “Two Birds with One Stone” strategy is utilized at the jammer node to realize effective rate degradation and information eavesdropping. A confrontation game between an intelligence-empowered FD jammer and its opponent is investigated. Specifically, to capture their adversarial relationship in an environment with incomplete information, a power-domain Bayesian-Stackelberg game is proposed. The existence of a Stackelberg equilibrium (SE) power solution is proved. The semi-closed-form solutions of SE are derived, which are proved to be asymptotically optimal (have a gap of less than 1% with the exact utility), and improves the jammer node 10% utility compared with the Nash equilibrium. Additionally, the SJE strategy outperforms the half-duplex (HD) and other benchmark schemes.

In this article, we investigate the jointly optimized resource allocation with hybrid multiple access in energy-efficient industrial Internet of Things (IIoT), where some devices (e.g., those for critical control devices) have higher transmission priority and stable energy supply while some devices (e.g., those for comprehensive sensors) may not. We consider a system model supporting wireless powered IIoT devices, with certain user terminal as a potential relay for the transmission between a hybrid access point and another user terminal. Constrained by the limited energy storage, the user needs to harvest energy before relaying and only the harvested energy is utilized for the following transmission. We propose a collaborative orthogonal and nonorthogonal multiple access protocol where two cooperation schemes with and without decoding the relay message are applied. Jointly considering time sharing in the transmission process, power splitting for simultaneous wireless information and power transfer, and transmit power allocation at the cooperative user, the achievable rate regions under the Rayleigh fading channel model are derived. Based on which, an optimization problem on resource allocation strategies is formulated and discussed. Both analytical and numerical results are provided, illustrating the impact of user geometry on the achievable rates as well as the optimal resource allocation with different cooperative strategies applied in different use cases. Aiming to enhance resource utilization, energy-efficient cooperation enables the combination of various transmission modes and networking classes in large scale networks, as well as a better use of ambient radio frequency signals for wireless powered transmissions.

Internet of Mobile Things (IoMTs) refers to the interconnection of mobile devices, for example, mobile phones, vehicles, robots, etc. For mobile data, strong extra processing resources are normally required due to the limited physical resources of the mobile devices in IoMTs. Due to latency or bandwidth limitations, it may be infeasible to transfer a large amounts of mobile data to remote server for processing. Thus, distributed computing is one of the potential solutions to overcome these limitations. We consider the device mobility in IoMTs. Two situations of the movement position of the mobile devices, i.e., unpredictable and predictable, are considered. In addition, three possible relative positions between the two server sets which respectively correspond to the positions of a mobile device for computation tasks offloading and for output results receiving, i.e., within the same server sets, with two different server sets and with two adjacent server sets, are studied. Coded schemes with high flexibility and low complexity are proposed based on Fountain codes to reduce the total processing time and latency of the distributed fog computing process in IoMTs for the above different situations. The latency related performance, i.e., the computation, the communication and the transmission loads, is analyzed. We also compare of the Fountain code-based and the uncoded schemes and numerical results demonstrate that shorter total processing time and lower latency can be achieved by the Fountain code-based schemes.

Introducing cooperative coded caching into small cell networks is a promising approach to reducing traffic loads. By encoding content via maximum distance separable (MDS) codes, coded fragments can be collectively cached at small-cell base stations (SBSs) to enhance caching efficiency. However, content popularity is usually time-varying and unknown in practice. As a result, cached content is anticipated to be intelligently updated by taking into account limited caching storage and interactive impacts among SBSs. In response to these challenges, we propose a multi-agent deep reinforcement learning (DRL) framework to intelligently update cached content in dynamic environments. With the goal of minimizing long-term expected fronthaul traffic loads, we first model dynamic coded caching as a cooperative multi-agent Markov decision process. Owing to the use of MDS coding, the resulting decision-making falls into a class of constrained reinforcement learning problems with continuous decision variables. To deal with this difficulty, we custom-build a novel DRL algorithm by embedding homotopy optimization into a deep deterministic policy gradient formalism. Next, to empower the caching framework with an effective trade-off between complexity and performance, we propose centralized, and partially and fully decentralized caching controls by applying the derived DRL approach. Simulation results demonstrate the superior performance of the proposed multi-agent framework.

The alternating direction method of multipliers (ADMM) has recently been recognized as a promising approach for large-scale machine learning models. However, very few results study ADMM from the aspect of communication costs, especially jointly with privacy preservation. We investigate the communication efficiency and privacy of ADMM in solving the consensus optimization problem over decentralized networks. We first propose incremental ADMM (I-ADMM), the updating order of which follows a Hamiltonian cycle. To protect privacy for agents against external eavesdroppers, we investigate I-ADMM with privacy preservation, where randomized initialization and step size perturbation are adopted. Using numerical results from simulations, we demonstrate that the proposed I-ADMM with step size perturbation can be both communication efficient and privacy preserving.

A batched sparse (BATS) code provides a novel two-stage coding structure for the multi-hop network, in which the outer code performed at the source node generates a potentially unlimited number of batches and the inner code at the intermediate network nodes applies network coding on packets belonging to the same batch. Previous works have studied the performance of BATS codes in the erasure channels, in which the packet loss rate ε is always assumed to be a constant on each link. However, in some application scenarios such as the Industrial Internet of Things (IIoTs) where there are a number of mobile nodes in the networks, the channel conditions could be time-variant due to the mobility of nodes, resulting the packet loss rate ε varying over time as well. Therefore this paper studies the rank distribution which is one of the most significant performance metric of BATS codes under time-variant channels by assuming the packet loss between links modeled as a random variable instead of a constant value. Closed-form expressions of rank distribution are obtained with the packet loss rate ε following two typical types of distributions. Both numerical and simulation results are provided to verify our analysis.

In this article, a general framework for enhancing the physical layer security (PLS) in the Internet of Things (IoT) systems via channel feedback is established. To be specific, first, we study the compound wiretap channel (WTC) with feedback, which can be viewed as an ideal model for enhancing the PLS in the downlink transmission of IoT systems via feedback. A novel feedback strategy is proposed and a corresponding lower bound on the secrecy capacity is constructed for this ideal model. Next, we generalize the ideal model (i.e., the compound WTC with feedback) by considering channel states and feedback delay, and this generalized model is called the finite state compound WTC with delayed feedback. The lower bounds on the secrecy capacities of this generalized model with or without delayed channel output feedback are provided, and they are constructed according to variations of the previously proposed feedback scheme for the ideal model. Finally, from a Gaussian fading example, we show that the delayed channel output feedback enhances the achievable secrecy rate of the finite state compound WTC with only delayed state feedback, which implies that feedback helps to enhance the PLS in the downlink transmission of the IoT systems.

As a hybrid MIMO architecture, beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter wave (mmWave) massive MIMO systems without obvious performance loss, in which, however, the number of users supported cannot be larger than that of RF chains. To break this fundamental limit, we introduce the concept of code-domain non-orthogonal multiple access (NOMA) into beamspace MIMO. A beam selection scheme is proposed first to maximize the sum-rate by utilizing the quasi-orthogonality of the beamspace channel. Furthermore, a low-complexity detection algorithm is developed to realize the transceiver design in mmWave communications using lens antennas. Finally, numerical results of the decoding complexity at receiver side are analyzed. Simulation results show that the proposed beamspace MIMO-NOMA in code domain can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.

In recent years, network densification and edge caching become effective approaches to reduce the burden on the fronthaul links and the content delivery latency for wireless communication systems. However, maximizing system spectral efficiency cannot directly provide any insight on their energy requirements/efficiency for cache-enabled millimeter-wave (mmWave) radio access networks (RANs). In this paper, we study the design of energy-efficient transceiver, consisting of analog and digital precoder/combiner, for the delivery phase of the downlink of cache-enabled mmWave RANs. Due to the non-convexity of the delivery rate and objective, the coupling between the digital and analog precoders/combiners, and the constant module constraint on the elements of analog precoders/combiners, the problem of interest is non-convex and hard to obtain the global optimal solution, even the local optimal solution. To this end, we first overcome these challenges one-by-one and then transform the original problem into tractable one. Finally, an algorithmic framework that converges to the Karush-Kuhn-Tucker solution with provable is developed to achieve the design of energy-efficient transceiver. Numerical results are provided to evaluate the performance of the proposed algorithm, where fully digital precoding is used as benchmark.

For the benefit of improved intermediate performance, recently online fountain codes attract much research attention. However, there is a trade-off between the intermediate performance and the full recovery overhead for online fountain codes, which prevents them to be improved simultaneously. We analyze this trade-off, and propose to improve both of these two performance. We first propose a method called Online Fountain Codes without Build-up phase (OFCNB) where the degree-1 coded symbols are transmitted at first and the build-up phase is removed to improve the intermediate performance. Then we analyze the performance of OFCNB theoretically. Motivated by the analysis results, we propose Systematic Online Fountain Codes (SOFC) to further reduce the full recovery overhead. Theoretical analysis shows that SOFC has better intermediate performance, and it also requires lower full recovery overhead when the channel erasure rate is lower than a constant. Simulation results verify the analysis and demonstrate the superior performance of OFCNB and SOFC in comparison to other online fountain codes.

To boost energy saving for the general delay-tolerant IoT networks, a two-stage, and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware N-threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.

A novel social-tie motivated relay selection scheme is proposed for dynamic device-to-device (D2D) communications overlaying cellular networks. Using the non-edge cellular users to forward data, the proposed relay selection scheme can improve the transmission performance of the cell-edge users as an explicit benefit of D2D relays. Meanwhile, the effects of both the physical layer and social layer on the relay selection are jointly considered, where social ties are regarded as not only the motivation of relay services, but also the metric of security performance. Moreover, a generalized satisfaction index is introduced for designing a uniform quality of experience (QoE) criterion that can map different quality of service (QoS) metrics such as rate, throughput, delay, into a unified metric, and hence, is beneficial for the tradeoff between QoE and resource efficiency of relay selection. Furthermore, a dynamic optimization process is constructed for analyzing the effects of both the mobility of users and the randomness of channel on the relay selection, with the aid of the Lyapunov framework and drift-plus-penalty (DPP) algorithm. Finally, numerical results validate the effects of the proposed relay selection scheme.

Wireless content caching in cellular networks is an efficient way to reduce the service delay and alleviate backhaul pressure. For the benefits of sharing spectral and storage resources, clustering in cached networks has recently attracted significant research interests. Meanwhile, since the multimedia content (e.g., video) of caching networks may require a huge transmission rates, millimeter wave (mmWave) communication is considered to be an efficient transmission scheme for cache-enabled networks. We investigate the ergodic rate and average service delay for typical user terminal (UT) in the clustered cache-enabled small cell networks (SCN) and ultra dense networks (UDN) with mmWave channels. In SCN, each cluster consists of cache-enabled UTs, and in the UDN a cluster is formed by cache-enabled UTs and small base stations (SBSs) with non-uniform caching capacity. The clusters are assumed to be discs and content sharing is only possible within clusters through mmWave device-to-device (D2D) tier and SBS tier communications. With stochastic geometry methods, the distributions of content sharing distance and signal-to-interference-noise-ratio (SINR) of typical UT in a cluster are derived for both SCN and UDN scenarios. To minimize the average service delay in high SINR region, we provide an algorithm to jointly optimize caching scheme for SBSs and UTs. By simulations, we validate our theoretical analysis and the performance of proposed caching scheme. The numerical results also show that there exists best radius in the design of cluster for UDNs.

To meet the extremely low latency constraints of industrial wireless control in critical applications, the wireless high-performance scheme (WirelessHP) has been introduced as a promising solution. The proposed design showed great improvements in terms of latency, but its performance in terms of reliability have not been fully tested yet. While traditional wireless systems achieve high reliability through packet retransmissions, this would impair the latency, and an approach based on channel coding is preferable in industrial applications. In this paper, a set of packet error rate (PER) tests is performed by applying concatenated Reed Solomon and convolutional codes to the WirelessHP physical layer, using a demonstrator based on a universal software radio peripheral platform. The effectiveness of channel coding to achieve 10-7level PER without retransmissions is shown in typical laboratory and factory environments.

With the development of healthcare 4.0, there has been an explosion in the amount of data such as image, medical text, physiological signals, lab tests, etc. Among them, medical records provide a complete picture of the associated clinical events. However, the processing of medical texts is difficult because they are structurally free, diverse in style, and have subjective factors. Assigning metadata codes from the International Classification of Diseases (ICD) presents a standardized way of indicating diagnoses and procedures, so it becomes a mandatory process for understanding medical records to make better clinical and financial decisions. Such a manual encoding task is time-consuming, error-prone and expensive. In this paper, we proposed a deep learning approach and a medical topic mining method to automatically predict ICD codes from text-free medical records. The result of the F1 score on Medical Information Mart for Intensive Care (MIMIC-III) dataset increases by 5% over the state of art. It also suitable for multiple ICD versions and languages. For the specific disease, atrial fibrillation, the F1 score is up to 96% and 93.3% using in-house ICD-10 datasets and MIMIC-III datasets, respectively. We developed an Artificial Intelligence based coding system, which can greatly improve the efficiency and accuracy of human coders, and meanwhile accelerate the secondary use for clinical informatics.

Millimeter Wave (mmWave) communication has attracted massive attentions, since the abundant available bandwidth can potentially provide reliable communication with orders of magnitude capacity improvements relative to microwave. However, the achievable rate of mmWave channels under latency and reliability constraints is still not quite clear. We investigate the achievable rates of mmWave channels by random coding error exponent (RCEE) with finite blocklength. With imperfect channel state information at the receiver, the exact and approximate analytical expressions of the training based maximum achievable rate are derived to capture the relationship among rate-latency-reliability. Additionally, the relationship between the training based maximum achievable rate and bandwidth is investigated. We show that there exists critical bandwidth to maximize the training based maximum achievable rate for the non-line-of-sight (NLoS) propagation. Numerical results show that the approximate expression of the training based maximum achievable rate are tight and can capture the tendency at low SNRs. In addition, results show that for a given rate, one can reduce both packet duration and decoding error probability by increasing bandwidth. Results also suggest that in some mmWave bands, e.g. 57-64 GHz band, the performance, i.e., Gallager function, is significantly affected by frequency selective power absorption.

In this paper, we propose the full-duplex and cloud radio access network (C-RAN) based multi-cell non-orthogonal multiple access schemes over 5G mobile wireless networks. To cope with the severe intra-cell and inter-cell interferences as well as perform the centralized optimization, we adopt the C-RAN architecture, where the baseband processing and resource management are conducted at a central unit (CU). With the goal of maximizing the weighted sum achievable rate, we formulate the sum rate maximization power allocation problem as a non-convex problem. Thanks to the hidden monotonicity structure of the considered problem, the optimal power allocation algorithm is developed by the monotonic optimization method. Besides, we propose another suboptimal algorithm by employing successive convex approximation method to obtain the close-to-optimal solution with a significantly reduced computational complexity. Extensive simulations are conducted to verify the effectiveness of our proposed power allocation schemes, and confirm the superiority of our proposed C-RAN architecture.

