Deploying six-dimensional movable antenna (6DMA) systems in Internet-of-Vehicles (IoV) scenarios can greatly enhance spectral efficiency. However, the high mobility of vehicles causes rapid spatio-temporal channel variations, posing a significant challenge to real-time 6DMA optimization. In this work, we pioneer the application of 6DMA in IoV and propose a low-complexity, instantaneous channel state information (CSI)-free dynamic configuration method. By integrating vehicle motion prediction with offline directional response priors, the proposed approach optimizes antenna positions and orientations at each reconfiguration epoch to maximize the average sum rate over a future time window. Simulation results in a typical urban intersection scenario demonstrate that the proposed 6DMA scheme significantly outperforms conventional fixed antenna arrays and simplified 6DMA baseline schemes in terms of total sum rate.

A version of the accepted manuscript is available in arXiv at arXiv:2603.04898v1 [cs.LG] (https://arxiv.org/abs/2603.04898). Comments: This paper has been accepted by infocom. The source code has been released at: https://github.com/qiongwu86/U-Parking . Submission history: From: Qiong Wu: [v1] Thu, 5 Mar 2026 07:38:51 UTC (499 KB).

Integrating communication, sensing, and power transfer (ICSPT) is an emerging network paradigm for the sixth-generation (6G) systems, which is able to provide concurrent communication and sensing functions while simultaneously wirelessly powering low-power Internet of Things (IoT) devices with shared spectrum and hardware resources. To enhance the performance of ICSPT in fading channels, the outage probability (OP)constrained robust transmit beamforming design (OP-RTBD) is proposed, and a transmit power minimization problem is formulated with imperfect channel state information (CSI) by jointly optimizing information, sensing, and energy beam vectors at the base station (BS), subject to OP constraints on the communication rate, sensing Cramér-Rao bound, and energy transfer. To solve the non-convex problem, we propose a Bernstein-type inequality (BTI)-based method to conservatively approximate the probabilistic constraints to handle the CSI uncertainty. Then, a semi-positive definite relaxation-based method is proposed to solve the approximated problem. Simulation results show that the proposed OP-RTBD achieves near-optimal performance compared to the exhaustive search method with only less than 4% deviation, and it also significantly reduces the transmit power compared to baselines. Moreover, OP-RTBD exhibits strong robustness, achieving performance very close to that in perfect CSI scenarios, with a deviation of only less than 10%. Besides, the simulation results indicate that the BS’s transmit power should be allocated with priority to communication requirements over sensing and power transfer demands. Additionally, they further demonstrate that to simultaneously meet communication, sensing, and power transfer requirements, our proposed OP-RTBD in ICSPT is more energy-efficient, reducing energy consumption by approximately 10% and 20% compared to SWIPT and ISAC, respectively.

This paper presents a mobile computing-based framework for distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenarios under intelligent transportation systems (ITS). A centralized trajectory planning problem is first formulated to optimize merging efficiency and safety. To eliminate reliance on a central controller, a distributed solution is developed using ADMM algorithm based on V2X communication, enabling CAVs to collaboratively compute trajectories in parallel by leveraging their onboard computing power. Building on this, a multi-vehicle model predictive control (MPC) problem is proposed to enhance system stability under strict constraints. To solve it efficiently, a Distributed Cooperative Iterative MPC (DCIMPC) method is introduced, which decomposes and reformulates the problem for real-time distributed execution across CAVs. Together, these methods form a mobile edge computing-driven control framework. Simulations and experiments demonstrate significant improvements in computational efficiency and system performance, highlighting the potential of mobile computing in cooperative CAV control.

This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption under strict coupling constraints, the complex non-convex optimization problem is decoupled into a hierarchical execution framework. First, a sequential distributed optimization algorithm based on Second-Order Cone Programming (SOCP) is proposed to optimize the 3D flight trajectory of each UAV, ensuring adaptive network coverage. Second, a novel hybrid resource scheduling paradigm synergizing Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) is developed. Within this framework, the DRL agent dictates the initial resource allocation, while the LLM acts as a semantic macro-scheduler to rectify long-tail allocation imbalances for failed and surplus tasks. Crucially, a reward decoupling mechanism is introduced to isolate DRL training from external LLM interventions, thereby ensuring policy convergence. Finally, the task offloading ratios are precisely determined via Linear Programming (LP) within an alternating optimization loop. Simulation results demonstrate that the proposed method significantly outperforms traditional multi-agent reinforcement learning baselines in terms of task success rate and system efficiency.

In federated learning (FL), although the original intention of “available but not visible” data is to allay data privacy concerns, it potentially brings new security threats, particularly poisoning attacks that target such “not visible” local data. Intuitively, such data poisoning attacks have great potential in stealthily degrading global FL outcomes, and are expected to be even stealthier if being enhanced by generative models like generative adversarial networks (GANs). However, existing defense methods have not been thoroughly challenged in this regard and generally fail to be aware of a local generation of seemingly legitimate poisoned data. With a growing concern on potentially stealthier attacks, in this paper, a cost-effective defense mechanism named Model Consistency-Based Defense (MCD) is proposed, which offers a comprehensive examination of available local models across multiple feature dimensions, providing an indirect yet effective means of identifying hidden data poisoning attackers. To push the limit of MCD against stealthier attacks, we propose a new GAN-based data poisoning attack model named VagueGAN and an unsupervised variant of it, which can be flexibly deployed to generate seemingly legitimate but noisy poisoned data. The consistency of GAN outputs revealed by VagueGAN helps strengthen MCD to work against stealthier GAN-based attacks as well as other mainstream ones. Extensive experiments on multiple open datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and Mini-Imagenet) indicate that our attack method better balances the trade-off between attack effectiveness and stealthiness with low complexity. More importantly, our defense mechanism is shown to be more competent in identifying a variety of poisoned data, particularly stealthier GAN-poisoned ones.

In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL.

In this paper, we address the problem of fair access and Age of Information (AoI) optimization in 5G New Radio (NR) Vehicle to Everything (V2X) Mode 2. Specifically, vehicles need to exchange information with the road side unit (RSU). However, due to the varying vehicle speeds leading to different communication durations, the amount of data exchanged between different vehicles and the RSU may vary. This may poses significant safety risks in high-speed environments. To address this, we define a fairness index through tuning the selection window of different vehicles and consider the image semantic communication system to reduce latency. However, adjusting the selection window may affect the communication time, thereby impacting the AoI. Moreover, considering the re-evaluation mechanism in 5G NR, which helps reduce resource collisions, it may lead to an increase in AoI. We analyze the AoI using Stochastic Hybrid System (SHS) and construct a multi-objective optimization problem to achieve fair access and AoI optimization. Sequential Convex Approximation (SCA) is employed to transform the non-convex problem into a convex one, and solve it using convex optimization. We also provide a large language model (LLM) based algorithm. The scheme's effectiveness is validated through numerical simulations.

This paper investigates the active reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and power transfer (ISCPT) networks, where rate-splitting multiple access (RSMA) scheme is employed to serve multiple downlink communication users. To promote the energy efficiency (EE) of such a system, we formulate an EE maximization problem by jointly optimizing the beamforming matrix, the sensing matrix, the active RIS matrix, the power splitting (PS) ratio vector, and the common rate allocation vector. Due to the non-convexity of the problem, we first design a successive convex approximation scheme with alternating optimization method (named SCA-AO) to solve it. As SCA-AO operates in an iterative manner, which is with relatively high computational complexity, we then design a mixture of experts (MoE)-based deep reinforcement learning (DRL) scheme with smooth clipping function (named MoE-SCF). In comparison, SCA-AO is able to achieve higher solution accuracy, while MOE-SCF has a shorter online execution response time. In order to integrate the advantages of both presented SCA-AO and MoE-SCF simultaneously, we further propose a hybrid MoE (H-MoE) scheme, where both the SCA-AO and the MoE-SCF are employed as expert strategies, and an opportunistic activator (OPA) is designed to dynamically select the best strategy generated by all expert combinations according to the performance evaluation function. Simulation results demonstrate that the proposed H-MoE promotes the system's EE by about 18.14% compared to traditional MoE, with similar response time. Additionally, compared to the SCA-AO, H-MoE significantly decreases the response time by approximately 56.17%, while only marginally compromising the EE performance by less than 3.1%.

Recently, on-device large models (e.g., 7B parameter LLMs and LVLMs) are increasingly deployed in real-world services such as mobile assistants, edge computing, and IoT systems, where low latency and resource efficiency are critical. Since large models trained on general-domain data are typically suboptimal for downstream services, In-Context Learning (ICL) technique is widely used to enhance their task-specific service performance without extra tuning. However, effective ICL methods generally necessitate a sufficient amount of supervised data to provide abundant service-related information, which usually leads to a long context (i.e., more texts or images input) for models to process. This long context issue can significantly degrade the real-time inference efficiency and also impact the information utilization effectiveness, particularly for on-device large models with limited long context modeling capacities. To tackle this challenge, we propose a Personalized Knowledge Refinement (PKR) framework to achieve efficient and effective ICL for on-device large models. Specifically, we first introduce a personalized knowledge extraction module, which analyzes the inference behaviors of the target model on small-scale supervised data and then convert these behavioral patterns into personalized service-specific knowledge as the instruction context. Furthermore, we propose an adaptive knowledge filtration mechanism to model the informativeness of the extracted knowledge and eliminate the redundant ones, further improving the knowledge encoding efficiency per unit of context length. Experiments based on 14 benchmarks spanning both textual and visual task services, and 4 large models, demonstrated that PKR consistently improves task accuracy while reducing context length and inference latency, making it a practical solution for real-world on-device services. Codes are released athttps://github.com/wanghl21/PKR.

The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

The goal of the acoustic scene classification (ASC) task is to classify recordings into one of the predefined acoustic scene classes. However, in real-world scenarios, ASC systems often encounter challenges such as recording device mismatch, low-complexity constraints, and the limited availability of labeled data. To alleviate these issues, in this paper, a data-efficient and low-complexity ASC system is built with a new model architecture and better training strategies. Specifically, we firstly design a new low-complexity architecture named Rep-Mobile by integrating multi-convolution branches which can be reparameterized at inference. Compared to other models, it achieves better performance and less computational complexity. Then we apply the knowledge distillation strategy and provide a comparison of the data efficiency of the teacher model with different architectures. Finally, we propose a progressive pruning strategy, which involves pruning the model multiple times in small amounts, resulting in better performance compared to a single step pruning. Experiments are conducted on the TAU dataset. With Rep-Mobile and these training strategies, our proposed ASC system achieves the state-of-the-art (SOTA) results so far, while also winning the first place with a significant advantage over others in the DCASE2024 Challenge.

Adapting pre-trained models has become the dominant approach for anomalous sound detection (ASD), where classifying the attributes of machine working status is commonly chosen as the deputy task for fine-tuning. However, attributes might be intractable to collect for some machines, causing the label to bear mixed granularity and thus deprecating the ASD performance. Therefore, we propose an adaptive proto-type learning scheme for fine-tuning pre-trained models, which adaptively scales coarse-grained labels to sub-centers so as to keep consistency with fine-grained labels. To deal with domain shift, we employ SMOTE to over-sample the prototypes of the target domain. The experiment on the DCASE 2024 ASD dataset demonstrates the efficacy of the proposed scheme, setting up a new milestone of 65.01% on both sets and outperforming the best system of the challenge. A detailed ablation study is also conducted to validate the effectiveness.

In the Internet of Vehicles (IoV), federated learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated self-supervised learning (FSSL) utilizes self-supervised learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to base station (BS), which poses a risk of privacy leakage. Simplified contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a deep reinforcement learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the central processing unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods.

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, cellular vehicle-to-everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultralow latency and high reliability in vehicle-to-vehicle (V2V) communication. This article proposes a method that integrates graph neural networks (GNNs) with deep reinforcement learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the graph sample and aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on vehicle-to-infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of Internet of Vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of reconfigurable intelligent surface (RIS), which provide alternative communication pathways to assist vehicle communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider an RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the deep deterministic policy gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the multiagent DDPG (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, twin delayed DDPG (TD3), and some typical stochastic schemes.

In the digital twin mobile edge network, the maintenance of the vehicle twin model and vehicular task processing in the server require the support of computing resources. In addition, they are performed simultaneously. Therefore, how to allocate resources for twin maintenance and task processing under limited server resources is crucial. However, current research tends to ignore the aspect of resource competition for twin maintenance. In this study, we analyze the delays of these two affected by resource allocation under a generic digital twin mobile edge network (DTMEN) to construct the optimization problem. For this problem, we transformed the problem using a Markov decision process. Meanwhile, we propose a multi-agent reinforcement learning (MADRL) based twin maintenance and task processing resource collaborative scheduling (TMTPRCS) algorithm to solve the problem. Experiments show that our proposed approach is effective in terms of resource allocation compared to other alternative algorithms.

Federated learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles’ local models instead of the local data. The gradients of vehicles’ local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error (QE), which further affects the model accuracy and training time. To do so, the total training time and QE become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this article, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.

Small-cell mobile edge computing (SE-MEC) networks amalgamate the virtues of MEC and small-cell networks, enhancing data processing capabilities of user devices (UDs). Nevertheless, time-varying wireless channels, dynamic UD requirements, and severe interference among UDs make it difficult to fully exploit the limited network resources and stably provide computing services for UDs. Therefore, efficient task offloading and resource allocation (TORA) is essential. Moreover, since multiple small cells are deployed, decentralized TORA schemes are preferred in practice. Thus, this paper aims to design distributed adaptive TORA schemes for SE-MEC networks. In pursuit of an eco-friendly design, an optimization problem is formulated to minimize the total energy consumption (TEC) of UDs subject to delay constraints. To effectively deal with network's dynamic characteristics, the reinforce learning framework is applied, where the TEC minimization problem is first modeled as a partially observable Markov decision process (POMDP), and then an efficient multi-agent proximal policy optimization (MAPPO)-based scheme is presented to solve it. In the presented scheme, each small-cell base station (SBS) serves as an agent and is capable of making TORA decisions only with its own local information. To promote collaboration among multiple agents, a global reward function is designed. A state normalization mechanism is also introduced into the presented scheme for enhancing learning performance. Simulation results show that although the proposed MAPPO-based scheme works in a distributed manner, it achieves very similar performance to the centralized one. In addition, it is demonstrated that the state normalization mechanism has a significant effect on reducing TEC.

In the typical radio frequency (RF)-based wireless power transfer (WPT) system, the wireless power station (WPS) connected to the grid transmits energy to charge low-power sensors via radio signals. Such a system may not be green and also difficult to deploy in some special areas including deserts and mountainous areas, because it depends on the grid. To achieve a green RF WPT system design, this paper considers that the WPS is powered by natural energy sources rather than the grid. To explore the maximal total amount of the energy that can be harvested by the sensors, we focus on the offline setting, so similar to many existing works on offline optimization, we assume that the WPS knows prior knowledge about energy arrivals and channel changes, and then formulate an optimization problem to maximize the total harvested energy via optimizing the WPS’s time-domain transmit power subject to multiple constraints, including the finite battery capacity at the WPS, the causal relationship between the natural energy harvesting and the WPT, and the transmit power budget of the WPS, where for practicality, the nonlinear energy harvesting (EH) model is also taken into account. To solve this non-convex problem, we first equivalently transform it by using the epigraph reformulation and the variable substitution, and then use the first-order Taylor expansion to get an approximate convex version. Then, we present a successive convex approximation (SCA)-based algorithm to improve the accuracy of the obtained solution for approaching the optimal one. For the special case with a single sensor, we further propose a branch and bound (BB)-based algorithm that is able to get a more accurate solution with lower complexity than the SCA-based one. Numerical results demonstrate that the proposed algorithms are able to achieve the near-global optimal solution. As the average recharge rate increases, compared with the other two baselines, i.e., the greedy power (GP) policy and the constant power (CP) policy, the total harvested energy achieved by the SCA-based algorithm is up to about 2.48 times and 1.37 times that of the baselines respectively. For the single-sensor case, the BB-based algorithm always outperforms the SCA-based one in terms of the total harvested energy while reducing the running time required for solving by about 90% on average.

Machine anomalous sound detection (ASD) has emerged as one of the most promising applications in the Industrial Internet of Things (IIoT) due to its unprecedented efficacy in mitigating risks of malfunctions and promoting production efficiency. Previous works mainly investigated the machine ASD task under centralized settings. However, developing the ASD system under decentralized settings is crucial in practice, since the machine data are dispersed in various factories and the data should not be explicitly shared due to privacy concerns. To enable these factories to cooperatively develop a scalable ASD model while preserving their privacy, we propose a novel framework named CoopASD, where each factory trains an ASD model on its local dataset, and a central server aggregates these local models periodically. We employ a pre-trained model as the backbone of the ASD model to improve its robustness and develop specialized techniques to stabilize the model under a completely non-iid and domain shift setting. Compared with previous state-of-the-art (SOTA) models trained in centralized settings, CoopASD showcases competitive results with negligible degradation of 0.08%. We also conduct extensive ablation studies to demonstrate the effectiveness of CoopASD.

Machine anomalous sound detection is a useful technique for various applications, but it often suffers from poor generalization due to the challenges of data collection and complex acoustic environment. To address this issue, we propose a robust machine anomalous sound detection model that leverages self-supervised pre-trained models on large-scale speech data. Specifically, we assign different weights to the features from different layers of the pre-trained model and then use the working condition as the label for self-supervised classification fine-tuning. Moreover, we introduce a data augmentation method that simulates different operating states of the machine to enrich the dataset. Furthermore, we devise a transformer pooling method that fuses the features of different segments. Experiments on the DCASE2023 dataset show that our proposed method outperforms the commonly used reconstruction-based autoencoder and classification-based convolutional network by a large margin, demonstrating the effectiveness of large-scale pre-training for enhancing the generalization and robustness of machine anomalous sound detection. In Task2 of DCASE2023, we achieve 2nd place with these methods.

Anomalous Sound Detection (ASD) has gained significant interest through the application of various Artificial Intelligence (AI) technologies in industrial settings. Though possessing great potential, ASD systems can hardly be readily deployed in real production sites due to the generalization problem, which is primarily caused by the difficulty of data collection and the complexity of environmental factors. This paper introduces a robust ASD model that leverages audio pre-trained models. Specifically, we fine-tune these models using machine operation data, employing SpecAug as a data augmentation strategy. Additionally, we investigate the impact of utilizing Low-Rank Adaptation (LoRA) tuning instead of full fine-tuning to address the problem of limited data for fine-tuning. Our experiments on the DCASE2023 Task 2 dataset establish a new benchmark of 77.75% on the evaluation set, with a significant improvement of 6.48% compared with previous state-of-the-art (SOTA) models, including top-tier traditional convolutional networks and speech pre-trained models, which demonstrates the effectiveness of audio pre-trained models with LoRA tuning. Ablation studies are also conducted to showcase the efficacy of the proposed scheme.

Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency. In this work, we reconceptualize the FL system from the perspective of network information theory, and formulate an original FL communication framework, FedNC, which is inspired by Network Coding (NC). The main idea of FedNC is mixing the information of the local models by making random linear combinations of the original parameters, before uploading for further aggregation. Due to the benefits of the coding scheme, both theoretical and experimental analysis indicate that FedNC improves the performance of traditional FL in several important ways, including security, efficiency, and robustness. To the best of our knowledge, this is the first framework where NC is introduced in FL. As FL continues to evolve within practical network frameworks, more variants can be further designed based on FedNC.

This article investigates rate-splitting multiple access (RSMA)-based visible light communication (VLC) networks with simultaneous lightwave information and power transfer (SLIPT). To effectively enhance the fairness among information decoding users (IDUs), we formulate an optimization problem to maximize the minimum data rate by optimizing the direct current bias vector, the common message rates of RSMA, and the transmit precoding vectors. In the problem, the IDUs’ minimum energy harvesting (EH) requirements, the total power budget of the light-emitting diode (LED) transmitters, and the linear operation region of LEDs are also considered as the system constrains. To solve the formulated nonconvex problem, epigraph reformulation is first employed to transform the nonconvex objective function. Then, a series of transformations is proposed and the semi-definite relaxation (SDR) method is adopted to address the rank-one precoding matrix constraint. After that, an iterative algorithm is proposed to obtain an effective suboptimal solution by applying the successive convex approximation. Extensive simulations show that the max–min rate (MMR) is inversely proportional to the number of IDUs and it decreases as the minimum EH requirement becomes more stringent, especially in the high-EH region. Moreover, the value of the maximum drive current imposes a significant impact on the system performance, particularly, the MMR becomes saturated for a given maximum drive current even if the total power budget is sufficient. Besides, RSMA can contribute to both spectral efficiency and energy efficiency greatly in comparison to the traditional multiple access scheme.

This article investigates the age of information (AoI) performance in a wireless powered communication network (WPCN), where a sensor node (SN) harvests energy from an energy transmitter (ET) and then transmits status information to its data receiver (DR) by using the harvested and accumulated energy. The AoI penalty is used as a performance metric to characterize the nonlinear feature of dissatisfaction with data obsolescence at the DR. We derive the closed-form expressions of the average AoI penalty and the average peak AoI (PAoI) penalty with the Rician fading model. To further explore the system performance limit in terms of AoI penalty, we formulate two optimization problems to minimize the average AoI penalty and the average PAoI penalty with respect to the battery discharge threshold. Particularly, we reveal the conditions for the existence of the average AoI penalty and average PAoI penalty. Simulation results show that there exists a unique optimal battery discharge threshold that minimizes the system’s average AoI penalty and a unique optimal battery discharge threshold that minimizes the system’s average PAoI penalty. Moreover, as expected, the average AoI penalty and the average PAoI penalty decrease with the increment of the Rician$K$-factor, and increase with the increment of the distance between ET and SN. Besides, the average AoI penalty and the average PAoI penalty first decrease with the increment of transmit power of ET and then tend to be flat. Additionally, a smaller data size of SN yields better system performance.

This article studies an intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) network, where the ground users (GUs) desire to receive information from a UAV. Downlink nonorthogonal multiple access (NOMA) is considered typically with two GUs being selected according to whether a Line-of-Sight (LoS) link between GUs and UAV exists. As the accurate channel information of LoS or Non-LoS (NLoS) links for multiple GUs is difficult to acquire, an approximate LoS region-based method is designed to select GUs as an alternative. In order to enhance the communication quality of the far GU, an IRS is deployed to assist the NLoS transmission. For such a system, we evaluate its outage performance in Nakagami-m fading. First, the central limit theorem (CLT) and Laplace transform (LT) are employed to derive the channel statistics of the UAV- IRS-user link. Then, asymptotic closed-form expressions of the outage probabilities are derived for the selected GUs based on Gaussian–Chebyshev quadrature approximation. Monte Carlo simulations validate the validness of our derived outage probabilities. It shows that the approximate LoS region-based scheme provides similar outage performance laws as the accurate LoS region-based one. Moreover, the outage probabilities of selected GUs in terms of NOMA-based protocol and orthogonal multiple access (OMA)-based protocol are analyzed. Simulation results confirm that the proposed NOMA-based protocol is capable of achieving superior performance compared with the OMA-based protocol by setting power allocation factor and targeted acrlong SINR thresholds of near GU and far GU properly. Specifically, when the rate threshold of near GU is relatively large or the rate threshold of far GU is relatively small, the outage performance derived by NOMA-based protocol performs better than OMA-based protocol in most of cases.

