Event cameras provide microsecond latency and high dynamic range, making them ideal for 3D perception tasks in traffic scenes with challenging lighting conditions. Yet existing methods often struggle to generalize to out-of-domain environments due to the limited availability of diverse training data. While synthetic data offers an easily accessible alternative, it introduces a significant sim-to-real gap, particularly in motion patterns. We tackle this challenge by introducing Motion-Adaptation Mamba (MA-Mamba), a dual-track framework that advances both architecture and data augmentation. At the architectural level, we introduce a lightweight Spatio-Temporal Association module that captures motion-induced appearance variations at arbitrary scales, and an Adaptive Memory Balancing module, built on the Mamba state-space framework, that adaptively filters memory updates to maintain stable scene context under diverse dynamics. At the data level, we design event-oriented augmentations that simulate varied motion patterns and apply priority-based masked sequence modeling to strengthen long-range spatio-temporal reasoning. Trained solely on synthetic data, MA-Mamba delivers substantial zero-shot gains on multiple real-world benchmarks, demonstrating strong robustness and generalizability.

Vehicular Fog Computing (VFC) is significantly enhancing the efficiency, safety, and computational capabilities of Intelligent Transportation Systems (ITS), and the integration of Unmanned Aerial Vehicles (UAVs) further elevates these advantages by incorporating flexible and auxiliary services. This evolving UAV-integrated VFC paradigm opens new doors while presenting unique complexities within the cooperative computation framework. Foremost among the challenges, modeling the intricate dynamics of aerial-ground interactive computing networks is a significant endeavor, and the absence of a comprehensive and flexible simulation platform may impede the exploration of this field. Inspired by the pressing need for a versatile tool, this paper provides a lightweight and modular aerial-ground collaborative simulation platform, termedAirFogSim. We present the design and implementation of AirFogSim, and demonstrate its versatility with five key missions in the domain of UAV-integrated VFC. A multifaceted use case is carried out to validate AirFogSim's effectiveness, encompassing several integral aspects of the proposed AirFogSim, including UAV trajectory, task offloading, resource allocation, and blockchain. In general, AirFogSim is envisioned to set a new precedent in the UAV-integrated VFC simulation, bridge the gap between theoretical design and practical validation, and pave the way for future intelligent transportation domains. Our code will be available athttps://github.com/ZhiweiWei-NAMI/AirFogSim.

Federated learning (FL) has been recognized as a promising paradigm to support distributed AI model training among wireless devices (WDs) under the coordination of an edge server (ES) without sharing local datasets. To alleviate computation burden of resource-limited WDs, model partitioning that leverages computing capability at the ES is further integrated into FL, yielding the split (S) FL framework. In this paper, we study online bandit model partitioning and resource allocation for SFL over dynamic wireless networks, aiming to minimize overall energy-latency cost (ELC). Unlike prior works focusing on offline static or online gradient-based model splitting and resource allocation, we consider a practical setting where the analytical expression of ELC function is unavailable, and instead only the function values at queried points are revealed. To tackle such a challenging mixed-integer non-linear programming problem in the online bandit context, novel Bayesian optimization (BO)-based approaches are put forth by relying on a Gaussian process (GP)-based surrogate model to actively select the model splitting points and resource allocation decisions per round via the low-complexity acquisition. Besides incorporating training model-specific structural information in the kernel design of the GP surrogate, an ensemble of GP models with data-adaptive weights is further leveraged to capture system dynamics. To cope with the challenging combinatorial nature and strong coupling over mixed action space during acquisition, an efficient alternating optimization approach is proposed building upon a novel contextual local search method. Numerical tests demonstrate that the proposed BO-based approaches outperform the contemporary baselines under various practical SFL settings.

This paper investigates a heterogeneous multi-vehicle, multi-modal sensing (H-MVMM) aided online precoding problem. The proposed H-MVMM scheme utilizes a vertical federated learning (VFL) framework to minimize pilot sequence length and optimize the sum rate. This offers a promising solution for reducing latency in frequency division duplexing systems. To achieve this, three preprocessing modules are designed to transform raw sensory data into informative representations relevant to precoding. The approach effectively addresses local data heterogeneity arising from diverse on-board sensor configurations through a well-structured VFL training procedure. Additionally, a label-free online model updating strategy is introduced, enabling the H-MVMM scheme to adapt its weights flexibly. This strategy features a pseudo downlink channel state information label simulator (PCSI-Simulator), which is trained using a semi-supervised learning (SSL) approach alongside an online loss function. Numerical results show that the proposed method can closely approximate the performance of traditional optimization techniques with perfect channel state information, achieving a significant 90.6% reduction in pilot sequence length.

In recent years, there has been significant progress in semantic communication systems empowered by deep learning. It has greatly improved the efficiency of information transmission. Nevertheless, traditional semantic communication models still face challenges, particularly due to their single-task and single-modal orientation. Many of these models are designed for specific tasks, which results in limitations when applied to multi-task communication systems. Moreover, these models often overlook the correlations among different modal data in multi-modal tasks. It leads to an incomplete understanding of complex information, causing increased communication payload and diminished performance. To address these limitations, Synesthesia of Machines (SoM) provides an effective framework for fusing multi-modal data, capturing their complementary relationships. Inspired by SoM, we propose a SoM-enabled multi-task semantic communication (SoMMSC) framework. In contrast to traditional semantic communication approaches, SoMMSC can effectively handle various tasks across multiple modalities. Furthermore, we design a fusion module based on Bidirectional Encoder Representations from Transformers (BERT) for multi-modal fusion. By leveraging the powerful semantic understanding capabilities and self-attention mechanism of BERT, we achieve effective fusion of different modalities. We compare our model with multiple benchmarks. Simulation results show that SoMMSC outperforms these models in terms of both performance and communication payload.

Deep learning-based channel state information (CSI) feedback schemes offer strong compression but are typically confined to fixed system configurations, limiting their generalizability and flexibility. To address this challenge, this work proposes WiFo-CF, a novel wireless foundation model tailored for CSI feedback. WiFo-CF uniquely accommodates heterogeneous configurations, including varying channel dimensions, feedback rates, and data distributions, within a unified framework through two key innovations: a multi-user, multi-rate self-supervised pre-training strategy and a Mixture of Shared and Routed Experts (S-R MoE) architecture. To support its large-scale pre-training, we introduce the first heterogeneous channel feedback dataset; its diverse patterns enable WiFo-CF to achieve superior performance on both in-distribution and out-of-distribution data across simulated and real-world scenarios. Furthermore, the learned representations effectively facilitate adaptation to downstream tasks such as CSI-based indoor localization, validating the model’s scalability and deployment potential.

Integrated sensing and communication (ISAC) at sub-THz frequencies is crucial for future air-ground networks. However, optimizing ISAC performance while managing operational latency is challenging due to unique propagation characteristics and hardware limitations. This paper introduces a multi-modal sensing fusion framework inspired by synesthesia of machine (SoM) to enhance sub-THz ISAC transmission. By exploiting inherent degrees of freedom in sub-THz hardware and channels, the framework succeeds in tuning the radio-frequency environment. It features squint-aware beam management to improve air-ground network adaptability, enabling dynamic three-dimensional ISAC links. By leveraging multi-modal information, the framework enhances ISAC performance and reduces latency. Visual data is used to rapidly localize users and targets, while a customized multi-modal learning algorithm optimizes the hybrid precoder. A new metric is proposed for comprehensive performance evaluation. Extensive experiments demonstrate that the proposed scheme significantly improves ISAC efficiency.

In recent years, there has been significant progress in semantic communication systems empowered by deep learning. It has greatly improved the efficiency of information transmission. Nevertheless, traditional semantic communication models still face challenges, particularly due to their single-modal orientation. These models often overlook the correlations among different modal data in multi-modal tasks. It leads to an incomplete understanding of complex information, causing increased communication overhead and diminished performance. To address these limitations, Synesthesia of Machines (SoM) provides an effective framework for fusing multi-modal data, capturing their complementary relationships. Inspired by SoM, we propose a SoM-enabled semantic communication (SoMSC) framework. In contrast to traditional semantic communication approaches, SoMSC can effectively handle tasks across multiple modalities. We design a fusion module based on Bidirectional Encoder Representations from Transformers (BERT) for multi-modal fusion. By leveraging the powerful semantic understanding capabilities and self-attention mechanism of BERT, we achieve effective fusion of different modalities. We compare our model with multiple benchmarks. Simulation results show that SoMSC outperforms these models in terms of both performance and communication overhead.

Rejecting outliers before applying classical robust methods is a common approach to increase the success rate of estimation, particularly when the outlier ratio is extremely high (e.g. 90%). However, this method often relies on sensor- or task-specific characteristics, which may not be easily transferable across different scenarios. In this paper, we focus on the problem of rejecting 2D-3D point correspondence outliers from 2D forward-looking sonar (2D FLS) observations, which is one of the most popular perception device in the underwater field but has a significantly different imaging mechanism compared to widely used perspective cameras and LiDAR. We fully leverage the narrow field of view in the elevation of 2D FLS and develop two compatibility tests for different 3D point configurations: (1) In general cases, we design a pairwise length in-range test to filter out overly long or short edges formed from point sets; (2) In coplanar cases, we design a coplanarity test to check if any four correspondences are compatible under a coplanar setting. Both tests are integrated into outlier rejection pipelines, where they are followed by maximum clique searching to identify the largest consistent measurement set as inliers. Extensive simulations demonstrate that the proposed methods for general and coplanar cases perform effectively under outlier ratios of 80% and 90%, respectively.

ABSTRACT Intelligent vehicles require extensive data processing to enhance safety and improve driver comfort. With limited onboard computing resources, these vehicles often offload tasks to nearby vehicles or servers for auxiliary processing to meet real‐time response requirements. However, the complexity and highly dynamic nature of the vehicular environment render the design of effective offloading strategies. While existing approaches can adapt to changes in environmental parameters within vehicular networks, they are fundamentally limited by their inability to process variable‐dimensional environmental information and make decisions that scale with network size. Traditional methods typically rely on fixed‐size input representations and static computational frameworks, which are inherently unsuitable for the dynamic and scalable nature of real‐world vehicular networks that require adaptive responses to varying network sizes. As a result, existing alternatives lack feasibility to highly dynamic real‐world vehicle networks that require adaptive responses to varying network sizes. To alleviate this limitation, we develop an original approach to address the task offloading and resource allocation problem with a scalable size, via a framework based on a graph neural network (GNN). Leveraging its neighbour aggregation mechanism, GNN effectively adapts to varying‐scale topologies in dynamic vehicular networks, ensuring robust performance regardless of network size. To evaluate our proposed approach, we conducted extensive simulations to analyse its performance. The experimental results demonstrate that our method provides a more scalable and real‐time capable solution, surpassing existing approaches by seamlessly handling dynamic network size variations.

Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and strongly depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and initial channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches.

Integrated sensing and communication (ISAC) emerges as a promising technology for 6G, particularly in the millimeter-wave (mmWave) band. However, the widely utilized hybrid architecture in mmWave systems compromises multiplexing gain due to the constraints of limited radio-frequency (RF) chains. Moreover, additional sensing functionalities exacerbate the impairment of spectrum efficiency (SE). In this paper, we present an optimized beam pattern modulation-embedded ISAC (BPM-ISAC) transceiver design, which spares one RF chain for sensing and uses the remaining ones for communication. To compensate for the reduced SE, index modulation across communication beams is applied. We formulate an optimization problem aimed at minimizing the mean squared error (MSE) of the sensing beampattern, subject to a symbol MSE constraint. This problem is then solved by sequentially optimizing the analog and digital parts. Both the multi-aperture structure (MAS) and the multi-beam structure (MBS) are considered in the analog part. We conduct theoretical analysis on the asymptotic pairwise error probability (APEP) and the Cramér-Rao bound (CRB) of direction of arrival (DoA) estimation. Numerical simulations validate the overall enhanced ISAC performance over existing alternatives.