Recently, power allocation has attracted more and more attention in order to optimize the performance of non-orthogonal multiple access (NOMA) systems. Different from existing works, the power allocation problems are investigated for cooperative NOMA systems with dedicated amplify-and-forward half-duplex relay (NOMA-HDR) and full-duplex relay (NOMA-FDR). From the fairness standpoint, the power allocation problems are formulated to maximize the minimum achievable user rate in the considered systems. The problems for both NOMA-HDR and NOMA-FDR systems with two-user and M-user are addressed. The closed-form power allocation policy of two-user NOMA-HDR system is obtained. Also, the optimal numerical power allocation policies for two-user NOMA-FDR and M-user NOMA-HDR systems are obtained. In addition, the problem for M-user NOMA-FDR systems is solved in noise-limited environment. Simulation results show that the proposed NOMA-HDR or NOMA-FDR scheme with power adaption clearly outperforms the NOMA-HDR or NOMA-FDR scheme with fixed power allocation. Besides, when the residual self-interference channel gain is small, the performance of NOMA-FDR system is better than the NOMA-HDR system.

In Industrial Internet of Things (IIoTs), data generated during manufacturing are collected by sensors and need be processed timely. The direction of data transmissions from sensors to processing centers (fog nodes) is often called uplink transmission. In this paper, the cases with single and multiple distributed fog nodes, which are also referred to as centralized and distributed models, are studied. Two distributed schemes based on batched sparse (BATS) codes are proposed separately for the uplink of these two models. The expected rank and the recovery probability of the information from sensors at fog node(s) are derived. Comparison results show that by using the proposed BATS-based schemes, improved transmission reliability can be achieved compared to the XOR-based network coding (NC) scheme.

Identifying relation from clinical texts is a complex and challenging task due to the specific biomedical knowledge. Existing methods for this work generally have the misclassification problem caused by sample class imbalance. In this paper, we propose a hierarchical attention-based convolutional long short-term memory (ConvLSTM) network model to solve this problem. We construct a sentence as multi-dimensional hierarchical sequence and directly learn local and global context information by a single-layer ConvLSTM network. Besides, a hierarchical attention-based pooling is built to capture the parts of a sentence that are relevant with the target semantic relation. Experiments on the 2010 i2b2/VA relation dataset show that our model outperforms several previous state-of-the-art models without relying on any external features.

In this paper, an intelligently cooperative communication network with cognitive users is considered, where in a primary system and a secondary system, respectively, a message is communicated to their respective receiver over a packet-based wireless link. The secondary system assists in the transmission of the primary message employing network coding, on the condition of maintaining or improving the primary performance, and is granted limited access to the transmission resources as a reward. The users in both systems exploit their previously received information in encoding and decoding the binary combined packets. Considering the priority of legitimate users, a selective cooperation mechanism is investigated and the system performance based on an optimization problem is analyzed. Both the analytical and numerical results show that the condition for the secondary system accessing the licensed spectrum resource is when the relay link performs better than the direct link of the primary transmission. We also extend the system model into a network with multiple secondary users and propose two relay selection algorithms. Jointly considering the related link qualities, a best relay selection and a best relay group selection algorithm are discussed. Overall, it is found that the throughput performance can be improved with multiple secondary users, especially with more potential users cooperating in the best relay group selection algorithm.

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both the technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of the IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by the existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks.

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks. In this tutorial paper, we review the various application scenarios, fundamental performance limits, and potential technical solutions for high-reliability and low-latency (HRLL) wireless IoT networks. We discuss physical, MAC (medium access control), and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. For the physical layer, we discuss the fundamental information-theoretic limits for HRLL communications, and then we also introduce a frame structure and preamble design for HRLL communications. Then practical channel codes with finite block length are reviewed. For the MAC layer, we first discuss optimized spectrum and power resource management schemes and then recently proposed grant-free schemes are discussed. For the network layer, we discuss the optimized network structure (traffic dispersion and network densification), the optimal traffic allocation schemes and network coding schemes to minimize latency.

In a typical wirelessly powered sensor network (WPSN), wireless chargers provide energy to sensor nodes by using wireless energy transfer (WET). The chargers can greatly improve the lifetime of a WPSN using energy beamforming by a proper charging scheduling of energy beams. However, the supplied energy still may not meet the demand of the energy of the sensor nodes. This issue can be alleviated by deploying redundant sensor nodes, which not only increase the total harvested energy but also decrease the energy consumption per node provided that an efficient scheduling of the sleep/awake of the nodes is performed. Such a problem of joint optimal sensor deployment, WET scheduling, and node activation is posed and investigated in this paper. The problem is an integer optimization that is challenging due to the binary decision variables and non-linear constraints. Based on the analysis of the necessary condition such that the WPSN be immortal, we decouple the original problem into a node deployment problem and a charging and activation scheduling problem. Then, we propose an algorithm and prove that it achieves the optimal solution under a mild condition. The simulation results show that the proposed algorithm reduces the needed nodes to deploy by approximately 16%, compared with a random-based approach. The simulation also shows that if the battery buffers are large enough, the optimality condition will be easy to meet.

In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multiple-input multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K -nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a near-optimal performance with a significantly lower complexity.

Spatial modulation (SM) and space shift keying (SSK) techniques have the unique advantages of their single-radio-frequency (RF) structures compared with conventional multiple-input-multiple-output (MIMO) techniques. However, the transmission rates of these techniques are decided by the maximal switching frequency or by the minimal switching time between the RF chain and transmit antennas, which has been a bottleneck for their applications in future broadband wireless communications. To alleviate this problem, we propose a class of novel offset SM (OSM) and offset SSK (OSSK) schemes, with the aid of channel state information (CSI) at the transmitter. Compared with conventional SM and SSK, the proposed OSM and OSSK schemes can reduce the switching frequency of the RF chain, by introducing an offset between the connected RF chain and the index of the spatial modulated antenna. In extreme conditions, the proposed OSM and OSSK can work without RF switching while maintaining the single-RF advantage of conventional SM and SSK schemes. Through theoretical analysis, we also develop the bit-error rate (BER) performance bounds for the proposed two schemes. Finally, our simulation results demonstrate that the proposed OSM and OSSK outperform their counterparts, including conventional SM, SSK, CSI-aided SM, and CSI-aided SSK, while having a simplified RF-switching structure.

In this paper, we investigate the optimal dynamic social-aware peer selection with spectrum-power trading to maximize the average sum energy efficiency (EE) of cellular users (CUs) for uplink transmission for an orthogonal frequency division multiple access cellular network with device-to-device (D2D) communications. Different from the previous studies, which mostly focus on how to exploit social ties in human social networks to construct the permutation of all the feasible peers, we consider dynamic peer selection with social awareness-aided spectrum-power trading in D2D overlaying communications. Specifically, the amount of transmit power from the D2D transmitters to relay the CUs for uplink transmission is determined by their social trust levels. Likewise, the D2D transmitters can gain the corresponding amount of spectrum from the CUs for D2D pair link communications, which can be regarded as the compensation of the power consumption for relaying CUs. We formulate the dynamic peer selection problems with social awareness-aided spectrum-power trading in cooperative D2D communications into the infinite-horizon time-average renewal-reward problems subject to time average constraints on a collection of penalty processes. And the Lyapunov optimization concepts-based drift-plus-penalty algorithms are proposed to solve them. The simulation results demonstrate the effectiveness of the proposed dynamic peer selection algorithms. And further performance comparison indicates that the proposed dynamic peer selection algorithms not only maximize the average EE of CUs but also guarantee higher privacy protection.

Millimeter-wave (mm-wave) communication has attracted increasing attention as a promising technology for 5G networks. One of the key architectural features of mm-wave is the possibility of using large antenna arrays at both the transmitter and receiver sides. Therefore, by employing directional beamforming, both mm-wave base stations (MBSs) and mm-wave user equipments (MUEs) are capable of supporting multi-beam simultaneous transmissions. However, most of the existing research results have only considered a single beam. Thus, the potentials of mm-wave have not been fully exploited yet. In this context, in order to improve the performance of short-range indoor mm-wave networks with multiple reflections, we investigate the challenges and potential solutions of downlink multi-user multi-beam transmission, which can be described as a beamspace multi-user multiple-input multiple-output (MU-MIMO) technique. We first exploit the characteristic of MBS/MUEs supporting multiple beams simultaneously to improve the efficiency of multi-user BF training. Then, we analyze the inter-user interference to avoid beam selection conflicts. Furthermore, we propose blockage control strategies and multi-user multi-beam power allocation solutions for the beamspace MU-MIMO. The theoretical and numerical results demonstrate that the beamspace MU-MIMO compared with single beam transmission can largely improve the rate performance and robustness of mm-wave networks.

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.

We investigate the energy-efficient power allocation for downlink transmission in orthogonal frequency division multiple access-based long term evolution systems. Aiming at realizing on-demand power allocation in cellular networks, we explore the available coordination between the base station and multiple users, and propose a new performance merit, namely, the demand side coordination energy efficiency (DSC-EE), which captures the system normalized EE and the demand side information. The proposed DSC-EE is designed to exploit individual disparities from both the entire system and the individual own expected utility perspectives. Our goal is to maximize the DSC-EE of the system via power allocation with a constraint on the maximum transmit power. Specifically, the objective function of DSC-EE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. The convergence property of the proposed algorithms and the meaningfulness of the proposed performance merit related to the EE are demonstrated by simulations. The comparison of four EE metrics, the EE and the rate fairness, global-EE, Sum-EE, and DSC-EE, shows that the DSC-EE maximization tends to achieve high implementation level of on-demand power allocation while ensuring the EE of the system. In addition, when the minimum rate replaces the expected rate in the DSC-EE, further performance comparison indicates the necessity and impact of the expected rate in the tolerable quality of service bias function.

The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.

With large available bandwidth, millimeter wave (mm-wave) communications have attracted considerable research interests because of their potential to achieve multi-giga bps rates. However, one of the main challenges for mm-wave is high pathloss. To address this problem, full-duplex (FD) relaying can be used to increase the effective transmission distance and the spectral efficiency. Thus, studying the application of FD relaying in mm-wave communications will be of value. However, one of the main challenges in FD mm-wave relaying is the residual self-interference (SI), which includes line-of-sight (LOS) and non-LOS parts. To eliminate the SI and improve the spectral efficiency, we propose an orthogonal matching pursuit-based SI-cancellation precoding algorithm. Then, we propose an energy consumption model and analyze the energy efficiency performance. We formulate the joint spectral efficiency and energy efficiency optimization problem, which can be transformed into a convex problem. The numerical results show that the FD precoding scheme can effectively eliminate the residual SI and achieve approximately twice the spectral efficiency of the conventional half-duplex system. We also show that in low-spectral-efficiency regions, the optimal energy efficiency can be achieved, but the achievable energy efficiency will decrease in high-spectral-efficiency regions.

Millimeter wave (mmWave) communication emerges as an enabling technology for Gbps transmission. A further performance enhancement can be achieved by incorporating mmWave communication into fog radio access networks (F-RANs), which alleviate the large path loss of mmWave signals by shortening the distance between transmitters and users and reduce the latency by caching at enhanced remote radio heads (eRRHs). The full benefit of mmWave F-RANs is leveraged on a joint design of signal processing at the centralized baseband unit (BBU) and distributed eRRHs. In this paper, we propose an energy efficient hybrid precoding design for the downlink mmWave F- RANs with wireless fronthaul links, where digital and hybrid precoders are exploited at the BBU and eRRHs, respectively. We develop an effective method to solve the resulting difficult precoding optimization problem and provide numerical results to demonstrate the effectiveness of the proposed mmWave F-RAN design.

Content caching at small base stations has been considered as an efficient way to alleviate the use of expensive backhauling. In this paper, we study the caching content update for a cache- enabled heterogeneous network. Through analyzing a benchmark content update policy, the problem of content consistency, which is caused by the distinct update time at each caching entity, is firstly revealed. As well, the close form representation of the consistency probability in a mobile environment is derived. Furthermore, a deterministic content update strategy is investigated, where the trade-off between consistency probability and storage cost is characterized. Detailed simulations are provided to support our analysis, as well as present the impacts of moving speed of mobile terminals and transmission bandwidth for content delivery on the consistency performance.

Millimeter wave (mmwave) communication operating in the band of 30-300 GHz is promising to provide Gbps data rates owing to its abundant spectrum resource, and has attracted increasing attention. Cooperative transmission, by converting undesired interferences into useful signals, is able to further improve performance of mmwave systems. In this paper, we propose a novel cooperative transmission scheme for mmwave communication networks, where each mobile user is cooperatively served by multiple access points (APs) that use hybrid precoders. Our goal is to maximize the system energy efficiency, which leverages on a joint design of the hybrid precoders of all APs. The formulated problem is a difficult nonlinear fractional programming subject to unit modulus constraints. We propose an efficient algorithm by incorporating penalty decomposition and block coordinate descent methods. Numerical results are provided to confirm the effectiveness of the proposed algorithm and reveal some important insights.

Physical layer authentication based on channel state information is an effective solution to preventing spoofing attacks in wireless communications by comparing the channel impulse responses. Existing theoretical analyses and experiments have proved the feasibility and efficiency in labs or offices. However, the environment of industrial wireless communication is significantly different. This paper applies physical layer authentication based on channel state information to measurements from four different industrial wireless communication scenarios, including indoor, outdoor, moving, and stationary scenarios. The analysis of the results allows to derive meaningful insights on the applicability of such a method to industrial wireless communications.

We consider cooperation in downlink non-orthogonal multiple access (NOMA) in a network supporting diverse communication modes. One user (UE2) exists as a potential relay between a base station (BS) and another user (UE1). With relaying the signal for UE1, UE2 obtains the opportunity for its own transmission to UE3 in D2D mode, meanwhile maintaining the transmission efficiency for UE1. On the basis of supporting different communication modes, we propose a NOMA-based cooperation scheme at UE2 to combine the relay message with its own. We derive achievable rate regions for two cases depending on the status of the Rayleigh fading channel of the UE2. We find solutions based on experiments through the transmit power allocation strategy at the UE2 and(/or) the BS. We show the impacts of our cooperative scheme and the corresponding user geometry on the achievable rates and the resource sharing strategies.

We analyze the delay performance of traffic dispersion in millimeter-wave (mm-wave) communications, where Nakagami-m fading channel is considered. To apply (min, +)-algebra network calculus in wireless communications, we develop a closed-form expression based on moment generating function (MGF), which characterizes the stochastic service process by staying in the bit domain, rather than transferring to the SNR domain. Subsequently, for traffic dispersion with mm-wave, we derive probabilistic delay bounds and effective capacity based on the obtained MGF of the cumulative service process. Besides, the impacts of several factors, e.g., the number of independent path, system gain (including antenna gain and adopted radio frequency) or Nakagami parameter, on the delay performance are studied. We not only comprehensively study the delay performance of traffic dispersion with mm-wave, but also demonstrate the feasibility and tractability of performance analysis.