This article investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted downlink multiuser multiple-input–single-output (MU-MISO) networks with the rate splitting multiple access (RSMA) scheme. A base station (BS) desires to simultaneously transmit messages to multiple users with the assistance of an STAR-RIS to enhance communication quality as well as extend the coverage of users. An optimization problem is formulated to maximize the achievable sum rate of the networks on the premise of satisfying the constraints on power budget at the BS, total common-stream rate of users, and individual users’ minimum rate requirements, via jointly optimizing the beamforming vectors, the common-stream rate allocation vector, and the transmission and reflection coefficients (TARCs) matrix. Due to the dynamic changes of communication links and the coupling of multiple variables, it is challenging to solve such a nonconvex optimization problem by utilizing traditional methods. Therefore, a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are designed properly. A constraint-satisfaction-processing (CSP) method is employed to further adjust the optimized transmit power to make sure that the obtained optimized results satisfy the power budget constraint. Simulation results show that the proposed PPO-based DRL algorithm converges well and achieves much better performance than several baselines, such as the soft actor–critic (SAC), the deep deterministic policy gradient (DDPG), the genetic algorithm (GA), the maximum ratio transmission (MRT), the zero-forcing (ZF), and the random methods. Moreover, it demonstrates that deploying STAR-RIS greatly enhances the system sum rate and user fairness compared to deploying traditional reflecting-only RIS (RO-RIS) and without RIS. Besides, it also shows that adopting the RSMA scheme achieves more notable performance gains than the nonorthogonal multiple access (NOMA) scheme in such a network.

This article investigates semantic communication in high-speed mobile Internet of Vehicles (IoV), focusing on spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We propose a semantic-aware spectrum-sharing (SSS) algorithm using deep reinforcement learning (DRL) with a soft actor-critic (SAC) approach. We start with semantic information extraction, redefining metrics for V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). We then apply the SAC algorithm to optimize decisions V2V and V2I spectrum-sharing decisions on semantic information. This optimization aims to maximize HSSE and enhance the success rate of effective semantic information transmission (SRS), including determining the optimal V2V and V2I sharing strategies, transmission power, and the length of transmitted semantic symbols. Experimental results show that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum-sharing algorithms and spectrum-sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.

Federated Learning (FL) is a promising distributed learning mechanism that revolutionizes our interaction with data in the IoT ecosystem. Due to the rapidly growing scale of smart devices and the limited transmission resources of networks, a simple, consistent and scalable FL framework aiming to address the communication bottleneck is urgently needed. In this work, we propose an efficient approach with Selective Aggregation of Models (SAM) to mitigate the communication overload in FL systems. The introduction of SAM enables each local client to upload its model with a certain probability, resulting in a significant reduction in costly communication expenses. We design the algorithm for SAM, analyze the convergence bound on non-convex objectives for heterogeneous data, which illustrates the impact of the selection probability as well as the set size of participating clients on the system performance, and assess the conservation for the network resource utilization by modeling queuing systems. We conduct various experiments to evaluate the performance of SAM, whose outcomes suggest that significant alleviation of the communication bottleneck can be accomplished with marginal cost of performance loss. It will also be shown that SAM is a communication-efficient method that can be freely applied to other frameworks.

As a promising learning paradigm integrating computation and communication, federated learning (FL) proceeds the local training and the periodic sharing from distributed clients. Due to the non-i.i.d. data distribution on clients, FL model suffers from the gradient diversity, poor performance, bad convergence, etc. In this work, we aim to tackle this key issue by adopting importance sampling (IS) for local training. We propose importance sampling federated learning (ISFL), an explicit framework with theoretical guarantees. Firstly, we derive the convergence theorem of ISFL to involve the effects of local importance sampling. Then, we formulate the problem of selecting optimal IS weights and obtain the theoretical solutions. We also employ a water-filling method to calculate the IS weights and develop the ISFL algorithms. The experimental results on CIFAR-10 fit the proposed theorems well and verify that ISFL reaps better performance, convergence, sampling efficiency, as well as explainability on non-i.i.d. data. To the best of our knowledge, ISFL is the first non-i.i.d. FL solution from the local sampling aspect which exhibits theoretical compatibility with neural network models. Furthermore, as a local sampling approach, ISFL can be easily migrated into other emerging FL frameworks.

Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially for the complex channels not modeled as the well-known typical ones. Recently, neural networks have been used in mutual information estimation and optimization. They are particularly considered as efficient tools for learning channel capacity. In this paper, we propose a cooperative framework to simultaneously estimate channel capacity and design the optimal codebook. First, we will leverage MIM-based GAN, a novel form of generative adversarial network (GAN) using message importance measure (MIM) as the information distance, into mutual information estimation, and develop a novel method, named MIM-based mutual information estimator (MMIE). Then, we design a generalized cooperative framework for channel capacity learning, in which a generator is regarded as an encoder producing the channel input, while a discriminator is the mutual information estimator that assesses the performance of the generator. Through the adversarial training, the generator automatically learns the optimal codebook and the discriminator estimates the channel capacity. Numerical experiments will demonstrate that compared with several conventional estimators, the MMIE achieves state-of-the-art performance in terms of accuracy and stability.

This paper studies the high-speed railway mobile networks (HSRMN), where multiple railway-side sensors (RSs) are deployed along the track to sense environmental data, and multiple train-mounted sensors (TSs) are deployed on the train to collect train data. Both RSs and TSs are scheduled to transmit their sensed data respectively to the ground base station (BS) in a time division multiple access (TDMA) mode. To keep the data received at the BS from the RSs as fresh as possible and also ensure that the TSs complete the given uploading tasks, an optimization problem is established to minimize the average age of information (AoI) of the data gathered from RSs by jointly optimizing sensors’ scheduling and transmission power control constrained by the maximum transmission power budget of RSs and TSs. Since the problem is non-convex and lacks an explicit expression of the objective function and the prior information about future channel state, we present a deep Q-learning network (DQN)-based method to solve it. Particularly, the BS is viewed as the agent, and the action space is constructed by scheduling policy and power control. To further accelerate the convergence speed of the presented DQN-based solution framework, an action space-reduced (ASR) version of the DQN-based method, i.e., the ASR-DQN-based method, is designed by deriving a closed-form solution to the optimal transmission power for a given sensors’ scheduling policy. Numerical simulations show that, compared to the DQN-based method, the ASR-DQN-based method decreases the number of episodes required for convergence by about 23% and reduces the running time by about 41%. Moreover, compared with three baselines, i.e., the random method, the round-robin method, and the deep-Sarsa method, our presented ASR-DQN-based method achieves the lowest average AoI and has the best robustness among these compared methods.

This paper investigates the radio-frequency-energy-harvesting-powered (RF-EH-powered) wireless Industrial Internet of Things (IIoT) networks, where multiple sensor nodes (SNs) are first powered by a wireless power station (WPS), and then collect status updates from the industrial environment and finally transmit the collected data to the monitor with their harvested energy. To enhance the timeliness of data, age of information (AoI) is used as a metric to optimize the system. Particularly, an expected sum AoI (ESA) minimization problem is formulated by optimizing the power adjustment policy for the SNs under multiple practical constraints, including the EH, the minimal signal-to-noise-plus-interference ratio (SINR) and the battery capacity constraints. To solve the non-convex problem with no explicit AoI expression, we transform it into a Markov decision problem (MDP) with continuous state space and action space. Then, inspired by the Soft Actor-Critic (SAC) framework in deep reinforcement learning, a SAC-based age-aware power adjustment (SAPA) method is proposed by modeling the power adjustment as a stochastic strategy. Furthermore, to reduce the communication overhead of SAPA, a multi-agent version of SAPA, i.e., MSAPA, is proposed, with which each SN is able to adjust its transmit power based on its local observations. The communication overhead of SAPA and MSAPA is also analyzed theoretically. Simulation results show that the proposed SAPA and MSAPA converge well with different numbers of SNs. It is also shown that the ESA achieved by the proposed SAPA and MSAPA is lower than that achieved by the baseline methods.

This paper investigates the energy-efficient coordinated beamforming design for multi-pair multiple-input single-output (MISO) networks with passive eavesdroppers. To be practical, it is assumed that only channel distribution information (CDI) of the network is known by the transmitters/sources, and the dynamic energy consumption model (DECM) is employed. In order to achieve a green network design, an energy efficiency (EE) maximization problem is formulated subjecting to the individual available power constraints, the rate outage probability constraints, and the information leakage probability constraints. To solve the formulated non-convex problem, semidefinite relaxation (SDR) and first-order lower bound are applied to transform the problem, and then an efficient algorithm is proposed based on successive convex approximation (SCA) and Dinkelbach's approaches. The proposed algorithm is theoretically proved to converge to a stationary point of the considered problem. Further, a distributed version of the proposed algorithm is designed, with which each transmitter is able to optimize its own beamforming vector with local CDI. Moreover, the computational complexities and the signaling overheads of the two developed algorithms are analyzed and compared. Simulation results show that both algorithms achieve good EE performance, and the EE performance achieved by the distributed algorithm is very similar to that achieved by the centralized one. Additionally, it is shown that similar to the conventional scenarios without eavesdroppers, the achieved system EE also has a saturation point w.r.t. the available power of the transmitters, and by employing our proposed algorithms, the network security is significantly enhanced.

Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users’ requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users’ personal information. Traditional federated learning (FL) can protect users’ privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then a popular content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.

Vehicles in platoons need to process many tasks to support various real-time vehicular applications. When a task arrives at a vehicle, the vehicle may not process the task due to its limited computation resource. In this case, it usually requests to offload the task to other vehicles in the platoon for processing. However, when the computation resources of all the vehicles in the platoon are insufficient, the task cannot be processed in time through offloading to the other vehicles in the platoon. Vehicular fog computing (VFC)-assisted platoon can solve this problem through offloading the task to the VFC which is formed by the vehicles driving near the platoon. Offloading delay is an important performance metric, which is impacted by both the offloading strategy for deciding where the task is offloaded and the number of the allocated vehicles in VFC to process the task. Thus, it is critical to propose an offloading strategy to minimize the offloading delay. In the VFC-assisted platoon system, vehicles usually adopt the IEEE 802.11p distributed coordination function (DCF) mechanism while having various computation resources. Moreover, when vehicles arrive and depart the VFC randomly, their tasks also arrive at and depart the system randomly. In this paper, we propose a semi-Markov decision process (SMDP) based offloading strategy while considering these factors to obtain the maximal long-term reward reflecting the offloading delay. Our research provides a robust strategy for task offloading in VFC systems, its effectiveness is demonstrated through simulation experiments and comparison with benchmark strategies.

Reconfigurable intelligent surface (RIS) has been proposed as a wireless coverage enhancement enabler. However, due to the passive feature of the RIS, it is challenging to acquire the instantaneous channel state information for RIS-user links. This paper investigates the outage-constrained transmission design for RIS-assisted multi-user multiple-input-single-output (MISO) systems under interference channel based on channel distribution information. The transmission design problem is formulated to maximize the sum transmission rate under constraints of the tolerable outage probability of each user, the power budget of each transmitter and the phase shift coefficient of each reflecting element. To solve the computational intractable problem, a block successive upper bound minimization (BSUM)-based algorithm is proposed where the feasible set is separated w.r.t. variables into several blocks, and for each block, a computationally efficient surrogate subproblem is formulated and solved. Furthermore, the non-decreasing behavior and optimality performance of the proposed algorithms are theoretically analyzed. Numerical results show that the proposed algorithm is more computational efficient than traditional alternative optimization based algorithm, and the proposed the outage-constrained transmission design is able to suppress the average outage rate to a required level as well as maximizing the sum transmission rate.

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn’t require CSI. In Ti-GSC, there are two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM), where SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, so no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term, are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, the BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. Besides, each term of the presented loss function has a great impact on the BLEU performance of Ti-GSC, where in Rician fading syntactic learning has the greatest impact, and in Rayleigh fading, the adversarial learning becomes important.

This paper investigates simultaneous wireless information and power transfer (SWIPT)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) networks with power splitting (PS) receivers and non-orthogonal multiple access (NOMA). By exploiting the conjugated beamforming method, the closed-form expressions of the information rate and the total harvested power at each user equipment (UE) are derived. To improve the system spectral efficiency, a sum rate maximization problem is formulated subjecting to the quality of service requirement at each UE and the power budget constraint at each access point by optimizing the UE clustering, the power control coefficients, and the PS ratios. To solve the formulated non-convex and mixed combinatorial problem, a machine learning-based approach is designed. Particularly, the UE clustering is first optimized by using a K-means based method and then the power control coefficients and the PS ratios are jointly optimized by a proposed multi-agent deep Q-network (MA-DQN) based method. The impact of the discount factor of the MA-DQN based method on the derived result is discussed. It is proved that by setting the discount factor as zero, the performance loss is negligible. Based on this observation, a zero-discount MA-DQN (0-γ MA-DQN) based method is further proposed to improve the computational efficiency. Also, the computational complexity of the proposed machine learning-based approach is analyzed. Simulation results show that the proposed machine learning-based approach outperforms various existing approaches. Moreover, it indicates that CF-mMIMO and NOMA could enhance the propagation performance of SWIPT while the proposed machine learning-based approach could facilitate resource allocation.

The fast-developing Artificial Internet of Things (AIoT) technologies enable the consistent monitoring of multiple machines, by which machine failures can be detected in the early phases, and production efficiency and system management can be greatly promoted, bringing huge significance for anomaly detection. However, in most cases, anomalies are not provided for training, and the lack of direct supervision deprecates the anomaly detection performance. For the application viewpoint, the detector is required to generalize well on multiple machines, except for being computationally efficient. The computational cost is strictly limited, which is a great challenge for mobile and embedded devices. In face of these issues, we propose MobileAnoNet, which decouples an end-to-end detector into a front-end feature extractor and a back-end anomaly detector. The front-end extractor, consuming most computation, is unified for all machine types, while the back-end detector is specialized for each machine type, improving the detection capacity. The model is trained by handy labels of machine types and working conditions, in which multiple classification heads are attached behind the feature extractor during training. The performance of the model is evaluated on two DCASE datasets focusing on machine audio anomaly detection. It's shown that MobileAnoNet achieves a general improvement of 6.9% and 8.8% on two datasets, respectively. The ablation study demonstrates that multi-task learning promotes the general representation capacity. The source code is available at: www.github.com/hqj-les30/MobileAnoNet.

Automatic detection of machine anomaly remains challenging for machine learning. We believe the capability of generative adversarial network (GAN) suits the need of machine audio anomaly detection, yet rarely has this been investigated by previous work. In this paper, we propose AEGAN-AD, a totally unsupervised approach in which the generator (also an autoencoder) is trained to reconstruct input spectrograms. It is pointed out that the denoising nature of reconstruction deprecates its capacity. Thus, the discriminator is redesigned to aid the generator during both training stage and detection stage. The performance of AEGAN-AD on the dataset of DCASE 2022 Challenge TASK 2 demonstrates the state-of-the-art result on five machine types. A novel anomaly localization method is also investigated. Source code available at: www.github.com/jianganbai/AEGAN-AD

Vehicular networks enable vehicles support some real-time applications through training data. Due to the limited computing capability of vehicles, vehicles usually transmit data to a road side unit (RSU) deployed along the road to process data collaboratively. However, vehicles are usually reluctant to share data with each other due to the inevitable data privacy. For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model through aggregation, thus the data privacy can be protected through sharing model instead of raw data. The traditional FL requires to update the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload local models to update the global model. However, vehicles may usually drive out of the coverage of the marked RSU before they obtain their local models through training, which reduces the accuracy of the global model. In this paper, a mobility-aware vehicular asynchronous federated learning (AFL) is proposed to solve this problem, where the RSU updates the global model once it receives a local model from a vehicle where the mobility of vehicles, amount of data and computing capability are taken into account. Simulation experiments validate that our scheme outperforms the conventional AFL scheme.

With the development of large-scale intelligent services, semantic communication has attracted significant interest from both academia and industry, which is expected to transmit valuable data traffic at sufficiently high speed with extremely low end-to-end latency. However, the generation and measurement of semantic messages is still an open problem. On the other hand, expansion which combines simple things into complex systems and even generates intelligence, is consistent with the evolution of human civilization and language systems. Motivated by this key idea, we apply it to semantic communication systems, measuring semantics carried by symbol sequences, and similarly investigate the semantic information system as Shannon did for digital communication systems. This work was the first to propose the concept of semantic expansion and knowledge collision, which may provide a new paradigm for semantic communications. We believe that expansion and collision will be the cornerstone of semantic information theory.

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and communication in FL from a view of pruning. By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models. Two specific schemes of FedLP are designed for scenarios with homogeneous local models and heterogeneous ones. Both theoretical and experimental evaluations are developed to verify that FedLP relieves the system bottlenecks of communication and computation with marginal performance decay. To the best of our knowledge, FedLP is the first framework that formally introduces the layer-wise pruning into FL. Within the scope of federated learning, more variants and combinations can be further designed based on FedLP.

This paper investigates the joint optimization of the task offloading and resource allocation in small cell mobile edge computing (MEC) networks, where multiple small-cell base stations (SBSs) integrating MEC servers provide computing services for user devices (UDs) in their cells. In pursuit of green network design and also saving energy of the UDs, an optimization problem is formulated to minimize the total energy consumption of UDs subjecting to the delay constraints. Since the existing optimization schemes based on traditional optimization theory cannot adapt to the time-varying channel and highly dynamic UD requirements due to their complexity, we propose an efficient learning-enabled joint task offloading and resource allocation scheme based on proximal policy optimization (PPO) framework. Simulation results show that the total energy consumption of UDs is significantly reduced by our proposed PPO-based scheme, and also show the trade-off between the delay constraints satisfaction probability and the total energy consumption.

Federated learning (FL) has recently emerged as an innovative paradigm to train models among distributed agents. Conventional FL considers the center as an aggregator and trains from distributed data, while the collected global information at the center is not effectively utilized. Thus, the restricted information from local observations may limit the model accuracy. If FL can introduce data sets from the network server, the distributed models may be largely improved by the extra global information. Since network agents may not be completely trusted, the center cannot directly broadcast its raw data for security concern. Then, how to combine the central sets with FL? In this article, we propose to add a learning model at the center, which obtains the central sets as input. The outputs can be transmitted to network agents and integrated into local models instead of the raw data. The central and local models could be trained to form an integration for intelligent inference. Then, what is the integrated performance gain comparing with the original horizontal FL (HFL) and how to implement it? To figure out these two problems, we propose the vertical-HFL (VHFL) scheme, where models of the center and agents are trained collaboratively. We further analyze its convergence and the related communication channel, proposing the theoretical bounds to guide the network implementation of VHFL. Some simulation results will demonstrate the effectiveness of our proposed VHFL scheme. It is expected that VHFL will be an important block for the next generation of smart services.

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

This paper investigates a multi-user wireless powered fog computing (FC) network, where multiple energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP), and then compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. In order to pursue the green computing network design, an optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs’ computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, it is first transformed into an approximate form, and then an alternating direction method of multipliers (ADMM)-based algorithm is designed to solve the transformed problem, based on which the successive convex approximation (SCA) is adopted to improve the approximating precision in an iterative way. With the proposed ADMM-based distributed algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI), which thus is more suitable for large-scale networks. For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also presented, and the computational complexities of the proposed ADMM-based algorithm and the CSB algorithm are analyzed. Simulation results show that the proposed distributed algorithm achieves a comparable performance with the CSB centralized algorithm and the exhaustive search method. It is also observed that to minimize the transmit power at the HAP, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional sum-computation-rate maximization design.

This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users’ quality of service and information security requirements and the UAV’s trajectory’s causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmit power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperforms existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.

This paper investigates the cache-assisted power allocation in time domain for high-speed railway communications (HSRC), where train users are divided into real-time users (RUs) and non-real-time users (NRUs) from the time-sensitive perspective. RU's real-time data rate demand is ensured by power allocation, and NRU's data amount requirement is guaranteed by releasing cached content. In order to maximize the mobile service amount (MSA) of HSRC, an optimization problem is formulated to find the optimal cache switching time and power distribution under the constraints of total available energy, maximum power, caching and releasing causality, RU's minimal data rate requirement and NRU's minimal data amount requirement. Since the formulated problem is non-convex, a two-stage algorithm is proposed. In the first stage, we fix the cache switching time and transform the problem to be convex, and then use Karush-Kuhn-Tucker (KKT) condition to determine the optimal power distribution. In the second stage, one-dimensional search is employed to find the optimal cache switching time. Simulation results show that our proposed method is able to guarantee the RU's data rate threshold all the time by sacrificing some MSA. Moreover, the increase of data rate threshold and speed lead to a decrease of MSA, while the cache usage rate has relatively weak influence on MSA. © 2022 IEEE.

This paper studies the cooperative base station switching-off for multiple mobile network operators (MNOs) in multiple-input single-output (MISO) networks. To save the energy consumption of the system and also guarantee MNOs’ profit, we formulate a power minimization problem by jointly optimizing the operation modes of BSs, the connection states between users and BSs, and the beamforming vectors of multi-antenna BSs. To tackle the formulated non-convex problem, a roaming-cost-based BS switching-off scheme is designed to first search the feasible BSs that can be switched off and then optimize the beamforming vectors. Simulation results show that the proposed scheme not only reduces network power consumption but also avoids the profit loss at each MNO. It is also observed that there exists a minimum power consumption and a maximum average profit gain in terms of the rate price. Besides, the proposed scheme has notable capability in improving the profit at the low rate price region.

We consider a wireless network where$N$nodes compete for a shared channel over the CSMA/CA protocol to deliver observed updates to a common remote monitor. For this network, we rate the information freshness of the CSMA/CA based network using the age of information (AoI). Different from previous work, the network we consider is unsaturated. To theoretically analyze the transmission behavior of the CSMA/CA based network, we, therefore, develop an equivalent and tractable Markov transmission model. Based on this newly developed model, the transmission probability, collision probability and average AoI of the network are obtained. Our numerical results show that as the packet rate and the number of nodes increase, both the transmission probability and collision probability are increasing; the average AoI first decreases and then increases as the packet rate increases and increases with the number of nodes.

Federated learning (FL) has recently emerged as a transformative paradigm that jointly train a model with distributed devices while avoiding the need for central data collection. Due to the limited observation range, the devices only contain local information, which limits the quality of trained models. In this case, combining the global information into FL may be helpful. However, in horizontal FL, the central agency only acts as a model aggregator without utilizing its global observation. Meanwhile, the global data may not be directly transmitted to agents for data security. Then how to utilize the global observation residing in the central agency while protecting its safety thus rises up as an important problem in FL. In this paper, we develop a vertical-horizontal federated learning (VHFL) scheme, where the global feature is shared with the agents in a procedure similar to that of vertical FL. It is shown by experiments that the proposed VHFL could enhance the accuracy compared with horizontal FL while protecting the central data from being announced.