In vehicular networks, collaborative decision-making can improve the recognition capability and driving efficiency of vehicles, while easy to suffer from false information injection from malicious nodes (MNs). Therefore, in this paper, we investigate collaborative decision-making in a hierarchical blockchain-enabled vehicular network in the presence of self-interested MNs. In order to improve the accuracy of collaborative decision-making, we propose a reputation-based decision-making approach, which consists of two parts: 1) We devise a Bayesian static game model for an attack-defense game between MNs and miners in the blockchains. By providing incentives in expected payoffs, the scheme motivates MNs to cease attacks, thereby reducing the dissemination of false information during the collaborative process. The pure-strategy Bayesian Nash equilibrium (BNE) of MN quitting attack is further studied. 2) We introduce a collaborative decision-making algorithm based on Dempster-Shafer Theory (DST) to facilitate precise decision-making in the presence of false information. This algorithm leverages the hierarchical blockchain architecture, integrating both historical and local reputation of a node to assess credibility, mitigating the influence of untrusted information. Simulation results demonstrate the effectiveness of our approach.

Despite the widespread implementation, the Adaptive Cruise Control (ACC) systems still fall short in delivering a satisfactory human-likely experience, primarily due to the heterogeneity in driving experience preferences and the unpredictable, heterogeneous nature of human driving behaviors. To address this critical gap, we introduce an innovative driving style-aware car-following model that effectively captures the varying cut-in tendencies of adjacent vehicles by utilizing the Maximum Entrop Inverse Reinforcement Learning (Max-Ent IRL) method. A distinct reward function is developed to replicate human driving behavior, which can achieve a harmonious equilibrium between efficiency, safety, and comfort. The efficacy of this model is rigorously evaluated through a comprehensive analysis on car-following episodes extracted from the Next Generation Simulation (NGSIM) I-80 dataset. A novel human-likely metric is utilized for evaluating the performance of the proposed model in comparison to standard benchmarks. The results demonstrably favor our approach, showing notable enhancements in efficiency, safety, and comfort. Additionally, the model’s versatility is confirmed by its ability to accommodate a wide spectrum of driving styles, as evidenced by the diverse weights learned from different driving styles. These findings highlight the significant potential of our model in advancing ACC technology for more human-oriented vehicular systems that align closely with the natural driving instincts and preferences of humans.

Real-world last-mile express datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile express dataset exists to support research in this field. In this paper, we introduce LaDe, the first publicly available last-mile express dataset with millions of packages from the industry. LaDe has three unique characteristics: (1)Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2)Comprehensive information. It offers original package information, task-event information, as well as couriers' detailed trajecotries and road networks. (3)Diversity. The dataset includes data from various scenarios, including package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. We verify LaDe on three tasks by running several classical baseline models per task. We believe that the large-scale, comprehensive, diverse feature of LaDe can offer unparalleled opportunities to researchers in the supply chain community, data mining community, and beyond. The dataset and code is publicly available at https://huggingface.co/datasets/Cainiao-AI/LaDe.

Recent advancements in 3D perception have led to a proliferation of network architectures, particularly those involving multi-modal fusion algorithms. While these fusion algorithms improve accuracy, their complexity often impedes real-time performance. This paper introduces VeXKD, an effective and Versatile framework that integrates Cross-Modal Fusion with Knowledge Distillation. VeXKD applies knowledge distillation exclusively to the Bird's Eye View (BEV) feature maps, enabling the transfer of cross-modal insights to single-modal students without additional inference time overhead. It avoids volatile components that can vary across various 3D perception tasks and student modalities, thus improving versatility. The framework adopts a modality-general cross-modal fusion module to bridge the modality gap between the multi-modal teachers and single-modal students. Furthermore, leveraging byproducts generated during fusion, our BEV query guided mask generation network identifies crucial spatial locations across different BEV feature maps in a data-driven manner, significantly enhancing the effectiveness of knowledge distillation. Extensive experiments on the nuScenes dataset demonstrate notable improvements, with up to 6.9\%/4.2\% increase in mAP and NDS for 3D detection tasks and up to 4.3\% rise in mIoU for BEV map segmentation tasks, narrowing the performance gap with multi-modal models.

To support various upper applications of intelligent vehicles ranging from driving assistance to automated planning and control, accurate localization, and tracking are the fundamental tasks. Given the limited versatility and efficiency of traditional single-vehicle multisensor and multivehicle multisensor localization and tracking solutions, this article presents an efficient distributed multivehicle cooperative tracking framework via multicast. Once the self-positioning data is locally fused with assistance from roadside units, each vehicle shares the local-fusion results with surrounding vehicles through multicast and observes surrounding vehicles with on-board sensing equipment. The vehicles can then jointly feed the local-fusion results, received multicast information, and observation results into a global filter to obtain accurate and robust cooperative tracking. By leveraging multicast, the communication load is reduced, which promotes the efficiency of communication resource utilization. By optimizing the data fusion procedure, the error caused by error correlation is eliminated and the sensitivity to nonideal conditions, including packet loss, interruption, time-varying cooperative vehicles, etc., is reduced, which improves the versatility of the framework in real-world applications. Furthermore, several practical issues, such as random communication delay, packet loss, communication load, and localization robustness are also involved. To verify the effect of the framework, both theoretical analyses and simulation results are presented to show the accuracy and robustness of our proposed cooperative tracking framework.

The U-Net-like coarse-to-fine network design is currently the dominant choice for dense prediction tasks. Although this design can often achieve competitive performance, it suffers from some inherent limitations, such as training error propagation from low to high resolution and the dependency on the deeper and heavier backbones. To design an effective network that performs better, we instead propose Recurrent Multiscale Feature Modulation (R-MSFM), a new lightweight network design for self-supervised monocular depth estimation. R-MSFM extracts per-pixel features, builds a multiscale feature modulation module, and performs recurrent depth refinement through a parameter-shared decoder at a fixed resolution. This network design enables our R-MSFM to maintain a more lightweight architecture and fundamentally avoid error propagation caused by the coarse-to-fine design. Furthermore, we introduce the mask geometry consistency loss to facilitate our R-MSFM for geometry consistent depth learning. This loss penalizes the inconsistency of the estimated depths between adjacent views within the nonoccluded and nonstationary regions. Experimental results demonstrate the superiority of our proposed R-MSFM both at model size and inference speed, and show state-of-the-art results on two datasets: KITTI and Make3D.

Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead of overestimating their willingness. To remunerate the participation intentions of vehicles as well as improve vehicular fog resource utilization in the large-scale VFC, the trading-based mechanism is a potential solution. In this article, we propose a many-to-many task offloading framework based on the vehicular trading paradigm. This framework enables computational resource trading across different VFC subsystems and decides the multi-tier task offloading results based on the trading consensus. The trading process is viewed as a partially observable Markov decision process (POMDP) and a Multi-Agent Gated actor Attention Critic (MA-GAC) approach is designed to reach an effective and stable offload-and -serve cooperation among vehicles. Theoretical analyses and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the coordinated MA-GAC approach not only benefits vehicles with higher long-term rewards but also optimizes the system social welfare in a distributed manner.

Vehicular fog computing (VFC) has been expected as a promising architecture that can make full use of computing resources of idle vehicles to increase computing capability. However, most current VFC architectures only focus on the local region and ignore the spatio-temporal heterogeneity of computing resources, resulting in that some regions have idle computing resources while others cannot satisfy the requirements of tasks. To further improve the overall computing resource utilization in the whole network, in this work, we propose a hierarchical VFC architecture, where neighboring regions can share their idle computing resources. Considering the high complexity of both inter- and intra-region cooperative task offloading in such a hierarchical VFC architecture, we put forward a distributed task offloading strategy based on multi-agent reinforcement learning in which the multi-agent reinforcement learning method is designed to learn each task vehicle’s offloading strategy in a distributed manner. Moreover, to tackle the inefficiency caused by the multi-agent credit assignment problem, we provide the counterfactual multi-agent reinforcement learning approach which exploits a counterfactual baseline to evaluate the action of each agent. Simulation results validate that the proposed hierarchical VFC architecture can effectively improve the global task computing efficiency and the proposed mechanism outperforms the baseline algorithms.

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require exact alignment between the narrow beams, which traditionally involves excessive signaling overhead. To address this issue, we propose a novel proactive beamforming scheme that integrates multi-modal sensing and communications via Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple neural network components with distinct functions. Unlike existing methods that rely solely on communication processing, our approach obtains comprehensive environmental features to improve beam alignment accuracy. We verify our scheme on the Vision-Wireless (ViWi) dataset, which we enriched with realistic vehicle drifting behavior. Our proposed MMFF-Net achieves more accurate and stable angle prediction, which in turn increases the achievable rates and reduces the communication system outage probability. Even in complex dynamic scenarios with adverse environment conditions, robust prediction results can be guaranteed, demonstrating the feasibility and practicality of the proposed proactive beamforming approach.

Vehicular fog computing (VFC) can make full use of computing resources of idle vehicles to increase computing capability. However, most current VFC architectures only focus on the local region and ignore the spatio-temporal distribution of computing resources, resulting that some regions have idle computing resources while others cannot satisfy the requirements of tasks. Therefore, we propose a hierarchical VFC architecture, where neighboring regions can share their idle computing resources. Considering that the existing centralized offloading mode is not scalable enough and the high complexity of cooperative task offloading, we put forward a distributed task offloading strategy based on multi-agent reinforcement learning. Moreover, to tackle the inefficiency caused by the multi-agent credit assignment problem, we provide the counterfactual multi-agent reinforcement learning approach which exploits a counterfactual baseline to evaluate the action of each agent. Simulation results validate that the hierarchical architecture and the distributed algorithm improves the efficiency of global performance.

Vehicular fog computing (VFC) is regarded as a promising framework for vehicular computing applications by utilizing local spare resources of nearby vehicles to conduct ubiquitous time-critical and data-intensive tasks. Meanwhile, how to provide stable and low-latency services through real-time task offloading has become a heated issue. Opposite to the traditional task-to-individual offloading manner, in this article, we propose a novel task-block (TB)-based offloading paradigm for VFC, in which the tasks are merged into blocks to be assigned and offloaded. This TB-based offloading paradigm effectively alleviates the offloading decision-making burden and, thus, reduces the overall computation latency in the dynamic vehicular environment. Faced with transmission-reliable and time-intensive requirements of TBs, we turn to cooperation among vehicles and further propose a TB-based overlapping matching-coalition (TBOMC) scheme integrating overlapping coalition formation (OCF) game with matching theory to address the complicated offloading problem. The OCF game framework encourages vehicular fog nodes to devote their resources and form collaborative computing groups in a distributed method. Numerical results demonstrate that the TBOMC scheme better exploits local computing capabilities and outperforms from 5% to 12% over other existing benchmarks.

Recent years have witnessed the exciting developments for the power grid. For instance, many traditional mechanical components are being replaced by modern electronics devices that can operate intelligently; new elements, such as renewable energy resources and various large-scale energy storage, are introduced into the grid to bring a new outlook on the system operation and control; smart appliances are produced to facilitate more customized and efficient energy usage; and advanced sensors, such as the phasor measurement units (PMUs) and the advanced metering infrastructure (AMI), are designed and implemented for real-time wide-area monitoring of the system conditions. In general, the power grid is increasingly organized and managed as an interconnected network of many different individual components that operate intelligently in a distributed but connected manner, as opposed to the traditional centralized fashion. In other words, the power grid is evolving into a big Internet of Things (IoT).

With increasing time-critical and computation-intensive tasks generated by mobile applications, vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on roadside units (RSUs) or cloud centers. In VFC, tasks are offloaded to vehicular fog nodes local to the client devices, which exploits the under-explored computational resources of nearby vehicles. In this paper, we propose a novel cooperative vehicular fog computing architecture from an overlapping perspective, termed Overlapping-enabled Cooperative Vehicular fog Computing (OCVC) to fully utilize vehicular fog nodes' local potential resources. Different from traditional cooperative VFC architecture where each vehicle only works in one fog computing group at one time, the proposed OCVC architecture enables vehicles to participate in different computing groups simultaneously, and thus is able to fully exploit potential computational resources in an overlapping manner. In addition, we provide a distributed OCVC scheme to solve the complicated computing group formation, overlapping resource allocation, and task assignment problem by employing the overlapping coalition formation (OCF) game framework and a heuristic offloading algorithm. We conduct simulations for performance comparison in terms of diversified performance metrics and numerical results show that the proposed OCVC scheme performs better than other benchmarks under different conditions.