We study a mobility prediction based proactive wireless caching scheme for two-tier cellular networks consisting of a base station (BS) tier and a device-to-device (D2D) tier. Two scenarios are considered: popular contents cached only at BSs, and popular contents cached at both BSs and MTs. We model user mobility as a Markov renewal process to predict user moving paths and residence time. Then we analyse the hit-rate performance to evaluate the presented schemes. By formulating content placement to maximize the hit-rate as optimization problems, we provide the optimal solution for the first scenario and develop a greedy mobility prediction based proactive wireless caching (MPPC) scheme for the second. Through analysis we show that the hit-rate achieved by MPPC is at least exp(1)-1/exp(1) of the optimal hit-rate. The numeric results show that the MPPC can dramatically improve the hit-rate performance, compared with random caching and most popular caching (MPC) schemes. We show that the hit-rate achieved by MPPC outperformances MPC by 26% at most when MTs are not able to cache. Besides we present the impact of the moving speed on the hit-rate performance of MPPC for MTs.

A social network is a social structure made up of a set of social actors and a set of dyadic ties between these actors. The actors form a number of communities. In communities, some actors want to transmit their information to all other actors. Each actor corresponds to a user equipment (UE). The UE of the actor which has information to be transmitted is also called the source, and the UEs of all other actors are called destinations. The information is transmitted from the source to destinations with the assistance of helpers, which can be small cell base stations (SCBSs). A novel information dissemination approach, namely expanded network coding with a finite buffer size (ENCFB), is proposed for the case when the buffer size of helpers is limited. The performance comparison of the uncoded information dissemination approach, the network coded approach and the ENCFB approach is conducted. Comparison results show that the ENCFB approach can significantly improve the performance of information dissemination when the buffer size is limited.

Recently, it has been shown that the time-varying multiple-access channel (MAC) with perfect channel state information (CSI) at the receiver and delayed feedback CSI at the transmitters can be modeled as the finite state MAC (FS-MAC) with delayed state feedback, where the time variation of the channel is characterized by the statistics of the underlying state process. To study the fundamental limit of the secure transmission over multi-user wireless communication systems, we re-visit the FS-MAC with delayed state feedback by considering an external eavesdropper, which we call the finite state multiple-access wiretap channel (FS-MAC-WT) with delayed feedback. The main contribution of this paper is to show that taking full advantage of the delayed channel output feedback helps to increase the secrecy rate region of the FS-MAC-WT with delayed state feedback. Moreover, by a degraded Gaussian fading example, we show the effects of feedback delay and channel memory on the secrecy sum rate of the FS-MAC-WT with delayed feedback.

Millimeter wave (mmwave) communication has attracted increasing attention owing to its abundant spectrum resource. The short wavelength at mmwave frequencies facilitates placing a large number of antennas in a small space, and the mmwave channels are likely to be sparse in the directions. These two new features promise enhanced security by directional precoding. To explore this potential, we investigate the design of directional hybrid digital and analog precoding for the multiuser mmwave communication system with multiple eavesdroppers. Particularly, we consider two cost-efficient sub-connected hybrid architectures, i.e., multi-subarray architecture and switched-phased-array architecture, and optimize the hybrid precoding under per-antenna constant envelope (CE) constraints. The goal of our design is to guarantee the receive quality of the legitimate users while minimizing the power leaked to the eavesdroppers, so as to realize a directional transmission for a general mmwave channel. The resulting problems are very challenging due to the nonlinear CE constraints and binary integer constraint from antenna selection. To address them, we leverage exact penalty function methods to find efficient solutions to the CE hybrid directional precoding. Our analysis shows that the proposed algorithm is able to converge to a stationary point under some mild conditions. Simulation results are finally provided to confirm the effectiveness of the proposed schemes and their superiority over the existing schemes under both single-path and multi-path mmwave channels.

For buffer-aided tandem networks consisting of relay nodes and multiple channels per hop, we consider two traffic allocation schemes, namely local allocation and global allocation, and investigate the end-to-end latency of a file transfer. We formulate the problem for generic multi-hop queuing systems and subsequently derive closed-form expressions of the end-to-end latency. We quantify the advantages of the global allocation scheme relative to its local allocation counterpart, and we conduct an asymptotic analysis on the performance gain when the number of channels in each hops increases to infinity. The traffic allocations and the analytical delay performance are validated through simulations. Furthermore, taking a specific two-hop network with millimeter-wave (mm-wave) as an example, we derive lower bounds on the average end-to-end latency, where Nakagami-m fading is considered. Numerical results demonstrate that, compared with the local allocation scheme, the advantage of global allocation grows as the number of relay nodes increases, at the expense of higher complexity that linearly increases with the number of relay nodes. It is also demonstrated that a proper deployment of relay nodes in a linear mm-wave network plays an important role in reducing the average end-to-end latency, and the average latency decays as the mm-wave channels become more deterministic. These findings provide insights for designing multi-hop mm-wave networks with low end-to-end latency.

This paper investigates the constellation/codebook design of a promising uplink multiple access technique, sparse code multiple access (SCMA), proposed for the fifth generation mobile networks. The application of bit-interleaved coded modulation with iterative multiuser detection is considered for uplink SCMA over fading channels. Extrinsic information transfer chart is used to aid the analysis and the design of multidimensional constellations, and the design criteria for multidimensional constellations and labelings optimization are thus established. Furthermore, a new and simple approach of multi-stage optimization for the multidimensional constellation design is proposed for SCMA, to improve the bit-error rate performance and alleviate the complexity of turbo multiuser detection. Numerical and simulation results are also provided to demonstrate the performance and verify the efficiency of the proposed scheme, compared with the state of the art.

The online property of fountain codes enables the encoder to efficiently find the optimal encoding strategy that minimizes the encoding overhead based on the instantaneous decoding state. Therefore, the receiver is able to optimally recover data from losses that differ significantly from the initial expectation. In this paper, we propose a framework to analyze the relationship between overhead and the number of recovered source symbols for online fountain codes based on random graph theory. Motivated by the analysis, we propose improved online fountain codes (IOFCs) by introducing a designated selection of source symbols. Theoretical analysis shows that IOFC has lower overhead compared with the conventional online fountain codes. We verify the proposed analysis via simulation results and demonstrate the tradeoff between full recovery and intermediate performance in comparison to other online fountain codes.

Wireless self-backhauling is a promising solution for dense millimeter wave (mmWave) small cell networks, the system efficiency of which, however, depends upon the balance of resources between the backhaul link and access links of each small cell. In this paper, we address the discrete power control and non-unified transmission duration allocation problem for self-backhauling mmWave cellular networks, in which each small cell is allowed to adopt individual transmission duration allocation ratio according to its own channel and load conditions. We first formulate the considered problem as a non-cooperative game G with a common utility function. We prove the feasibility and existence of the pure strategy Nash equilibrium (NE) of game ' under some mild conditions. Then, we design a centralized resource allocation algorithm based on the best response dynamic and a decentralized resource allocation algorithm (DRA) based on control-plane/user-plane split architecture and loglinear learning to obtain a feasible pure strategy NE of game G. For speeding up convergence and reducing signaling overheads, we reformulate the considered problem as a non-cooperative game G' with local interaction, in which only local information exchange is required. Based on DRA, we design a concurrent DRA to obtain the best feasible pure strategy NE of game q'. Furthermore, we extend the proposed algorithms to the discrete power control and unified transmission duration allocation optimization problem. Extensive simulations are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.

Multi-beam concurrent transmission is one of promising solutions for a millimeter-wave (mmWave) network to provide seamless handover, robustness to blockage, and continuous connectivity. Nevertheless, one of the major obstacles in multi-beam concurrent transmissions is the optimization of beam pair selection, which is essential to improve the mmWave network performance. Therefore, in this paper, we propose a novel heterogeneous multi-beam cloud radio access network (HMBCRAN) architecture which provides seamless mobility and coverage for mmWave networks. We also design a novel acquirement method for candidate beam pair links (BPLs) in HMBCRANs architecture, which reduces user power consumption, signaling overhead, and overall time consumption. Based on HMBCRANs architecture and the resulted candidate BPLs for each user equipment, a beam pair selection optimization problem aiming at maximizing network sum rate is formulated. To find the solution efficiently, the considered problem is reformulated as a non-operative game with local interaction, which only needs local information exchanging among players. A decentralized algorithm based on HMBCRANs architecture and binary log-linear learning is proposed to obtain the optimal pure strategy Nash equilibrium of the proposed game, in which a concurrent multi-player selection scheme and an information exchanging protocol among players are developed to reduce the complexity and signal overheads. The stability, optimality, and complexity of the proposed algorithm are analyzed via theoretical and simulation method. The results prove that the proposed scheme has better convergence speed and sum rate against the state-of-the-art schemes.

We study the maximum achievable rate of a two-hop amplified-and-forward (AF) relaying millimeter-wave (mm-wave) system, where two AF relaying schemes, i.e., half-duplex (HD) and full-duplex (FD) are discussed. By considering the two-ray mm-wave channel and the Gaussian-type directional antenna, jointly, the impacts of the beamwidth and the self-interference coefficient on maximum achievable rates are investigated. Results show that, under a sum-power constraint, the rate of FD-AF mm-wave relaying outperforms its HD counterpart only when antennas with narrower beamwidth and smaller self-interference coefficient are applied. However, when the sum-power budget is sufficiently high or the beamwidth of directional antenna is sufficiently small, direct transmission becomes the best strategy, rather than the AF relaying schemes. For both relaying schemes, we show that the rates of both AF relaying schemes scale as O(min{θm-1, θm-2}) with respect to beamwidth θm, and the rate of FD-AF relaying scales as O(μ-(1/2)) with respect to self-interference coefficient μ. Also, we show that ground reflections may significantly affect the performance of mm-wave communications, constructively or destructively. Thus, the impact of ground reflections deserves careful considerations for analyzing or designing future mm-wave wireless networks.

Low latency is critical for many applications in wireless communications, e.g., vehicle-to-vehicle, multimedia, and industrial control networks. Meanwhile, for the capability of providing multi-gigabits per second rates, millimeter-wave (mm-wave) communication has attracted substantial research interest recently. This paper investigates two strategies to reduce the communication delay in future wireless networks: traffic dispersion and network densification. A hybrid scheme that combines these two strategies is also considered. The probabilistic delay and effective capacity are used to evaluate performance. For probabilistic delay, the violation probability of delay, i.e., the probability that the delay exceeds a given tolerance level, is characterized in terms of upper bounds, which are derived by applying stochastic network calculus theory. In addition, to characterize the maximum affordable arrival traffic for mm-wave systems, the effective capacity, i.e., the service capability with a given quality-of-service requirement, is studied. The derived bounds on the probabilistic delay and effective capacity are validated through simulations. These numerical results show that, for a given sum power budget, traffic dispersion, network densification, and the hybrid scheme exhibit different potentials to reduce the end-to-end communication delay. For instance, traffic dispersion outperforms network densification when high sum power budget and arrival rate are given, while it could be the worst option, otherwise. Furthermore, it is revealed that, increasing the number of independent paths and/or relay density is always beneficial, while the performance gain is related to the arrival rate and sum power, jointly. Therefore, a proper transmission scheme should be selected to optimize the delay performance, according to the given conditions on arrival traffic and system service capability.

Ultra-dense networks (UDNs) are the promising technology for the fifth-generation wireless networks and beyond to significantly boost network capacity and improve network coverage by exploiting spatial spectrum reuse through the deployment of massive base stations (BSs). In this paper, the fundamental tradeoffs of non-orthogonal multicast, multicast, and unicast in the UDN are studied, to understand the impact of network densitification on them and provide some insights on UDN deployment. Non-orthogonal multicast with imperfect channel estimation and successive interference cancellation is also investigated. To evaluate the performance, a tractable model for performance analysis is developed by using stochastic geometry, and then the analytical expressions for downlink signal-to-interference-plus-noise ratio coverage probability, spectrum efficiency, area traffic capacity, and energy efficiency are derived. The numerical results together with the Monte Carlo simulations are also provided. The results demonstrate that non-orthogonal multicast can further improve the performance of multicast and achieve higher spectrum efficiency, area traffic capacity, and energy efficiency than unicast from the low-to-high BS density regions, but suffers from inferior performance to unicast in the very high BS density region. The results also show that the non-orthogonal multicast and multicast exhibit different performance trends from unicast.

We consider optimized cooperation in joint orthogonal multiple access and nonorthogonal multiple access in industrial cognitive networks, in which lots of devices may have to share spectrum and some devices (e.g., those for critical control devices) have higher transmission priority, known as primary users. We consider one secondary transmitter (less important devices) as a potential relay between a primary transmitter and receiver pair. The choice of cooperation scheme differs in terms of use cases. With decode-and-forward relaying, the channel between the primary and secondary users limits the achievable rates especially when it experiences poor channel conditions. To alleviate this problem, we apply analog network coding to directly combine the received primary message for relaying with the secondary message. We find achievable rate regions for these two schemes over Rayleigh fading channels. We then investigate an optimization problem jointly considering orthogonal multiple access and nonorthogonal multiple access, where the secondary rate is maximized under the constraint of maintaining the primary rate. We find both analytical solutions as well as solutions based on experiments through the time sharing strategy between the primary and secondary system and the transmit power allocation strategy at the secondary transmitter. We show the performance improvements of exploiting analog network coding and the impacts of cooperative schemes and user geometry on achievable rates and resource sharing strategies.

In an industrial automation system, one of the most important parts is control loop. Fog computing is a potential solution for industrial control in time-critical applications as it provides distributed computing services closer to the connected devices. However, a huge amount of data exchanging among fog nodes causes high communication load, which constrains the overall response time from fog nodes to actuators. In this paper, we consider the erasure environment, batched sparse (BATS) codes are applied to the Map and the Data Shuffling stages of distributed fog computing process to reduce both the communication and the computation loads. The communication loads of the uncoded, the coded, and the proposed BATS-based schemes over erasure channels are calculated, respectively. Numerical results show that the BATS-based scheme can reduce the communication and the computation loads simultaneously, and furthermore reduce the overall response time from fog nodes to the actuators.

Interference channels with confidential messages are studied under strong secrecy constraints, based on the framework of channel resolvability theory. It is shown that if the random binning rate for securing a confidential message is above the resolution of its corresponding wiretapped channel, strong secrecy can be guaranteed. The information-spectrum method introduced by Han and Verdú is generalized to an arbitrary interference channel to obtain a direct channel resolvability result as a first step. For stationary and memoryless channels with discrete output alphabets, the results show that the achievable rates under weak and strong secrecy constraints are the same. This result is then generalized to channels with continuous output alphabets by deriving a reverse direction of Pinsker's inequality to bound the secrecy measure from above by a function of the variational distance of relevant distributions. As an application, Gaussian interference channels are studied in which the agreement between the best known weak and strong secrecy rate regions also appear. Following the footsteps of Csiszár, Hayashi and of Bloch and Laneman, these results provide further evidence that channel resolvability is a powerful and general framework for strong secrecy analysis in multiuser networks.