Inspired by diversity technology, we rethink the model enhancement from the view of wireless communication and propose a space-time framework for ensemble learning, called diversity learning. Such framework provides a new perspective that links the multi-model learning with the multi-channel commu-nication. In this paper, 2×1 diversity learning is mainly studied whose efficiency is guaranteed theoretically. We also evaluate the proposed scheme on two popular image classification tasks, MNIST and CIFAR-10. The results elucidate that the diversity learning reaps superiority on model enhancement, convergence, complexity and robustness compared to single models as well as weighting ensemble approach. Furthermore, the diversity schemes can be deployed in several emerging distributed learning systems, especially the mobile scenarios such as edge computing and cooperative learning where the resources for computation and communication are restricted.

In multiservice systems, multiple different Age of Information (AoI) penalty functions and corresponding algorithms are required to be deployed, which may result in high deployment complexity. Motivated by this, we propose a universal function$f(t)=\beta e^{\alpha t} -\beta $called$\alpha $-$\beta $AoI penaltyfunction to characterize different nonlinear forms of AoI penalty. With the presented$\alpha $-$\beta $AoI penalty function, we analyze the performance of wireless-powered communication networks (WPCNs), where a sensor first harvests energy from a wireless power station (WPS) and then transmits the generated update to its data collector. The sensor is equipped with a battery of limited energy capacity. When the battery of the sensor node is fully charged, the sensor generates a status update and uses all available energy to transmit it. A closed-form expression of the system average$\alpha $-$\beta $AoI penalty is derived by using some limit methods. In order to minimize the average$\alpha $-$\beta $AoI penalty of the system, an optimization problem is formulated to optimize the battery capacity. Simulation results demonstrate the correctness of our theoretical analysis results and show that there is a unique optimal battery capacity that optimizes the system AoI performance. Moreover, when the system is with the exponential-shape AoI penalty function ($\beta >0$and$\alpha >0$), with the increment of$\alpha $and$\beta $increase, the average$\alpha $-$\beta $AoI penalty also increases. Differently, when the system is with the logarithmic-shape AoI penalty function ($\beta < 0$and$\alpha < 0$), with the increment of$\alpha $and$\beta $, the average$\alpha $-$\beta $AoI penalty decreases.

This article studies the unmanned aerial vehicle (UAV)-assisted wireless powered network, where a UAV is dispatched to wirelessly charge multiple ground nodes (GNs) by using radio frequency (RF) energy transfer and then the GNs use their harvested energy to upload the sensed information to the UAV. At each moment, the UAV is scheduled to charge the GNs or only one GN is scheduled to upload its data. An optimization problem is formulated to minimize the average Age of Information (AoI) of the GNs by jointly optimizing the trajectory of the UAV and the scheduling of information transmission and energy harvesting of GNs. As the problem is a combinational optimization problem with a set of binary variables, it is difficult to be solved. Thus, it is modeled as a Markov problem with large state spaces and a deep${Q}$network (DQN)-based scheme is proposed to find its near-optimal solution on the basis of the deep reinforcement learning (DRL) framework. Two nets are structured with artificial neural network (ANN), where one is for evaluating the reward of the action performed in current state, and the other is for predicting realistic action. The corresponding state spaces, the efficient action spaces, and reward function are designed. Simulation results demonstrate the convergence of the proposed DQN scheme, which also show that the proposed DQN scheme gets much smaller average AoI than the three other known schemes. Moreover, by involving the energy punishment in the reward, the UAV may save its energy but yield higher AoI. Additionally, the effects of the packet size, the transmit power, and the distribution area of GNs on the GNs’ average AoI are also discussed, which are expected to provide some useful insights.

In this article, we consider the timeliness of information transmissions in a three-node industrial wireless sensor network (IWSN) in terms of Age of Information (AoI). In this network, a sensor monitors the ambient environment and transmits the sensed information to a remote monitor directly or through a relay node. In particular, we are interested in how the timeliness of the system is changed by decomposing the long-distance transmission with a relay and by enabling parallel transmissions over the two hops with a packet buffer. To this end, we derive the average AoIs of the transmissions over the direct-link, the relay-links with and without a buffer in a closed form. The obtained results show that the relay-link with a buffer outperforms the other two links, while the relay-link without a buffer outperforms the direct-link only if the relay is properly placed and the sensor–monitor distance is relatively large. On the condition that the average transmission times over the direct-link and the relay-link without a buffer are equal, we further evaluate how fast the average AoI can be reduced by using a relay or a packet buffer, as the packet rate approaches the maximum feasible rate over the links. It is shown that, although the sensor–monitor distance dominates the average AoIs of the links, the gains of using the relay and the buffer do not change much with the distance and are approximately constant.

In order to introduce deep learning technologies into anomaly detection, generative adversarial networks (GANs) are considered as important roles in the algorithm design and realistic applications. In terms of GANs, event probability reflected in the objective function has an impact on the event generation, which plays a crucial part in GAN-based anomaly detection. The information metric, e.g., Kullback–Leibler divergence in the original GAN, makes the objective function have different sensitivity on different event probability, which provides an opportunity to refine GAN-based anomaly detection by influencing data generation. In this article, we introduce the exponential information metric into the GAN, referred to as message importance measure (MIM)-based GAN, whose superior characteristics on data generation are discussed in theory. Furthermore, we propose an anomaly detection method with MIM-based GAN, as well as explain its principle for the unsupervised learning case from the viewpoint of probability event generation. Since this method is promising to detect anomalies in Internet of Things (IoT), such as environmental, medical, and biochemical outliers, we make use of several data sets from the online outlier detection data set (ODDS) repository to evaluate its performance and compare it with other methods.

Platooning strategy is an important part of autonomous driving technology. Due to the limited resource of autonomous vehicles in platoons, mobile-edge computing (MEC) is usually used to assist vehicles in platoons to obtain useful information, increasing its safety. Specifically, vehicles usually adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit large amount of data to the base station (BS) through vehicle-to-infrastructure (V2I) communications, where the useful information can be extracted by the edge server connected to the BS and then sent back to the vehicles to make correct decisions in time. However, vehicles may be moving on different lanes with different velocities, which incurs the unfair access due to the characteristics of platoons, i.e., vehicles on different lanes transmit different amount of data to the BS when they pass through the coverage of the BS, which also results in the different amount of useful information received by various vehicles. Moreover, age of information (AoI) is an important performance metric to measure the freshness of the data. Large average age of data implies not receiving the useful information in time. It is necessary to design an access scheme to jointly optimize the fairness and data freshness. In this article, we formulate a joint optimization problem in the MEC-assisted V2I networks and present a multiobjective optimization scheme to solve the problem through adjusting the minimum contention window under the IEEE 802.11 DCF mode according to the velocities of vehicles. The effectiveness of the scheme has been demonstrated by simulation.

Vehicular edge computing (VEC) is envisioned as a promising approach to process the explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates power to process partial tasks through offloading and the remaining tasks through local execution. During the offloading, each VU adopts the multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) channel to improve the channel spectrum efficiency and capacity. However, the channel condition is uncertain due to the channel interference among VUs caused by the MIMO-NOMA channel and the time-varying path loss caused by the mobility of each VU. In addition, the task arrival of each VU is stochastic in the real world. The stochastic task arrival and uncertain channel condition affect greatly on the power consumption and latency of tasks for each VU. It is critical to design an optimal power allocation scheme considering the stochastic task arrival and channel variation to optimize the long-term reward, including the power consumption and latency in the MIMO-NOMA VEC. Different from the traditional centralized deep reinforcement learning (DRL)-based scheme, this article constructs a decentralized DRL framework to formulate the power allocation optimization problem, where the local observations are selected as the state. The deep deterministic policy gradient (DDPG) algorithm is adopted to learn the optimal power allocation scheme based on the decentralized DRL framework. Simulation results demonstrate that our proposed power allocation scheme outperforms the existing schemes.

As an emerging technique, mobile-edge computing (MEC) introduces a new scheme for various distributed communication-computing systems, such as industrial Internet of Things (IoT), vehicular communication, smart city, etc. In this work, we mainly focus on the timeliness of the MEC systems where the freshness of the data and computation tasks is significant. First, we formulate a kind of age-sensitive MEC models and define the average Age-of-Information (AoI) minimization problems of interests. Then, a novel mixed-policy-based multimodal deep reinforcement learning (RL) framework, called heterogeneous multiagent actor–critic (H-MAAC), is proposed as a paradigm for joint collaboration in the investigated MEC systems, where edge devices and center controller learn the interactive strategies through their own observations. To improve the system performance, we develop the corresponding online algorithm by introducing the edge federated learning mode into the multiagent cooperation whose advantages on learning convergence can be guaranteed theoretically. To the best of our knowledge, it is the first joint MEC collaboration algorithm that combines the edge federated mode with the multiagent actor–critic RL. Furthermore, we evaluate the proposed approach and compare it with popular RL-based methods. As a result, the proposed algorithm not only outperforms the baselines on average system age, but also promotes the stability of training process. Besides, the simulation outcomes provide several insights for collaboration designs over MEC systems.

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically.

Unmanned aerial vehicles (UAVs) with huge-capacity batteries could be employed to wirelessly charge the ground sensor users (GSUs) and enhance the coverage of aerial wireless networks in outdoor Internet of Things (IoT). This paper investigates the information and energy coverage of UAV-enabled simultaneous wireless information and power transfer (SWIPT) networks. Both power splitting (PS) and time switching (TS) receiver architectures are considered. By using stochastic geometry approach, the general and explicit expressions of the information coverage probability (ICP), the energy coverage probability (ECP) and the joint information and energy coverage probability (JIECP) are derived under the nonlinear and linear energy harvesting (EH) models, respectively. Particularly, the Laplace transform and the probability generating functional (PGFL) are used to derive the ICP. And, Campbell’s theorem and the maximum function are applied to obtain the ECP and the JIECP, respectively. To achieve the optimal UAVs’ deployment density, the maximization optimization problems are formulated for the PS-based and TS-based systems, respectively. By using the series expansion of$Q(x)$($Q$-function) with large$x$, the closed-form approximating optimal solutions to the formulated problems are obtained. Monte Carlo simulations validate the correction of our obtained theoretical results, and numerical results show that the performance of the PS-based system is superior to that of the TS-based one. Moreover, when the energy requirement of GSUs or the transmit power of UAVs is relatively large, or when the information requirement of GSUs or the UAV deployment density is relatively small, compared with the nonlinear EH model, the analysis bias caused by traditional linear EH model is relatively large and in these cases, traditional linear EH model cannot be used to replace the nonlinear EH one for the system performance analysis or optimal system design.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

Recently, federated learning (FL) has been treated as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their data sets. Different from centralized training on some collected data sets, FL training suffers a lot of constraints from limited resources in the network. Therein, the bandwidth and package loss restrict interactions in training. Meanwhile, the highly distributed data sets and limited computation could also affect its convergence. To figure out the specific impact, we analyze the convergence rate of FL training considering both communication and training. Further taking in training costs in terms of time and power, the closed-form optimal settings for communication networks are proposed with principles to assist the parameter selection. The results build a bridge between AI and communication, giving us an intuitive knowledge of how the background system could influence the distributed training process.

We consider an Internet-of-Things (IoT) system in which a sensor observes a phenomenon of interest with exponentially distributed intervals and delivers updates to a monitor with random service times. At the monitor, the received updates are used to make decisions with deterministic or random intervals. For this system, we investigate the freshness of the received updates at decision epochs using the age upon decisions (AuDs) metric. With the first-come-first-served (FCFS) policy, theoretical results show that: 1) when the decisions are made with exponentially distributed intervals, the average AuD of the system is smaller when the service time (e.g., transmission time) is uniformly distributed than when it is exponentially distributed and would be the smallest if it is deterministic; 2) when the decisions are made periodically, the average AuD of the system is larger than, and decreases with decision rate to, the average AuD of the corresponding system with Poisson decision intervals; and 3) the probability of missing to use a received update for any decisions is decreasing with the decision rate and is the smallest if the service time is deterministic. When the last-come-first-served (LCFS) policy with preemption is used, we observe that systems with Poisson service processes perform the best while systems with periodic service processes perform the worst. For IoT-based monitoring systems, therefore, it is suggested to use deterministic monitoring schemes, deterministic transmitting schemes, and Poisson decision schemes so that the received updates are as fresh as possible at the time they are used to make decisions.

This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV's trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV's trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush-Kuhn-Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

This article investigates a multifog server (FS)-assisted nonorthogonal multiple access (NOMA)-based wireless powered network, where an energy-limited wireless device (WD) first harvests energy from a power transmitter (PT) and multiple helping FSs and then uses the harvested energy to partially offload its computing task to the FSs with NOMA for computing. To explore the WD's performance limit in terms of achievable computation rate, an optimization problem is formulated by jointly optimizing the time assignment, the power allocation, and the computation frequency under multiple system constraints. Since the problem is nonconvex with no known solution, an efficient solution approach is designed to achieve the ε-optimal solution, in which the transmit power vector and the computation frequency are jointly optimized with fixed-time assignment, and then, a golden section search (GSS)-based algorithm is designed to find the optimal time assignment. For the case when the FS is with sufficiently strong computation capability, some semiclosed-form results are derived. Numerous results show that our proposed design achieves much higher computation rate than benchmark schemes. Moreover, with the increment of the helping FSs, the achievable computation rate increases while the increasing rate is decreased. Besides, by employing NOMA, the WD's computation rate is also improved compared with orthogonal multiple access (OMA)-based scheme. Additionally, in such a system, with nonlinear energy harvesting (EH) model adopted, the more the helping FSs are deployed, the more the performance loss caused by the traditional linear EH model can be reduced.

A UAV-aided wireless power transfer and data collection network is studied, where it is assumed that when the harvested energy at the sensor node (SN) cannot surpass its circuit activation threshold or the received data rate at UAV falls below a minimal required rate threshold, the information outage occurs. The closed-form expressions of energy outage probability and rate outage probability are derived at first, and then the overall outage probability and coverage performance of the system are analyzed. Based on which, an optimization problem is formulated to minimize the overall outage probability by optimizing UAV's elevation angle and the time splitting (TS) factor. Since the problem is non-convex and has no known solution, an alternating optimization (AO)-based algorithm with Golden-section (GS) based linear search method is designed to find the global optimal solution. In order to explore the maximum coverage area of the UAV for a given tolerable outage probability, another optimization problem is also formulated to maximize the coverage range by optimizing UAV's elevation angle. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution of the optimal elevation angle for maximizing the coverage area is derived. Monte Carlo simulations verify the accuracy of the derived closed-form expression of the overall outage probability and the semi-closed-form expressions of the optimum UAV's elevation angle and TS factor. It shows that there exist a unique optimum elevation angle and the TS factor to achieve the minimum overall outage probability, and significant performance gain can be obtained by using our proposed optimization scheme. The developed theoretical results can be useful to the design of UAV-aided wireless communication systems with wireless power transfer.

Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. As FL does not collect and store the data centrally, it requires frequent model exchange through the wireless network. However, since the aggregation in FL can be partially participated with synchronized frequency, its communication pattern is different from the conventional network. Therein, limited bandwidth and package loss restrict interactions in training. Thus, the network scheduling could largely affect the FL convergence. To figure out the specific effects, we analyze the convergence rate of FL regarding the joint impact of communication and training. Combining it with the network model, we formulate the optimal scheduling problem for FL implementation. The theoretical results could guide the hyper-parameter design in the network and explain the principle of how the wireless communication could influence the FL training process.

This paper investigates a multi-node wireless powered communication network (WPCN), where a hybrid access point (HAP) first charges an Internet of Thing (IoT) device wirelessly with the assistance of multiple selfish wireless nodes (WNs), and then the IoT device uses the harvested energy to transmit real-time status updates to the HAP. Two incentive schemes, i.e., the energy-incentive scheme and the price-incentive scheme, are designed to overcome the selfishness of the WNs and enhance the per-packet AoI performance. For the energy-incentive scheme, an AoI-energy utility function is defined and an optimization problem is formulated to maximize the AoI-energy utility value of the HAP-IoT device pair. By using equality constraint elimination and Lagrangian method, the problem is solved and some closed-form solutions are derived to obtain the optimal solution. For the price-incentive scheme, an AoI-price utility function is defined and a Stackelberg game is established to maximize the utility of the HAP-IoT device pair. By using function transformation and Lagrange method, some semi-closed-form solutions are derived to maximize their own profits of the HAP and WNs in a distributed way. Numerical results show that our proposed two incentive mechanisms are able to achieve higher network utility values than the benchmark scheme. The more the number of WNs, the lower the AoI of each status update packet and the higher utility value of the HAP. It also shows that by positioning WNs closer to the IoT device, the better per-packet AoI performance can be achieved by both incentive mechanisms. Additionally, for the energy-incentive mechanism, its achieved AoI gain and energy gain decrease with the increment of the distance between the HAP and the IoT device. But for the price-incentive mechanism, the opposite phenomenon is observed.

This paper investigates the relay-assisted wireless information and power transfer enabled hybrid visible light communication (VLC)-radio frequency (RF) network, where a light emitting diode (LED) access point (AP) serves multiple information users (IUs) and multiple energy harvesting users (EHUs). IUs are allowed to receive information from the LED AP through time-division-multiple-access (TDMA) manner by either the single-hop VLC-ONLY mode or the relay-assisted dual-hop VLC-RF mode, while EHUs harvest energy via the VLC links. An optimization problem is formulated to maximize the achievable information rate of IUs by jointly optimizing the access mode selection, the direct current (DC) offset at the LED AP, the peak amplitude of the alternating current (AC) component at the LED AP, the electrical power allocated to the LED AP and the power allocation at relay, subject to the energy harvesting (EH) requirement constraints of EHUs. To tackle the non-convex problem with binary variables, we first decompose it into two subproblems in terms of the two access modes. Then, the subproblems are equivalently transformed and solved by the proposed successive convex approximation (SCA)-based algorithms. Simulation results show that significant performance gain can be achieved by optimizing the DC offset. It is also observed that the area where the VLC-ONLY mode is superior to the VLC-RF mode is enlarged with the decrease of the minimal EH requirement. Besides, the achievable information rate of IUs by the VLC-RF mode first increases and then decreases with the increment of the distance between the relay and the LED AP.

This paper investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

In this article, we consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as age upon decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, and controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision-making process. The theoretical results show that: 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and will be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrival process are periodic, the average AuD is larger, but decreases with decision rate to, the average AuD of the corresponding system with the Poisson decisions (i.e., random); and 3) when both the decision process and the arrival process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making the periodical updates and decisions with properly controlled offset is also a promising solution, if the timing information of the two processes can be accessed by the monitor.

We consider an Internet of Things (IoT)-based sensing system, in which an energy harvesting powered sensor observes the phenomenon of interest and transmits its observations to a remote monitor through a Gaussian channel. Based on the received signals, the monitor makes estimations of source signals with some distortion requirement. We measure the timeliness of the recovered signals using the Age of Information (AoI), which could be reduced by transmitting observations more frequently (i.e., with shorter intervals). We evaluate the recovery distortion with the mean-squared-error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor is quite limited, however, the frequency and power of transmissions cannot be increased at the same time. Thus, we shall investigate the timeliness-distortion tradeoff of the system by minimizing the average weighted sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we investigate the offline optimization of the system and propose a backward water-filling-based power allocation scheme, as well as a genetic-based joint transmission scheduling and power control algorithm. Third, we formulate the online power control as a Markov decision process (MDP) and solve the problem with an iterative algorithm, which closely approaches the tradeoff limit of the system. We show that the optimal transmit power is a monotonic and bivalued function of current AoI and distortion. Finally, we present our results via simulations and extend the results on the save-and-transmit policy to fading sensing systems.

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV's trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV's CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV's computing energy can also be greatly reduced.

Heavy data load and wide cover range have always been crucial problems for big data processing in Internet of Things (IoT). Recently, mobile-edge computing (MEC) and unmanned aerial vehicle base stations (UAV-BSs) have emerged as promising techniques in IoT. In this article, we propose a three-layer online data processing network based on the MEC technique. On the bottom layer, raw data are generated by distributed sensors with local information. Upon them, UAV-BSs are deployed as moving MEC servers, which collect data and conduct initial steps of data processing. On top of them, a center cloud receives processed results and conducts further evaluation. For online processing requirements, the edge nodes should stabilize delay to ensure data freshness. Furthermore, limited onboard energy poses constraints to edge processing capability. In this article, we propose an online edge processing scheduling algorithm based on Lyapunov optimization. In cases of low data rate, it tends to reduce edge processor frequency for saving energy. In the presence of a high data rate, it will smartly allocate bandwidth for edge data offloading. Meanwhile, hovering UAV-BSs bring a large and flexible service coverage, which results in a path planning issue. In this article, we also consider this problem and apply deep reinforcement learning to develop an online path planning algorithm. Taking observations of around environment as an input, a CNN network is trained to predict action rewards. By simulations, we validate its effectiveness in enhancing service coverage. The result will contribute to big data processing in future IoT.

Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results.

This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested energy to offload their computation tasks to fog/cloud servers via the HAP or compute their tasks locally. To pursue multi-user fairness, an optimization problem is formulated to maximize the minimal energy balance among all users by jointly optimizing time assignments, computation central processing unit (CPU) frequencies, and the computing mode selection. Since the problem is mixed-integer combinatorial non-convex, which is intractable, a generalized Benders decomposition (GBD)-based method is proposed, which guarantees the globally optimal solution. To release the high computational complexity of the proposed GBD-based method, a penalized successive convex approximation (P-SCA)-based algorithm is designed as an alternative to obtain a suboptimal solution with low computational complexity. Numerical results show that among different optimizable factors in the system, computing mode selection is the dominant one on affecting the system performance. Moreover, for each user, local computing is a better choice, if it is with relatively poor channel gain and small local computing delay. Otherwise, fog/cloud computing may be a better choice. Additionally, for the users with relatively high channel gains, if their local computing delays are less than those selecting fog computing, cloud computing should be a better choice.

This paper studies the secrecy energy efficiency (SEE) for MISO power-splitting (PS) SWIPT networks in the presence of multiple passive eavesdroppers (Eves), where the non-linear energy harvesting (EH) model and the dual-layer PS receiver architecture are employed. With only channel distribution information (CDI) of Eves known and the artificial noise (AN) embedded into the transmit signals at the transmitter, a SEE maximization problem is formulated under constraints of the minimal rate and EH requirements of legitimate receivers and the power budget at the transmitter. To tackle the difficulty caused by the fractional objective function and the probability constraints in solving the considered problem, the second-layer PS ratios are firstly optimized by bisection and sum-of-ratios maximization methods, and then the transmit beamforming vectors, the AN covariance matrix and the first-layer PS ratios are jointly optimized by using successive convex approximation (SCA) and Dinkelbach's methods. The proposed solution approach is theoretically proved to converge to a stationary point of the SDR form of the considered problem, which is further shown to be the optimal one. Numerical results show that our proposed design achieves the highest SEE over traditional power minimization and secrecy rate maximization designs. Moreover, when the rate requirement is larger than a threshold or the available power is less than a threshold, traditional power minimization design or secrecy rate maximization design is able to achieve a similar SEE to our proposed design. Besides, the dual-layer PS receiver architecture is able to improve the EH efficiency and system SEE.