Simultaneous wireless information and power transfer (SWIPT) is a prospective technology that can handle the energy consumption and communication requirements in the Internet of Things. Resonant beam SWIPT (RB-SWIPT) scheme utilizes narrow optical beam as carrier and with spatially separated resonator structure, which can support high power and high rate SWIPT for mobile devices. However, the performance of original RB-SWIPT systems is limited by returning beam interference and transmission loss. In this paper, we propose a RB-SWIPT scheme for transmission-enhanced and anti-interference. The telescope internal modulator (TIM) and second harmonic generator (SHG) are adopted in the proposed system. The TIM can compress beams to reduce the transmission loss. The SHG can generate frequency-doubled beams to avoid interference. To evaluate the proposed system, we establish mathematical models to depict the beam transmission, energy conversion, electric power output and data receiving. Numerical results illustrate that the proposed system can achieve 18 bit/s/Hz spectral efficiency and deliver 8 W power over 100 m distance.

Wireless multi-casting is an efficient transmission modality for downlink network control and content sharing. Conventional multi-casting amounts to finding an optimized beamforming vector under a specific performance metric and without the need of a dedicated treatment at the receiver. However, the so-calledone-fit-allstrategy is not directly applicable to mmWave systems equipped with 1-bit digital-to-analog converters (DAC), prompting us to develop a new multi-casting framework. The overarching design is built upon a vector-based instead of a scalar-based modulation, with which the multi-casting exhibits special features on both the transmitter and receiver. Specifically, we start by revealing the theoretical fundamentals for multi-casting under different 1-bit setups. Then, we design an iterative scheme to generate high-order constellations by establishing the equivalence between the codeword basis and low-order constellations. Finally, effective detection solutions are individually proposed for low-order and high-order constellations per their specific signal structures. Extensive analyses and simulations have been carried out to corroborate the decency of the proposed multi-casting strategy.

5G communication network has become a major pillar in the evolution of interconnected industrial systems. However, the introduction of 5G network may lead to unknown risks in the systems. To reveal the impact of network threats on 5G-based industrial systems, a 5G network security analysis approach combining formal modeling and attack penetration is proposed. Firstly, the 5G network models based on topology and transmission events are established to cope with diverse and hidden attack routes and behaviors. Then, the attack module is integrated into the network model. With attack penetration to the models, potential vulnerabilities are exploited and quantified based on the hierarchical-topology model, and network reliability is evaluated based on the transmission-event model. The simulation results identify and quantify network vulnerabilities under various attacks, including access authentication failure, destruction of data integrity, illegal control of Network Functions (NFs), and malicious consumption of shared slicing resources. Meanwhile, a more unpredictable outcome is that there is a threshold of access probability,$\alpha $, to measure the impacts of attacks against the bearer network and core network on reliability. Finally, a practical case about the impact of network security on a 5G-based coupled-tank system is discussed, which further proves the feasibility of our approach.

In this paper, we study a cooperative perception scheme in a vehicular sensor network, attempting to fuse the semantic information provided by different sensors at multiple vehicles, so as to expand the vehicle’s perception range, eliminate blind spots, improve the ability to handle environmental interference and enhance the accuracy and robustness of the perception results. The key to guide the fusion process is the evaluation of the confidence levels of the outputs provided by various machine learning schemes implemented at individual sensors in the vehicular sensor network. We first propose an evaluation criterion termed as Environmental Sensitivity (ES), which is used to measure the sensitivity of the network to environmental changes. Based on the ES, we further evaluate the confidence of the perception output of neural networks and quantify the confidence level considering the abnormal level of the input data, the general performance of the perception algorithm, the detection performance and the ES of the network. Semantic information fusion algorithm is then developed based upon the confidence levels. Experiment results are provided to validate the proposed fusion method in various scenarios.

Confronted with the increasing computation-intensive requirements of vehicular applications, vehicular fog computing (VFC) has emerged as the promising solution to mitigate the load at the edge of vehicular network. In VFC, vehicles are employed as vehicular fog nodes to provide reliable services with applicability. However, considering the individual serving and offloading intentions of the privately-owned vehicles, the many-to-many task offloading in dynamic vehicular environment becomes a challenging problem. In this paper, we propose a distributed dynamic many-to-many task offloading framework based on vehicle-to-vehicle (V2V) trading paradigm to improve the fog resource utilization in VFC. In order to reach an effective and stable offload-and-serve cooperation between vehicles as service demanders and vehicles as computation providers in the proposed framework, we formulate the trading process as a partially observable Markov decision processes (POMDP) and design a Multi-Agent Gated actor Attention Critic (MA-GAC) approach, leading to an efficient offloading optimization process in a distributed manner. Theoretical analysis and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the proposed MA-GAC approach outperforms other benchmarks in the dynamic environment.

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload in vehicular network. Since individual vehicular fog node is incapable of providing ultra-reliable and low-latency services constrained by limited resources, cooperation among vehicles becomes an attractive attempt to promote quality of service (QoS). In this paper, we propose a novel Overlapping-enabled Cooperative Vehicular Computing architecture in VFC, termed OCVC, to fully utilize vehicular fog nodes' local potential resources. The proposed OCVC architecture enables vehicles to participate in different fog groups simultaneously different from traditional cooperative computing architecture. In addition, we propose a distributed OCVC scheme to solve the complicated computing group for-mation, overlapping resource allocation, and task assignment problem based on overlapping coalition formation (OCF) game framework. We conduct experiments in several metrics and numerical results show that the proposed OCVC scheme per-forms at least 5 % better than other benchmarks under different conditions.

Abstract Vehicle behaviour prediction provides important information for decision‐making in modern intelligent transportation systems. People with different driving styles have considerably different driving behaviours and hence exhibit different behaviour tendency. However, most existing prediction methods do not consider the different tendencies in driving styles and apply the same model to all vehicles. Furthermore, most of the existing driver classification methods rely on offline learning that requires a long observation of driving history and hence are not suitable for real‐time driving behaviour analysis. To facilitate personalised models that can potentially improve vehicle behaviour prediction, the authors propose an algorithm that classifies drivers into different driving styles. The algorithm only requires data from a short observation window and it is more applicable for real‐time online applications compared with existing methods that require a long term observation. Experiment results demonstrate that the proposed algorithm can achieve consistent classification results and provide intuitive interpretation and statistical characteristics of different driving styles, which can be further used for vehicle behaviour prediction.

With the unprecedented development of smart vehicles and roadside units equipped with wireless connectivity, the transportation system is undergoing revolutionary changes in the past decade or two. Bearing safety and efficiency as the utmost objectives, the vehicular environments are witnessing explosive increase of various sensors onboard vehicles and equipped at transportation infrastructures. On the one hand, these sensors are destined to be wirelessly connected to provide more comprehensive situational awareness for transportation purposes. On the other hand, the abundance of sensor data of the environment can potentially shed light on the channel propagation characteristics that lie at the core of any communications system design. The integrated sensing and communications (ISACs) is henceforth both necessary and natural in vehicular communications networks (VCN). Different from existing ISAC works that target generic environments but are limited to dual-function radar-communications (DFRC), in this article we focus on transportation scenarios and applications but take a wholistic view of ISAC possibilities. First, we argue that, even though many sensors in transportation settings are nonradio-frequency (RF)-based, functional ISAC (fISAC) is feasible and necessary, in both communication-centric (CC) or sensing-centric (SC) modes. To facilitate this, the concept of synesthesia is introduced to ISAC to accommodate “machine senses” in the RF and non-RF formats. We then zoom in to RF-based sensors and propose the so-termed signaling ISAC (sISAC), with either unified-hardware (UH) or separate-hardware (SH) platforms, and delineate the unique issues arising in transportation settings. Several transportation-specific case studies are included to demonstrate these various ISAC regimes. Toward the end, the relationships of these ISAC subcategories are discussed with a roadmap laid out.

Unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian, with its unique advantages of flexibility, convenience, and wide coverage. As flying stations, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this article, we investigate a two-way multihop UAV relaying network, where there are two ground users as sources and multiple UAVs as relays to help the two ground sources exchange information. For the purpose of enhancing the efficiency of the investigated UAV-assisted relaying, we come up with a productive two-way multihop UAV relaying pattern, which can achieve a data rate of$({1}/{2})$data packets per time slot with the decode-and-forward protocol. Then, we further formulate a joint transmit power and trajectory optimization problem for the UAVs in this two-way multihop relaying scenario. The formulated problem is nonconvex which makes it difficult to solve directly; hence, we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Numerical results demonstrate that our proposed two-way multihop UAV relaying network achieves significant throughput gains compared with other benchmark schemes.

In this article, with the aim to enhance the secure transmission and improve the utilization of spectrum resources in Internet of Things (IoT), a multicarrier nonorthogonal multiple access (MC-NOMA)-assisted full duplex (FD) network is investigated, in which nonorthogonal multiple access (NOMA) is implemented in both uplink and downlink transmissions. The lightweight and low-power physical layer security (PLS) technology is employed to protect the information from eavesdropping. Taking the imperfect channel state information (CSI) into account, we formulate a problem to optimize the beamforming vector, artificial noise (AN), transmit power, and subcarrier assignment policy aiming to maximize the worst case sum secrecy rate under the Quality of Service (QoS) and power consumption constraints. Since the formulated problem is nonconvex and difficult to be solved, we decompose it into two joint optimization subproblems. The first is resource allocation with given subcarrier assignment, which is solved by using the block coordinate descent (BCD) approach. The second is subcarrier assignment solved by the matching theory. Our simulation shows that the proposed scheme is robust against the CSI imperfectness of the eavesdropping and self-interference channels, while providing significant sum secrecy rate improvement compared with the orthogonal multiple access (OMA), half duplex (HD) systems, and other benchmark schemes.

In autonomous driving, environment perception is the fundamental task for intelligent vehicles which provides the necessary environment information for other applications. The main issues in existing environment perception can be categorized into two aspects. On the one hand, all sensors are prone to measurement errors and failures. On the other hand, in complex driving environments, vehicles may encounter a variety of blind spots caused by vehicle occlusions, overlaps, and harsh weather conditions, which will cause sensors to experience low-quality data or to miss crucial environmental information. To cope with these issues, a multivehicle and multisensor (MVMS) cooperative perception method is presented to construct the occupancy grid map (OGM) of vehicles in a global view for the environment perception of autonomous driving. Distinct from existing environment perception methods, our proposed MVMS-OGM not only provides continuous geographical information but also captures and fuses continuous information with soft occupancy probabilities, resulting in more comprehensive and raw environmental information. Simulations and real-world experiments demonstrate that the proposed approach not only expands the perception range in comparison with single-vehicle sensing but also better captures the uncertainty of sensor data by fusing the occupancy probabilities with soft information.

In recent years, traffic flow prediction has attracted more and more interest from both academia and industry since such information can provide effective guidance for traffic management or driving planning and enhance traffic safety and efficiency. But due to the complicated spatial-temporal dependence in actual roads and the limitation of intersection monitoring equipment, there are still many challenges in spatial-temporal traffic flow prediction. In this paper, we propose a novel hierarchical traffic flow prediction protocol based on spatial-temporal graph convolutional network (ST-GCN), which incorporates both spatial and temporal dependence of intersection traffic to achieve a more accurate traffic flow prediction. Different from existing works, our proposed protocol with the Adjacent-Similar algorithm can also effectively predict the traffic flow of the intersections without historical data. Experiments based on practical traffic data of the city of Qingdao, China demonstrate that our proposed ST-GCN-based traffic flow prediction protocol outperforms the state-of-the-art baseline models. Moreover, as for the intersections without historical data, we can also obtain a good prediction accuracy.

Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent transportation systems. Common practices to reason about the future trajectory are through their historical paths. However, the impact of traffic context is ignored, which means the beneficial environment information is deserted. Although a few methods are proposed to exploit the surrounding vehicle information, they simply model the influence according to spatial relations without considering the temporal information among them. In this paper, a novel multi-vehicle collaborative learning with spatio-temporal tensor fusion model for vehicle trajectory prediction is proposed, which introduces a novel auto-encoder social convolution mechanism and a fancy recurrent social mechanism to model spatial and temporal information among multiple vehicles, respectively. Furthermore, the generative adversarial network is incorporated into our framework to handle the inherent multi-modal characteristics of the agent motion behavior. Finally, we evaluate the proposed multi-vehicle collaborative learning model on NGSIM US-101 and I-80 benchmark datasets. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art for vehicle trajectory prediction. Additionally, we also present qualitative analyses of the multi-modal vehicle trajectory generation and the impacts of surrounding vehicles on trajectory prediction under various circumstances.

Hop count distribution (HCD), generally formulated as a discrete probability distribution of the hop count, constitutes an attractive tool for performance analysis and algorithm design. This paper devotes to deriving an analytical HCD expression for a finite ad hoc network under the minimum hop-count routing protocols. Formulating the node distribution with binomial point process, the network is provided as a bounded area with all nodes randomly and uniformly distributed. Considering an arbitrary pair of source node (SN) and destination node, an innovative and straightforward definition is presented for HCD. In order to derive HCD out, an original mathematical framework, named as the equivalent area replacement method (EARM), is proposed and verified. Under the EARM, HCD is derived by first considering the special case where SN locates at the network center and then extending to the general case where SN is randomly distributed. For each case, the accuracy of our HCD model is evaluated by simulation comparison. Results show that our model matches well with the simulation results over a wide range of parameters. Particularly, the derived HCD outperforms the existing formulations in terms of the Kullback Leibler divergence, especially when SN is randomly distributed.

Based on the advantages of small size, light weight, as well as flexible deployment and recycling, unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian. As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this paper, we investigate full-duplex (FD) UAV relaying for multiple source-destination pairs. To fully exploit the flying flexibility of the UAV in serving multiple source-destination pairs, we propose a scheduling protocol that exploits time division multiple access (TDMA) to serve different source-destination pairs in turns when flying along an optimized trajectory. Then, we further formulate a joint optimization problem of the TDMA-based user scheduling and the dynamic UAV trajectory to maximize the system throughput. The formulated problem is non-convex which makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that our proposed TDMA-based protocol outperforms the OFDMA-based ones with fixed UAV position/trajectory when the UAV helps relay information for multiple source-destination pairs.

Unmanned aerial vehicles (UAVs) facilitate information collection greatly in the Internet-of-Things (IoT) systems due to their superior flexibility and mobility. On the other hand, nonorthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in fifth-generation networks. The integration of NOMA into UAV-assisted wireless networks shows great potential, but how to determine the user grouping and power allocation in NOMA according to the high mobility of UAV is challenging. In this article, we propose a general NOMA-enabled UAV-assisted data collection (NUDC) protocol to maximize the sum rate of a wireless sensor network (WSN), where the location of UAV, sensor grouping, and power control are jointly considered. Moreover, a joint signal-to-interference ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to obtain the appropriate sensor grouping and the optimal power control solutions efficiently, in which the hypergraph and the greedy coloring algorithm are exploited to find out the optimized group relationships. Extensive simulation results demonstrate the efficiency of our proposed protocol.

Recent years have witnessed exciting developments in our transportation system with increasingly intelligent vehicles and infrastructure. The transportation system is envisioned to be highly heterogeneous, consisting of diverse participants with mixed intelligence and connectivity. Among them, autonomous vehicles have the highest intelligence and connectivity level and could contribute greatly to the operation of the transportation system in an efficient and reliable manner. However, the current design of autonomous driving techniques is mostly concerned with the autonomous vehicle at the individual level, and the overall transportation system does not provide proactive support to autonomous driving. In fact, the increasing intelligence and connectivity in transportation could be leveraged to significantly enhance the safety and efficiency of individual vehicles and the entire system. To facilitate this, vehicles need to interact and cooperate both among themselves and with the transportation infrastructure and management. In this article, we propose the societal intelligence (SI) framework. Different from the existing multientity intelligence frameworks, SI allows for much diverse interactions among the multiple entities at different levels and is thus suitable for transportation. In addition, we also render the driving process into four functional layers and demonstrate how the social intelligence framework can adapt to these layers, respectively.

Based on the advantages of small size, lightweight, as well as flexible deployment and recycling, unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian. As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this article, we investigate full-duplex (FD) UAV relaying for multiple source-destination pairs. To fully exploit the flying flexibility of the UAV in serving multiple source-destination pairs, we propose a scheduling protocol that exploits time-division multiple access (TDMA) to serve different source-destination pairs in turns when flying along an optimized trajectory. Then, we further formulate a joint optimization problem of the TDMA-based user scheduling, the dynamic UAV trajectory, and the UAV transmit power to maximize the system throughput. The formulated problem is nonconvex that makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that our proposed FD-based UAV relaying network achieves significant throughput gains compared with the half-duplex (HD) baseline, and the TDMA-based protocol outperforms the OFDMA-based ones with fixed UAV position/trajectory when the UAV helps relay information for multiple source-destination pairs.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. In most existing NOMA user grouping approaches, users are grouped into disjoint groups, which may lead to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then, we address this problem by exploiting the overlapping coalition formation (OCF) game framework, and we further propose an OCF-based algorithm in which each user can be self-organized into a desirable overlapping coalition structure. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed OCF-based GuG NOMA scheme achieves significant performance gains in terms of system sum rate.

In-home health networks greatly rely on the massive connected monitoring devices. Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can connect more monitoring devices and enhance the spectrum efficiency (SE) performance, which makes it an ideal solution to in-home health networks. However, conventional NOMA (C-NOMA) is mostly constrained to single-carrier scenario. The problem of multi-carrier NOMA lies in the inter-carrier interference (ICI) from neighboring carriers. In this article, we propose a sparse vector coding-based NOMA (SVC-NOMA) to suppress the ICI. We give closed-form expressions of capacity and symbol error rate (SER) performances for both C-NOMA and SVC-NOMA within the considered multi-carrier scenario. Simulation results demonstrate that compared to C-NOMA, SVC-NOMA has better capacity and SER performances. In addition, we find from our results that there is a trade-off between SVC-NOMA's ICI suppression ability and the system capacity performance.

To enable next-generation mmWave cellular, it is vital to design high-performance precoding schemes for wideband multi-user (wMU) mmWave massive MIMO (mMIMO). As existing approaches are mostly ad-hoc, thereby lacking performance guarantee, we will tailor an enhanced transceiver design explicitly for wMU mmWave mMIMO, with the goal of maximizing mutual information (MI). The proposed scheme follows the prevalent hybrid block diagonalization (HBD-)based framework that is well-known for balancing the transmitter-end processing flexibility and the user-end detection complexity. In this paper, we for the first time prove that HBD is optimal in the sense of MI. In terms of the transceiver design, we start by decoupling the hybrid processing into a two-stage analog and digital processing, and then derive the MI bounds associated with HBD. By optimizing the tight MI bound, excellent HBD transceivers are devised for both the multi-aperture structure (MAS) and the multi-beam structure (MBS). The proposed HBD technique does not rely on substantial computational complexity, striking channel sparsity, or high-resolution analog beamformers, and can achieve a superb MI performance even with inferior hardware configurations.

In Vehicle-to-Everything (V2X) networks, where all vehicles and infrastructures are interconnected for information sharing, data dissemination is increasingly playing a significant role in superior and pluralistic communication services. To empower the efficiency of data dissemination, in this paper, we propose a novel unmanned aerial vehicle (UAV)-enabled scheduling protocol consisting of a proactive caching policy and a file sharing strategy in V2X networks. In the proactive caching process, we deploy UAVs as flying base stations (BSs) with caching capability, where we propose a UAV dynamic trajectory scheduling (DTS) algorithm to optimize the caching duration. Whereas in the file sharing strategy, based on the previous vehicular caching status, we provide a framework of file sharing cycle for data dissemination scheduling and employ a channel prediction algorithm to alleviate communication overhead. Moreover, we propose a relay ordering algorithm to effectively improve the file sharing process. Simulation results demonstrate that, our proposed scheduling protocol can enhance the efficiency of data dissemination and achieve an improved network performance in terms of caching process, system throughput, and file sharing latency in V2X networks.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then we further provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently, in which the established machine learning-based model is exploited to explore the relative relationships of channel gains of users and obtain several fixed grouping patterns via Merge operation. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed GuG scheme achieves significant performance gains in terms of system sum rate.

Referring to the system equipped with single-bit converters, 1-bit mmWave communications is gaining increasing attention for its superb cost efficiency. However, the inherent non-linear distortion renders the detectors designed for classical transparent communications inapplicable, leading to an urgent need for novel detecting solutions dedicated to 1-bit systems. Although a few endeavours have been made towards learning-based (as opposed to the traditional model-based) detectors for multi-user (MU) 1-bit systems, they are exclusively limited to narrowband channels and fail to cope with the multi-path effects inevitable to mmWave systems. In this paper, we first design a learning-based detector (LeaD) for general wideband multi-user (wMU) scenarios. Though stemming from block-based detection, the classic workhorse for transparent systems, LeaD faces either unaffordable complexity or unacceptable data rate in 1-bit systems. Given the impracticability of block-based detection, we resort to the serial detection mechanism and henceforth devise a so-termed model-enhanced (Me-)LeaD by utilizing the channel delay-domain information. Me-LeaD can be further augmented by exploiting the channel angular-domain information. Underpinned by a judiciously tailored method for extracting tbe model information, the proposed Me-LeaD demonstrates a decent overall performance in general 1-bit wMU scenarios.

Sparse code multiple access (SCMA), which helps improve spectrum efficiency (SE) and enhance connectivity, has been proposed as a non-orthogonal multiple access (NOMA) scheme for 5G systems. In SCMA, codebook design determines system overload ratio and detection performance at a receiver. In this paper, an SCMA codebook design approach is proposed based on uniquely decomposable constellation group (UDCG). We show that there are N+1 ( N ≥ 1) constellations in the proposed UDCG, each of which has M (M ≥ 2) constellation points. These constellations are allocated to users sharing the same resource. Combining the constellations allocated on multiple resources of each user, we can obtain UDCG-based codebook sets. Bit error ratio (BER) performance will be discussed in terms of coding gain maximization with superimposed constellations and UDCG-based codebooks. Simulation results demonstrate that the superimposed constellation of each resource has large minimum Euclidean distance (MED) and meets uniquely decodable constraint. Thus, BER performance of the proposed codebook design approach outperforms that of the existing codebook design schemes in both uncoded and coded SCMA systems, especially for large-size codebooks.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. In most existing NOMA user grouping approaches, users are grouped into disjoint groups, which may lead to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then, we address this problem by exploiting the overlapping coalition formation (OCF) game framework, and we further propose an OCF-based algorithm in which each user can be self-organized into a desirable overlapping coalition structure. Simulation results verify the efficiency of GuG in NOMA systems and show that our proposed OCF-based GuG NOMA scheme achieves significant performance gains in terms of system sum rate.

Non-orthogonal multiple access (NOMA) provides high spectral efficiency and supports massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. We formulate a joint power control and GuG optimization problem, and then provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently. Simulation results show significant performance gains in terms of system sum rate.

In recent years, massive multiple-input multiple-output (MIMO) has been regarded as a promising technique in the fifth-generation (5G) communication systems. With the ability of focusing transmission beams on users, massive MIMO has a natural advantage in the field of physical layer security to improve the system secrecy performance. However, in practical mobile systems, the imperfect channel state information (CSI) caused by the channel estimation error and the transmission and processing delay will have a non-negligible impact on the system performance. In this paper, we investigate secure communications in a multi-user massive MIMO-enabled vehicular communication networks. Considering the influence of imperfect CSI on the secrecy performance, we derive a tight asymptotic lower bound of the system secrecy capacity under both perfect and imperfect CSI. Moreover, we further analyze the impact of vehicle speed on the system secrecy performance and propose a channel prediction scheme based on (Long Short-Term Memory) LSTM model to compensate for the negative effects of imperfect CSI, which can improve the system secrecy performance in high mobility scenario. Simulation results show that the imperfect CSI severely reduces the system secrecy capacity, but its negative effects can be effectively alleviated through the designed LSTM-based channel prediction and compensation scheme.