The abundance of spectrum in the millimeter-wave (mm-wave) bands makes it an attractive alternative for future wireless communication systems. Such systems are expected to provide data transmission rates in the order of multi-gigabits per second in order to satisfy the ever-increasing demand for high rate data communication. Unfortunately, mm-wave radio is subject to severe path loss, which limits its usability for long-range outdoor communication. In this paper, we propose a multi-hop mm-wave wireless network for outdoor communication, where multiple full-duplex buffered relays are used to extend the communication range, while providing end-to-end performance guarantees to the traffic traversing the network. We provide a cumulative service process characterization for the mm-wave propagation channel with self-interference in terms of the moment generating function of its channel capacity. Then, we then use this characterization to compute probabilistic upper bounds on the overall network performance, i.e., total backlog and end-to-end delay. Furthermore, we study the effect of self-interference on the network performance and propose an optimal power allocation scheme to mitigate its impact in order to enhance network performance. Finally, we investigate the relation between relay density and network performance under a sum power constraint. We show that increasing relay density may have adverse effects on network performance, unless the self-interference can be kept sufficiently small.

The lifetime of a wireless sensor network (WSN) determines how long the network can be used to monitor the area of interest. Hence, it is one of the most important performance metrics for WSN. The approaches used to prolong the lifetime can be briefly divided into two categories: reducing the energy consumption, such as designing an efficient routing, and providing extra energy, such as using wireless energy transfer (WET) to charge the nodes. Contrary to the previous line of work where only one of those two aspects is considered, we investigate these two together. In particular, we consider a scenario where dedicated wireless chargers transfer energy wirelessly to sensors. The overall goal is to maximize the minimum sampling rate of the nodes while keeping the energy consumption of each node smaller than the energy it receives. This is done by properly designing the routing of the sensors and the WET strategy of the chargers. Although such a joint routing and energy beamforming problem is non-convex, we show that it can be transformed into a semi-definite optimization problem (SDP). We then prove that the strong duality of the SDP problem holds, and hence, the optimal solution of the SDP problem is attained. Accordingly, the optimal solution for the original problem is achieved by a simple transformation. We also propose a lowcomplexity approach based on pre-determined beamforming directions. Moreover, based on the alternating direction method of multipliers, the distributed implementations of the proposed approaches are studied. The simulation results illustrate the significant performance improvement achieved by the proposed methods. In particular, the proposed energy beamforming scheme significantly outperforms the schemes where one does not use energy beamforming, or one does not use optimized routing. A thorough investigation of the effect of system parameters, including the number of antennas, the number of nodes, and the number of chargers, on the system performance is provided. The promising convergence behavior of the proposed distributed approaches is illustrated.

In frequency hopping (FH) wireless communications, finding an effective transmission strategy to properly mitigate jamming has been recently considered as a critical issue, due to the inherent broadcast nature of wireless communications. Recently, game theory has been proposed as a powerful tool for dealing with the jamming problem, which can be considered as a player (jammer) playing against a user (transmitter). Different from existing results, in this paper, a bimatrix game framework is developed for modeling the interaction process between the transmitter and the jammer, and the sufficient and necessary conditions for Nash equilibrium (NE) strategy of the game are obtained under the linear constraints. Furthermore, the relationship between the NE solution and the global optimal solution of the corresponding quadratic programming is presented. In addition, a special analysis case is developed based on the continuous game framework in which each player has a continuum of strategies. Finally, we show that the performance can be improved based on our game theoretic framework, which is verified by numerical investigations.

This paper studies beam training design for a codebook- based beamforming millimeter wave (mmwave) system where multiple antenna arrays are employed and each array is capable of beamforming independently. To reduce the training overhead and the complexity of subsequent beam direction search, we propose a cooperative multi- subarray beam training method. Specifically, from the perspective of excluding noneffective beam direction combinations and thus reducing search space, method and criterion of beam superposition are proposed to construct a wide beam from multiple narrow beams corresponding to multiple subarrays. Then, a cooperative multisubarray beam training scheme is proposed based on the proposed criterion. Finally, simulation results show that the proposed scheme achieves a spectral efficiency close to that of the optimal exhaustive search scheme, while has greatly reduced training overhead and computational complexity.

This paper investigates the asymptotic performance of bit-interleaved coded modulation (BICM) with iterative multiuser detection and decoding in uplink sparse code multiple access (SCMA) systems. The extrinsic information transfer (EXIT) characteristics analysis of the joint multiuser detector for SCMA is provided, and shows that the average detection reliability for multiple users converges to the single-user case, if ideal feedback from the decoder is available to the detector. We develop a tight analytical bound on the convolutionally encoded bit-error rate (BER) for independent Rayleigh fadings, based on the single-user bound with arbitrary multidimensional constellations. Moreover, we analyze the achievable coding and diversity gains of the SCMA-BICM system with iterative receiver. Simulations are carried out to verify the effectiveness of the analysis.

Wireless sensor networks (WSNs) consist of energy limited sensor nodes, which limits the network lifetime. Such a lifetime can be prolonged by employing the emerging technology of wireless energy transfer (WET). In WET systems, the sensor nodes can harvest wireless energy from wireless charger, which can use energy beamforming to improve the efficiency. In this paper, a scenario where dedicated wireless chargers with multiple antennas use energy beamforming to charge sensor nodes is considered. The energy beamforming is coupled with the energy consumption of sensor nodes in terms of data routing, which is one novelty of the paper. The energy beamforming and the data routing are jointly optimized by a non-convex optimization problem. This problem is transformed into a semidefinite optimization problem, for which strong duality is proved, and thus the optimal solution exists. It is shown that the optimal solution of the semi-definite programming problem allows to derive the optimal solution of the original problem. The analytical and numerical results show that optimal energy beamforming gives two times better monitoring performance than that of WET without using energy beamforming.

In this paper, a multi-user multi-relay network with wireless energy harvesting (EH) and transferring (ET) is studied. In our system, a simultaneous two-level cooperation, i.e., information-level and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. The energy minimization problem that takes into account the energy causality and outage probability constraints is formulated. However, the optimization problem is non-convex and hard to be solved directly. Alternatively, an approximation technique is adopted to convert it into a convex one. By solving the convex problem, efficient power allocation and ET policies are designed. Numerical results show that the proposed algorithm is able to achieve a near-optimal performance and outperforms the state of arts.

To overcome the severe path loss and provide higher spectral efficiency for 60 GHz wireless communications, highly directional antennas, which indicate high antenna gains, are widely employed. In this work, we focus on the trade-off between the beamwidth of directional antennas and the interference produced by random concurrent transmissions, in terms of two important statistics, i.e., the expectation and variance. A specific regular network is considered, which consists of multi-layer rings, and a typical receiver with a fixed orientation is placed in the network center. We derive closed-form expressions of interference statistics associated with the beamwidth, which are followed by upper and lower bounds. In addition, we demonstrate that the performance gain provided by directional antennas approximately grows in the fashion of the reciprocal of beamwidth, and an approximation for the expectation of signal-to-interference-and-noise ratio (SINR) is also presented. Numerical results show that, analytic expressions coincide with simulations, and derived bounds are valid. The benefits of utilizing directional antennas are quantified and verified. Furthermore, the impacts of the factors, such as path loss exponent and side-lobe gain, are also studied, comprehensively.

Wireless caching enables popular files to be stored at the base stations (BSs) in advance, which has been considered as an efficient way to reduce the service delay and alleviate heavy burdens on the backhaul links. In this paper, we study the BS clustering scheme for the cooperative wireless caching networks (CWCNs) with heterogeneous file preference among users and BSs, and propose the minimum cost (MC) based clustering scheme. We first introduce the weight-based cost function, which characterizes the trade-off between service delay and transmission cost, and then formulate the clustering as the optimization problem. Furthermore, two clustering algorithms are developed to solve the optimization problem. The results show that the proposed scheme can achieve lower cost compared with random clustering scheme.

We study a downlink non-orthogonal multiple access (NOMA) system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Numerical results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of arts.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. In particular, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the instantaneous cross-channel gain by observing the power adaption. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of art, we show the advantages of the proposed proactive estimator.

The lifetime of a wireless sensor network (WSN) is limited by the lifetime of the individual sensor nodes. A promising technique to extend the lifetime of the nodes is wireless energy transfer. The WSN lifetime can also be extended by exploiting the redundancy in the nodes' deployment, which allows the implementation of duty-cycling mechanisms. In this paper, the joint problem of optimal sensor node deployment and WET scheduling is investigated. Such a problem is formulated as an integer optimization whose solution is challenging due to the binary decision variables and non-linear constraints. To solve the problem, an approach based on two steps is proposed. First, the necessary condition for which the WSN is immortal is established. Based on this result, an algorithm to solve the node deployment problem is developed. Then, the optimal WET scheduling is given by a scheduling algorithm. The WSN is shown to be immortal from a networking point of view, given the optimal deployment and WET scheduling. Theoretical results show that the proposed algorithm achieves the optimal node deployment in terms of the number of deployed nodes. In the simulation, it is shown that the proposed algorithm reduces significantly the number of nodes to deploy compared to a random-based approach. The results also suggest that, under such deployment, the optimal scheduling and WET can make WSNs immortal.

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

For the potential of providing rates of multiple Giga-bps in a single channel, millimeter wave (mmWave) communications have recently attracted substantial research interest. While mmWave technology is already being used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave frequencies in mobile networks, where transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, many significant technical challenges must be tackled. The main objective of this IEEE JSAC Special Issue on “Millimeter wave communications for future mobile networks” is to collect the most recent technical advances in mmWave for future mobile networks. The response from the community to the call has been overwhelming. We received 96 submissions with a call period short than 4 months. Many of the submissions are from the most well known research groups in the field. After a strict review process, we decided to accept 38 papers, which will be published in two issues. The papers were selected based on the technical relevance and merits. Unfortunately, due to space limitations, a number of interesting papers were not selected, despite the merits that they had. We sincerely hope those papers can find other publishing venues.

We study a downlink non-orthogonal multiple access system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability and ergodic sum rate with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Furthermore, by taking the fairness between the near user and far user into account, we characterize the optimal power allocations with closed-form expressions at the BS and relay to maximize the minimum achievable rate of users. Simulation results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of the art.

Multimedia broadcast/multicast service (MBMS) transmission, which distributes the media content to multiple users on the same radio resources by using point-to-multipoint communications, is a highly spectrum efficient mechanism for multimedia communications. In this paper, we study the application of power domain non-orthogonal transmission to MBMS enhancements in a K-tier heterogeneous network, in order to satisfy the ever-increasing demands for emerging applications and performance requirements. Then, we present non-orthogonal multi-rate MBMS transmission (NOMRMT) and non-orthogonal multi-service MBMS transmission schemes and investigate their performance by using stochastic geometry. A tractable mode is developed to analyze the performance of asynchronous and synchronous non-orthogonal MBMS transmission. Based on this model, analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, average number of served users, and sum rate are derived. The results demonstrate that non-orthogonal MBMS transmission can achieve better performance than the orthogonal one, while synchronous non-orthogonal MBMS transmission is superior to the asynchronous one.

Millimeter wave (mmWave) wireless networks can operate in single-cell point-to-multipoint mode to provide local multicast services efficiently. In this paper, the performance of multicast mmWave wireless networks is studied, through stochastic geometry. Then, the use of power domain non-orthogonal multiple access (NOMA) for enhancing mmWave multicasting is also investigated. Furthermore, we study multicasting in two-tier mmWave heterogeneous networks, and propose a novel cooperative NOMA multicast scheme. Analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, the average number of served users, and the sum multicast rate are derived, in order to assess the performance of these schemes. Finally, we discuss the maximum sum multicast rates, by formulating them as optimization problems, and also develop efficient golden section search algorithms to solve them. The offered solutions reveal the impact of data transmission rate and power allocation on the sum multicast rate. Both analytical and numerical results demonstrate that NOMA can significantly improve the mmWave multicasting, while the proposed cooperative NOMA mmWave multicast scheme can further improve the NOMA mmWave multicasting.

We investigate the error performance of multidimensional constellations in the multiple access and broadcast channels. More specifically, we provide closed-form expressions for the pairwise error probability (PEP) of the joint maximum likelihood detection, for multiuser signaling in the presence of additive white Gaussian noise and Rayleigh fading. Arbitrary numbers of users and multidimensional signal sets are assumed, while the provided formula for the PEP is a function of the dimension-wise distances of the multidimensional constellation. Furthermore, a useful upper bound on the average symbol error probability is also obtained through the union bound. The analysis is applied to the sparse code multiple access systems. The analytical results are validated successfully through simulations, and show their importance in the multidimensional constellation design.

In this paper, a multi-user multi-relay network with integrated energy harvesting and transferring (IEHT) strategy is studied. In our system, a simultaneous two-level cooperation, i.e., information- and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. For generality purposes, the Nakagami-m fading channels that are independent but not necessarily identically distributed (i.n.i.d.) are considered. The problem of energy efficiency maximization under constraints of the energy causality and a predefined outage probability threshold is formulated and shown to be non-convex. By exploiting fractional and geometric programming, a convex form-based iterative algorithm is developed to solve the problem efficiently. Close-to-optimal power allocation and energy cooperation policies across consecutive transmissions are found. Moreover, the effects of relay locations, wireless energy transmission efficiency, battery capacity as well as the existence of direct links are investigated. The performance comparison with the current state of solutions demonstrates that the proposed policies can manage the harvested energy more efficiently.

To meet the ever-increasing demands on higher throughput and better network delay performance, 60 GHZ networking is proposed as a promising solution for the next generation of wireless communications. To successfully deploy such networks, its important to understand their performance first. However, due to the unique fading characteristic of the 60 GHz channel, the characterization of the corresponding service process, offered by the channel, using the conventional methodologies may not be tractable. In this work, we provide an alternative approach to derive a closed-form expression that characterizes the cumulative service process of the 60 GHz channel in terms of the moment generating function (MGF) of its instantaneous channel capacity. We then use this expression to derive probabilistic upper bounds on the backlog and delay that are experienced by a flow traversing this network, using results from the MGF-based network calculus. The computed bounds are validated using simulation. We provide numerical results for different networking scenarios and for different traffic and channel parameters and we show that the 60 GHz wireless network is capable of satisfying stringent quality-of-Service (QoS) requirements, in terms of network delay and reliability. With this analysis approach at hand, a larger scale 60 GHz network design and optimization is possible.