This paper studies the robust energy efficient beamforming design for MISO systems where only channel distribution information (CDI) is assumed to be available at the transmitter. To capture the general relationship between the data transmission and the energy consumption, the dynamic energy consumption model (DECN) is adopted. An optimization problem is formulated to maximize the system energy efficiency under the constraints of rate outage probability and total available power. The problem is difficult to tackle due to the fractional objective function and the information outage constraints. To solve it, the semidefinite relaxation (SDR) is applied at first and then, a solution approach based on the successive convex approximation (SCA) and the Dinklebach's methods is presented. It is proved that our proposed solution approach is able to converge to a stationary point of the formulated optimization problem. Numerical results demonstrate that DECN has a great impact on system EE. It is observed that there is a saturation point on the system EE in term of available power and the required power corresponding to the saturation point of EE highly depends on circuit power. Particularly, higher circuit power leads to a larger required power but a smaller maximal system EE.

This paper investigates the optimal design of wireless- powered hierarchical fog-cloud computing networks, where energy-constrained users first harvest energy from a hybrid access point (HAP) and then offload their computation tasks to fog/cloud servers via the HAP or compute the tasks locally by us- ing the harvested energy. An optimization problem is formulated to maximize the minimal energy balance among multiple users by jointly optimizing offloading decisions, communication and computation resource allocations in the system, where computational capacity, processing delay, energy harvesting (EH) and energy consumption constraints are considered. To efficiently solve such a mixed-integer combinatorial non-convex problem, a penalized successive convex approximation (P-SCA)- based algorithm is designed, which is able to converge to a suboptimal solution with the polynomial time computational complexity. Numerical results show that compared to communication and computation resource allocation, the offloading decision is the dominant factor on affecting the system performance. It is also found that local computing is a better choice for users with relatively poor channel gains while fog/cloud computing is a better choice for users with relatively good channel gains. Specifically, cloud computing is preferred if the cloud computational capacity is strong enough and the wired-link data rate is high enough; Otherwise, fog computing is preferred. Besides, more users are served, less max-min energy balance can be obtained.

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, edge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed, where distributed raw data are collected and processed by edge servers. The edge servers are supposed to split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of the limited edge computation capability, part of the raw data may be directly transmitted to the cloud. To manage limited resources in an online manner, we propose an algorithm based on Lyapunov optimization, which jointly optimizes the policy involving edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay while saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT.

This paper investigates the efficient cooperation in wireless-powered communication networks, where an access point (AP) and multiple helpers first together charge up a sensor via radio-frequency (RF)-based wireless power transfer (WPT), and then the sensor uses the harvested energy to transmit real-time status updates to the AP. Due to the selfishness of the helpers, payment is provided as an incentive to them by the sensor-AP communication pair. For such a system, a Stackelberg game approach is designed to establish efficient cooperation between the helpers and the sensor-AP communication pair. An Age of Information (AoI)-based utility and a profit-based utility are defined for the sensor-AP pair and the helpers, respectively. Optimization problems are formulated to maximize their utilities. An explicit expression of the optimal transmit power of the helper is derived, with which a Dinkelbach's Programming (DP)-based algorithm is designed to jointly find the optimal payment price and transmit power at the AP, and at the same time, the Stackelberg equilibrium (SE) is achieved. Moreover, a closed-form expression of the minimum AoI of the system is also presented. Numerical results show that the more helpers there exist, the higher payment price the AP should fix, and meanwhile, the lower AoI of the sensor-AP communication pair can be achieved. Besides, it is shown that by increasing the transmit power at the AP, the optimal average AoI could not be reduced evidently.

This paper investigates the information-energy (I-E) region for simultaneous wireless information and power transfer (SWIPT) system in mobility scenarios, where a moving transmitter transmits information and energy to a power splitting (PS)-based receiver. An optimization problem is formulated to explore the system I-E region under the nonlinear energy harvesting (EH) model by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver. Since the problem is nonconvex, a successive convex approximate-based (SCA-based) algorithm is proposed, which is able to find the sub-optimal solution with low complexity. For comparison, the I-E region of the system under the linear model is also studied, where some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the linear EH model, the nonlinear EH model yields a smaller I-E region due to the limitations of EH circuit features. Nevertheless, using the nonlinear EH model avoids the false achievable I-E region for practical mobile SWIPT systems. Besides, it shows that the higher moving speed yields the smaller I-E region. Moreover, with the increment of the required information amount, the harvested energy bias caused by the linear EH model decreases, but the bias ratio increases.

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with the increment of time between two received updates. Due to EH, a waiting time is required at the sensor before transmitting the status update. An optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. A two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and the inner loop presents a semi-closed-form expression of the optimal waiting time policy based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results show that our proposed optimal transmission policy outperforms the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are in slight load. It also shows that the system U-AoI first decreases and then keeps unchanged with the increments of EH circuit's saturation level.

With the rapid growth of the data volume and the fast increasing of the computational model complexity in the scenario of cloud computing, it becomes an important topic that how to handle users' requests by scheduling computational jobs and assigning the resources in data center.In order to have a better perception of the computing jobs and their requests of resources, we analyze its characteristics and focus on the prediction and classification of the computing jobs with some machine learning approaches. Specifically, we apply LSTM neural network to predict the arrival of the jobs and the aggregated requests for computing resources. Then we evaluate it on Google Cluster dataset and it shows that the accuracy has been improved compared to the current existing methods. Additionally, to have a better understanding of the computing jobs, we use an unsupervised hierarchical clustering algorithm, BIRCH, to make classification and get some interpretability of our results in the computing centers.

This paper investigates the simultaneous wireless information and power transfer (SWIPT) networks with coexisting power-splitting users (PSUs) and time-switching users (TSUs) under the nonlinear energy harvesting (EH) model, where a multiantenna hybrid access point (H-AP) transmits information and power to multiple PSUs and TSUs. For such a network, an optimization problem is formulated to minimize the required transmit power at the H-AP subject to users' information rate and harvested energy constrains by jointly optimizing the H-AP's transmit beamforming vectors, PSUs' power splitting (PS) ratios, and TSUs' time switching (TS) factors. Due to the interferences among PSUs and TSUs, and the nonlinear EH model, the problem is nonconvex and has no known solution method. Thus, a two-layer algorithm is first presented based on semidefinite relaxation (SDR) and it is theoretically proved that the global optimum is achieved. However, since 1-D search is adopted by the two-layer algorithm, which may be too computationally exhaustive, a successive convex approximate-based (SCA-based) algorithm is then proposed as an alternative, which is able to find the near-optimal solution with low complexity by using the first-order approximation. The numerical results show that with the same EH requirements, TSUs are more likely to enter into the saturation region compared with PSUs, but their EH efficiency is higher than that of PSUs. It is also shown that the minimal required transmit power under the nonlinear EH model is much lower than that under the linear one. Although after the saturation point of the nonlinear EH model, the linear one yields a lower required transmit power, it is a fake result, because the linear EH model mismatches the nonlinearity of practical EH circuits.

This paper investigates a fog computing-assisted multiuser simultaneous wireless information and power transfer network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multiuser scheduling, the time assignment, the sensors' transmit powers, and the PS ratios. Since, the problem is a mixed integer programming problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions, and theoretical analysis. For a scheduled sensor, the closed-form and semi-closed-form solutions to achieve its minimal required energy are derived, and then an efficient multiuser scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server, the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.

This paper considers an MISO simultaneous wireless information and power transfer (SWIPT) system, where one transmitter serves multiple authorized receivers in the presence of several potential eavesdroppers (idle receivers). To prevent the information interception by eavesdroppers, artificial noise (AN) is embedded into the transmit signals. The non-linear energy harvesting (EH) model is adopted and a novel power-splitting (PS) EH receiver architecture is proposed. Stochastic uncertainty channel model (SUM) is considered for the idle receivers due to outdated channel feedback. A global energy efficiency (GEE) maximization problem is formulated by jointly optimizing the transmit beamforming vectors, the AN covariance matrix, and the PS ratios, under the minimal rate and secure transmission constraints of authorized receivers, the EH requirement constraints of idle receivers, and the total available power constraint at the transmitter. Since the problem is non-convex with no known solution, it is solved based on the following solution framework. Firstly, the PS ratios are optimized by using the bisection method and successive convex approximation (SCA), and then, the transmit beamforming vectors and the AN covariance matrix are jointly optimized by using a Dinkelbach's Algorithm based method, where SCA is applied to solve its inner subproblem. It is theoretically proved that by involving AN, the system GEE can be improved. Numerous results show that system GEE first increases and then keeps unchanged with the increment of the total available power, but it first keeps unchanged and then decreases with the increment of the minimal rate requirement. It is also observed that compared with traditional EH receiver architecture and linear EH model, our proposed PS EH receiver architecture is able to achieve higher GEE and avoid false output power at idle receivers.

This paper investigates a simultaneous wireless information and power transfer (SWIPT)-enabled non- orthogonal multiple access (NOMA) network with full- duplex (FD) relaying, where a multi-antenna source transmits information to two users. The nearby user is with multiple antennas, which receives its own information and harvests energy from the signals transmitted by the source and also help forward information to the far-end user. For such a system, an optimization problem is formulated to minimize the required transmit power by jointly optimizing beamforming vectors and power splitting (PS) ratio under the energy harvesting and users' data rate constraints of both users. As the problem is non- convex with unknown solution, a bilevel- optimization method is proposed to solve it via semidefinite relaxation (SDR) and the global optimal solution is achieved with perfect self- interference cancellation. However, since self- interference may not be cancelled perfectly in practice, a successive convex approximation (SCA) based algorithm with low complexity is proposed to obtain a near optimal solution. Numerical results show that integrating NOMA, FD relaying and SWIPT in a single communication system is able to greatly reduce the required transmit power. Besides, the effects of the parameters including the data rate threshold and the energy storage amounts, on the system performance are also discussed.

In this paper, we focus on the mutual information, which can characterize the transmission ability because it shows correlation between channel input and channel output. Shannon entropy and mutual information are the cornerstones of information theory. In addition, Chernoff information is another fundamental channel information measure, and it describe the maximum achievable exponent of the error probability in hypothesis testing. Uased on alternating conditional expectation (ACE) algorithm, we decompose these two mutual information. In fact, their decomposition results are similar in big data prespective. In this sense, these two kinds of mutual information are just different measures of the same information quantity. This paper also deduces that the channel performance only depends on channel parameters and the decomposition results of a new proposed mutual information should agree with the impact of the parameters.

This paper mainly focuses on the utilization frequency in receiving end of communication systems, which shows the inclination of the user about different symbols. When the using number is limited, a specific utility distribution is proposed on the best effort in term of fairness, which is also the closest one to occurring probability in the relative entropy. Similar to a switch, the parameter of this special utility distribution can be selected to make it satisfy the personalized user demands: negative parameter means the user focus on high-probability events and positive parameter means the user is interested in small-probability events. In fact, the utility distribution can be regraded as a measure of message importance in essence. It illustrates the meaning of message importance measure (MIM), and extend it to the general case by selecting the parameter. Based on it, we connect personalized user demands to the message importance. Numerical results show that this utility distribution characterizes the message importance like MIM and its parameter determines the concern of users like a switch.

This paper investigates the artificial noise (AN)-aided multi-cell coordinated beamforming (MCBF) for secure simultaneous wireless information and power transfer in both centralized and distributed manners. The proposed transmit design is formulated into a power-minimization problem to guarantee the authorized users' information and energy harvesting (EH) requirements while avoiding the information interception by unauthorized users. Power splitting receiver architecture and the non-linear EH model are employed. Both perfect and imperfect channel state information (CSI) cases are considered. For the perfect CSI case, the non-robust design is presented by applying semi-definition relaxation (SDR). When no user harvests energy, the global optimum is guaranteed, and when some users harvest energy, approximate global optimum is achieved. For the imperfect CSI case, the worst-case robust design under the deterministic uncertainty channel model is studied, where a solving approach based on SDR and S-procedure is proposed, and the statistically robust design under the stochastic uncertainty channel model is also studied, where an upper bound to the global optimum is obtained by using SDR and Bernstein-type inequality. We further propose a distributed AN-aided MCBF design framework by using an alternating direction method of multipliers for the non-robust, worst-case robust, and statistically robust designs, with which each BS is able to optimize its own transmit design with the local CSI. Simulation results demonstrate our theoretical analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It also shows that employing the non-linear EH model is able to avoid false output power and save power consumption at the BSs.

The storage and the transmission of messages in big data are discussed in this paper, where message importance is taken into account. To this end, we propose to use non-parametric message importance measure (NMIM) as a measure of message importance, which can characterize the uncertainty of random events like Shannon entropy and Rényi entropy. We prove that NMIM sufficiently describes the two key characters of big data, i.e., the rare events finding and the large diversities of events. Based on NMIM, we then propose an effective compressed encoding mode for data storage, and discuss the transmission of messages over some typical channel models with limited message importance loss. Our numerical results show that the proposed strategy occupies less storage space without losing too much important information, and the maximum received entropy rate increases with the increasing of message importance loss until it reaches saturation, which contributes to designing of better practical communication system.

This paper considers the placement of unmanned aerial vehicle base stations (UAV-BSs) with criterion of minimum UAV-recall-frequency (UAV-RF), indicating the energy efficiency of mobile UAVs networks. Several different power consumptions, including signal transmit power, on-board circuit power and the power for UAVs mobility, and the ground user density are taken into account. Instead of conventional empirical stochastic models, this paper utilizes a pattern formation system to track the instable and non-ergodic time-varying nature of user density. We show that for a single time-slot, the optimal placement is achieved when the transmit power of UAV-BSs equals their on-board circuit power. Then, for multiple time-slot duration, we prove that the optimal placement updating problem is an integer nonlinear programming coupled with an inherent integer linear programming. Since the original problem is NP-hard and cannot be solved with conventional recursive methods, we propose a sequential-Markov-greedy-decision strategy to achieve near minimal UAV-RF in polynomial time. Furthermore, we prove that the increment of UAV-RF caused by inaccurate predicted user density is proportional to the generalization error of learned patterns. Here, in regions with large area, high-rise buildings, or low user density, large sample sets are required for effective pattern formation.

This paper investigates the secure transmit design for simultaneous wireless information and power transfer system under the non-linear energy harvesting (EH) model, where a transmitter sends confidential information and transfers energy to multiple information receivers (IRs) and EH receivers (ERs) with the existence of multiple eavesdroppers (Eves). To prevent confidential information leakage, multiple artificial redundant signals (MARSs) are embedded in the transmit signals. The goal is to minimize the total transmit power by jointly optimizing transmit beamforming vectors and the covariance matrixes of MARSs, such that the minimal information rate and EH requirements at IRs and ERs are guaranteed while making the received signal-to-Interference ratio at ERs and Eves lower than their information decoding thresholds. Both the non-robust and the robust designs are studied. For the non-robust design, the optimal solution is derived. For the robust design, an approximate optimal solution is obtained by using Gaussian randomization procedure. Simulation results show that compared with traditional non-MARS-aided beamforming design, our proposed design is superior in terms of the total required transmit power. It also shows that employing the non-linear EH model can avoid false output power at the ERs and/or save power at the transmitter.

This paper mainly considers the compressed storage problem for big data in wireless communication systems, where the message importance is taken into account. Similar to Shannon Entropy and Renyi Entropy, we first define a non-parametric message important measure (NMIM) as a measure for message importance. It can characterize the uncertainty of random events. It is proved that it can sufficiently describe the two key characters of big data: rare events finding and large diversities of events. Based on NMIM, we propose an effective compressed encoding mode for data storage in wireless communication systems. Numerical simulation results show that using our developed strategy takes up very little storage space without losing too much message importance.

In the downlink of high-speed railway communication systems equipped with distributed transmit antennas, high mobility leads to fast time-varying received signal to noise ratios. In this case, dynamic time-domain power allocation and antenna selection could be jointly optimized to improve the system energy efficiency. This paper considers this problem in such a simple way where dynamic switching between multiple-input-multiple-output and single-input-multiple-output is allowed and exclusively utilized, while the sparse scattering terrains and delay-sensitive traffic flows are taken into account. The original optimization problem is a typical mixed integer nonlinear programming (MINLP) problem. Instead of conventional iteration based methods, such as the extended cutting plane method, we propose a low-complexity and direct solution by exploiting the physical nature of original problem, which can be utilized in real time. Theoretical results show that our proposed method can be viewed as the generalization of channel-inversion associated with transmit antenna selection. Also, compared with methods without dynamic antenna selection, our method significantly decreases the average transmit power.

This work focuses on an energy harvesting communication system where a static sensor harvests energy from the environment and transmits information to a moving agent. Assuming that the battery is infinite and the sensor always has information to transmit, we first maximize the channel service theoretically, and then implement the event-triggered scheduling to control the time for starting transmission. We prove that energy depletion time is only relevant to the initial energy in the battery. There is no relation between the harvesting power and the energy depletion time. We provide the transmission triggered condition by deriving the initial energy threshold. Moreover, we extend the transmission model in single time-slot to an M/G/s(0) queuing system in multiple time slots and discuss the blocking probability caused by the event-triggered strategy. Our numerical results show the channel service of the scheme along with constant PA under various different configurations.

This paper investigates the joint optimal beamforming and power-splitting receiver architecture design for simultaneous information and power transfer (SWIPT) under non-linear energy harvesting (EH) circuit environment, where hybrid access point (H-AP) equipped with multiple antennas simultaneously transmits information and power to multiple single-antenna users. For such a system, in order to achieve green communication design, we formulate an optimization problem to minimize the total transmit power of H-AP subjecting to the required signal-to-interference-plus- noise ratio (SINR) and the harvested power constrains at each user under non-linear EH model. Since the problem is non-convex, the relaxed semidefinite program (SDP) is used to solve it, and it is proved that the semidefinite relaxation (SDR) process is tight and our optimal solution achieves the global optimum. Numerical results show that a considerable gain could be achieved if the SWIPT beamforming vector and the power splitting ratios are jointly designed under the non-linear EH model compared with traditional linear EH model, since the non-linear EH model captures and matches the real EH circuits' non-linear features. Moreover, the feasible region of traditional linear EH model in the non-linear EH circuit environment is also characterized.

This paper investigates the power minimization problem for a simultaneous wireless information and power transfer (SWIPT) system in MISO wiretap networks, where one multiple-antenna transmitter intends to transmit required amount of information and energy to its legitimate receiver while restrict the information leakage to an eavesdropper (Eve). The nonlinear EH model is employed for SWIPT. Two uncertainty MISO channel models are considered for the legitimate receiver, i.e. the deterministic uncertainty model (DUM) and the stochastic uncertainty model (SUM), and the Eve is assumed not to feed back its channel to the transmitter. For the DUM, the worst-case design with global optimum is solved by our proposed method based on semidefinite relaxation (SDR) and S-procedure. For the SUM, the statistically robust design with a tight upper bound to global optimum is obtained by our proposed method based on SDR and Bernstein-type inequality. Numerous simulation results demonstrate the validity and efficiency of our proposed robust transmit design methods. Compared with the traditional linear EH model, employing the nonlinear EH model can avoid false output power at the legitimate receiver or save power consumption at the transmitter as the real circuits are working in the nonlinear output field rather than the linear one.

This paper investigates how to achieve secure information transmission in multi-cell multi-user networks, where artificial noise (AN) aided multi-cell coordinated beamforming (MCBF) is designed to guarantee the authorized users' QoS requirements while avoiding the information being intercepted by unauthorized users. To realize the green communication target, we formulate an optimization problem to minimize the total transmit power by jointly optimizing the beamforming and AN vectors at all BSs. Since the problem is nonconvex and not easy to be solved by using existing solution methods, we then solve it by applying semi-definition relaxation (SDR) and prove that our proposed method can guarantee the global optimal solution under full channel state information (CSI). Moreover, we further design a distributed AN-aided MCBF for the system by using alternating direction method of multipliers (ADMM), with which each BS can calculate the beamforming and AN vectors with its local CSI. Simulation results demonstrate our analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It is also observed that for the same secure transmission requirement, the total power consumed by our proposed AN-aided MCBF decreases with the increment of transmit antennas, where less part of the power is used by AN and more part is used for information beamforming.

Message importance measure (MIM) is applicable to characterize the importance of information in the scenario of big data, similar to entropy in information theory. In fact, MIM with a variable parameter can make an effect on the characterization of distribution. Furthermore, by choosing an appropriate parameter of MIM, it is possible to emphasize the message importance of a certain probability element in a distribution. Therefore, parametric MIM can play a vital role in anomaly detection of big data by focusing on probability of an anomalous event. In this paper, we propose a parameter selection method of MIM focusing on a probability element and then present its major properties. In addition, we discuss the parameter selection with prior probability, and investigate the availability in a statistical processing model of big data for anomaly detection problem.

Massive Multiple-input Multiple-output (MIMO) adaption is one of the primary evolving objectives for the next generation high speed train (HST) communication system. In this paper, we consider how to design an efficient low-complexity location-aware beamforming for the multi-user (MU) massive MIMO system in HST scenario. We first put forward a lowcomplexity beamforming based on location information, where multiple users are considered. Then, without considering interbeam interference, a closed-form solution to maximize the total service competence of base station (BS) is proposed in this MU HST scenario. Finally, we present a location-aid searching-based suboptimal solution to eliminate the inter-beam interference and maximize the BS service competence. Various simulations are given to exhibit the advantages of our proposed massive MIMO beamforming method.

We consider content delivery to users in a system consisting of a macro base station (BS), several energy-harvesting small cells (SCs), and many users. Each SC has a large cache and stores a copy of all contents in the BS. A user's content request can be either handled by the SC for free if it has enough energy, or by the BS that has a cost. Each user has a finite cache and can store some most popular contents. We propose an efficient coded content caching schemes and an optimal transmission schemes for this system to maximally off-load the data traffic from the macro BS to the energy-harvesting SCs, and therefore minimize the power consumption from the grid. Specifically, the proposed coded caching scheme stores fractions of some most popular contents, such that contents requested from multiple users can be simultaneously delivered by the BS or SC. Moreover, the optimal transmission policy is formulated and solved as a Markov decision process. Extensive simulation results are provided to demonstrate that the proposed coded caching and transmission schemes can provide significantly higher traffic off-loading capability compared with systems with no caching or with uncoded caching, as well as systems that employ heuristic-based transmission schemes.