Unmanned aerial vehicles (UAVs) facilitate information collection greatly in Internet of Things (IoT) systems. On the other hand, non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G networks. The integration of NOMA into UAV-assisted wireless networks shows great potential, but how to determine the user grouping and power allocation in NOMA according to the different locations of UAV is challenging. In this paper, we propose a general NOMA-enabled UAV-assisted data collection (NUDC) protocol to solve the formulated sum rate maximization problem such that the location of UAV, sensor grouping, and power control are jointly considered. Moreover, a joint signal-to-interference-ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to obtain the appropriate sensor grouping and the optimal power control solutions efficiently. Extensive simulation results demonstrate the effectiveness of our proposed protocol.

This paper investigates the transceiver design for downlink hybrid mmWave multi-user multi-carrier massive MIMO systems. In order to balance the processing complexity and the design flexibility, we adopt a prevalent hybrid precoding technique named hybrid block diagonalization (HBD) for downlink multi-user transmission. Aimed at maximizing the end-to-end mutual information (EEMI), a novel virtual EEMI assisted two-stage HBD scheme is judiciously devised. Apart from a low implementing complexity, the developed scheme is a more generic HBD solution, as it not only takes the frequency selectivity into account, but also removes the reliance on the high-resolution analog network. Simulations show that, even when applied with an inferior hardware configuration, the proposed HBD could still remarkably outperform its counterparts in terms of the EEMI performance at different levels of channel sparsity.

For autonomous driving of unmanned vehicles in intelligent transportation systems, multi-vehicle cooperative perception supported by vehicular networks can greatly improve the accuracy and reliability of the perception decisions. Currently, the perception decisions for a single vehicle are mostly provided by neural networks. Therefore, in order to fuse the perception decisions from multiple vehicles, the credibility of the neural network outputs needs to be studied. Among various factors, the environment is one of the most important affecting vehicles' perception decisions. In this paper, we propose a new evaluation criteria for the neural networks used in the perception module of unmanned vehicles. This criterion is termed as Environmental Sensitivity (ES), indicates the sensitivity of the network to environmental changes. We design an algorithm to quantitatively measure the ES value of different perception networks based on the extracted features. Experimental results show that our algorithm can well capture the sensitivity of the network in different environments and the ES values will be helpful to the subsequent decision fusion process.

The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.

Unmanned aerial vehicle (UAV)-assisted communications are expected to become an important part of the next generation mobile communication systems, due to the high mobility of the UAVs. Non-orthogonal multiple access (NOMA) is regarded as a rosy technology in the fifth generation (5G) mobile communication systems, since it can effectively improve the spectral efficiency. In this paper, we combine the advantages of the UAV-assisted communications and NOMA, and propose a device-to-device (D2D)-enhanced UAV-NOMA network architecture, in which D2D is introduced to increase the file dispatching efficiency. Resource reuse based on spatial reuse is also allowed to further improve the spectral efficiency. Then, we propose a graph-based file dispatching (GFD) protocol to control the interference and minimize the UAV-assisted file dispatching mission time. Simulation results verify the advantages of our proposed D2D-enhanced UAV-NOMA network architecture and the efficiency of our designed GFD protocol.

Most recently, vehicular fog computing (VFC) has been regarded as a novel and promising architecture to effectively reduce the computation time of various vehicular application tasks in Internet of vehicles (IoV). However, the high mobility of vehicles makes the topology of vehicular networks change fast, and thus it is a big challenge to coordinate vehicles for VFC in such a highly mobile scenario. In this paper, we investigate the joint task assignment and resource allocation optimization problem by taking the mobility effect into consideration in vehicular fog computing. Specifically, we formulate the joint optimization problem from a Min-Max perspective in order to reduce the overall task latency. Then we decompose the nonconvex problem into two sub-problems, i.e., one to one matching and bandwidth resource allocation, respectively. In addition, considering the relatively stable moving patterns of a vehicle in a short period, we further introduce the mobility prediction to design a mobility prediction-based scheme to obtain a better solution. Simulation results verify the efficiency of our proposed mobility prediction-based scheme in reducing the overall task completion latency in VFC.

Underwater optical wireless sensor networks (UOWSNs) have been attracting many interests for the advantages of high transmission rate, ultrawide bandwidth, and low latency. However, due to limited energy resources and highly dynamic topology caused by the water flow movement, it is challenging to provide a low-consumption and reliable routing in UOWSNs. To tackle this issue, in this article, we propose an efficient routing protocol based on multiagent reinforcement learning, termed as DMARL, for UOWSNs. The network is first modeled as a distributed multiagent system, and residual energy and link quality are considered into the routing protocol design to improve the adaptation to a dynamic environment and the support of prolonging network life. Additionally, two optimization strategies are proposed to accelerate the convergence of the reinforcement learning algorithm. On the basis, a reward mechanism is provided for the distributed system. The simulation results show that the DMARL-based routing protocol has low energy consumption and high packet delivery ratio (over 90%), and it is suitable for networks where the average number of neighbor nodes is less than 14.

The mobility status of self and surrounding vehicles provides important information to various tasks in autonomous driving (AD) and intelligent transportation system (ITS). Accordingly, a precise, stable, and robust mobility tracking framework is essential. Compared with self-tracking that relies only on mobility observations from onboard sensors [e.g., global positioning system (GPS), inertial measurement unit (IMU), and camera], cooperative tracking markedly increases the precision and reliability of the mobility information by integrating observations from roadside units (RSUs) and nearby vehicles through vehicle-to-everything (V2X) communications in the Internet of Vehicles (IoV). Nevertheless, cooperative tracking can be quite vulnerable if there are malicious users sending bogus observations in the cooperative network. In this article, we present a malicious user detection framework, which includes two sequential detection algorithms and a secure mobility data exchange and fusion model to detect and remove bogus mobility information and integrate proposed detection algorithms with previous data fusion algorithms, which secures the cooperative mobility tracking in AD, ITS. Simulations validate the effectiveness and robustness of the proposed framework under different types of attacks.

As a newly emerging communication assistant equipment, unmanned aerial vehicles (UAVs) can be exploited to dispatch data files quickly to specific areas and support rapid deployment of communication links in complex terrain, which is of great significance for specific communication demands in disaster and remote areas. Nonorthogonal multiple access (NOMA), as a rosy technology in the fifth generation (5G) and future mobile communication systems, has been widely studied because of its ability in improving spectral efficiency and reducing transmission latency to enhance the overall Quality of Service (QoS) and meet the strict communication requirements. Based on these, in this article, we propose a device-to-device (D2D)-enhanced UAV-NOMA network architecture, in which D2D is introduced to increase the file dispatching efficiency. In our proposed D2D-enhanced UAV-NOMA network, the ground users (GUEs) that have already received file blocks (FBs) are allowed to reuse the time-frequency resources assigned to NOMA links to share their FBs with other GUEs, which significantly improves the efficiency of file dispatching. But this also leads to a complicated interference environment. In order to effectively manage the interference and minimize the UAV-assisted file dispatching mission time, we propose a graph-based file dispatching (GFD) protocol, in which the complicated joint optimization problem is decomposed to be solved efficiently and graph theory-based algorithms are proposed for resource allocation. The simulation results verify the advantages of our proposed D2D-enhanced UAV-NOMA network architecture and the efficiency of our designed GFD protocol in minimizing the total UAV-assisted file dispatching mission time.

In this article, a simultaneous wireless information and power transfer (SWIPT) system with full-duplex (FD) Internet of Things (IoT) nodes is considered and investigated. We induce the adaptive interference decoding (AID) strategy as well as the switch and inverter structure with antenna selection (SIAS)-based hybrid precoding scheme to the SWIPT system. A joint optimization of hybrid precoder, decoding rule, and power splitting (PS) ratio problem is formulated to minimize the total transmission power, while satisfying the data rate and harvested energy constraints. As it is a nonconvex problem, we propose a suboptimal solution with three stages. In the first stage, a search algorithm which can reduce the complexity of exhaustive search is proposed to decide the sending mode of each node. In the second stage, we utilize the semidefinite relaxation (SDR) approach to find the optimal digital precoder and PS ratios. In the last stage, we propose an alternate minimization algorithm to obtain the hybrid precoding vectors. The simulation results show that our proposed suboptimal solutions can achieve a better performance in terms of power consumption and outage probability compared with those for treating interference as noise (IAN) systems and the digital precoding schemes. Both AID strategy and SIAS-based hybrid precoding are beneficial to the FD SWIPT system. Moreover, the self-interference causes little effect on the system performance as long as it can be eliminated up to 30 dB, which can be easily achieved by the antenna separation technique.

In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it's also able to be applied in future channel predictions in case of large amount of measured data available.

In mmWave massive multiple-input multiple-output (mMIMO) systems, hybrid (digital/analog) structure has been a prevalent option to balance system cost and performance. To facilitate transceiver design in hybrid mmWave mMIMO, acquiring an accurate channel state information is critical. To this end, a novel doubly-sparse approach is proposed to estimate doubly-selective mmWave channels under hybrid mMIMO. Via the judiciously designed training pattern, the well-utilized beamspace sparsity alongside the under-investigated delay-domain sparsity that mmWave channels exhibit can be jointly exploited to assist channel estimation. Thanks to our careful two-stage (random-probing and steering-probing) design, the proposed channel estimator possesses strong robustness against the double (frequency and time) selectivity whilst enjoying the benefits brought by the exploitation of double sparsity. Compared with existing alternatives, our proposed mmWave channel estimator not only works in doubly-selective channels, but also largely reduces the training overhead, storage demand as well as computational complexity.

In recent years, autonomous driving has attracted a vast amount of attention from both industry and academia, which has introduced large amounts of region-related data. In order to release the burden in core networks caused by the communication demands for such data, various in-vehicle storage (IVS) systems have been widely studied to bring contents closer to users. To cope with the mobility issue hindering the realization of IVS systems, in this paper, we propose a relay selection strategy (RSS) consisting of the relay map construction (RMC) algorithm and the relay pair matching (RPM) algorithm with the assistance of the vehicle route information. By considering both the potential transmission amount and the waiting time, the proposed RSS generates an overall optimal relay assignment for the IVS system. The performance gain of the proposed RSS compared with the baseline is evaluated by a realistic simulator in terms of the relay failure ratio, the retrieval throughput, and the RSU consumption ratio. Simulation results show that the proposed RSS achieves higher efficiency and robustness than the baseline.

Vehicle-to-Everything (V2X) communications refers to an intelligent and connected vehicular network where all vehicles and infrastructure systems are interconnected with each other. Data dissemination is playing an increasingly significant role in enhancing the network connectivity and data transmission performance. However, conventional scenarios and protocols cannot satisfy the growing pluralistic and superior quality of services (QoS) requirements of included vehicles. Therefore, in this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted data dissemination protocol with proactive caching at the vehicles and an advanced file sharing strategy for revolutionizing communications. Specifically, in the proactive caching phase, we employ UAVs to act as flying base stations (BSs) for information interactions. Considering the time-variant network topology, we further propose a spatial scheduling (SS) algorithm for the trajectory optimization of each UAV, which can expedite the caching process and boost the system throughput. Then in the file sharing phase, based on the previous caching status, we provide a relay ordering algorithm to enhance the network transmission performance. Numerical results verify that our proposed UAV-assisted data transmission protocol can achieve a desirable system performance in terms of the downloading process, network throughput, and average data delivery delay.

Data dissemination is a promising application in vehicular ad-hoc networks (VANETs) to overcome the limitation in the connection time of specific vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, and provide efficient large data file transfer from road-side units (RSUs) to vehicles therein. Unmanned aerial vehicles (UAVs), recently regarded as an effective supplement in wireless networks, can provide line-of-sight (LoS) links with better channel quality, and their high flexibility and maneuverability are beneficial for on-demand deployment in communication systems. In this paper, by employing UAVs as flying relays with data caching capability in VANETs, we design an enhanced UAV-aided data dissemination protocol. Specifically, we propose a centralized UAV trajectory scheduling algorithm based dynamic programming (CTS-DP) to optimize the flying routes of UAVs. Then, based on the scheduled trajectories of UAVs, we further propose a centralized UAV-aided data dissemination scheduling strategy to achieve both effective and efficient coordination of the RSUs, UAVs, and vehicles for data dissemination. Numerical simulations in vehicular scenarios verify the efficiency of the proposed protocol with dynamic UAV trajectory scheduling in terms of downloading progress, data dissemination delay, and system throughput.