In Wireless Sensor Networks (WSNs), to supply energy to the sensor nodes, wireless energy transfer (WET) is a promising technique. One of the most efficient procedures to transfer energy to the sensor nodes consists in using a sharp wireless energy beam from the base station to each node at a time. A natural fundamental question is what is the lifetime ensured by WET and how to maximize the network lifetime by scheduling the transmissions of the energy beams. In this paper, such a question is addressed by posing a new lifetime maximization problem for WET enabled WSNs. The binary nature of the energy transmission process introduces a binary constraint in the optimization problem, which makes challenging the investigation of the fundamental properties of WET and the computation of the optimal solution. The sufficient condition for which the WET makes WSNs immortal is established as function of the WET parameters. When such a condition is not met, a solution algorithm to the maximum lifetime problem is proposed. The numerical results show that the lifetime achieved by the proposed algorithm increases by about 50% compared to the case without WET, for a WSN with a small to medium size number of nodes. This suggests that it is desirable to schedule WET to prolong lifetime of WSNs having small or medium network sizes.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access the licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the EE maximization as a joint optimization problem of the spectrum sensing duration and the transmit power of the cognitive transmitter. Specially, we consider the constraints of both the collision probability between the primary and cognitive transmission and the outage probability of the cognitive transmission. Since the joint optimization problem of the sensing duration and the transmit power is hard to be solved directly, we decompose it into two sub-problems with the spectrum sensing duration and the transmit power as variables, respectively. Based on the analytical solvers of the two sub-problems, we propose an iterative-based algorithm to solve the joint optimization problem. Finally, numerical results are provided to validate the analysis and performance of our proposed algorithms.

Contamination in drinkable water distribution networks can be potentially monitored by new and agile mobile sensor networks. These sensor networks are composed of static sensor nodes, which are pre-installed, of mobile sensor nodes, which are released into the water network for a more punctual monitoring, and of sink nodes, which are used to collect data from mobile sensor nodes. Thus, the activation of the sink nodes as well as the release locations of the mobile nodes must be carefully decided to ensure timely and accurate event detections. Unfortunately, no approach can be found in the literature to optimally determine the release locations of the mobile sensor nodes and the activation of the sink nodes. In this paper, a novel optimization approach to solve such a problem is posed. The problem is particularly challenging due to the potential large size of the networks, the undetermined movement of the mobile sensor nodes, the integer decision variables associated to the release locations of these mobile sensor nodes, and the binary decision variables associated to the activation of the sink nodes. To account for the mobile node mobility across the water distribution network, a stochastic mobility model is considered. It is shown that the objective function of the optimization problem exhibits submodular properties, which allow establishing a mobile nodes release algorithm. The benefits and efficiency of the proposed algorithm are illustrated by analysis and numerical evaluations. It is concluded that the proposed optimization based approach allows efficient monitoring of water distribution networks by mobile sensor nodes.

This paper investigates error performance of sparse code multiple access (SCMA) networks with multiple access channels (MAC) and broadcast channels (BC).We give the closed-form expression for the pairwise error probability (PEP) of joint maximum likelihood (ML) detection for multiuser signals over additive white Gaussian noise (AWGN) and Rayleigh fading channels with an arbitrary number of users and multidimensional codebooks. An upper bound for the average symbol error rate (SER) is calculated. The bound is tight in both AWGN channel and Rayleigh fading channels for high SNR regions. The analytical bounds are compared with simulations, and the results confirm the effectiveness of the analysis for both AWGN and Rayleigh fading channels.

The problem of the centralized caching is studied in a two-layer network. The first layer of the network is constructed by a server and K1helpers, and the second layer consists of K1orthogonal sub-networks, each of which contains a helper and K2users. The pioneer caching design in the two-layer network is to directly apply the Maddah-Ali & Niesen (MAU) centralized caching [1] into individual layers, such that single-layer multicast opportunities (SMO) are deployed. In this paper, a joint caching (JC) algorithm is developed by exploiting both the SMO and the correlations of caching contents across two layers, namely, cross-layer storage correlations (CSC). Furthermore, cross-layer multicast opportunities (CMO) can also be created by applying the MAU scheme between the server and users. In order to simultaneously obtain the caching gains from SMO, CSC, and CMO, a hybrid caching scheme is proposed and demonstrated to be order-optimal when the storage sizes at both helpers and users are limited. In other words, the achievable rate region lies within a constant multiplicative and additive gap of the information-theoretic bounds. In particular, the multiplicative and additive factors can be carefully characterized to be 1/48 and 4, respectively.

Existing studies on cognitive two-way relay networks assume that the primary users (PUs) transmit data with two power levels. In practice, it is possible that the PUs may adopt multiple power levels to maximize their throughputs or to compensate for wireless link fading. In such a scenario, there exist two key problems: one is the detection accuracy for the secondary users (SUs), and the other is the throughput tradeoff between PUs and SUs. To address the detection accuracy challenge, we propose a cooperative soft-combination-based energy detector and demonstrate its benefit over other hard-combination-based detectors. To solve the throughput tradeoff problem, we derive the throughput expressions for both PUs and SUs and analyze the impact of our detection accuracy on the throughputs. It is shown that the SU's throughput varies with the number of the PU's transmit power levels and achieves a maximum value when the number is small. We also find that increasing the detection capability of the SU reduces its own throughput, while increases the PU's throughput.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access a licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the average EE maximization problem in fading channels as a joint optimization problem of the spectrum sensing duration and the transmit power of cognitive users. Meanwhile, we consider the constraints of both the collision probability between the primary and cognitive transmissions and the outage probability of cognitive transmissions. However, the joint optimization problem subject to the constraints is complicated and it is computationally hard to obtain the optimal solution. Alternatively, we develop two algorithms, i.e., a linear search algorithm and an iterative-based algorithm, with considerable complexity to solve the problem. Numerical results verify the correctness of both algorithms and show that the proposed algorithms can achieve the performance close to that of the exhaustive search algorithm and outperform the state of arts.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-channel gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT's relaying on the primary transmission and observe that the impact is related to the CT's location. By introducing a factor φ (0 ≤ φ ≤ 1) to denote the probability that the CT's relaying improves the primary transmission instead of causes interference, we design the CT location as a function of φ. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

We study the repair problem in distributed storage systems where storage nodes are connected through packet erasure channels and some nodes are dedicated to repair [termed as dedicated-for-repair (DR) storage nodes]. We first investigate the minimum required repair-bandwidth in an asymptotic setup, in which the stored file is assumed to have an infinite size. The result shows that the asymptotic repair-bandwidth over packet erasure channels with a fixed erasure probability has a closed-form relation to the repair-bandwidth in lossless networks. Next, we show the benefits of DR storage nodes in reducing the repair bandwidth, and then we derive the necessary minimal storage space of DR storage nodes. Finally, we study the repair in a nonasymptotic setup, where the stored file size is finite. We study the minimum practical-repair-bandwidth, i.e., the repair-bandwidth for achieving a given probability of successful repair. A combinatorial optimization problem is formulated to provide the optimal practical-repair-bandwidth for a given packet erasure probability. We show the gain of our proposed approaches in reducing the repair-bandwidth.

The energy efficiency (EE) of a multi-user multi-relay system with the maximum diversity network coding (MDNC) is studied. We explicitly find the connection among the outage probability, energy consumption, and EE, and formulate the maximizing EE problem under the outage probability constraint. Relay scheduling (RS) and power allocation (PA) are applied to schedule the relay states (transmitting, sleeping, and so on) and optimize the transmitting power under the practical channel and power consumption models. Since the optimization problem is NP hard, to reduce computational complexity, the outage probability is first tightly approximated to a log-convex form. Furthermore, the EE is converted into a subtractive form based on the fractional programming. Then, a convex mixed-integer nonlinear problem is eventually obtained. With a generalized outer approximation algorithm, RS and PA are solved in an iterative manner. The Pareto-optimal curves between the EE and the target outage probability show the EE gains from PA and RS. Moreover, by comparing with the no network coding (NoNC) scenario, we conclude that with the same number of relays, MDNC can lead to EE gains. However, if RS is implemented, NoNC can outperform MDNC in terms of the EE when more relays are needed in the MDNC scheme.

We propose a novel two-timeslot two-way full-duplex (FD) relaying scheme, in which the access link and the backhaul link are divided in the time domain, and we study the average end-to-end rate and the outage performance. According to the user equipment capability and services, we investigate two scenarios: three-node I- and four-node Y-relaying channels. Among various relaying protocols, the well-known amplify-and-forward and decode-and-forward are considered. Closed-form expressions for the average end-to-end rate and the outage probability, under the effect of residual self-interference and inter-user interference, are presented. The results show that the proposed two-timeslot two-way FD relaying scheme can achieve higher rate and better outage performance than the half-duplex one, when residual self-interference is below a certain level. Therefore, this relaying scheme presents a reasonable tradeoff between performance and complexity, and so, it could be efficiently used in the fifth-generation wireless networks.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper (respectively, lower) bounding models, which can be used to calculate the outer (respectively, inner) bounds for the capacity region of the original noisy network. Based on the network equivalence framework, this paper presents scalable upper and lower bounding models for wireless networks with potentially many nodes. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels and broadcast channels. The upper bounding model, consisting of only point-to-point bit pipes, is constructed by first extending the one-shot upper bounding models developed by Calmon et al. and then integrating them with network equivalence tools. The lower bounding model, consisting of both point-to-point and point-to-points bit pipes, is constructed based on a two-step update of the lower bounding models to incorporate the broadcast nature of wireless transmission. The main advantages of the proposed methods are their simplicity and the fact that they can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper and lower bounds can approach the capacity in some setups.

In this paper, we systematically study the average rate and outage probability tradeoffs of full-duplex two-way and one-way relaying under residual self-interference. Among various relaying protocols, two common of them are considered: amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, we consider the application of physical-layer network coding (PNC) and analog network coding (ANC) to full-duplex two-way relaying. Novel closed-form expressions for the average rate and outage probability, are presented. The results show that full-duplex two-way relaying can achieve higher rate than one-way relaying in the medium to high signal-to-noise ratio (SNR) region, at the cost of a certain loss in the outage performance. Moreover, DF protocol can achieve better outage performance than the AF one, but it suffers from a certain loss in the rate in the high SNR region. It is also shown that PNC can further improve the rate and outage performance. In addition, the results clearly reveal the effects of time multiplexing, forward protocol, and network coding on relaying systems, which would shed light on designing practical full-duplex relaying schemes.

The recent development of low cost wireless sensors enables water monitoring through dense wireless sensor networks (WSN). Sensor nodes are battery powered devices, and hence their limited energy resources have to be optimally managed. The latest advancements in compressive sensing (CS) provide ample promise to increase WSNs lifetime by limiting the amount of measurements that have to be collected. Additional energy savings can be achieved through CS-based scheduling schemes that activate only a limited number of sensors to sense and transmit their measurements, whereas the rest are turned off. The ultimate objective is to maximize network lifetime without sacrificing network connectivity and monitoring performance. This problem can be approximated by an energy balancing approach that consists of multiple simpler subproblems, each of which corresponds to a specific time period. Then, the sensors that should be activated within a given period can be optimally derived through dynamic programming. The complexity of the proposed CS-based scheduling scheme is characterized and numerical evaluation reveals that it achieves comparable monitoring performance by activating only a fraction of the sensors.

The problem of transmitting confidential messages in the M × K wireless X network is considered, in which each transmitter intends to send one confidential message to every receiver. In particular, the secrecy degrees of freedom (SDOF) of the considered network are studied by an artificial noise alignment (ANA) approach, which integrates interference alignment and artificial noise transmission. At first, an SDOF upper bound is derived for the M × K X network with confidential messages (XNCM) K(M-1)/K+M-2 to be equation. By proposing an ANA approach, it is shown that the SDOF upper bound is tight when either K = 2 or M = 2 for the considered XNCM with time/frequency varying channels. For K, M ≥ 3, it is shown that an SDOF of K(M-1)/K+M1 equation can be achieved, even when an external eavesdropper appears. The key idea of the proposed scheme is to inject artificial noise into the network, which can be aligned in the interference space at receivers for confidentiality. The proposed method provides a linear approach for secure interference alignment.

The secrecy degrees of freedom (SDOF) of the multiple-input single-output (MISO) broadcast channel with confidential messages (BCC) is studied. The network consists of a two-antenna transmitter and two single-antenna receivers, each demanding a confidential message from the transmitter. The problem is investigated with mixed channel state information at transmitter (CSIT), which is a combination of perfect delayed CSIT and inaccurate current CSIT. When the variance of the estimation error for the current CSIT scales with O(P-α), with α ∈ [0, 1], it is shown that the optimal sum SDOF of the considered BCC is 1+α. Furthermore, the optimal SDOF region of the considered MISO BCC is shown to be a polygon scaling with α. The proposed scheme is based on an artificial noise alignment that can combine the benefits of both types of delayed and current CSIT. These results can be seen as an extension of results of Yang et al. and Gou-Jafar to multiuser networks with secrecy constraints.

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.

We investigate the erasure floor performance of distributed Luby transform (DLT) codes for transmission within a multi-source, single-relay, and single-destination erasure-link network. In general, Luby transform (LT) codes exhibit a high erasure floor due to poor minimum-distance properties, which can be improved by maximizing the minimum variable-node degree. The same behavior is observed for DLT codes, and therefore a new combining scheme at the relay is proposed to maximize the minimum variable-node degree in the decoding graph. Furthermore, the encoding process at the sources and the combining scheme at the relay are coordinated to improve the transmission overhead. To characterize the asymptotic performance of the proposed DLT codes, we derive closed-form density-evolution expressions, considering both lossless and lossy source-relay channels, respectively. To support the asymptotic analysis, we evaluate the performance of the proposed DLT codes by numerical examples and demonstrate that the numerical results correspond closely to the analysis. Significant improvements in both the erasure floor and transmission overhead are obtained for the proposed DLT codes, as compared to conventional DLT codes.

The problem of transmitting confidential messages in M x K wireless X networks is considered in which each transmitter intends to send one confidential message to every receiver. In particular, the secure degrees of freedom (SDOF) of the considered network are studied based on an artificial noise alignment (ANA) approach, which integrates interference alignment and artificial noise transmission. At first, an SDOF upper bound is derived for the M x K X network with confidential messages (XNCM) to be K(M-1)/K+M-2. By proposing an ANA approach, it is shown that the SDOF upper bound is tight when K = 2 for the considered XNCM with time-/frequency-varying channels. For K ≥ 3, it is shown that SDOF of K(M-1)/K+M-1 can be achieved, even when an external eavesdropper is present. The key idea of the proposed scheme is to inject artificial noise into the network, which can be aligned in the interference space at receivers for confidentiality. Moreover, for the network with no channel state information at transmitters, a blind ANA scheme is proposed to achieve SDOF of K(M-1)/K+M-1 for K, M ≥ 2, with reconfigurable antennas at receivers. The proposed method provides a linear approach to secrecy coding and interference alignment.

Indoor communications in the 60 GHz band is capable of supporting multi-gigabit wireless access thanks to the abundant spectrum and the possibility of using dense antenna arrays. However, the high directivity and penetration loss make it vulnerable to blockage events, which can be frequent in indoor environments. Given network topology information in sufficient precision, we investigate the average throughput and outage probability when the connection between any two nodes can be established either via the line-of-sight (LOS) link, through a reflection link, or by a half-duplex relay node. We model the reflection link as an LOS with extra power loss and derive the closed-form expression for the relative reflection loss. For networks with a central coordinator and multiple relays, we also propose a generic algorithm, maximum throughput path selection (MTPS), to select the optimal path that maximizes the throughput. The complexity of the MTPS algorithm is O(n2) for networks equipped with n relays, whereas a brute-forced algorithm has complexity of O(n · n!). Numerical results show that increasing the number of relays can significantly increase the average throughput and decrease the outage probability, and resorting to reflection paths provides significant gains when the probability of link blockage is high.