This paper investigates the optimal resource allocation in wireless powered communication network with user cooperation, where two single-antenna users first harvest energy from the signals transmitted by a multi-antenna hybrid access point (H-AP) and then cooperatively send information to the H-AP using their harvested energy. To explore the system information transmission performance limit, an optimization problem is formulated to maximize the weighted sum-rate (WSR) by jointly optimizing energy beamforming vector, time assignment, and power allocation. Besides, another optimization problem is also formulated to minimize the total transmission time for given amount of data required to be transmitted at the two sources. Because both problems are non-convex, we first transform them to be convex by using proper variable substitutions and then apply semi-definite relaxation to solve them. We theoretically prove that our proposed methods guarantee the global optimum of both problems. Simulation results show that system WSR and transmission time can be significantly enhanced by using energy beamforming and user cooperation. It is observed that when the total amount of information of two users is fixed, with the increase of the information amount of the user relatively farther away from the H-AP, the transmission time of the user cooperation scheme decreases while that of the direct transmission increases. Besides, the effects of user position on the system performances are also discussed, which provides some useful insights.

This paper considers a wireless powered communication network (WPCN) with group cooperation, where two communication groups cooperate with each other via wireless power transfer and time sharing to fulfill their expected information delivering and achieve “win-win” collaboration. To explore the system performance limits, we formulate optimization problems to maximize the weighted sum-rate (WSR) and minimize the total consumed power. The time assignment, beamforming vector and power allocation are jointly optimized under available power and quality of service requirement constraints of both the groups. For the WSR-maximization, both fixed and flexible power scenarios are investigated. As all problems are non-convex and have no known solution methods, we solve them by using proper variable substitutions and the semi-definite relaxation. We theoretically prove that our proposed solution method guarantees the global optimum for each problem. Numerical results are presented to show the system performance behaviors, which provide some useful insights for future WPCN design. It shows that in such a group cooperation-aware WPCN, optimal time assignment has the greatest effect on the system performance than other factors.

Highly mobile wireless communication attracts much more attention currently due to the rapid development of high speed railways (HSRs) all over the world. Although the single train scenario has been well studied by now, two trains encountering scenario over the general two-way railways is also an important problem deserving to investigate. To this end, this paper concentrates on the uplink information transmission of HSRs in the two trains encountering scenario, which is modeled as a time- varying partial multiple access channel. In order to evaluate the transmission performance, the achievable rate region is utilized as a metric to characterize the tradeoff between the rates that each train can obtain under limited channel source constraint. With the help of superposition modulation and sequential interference cancelling, an optimal adaptive power allocation scheme aided by real-time position information is proposed to achieve the maximal boundary of the achievable rate region, namely alleviating the effect of encountering on information transmission to the largest extent. According to the numerical results, great improvement can be obtained by new proposed adaptive power allocation along time.

Channel estimation is a major overhead factor in large-scale multiple-input multiple-output (MIMO) systems, especially under the high-speed railway scenarios. This paper proposes a position-aided channel estimation scheme for high-speed railway communication systems, where both the transmitter and the receiver are equipped with large-scale antenna linear arrays. By joint spatio-temporal correlation, the pilot overhead can be significantly reduced. Furthermore, the optimal design of transmit power and time interval partition between the training and data phases as well as the antenna size are presented accordingly. Both analytical and simulation results show that the system throughput with position-aided channel estimation does not deteriorate significantly as the mobility increases, which is sharply in contrast with the conventional one that intends to re- estimate the entire channel matrix each block.

This paper investigates the energy efficient resource allocation for OFDM relay networks, where K users receive information via L helping relays. For such a system, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the relay selection, subcarriers assignment and power allocation under the proportional rate constraints and available power constraint. Since this problem is non-convex with integer variables, which is nontrivial to be solved by using known methods, we design an efficient low- complexity algorithm to solve it. Simulation results show that by using our proposed resource allocation scheme, the approximate optimal results can be achieved. It is also shown that the circuit power (including a rate-dependent part and a constant part) in the consumed power has a great impact on limiting the EE resource allocation to obtain a high spectral efficiency. Besides, the effects of the relay selection, subcarrier assignment and power allocation on the system performance are also discussed via simulations.

Millimeter wave provides a promising approach for meeting the ever-growing traffic demand in next generation wireless networks. It is crucial to obtain the channel state information in order to perform beamforming and combining to compensate for severe path loss in this band. In contrast to lower frequencies, a typical millimeter wave channel consists of a few dominant paths. Thus it is generally sufficient to estimate the path gains, angles of departure (AoDs), and angles of arrival (AoAs) of those paths. Proposed in this paper is a dual timescale model to characterize abrupt channel changes (e.g., blockage) and slow variations of AoDs and AoAs. This work focuses on tracking the slow variations and detecting abrupt changes. A Kalman filter based tracking algorithm and an abrupt change detection method are proposed. The tracking algorithm is compared with the adaptive algorithm due to Alkhateeb, Ayach, Leus and Heath (2014) in the case with a single radio frequency chain. Simulation results show that to achieve the same tracking performance, the proposed algorithm requires much lower signal-to-noise ratio (SNR) and much fewer pilots than the other algorithm. Moreover, the change detection method can always detect abrupt changes with moderate number of pilots and SNR.

In this paper, we present a practical simple location-aided umbrella-shaped beamforming scheme with transmit diversity of massive Multiple-input Multiple-output (MIMO) system for high speed railway scenarios. Unlike conventional schemes which combines space-time block coding (STBC) with adaptive beamforming or orthogonal switched beamforming, our scheme needs neither uplink channel covariance matrix (UCCM) nor downlink CCM (DCCM) but precalculates the beamforming weights with the help of train location information, which can be completed through pure off-line calculation and therefore reduce system implementation complexity. A closed-form solution of power allocation optimization is derived and the performance of our scheme is verified with simulations from the perspectives of instantaneous received signal-to- noise ratio (SNR), bit error rate (BER) and handover success probability. It indicates that the performance of our scheme approaches to the combination scheme of STBC and adaptive beamforming (STBC-ABF) without introducing any on-line system complexities.

High mobility may destroy the orthogonality of subcarriers in OFDM systems, resulting in inter-carrier interference (ICI), which may greatly reduce the service quantity of high speed railway (HSR) wireless communications. This paper focuses on ICI mitigation in the HSR downlinks with distributed transmit antennas. In such a system, its key feature is that the ICIs are caused by multiple carrier frequency offsets corresponding to multiple transmit antennas. Meanwhile, the channel of HSR is fast time varying, which is another big challenge in the system design. In order to get a good performance, low complexity real-time ICI reduction is necessary. To this end, we first analyzed the property of the ICI matrix and then propose a low complexity ICI reduction method based on location information. For evaluating the effectiveness of the proposed method, the maximum and minimum remaining interference after ICI reduction is analyzed and the service quantity is also discussed. Numerical results are presented to verify our theoretical analysis and the effectiveness of the proposed ICI reduction method. One important observation is that our proposed ICI mitigation method can achieve almost the same service quantity with that obtained on the case without ICI when the velocity of the train is 300km/h.

Deploying multiple antennas on high speed trains is an effective way to enhance the information transmission performance for high speed railway (HSR) wireless communication systems. However, how to efficiently deploy N (N ≥ 2) antennas on a train has not been studied yet. In this paper, we investigate efficient antenna deployment strategies for HSR communication systems where two multi-antenna deployment strategies, i.e., the equidistant strategy and the fixed-interval strategy, are considered. To evaluate the system performance, mobile service amount and outage time ratio are introduced. Theoretical analysis and numerical results show that, when the length of the train is not very large, for N = 2 case, by increasing the distance of neighboring antennas in a reasonable region, the system performance can be enhanced, and for N> 2 case the two strategies have much difference performance behavior in terms of instantaneous channel capacity, and the fixed-interval strategy may achieve much better performance than the equidistant one in terms of service amount and outage time ratio when the antenna number is much large.

To attain seamless handover and reduce the handover failure probability for high-speed railway (HSR) systems, this paper proposed a remote antenna unit (RAU) selection assisted handover scheme based on two HST antennas and distributed antenna system (DAS) cell architecture. The RAU selection is adopted to provide high quality received signals for trains in DAS cells and the two HST antennas are employed on trains to realize seamless handover. Moreover, to efficiently evaluate the system performance, a new metric termed as handover occurrence probability is define for describing the relation between handover occurrence position and handover failure probability. We derive the expressions of the received signal strength, the handover trigger probability, the handover occurrence probability, the handover failure probability and the communication interruption probability of our proposed method. Numerical experimental results are provided to compare our proposed scheme with traditional handover scheme and some existing ones. It is shown that, our proposed scheme is able to achieve the lowest handover failure probability and communication interruption probability among all schemes.

In this study, the caching problem is investigated. Assuming that the users are only equipped with buffer of small sizes, the peak rate of caching problem is investigated in this study. In contrast to recent results in the literature, this study shows that under some specific condition, i.e. if the number of users is no less than the amount of files in the server, a lower peak rate of caching is achievable. Furthermore, this new presented peak rate of caching is demonstrated to coincide with the well‐known cut‐set bound.

For a two-way relay network (TWRN) with three nodes, we discuss the performance optimization of digital network coding (DNC) and physical network coding (PNC) schemes under the energy harvesting (EH) constraints and peak power constraints. We also consider the energy transfer between nodes, which is referred to as energy co-operation. To find the maximal achievable performance, we first consider the case of offline scheduling, formulate the corresponding optimization problems, find the optimal solutions, as well as present some useful theoretical properties on optimality. Then we move to the online scheduling, and propose both dynamic programming and some intuitive policies to approach the performance of its offline counterpart. Numerical results show that PNC outperforms DNC due to the higher spectrum efficiency and the intrinsic coding gains, under the same conditions. Furthermore, it is observed that if the relay harvests much more energy and shares it with the two source nodes, DNC with energy co-operation scheme has the potential to perform comparable to or even better than PNC without energy co-operation scheme, which validates the importance of energy co-operation in contemporary communication systems.

This paper investigates the energy efficiency (EE) in multiple relay-aided OFDM systems, where decode-and-forward (DF) relay beamforming is employed to help the information transmission. In order to explore the system performance behavior with user fairness for such a system, an optimization problem is formulated to maximize the EE by jointly considering multiple factors, i.e., the transmission mode selection (DF relay beamforming or direct-link transmission), the helping relay set selection, the subcarrier assignment and the power allocation at the source and relays on subcarriers, under nonlinear proportional rate fairness constraints, where both transmit power consumption and linearly rate-dependent circuit power consumption are taken into account. To solve the nonconvex optimization problem, we propose a low-complexity scheme to approximate it. Simulation results demonstrate its effectiveness. The effects of the circuit power consumption on system performance is also studied and it is observed that with either the constant or the linearly rate-dependent circuit power consumption, system EE grows with the increment of system average channel-to-noise ratio (CNR), but the growth rates show different behaviors. For the constant circuit power consumption, system EE increasing rate is an increasing function of the average CNR, while for the linearly rate-dependent one, system EE increasing rate is a decreasing function of the average CNR. This observation is very important, which indicates that by deducing the circuit dynamic power consumption per unit data rate, system EE can be greatly enhanced. Besides, we also discuss the effects of the number of users and subcarriers on the system EE performance.

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF-CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF-CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF-AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF-AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC.

To meet the surging demand for throughput, 5G cellular networks need to be more heterogeneous and much denser, by deploying more and more small cells. In particular, the number of users in each small cell can change dramatically due to users' mobility, resulting in random and time varying uplink interference. This paper considers the uplink interference in a 5G heterogeneous network, which is jointly covered by one macro cell and several small cells. Based on the Lévy flight moving model, a mobility-aware interference model is proposed to characterize the uplink interference from macro cell users to small cell users. In this model, the total uplink interference is characterized by its moment generating function, for both closed subscriber group (CSG) and open subscriber group (CSG) femto cells. In addition, the proposed interference model is a function of basic step length, which is a key velocity parameter of Lévy flights. It is shown by both theoretical analysis and simulation results that the proposed interference model provides a flexible way of evaluating the system performance in terms of success probability and average rate.

Wireless energy transfer, namely, radio frequency (RF)-based energy harvesting, is a potential way to prolong the lifetime of energy-constrained devices, especially in wireless sensor networks. However, due to huge propagation attenuation, its energy efficiency is regarded as the biggest bottleneck to wide applications. It is critical to find appropriate transmission policies to improve the global energy efficiency in this kind of system. To this end, this paper focuses on the sensor networks scenario, where a mobile control center powers the sensors by RF signal and also collects information from them. Two related schemes, called harvest-and-use scheme and harvest-store-use scheme, are investigated. In the harvest-and-use scheme, as a benchmark, both constant and adaptive transmission modes from sensors are discussed. In the harvest-store-use scheme, we propose a new concept, the best opportunity for wireless energy transfer, and use it to derive an explicit closed-form expression of optimal transmission policy. It is shown by simulation that a considerable improvement in terms of energy efficiency can be obtained with the help of the transmission policies developed in this paper. Furthermore, the transmission policies are also discussed under the constraint of fixed information rate. The minimal required power, the performance loss from the new constraint, and the effect of fading are then presented.

We consider providing broadband wireless services for passengers in a high-speed train and local users (low-mobility users) in the same OFDMA system. For the train, a two-hop architecture, under which, passengers communicate with base stations (BSs) via a mobile relay (MR) is employed. Then all passengers in the train can be treated as one large user represented by the MR. Since the channel conditions of the MR and local users are different in both large scale-fading and small-scale fading, we allocate the system resources over two time-scales for them. We formulate the resource allocation problem in the downlink OFDMA system as a capacity optimization problem for the MR subject to the sum capacity constraint of local users. We discuss the problem in two cases where perfect intercarrier interference (ICI) cancellation and no ICI cancellation are applied at the MR. The corresponding capacities and optimal power and subcarrier allocation (OPSA) policies are presented for each case. The capacities obtained in these two cases bound the achievable transmission rate of the MR and provide meaningful guidance for the application of ICI cancellation. Finally, the implementation issues of resource allocation policies are discussed to simplify application in practical scenarios.

Abstract We investigate the multicast throughput of a butterfly network, which may be a promising topology for network coding application in next‐generation wireless communication systems. The butterfly network consists of two sources, two destinations and a relay, where each destination requires decoding of data from two independent sources. It is assumed that all the nodes are operated in half‐duplex mode. Each end‐to‐end packet transmission should be completed in a two‐phase period. In order to reduce processing complexity and multiple interference, other nodes should keep silent when the relay transmits a signal. By using Avestimehr, Diggavi and Tse's deterministic model, we first introduce a deterministic butterfly network and demonstrate that its maximal multicast rate region can be achieved by employing a network coding policy. According to the results obtained in deterministic case, we then put forward a near‐optimal design on the transmitted signal and decoding scheme for Gaussian scenarios based on a nested lattice code. It is proved that the gap between the achievable rate region and an outer bound is less than 3bits/s/Hz, which is not related to the signal‐to‐noise ratio. That is, the proposed scheme can approach the maximal multicast throughput. Finally, numerical results demonstrate that the gap is robust to both channel gains and time division of the two phases. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Multiple‐Input, Multiple‐Output (MIMO)‐orthogonal frequency division multiplexing (OFDM) is a promising technique in 5G wireless communications. In high‐mobility scenarios, the transmission environments are time‐varying and/or the relative moving velocity between the transmitter and receiver is also time‐varying. In the literature, most of previous works mainly focused on fixed subcarrier group size and precoded the MIMO signals with unitary channel state information. In this way, the subcarrier grouping may naturally lead to big loss of channel capacity in high‐mobility scenarios because of the channel state information difference on the subcarriers in each group. To employ the MIMO‐OFDM technique, adaptive subcarrier grouping scheme may be an efficient way. In this paper, we first consider MIMO‐OFDM systems over double‐selective i.i.d. Rayleigh channels and investigate the quantitative relation between subcarrier group size and capacity loss theoretically. With developed theoretical results, we also propose an adaptive subcarrier grouping scheme to satisfy the preset capacity loss threshold by adjusting grouping size with the sensed environmental information and mobile velocity. Theoretical analysis and simulation results show that to achieve a better system capacity, a sparse scattering, lower signal‐to‐noise ratio, and lower velocity as well as properly large antenna number are matched with larger subcarrier group size. One important observation is that if the antenna number is too large and higher than a threshold, which will not bring any additional gain to the subcarrier grouping. That is, the system capacity loss will converge to a lower bound expeditiously with respect to antenna number, which is given in theory also. Copyright © 2016 John Wiley & Sons, Ltd.

This paper investigates the simultaneous wireless power and information transfer (SWPIT) for network-coded two-way relay transmission from an information theoretical viewpoint, where two sources exchange information via an energy harvesting relay. By considering the time switching (TS) relay receiver architecture, we present the TS-based two-way relaying (TS-TWR) protocol. In order to explore the system throughput limit with data rate fairness, we formulate an optimization problem under total power constraint. To solve the problem, we first derive some explicit results and then design an efficient algorithm. Numerical results show that with the same total available power, TS-TWR has a certain performance loss compared with conventional non-EH two-way relaying due to the path loss effect on energy transfer, where in relatively low and relatively high SNR regimes, the performance losses are relatively small.

The relay scheme and the corresponding rate performance are considered for a half-duplex relay channel in which the source can only transmit information with fixed power. As different relay strategies result in different rate performance, we first present a decision criterion for selecting between Decode-and-Forward (DF) and Compress-and-Forward (CF) strategies by thoroughly analyzing their achievable rate. Based on the analysis result, the maximum of the DF rate and CF rate can be achieved by strategy selection procedure. To further obtain a larger rate, we put forward a hybrid DF-CF scheme in which the strategy selection between DF and CF is combined with active relay power allocation efficiently. It is shown that the concave envelope of the maximum of DF rate and CF rate is achievable via our new developed hybrid scheme. For the convenience of implementation, we also present a suboptimal setting for the hybrid DF-CF scheme. Numerical results show that the suboptimal setting can achieve a rate approaching the maximal rate.

Wireless energy transfer, namely RF-based energy harvesting, is a potential way to prolong the lifetime of energy-constrained devices, such as wireless sensor networks. However, due to the huge propagation attenuation, energy efficiency is widely regarded as the biggest bottleneck for large-scale applications. Thus, it is significantly essential to explore appropriate transmission policies for the system to improve the global energy efficiency. To this end, this paper focuses on the optimum transmission policies in sensor networks scenario, where a mobile control center powers the sensor node by RF signal and then collects information from sensor node. Based on whether there is an energy storage at the sensor node, two related schemes, called as harvest-and-use scheme and harvest-store-use scheme, are investigated, respectively. In harvest-and-use scheme, as a baseline, both constant and optimal adaptive transmission strategies are discussed. In harvest-store-use scheme, an explicit closed-form expression of optimal transmission policy is derived in terms of throughput maximization, which can greatly enhance the system performance by opportunistic energy transfer with the help of energy storage. According to the simulation results, a considerably large improvement can be observed by employing the optimum transmission policy developed in this paper.

In 5G networking, it is promising to integrate cellular system and wireless local area networks (WLAN) to enhance the throughput. The access point of the WLAN can be authenticated as a relay receiver in cellular system to assist the communication between the base station and the user equipment. In this paper, we model the integrated system as a Receiver Frequency Division Gaussian Relay Channel (RFD-GRC) and study how to improve the achievable transmission rate by adopting Decode-and-Forward (DF) and Compress-and-Forward (CF) schemes in the system.Specifically, making use of the orthogonality between the source frequency band (SFB, the cellular system frequency band) and the relay frequency band (RFB, the WLAN frequency band), we independently divide the available SFB and RFB into two subbands and adopt DF and CF in the two subbands, respectively. Joint bandwidth and power allocation of this hybrid DF-CF scheme is optimized, resulting in a cooperation gain larger than that achieved by DF and CF schemes individually.A sub-optimal setting for the hybrid DF-CF scheme is also given, simplifying the implementation and approaching the optimal rate performance. Numerical analysis confirms the effectiveness of the new scheme.

A wideband field-programmable gate array (FPGA) based hardware implementation for the m parameter estimation of the Nakagami-m fading channel is introduced. It requires fresh estimation of the noise spectrum power density to actually evaluate the signal-to-noise ratio (SNR), and with it, one can efficiently measure the m parameter of Nakagami-m fading channel. The hardware employs a Xilinx Virtex-6 SX475T-2c FPGA integrated Minibee platform operated in Centos system, in which a wideband quadrature modulator ADL5375 is integrated, with output frequency ranging from 400MHz to 6GHz. Such a hardware framework enables the measure system to function well in wireless wideband systems. In addition, we utilize it to conduct real-scenario estimations and compare the results with the software simulations. It also indicates our developed m parameter ML estimation has a better performance compared with some known estimation algorithms.

Network coding is famous for its capability in significantly improving the throughput of network. The successful decoding of the network coded data relies on some side information of the original data. In that framework, independent data flows are usually decoded first and then network coded by relay nodes. If appropriate signal design is adopted, physical layer network coding is a natural way in wireless networks. In this work, a network coding tree algorithm which enhances the efficiency of the multiple access system (MAS) is presented. For MAS, researchers try to avoid the collisions but collisions happen frequently under heavy load. By introducing network coding into MAS, our proposed algorithm achieves a better trade-off between average delay and system throughput. When multiple users transmit signal in a time slot, the sum signals are saved and used to jointly decode the collided frames after some component frames of the network coded frame are received. Splitting tree structure is extended to our proposed algorithm for collision solving. The system throughput and average delay of frames are presented in a recursive way. Besides, extensive simulations show that network coding tree algorithm enhances the system performance in terms of system throughput and average frame delay compared with other algorithms.

In fading channels, power allocation over channel state may bring a rate increment compared to the fixed constant power mode. Such a rate increment is referred to power allocation gain. It is expected that the power allocation gain varies for different relay protocols. In this paper, Decode-and-Forward (DF) and Compress-and-Forward (CF) protocols are considered. We first establish a general framework for relay power allocation of DF and CF over channel state in half-duplex relay channels and present the optimal solution for relay power allocation with auxiliary parameters, respectively. Then, we reconsider the power allocation problem for one hybrid scheme which always selects the better one between DF and CF and obtain a near optimal solution for the hybrid scheme by introducing an auxiliary rate function as well as avoiding the non-concave rate optimization problem. Simulation results show that the developed power allocation solutions bring significant rate gains in various fading relay channels compared to constant power allocation mode.

In high speed railways (HSRs) communication system, the wireless channel between the train and base station varies strenuously due to high mobility, which makes it very essential to implement appropriate adaptive algorithms to guarantee the quality-of-service (QoS). What's more, how to evaluate the performance limits in this new scenario must also be considered. To this end, this paper investigates the performance limits of wireless communication in HSRs scenario. Since the information transmitted between train and base station usually has diverse QoS requirements, a QoS-distinguished achievable rate region is utilized to characterize the transmission performance in this paper, which can be regarded as a generalized case of traditional ergodic capacity and outage capacity with unique QoS requirement. The specific adaptive algorithm that can achieve the maximal boundary of achievable rate region is also derived. Compared with conventional strategies, the advantages of the proposed strategy are validated in terms of green communication, namely minimizing average transmit power.