The widespread deployment of mmWave communication systems is an unstoppable trend, but its success will heavily rely on well-designed transceivers to combat the severe propagation loss. To acquire the accurate channel state information (CSI) so that the transceiver design can be facilitated, in this paper, we provide a new time-domain channel estimation scheme for hybrid mmWave massive multiple-input multiple-output (mMIMO) systems. In addition to utilizing the well-known angular sparsity, the delay-domain sparsity will also be exploited to accomplish the channel estimation with satisfactory accuracy yet affordable complexity. The successful combination of these two types of sparsity is attributed to a judiciously designed training pattern. Thanks to our innovative exploitation of the double sparsity, a satisfactory performance can be achieved together with largely reduced training overhead, storage demand, and computational complexity.

Differential spatial modulation (DSM) is a novel multiple-input-multiple-output (MIMO) transmission technology that uses the transmit antenna index matrices to carry part of the information in non-coherent communication scenarios. However, when high-order modulation should be used for spectral efficiency considerations, the conventional use of constant modulus Phase Shift Keying (PSK) constellations in DSM would result in significant performance loss, whereas using non-constant modulus constellations such as Quadrature Amplitude Modulation (QAM) in DSM is very challenging because the peak signal power may grow without bound through the differential iterative processing. In this work, a generalized transmission scheme that uses non-constant modulus constellations for DSM signaling is investigated. By normalizing the power of all symbols in the previous transmit matrix when performing differential transmission, the Precoding Normalized DSM (PN-DSM) scheme which can adopt non-constant modulus constellations is proposed. In addition to the legacy QAM, another two non-constant modulus constellations in the literature, Amplitude Phase Shift Keying (APSK) and star-QAM, are considered for PN-DSM transmissions. It is revealed by numerical studies that, compared with the legacy QAM, APSK and star-QAM are more robust to the error propagation issue of the proposed PN-DSM scheme. In addition, because of the increased minimum distance in the constellation, the star-QAM slightly outperforms the APSK in terms of the average error rate performance.

Data dissemination is playing a crucial role in improving the connectivity and performance in hybrid vehicular ad-hoc networks (VANETs) by exploiting both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links, which can achieve effective data sharing and distribution between road-side units (RSUs) and vehicles. Recently, unmanned aerial vehicles (UAVs), acting as flying base stations (BSs) or relays with caching capability, have been widely investigated as an effective and enhanced communication support from the sky to provide the ground users improved quality of services (QoS) in a variety of circumstances. In this paper, by fully exploiting the advantages of UAVs introduced in VANETs, we design an advanced UAV-aided cooperative data dissemination scheduling strategy to improve the data dissemination performance in VANETs. Considering the mobility of the involved UAVs, we further propose a three-dimensional (3D) spatial dynamic programming (SDP) algorithm for the trajectory scheduling of UAVs to optimize the network transmission utility. Simulations results verify that, compared with other data dissemination strategies, our proposed UAV-aided cooperative data dissemination strategy can efficiently achieve a better system performance in terms of downloading progress and transmission delay.

In this paper, we propose a novel hybrid spatial-modulation (SM) based virtual MIMO relaying protocol with simultaneous wireless information and power transfer (SWIPT). The multiple relay nodes (RNs) employ a dualhop half duplex (HD) decode-and-forward (DF) protocol and harvest the radio frequency (RF) energy from the source node (SN) with the power splitting (PS) SWIPT method used. In the first hop, pre-coding aided spatial modulation (PSM) technique is used, where a portion of relay nodes (RNs) are activated to receive the transmitted signals with the zero-forcing (ZF) linear pre-coding employed at the source node (SN). The received signals at the RNs are divided into two parts by the power splitter denoted as energy signals and information signals. In the second hop, the information signals are transmitted from the active RNs to the destination node (DN) with the harvested energy, which makes the information transmission for this link SM-like. With the capacity upper bound (UB), the optimal system throughput can be analyzed and water-filling power allocation method is utilized to further boost the system throughput. With the additional information carried by the indices of the RNs, the proposed protocol can demonstrate better throughput performance against both the best RNs selection (BRS) and fix RNs (FR) virtual MIMO relaying schemes with the similar PS-SWIPT method and computation complexity. The Montecarlo simulations are conducted to verify our theoretical analysis.

Vehicle-to-everything (V2X) communications are regarded as the key technology in future vehicular networks due to its ability in improving the traffic efficiency and safety, and reducing congestion. Recently, non-orthogonal multiple access (NOMA), as a promising solution in the fifth generation (5G) mobile communication systems, has drawn much attention because it can significantly improve the network throughput and lower the accessing and transmission latency to meet the requirements of many 5G-enabled applications. Noticing these, in this paper, we propose to introduce NOMA in device-to-device (D2D)-enhanced V2X networks, where D2D-enabled resource sharing based on spatial reuse for different V2X communication groups are permitted through centralized resource management. Such an enhanced NOMA-V2X architecture results in a more complicated and challenging interference scenario. In order to efficiently solve the interference management and resource allocation problem in the NOMA-V2X network, we construct a weighted 3-partite interference hypergraph to model the relationships among different communication groups. Then, based on the constructed hypergraph, we further propose an interference hypergraph-based 3-dimensional matching (IHG-3DM) resource allocation protocol with a greedy 3DM algorithm. Simulation results verify the efficiency of our proposed IHG-3DM resource allocation protocol for NOMA-V2X communications in improving the network throughput.

The current age is witnessing speedy revolution of vehicles from the hundred-year old moving metal box on four wheels into a new species with dazzling intelligence. To enable such intelligence, the nervous system heavily hinges upon the connectivity among vehicles as well as between vehicles and the transportation infrastructure. With such intelligence, humans would be relieved from the driving duties and naturally convert the vehicle into moving offices or entertainment rooms, thus imposing unprecedented burden to the connectivity to the world beyond the vehicle. Due to the mobile nature of vehicles, wireless naturally becomes the rescue. However, though wireless has been, to some extent, deployed on vehicles for more than half a century, the current wireless-vehicle interactions are, to the best, a mere combination, in which the wireless systems are designed accounting for the mobile environment, but do not have much to do with the vehicle core functions. In this paper, we will discuss the challenges, progresses and perspectives of the present-to-the-near-future vehicular wireless channels, wireless-vehicle combination, as well as the more demanding wireless-vehicle integration.

Collisions caused by the hidden terminal effects may result in severe packet corruption and performance degradation in multihop IEEE 802.15.4-compliant wireless sensor networks (WSNs). In order to avoid such collisions through scheduling protocols, it is important to first recognize these collisions by distinguishing them from some other noncollision cases (e.g., path loss, multipath fading, shadow fading, and IEEE 802.11 interference), which may also lead to similar consequences. In this paper, we focus on the collision recognition problem in multihop IEEE 802.15.4-compliant WSNs. First, through a series of measurements of the error properties in various collision and noncollision scenarios, we investigate the statistical behaviors of error patterns including the bit error rate and error position distribution, which reveal obvious differences between collision and noncollision cases in terms of bit- and symbol-level error position distribution. Based on these observations, we further propose a machine learning-based collision recognition mechanism by inserting some redundant blocks in a data frame. The inserted blocks are known to both the sender and receiver, thereby it enables the receiver to recognize the error patterns only according to the redundant sequences. Moreover, a mutual information-guided byte selection technique is also provided to effectively improve the recognition accuracy. Finally, the proposed mechanism is verified under three different transmission environments. The experimental results show that the proposed mechanism achieves good recognition accuracy over 90% with 94% coding efficiency.

The new era of the Internet of Things (IoT) is prompting the evolution of conventional vehicle ad-hoc networks (VANETs) into the Internet of Intelligent Vehicles (IoIV). Different from VANETs, where a vehicle is essentially considered as a node disseminating messages, the emerging IoIV paradigm is expected to regard each vehicle as a smart object equipped with a powerful multisensor platform, unprecedented communication capability, computing units, and Internet protocol (IP)-based connectivity. As such, the vehicles in IoIV are highly efficient in a broad array of vehicular and transportation applications. As a unique subset of general purpose IoT, IoIV can benefit from the existing research on VANET, which lays the foundation toward a more pervasive and ubiquitous communications and networking core that is essential for IoIV. Nevertheless, research in many aspects of IoIV, especially those that are application-driven and data-oriented ones, is still at its infancy.

Idle time windows (ITWs) consist of one critical trigger for various functions in green intelligent network management and traffic scheduling in mobile networks. In this paper, we study the ITW prediction in mobile networks based on network subscribers' demand and mobility behaviors observed by network operators. We first innovatively formulate the ITW prediction into a regression problem with an ITW presence confidence index that facilitates direct ITW detection and estimation. Feature extraction on the demand and mobility history is then proposed to capture the current trends of subscribers' demand and mobility as well as to account for the periodicity underlying subscribers' demand and mobility patterns as exogenous inputs. In light of feature engineering, a deep learning-based ITW prediction model is proposed, which consists of two components, namely the representation learning network and the output network. The representation learning network is aimed to learn effective patterns, whereas the output network is designed to produce the desired ITW presence confidence index and the ITW estimate by integrating the learned representation and exogenous inputs. In this paper, a novel temporal graph convolutional network (TGCN) for the representation learning network is proposed to effectively capture the graph-based spatiotemporal input features. The experiment results validate the proposed direct ITW prediction formulation and demonstrate the superiority of the proposed TGCN in terms of both ITW detection and ITW estimation performance, which can achieve a significant intersection-over-union (IoU) improvement compared with baselines.

Millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) has been recognized as a promising candidate for 5G communications for its capability of supporting Gb/s transmission. However, it is a common exercise to deploy a limited number of radio-frequency (RF) chains at mmWave mMIMO transceivers due to hardware complexity and cost. As a result, the potential multiplexing gain (MG), which is restricted by the smaller number of RF chains at the transmitter and receiver, is markedly compromised. In order to boost the MG and spectral efficiency (SE), we innovatively develop a novel index modulation termed as the generalized beamspace modulation (GBM). The acquisition of (sub-)beamspace is owing to a natural exploitation of the unique features of mmWave mMIMO. Based on the (sub-)beamspace, a complete GBM transceiver is designed and optimized. Unlike existing alternatives that are largely digital based, our GBM is tailored for the hybrid structure of mmWave mMIMO and can, thereby, realize efficient spatial multiplexing despite the limited RF chains. Extensive analyses and simulations have demonstrated remarkable superiority of GBM over existing counterparts in terms of the error performance and SE.

In recent years, physical layer security has been regarded as a promising technique to facilitate secure communications in next generation mobile systems, where theoretically massive MIMO can significantly enhance the system secrecy performance under its advantage in shaping the transmitted signals to null the interference or leakage. However, in practical systems, the achievable secrecy performance under imperfect channel state information (CSI) deserves further investigation. In this paper, we give a detailed analysis about the physical layer security problem in a multi-user massive MIMO system with imperfect CSI. The considered imperfect CSI includes both the outdated CSI due to the transmission and processing delay, and the channel estimation error. We first derive a tight asymptotic lower bound of the ergodic system secrecy capacity under imperfect CSI, and then analyze how imperfect CSI affects the system secrecy performance. Moreover, we propose a channel prediction scheme that can result in more accurate CSI, in order to alleviate the negative effect on the achievable system secrecy capacity caused by imperfect CSI. Simulation results reveal that the imperfect CSI greatly reduces the system secrecy capacity, while our designed channel prediction scheme can effectively mitigate the harmful impact of imperfect CSI and thus improve the system secrecy performance.

In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to-EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EV-provider-optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging-based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs.