In this paper, we investigate the performance of a wireless relay network with multiple transmission sessions, in which multiple groups of source nodes communicate with their respective destination nodes via a shared wireless relay network. A multiple transmission session model with network code division multiplexing (NCDM) scheme is proposed to remove the inter-session interference at each destination. The fundamental idea of the NCDM scheme takes advantage of the property of G Θ HT= 0 of the low-density generator matrix (LDGM) codes. Based on the analysis of the NCDM scheme, we investigate the relationship among the equivalent received signal vector, the number of sessions and the column weight of the generator matrix. New code design criteria for the construction of the generator matrix is proposed. We further evaluate the multiple transmission session model with the proposed NCDM scheme in terms of throughput and complexity. Our evaluation demonstrates that the proposed scheme not only has a linear computational complexity, but also shows a similar error performance in the AWGN case and a considerable throughput improvement compared with its counterpart, which is referred to as a serial session scheme, where groups of source nodes communicate with their respective destinations in a time division manner.

The problem of exact repair of a failed node in multi-hop networked distributed storage systems is considered. Contrary to the most of the current studies which model the repair process by the direct links from surviving nodes to the new node, the repair is modeled by considering the multi-hop network structure, and taking into account that there might not exist direct links from all the surviving nodes to the new node. In the repair problem of these systems, surviving nodes may cooperate to transmit the repair traffic to the new node. In this setting, we define the total number of packets transmitted between nodes as repair-cost. A lower bound of the repair-cost can thus be found by cut-set bound analysis. In this paper, we show that the lower bound of the repair-cost is achievable for the exact repair of MDS codes in tandem and grid networks, thus resulting in the minimum-cost exact MDS codes. Further, two suboptimal (achievable) bounds for the large scale grid networks are proposed.

In this paper, we propose a threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying uplink system with network coding. In the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We first derive the bit error rate (BER) expression at the destination, based on which, we optimize the threshold to minimize the BER. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error performance.

The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper/lower bounding models for the original noisy network. This paper presents scalable upper bounding models for wireless networks, by firstly extending the “one-shot” bounding models developed by Calmon et al. and then integrating them with network equivalence tools. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels (MACs) and broadcast channels (BCs). The main advantages of the proposed method is its simplicity and the fact that it can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper bounds can approach the capacity in some setups.

Luby transform (LT) codes are a class of rateless codes that automatically adapt their rate to the quality of the communication channel. In the original LT codes, fixed check-node degree distributions are used to combine variable nodes uniformly at random to extend the code graph and produce code bits. Here we propose a different approach: we design a sequence of rate-compatible degree distributions, and develop an algorithm that produces code bits in a manner such that the resulting degree distributions follow the designed sequence. Using this new design framework, we develop low-complexity LT codes suitable for time-varying noisy channels. Performance and complexity of the proposed LT codes are measured in terms of bit error rate and average number of edges per information and coded bit, respectively. Numerical examples illustrate the resulting trade-off between performance and complexity of the designed LT codes.

We consider a scenario consisting of a primary and a secondary system, each represented by a pair of a transmitter and a receiver. The secondary transmitter assists in the retransmission of the primary message, which prevents the primary performance from being degraded by allowing the secondary system to access the transmission resources. Two network coding schemes applied in retransmission phase are investigated, the stationary network coding (SNC) scheme and the adaptive network coding (ANC) scheme. For each scheme we derive analytical results on packet throughput and infer that the ANC scheme outperforms the SNC scheme. We then provide a numerical performance comparison and a numerical optimization of the secondary packet throughput. Our main result shows cooperation can provide a significant performance improvement through effective network coding.

This paper investigates the performance of a two-way decode-and-forward (DF) multi-antenna relay network. A joint antenna selection scheme for all nodes is first proposed based on the maximizing the worse received signal to noise ratio (SNR) of two end users. Then, we derive the probability density function (PDF) and cumulative distribution function (CDF) of the received SNRs of both users. We also achieve the closed-form expressions of average bit error rate (BER) and outage probability of the relay system. Furthermore, we reveal the asymptotic behavior of our system when transmitting SNR or the number of antennas is large. Our analysis shows that the proposed DF antenna selection scheme achieves full diversity. The numerical results finally verify the accuracy of our analysis.

We focus on the design of distributed Luby transform (DLT) codes for erasure networks with multiple sources and multiple relays, communicating to a single destination. The erasure floor performance of DLT codes improves with the maximum degree of the relay-degree distribution. However, for conventional DLT codes, the maximum degree is upper bounded by the number of sources. An additional constraint is that the sources are required to have the same information block length. We introduce a D-bit buffer for each source-relay link, which allows the relay to select multiple encoded bits from the same source for the relay-encoding process; thus, the number of sources no longer limits the maximum degree at the relay. Furthermore, the introduction of buffers facilitates the use of different information block sizes across sources. Based on density evolution, we develop an asymptotic analytical framework for optimization of the relay-degree distribution. We further integrate techniques for unequal erasure protection into the optimization framework. The proposed codes are considered for both lossless and lossy source-relay links. Numerical examples show that there is no loss in erasure rate performance for transmission over lossy source-relay links, as compared with lossless links. Additional delays, however, may occur. The design framework and our contributions are demonstrated by a number of illustrative examples, showing the improvements obtained by the proposed buffer-based DLT codes.

In this paper, we propose a threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying system with network coding, where two sources communicate with the destination with the help of a relay. Specifically in the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We are interested in optimizing the TOB-SF protocol in fading channels, and consider both the uncoded and low-density parity check coded systems. In the uncoded system, we first derive the bit error rate (BER) expressions at the destination, based on which, we derive the optimal threshold. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. In the coded system, we first optimize the LLR threshold. Then we develop a methodology to track the BER evolution at the destination by using Gaussian approximations. Based on the BER evolution, we further optimize the power allocation at the relay which minimizes the system BER. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error performance.

A downlink two-hop MISO broadcast network is considered, with a two-antenna source communicating to 2 single-antenna destinations, via multiple single-antenna relays in between. The sum degrees of freedom (DOF) of the network with mixed channel state information at the transmitter (CSIT) is investigated. The mixed CSIT consists of accurate delayed CSIT and inaccurate instantaneous CSIT, and its availability is limited within each hop, i.e., the source is oblivious to the channels of the second hop. Given a transmission power P and a real value α ∈ [0, 1], if the variance of the error for instantaneous CSIT decreases as O(P-α), it is shown that the sum optimal DOF of the considered network is d = 4+2α/3 when there exist at least 3 intermediate relays. The result can be extended to the MIMO and multiple-hop cases. The proposed achievable schemes essentially combine the concept of retrospective interference alignment based on delayed CSIT and linear beamforming based on inaccurate instantaneous CSIT into an integrated form. Our results show that, in multi-hop MISO broadcast networks, delayed CSIT and inaccurate instantaneous CSIT can be exploited simultaneously to benefit network DOF.

We consider a downlink two-hop MISO broadcast network with a 2-antenna source communicating to 2 single-antenna destinations, via 2 single-antenna relays. We investigate spectrally efficient transmission and the associated achievable sum degrees of freedom (DoF) with mixed channel state information at the transmitter (CSIT), which consists of perfect delayed CSIT and imperfect instantaneous CSIT. When the variance of the estimation error of the instantaneous CSIT lies on level of O(P-α) for the transmission power P and some α ∈ [0, 1], we show that the sum DoF 4-2α/3-2α ∈ [4/3, 2] can be achieved by a novel interference alignment (IA) scheme. The result shows that rather than exploiting only delayed or imperfect instantaneous CSIT, the transmission design taking advantages of both can achieve higher sum DoF.

We study the repair problem of distributed storage systems in erasure networks where the packets transmitted from surviving nodes to the new node might be lost. The fundamental storage-bandwidth tradeoff is calculated by multicasting analysis in erasure networks. The optimal tradeoff bound can be asymptotically achieved when the number of transmission (packets) goes to infinity. For a limited number of transmission, we study the probability of successful regenerating. Then, we investigate two approaches of increasing the probability of successful regenerating, namely, by connecting more surviving nodes or by increasing the storage space of nodes. Using more nodes may pose larger delay and in certain situation it might not be possible to connect to more nodes too. We show that in addition to reducing repair bandwidth, increasing storage space can also increase reliability for repair.

There have been emerging lots of applications for distributed storage systems e.g., those in wireless sensor networks or cloud storage. Since storage nodes in wireless sensor networks have limited battery, it is valuable to find a repair scheme with optimal transmission costs (e.g., energy). The optimal-cost repair has been recently investigated in a centralized way. However a centralized control mechanism may not be available or is very expensive. For the scenarios, it is interesting to study optimal-cost repair in a decentralized setup. We formulate the optimal-cost repair as convex optimization problems for the network with convex transmission costs. Then we use primal and dual decomposition approaches to decouple the problem into subproblems to be solved locally. Thus, each surviving node, collaborating with other nodes, can minimize its transmission cost such that the global cost is minimized. We further study the optimality and convergence of the algorithms. Finally, we discuss the code construction and determine the field size for finding feasible network codes in our approaches.

Motivated by the framework of network equivalence theory [1], [2], we present capacity lower bounding models for wireless networks by construction of noiseless networks which can be used to calculate an inner bound for the corresponding wireless network. We first extend the “one-shot” lower bounding model [6] to many-user scenarios, and then propose a two-step update of the one-shot models to incorporate the broadcast nature of wireless transmission. The main advantage of the proposed lower bounding method is its simplicity and the fact that it can be easily extended to larger networks. We demonstrate by examples that the resulting lower bounds can even approach the capacity in some setups.

We propose an adaptive decoding scheme for Luby Transform (LT) codes over noisy channels which exhibits lower complexity as compared to the conventional LT decoder. The corresponding modified degree distributions have been derived for the low-complexity LT decoder. The complexity and performance comparison demonstrate that the decoding complexity can be reduced with negligible degradation in bit error rate performance.

We consider a downlink two-hop MISO broadcast network with a 2-antenna source communicating to 2 single-antenna destinations, assisted by 2 single-antenna intermediate relays. We investigate spectrally efficient transmission schemes and their achieved sum degrees of freedom (DoF), with quantized delayed channel state information (CSI) feedback. Assuming Grassmannian vector quantization, we study two feedback scenarios according to the feedback range limit, namely global-range feedback, i.e., the source can receive the feedback signals from both the relays and the destinations, and one-hop-range feedback, i.e., each node can only attain the feedback information of its upcoming hop. We establish a sum DoF lower bound for each case. Our results reveal that when the quantization rate at relays BR= α1log2(SNR) and at destinations BD= α2log2(SNR) for min{α1, α2} ≥ 1, the optimal sum DoF 4 over 3 can be achieved with finite-rate delayed feedback.

We investigate capacity bounds for a wireless multicast relay network where two sources simultaneously multicast to two destinations with the help of a full-duplex relay node. The two sources and the relay use the same channel resources (i.e. co-channel transmission). We assume Gaussian channels with time-invariant channel gains which are known by all nodes. The two source nodes are connected by orthogonal limited-rate error-free conferencing links. By extending the proof of the converse for the Gaussian relay channel and introducing two lemmas on conditional (co-)variance, we present two genie-aided outer bounds of the capacity region for this multicast relay network. We extend noisy network coding to use source cooperation with the help of the theory of network equivalence. We also propose a new coding scheme, partial-decode-and-forward based linear network coding, which is essentially a hybrid scheme utilizing rate-splitting and messages conferencing at the source nodes, partial decoding and linear network coding at the relay, and joint decoding at each destination. A low-complexity alternative scheme, analog network coding based on amplify-and-forward relaying, is also investigated and shown to benefit greatly from the help of the conferencing links and can even outperform noisy network coding when the coherent combining gain is dominant.

We investigate the achievable degrees of freedom (DoF) in K-user X networks (K×K X networks) with delayed channel state information at transmitters (CSIT), where partial cooperation (i.e. message sharing) is potentially allowed among transmitters. We consider two possible cooperation scenarios. In the first scenario one of the transmitters serves as a super node which can obtain the messages of the other transmitters. By proper interference alignment (IA) design, we prove that a DoF 2K/K+1 can be achieved almost surely. In the second scenario, there is no super node but each transmitter shares its message to its left-side neighbor. We show that when K = 3, DoF 7/5 is achievable. In both cases, the achieved DoF are shown to be improved compared with non-cooperative X networks. Moreover, we use a simple example to show that sharing a subset of messages may also improve DoF.

Noisy network coding (NNC) has been shown to outperform standard compress-and-forward (CF) in networks with multiple relays and/or multiple destinations. Recently, short-message noisy network coding (SNNC) has been proved to achieve the same rate region as NNC for independent sources but with significantly reduced encoding delay and decoding complexity. In this paper, we show that when partial cooperation between source nodes is possible, by performing rate-splitting, message exchange, and superposition coding with proper power allocation at the source nodes, SNNC can achieve a strictly larger rate region than NNC. The gain comes from coherent combining at all the receiving nodes.

We investigate the design and performance of spatially-coupled rateless codes. A modified encoding process is introduced for spatially-coupled Luby Transform (SCLT) codes which leads to an almost regular variable-node degree distribution at the encoding graph. The proposed SCLT codes outperform its counterparts significantly over binary erasure channels, particularly in the erasure floor region. To further improve the erasure floor performance, the approach of spatial coupling is then extended to Raptor codes by concatenating a high-rate pre-coder to the SCLT codes. It is shown that the spatial coupling improves the convergence threshold of Raptor codes. Different ensembles of spatially-coupled Raptor codes are constructed depending on whether pre-coders and/or LT codes are spatially-coupled. The performance of different ensembles of spatially-coupled Raptor codes is then evaluated and compared based on density evolution, leading to an improved spatially-coupled Raptor code in terms of convergence threshold and lower complexity.

Cooperative communication is shown to be an efficient method of combating fading in wireless networks. By ”sharing” their antennas cooperating single-antenna nodes create a virtual multi-antenna system and therefore, benefit from spatial diversity. Network coding being a particular cooperative communication technique provides substantial gains in data rate, especially in networks with many sink nodes. By allowing intermediate nodes of a network to mix the incoming data streams, one can achieve the multicast capacity. Recently, Xiao and Skoglund showed that binary network coding (BNC) is not optimal for multi-user multi-relay networks in terms of diversity and proposed diversity network coding (DNC) scheme that outperforms BNC approach. Following this approach we examine potential diversity gains of using the DNC scheme for multi-source line networks. We show that the DNC technique outperforms both conventional time-orthogonal transmission and the BNC scheme. Further, the problem of optimal scheduling of the transmission is explored. We formulate the optimization problem and propose efficient solutions. Numerical results are presented to support theoretical findings.