We consider providing services for passengers in a high-speed train and local users (quasi-static users) in a single OFDMA system. For the train, we apply a two-hop architecture, under which, passengers communicate with base stations (BSs) via a mobile relay (MR) installed in the train cabin. With this architecture, all passengers in the train can be represented by the MR. Since the channels of the MR and local users vary differently, we consider allocating system resources (power and subcarriers) over two time-scales for them. We formulate the problem as a capacity optimization problem for the MR subject to the sum capacity constraint of local users. We treat the inter-carrier interference (ICI) at the MR as additive Gaussian noise and derive an explicit expression for the ICI using the two-path Doppler spread model. Then we discuss the optimization problem and propose an optimal power and subcarrier allocation (OPSA) policy. The capacity obtained using OPSA is compared with that of constant power and subcarrier allocation (CPSA) policies. Simulation results justify the optimality of the OPSA. Besides, by comparing the capacity bounds achieved by OPSA with and without ICI, we find that only in specific regions, where the gap between the capacity bounds is large, do practical ICI cancellation methods provide meaningful rate gain.

This paper investigates the simultaneous wireless information and energy transfer for the non-regenerative multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) relaying system. By considering two practical receiver architectures, we present two protocols, time switching-based relaying (TSR) and power splitting-based relaying (PSR). To explore the system performance limits, we formulate two optimization problems to maximize the end-to-end achievable information rate with the full channel state information (CSI) assumption. Since both problems are non-convex and have no known solution method, we firstly derive some explicit results by theoretical analysis and then design effective algorithms for them. Numerical results show that the performances of both protocols are greatly affected by the relay position. Specifically, PSR and TSR show very different behaviors to the variation of relay position. The achievable information rate of PSR monotonically decreases when the relay moves from the source towards the destination, but for TSR, the performance is relatively worse when the relay is placed in the middle of the source and the destination. This is the first time such a phenomenon has been observed. In addition, it is also shown that PSR always outperforms TSR in such a MIMO-OFDM relaying system. Moreover, the effects of the number of antennas and the number of subcarriers are also discussed.

A relay channel (RC), consisting of a source, a relay, and a destination, is a basic transmission unit of cooperative communication networks. The capacity of an RC is not known in general. In this paper, an SNR decomposition (SD) strategy is presented to implement time sharing, which provides a new tractable and achievable rate for a full-duplex Gaussian RC. More specifically, we first expand the SNR of a relay destination channel (SNR-RD) into two terms under the relay power constraint and divide the system into two subbands. Then, we assign the obtained SNR-RD for each subband and employ decode-forward (DF) or compress-forward (CF) according to the assigned SNR-RD. It is shown that the achievable rate of the SD strategy is competitive with that of superposing CF on DF. As the superposition structure requires a sophisticated codeword design, the SD strategy provides another practical combination structure of DF and CF strategies. Approximations for the SNR-RD and bandwidth allocation for subbands are also given. Based on the obtained results, two application scenarios, i.e., mobile relay and quasi-static fading RCs, are also considered. Finally, various numerical results are shown to support our developed theoretical results.

Abstract To provide stable and high data rate wireless access for passengers in the train, it is necessary to properly deploy base stations along the railway. We consider this issue from the perspective of service, which is defined as the integral of the time‐varying instantaneous channel capacity. With large‐scale fading assumption, it will be shown that the total service of each base station is inversely proportional to the velocity of the train. Besides, we find that if the ratio of the service provided by a base station in its service region to its total service is given, the base station interval (i.e., the distance between two adjacent base stations) is a constant regardless of the velocity of the train. On the other hand, if a certain amount of service is required, the interval will increase with the velocity of the train. The aforementioned results apply not only to simple curve rails, like line rail and arc rail, but also to any irregular curve rail, provided that the train is traveling at a constant velocity. Furthermore, the new developed results are applied to analyze the on–off transmission strategy of base stations. Copyright © 2013 John Wiley & Sons, Ltd.

In full-duplex Gaussian relay channels, neither Decode-and-Forward (DF) nor Compress-and-Forward (CF) can achieve a larger rate than the other for all the channel gain combinations. Combining DF and CF strategies, we show that a new achievable rate, which is the concave envelop of the maximal rate achieved by DF and CF with respect to the source power, is achievable. To this end, we actively adjust the transmission power of the source for different time and switch the transmission strategy between DF and CF according to the source power. It is proved that when the signal to noise ratio (SNR) of the source-destination link falls into a certain range, the new achievable rate is strictly larger than that achieved by pure DF and pure CF. The optimal power allocation and corresponding time proportions are also obtained. Numerical results show that the new achievable rate is also competitive with the rate achieved by superposing CF on DF. As strategy switching avoids complex codeword constructions, it is more practical than superposition structures to be implemented in relay systems.

Harvesting energy from ambient environment is a new promising solution to free electronic devices from electric wire or limited-lifetime battery, which is significant in sensor networks and body-area networks. This paper investigates the fundamental limits of information transmission in data acquisition system with RF-based energy harvesting sensor node, in which the host node acts not only as an information source but also as an energy source for the sensor node while only information is transmitted back from sensor to host node. From a view of system level, achievable capacity-rate region and capacity-rate function are proposed as metrics to measure the transmission performance. The tradeoff relationship of two way information rates between sensor and host node in a time division duplex system is obtained and the corresponding optimal transmission policy is also given. At last, a typical application in terms of minimizing required transmit power, namely green system, is introduced to validate the results developed in this paper.

This work considers the achievable rate region of full-duplex Gaussian relay channel. Under Gaussian signaling, it was found that neither Decode-Forward (DF) nor Compress-Forward (CF) can achieve better performance than the other for all channel gains. Recently, it was verified that superposing CF on DF in one band has a better performance than both DF and CF for Gaussian signaling. In this work, we consider another combining structure of CF and DF by making use of Subband Division (SD). It will show that our new developed strategy will achieve a larger rate for Gaussian signaling by comparing with DF lower bound, CF lower bound and the result of superposing CF on DF. In addition, a closed form solution of the achievable rate is found, in which the subband division factors are also given. Numerical results confirm our developed theoretical results.

Two user Gaussian interference channel (GIC) consists of two source-destination pairs which transmit independent messages and interfere with each other. The best achievable rate region, referred to HK sum rate bound, requires the sources to split the information into public messages and private messages. As Gaussian signaling holds the potential of approaching the capacity, finding the HK sum rate achieved by Gaussian signaling is of great importance. However, The optimal power allocation over messages for Gaussian signaling are not known yet This work clearly describes the optimal power allocation and corresponding sum rate achieved by Gaussian signaling without time sharing (TS) in closed form. It lays a foundation for finding the TS strategy achieving the optimal sum rate. The obtained power allocation indicates that without TS, message splitting may not be always necessary. Besides, the conditions for using and not using message splitting are also characterized in detail.

Quality of service (QoS) requirements are usually different from user to user in a multiaccess system, and it is necessary to take the different requirements into account when allocating the shared resources of the system. In this paper, we consider one QoS criterion-average packet delay in a multiaccess system, and we combine information theory with queueing theory in an attempt to analyze whether a multiaccess system can meet the different delay requirements of all users. When the queue state information is not available to the central scheduler, we show that static rate allocation achieves the best performance. Based on this result, we provide a polynomial-time algorithm for deciding whether a system can meet the different delay requirements of all users. In cases where the system cannot meet the needs of all users, we prove that as long as the sum power is larger than a threshold, there is always an approach to adjust the transmission power of each user to make the system delay feasible if power reallocation is available.

In this paper, we consider one quality-of-service (QoS) criterion, average packet delay (queueing delay plus service time), in a multiaccess system and investigate the basic problem whether a multiaccess system can meet the different average packet delay requirements of all users by combining information theory with queueing theory. Two different cases of the central scheduler with and without queue state information (QSI) are discussed. If the QSI is not available to the central scheduler, we show that static rate allocation policies (SRAPs) can achieve better average packet delay performance than probabilistic rate allocation policies. Based on this conclusion, the delay feasibility checking process reduces to checking whether the required service rate vector lies in the multiaccess capacity region. We find that for users with equal transmit power, only N inequalities are necessary for the checking process, whereas for users with unequal transmit powers, we provide a polynomial-time algorithm for such a decision. Furthermore, if the system cannot satisfy the average packet delay requirements of all users, we prove that as long as the sum power is larger than a threshold, there is always an approach to adjust the transmit powers of different users to satisfy the average packet delay requirements. On the other hand, if the QSI is available to the central scheduler, we propose two dynamic scheduling algorithms to achieve proportional average packet delay and compare their performances with optimal SRAP by simulations.

Space time network coding (STNC) is a recently proposed time-division multiple-access (TDMA)-based cooperative relaying scheme for multi-relay wireless systems, which can achieve full diversity order with low transmission delay by taking advantage of the concepts of network coding and distributed space time coding. However, STNC does not fully exploit the benefit of the broadcast nature of wireless channels, since it only allows relays to receive signals from the sources. To explore the potential capacity of STNC-based systems, in this paper, we propose a new cooperative relaying scheme, termed space-time network coding with overhearing relays (STNC-OR), by allowing each relay to collect the signals transmitted from not only the sources but also its previous relays. Then, we derive some explicit expressions for the outage probability and symbol error rate (SER) for STNC-OR with decode-and-forward relaying over independent non-identically distributed (i.n.i.d) Rayleigh fading channels. For comparison, we also derive the explicit expression of the outage probability for STNC. To further improve the performance of STNC-OR, we investigate the effect of relay ordering on the performance of STNC-OR and then present the optimal relay ordering algorithm. Further, a suboptimal relay ordering is also designed to reduce the complexity. Extensive simulation and numerical results are presented finally to validate our theoretical analysis. It is shown that the proposed STNC-OR achieves much lower outage probability and SER than STNC and traditional pure TDMA relaying schemes.

ABSTRACT In this paper, we study the channel service process of a multiple‐input multiple‐output (MIMO) system over an independent and identically distributed (i.i.d.) fading channel. One key problem of communication over fading MIMO channels is what kind of service the MIMO channel can provide. In this paper, this problem is investigated in terms of channel service process. Assuming that the channel state information is available at the receiver, the channel service process S(t) is defined as the integral of the instantaneous channel capacity over a time interval of length t, which specifies the service provided by the channel during the period. Using the characteristic function approach and the infinitely divisible law, it is proved that the channel service process S(t) is a deterministic linear function of time t, other than any curve form or a stochastic process. Specifically, , where is a constant equal to the corresponding ergodic capacity. This result has two implications: (i) i.i.d. fading MIMO channels can support a constant rate traffic stream of rate without higher layer transmission delay; (ii) the ergodic capacity is the actual transmission capacity of the fading channel, other than only a statistical average value. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT To evaluate mobile communication systems, it is important to develop accurate and concise fading channel models. However, fading encountered in mobile communication is usually non‐stationary, and the existing methods can only model quasi‐stationary or piecewise‐stationary fading instead of general non‐stationary fading. To address this, this paper proposes an evolutionary spectrum (ES)‐based approach to modeling non‐stationary fading channels. Our ES approach is more general than the existing piecewise‐stationary models and is capable of characterizing a general non‐stationary fading channel that has an arbitrary ES (or time‐varying power spectral density); our ES approach is parsimonious and is also able to generate stationary fading processes. As an example, we show how to apply our ES approach to generating stationary and non‐stationary correlated Nakagami‐mfading channel processes. Simulation results show that the ES of the channel gain process produced by our ES‐based channel model agrees well with the user‐specified ES, indicating the accuracy of our ES‐based channel model. Copyright © 2011 John Wiley & Sons, Ltd.

Modeling of wireless channels, especially non-stationary fading channels, is important for design and performance analysis of wireless communication systems. Recently, we proposed a new approach to modeling non-stationary fading channels, based on the theory of evolutionary spectrum ES. In this paper, we develop a time-varying autoregressive AR model for a non-stationary flat fading channel; specifically, we develop a method to determine the time-varying coefficients of the AR channel model, given the ES of a non-stationary process. Furthermore, with the ES theory, we develop a trace-driven time-varying AR channel simulator to generate a non-stationary flat fading process. Simulation results show that the ES of the channel gain process produced by our joint ES-and-AR-based channel model agrees well with the user-specified ES, indicating the accuracy of our joint ES-and-AR-based channel model. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Signal‐time coding (STC) is a newly proposed transmission scheme for half‐duplex relay networks, which is able to achieve higher information flow rate by combining the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in the time domain. However, most of the results for STC are only obtained under the ideal assumptions that the signal detections at physical layer are perfect and there are still a lot of fundamental problems to be explored. This paper considers the implementing issues of STC at physical layer in additive white Gaussian noise relay networks. Firstly, a performance evaluation criterion, the reliable information per symbol (RIPS), is proposed to characterize the performance of STC in noisy wireless networks. Secondly, a new construction scheme based on route ID for the codeword of STC is presented, and some structural properties of the codeword of STC are investigated. Thirdly, the error probabilities of STC in both the signal domain and the time domain are discussed. Furthermore, two implementing schemes, that is, the energy detection based STC (ED‐STC) and the symbol detection based STC (SD‐STC), are proposed, and their performance bounds in terms of RIPS are discussed. Numerical analyses show that both ED‐STC and SD‐STC outperform traditional transmission methods in terms of effective information rate even under some practical conditions. Copyright © 2011 John Wiley & Sons, Ltd.

This paper analyzes the outage probability of space-time network coding (STNC) with amplify-and-forward (AF) relays in a cooperative relaying system, where multiple sources transmit their information to a common destination with the help of multiple AF relays in time-division multiple-access (TDMA) mode. We derive an approximate closed-form expression of the outage probability for STNC with an arbitrary number of AF relays for independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels. Numerical results validated our analysis. Moreover, with the developed result, we also discuss the impact of the transmit signal-to-noise ratio (SNR), the outage threshold, the number of relays and the nonorthogonal codes on the system performance.

This paper investigates the resource allocation for two-way relay networks with symmetric data rates from an information theoretic perspective, where a round of information exchange between two sources requiring equal end-to-end transmission rates is considered to be completed by a muti-access (MAC) phase and a broadcast (BC) phase. Decode-and forward (DF) protocol is employed. In this case, we formulate an optimization problem to maximize the sum rate of the system under total available energy. Our goal is to seek the jointly optimized time assignment between the MAC and BC phases and the power allocation among the source and relay nodes. Since the problem is difficult to solve in general, we firstly discuss it in two extreme cases by considering very low and very high system available energy. By doing so, we find an interesting result that in the very high energy case, the optimal ratio of the time assigned for the MAC phase to that assigned for the BC phase is a constant, i.e., 2 : 1. Further, we adopt such constant time assignment to general cases, and derive a closed-form power allocation for two-way relay transmissions. Extensive numerical results vitiated the proposed joint resource allocation and show that the maximum system sum-rate can be approached by our scheme, which obviously excels traditional equal time assignment and equal power distribution schemes.

Outage capacity is one of the widely accepted metrics to predict the maximum reliable information rate over fading channel with channel side information (CSI) at the receiver only. This metric is calculated with the assumption that since CSI is not available at the transmitter side, transmission strategies such as rate adjustment cannot be employed to match channel varying to improve the system spectral efficiency. But it is possible to adjust transmission rate at the receiver side instead of adjusting at the transmitter side. In this case, is the widely accepted concept on outage capacity still true? To answer this question, we evaluate spectrum efficiency of two different systems that use rateless codes for several modulation schemes over Rayleigh flat fading channel by theoretical analysis and simulation in the paper. It is shown that outage capacity cannot be well used to predict maximum reliable information rate for fading channels in middle regime of signal to noise ratio.

We investigate the multicast throughput of asymmetrical butterfly network which consists of two sources, two destinations and a half-duplex relay, where each destination needs to decode the data from two independent sources. In order to reduce processing complexity and multiple interference, other nodes should keep silent when the relay transmits signals. We first present an explicit expression of the deterministic maximal multicast throughput by using Avestimehr, Diggavi and Tse's deterministic approach. With the insight of the obtained result, we put forward a near-optimal network coding strategy to approach the maximal multicast throughput of this network. Finally, we compare our achievable rate region with an outer bound and show the gap between them is less than a constant 2.45 bits in some cases.

Adaptive modulation and coding (AMC) and incremental redundancy (IR) are two popular rate adaptive systems to improve system capacity, peak data rate and reliable coverage of transmission in wireless mobile communications suffering from time-varying channel conditions. Adaptive demodulation (ADM) using rateless codes has been considered as an alternative solution for these rate adaptive systems in order to avoid their resource consumptions. In ADM system, data stream is modulated with a fixed modulation scheme at the transmitter but it is demodulated at a non-fixed rate at the receiver. If the signal to noise ratio (SNR) of the communication channel is not high enough to demodulate the message to reach predefined bit error rate (BER), demodulation level is then decreased by treating some of the bits in each symbol as erasures and being discarded. In this paper, we shall propose a log-likelihood ratio (LLR) based ADM algorithm to select the bits to demodulate. In our algorithm we shall select the bits to demodulate in whole information packet by comparing their probability to be correct instead of using modified constellation diagram to select bits per symbol. Therefore, the probability of discarding incorrect bits will increase, resulting in BER performance and transmission rate improvement. In addition, our developed algorithm can be applied easily to any modulation scheme and the corresponding demodulation rate can be selected as any rational rate value which relaxes the limitation of ADM systems. Various simulations show that our proposed LLR based ADM technique significantly outperforms the conventional ADM algorithm in term of BER performance.

In this paper, we investigate the joint subcarrier-pairing and resource allocation scheme for multi-relay aided two-way relay OFDM networks, where power allocation, subcarriers assignment and relay selection are taken into account. It is assumed that amplify-and-forward relaying protocol is deployed on all relay nodes to assists the information exchange between two sources via orthogonal subchannels. In this case, we formulate an optimization problem to maximize the total end-to-end transmission rate of the system under individual power constraints at each node. The goal is to seek the jointly optimized subcarrier pairing, subcarrier-pair-to-relay selection and power allocation. To solve the problem, we derive an asymptotically optimal scheme by adopting the dual decomposition approach of mixed-integer programming problems. Finally, simulation results are presented to demonstrate the performance of the proposed scheme.

Multiple input multiple output (MIMO) relay channels has great application potentials in wireless communication systems and therefore attracts both academia and industrial attentions recently. In this paper, we consider the MIMO relay channels where both source node and relay node are equipped with multiple antennas and the destination node has a single antenna. We address the optimal beamforming design when decode-and-forward half-duplex relaying protocol is deployed. Specifically, we develop an efficient algorithm to determine the optimal beamforming vector at the source when transmitting to relay and destination at the same time. Based on the exact channel capacity formulation, the proposed algorithm can achieve arbitrarily high accuracy with low computational complexity.

This paper investigates the resource allocation problem to minimize the downlink transmission delay under power constraints for two-way relay transmission using network coding over OFDM channels, where two sources with unbalanced traffic exchange their information via a relay node with network coding deployed. Since the explicit solution to this optimization problem is hard to obtain and with extremely high computation complexity even for numerical solution, we propose low-complexity suboptimal algorithms for the problem, where subcarrier assignment is carried out by assuming an equal power distribution at first and then optimal power allocation is executed to minimize the transmission delay. By simulations, the proposed resource allocation scheme is shown to achieve less than 1.01 times the optimal delay and outperform the strategies without network coding in overwhelming majority cases.

This paper considers the multicast throughput of a special butterfly network which is made up of two sources, two destinations and a half-duplex relay. Each destination requires to decode the data from two independent sources. All the nodes are assumed to be half-duplex. When the relay transmits signal, other nodes of the system should keep in receiving state to reduce processing complexity and interference. First, we present an explicit expression of the maximal multicast throughput in deterministic butterfly network which is derived from Aves-timehr, Diggavi and Tse's deterministic model. Based on the result obtained in deterministic butterfly network, we propose a near-optimal transmitting and decoding policy to approach the maximal throughput by using network coding in Gaussian butterfly network. Finally, we evaluate the gap between our achievable rate region with an outer bound and show it consists of a constant item which is less than 2.45 bits and a variable item which only exists in some cases and increases logarithmically with the signal to noise ratio (SNR) over all the links.

Analysis of effective information transmission rate over fading channels has attracted much attentions in the last few years. Ergodic capacity and outage capacity, as two conventional indices, have been widely investigated in various scenarios. However, there exists a gap between them for any fixed average signal to noise ratio. Thus, one problem is raised naturally: How to fill this gap? To answer it, we shall propose a new concept, є-overflow rate, which is used to characterize the transmission capability of a fading channel when a finite size buffer is employed at the transmitter. With this buffer, the constant rate source data stream is matched with the time varying channel status so that the fading channel can support a higher rate source data stream. It will be proved that the є-overflow rate is larger than the є-outage capacity under the same outage constraint and can converge to the ergodic capacity in all signal to noise ratio region.

ABSTRACT Secure transmission of information over wireless channels in the presence of an eavesdropper has attracted much attention recently. Previous work assumes that a transmitter has perfect knowledge of the channel side information (CSI) of the legitimate channel and the eavesdropper's channel. To loosen this strong requirement, this paper considers the case where a transmitter has perfect knowledge of the CSI of the legitimate channel and only the average channel gain of the eavesdropper's channel. In this case, some information may be leaked to the eavesdropper because the eavesdropper's channel may have a higher gain than the legitimate channel. Hence, we propose, for the first time, to study leakage‐probability‐constrained secrecy capacity of a fading channel, that is, secrecy capacity under the constraint on leakage probability. We also propose an optimal power allocation strategy that maximizes leakage‐probability‐constrained secrecy capacity under average power constraint. Moreover, we study the impact of the bias of the average gain estimate of the eavesdropper's channel on leakage‐probability‐constrained secrecy capacity. Copyright © 2011 John Wiley & Sons, Ltd.

In the paper, we study the service process S(t) of an independent and identically distributed (i.i.d.) Nakagami-m fading channel, which is defined as the amount of service provided, i.e., the integral of the instantaneous channel capacity over time t. By using the Moment Generation Function (MGF) approach and the infinitely divisible law, it is proved that, other than certain generally recognized curve form or a stochastic process, the channel service process S(t) is a deterministic linear function of time t, namely, S(t)=c_m* \cdot t where c_m* is a constant determined by the fading parameter m. Furthermore, we extend it to general i.i.d. fading channels and present an explicit form of the constant service rate c_p*. The obtained work provides such a new insight on the system design of joint source/channel coding that there exists a coding scheme such that a receiver can decode with zero error probability and zero high layer queuing delay, if the transmitter maintains a constant data rate no more than c_p*. Finally, we verify our analysis through Monte Carlo simulations.