Spatial modulation orthogonal frequency division multiplexing with subcarrier index modulation (ISM-OFDM) is a novel OFDM technique that conveys additional information bits by active antenna indices and grouped subcarrier indices. However, it takes a huge amount of resources to acquire the channel state information (CSI) at the receiver. In this paper, we propose two differential spatial frequency modulation with orthogonal frequency division multiplexing (DSFM-OFDM) systems, termed DSFM-OFDM-single (DSFM-OFDM-S) and DSFM-OFDM- multiple (DSFM-OFDM-M) over space-time-frequency, which completely avoid the need for CSI at the transmitter and receiver. In DSFM-OFDM, the transmitted symbols are modulated through ordinary signal modulation. And then part of some additional information bits are transmitted by selecting a permutation of the active antennas, whereas another part of it are employed to determine the subcarrier index matrices. Simulation results show that at low spectral efficiency, the performance of the proposed two schemes are better than differential spatial modulation (DSM) with the high SNR. Besides, the performance degradation of DSFM-OFDM-M and DSFM- OFDM-S over ISM-OFDM can be less than 6dB. And that the loss decreases as the number of receive antennas decreases.

As a recently emerging technology, index-based modulation (IBM) has been attracting increasing research interests for its improved bit error rate (BER) performance and power efficiency. At present, the applications of two typical schemes named spatial modulation (SM) and subcarrier index modulation (IM), as well as their variants are introduced to microwave systems. To make IBM applicable in mmWave systems, the special properties of channel environments and system architectures should be taken into account. In this paper, we present a novel IBM scheme termed as generalized beamspace modulation (GBM) for mmWave beamspace multiple-input multiple-output (MIMO) systems. Unlike the frequency or spatial domain in which the existing IBM schemes are typically performed, GBM is implemented in the beamspace. To achieve near- optimal BER performance in GBM systems, a general effective beamspace channel (EBC) optimization method is derived based on the minimum asymptotic pairwise error probability (APEP) criterion. The optimal maximum-likelihood (ML) detector and the lowcomplexity detector are both provided. Thanks to our proposed GBM scheme, the BER performance can be noticeably enhanced compared to plain mmWave systems, with a smaller number of active frequency chains are used during transmission.

In recent years, wireless communication demands from high-speed vehicles rapidly increase, due to the enrichment of various vehicular applications. Such high demands result in a compelling burden on the supporting infrastructure in vehicular networks, which in turn deteriorate the quality of experience (QoE) of vehicular users. In-device caching/storage has been attracting increasing research interests, which can significantly alleviate the burden of the wireless infrastructure and enhance the network throughput via spatial frequency reuse. However, its realization in vehicular scenarios is significantly hindered by the high mobility of vehicles. To cope with such issue, we propose a relay-and-repair based in-vehicle storage ( R2IVS) system with two storage maintaining mechanisms, namely the storage relay and the storage repair, to account for the vehicle mobility and vehicle-to-vehicle (V2V) communications vulnerability. The proposed R2IVS scheme is evaluated in a general vehicular network setting based on a microscopic stochastic vehicle mobility model combined with the classic Markov chain model of IEEE 802.11p media access control (MAC) function. Theoretical analysis and simulation results demonstrate the superiority of our proposed R2IVS system compared with the one directly fetching contents from wireless infrastructure.

Vehicular communication network is a core application scenario in the fifth generation (5G) mobile communication system which requires ultra high data rate and ultra low latency. Most recently, non-orthogonal multiple access (NOMA) has been regarded as a promising technique for future 5G systems due to its capability in significantly improving the spectral efficiency and reducing the data transmission latency. In this paper, we propose to introduce NOMA in D2D-enabled V2X networks, where resource sharing based on spatial reuse for different V2X communications are permitted through centralized resource management. Considering the complicated interference scenario caused by NOMA and spatial reuse-based resource sharing in the investigated NOMA-integrated V2X networks, we construct an interference hypergraph to model the interference relationships among different communication groups. In addition, based on the constructed hypergraph, we further propose an interference hypergraph-based resource allocation (IHG-RA) scheme with cluster coloring algorithm, which can lead to both effective and efficient QoS-guaranteed resource block (RB) assignment with low computational complexity. Simulation results verify the efficiency of our proposed IHG-RA scheme for NOMA-integrated V2X communications in improving the network sum rate.

In recent years, wireless communication demands from high-speed vehicles rapidly increase, due to the enrichment of various emerging vehicular applications including road safety, intelligent transportation, in-vehicle entertainments, self- driving, etc. Such high communication demands result in significant burdens on the supporting infrastructure in vehicular networks, which in turn deteriorate the quality of experience (QoE) of vehicular users. In the literature, local popular content caching is an effective approach to alleviate the burden on core networks as well as reducing the content downloading delay. However, the burden is simply shifted to RSUs if popular contents are cached at RSUs. In addition, the frequency reuse in space is also inadequate. Caching popular contents at vehicles may be an effective approach to solve the aforementioned problems. However, the critical issue of vehicle- based storage schemes is the high mobility nature of vehicles, which could lead to loss of stored data due to the storage vehicles' leaving the region of interests. To cope with the vehicle mobility issue, we propose a vehicle-based distributed storage scheme via local V2V communications in this paper. The key of the proposed data storage scheme is to maintain the survival of the stored data in a designated region by transferring the stored data from leaving vehicles to incoming vehicles at the entrance/exit of the region, in order to prevent data loss due to the high mobility of vehicles. Structured redundancy via erasure coding is also introduced in order to combat the volatile V2V links. Theoretical analysis and numerical results validate the effectiveness of our proposed vehicle-based distributed storage scheme and suggest that the stored data could survive for up to tens of hours under a typical road condition.

In recent years, physical layer security has been regarded as a promising concept to provide secure communications in next generation mobile systems, where theoretically massive MIMO can significantly enhance the system secrecy performance due to its advantage in shaping the transmitted signals to null the interference or leakage. However, in practical systems, the channel state information is often imperfect due to the outdated channel effect and the channel estimation error. In this paper, we investigate the physical layer security problem in a multi-user massive MIMO system under imperfect CSI. We first derive a tight asymptotic lower bound of the ergodic system secrecy capacity under imperfect CSI, and then analyze how imperfect CSI affects the system secrecy performance. Simulation results reveal the negative impact of the imperfect CSI on the secrecy performance of massive MIMO systems and the accuracy of our theoretical derivations and analysis.

In high-speed vehicular ad-hoc networks (VANETs), cooperative data dissemination is an effective solution to amend the limited connection time of communication links between roadside units (RSUs) and vehicles. Existing data dissemination strategies utilize efficient cooperation of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links to maintain the data transmissions and thus improve the system performance. Recently, unmanned aerial vehicle (UAV) is widely utilized in communication systems, which has a high probability of line-of-sight (LoS) links with better channel quality and can be dynamically deployed. In this paper, we propose a novel UAV-assisted data dissemination scheduling strategy in VANETs. The recursive least squares (RLS) algorithm is utilized to predict the vehicle mobility with low complexity and high prediction accuracy. To enhance the transmission utilities of the UAVs, we further propose a maximum vehicle coverage (MVC) algorithm to schedule the two-dimensional (2D) movements of the UAVs during the process of data dissemination. Simulations in both urban and highway scenarios verify that the proposed UAV-assisted data dissemination strategy achieves a significant reduction of data dissemination delay and an improvement of system throughput.

The mobile network is at a critical tipping point. On the one hand, it is heavily stressed by the explosive increase of traffic, and the steep transition from a smartphone-dominated market to one that is dazzling diverse, all subject to the same electromagnetic theory-ruled physical world. On the other hand, it is hopefully excited by the rapid development of data science, more powerful and versatile computing, and increasingly affordable hardware. It seems that we have come to the point at which in order to make the ends meet, one would need to put all the pieces together. In this paper, we aspire to establish a holistic framework for mobile big data (MBD) based network intelligence. Combining the top-down and bottom-up approaches, we put in context the recent development in the mobile network architecture, resource-based management theory, MBD orchestration, and data analytics, and establish a hierarchical network intelligence architecture fueled by MBD analytics. This architecture nurtures holistic understanding on how the multidimensional, multilateral, and multigranular MBD, together with its processing, can be plugged into the network architecture.

In recent years, the explosive development of mobile communications and networking, together with the wave of Internet of Things (IoT), has led to super-complex systems, which are difficult to model and manage. At the same time, such systems are generating a large amount of data on a real-time basis, from both the user and network sides. How to utilize such data to relieve the dependence on restrictive, sometimes even unrealistic, system models are the key leading to more efficient and effective future networks, especially when under various resource constraints as in IoT systems. Fortunately, recent advancements in artificial intelligence (AI), empowered by modern machine learning algorithms, have demonstrated remarkable success in a variety of fields and are stimulating numerous data-driven approaches as well as applications. Combining the availability of big data in complex IoT communication networks and the recent advancements in AI, it now comes the time to renovate how we resolve network management issues to more efficiently and effectively fulfill the dynamic demands of network subscribers, especially in the presence of stringent network resource constraints. With the fuel (IoT data) and the engine (AI), data-driven network management will enable us to dynamically and adaptively meet the spatio-temporal network demands in the most resource-aware and resource-smart manner.

The demand forecasting plays a crucial role in the predictive physical and virtualized network management in cellular networks, which can effectively reduce both the capital and operational expenditures by fully exploiting the network infrastructure. In this paper, we study the per-cell demand forecasting in cellular networks. The success of demand forecasting relies on the effective modeling of both the spatial and temporal aspects of the per-cell demand time series. However, the main challenge of the spatial relevancy modeling in the per-cell demand forecasting is the irregular spatial distribution of cells in a network, where applying grid-based models (e.g., convolutional neural networks) would lead to degradation of spatial granularity. In this paper, we propose to model the spatial relevancy among cells by a dependency graph based on spatial distances among cells without the loss of spatial granularity. Such spatial distance-based graph modeling is confirmed by the spatiotemporal analysis via semivariogram, which suggests that the relevancy between any two cells declines as their spatial distance increases. Hence, the graph convolutional networks and long short-term memory (LSTM) from deep learning are employed to model the spatial and temporal aspects, respectively. In addition, the deep graph-sequence model, graph convolutional LSTM, is further employed to simultaneously characterize both the spatial and temporal aspects of mobile demand forecasting. Experiments demonstrate that our proposed graph-sequence demand forecasting model could achieve a superior forecasting performance compared with the other two proposed models as well as the traditional auto regression integrated moving average time series model.

The low power wide area network (LPWAN) technologies, which is now embracing a booming era with the development in the Internet of Things (IoT), may offer a brand new solution for current smart grid communications due to their excellent features of low power, long range, and high capacity. The mission-critical smart grid communications require secure and reliable connections between the utilities and the devices with high quality of service (QoS). This is difficult to achieve for unlicensed LPWAN technologies due to the crowded license free band. Narrowband IoT (NB-IoT), as a licensed LPWAN technology, is developed based on the existing long-term evolution specifications and facilities. Thus, it is able to provide cellular-level QoS, and henceforth can be viewed as a promising candidate for smart grid communications. In this paper, we introduce NB-IoT to the smart grid and compare it with the existing representative communication technologies in the context of smart grid communications in terms of data rate, latency, range, etc. The overall requirements of communications in the smart grid from both quantitative and qualitative perspectives are comprehensively investigated and each of them is carefully examined for NB-IoT. We further explore the representative applications in the smart grid and analyze the corresponding feasibility of NB-IoT. Moreover, the performance of NB-IoT in typical scenarios of the smart grid communication environments, such as urban and rural areas, is carefully evaluated via Monte Carlo simulations.

Wireless power transfer (WPT) is a promising solution to provide convenient and perpetual energy supplies to electronics. Traditional WPT technologies face the challenge of providing Watt-level power over meter-level distance for Internet of Things (IoT) and mobile devices, such as sensors, controllers, smart-phones, laptops, etc. Distributed laser charging (DLC), a new WPT alternative, has the potential to solve these problems and enable WPT with the similar experience as WiFi communications. In this paper, we present a multimodule DLC system model, in order to illustrate its physical fundamentals and mathematical formula. This analytical modeling enables the evaluation of power conversion or transmission for each individual module, considering the impacts of laser wavelength, transmission attenuation, and photovoltaic-cell (PV-cell) temperature. Based on the linear approximation of electricity-to-laser and laser-to-electricity power conversion validated by measurement and simulation, we derive the maximum power transmission efficiency in closed-form. Thus, we demonstrate the variation of the maximum power transmission efficiency depending on the supply power at the transmitter, laser wavelength, transmission distance, and PVcell temperature. Similar to the maximization of information transmission capacity in wireless information transfer (WIT), the maximization of the power transmission efficiency is equally important in WPT. Therefore, this paper not only provides the insight of DLC in theory, but also offers the guideline of DLC system design in practice.