We study the error and diversity-multiplexing tradeoff (DMT) performance of a (secondary) multi-user relay network, where a class of finite field network codes are applied in the relays to efficiently provide spatial diversity. To eliminate spectral efficiency loss induced by half-duplex limitation we adopt the cognitive radio overlay spectrum sharing concept and consider aligning the relays' operation with that of a primary system. To compensate the interference introduced by the secondary relaying, the secondary destination also transmits the primary signals to boost the signal power of the primary system. We also consider exploiting Automatic Repeat Request (ARQ) feedback signals from the secondary destination to minimize the energy consumption of the secondary system. In addition, by allowing multiple secondary sources to transmit non-orthogonally, the performance can be further enhanced.

We investigate the design of network codes for multiple-user multiple-relay (MUMR) wireless networks with slow fading (quasi-static) channels. In these networks, M users have independent information to be transmitted to a common base station (BS) with the help of N relays, where M ≥ 2 and N ≥ 1 are arbitrary integers. We investigate such networks in terms of diversity order to measure asymptotic performance. For networks with orthogonal channels, we show that network codes based on maximum distance separable (MDS) codes can achieve the maximum diversity order of N+1. We further show that the MDS coding construction of network codes is also necessary to obtain full diversity for linear finite field network coding (FFNC). Then, we compare the performance of the FFNC approach with superposition coding (SC) at the relays. The results show that the FFNC based on MDS codes has better performance than SC in both the high rate and the high SNR regime. Further, we discuss networks without direct source-to-BS channels for N ≥ M. We show that the proposed FFNC can obtain the diversity order N-M+1, which is equivalent to achieving the Singleton bound for network error-correction codes. Finally, we study the network with nonorthogonal channels and show our codes can still achieve a diversity order of N+1, which cannot be achieved by a scheme based on SC.

We investigate the error floor performance of Luby Transform (LT) codes over the additive white Gaussian noise channel. We first derive a lower bound on the bit error rate for an LT code, which we subsequently use to show that the corresponding error floor is predominantly caused by low-degree variable nodes. Based on this observation, we propose a modified encoding scheme for LT codes that provides a lower error floor with no increase in encoding and decoding complexities. The convergence behavior of the proposed scheme is analyzed using extrinsic information transfer charts, and shown to be similar to the original LT code. Numerical examples demonstrate the improvements of the modified LT code as a stand-alone code and as a component code of a Raptor code.

We investigate the capacity bounds for a wireless multicast relay network where two sources simultaneously multicast to two destinations through Gaussian channels with the help of a full-duplex relay node. All the individual channel gains are assumed to be time-invariant and known to every nodes in the network. The transmissions from two sources and from the relay use the same channel resource (i.e. co-channel transmission) and the two source nodes are connected with an orthogonal error-free backhaul. This multicast relay network is generic in the sense that it can be extended to more general networks by tuning the channel gains within the range [0, ∞). By extending the proof of the converse developed by Cover and El Gamal for the Gaussian relay channel, we characterize the cut-set bound for this multicast relay network. We also present a lower bound by using decoding-and-forward relaying combined with network beam-forming.

We study the design of network codes for M-source, N-relay wireless networks over slow fading channels. Specifically, vector-wise binary field network coding (BFNC) schemes are proposed. In the construction of our BFNC schemes, we utilize a diversity achieving criterion which can be expressed in terms of the linear independence of quasi-cyclic matrices. Our codes can be implemented with low-complexity encoders at the relays as only binary operations are used. Meanwhile at the destination, for small code lengths, ML decoder can be applied. For large code lengths, we propose a modified BP decoder with low decoding complexity. From analysis and simulations, we show that our proposed BFNC schemes can achieve full diversity for the ML decoder, as well as full diversity for the modified BP decoder we propose for large block lengths. Our simulations also show that our proposed BFNC schemes achieve a higher coding gain relative to previous network coding schemes.

We study the impact of multiple access protocols on the diversity-multiplexing tradeoff (DMT) performance in wireless multi-user relay networks. In the networks K half-duplex decode-and-forward (DF) relays employ a class of finite field network codes to assist in the communication between M independent sources and a common destination. The sources and relays are divided into individual clusters. The nodes within one cluster transmit non-orthogonally while the transmissions of different clusters span orthogonal channels. We provide the method to calculate the achievable DMT for each clustering strategy. The network DMT performance can thus be optimized by properly clustering the sources and the relays.

We consider a base-station broadcasting a set of order-insensitive packets to a user population over packet-erasure channels. To improve efficiency we propose a relay-aided transmission scheme using instantaneously-decodable binary network coding. Our proposed scheme ensures that a coded packet can be immediately decoded at the user side without delay. Moreover, only binary operations are required in the encoding and decoding processes, which decrease the computational complex. We further analyze the performance of the resulting broadcast scheme, and show that significant improvements in transmission efficiency are obtained as compared to previously proposed ARQ and network-coding- based schemes.

In distributed storage systems reliability is achieved through redundant storage nodes distributed in the network. Then a data collector can recover source information even if some nodes fail. To maintain reliability, an autonomous and efficient protocol should be used to reconstruct the failed node. The repair process causes traffic in the network. Recent results in e.g., [1], [2] found the optimal traffic-storage tradeoff, and proposed regenerating codes to achieve the optimality. We investigate the link costs and the impact of network topologies during the repair process. We formulate the minimum cost repair problem in joint and decoupled methods. We investigate the required field size for the joint method. For the decoupled method, we show that the optimization problem is linear for the linear cost. We further show that the cooperation of surviving nodes could efficiently exploit the network topology and reduce the repair cost. The numerical results in tandem, star and grid networks show the benefits of our methods in term of the repair cost.

Optimal power allocation in a multi-hop cognitive radio network is investigated. Information transmitted from the source passes through several wireless relay nodes before reaching the destination. At each hop, the received signal is decoded, re-encoded and retransmitted to the following node. Transmissions at every hop are overheard by nearby nodes and therefore cause interference. We study optimal power allocation strategies that maximize the end-to-end throughput of the network under the constraint of strictly limited interference to external users. We show that for networks that can be modeled as a line topology the optimal solution is achieved when the capacities of every intermediate link are equal and the interference power constraint is satisfied with equality. High- and low-SNR approximations that simplify the problem of finding the optimal power allocation are presented as well. The numerical results show good performance compared to schemes with equal power allocation.

We investigate efficient scheduling algorithms for a relay-aided broadcasting system using random network codes, where our objective is to maximize the transmission efficiency. The broadcast from a base-station (BS) is divided into an information phase and a redundancy phase, where the half-duplex relay assists in the redundancy phase. Time-division transmission is used over packet-erasure channels, where the erasure probabilities of the BS-to-relay and relay-to-user links are lower than the BS-to-user links. Following the information phase, each user provides feedback on the status of received packets to the BS and the relay, which in turn both generate redundancy packets for the redundancy phase. To improve efficiency, we formulate a scheduling problem for the transmissions of redundancy packets from the BS and the relay. We consider two scenarios; namely instantaneous feedback after each redundancy packet, and feedback after multiple redundancy packets. In the first case the schedule is determined using a greedy algorithm, while in the second case the schedule is determined using dynamic programming. To determine the performance with instantaneous feedback, we develop an analytic approach based on a Markov chain. Numerical results show that the transmission efficiency of the dynamic programming algorithm is close to the performance of the greedy algorithm, but requires significantly less feedback.

We consider serial concatenation of a Luby Transform (LT) code with a differential quadrature phase-shift-keying (DQPSK) modulator for transmission over an additive white Gaussian noise (AWGN) Channel. Assuming a target average rate for the operation of the rateless LT DQPSK scheme, the degree distribution of the LT code is optimized in terms of convergence threshold using extrinsic information transfer (EXIT) charts. From the EXIT chart analysis, we show that the proposed LT DQPSK scheme has a similar convergence performance, but lower complexity, as compared to a Raptor code with differential modulation, and a LDPC code optimized for DQPSK. The EXIT chart analysis framework is also applied to evaluate the throughput performance for the three schemes in terms of the average code rate as a function of the signal-to-noise ratio. The comparison demonstrates that the proposed structure is well-suited for adaptive-rate transmission over a wide range of rates.

In this article, we consider practical approaches to Costa precoding (also known as dirty paper coding). Specifically, we propose a symbol-by-symbol scheme for cancellation of interference known at the transmitter in a relay-aided downlink channel. For finite-alphabet signaling and interference, we derive the optimal (in terms of maximum mutual information) modulator under a given power constraint. A sub-optimal modulator is also proposed by formulating an optimization problem that maximizes the minimum distance of the signal constellation, and this non-convex optimization problem is approximately solved by semi-definite relaxation. For the case of binary signaling with binary interference, we obtain a closed-form solution for the sub-optimal modulator, which only suffers little performance degradation compared to the optimal modulator in the region of interest. For more general signal constellations and more general interference distributions, we propose an optimized Tomlinson-Harashima precoder (THP), which uniformly outperforms conventional THP with heuristic parameters. Bit-level simulation shows that the optimal and sub-optimal modulators can achieve significant gains over the THP benchmark as well as over non-Costa reference schemes, especially when the power of the interference is larger than the power of the noise.

We investigate cooperative network coding strategies for relay-aided two-source two-destination wireless networks with a backhaul connection between the source nodes. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding schemes, namely, finite field and linear network coding, and lattice coding. To further exploit the backhaul connection, we also propose network coding based beamforming. We measure the performance in term of achievable rates over Gaussian channels, and observe significant gains over benchmark schemes. We derive the achievable rate regions for these schemes and find the cut-set bound for our system. We also show that the cut-set bound can be achieved by network coding based beamforming when the signal-to-noise ratios lie in the sphere defined by the source-relay and relay-destination channel gains.

We study the impact of multiple access strategies on the diversity-multiplexing tradeoff (DMT) performance in wireless multi-user relay networks. The networks contain multiple independent sources, multiple half-duplex decode-and-forward (DF) relays, and one common destination. Instead of separately retransmitting each source message, the relays employ a class of spectrally efficient finite field network codes to assist the sources. It is shown that fully orthogonal or fully non-orthogonal transmission among sources/relays does not necessarily provide optimized DMT performance. We propose a novel transmission protocol that divides the sources and relays into individual clusters. The nodes within one cluster transmit non-orthogonally while the transmissions of different clusters span orthogonal channels. We provide the method to calculate the achievable DMT for each clustering strategy. The network DMT performance can thus be optimized by properly clustering the multiple sources and relays.

We study joint network and channel code design to optimize delay performance. Here the delay is the transmission time of information packets from a source to sinks without considering queuing effects. In our systems, network codes (network layer) are on top of channel codes (physical layer) which are disturbed by noise. Network codes run in a rateless random method, and thus have erasure-correction capability. For the constraint of finite transmission time, transmission errors are inevitable in the physical layer. A detection error in the physical layer means an erasure of network codewords. For the analysis, we model the delay of each information generation in the network layer as independent, identically distributed random variables. The calculation approaches for delay measures are investigated for coded erasure networks. We show how to evaluate the rate and erasure probability of a set of channels belonging to one cut. We also show that the min-cut determines the decoding error probability in the sinks if the number of information packets is large. We observe that for a given amount of source information, larger packet length leads to fewer packets to be transmitted but higher physical-layer detection error probabilities. Further, longer transmission time (delay) in the physical-layer causes smaller detection error probability at the physical layer. Thus, both parameters have opposite impacts on the physical and network layer, considering delay. We should find the optimal values of them in a cross-layer approach. We then formulate the problems of optimizing delay performance, and discuss solutions for them.

We investigate LDPC code design for a multi-source single-relay system, with uniform phase-fading Gaussian channels. We specifically consider the asymmetric channels for multiple sources, where the channel condition for each source in the system is different. We focus on LDPC code design when network coding (NC) at the relay is utilized. For the asymmetric sources, we firstly introduce a binary field rate splitting theorem which is used to discover an appropriate NC scheme at the relay. This NC scheme is then used to determine the achievable rates of each source and the whole system. These steps assist us in the development of the main contribution of our work, namely, network coded multi-edge type LDPC (NCMET-LDPC) code design. Extrinsic mutual information transfer (EXIT) chart analysis is utilized to optimize the code profiles. Our results demonstrate two key points. (1) From the whole system point of view, our NCMET-LDPC codes achieve better error performance than that of LDPC codes designed for the system without NC. (2) As a consequence of the binary field rate-splitting theorem, our NCMET-LDPC codes also guarantee better error performance of each asymmetric source. The improvement in error performance is typically about 0.3 dB relative to a system without NC.

We investigate the design and use of systematic binary deterministic rateless (BDR) codes for information transmission over block-erasure broadcast channels. BDR codes are designed to obtain a level of maximal distance separable (MDS) properties, making these codes ideal for the considered broadcast scenario. For a certain number of encoded redundancy blocks, we derive an expression for the probability that the MDS properties are maintained. Moreover, if limited feedback is available, we extend the BDR coding protocol to further improve the system performance. Numerical results show that for a finite number of source blocks and as the number of users grows the proposed systematic BDR codes performs significantly better than LT codes. The proposed schemes with feedback have better performance than traditional ARQ schemes.

We study joint network-coding and channel-coding design to optimize delay performance. In our systems, network codes (network layer) are on top of channel codes (physical layer). Network codes run in a rateless random method, i.e., source and intermediate nodes randomly produce and transmit codewords until transmission succeeds. Thus, the rateless network codes have block-erasure-correction capability. The physical layer codewords are disturbed by channel noise. For the constraint of finite transmission time (finite block length), transmission errors are inevitable in the physical layer. We show that the physical-layer erasure probability is affected by both layers. Then, there is an interaction between network codes and channel codes, concerning the delay measure of the networks. We model the delay of each generation as the independent, identically distributed random variables. We show tradeoffs between the network layer and the physical layer on the length of network codewords, and on the transmission time of physical-layer codewords. To measure delay performance, we use expected delay and network-layer decoding error probability of a given delay, which are measures for networks without and with strict maximum-delay constraints, respectively. We show how to evaluate these measures for the coded networks with erasure channels. Then, we formulate problems to optimize the delay performance, and solutions are suggested.

We investigate a relay-aided multi-cell broadcasting system using random network codes, where the focus is on devising efficient scheduling algorithms between relay and base stations. Two scheduling algorithms are proposed based on different feedback strategies; namely, a one-step scheduling algorithm with instantaneous feedback for each redundancy packet; and a multi-step scheduling algorithm with feedback only after multiple redundancy packets. For the latter case, dynamic programming is applied to determine optimal scheduling. Numerical results show that the transmission efficiency of the multi-step algorithm approaches that of the one-step algorithm, but requires significantly less feedback. They both significantly outperform corresponding ARQ and random scheduling approaches.

We study the impact of efficient network coding and multiple access techniques in a two-source two-relay one-destination wireless network through the diversity-multiplexing tradeoff (DMT) analysis. We compare the standard relaying protocol without network coding and the protocols using either a binary network coding (BNC) or an appropriately designed non-binary finite-field network coding (FFNC) at the relays. It is shown that the use of the non-binary FFNC strictly outperforms the other two protocols in terms of DMT. In addition, we propose a new transmission strategy based on the non-binary FFNC design and the non-orthogonal multiple access technique to further improve the DMT performance. Our results highlight the advantages of applying appropriate network coding in multi-source multi-relay networks.