ABSTRACT In this paper, we consider information transmission over a block Rayleigh fading channel, where a finite size buffer is employed to match the source traffic with the channel service capability. Given the buffer size, the transmission capability of a block fading Rayleigh channel is characterized from two aspects: (i) the buffer behavior when the input traffic rate is constant; and (ii) the traffic rate that can be supported by the channel for a given overflow probability constraint. For the first problem, the stationary distribution of the queue length in the buffer is derived by discretizing the queue length using a uniform quantization strategy. It is also shown that the overflow probability of the finite size buffer decreases exponentially with buffer size. An explicit upper bound on the overflow probability is also given. For the second one, a new concept of ε‐overflow rate is proposed to measure the transmission capability of a block fading channel under overflow probability constraints. It will be shown that the ε‐overflow rate is larger than the ε‐outage capacity under the same outage constraint and will meet the great gap between outage capacity and ergodic capacity as the overflow probability constraint varies. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract This paper investigates the topology control problem with the goal of minimizing mutual interferences in wireless ad hoc networks. It is known that interference is considered as a relationship between link and node in previous works. In this paper, we attempt to capture the physical situation of space‐division multiplex more realistically by defining interference as a relationship between any two bidirectional links. We formulate the pair‐wise interference condition between any two bidirectional links, and demonstrate that the interference condition is equivalent by employing the equal‐power allocation strategy and by employing the minimum‐power allocation strategy. Then we further study the typical interference relationship between a link and its surrounding links. To characterize the extent of the interference between a link and its surrounding links, a new metric, the interference coefficient, is given, and its property is explored in detail by means of analysis and simulation. Based on the insight obtained, a centralized algorithm, BIMA, and a distributed algorithm, LIMA, are proposed to control the network interference. Our simulation indicates that BIMA can minimize the network interference while conserving energy and maintaining good spanner property, and LIMA has relatively good interference performance while keeping low node degree, compared with some well‐known algorithms. Besides, both BIMA and LIMA show good robustness to additive noises in terms of interference performance. Copyright © 2010 John Wiley & Sons, Ltd.

ABSTRACT The grail of next‐generation wireless networks is providing real‐time services for delay‐sensitive applications, which require that the wireless networks provide QoS guarantees. The effective capacity (EC) proposed by Wu and Negi provides a powerful tool for design of QoS provisioning mechanisms. In this paper, we intend to generalize their formula for the effective capacity of a correlated Rayleigh fading channel; specifically, we derive a closed form approximate EC formula for a special correlated Nakagami‐m fading channel, for which the inverse of the correlation coefficient matrix is tridiagonal. To verify its accuracy via simulation, we develop a Green‐matrix based approach, which allows us to analytically obtain the effective capacity (given the joint probability density function of a correlated Nakagami‐m fading channel) while being able to simulate the corresponding channel gain process. Simulation results show that our EC formula is accurate. Furthermore, to facilitate the application of the EC theory to the design of practical QoS provisioning mechanisms, we propose a simple algorithm for estimating the EC of an arbitrary correlated Nakagami‐m fading channel, given channel measurements; simulation results demonstrate the accuracy of our proposed EC estimation algorithm showing its suitability in practice. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous work on PF only considers the single cell scenario. This paper focuses on the problem of achieving network‐wide PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using the dual method. By decomposing the dual objective function, we get a distributed pricing based algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still apply in the presence of time‐varying rates. The proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that the proposed distributed network‐wide PF scheduling algorithm achieves almost the same performance as the centralized one. Compared with traditional local PF (LPF) scheduling, the network‐wide PF scheduling achieves higher throughput, lower throughput oscillation, and greater fairness. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we first introduce the new proposed measure on channel capacity, reliable throughput, which set up a coherent relationship among the error detection probability, signal transmission rate and channel capacity in a systematic way. Based on the new measure, we then discuss the efficient transmission rate for single input single output (SISO) Rician fading channels and further confirm such a finding for Nakagamim fading channels that both high transmission efficiency and low outage probability can be achieved simultaneously only for either very high signal to noise ratio case or very low signal to noise ratio case. Furthermore, we apply the developed reliable throughput to discuss Multiple Input Multiple output (MIMO) transmission over Rayleigh fading channels and get some important insights that (1) MIMO system will have much higher reliable date rate and much lower outage probability compared to the single input single output system. (2) For the same diversity order, the system with more receiver antennas will have higher reliable data rate as the signal to noise ratio is relatively low. In contrast, as the signal to noise ratio is relatively high, the system with the same numbers of antennas at the transmitter and receiver will have higher reliable data rate.

Opportunistic Network Coding (ONC) has attracted much attention recently. The main improvement of ONC over the traditional network coding is that ONC allows the encoding node to decide whether it employs network coding based on the status of all its input streams. However, due to the nature of fading, wireless links may not always be reliable. Thus, the performance gain of ONC over traditional methods may be diminished due to wireless links in deep fading. Fortunately, some techniques, such as ARQ (Automatic Repeat reQuest) and cooperative diversity etc. can be used to mitigate it. In this paper, we consider the wireless butterfly network topology, a basic component of complex wireless networks. In order to improve the network performance via taking the advantage of ONC, ARQ and cooperative diversity, we propose a new Cooperationbased Opportunistic Network Coding (CP-ONC) protocol and then analyze its performance in terms of network throughput and delay. The advantage of CP-ONC over ONC is that it employs truncated ARQ and cooperative diversity to enhance the reliability of the wireless links. Various simulations show that CP-ONC achieves better gain over ONC in terms of network throughput and delay, especially in low Signal to noise ratio (SNR) region.

Cooperative relaying and network coding have attracting more attention recently. In this paper, we combine these two techniques together and present a Network Coding-aware Cooperative Relaying (NC2R) scheme for downlink cellular networks, in which two relay nodes are used to assist base stations in transmitting signals to cell-edge users. Moreover, we analyze its SINR performance and its spectral efficiency. Extensive simulations show that NC2R greatly improves the downlink transmission performance for users located near celledge regions, and outperforms existing known relaying schemes in terms of blocking probability and spectral efficiency. In addition, the effects of relay position on the performance of NC2R are also discussed.

In the paper, we consider the end-to-end routing and link scheduling problem for multi-hop wireless sensor networks. The efficient link scheduler under our consideration is intended to assign time slots to different users so as to minimize channel usage subject to constraints on data rate, delay bound, and delay bound violation probability. We also present a coupled robust multi-path routing structure satisfying the restriction of flows over fading channels based on an SINR-based interference model. Here the effective capacity (EC) model is used and then the joint routing and link scheduling can be formulated as a mixed integer optimization problem. Moreover, because the mixed integer optimization problem is NP-complete, we propose a computationally feasible EC-based Column-Generation-Algorithm (EC-CGA) to search for a sub-optimal solution. Simulation results are given to evaluate the performance of our proposed scheme.

Signal-Time Coding (STC), a novel transmission mechanism, was proposed recently. It combines the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in the time domain and can achieve higher information flow rate in some cases for relay networks. However, there are still many fundamental problems to be investigated. This paper considers the implementing issue of STC in AWGN relay networks. Firstly, an energy detection based STC (ED-STC) scheme is proposed and the error probabilities of ED-STC in both the signal domain and the time domain are given. Secondly, a performance evaluation criterion, the reliable information per symbol (RIPS), is proposed to characterize the performance of STC in noisy wireless networks. Moreover, the performance bounds of the RIPS of ED-STC are derived. Numerical analysis show that ED-STC outperforms traditional transmission method in terms of effective information rate within some practical conditions.

Cooperative transmission has attracted much attention recently, but it mainly focused on one S-D pair or multiple source one destination case (uplink). This paper, however, investigates a multi-source-multi-destination transmission system with a single assisting relay. Under a simple scheduling strategy, outage probability and outage capacity are derived. Moreover, optimal system throughput is proposed and analyzed. Fair access strategy of each S-D pair is also given and demonstrated. Furthermore, relay selection strategy is presented and demonstrated.

In the paper, we study the channel service process of an independent and identically distributed (i.i.d.) Rayleigh channel. The channel service process S(t) is defined as the amount of service provided by the channel, i.e., integral of the instantaneous channel capacity over a time interval of length t. The channel side information (CSI) is assumed available at the receiver. Using the Moment Generation Function (MGF) approach and the infinitely divisible law, it is proved that the channel service process S(t) is a deterministic linear function of time t other than the generally recognized certain curve form, namely, S(t) = c* · t, where c* is a constant.

Dual-cast transmission appears naturally in network coding-based two-way relay systems, where the relay terminal needs to send network coded information to both end users simultaneously. In this paper, we investigate the optimal beamforming design for wireless dual-cast channels. Specifically, we present an efficient closed-form solution of the optimal beamforming vectors and their achievable maximal dual-cast rate for both orthogonal and nonorthogonal user channel cases. Comparison with conventional non-network-coding based schemes shows that the proposed design of dual-cast beamforming with network coding can best explore the capacity benefit of multiple antennas at the relay terminal.

The diamond relay network is an efficient cooperative networking configuration in which the source node cooperates with two selected neighbors. For such networks, we investigate the impact of the relay positioning on the performance. In particular, considering an opportunistic protocol based on the use of relaying buffers, we provide sets of constraints which, when satisfied, give sufficient conditions for simultaneously guaranteeing network stability and throughput improvement. These results are given for both symmetric and asymmetric relay locations. In contrast to prior work dealing with the diamond relay network, we also break the strong hypothesis of no interlink interference between the source and relay transmissions. Our results provide design guidelines for relay selection by establishing areas of feasible relay locations, whilst also identifying optimal positions which lead to maximum throughput gain.

The design of a scheduling scheme is crucial for the efficiency and user-fairness of wireless networks. Assuming that the channel quality information (CQI) of all users is available to a central controller, a simple scheme which maximizes the sum-log utility function has been shown to guarantee proportional fairness. This work studies a more general problem which takes both the CQI acquisition and the user scheduling into account. First, in case the statistics of the channel quality is available to the controller, a joint channel probing and proportional fair scheduling scheme is developed based on the optimal stopping time theory. The convergence and optimality of the scheme is proved. Next, the problem is further studied in the case where the channel statistics are not available to the controller, and a joint learning, probing and scheduling scheme is designed by solving a generalized bandit problem. Furthermore, it is shown that the multiuser diversity gain does not always increase as the number of users increases. Numerical results demonstrate that the proposed scheduling schemes can provide significant gain over existing schemes.

Abstract The next generation wireless networks call for quality of service (QoS) support. The effective capacity (EC) proposed by Wu and Negi provides a powerful tool for the design of QoS provisioning mechanisms. In their previous work, Wu and Negi derived a formula for effective capacity of a Rayleigh fading channel with arbitrary Doppler spectrum. However, their paper did not provide simulation results to verify the accuracy of the EC formula derived in their paper. This is due to difficulty in simulating a Rayleigh fading channel with a Doppler spectrum of continuous frequency, required by the EC formula. To address this difficulty, we develop a verification methodology based on a new discrete‐frequency EC formula; different from the EC formula developed by Wu and Negi, our new discrete‐frequency EC formula can be used in practice. Through simulation, we verify that the EC formula developed by Wu and Negi is accurate. Furthermore, to facilitate the application of the EC theory to the design of practical QoS provisioning mechanisms in wireless networks, we propose a spectral‐estimation‐based algorithm to estimate the EC function, given channel measurements; we also analyze the effect of spectral estimation error on the accuracy of EC estimation. Simulation results show that our proposed spectral‐estimation‐based EC estimation algorithm is accurate, indicating the excellent practicality of our algorithm. Copyright © 2010 John Wiley & Sons, Ltd.

The secure transmission of information over wireless systems in the presence of an eavesdropper has attracted much attention. In this paper, we consider the case that full CSI of the legitimate user but only the average channel gain of the eavesdropper is known at the transmitter. In such setting, some information will be inevitably leaked to the eavesdropper from the information-theoretic view and this was not quantitatively evaluated before. A secrecy transmission framework with leakage threshold is thereby proposed. The secrecy capacity satisfying this leakage probability constraints is derived along with an optimal power allocation strategy. For Rayleigh and Nakagami-m fading channels, numerical results indicate that our framework can work well with transmitter knowledge of only average channel gain of the eavesdropper.

Network Coding can greatly increase the network transmission efficiency by combining multiple input packets into one packet algebraically before forwarding. However, the first arriving packets need to wait for the later ones before coding, which may cause additional delay. In this work, we will investigate the optimal queuing and scheduling strategy that can minimize the delay introduced by network coding, as well as the tradeoff between delay and efficiency of network coding. We will characterize the arriving packet processes into two classes: Uniform and Random. For uniform arriving processes, we will put forward the delay-minimized queuing schemes for both equal rates and unequal rates flows and analyze their performances. For random arriving processes, a half-opportunistic scheduling strategy that can jointly control delay, queue lengths and efficiency will be proposed. Finally, we will study the effect of processing time on the whole queuing system.

The design of scheduling schemes for wireless communication systems has been driven by a compromise between the objectives of system throughput and fairness among users. In case the quality of all user channels is known to the controller, proportional fair scheduling has been well understood. However, to acquire the channel quality information may consume substantial amount of resources. In this work, it is assumed that probing for channel quality information takes a fraction of the coherence block, so that the amount of time for data transmission is reduced. A simple strategy for channel probing and scheduling is proposed, which achieves the maximum throughput under the proportional fairness constraint. It is found that when probing cost is taken into account, the multi-user diversity gain does not always increase as the number of users increases. Simulation results show that the proposed strategy significantly outperforms existing schemes when the channel probing cost is taken into account.

Nowadays proportional fairness (PF) scheduling has attracted much attention in various wireless systems. But most previous work just considers the systems with only one base station (or data center), which just achieves local PF. In this paper we consider the problem of achieving global PF for the multiple base station multiple user scenario. Compared with previous works in the literature, the main contributions of this paper are threefold: (1) The PF rule is employed in the multiple base station multiple user case. Here we propose an approximate gradient projection based PF scheduling scheme, GP-PF, to approach the global PF optimality. And the convergence of the proposed algorithm is proved. (2) We study the communication and computation complexity of GP-PF and show that the developed GP-PF algorithm can be implemented either in a user selection mode, or in a random accessing way. And GP-PF applies to distributed systems in the sense that it does not need any inter base station cooperation at all. (3) By simulation, it is shown that global PF leads to higher throughput and greater fairness for users than local PF.

Diamond relay network is a kind of efficient cooperative network, in which the source user selects two neighbors as relays and cooperates with both of them. In our previous work, we have studied its transmission rates for different coding and relaying schemes and proved that the performance of SRP scheme (Spatial Reuse Pattern) is better than others. To further improve the performance, we incorporated the opportunistic scheduling and studied how to use buffers adapted to the time varying Rayleigh fading channel. However, the position information of users has not been considered yet which actually has a great influence on the performance of the diamond relay channel. Thus, in this paper, it is the first time that we study how to select the position of two relays to guarantee the performance of the throughput and delay constraint. This study facilitates the practical application of diamond relay networks since the source user knows that from where selecting neighbors as relays will bring nice performance. Finally, the simulation results confirm our analysis and some interesting illustrations show much novelty.

Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous works on PF only consider the single cell scenario. This paper focuses on the problem of achieving global PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using dual method. By decomposing the dual objective function, we get a pricing based PF algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still achieve network wide PF in the presence of time varying rates. We show that the proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that compared with traditional local PF scheduling, global PF scheduling achieves higher throughput, lower throughput oscillation and greater fairness.

Abstract The diamond relay channel, or diamond relay network, is a kind of efficient cooperative network in which the source node cooperates with two neighbors. In this paper, we study the impact on the performance of such a network caused by the relays' position for a general Nakagami‐m fading channel, which extends the previous work for a special Rayleigh fading case. In particular, the symmetric scenario for the two relays is investigated but the channel conditions of the two source‐relay‐destination links may have great difference due to the different values of m for the four Nakagami‐m fading channels. We study how to select the position of the two relays and give clear restrictions of their positions based on the requirement of the throughout improvement and network stability. Finally, we show how to find the optimal position to achieve the largest throughput improvement. The numerical results confirm our analysis and the interesting illustrations show much insight on the cooperative neighbor selections. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Diamond relay network model, as a basic transmission model, has recently been attracting considerable attention in wireless ad hoc networks. Node cooperation and opportunistic scheduling scheme are two important techniques to improve the performance in wireless scenarios. In the paper we consider such a problem how to efficiently combine opportunistic scheduling and cooperative modes for the Rayleigh fading scenario in the diamond relay network. To do so, we first compare the throughput of SRP (Spatial Reused Pattern) and AFP (Amplify Forwarding Pattern) in the half‐duplex case with the assumption that channel side information is known to all and then come up with a new scheduling scheme. It will be verified that only switching between SRP and AFP simply does little help to obtain an expected improvement because SRP is always superior to AFP on average due to its efficient spatial reuse. To improve the throughput further, we put forward a new processing strategy in which buffers are employed at both relays in SRP mode. By efficiently utilizing the links with relatively higher gains, the throughput can be greatly improved at a cost of queuing delay. Furthermore, we shall quantitatively evaluate the queuing delay and the tradeoff between the throughput and the additional queuing delay. Finally, to realize our developed strategy and make sure it always run at stable status, we present two criteria and an algorithm on the selection and adjustment of the switching thresholds. Copyright © 2009 John Wiley & Sons, Ltd.

This paper considers minimum-energy routing problem in Poisson random ad-hoc networks with Nakagami-m fading channels. We first formulate an analytical model for the transmission power, subject to a certain packet reception probability, under the assumption that the users employ proper power control and slotted ALOHA protocol. Based on this, we consider five routing strategies and compare their energy performances. Our results show that, long-hop routing in Nakagami-m networks can be more energy efficient than short-hop routing in certain scenarios, especially under light traffic and significant channel fading. When the interference can not be neglected, short-hop routing is typically better. It is also observed that when the path loss exponent is small, intelligent MAC mechanisms are critical for the energy efficiencies of routing strategies.

In this paper, we consider two fundamental problems in log-normal fading networks. One is the energy efficiency. The other is the characterization of the internode interferences and spacial reuse. We first introduce a power efficiency factor as a new parameter to measure the transmit efficiency and obtain an optimal transmit power allocation of the concurrent transmitters in such fading environment. The internode interference caused by the simultaneous transmissions within a local area is then analyzed. The analysis will also be extended to the whole network within unlimited region where the interference accumulative impact is taken into account. By doing so, we set up a direct link between the interference double disc model with the statistical accumulative interference model and derive the optimal spatial reuse distance to guarantee the network efficiency. Finally, we propose a concurrent access strategy, OCMAC (opportunistic concurrent MAC), which can efficiently mitigate the internode interference through the node coordination before the simultaneous transmissions while keeping a relatively low transmit power and high spatial reuse efficiency.

Multiuser diversity is inherent in wireless networks due to independent channel variations of different users. It requires that the transmitter has the knowledge of the channel state information (CSI) of each user in the downlink. Too much feedback will bring a heavy load to the system in some cases. This paper investigates the problem of exploiting multiuser diversity in a one source multiple destinations wireless transmission system with limited feedback. A strategy for obtaining the optimal multiple rate level and the maximum achievable sum rate is proposed in this paper. Moreover, the scheduling outage probability, the probability that rates of all users are below a threshold, is also analyzed. For one-bit feedback, a tight bound of the achievable rate is obtained. It is shown that the achievable rate nearly capture the order of the double-logarithmical of the number of users in full CSI systems. Numerical results are also presented. The achievable sum rate is close to the full CSI capacity via limited feedback as the number of users is large enough.

Joint channel-aware and queue-aware scheduling has been studied widely for direct downlink transmission in cellular networks. In this paper, we combine this cross-layer scheduling concept with opportunistic relaying for slotted multiuser packet transmissions. Cooperative relaying can bring additional degree of freedom, resulting in the system performance improvement in terms of throughput and fairness. Specifically, we propose two scheduling algorithms for selecting the destination user and its assistant user based on the instantaneous channel information and buffer status. Through simulation, we show that the proposed algorithms expand the system stable load region and reduce the average delay while improving the fairness among users with heterogeneous channel conditions compared to conventional schemes.

It is known that interference is considered as a relationship between link and node in previous works. In this paper, we attempt to capture the physical situation of space-division multiplex more realistically by defining interference as a relationship between any two undirected links. Here a new metric, the average partial interference coefficients, is given. Then we find that the coefficients are almost independent with node density and can be approximated by different linear functions of the length of link respectively. Based on the insight obtained, a new topology control algorithm, the blocked interference minimum algorithm (BIMA), is proposed to control the network interference. Our simulation indicates that the network topologies produced by BIMA show good performance in terms of network interference, energy cost and node degree.

Very recently, the advancements of these years in the realms of sensors, wireless communications, and pervasive computing have been proposed to facilitate the health-care service and/or disaster relief. As essential components of these tele-medical systems, the wireless sensor networks for medical purpose become a growing sub-field in the Wireless and Pervasive Communications, aiming to provide both reliable signal propagation and low network latency for physiological information. However, a substantial number of existing systems reveal that the two merits typically contradict each other, which calls for a reasonable tradeoff between them. In this paper, we propose a novel transmission strategy that allows retransmissions while still maintaining low latency for E-health wireless sensor networks. The newly developed strategy incorporates the techniques of collaborative beamforming (CB) and dirty paper (DP) coding, which simultaneously facilitate a successive information flow and allow timely retransmission of unsuccessfully decoded message. As will be shown in this paper, the two aspects of requirements on E-health sensor networks, i.e. high reliability and low latency, come to a reasonably desirable tradeoff in our CB-DP scheme. Then, we derive an explicit expression of outage probability of this scheme as a function of wireless channel characteristics, the number of participating sensor nodes, transmission power and retransmission power budget. Finally, some numerical results demonstrating the effectiveness of this transmission strategy are also provided.

Nowadays efficient scheduling with high fairness has attracted much attention in wireless cellular systems. In this paper we consider the downlink transmission for multiple-base-station scenario, where the proportional fairness of multiple users is taken into account. Compared with previous works in the literature, the main contributions of this paper are three-fold: (1) The proportional fairness rule is firstly employed in the multiple-base-station cooperation case. Here we propose a cooperative proportional scheduling (CPF) scheme which maximizes the sum-log utility function using gradient descent rule. (2) We show that when adopting CPF scheduling, the limiting behavior of the throughput converges to an ordinary differential equation (ODE). The limit of each scheduler-user pair's throughput is obtained by solving a fixed-point problem. (3) We propose a distributed implementation of CPF. In the developed distributed mode, the base stations are allowed to exchange their messages of local throughput in a completely distributed and asynchronous way, which makes it realizable in practice.

In this paper, we first investigate the network information flow over a relay network topology and find that the reliable achievable information rate is beyond the achievable upper bound using the conventional encoding/modulation techniques when the relay and the destination are with carrier sensing. We then propose a signal-time coding approach which combines the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in time domain. Such a hybrid signal-time coding approach can be considered as an integrated codec/modem processing in the two dimensional combinatorial space: Signal domain and temporal domain. The main feature of the proposed signal-time coding is that one can separately design the codec/modem in the signal domain and in the time domain. Therefore, the well known efficient codec/modems, such as LDPC, Turbo coding, TCM etc. in the signal domain can be employed here. A tight lower bound is explicitly presented for the signal phase coding/modulation efficiency in the time domain. In addition, we also present an iterative method to construct the code book for the signal phase coding/modulation in the time domain. Finally, consider its applications in the additive white Gaussian noisy (AWGN) relay networks and obtain some interesting results.

Because wireless network coding technology may increase the total throughput in wireless networks, it has attracted a lot of attentions. However, there is few work focusing on wireless network coding over fading channels. In fact, signal fading usually occurs in wireless communications, resulting in the performance degradation of wireless networks seriously in some scenarios. To improve the throughput of wireless networks, we analyze network coding over Rayleigh fading channels, and formulate the fading compensation as an optimization problem. By solving the optimization problem, the optimal data rate is obtained. Numerical results and simulation results indicate that if the relay node transmits packets at the optimal data rate, the total throughput will increase. We also consider the selection of relay nodes, and give the optimal assignment location of relay nodes, which will increase the total throughput. In addition, based on the concept of optimal data rate proposed in this paper, an opportunistic optimal network coding (OONC) scheme is presented, which performs well under various situations.

We first consider a topology consisting of one source, two destinations and one relay. For such a topology, it is shown that a network coding based cooperative (NCBC) multicast scheme can achieve a diversity order of two. In this paper, we discuss and analyze NCBC in a systematic way as well as compare its performance with two other multicast protocols. The throughput, delay and queue length for each protocol are evaluated. In addition, we present an optimal scheme to maximize throughput subject to delay and queue length constraints. Numerical results will demonstrate that network coding can bring significant gains in terms of throughput.

This paper addresses Rayleigh fading networks, and in particular, wireless ad-hoc and sensor networks over Rayleigh fading channels. First, we will model Rayleigh fading networks and show how to map the wireless fading channel to the upper layer parameters for cross-layer design. Based on the developed fading network model, we will consider two scarce resources of wireless networks, namely energy and medium, and develop a cross-layer way to improve their efficiency. In particular, we will first study the energy-efficiency and introduce a new parameter, energy cost factor, as the counterpart of transport capacity in wireless transmission. The new parameter will be used to design energy-efficient networks. As to the medium resource, we will bring forward the medium resource space, which not only organizes various medium resources in a systematic way but also considers a third dimension related to space reuse and internode interference. Finally, we will give a general discussion on the cross-layer design and show how power control and route selection jointly contribute to improving the resource efficiency. A few particular routing algorithms will also be studied in detail.

In this paper, we describe an efficient and energy conservative unicast routing technique for multi-hop wireless sensor networks over Rayleigh fading channels, which we shall refer to as assistant opportunistic routing (AsOR) protocol. In contrast to previous works, this protocol is presented from a systematic energy conservation perspective. During the source-destination transmission, the AsOR protocol forwards the data stream through a sequence of nodes, which are classified as three different node sets, namely, the frame node, the assistant node and the unselected node. The frame nodes are indispensable to decode-and-forward while the assistant nodes provide protections for unsuccessful opportunistic transmissions. Based on the AsOR protocol, each predetermined route can be divided into several disjoint segments, and we establish a mathematical model to characterize the energy consumptions for each node in one segment. Furthermore, we provide a method for selecting the optimal value N*, the number of nodes in one transmission segment, which can lead to the minimum average energy consumption. Numerical results will confirm that the proposed protocol is energy conservative compared with other two traditional routing protocols both in slow and fast Raleigh fading channels and that the method for searching the optimal value N* is efficient. Finally, robustness analysis for the theoretical results are presented.

Abstract Network topology construction and its channel assignment for each node in the constructed network topology are two main problems in the initialization of topology building. Topology control is an effective way to solve the problem of topology building. To investigate the joint effect of topology control and channel assignment, we propose a joint processing scheme composed of a k‐Neighbor topology control algorithm and a greedy channel assignment (GCA) algorithm in this paper. Based on this joint processing scheme, the relationships between the energy consumption, the total required channel number and the network connectivity are discussed. We also discuss the impact of some parameters on the performance of networks in terms of the path loss factor, node density, maximum node degree, etc. Our main contributions in this paper is that we find that topology control has a good effect on improving the performance of channel assignment, and the proposed joint processing scheme can reduce the required channel number effectively, compared with its theoretical upper bound. In particular, if the node degree in a network is not more than k, various simulations indicate that the required channel number is not more than 2k + 1. Copyright © 2008 John Wiley & Sons, Ltd.

This paper investigates minimizing mutual interferences in wireless ad hoc networks by means of topology control. Prior work defines interference as a relationship between link and node. This paper attempts to capture the physical situation more realistically by defining interference as a relationship between link and link. We formulate the pair-wise interference condition between two links, and show that the interference conditions for the minimum-transmit-power strategy and the equal-transmit-power strategy are equivalent. Based on the pair-wise definition, we further investigate the "typical" interference relationship between a link and all other links in its surrounding. To characterize the extent of the interference between a link and its surrounding links, we define a new metric called the interference coefficient. We investigate the property of interference coefficient in detail by means of analysis and simulation. Based on the insight obtained, we propose a topology control algorithm - minimum interference algorithm (MIA) - to minimize the overall network interference. Simulation results indicate that the network topologies produced by MIA show good performance in terms of network interference and spanner property compared with known algorithms such as LIFE, Gabriel Graph and k-NEIGH.

For network coding, each received packet is the combination of multiple independent packets. Thus, the decoding of the whole block of packets may fail even if only one combined packet is incorrectly received, which is referred to as the Error Propagation problem in this work. To solve this problem and make network coding more robust, we shall propose a distributed product code and a corresponding iterative decoding algorithm. By our scheme, the relays can simply forward its incorrectly received packets to the destination without retransmission, hence the transmission delay can be reduced. Simulation result will show that this product coding approach can improve the BER performance of network coding compared to the conventional link-by-link error correction.

The energy spreading transform (EST) has recently been proposed as a technique for the multiple input and multiple output (MIMO) fading channels, ending up with an EST-based iterative detection scheme for MIMO systems. In this paper, we develop a novel concept of capacity with iterative detection, which enables us to evaluate the contribution of the iterative data processing employed in this MIMO scheme to the achievable rate region. We then discuss the diversity gain of the EST-based MIMO system. In particular, we address the case where a technique of data rate adaption to signal-to-noise ratio (SNR) is employed, given that the achievable rate increases with the increment of SNR. Some simulation results are also given to demonstrate the theoretical results obtained in this paper.

Network coding is a powerful coding technique that has been proved to be very effective in achieving the maximum multicast capacity. It is especially suited for new emerging networks such as ad-hoc and sensor networks. In this work, we investigate the multicast routing problem based on network coding and put forward a practical algorithm to obtain the maximum flow multicast routes in ad-hoc networks. The "conflict phenomenon" that occurs in undirected graphs will also be discussed. Given the developed routing algorithm, we will present the condition for a node to be an encoding node along with a corresponding capacity allocation scheme. We will also analyze the statistical characteristics of encoding nodes and maximum flow in ad-hoc networks based on random graph theory.

This paper addresses the antenna assignment problem for distributed multiple-antenna architecture that enables wireless communication between onboard and ground in subway and railway. We propose a class of algorithms that match the assignment pattern to the large scale fading. Such algorithms are not constrained by short coherence time inherited in subway and railway. We will then derive an optimal antenna assignment strategy employing maximum ratio combining. Simulation results will be provided to demonstrate the advantages of the proposed strategy, which can also be applied to the recently proposed information raining system.

Time-slotted ALOHA packetized multiple-access has been extensively used in satellite communications, and has recently been attracting considerable attention in wireless Ad- hoc networks. In this paper, we consider a time-slotted ALOHA system which combines multiple-access, broadcasting channels and rate splitting. This system allows some transmission bits to be reliably received even when collisions occur and more bits to be reliably received in the absence of collisions. In contrast to previous work, our work focuses on the case in which the transmission channels obeyi.i.d(independent and identically distributed) Rayleigh fading. Two fundamental problems are considered. The first one is the calculation of the system capacity, and the second one is how to select an appropriate channel coding scheme with which the system achieves its capacity. We shall review the single time slotted capacity, and present the total capacity expression with the knowledge of channel side information. We will then derive a threshold for the transmission probability in a single time slot provided that all the users have the same transmission probability. It will be shown that when the transmission probability is greater than the derived threshold, low-resolution codes can help the system achieve its capacity. We shall also present an explicit estimation method for computing the threshold, and prove that it can be extended to the more general case when the transmission probabilities are approximately equal.

Topology control and channel assignment are two important methods to improve the performance of wireless ad hoc networks. In this paper, we investigate such a combination scheme composed of a k -Neighbor topology control algorithm and a greedy channel assignment (GCA) algorithm. Based on this combination scheme, we discuss the impact of some parameters on the performance of networks in terms of the path loss factor, node density, maximum node degree, etc. Simulation results will show that such a combination scheme can reduce the required channel number effectively, compared with its theoretical upper bound. In particular, if the node degree in a network is not more than k, various simulations indicate that the required channel number is not more than 2 k + 1.

High-speed Internet access in subway and railway systems has been receiving more and more attention in recent years. A critical issue for such application is the ground to train communications. One potential solution is to place multiple repeaters along the track and multiple antennas on the roof of the vehicle and then establish multiple point to point links through the use of DS/SSMA. In this paper, we will show that the antenna/repeater assignment can significantly affect system performance when this solution is employed. We will then derive an optimal antenna assignment strategy that maximizes the system throughput. Simulation results will be provided to demonstrate the advantages of the proposed strategy, which can also be applied to the recently proposed information rain system.

In this paper, we consider the time-slotted ALOHA packetized multiple-access system where the transmission channels obeyi.i.d(independent identically distributed) Rayleigh fading. Two fundamental problems are considered. The first one is the calculation of system capacity, and the second one is how to select a channel coding for the system achieving its capacity. Here we firstly review the single time slotted capacity, and present the total capacity expression with the knowledge of channel side information. Then we deduce a thresholdthetasfor transmission probability in a single time slot provided that all the users have the same transmission probability. It will be proved that when transmission probability is greater than the thresholdthetas, low-resolution code can help the system achieve its capacity. Moreover, we present an explicit threshold estimation, and prove that it can be extended to a more general case when transmission probabilities are approximately equal. In the end, simulation and numerical results show the validity and robustness of theoretical results.

IP radio access networks (RAN) are expected to employ CDMA as the next generation access networks (Bu et al., 2004). In the previous work, the problem about congestion of voice in IP-based CDMA RAN was examined and several policies were brought forward and evaluated with the assistance of simulations (Kasera et al., 2005). In this paper, we first develop a mathematical model to investigate the multi-region soft-handoff of the IP-based CDMA RAN and analyze its systematic characteristics. The effectiveness of the developed mathematical model is demonstrated by simulations. We then propose a max-leg-limit control policy to improve the system performance in terms of the blocking rate and the number of users. We also extend it to a more general situation, where instant video, instant data and voice are provided within an IP-based CDMA RAN. For such a multiple priority instant service system, we propose two control mechanisms, borrow-leg and router buffer preservation, to control the quality of services. Simulation shows that the control mechanisms are effective in keeping the quality of multi-priority services.

In wireless multi-hop networks such as ad-hoc and sensor networks, one node may receive signals from several other nodes simultaneously due to the broadcast nature of the wireless medium. That results in the reduction of bandwidth usage and system efficiency. To deal with this problem, several approaches have been developed to avoid signal collision by appropriate protocols. In this work and in contrast to previous works, we put forward a unicast strategy that can recover the desired signal from the collided signals in wireless multi-hop networks. The proposed strategy is featured as a physical layer network coding scheme that can greatly increase the throughput of unicast in wireless multi-hop networks without synchronization nor power control among the different transmitters, thus, making it ideally fit for distributed networks.

In this correspondence, we investigate the link failure problem based on the recent results of network coding. We propose a concept, named capacity factor of a network, which is the minimum link set that can influence the network capacity, as our basic tool. We define the capacity rank to each link of the network to characterize its criticality and present the concept of the p-stable network. Based on these notions, an upper bound for the capacity factor size is derived and a family of p-stable networks is constructed

A wireless ad-hoc network can be roughly considered as one consisting of a collection of mobile nodes distributed in a finite region, which adopts a non-centralized and self-organized structure. In such networks, messages are transmitted, received and forwarded in a finite geometrical region. In addition, the transmission of messages is highly dependent on the locations of the mobile nodes. As a result, the geometrical relationships between the nodes, and especially the distance between them are of fundamental importance. In this paper, we propose a space decomposition method to analyze the probability distribution of the distance between nodes in an ad-hoc network. In particular, we derive two theoretical expressions for the probability distribution of the distance between nodes under the assumption that the nodes are independently and uniformly distributed in either a rectangular region or hexagonal region. Further results on the node degree distribution and max-flow capacity of the network are then presented based upon these expressions

Network coding is an effective way to achieve the maximum flow of multicast networks. In this letter, we focus on the statistical properties of the maximum flow or the capacity of network coding for ad-hoc networks based on random graph models. Theoretical analysis shows that the maximum flow can be modelled as extreme order statistics of Gaussian distribution for both wired and wireless ad-hoc networks as the node number is relatively large under a certain condition. We also investigate the effects of the nodes' covering capabilities on the capacity of network coding.

To reduce the cost and complexity of network coding in multicast networks, we propose an algorithm to obtain the routes of the maximum flow and the encoding nodes. Thus the coding operation can be taken at these nodes only, rather than throughout the whole network. A practical approach for wireless Ad-hoc network is also given. Moreover, based on a random graph model of Ad-hoc network, we will show that the encoding node number and the maximum flow value between a source and destination pair approximately obey the geometric and the Poisson distribution, respectively.

Capacity Analysis of network coding is a fundamental problem in communication networks. In this paper, we investigate this problem by generalizing the conventional random graph model as G(n, P,C), where the connecting probability between each pair of nodes p obeys an independent and identical distribution and all the links each have an independent and identical transmission capacity distribution. A tight upper bound of the average value of the maximal flow will be derived based on the proposed random graph model. Moreover, the averaged value and the variance of the maximal flow shall be investigated by some simulations, which demonstrate the effectiveness of our theoretical analysis.

In multiuser wireless communication systems, dynamic allocation of transmit power is an important means to deal with the time-varying nature both at physical layer and at network layer. Optimal power allocation with perfect channel and buffer state information is studied in this paper. We first build up the cross-layer model by integrating that of physical layer and network layer and then formulate the optimization problem on power allocation. We prove that the optimal solution to power allocation problem can be regarded as an extension of the traditional water-filling (TWF) technique, which is called "water-filling in cellar" (WFIC) policy. The corresponding dynamic programming algorithm is presented. Finally, numerical experiments are employed to illustrate the advantage of our proposed policy.

In this paper, we investigate the performance of using a square array of four antennas for selection combining (SC), maximal-ratio combining (MRC) of Nakagami correlated signals. The closed form of average output signal-to-noise ratio (SNR) and bit error rate (BER) with SC for arbitrary 4 correlated branches will be derived and the expression of BER with MRC is also derived by utilizing the method of characteristic function (CF). Two models for angle of arrival (AOA), e.g. uniform and Gaussian, will be considered, as well as the comparison between their system performances. The effect of correlation coefficients and fading factor on the system performance are discussed by numerical results, which show that the uniform distributed AOA system outperforms the Gaussian distributed AOA system with small angular spread.

Narrowband interference (NBI) will degrade the performance in an OFDM system not only on the overlapped subcarriers, but also on the nearby subchannels due to the spectral leakage effect of DFT demodulation. In this paper we proposed a novel NBI suppression method in the case that NBI is caused by a narrowband digital communication system. We estimate the "transmitted data" of NBI signal and reconstruct its waveform, by measuring interference information on certain unmodulated subcarriers. And then subtract estimated disturbance in frequency domain. Simulation results show that this method can achieve an average SINR gain about 6 dB on a multipath fading channel, when the interference has equal power with the desired signal and twice bandwidth of an OFDM bin. With less bandwidth of NBI, more performance gain will be obtained.

Abstract A differential detection scheme for transmit diversity was proposed by Tarokh, which can achieve full diversity order without the requirement to estimate the channel state at the receiver. This paper investigates the potential of using multiple receive antennas for differential space time coded MPSK signals over correlated Nakagami fading channels. We also investigate the effect of the carrier frequency offset (CFO) and channel correlation on its performance and present some results on its maximal tolerable frequency offsets for different MPSK signals. The results have shown that the differential encoding transmit diversity is very robust to the CFO and channel correlation. Copyright © 2004 John Wiley & Sons, Ltd.

Diagonal algebraic space time (DAST) block codes was proved to outperform the codes from orthogonal design with the equivalent spectral efficiency when the number of transmit antennas employed is larger than 2. However, due to the limitation on the signal constellation with complex integer points, no 3 bits/symbol DAST block code was studied previously. In this paper, we propose a general form of an 8-star-PSK constellations with integer points and present some theoretical results on the performance of the equivalent 8-star-PSK modulations. By using our proposed 8-star-PSKs, we present a search algorithm to construct optimal DAST codes with 3 bits per symbol under some criteria and investigate their performance over flat Rayleigh fading channels.

A proportional-delay model for Internet differentiated services was proposed recently. Under this model, the average waiting times among different classes of traffic is kept as constant specified ratios. Adaptive waiting time priority (AWTP) scheduling algorithm has been demonstrated to be an efficient one to realize such proportional differentiated services if a good set of priority control parameter exists. However, the existence on such a good set of priority control parameters was not completely solved in the AWTP algorithm. In this paper, we mainly consider the searching problem on the good set of priority control parameters and its effect on the system performance. We propose an improved searching algorithm for obtaining the priority control parameters. Compared to that proposed by Leung. Lui and Yau (LLY), the new searching algorithm can greatly improve the successful searching probability for priority control parameters. Furthermore. We also investigate the selection problem on the priority control parameters when our searching algorithm does not converge and present a fast table looking-up method on the selection of the priority control parameters. Simulation results have clearly demonstrated that our table looking-up selection method on the priority control parameters can provide a very good performance in adaptive proportional delay differentiated services.

Cross layer design is a promising approach in mobile ad hoc networks (MANET) to combat the fast time-varying characteristics of wireless links, network topology, and application traffic. In this paper, we employ cross layer design to develop a novel-scheduling scheme with two optimisations aimed at service differentiation. The scheduling scheme is executed at the network layer of every station according to the channel conditions estimated by the MAC layer. The optimizations are based on traffic property sharing and packet timeout period interaction to reduce the packet collisions and improve network performance. We evaluate the proposed scheme under different network loads in terms of packet delivery ratio, average end-to-end delay and delay jitter. The simulation results show that our scheme can provide different service differentiations for time-bounded and best effort traffics. In particular, we can guarantee the delay and delay jitter requirements of time-bounded traffic.

IP over WDM networks has been receiving much attention as a promising approach to building the next generation Internet since it can reduce complexities and overheads associated with the ATM and SONET layers. Provision of quality of service (QoS) is one important topic in next generation Internet. An optical burst switching scheme to support basic QoS at the WDM layer, the offset-time-based QoS scheme, was proposed by Yoo, Qiao and Dixit (2000, 2001) (YQD scheme). In this paper, we propose a union bound method to estimate the loss probability for different priority services, which is an extension of the bounds given by Yoo, Qiao and Dixit. It is proved that the proposed union bound is tighter than that given by Yoo, Qiao and Dixit. Meanwhile, based on the conservation law, we also present a method to estimate the required maximum delay length of fiber delay lines (FDLs) if FDL buffers are employed in the YQD scheme in IP over wavelength division multiplexing (WDM) networks. Some numerical results on the effect of fiber delay lines on the system performance and the required maximum delay length of FDLs for different service classes are presented.

In this paper, we consider the subchannel detection problem in forward links for the multicarrier (MC) DS-CDMA system when some different subchannel allocation policies are used. An optimal subchannel decision algorithm is proposed based on the maximum-likelihood (ML) criterion. The theoretical analysis and simulation results are presented. We also discuss the parameter selection problem for the length of the training sequence in the system model in Pingyi Fan et al. by using the proposed ML detection algorithm. The results show that the subchannel allocation schemes in Pingyi Fan et al. is feasible since only a few symbols overhead are required.

We present a noncoherent coded 16QAM (NC-16QAM) scheme by modifying the trellis-coded 16QAM (TC-16QAM) scheme. Our simulation results show that the performance of the NC-16QAM with noncoherent detection is close to the one of the original TC-16QAM with coherent detection. The NC-16QAM is an extension of the NC-8PSK previously obtained by Wei and Lin (see IEEE Commun. Lett., vol.2. p.260-62, 1998). A noncoherent initial phase estimation algorithm is also proposed.

In this paper, we systematically study modulated codes (MC) that are encoded after binary-to-complex symbol mapping. The main advantage of modulated codes is that their encoding arithmetic operations and the intersymbol interference (ISI) channel arithmetic operations are all defined on the complex field and therefore can be algebraically combined together. With MC, the ISI is not treated as distortion but diversity gain. The performance analysis and simulation results of MC over the ISI channel are presented.

In this paper, we propose a block coded modulation (BCM) technique to reduce the peak to average power ratio (PAPR) in OFDM systems. In the proposed technique, binary blocks are mapped to M-ary blocks and M-ary blocks of small sizes with low PAPR are selected. Large size M-ary blocks with low PAPR are constructed by using the selected small size M-ary blocks. Similar to trellis coded modulation, with this technique variable rates ranging from 1 to a rate much higher than 1 of BCM can be obtained upon different requirements of the random error correction capability in the system, given that the PAPR is below a fixed value. PAPR gain is defined by comparing with the uncoded OFDM system. Optimal coding gain for the BCM given a PAPR gain is also obtained in various cases.

In this paper, we present a noncoherent coded 16QAM (NC-16QAM) scheme by modifying the trellis-coded 16QAM (TC-16QAM) scheme. The simulation results show that the performance of the NC-16QAM with noncoherent detection is close to the one of the original TC-16QAM with coherent detection. The NC-16QAM in this paper is an extension of the NC-8PSK recently obtained by Wei and Lin (see IEEE Communications Letters, vol.2, no.9, p.260-2, Sept. 1998). A noncoherent initial phase estimation algorithm is also proposed.

We investigate the effect of time delay spread on trellis coded modulation (TCM) in a wireless radio environment where equalization is not employed to mitigate the effects of frequency selective fading when the time delay spread is small. Using a random variable decomposition technique and a Gaussian approximation of the intersymbol interference terms, we obtain explicit bounds for the pairwise error probability of TCM over multipath Rayleigh fading channels characterized by various power delay profiles. A method to calculate an upper bound of the bit error rate (BER) based on Jamali and LeNgoc (1995) bound is also presented. These bounds are used to evaluate TCM performance as well as investigate the delay spread tolerance limit of TCM, including I-Q TCM, over frequency selective fading channels.