We investigate cooperative strategies for relay-aided multi-source multi-destination wireless networks with backhaul support. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding (NC) schemes, namely, finite field NC (FNC), linear NC (LNC), and lattice coding. To further exploit the backhaul connection, we also propose NC-based beam-forming (NBF). We measure the performance in term of achievable rates over Gaussian channels and observe significant gains over a benchmark scheme. The benefit of using backhaul is also clearly demonstrated in most of scenarios.

It has been shown in the literature that network coding can improve the transmission efficiency of wireless broadcasting as compared to traditional ARQ schemes. In this paper, we propose an improved network coding scheme that can asymptotically achieve the theoretical lower bound on transmission overhead for a sufficiently large number of information blocks. The proposed scheme makes use of an index allocation algorithm that distributes information blocks that have been erased during transmission into a minimum number of encoding sets, where each set represents the erased blocks to be jointly network encoded and retransmitted. Numerical results show that the proposed scheme enables higher transmission efficiencies than traditional ARQ, and previously proposed networks coding schemes for wireless broadcasting.

We propose a new scheme for cooperative wireless networking based on linear network codes. The network consists of multiple (M ≥ 2) users having independent information to be transmitted to a common basestation (BS), assuming block-fading channels with independent fading for different codewords. The users collaborate in relaying messages. Because of potential transmission errors in links, resulting in erasures, the network topology is dynamic. To efficiently exploit the diversity available by cooperation and time-varying fading, we propose the use of diversity network codes (DNCs) over finite fields. These codes are designed such that the BS is able to rebuild the user information from a minimum possible set of coded blocks conveyed through the dynamic network. We show the existence of deterministic DNCs. We also show that the resulting diversity order using the proposed DNCs is 2 M - 1, which is higher than schemes without network coding or with binary network coding. Numerical results from simulations also show substantial improvement by the proposed DNCs over the benchmark schemes. We also propose simplified versions of the DNCs, which have much lower design complexity and still achieve the diversity order 2 M - 1.

We investigate the design of network codes for multiple-user multiple-relay (MUMR) wireless networks. In the networks, multiple (M ges 2) users have independent information to be transmitted to a common base station (BS), with the help of N (N ges 2) relays. The networks consist of independent quasistatic fading channels. We investigate such networks in terms of outage probabilities (to measure asymptotic performance), and propose network codes with linearly independent global encoding kernels for all possible source-relay channel situations (outage or not) to achieve asymptotic optimality. We compare the performance of proposed finite-field network coding (FFNC) and superposition coding (real-domain network coding) in the relays. The results show that proposed FFNC has better performance than superposition coding in high rate regions.

We propose a new method of applying network coding for cooperative wireless networks. The network consists of multiple (M ges 2) users having independent information to be transmitted to a common base station (BS). These users form partners and relay information for each other. The transmission blocks are subject to block-fading with independent fading coefficients for each block. Designed non-binary network codes over finite fields are used on top of channel codes. Assuming perfect error detection, erroneous blocks out from channel decoders are discarded (erasure). Thus, relaying nodes may not have information messages of some partners (erasure in inter-user channels), and the BS may not decode some blocks correctly either. The network topology from the point of view of network coding is dynamic. To improve performance, we propose dynamic-network codes (deterministic codes for dynamic networks) for the cooperative networks. The codes are designed such that the BS can rebuild user information from a minimum possible set of coding blocks. In this sense, dynamic-network codes achieve the min-cut for cooperative networks with a dynamic topology. For block fading channels, the proposed scheme obtains high asymptotic performance. For two-user networks, we calculate the resulting outage probabilities. We also present simulations with specific channel codes. Numerical results show substantial improvement over previous schemes. Then, we generalize the results to multiple-user (M > 2) networks. We investigate the existence of deterministic dynamic-network codes for multiple-user networks, and show that the diversity order of the proposed scheme can achieve 2M - 1.

We investigate sink decoding approaches and performance analysis for a network with intermediate node encoding (coded network). The network consists of statistically independent noisy channels. The sink bit error probability (BEP) is the performance measure. First, we investigate soft-decision decoding without statistical information on the upstream channels (the channels not directly connected to the sink). Numerical results show that the decoder cannot significantly improve the performance from a hard-decision decoder. We develop union bounds for analysis. The bounds show the asymptotic (regarding SNR: signal-to-noise ratio) performance of the decoder. Using statistical information about the upstream channels, we can find the error patterns of final hop channels (channels directly connected to sinks).With the error patterns, maximum-likelihood (ML) decoding can be performed, and a significant improvement in the BEP is obtained. To evaluate the union bound for the ML decoder, we use an equivalent point procedure. It is reduced to the least-squares problem with a linear constraint in the medium-to-high SNR region. With deterministic knowledge of the errors in the upstream channels, a genie-aided decoder can further improve the performance. We give the union bound for the genie decoder, which is straightforward to evaluate. By analyzing these decoders, we find that knowledge about the upstream channels is essential for good sink decoding.

We investigate a systematic construction of binary deterministic rateless codes (BDRCs). The codes are for networks with erasure channels. With a maximum distance separable (MDS) property, non-systematic BDRCs were first proposed in [1] with encoding complexity O(K), and decoding complexity is O(K2). Here K is the length of information bits. To reduce complexity, we study systematic-BDRCs (SBDRCs). For SBDRCs, the source first transmits m - 1 uncoded blocks, where m is the number of source blocks. Then, the source produces and transmits coded blocks in a rateless way. These coded blocks are produced using only cyclic-shift and XOR (exclusive or). The SBDRCs can use a large number of information blocks (potentially infinite m). On receiving any m distinct blocks (uncoded or coded), a sink can rebuild the source. The SBDRCs have encoding complexity O(∈K), and decoding complexity O(∈2K2), where ∈ is the source-to-sink block erasure probability.

We investigate the calculation approach of the sink bit error probability (BEP) for a network with intermediate node encoding. The network consists of statistically independent noisy channels. The main contributions are, for binary network codes, an error marking algorithm is given to collect the error weight (the number of erroneous bits). Thus, we can calculate the exact sink BEP from the channel BEPs. Then we generalize the approach to nonbinary codes. The coding scheme works on the Galois field 2m, wheremis a positive integer. To reduce computational complexity, a subgraph decomposition approach is proposed. In general, it can significantly reduce computational complexity, and the numerical result is also exact. For approximate results, we discuss the approach of only considering error events in a single channel. The results well approximate the exact results in low BEP regions with much lower complexity.

We investigate sink decoding methods and performance analysis approaches for a network with intermediate node encoding (coded network). The network consists of statistically independent noisy channels. The sink bit error probability (BEP) is the performance measure. We first discuss soft-decision decoding without statistical information on the upstream channels (the channels not directly connected to the sink). The example shows that the decoder cannot significantly improve the BEP from the hard-decision decoder. We develop the union bound to analyze the decoding approach. The bound can show the asymptotic (regarding SNR: signal-to-noise ratio) performance. Using statistical information of the upstream channels, we then show the method of maximum-likelihood (ML) decoding. With the decoder, a significant improvement in the BEP is obtained. To evaluate the union bound for the ML decoder, we use an equivalent signal point procedure. It can be reduced to a least-squares problem with linear constraints for medium-to-high SNR.

We investigate a deterministic binary coding approach for combination networks. In the literature, network coding schemes with large alphabet sizes achieve the min-cut capacity. Here, we propose an approach using binary (GF(2)) sequences instead of going to a large alphabet size. In the encoding process, only cyclic-shifting and XOR operations are used. The encoding complexity is linear with the length of information bits. The transfer matrix is sparse, and the decoder can perfectly decode source information by a sparse- matrix processing approach. Our approach does not use any redundant bits, and achieves the min-cut capacity. Further, the code blocks can be produced in a rateless way. The sink can decode source information from any subset of code blocks, if the number of received distinct blocks is the same as that of the information blocks. Thus, we use the code for general networks with erasure channels. The proposed binary rateless codes have quite small overheads and can work with a small number of blocks. With high probability, the codes behave as maximum distance separable (MDS) codes.

We investigate the analysis and design of low density generator matrix (LDGM) codes for continuous phase modulation (CPM). The system uses LDGM codes as an outer code for CPM. For additive white Gaussian noise channels, we derive the union bound to analyze the error floor performance. Design principles for lowering error floors are suggested from this analysis. We propose a design approach of jointly considering the LDGM code degree and the CPM modulation index. Then we consider the rate-adaptive system for slowly fading channels. By changing the rate of the LDGM codes, the information rate of the CPM signals is adapted according to channel variations. We use a low-rate LDGM code as the mother code. Higher rates are achieved by puncturing the output of these codes. To exploit the rate-flexible property of punctured LDGM codes, a rate function is proposed to calculate the rate of each transmitted block. Thus, we can have a quasi-continuous information rate. Numerical results show that this approach can improve the energy efficiency from a discrete-rate adaptation. Using the rate-adaptive approach, up to 11 dB transmitted energy gain can be achieved from the non-adaptive scheme in the low bit-error-rate region (smaller than 10-3) for minimum shift keying (MSK).

We investigate the physical layer bit error probability (BEP) of a network using network coding (coded network). The network consists of statistically independent noisy channels. Thus, transmitted bits are subject to the noise disturbance. An error marking algorithm is given to calculate the error weight (the number of erroneous bits) in the sinks. Then we can calculate the sink BEP of a coded network from the channel BEPs. We formulate the minimizing BEP problem. We show that coding schemes with the same flow may have different sink BEPs. Two approaches are suggested to reduce the complexity.

We investigate energy-efficient network coding with a bit error probability (BEP) constraint. The network consists of statistically independent binary noisy channels. An error marking algorithm is used to calculate the number of erroneous bits in the sinks. Then we can accurately calculate the BEP in the sinks from the channel BEPs. For a given coding scheme, we formulate the optimal energy allocation problem. We show that the problem is convex for BPSK modulation. Then, we check the problem of the joint optimal network coding and energy allocation. A procedure is formulated to solve the problem. A pruning rule is suggested to reduce the search effort. Numerical results show the energy saving from the equal energy allocation and non-optimal coding

We investigate rate-adaptive continuous phase modulation (CPM) with punctured low-density generator matrix (LDGM) codes. We give an adaptive principal component analysis (PCA) CPM receiver for slow-fading channels. With the merit of the PCA receiver, the new receiver avoids the eigenvalue decomposition in every symbol interval, and uses a fixed filter-bank. By changing the rate of the LDGM codes, the information rate of CPM signals is adapted to the channel state information (CSI). We use a low rate LDGM code as the mother code, and higher rates are achieved by puncturing the output of the LDGM codes. To exploit the rate-flexible property of punctured LDGM codes, a rate function is proposed to calculate the rate of each transmitted block. Thus, we can have a quasi-continuous information rate. Numerical results for MSK show that up to 11 dB transmitted energy gain can be achieved in the low bit-error-rate region (smaller than 10-3), compared to the non-adaptive systems

We investigate the bit error probability (BEP) of a network using network coding (coded network). The network consists of independent binary noisy channels. An error marking algorithm is used to calculate the error weight (the number of erroneous bits) in the sinks. Hence, we can calculate the sink BEP of a coded network from the channel BEPs. Then we formulate the minimizing BEP problem. We show that coding schemes with the same flows may have different BEPs. Two approaches are given to reduce the complexity for the problem

In this paper, we investigate serially concatenated continuous phase modulation (SCCPM) with convolutional codes (CC) over rings. The transmitted signals are disturbed by additive white Gaussian noise. The properties for systems with both infinite and finite block lengths are investigated. For an infinite-length system, we check the convergence threshold using the extrinsic information transfer chart. For a finite-length system, we use union-bound techniques to estimate the error floors. In the union-bound analysis, we consider both the order and the position of nonzero permuted symbols. A simple method for determining a CPM error event through the sum of the input symbol sequence is shown. Thus, we can determine if the output symbol sequence of an error event in the ring CC can form an error event in CPM. Two properties concerning the interleaver gain (IG) are investigated. A recursive search algorithm for the maximal IG is shown. Compared with previous SCCPM with a binary CC, the proposed system shows an improvement concerning the convergence threshold or error floors

We investigate the low density generator matrix (LDGM) codes for continuous phase modulation (CPM). The nonsystematic version of LDGM codes is used in the scheme. The overall system has linear encoding complexity due to the low complexity of the LDGM codes. A property of the check node degree of the LDGM code is shown. We use the EXIT chart/function to optimize the LDGM codes. The EXIT function of CPM with a fixed SNR (signal-to-noise-ratio) is shown. We derive the union bound to analyze the error floor performance. Design approaches for lowering error floors are suggested from the analysis process. Numerical results show that this scheme converges earlier (lower SNR) than best found serially concatenated CPM (SCCPM) for iterative decoding while maintaining comparable error floors.

We propose a new scheme of serially concatenated continuous phase modulation (SCCPM) by using nonsystematic low density generator matrix (LDGM) codes as the outer code. A property of the LDGM code degree is investigated. We use the exit chart/function to optimize the LDGM codes. The exit function of CPM with a fixed SNR (signal-to-noise-ratio) is shown. We derive the union bound to analyze the error floor performance. Design principles are proposed from the analysis process. Numerical results show that this scheme converge earlier (lower SNR) than previous SCCPM for iterative decoding.

We investigate the union bound for symbol interleaved serially concatenated codes (SCC) with non-uniform error properties. We calculate the distance spectrum of the SCC using the input difference symbol sequence spectrum (DSSS) of the inner code. Then we modify the trellis search algorithm for general codes to calculate the DSSS. We also show the mapping approach between difference symbols with the different alphabet size. For the symbol interleaver, we use the multinomial coefficient to calculate the probability of the permuted sequence. Numerical results for symbol interleaved serially concatenated continuous phase modulation (SCCPM) show that the bound is tight in the medium to high signal-to-noise ratio (SNR) region.

Serially concatenated continuous phase modulation (SCCPM) systems with symbol interleavers are investigated. The transmitted signals are disturbed by additive white Gaussian noise (AWGN). Iterative detection with extrinsic information is used at the receiver side. Compared to bit interleaved SCCPM systems, this scheme shows a substantial improvement in convergence threshold at the price of a higher error floor. In addition to showing this property, we also investigate the underlying reason by error event analysis. In order to estimate bit error rate performance, we generalize traditional union bounds for a bit interleaver to the non-binary interleaver. For the latter, both the order and the position of permuted non-zero symbols have to he considered. From the analysis, some principal properties are presented. Finally some design principles are proposed. The paper concentrates on SCCPM, but the proposed analysis methods and conclusions can he widely used in many other systems such as serially concatenated trellis coded modulation, et cetera.

Serially concatenated continuous phase modulation (SCCPM) systems with ring convolutional codes (CC) are investigated. Both EXIT chart and union bound techniques are used to compare with the simulation results and are used as analysis tools. The latter is for the first time generalized to a nonbinary interleaver for a serially concatenated system, where both the order and position of the permuted nonzero symbols have to be considered.