We investigate the problem of resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA) broadband network with an eavesdropper under the condition that both legitimate users and the eavesdropper are with imperfect channel state information (CSI). We consider three kinds of imperfect CSI: (1) noise and channel estimation errors, (2) feedback delay and channel prediction, and (3) limited feedback channel capacity, where quantized CSI is studied using rate-distortion theory because it can be used to establish an informationtheoretic lower bound on the capacity of the feedback channel. The problem is formulated as joint power and subcarrier allocation to optimize the maximum-minimum (max-min) fairness criterion over the users’ secrecy rate. The problem considered is a mixed integer nonlinear programming problem. To reduce the complexity, we propose a two-step suboptimal algorithm that separately performs power and subcarrier allocation. For a given subcarrier assignment, optimal power allocation is achieved by developing an algorithm of polynomial computational complexity. Numerical results show that our proposed algorithm can approximate the optimal solution.

In this paper, we propose a novel digital filter-and-forward full-duplex (FF-FD) relaying system to exploit the loop-back signal (LBS). Unlike treating the LBS as a generic interference, we utilize the fact that the LBS actually conveys redundant information from the source to the destination in the relaying communication scenarios. Thus, the LBS could potentially be exploited instead of being cancelled. We prove that the FF-FD can achieve higher system achievable rate (SAR) than the amplify-and-forward full-duplex (AF-FD) system. To maintain the system linearity, a digital FF-FD relay is proposed without non-linear distortion induced in the digital domain. The relay can be modeled as a linear digital filter to exploit the LBS via linear filtering. The design challenge at the relay is accordingly shifted to optimizing its discrete frequency response (DFR) rather than the complicated interference cancellation design. Based on the metric of maximum SAR, the DFR is efficiently optimized with and without CSI at the source. A low-complexity yet near-optimal alternative is also provided to reduce the computation cost for large multi-carrier systems. Both theoretical analyses and simulations validate the considerable advantages of the proposed FF-FD over the conventional AF-FD, making FF-FD an appealing candidate for future relaying communications.

Full-duplex has emerged as a promising technology that enables a communication node to transmit and receive at the same time and same frequency band. One limitation of full-duplex is that the self-interference (SI) is very strong. In this paper, an effective SI cancellation scheme operated in the digital domain is proposed. It is facilitated by the property that the SI channel is reciprocal and the transmitted data is known by both the transmitter and the receiver. It converts the strong SI to the inter-symbol interference through a combination of the signals received in successive time slots. This conversion leads to a reduction in the number of independent signal flows but can be partially compensated for by transmitting more bits using spatial modulation and involving more time slots in the SI cancellation, which enhances the achievable rate. To achieve optimal performance, the transmitted symbols at one node need to be artificially rotated. We also derive a closed-form expression for an upper bound on the average bit error rate, and its error-free information transmission capability is investigated. Monte Carlo simulations over Rayleigh fading channels between two nodes are conducted and advantages are revealed.

Distributed storage systems (DSSs) are widely employed in data centers and sensing networks to resist storage node failures. Structured redundancy is introduced to DSS by various coding schemes to efficiently account for failures of storage nodes. The allocation of the coded data blocks to storage nodes is another factor that impacts the data reliability. In this paper, we investigate the coding and allocation problem on multiple data objects in DSS. We propose a cross-object coding and allocation (COCA), which amounts to encoding and symmetric allocation on one large virtual data object aggregated by multiple data objects. We first explore the benefits of the proposed COCA scheme and find its reliability improvement in terms of joint successful recovery probability. However, such reliability improvement comes at the cost of increased data retrieval complexity. Hence, an optimization problem is formulated to explore the tradeoff between data reliability and data retrieval complexity. By employing a coalition formation game to model the process of the data objects grouping, we also propose a coalition-formation-based grouping algorithm to provide a suboptimal solution with greatly reduced computation complexity. Simulations validate the reliability improvement of our proposed COCA scheme and the effectiveness of our proposed coalition-formation-based algorithm.

In this paper, an effective low complexity generalized spatial modulation (GenSM) scheme with transmit antenna grouping is proposed for massive multi-input multi-output (MIMO) system to deal with the channel correlation among transmit antennas. In the proposed scheme, all transmit antennas are divided into several equal-sized groups, and spatial modulation (SM) is carried out to select one active antenna in each group independently. Two different grouping methods, i.e., block grouping and interleaved grouping, are introduced to optimize the error performance in low and high signal-to-noise ratio (SNR) region, respectively. In consideration of the large amount of transmit antennas in a massive MIMO system, both linear and 2-dimensional transmit antenna arrays are considered in our design. To evaluate the performance, a closed-form expression of the average bit error probability (ABEP) upper bound is derived for all proposed grouping methods and Monte-Carlo simulations are conducted to verify the analysis and reveal the performance gain of the proposed scheme in terms of bit error rate (BER) in comparison with conventional GenSM.

In this paper, we investigate the physical layer security issue in a cellular network with a full-duplex (FD) base station and multiple uplinks and downlinks. We provide a novel cooperative jamming mechanism in order to enhance the secrecy performance for the investigated scenario. The total bandwidth is divided into multiple resource blocks (RBs). In our investigation, each uplink or downlink can acquire at most one RB, and each RB can only be assigned to one uplink and one downlink. We formulate the secrecy capacity maximization problem as a joint RB assignment and power allocation problem, and then propose a bipartite graph based propose-and-decide algorithm (BGPDA) to solve this problem effectively and efficiently. The numerical simulations verify the efficiency of our proposed algorithm.

Mobile cloud storage (MCS) is being extensively used nowadays to provide data access services to various mobile platforms such as smart phones and tablets. For cross-platform mobile apps, MCS is a foundation for sharing and accessing user data as well as supporting seamless user experience in a mobile cloud computing environment. However, the mobile usage of smart phones or tablets is quite different from legacy desktop computers, in the sense that each user has his/her own mobile usage pattern. Therefore, it is challenging to design an efficient MCS that is optimized for individual users. In this paper, we investigate a distributed MCS system whose performance is optimized by exploiting the fine-grained context information of every mobile user. In this distributed system, lightweight storage servers are deployed pervasively, such that data can be stored closer to its user. We systematically optimize the data access efficiency of such a distributed MCS by exploiting three types of user context information: mobility pattern, network condition, and data access pattern. We propose two optimization formulations: a centralized one based on mixed-integer linear programming (MILP), and a distributed one based on stable matching. We then develop solutions to both formulations. Comprehensive simulations are performed to evaluate the effectiveness of the proposed solutions by comparing them against their counterparts under various network and context conditions.

In this paper, we investigate the relay selection (RS) problem in power splitting (PS) based energy-harvesting (EH) half-duplex (HD) relay networks, where the relays are wirelessly powered by harvesting a portion of the received RF signal power. We expand the relay selection problem in PSEH-HD relay networks to allow multiple relays to cooperate simultaneously. Furthermore, the optimal PS factor is obtained in closed form to facilitate the RS. Simulations show that neither the single RS nor the all-participate RS is optimal across all SNR levels. To improve the capacity of the network by better exploiting the cooperative diversity, a heuristic RS strategy with quadratic complexity is then proposed. The performance of our proposed updated relay ordering based relay selection (URO-RS) strategy is evaluated by simulations, which achieves near-optimum performance in comparison with the exhaustive search based RS strategy but with significantly reduced complexity.

In this paper, we investigate the flexible and efficient charging mechanism for electric vehicles (EVs). We first provide a developed V2V charging concept, termed as cooperative V2V charging, which enables active cooperation through charging and discharging operations between EVs as energy consumers and EVs as energy providers and is beneficial to both sides. Then, based on the defined utilities of EVs as energy consumers and EVs as energy providers, we propose a novel stable matching based cooperative V2V charging mechanism by taking each EV's individual rationality into consideration. Furthermore, we provide two efficient stable V2V matching algorithms, resulting in optimal V2V matching solutions in terms of the utilities of EVs as energy consumers and the utilities of EVs as energy providers, respectively. Simulation results verify the efficiency of our proposed stable matching based cooperative V2V charging mechanism in improving the utilities of both EVs as energy consumers and EVs as energy providers as well as reducing the energy consumption of the EVs compared with the traditional EV charging protocol.

Vehicular ad-hoc networks (VANETs), as an important component of intelligent transportation systems (ITS), have been attracting more and more research interests for their various promising applications. Although various MAC protocols have been proposed, efficient medium access remains a significant challenge in VANETs, especially in improving the network throughput in heavy traffic vehicular networks. In this paper, we propose an interference-free graph based time-division multiple access (IG-TDMA) protocol for VANETs. In the proposed protocol, roadside units (RSUs), as centralized controllers, collect the information from active vehicles and construct the interference-free graph based on the vehicle locations and a preset interference-free threshold. We further propose a communication link selection algorithm, which can help the RSUs make efficient and effective scheduling decisions with high spatial reuse efficiency and low computational complexity. Simulations verify that the proposed IG-TDMA protocol can improve the network performance significantly compared with the IEEE 802.11p CSMA/CA based EDCA scheme and traditional TDMA protocol.

Sensor fusion maybe used to improve detection performance in applications. The idea is to make decisions locally, and then transmit them to a global fusion centre where the global decision is made. For global decision making, Bayes or Neyman–Pearson reasoning determines the optimal use of the local decision variables. However, the determination of the local decision variables that minimise global error probability is intractable. In this study, the authors design local decisions that maximise the mutual information between a binary decision variable and the underlying binary state. This serves as a benchmark against which globally optimum solutions maybe compared. Then, they use the theory of large deviations (LDs) to determine a local decision rule that minimises asymptotic global error probability. The use of LD produces a one‐dimensional search on a receiver operating characteristic curve to equalise the error exponents for local false alarm and miss probabilities. Many interesting properties of the LD solution are proved. Numerical results illustrate the performance of the asymptotically optimum decision rule for finite collections of sensors.

Named data networking (NDN) is a representation and implementation of an information centric network, which is considered as one of the next generation of network architectures. To the best of the authors’ knowledge, very few studies have considered multiple constrained quality‐of‐service (QoS) routing in NDN. In this study, a particle swarm optimisation‐forwarding information base (PSO‐FIB) algorithm that uses the forwarding experiences of particles to maintain the forwarding probability of each entry in the FIB is proposed. Illustrating the interaction of PSO‐FIB with the routing layer, the simulation results show that PSO‐FIB can support multi‐constrained QoS routing and achieve better performance in terms of successful delivery rate and average cost compared with random and ant colony optimisation strategies.

To cope with the growing trend of asymmetric data traffic, the bidirectional dynamic networks (BDNs) dynamically allocate the number of uplink and downlink remote radio heads (RRHs), which facilitates simultaneous uplink and downlink communications. In this study, the authors derive the asymptotic approximations of the achievable uplink and downlink rates using maximum ratio transmission precoder and maximum ratio combination receiver, as the RRH antenna number ( M ) approaches infinity. Considering an optical fibre connected backhaul network, a practical power consumption model is presented to study the system energy efficiency (EE). Based on the asymptotic analysis, they exploit the power scaling laws that both the uplink and downlink powers should scale down to 1/ M to maintain a desirable uplink or downlink rate. Numerical results verify that when M is large, the BDN system outperforms the dynamic time division duplex system in both the spectral efficiency and EE.

In the past decade, the smart phone evolution has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications, leading to an unprecedented mobile data volume generated from the mobile devices, content servers, and network operators, which are mainly nonstructured. In this big data era, such nonstructured data fragments are pieced together such that, drastically differing from the traditional practice where services determine and define the data, data is becoming a proactive entity that may drive and even create new services. Compared with the so-termed 5V characteristics of generic big data, namely volume, variety, velocity, veracity, and value, mobile big data is distinct in its unique multidimensional, personalized, multisensory, and real-time features. In this survey, we provide in-depth and comprehensive coverage on the features, sources and applications of mobile big data, as well as the current state-of-the-art, challenges and opportunities for research and development in this field, with an emphasis on the user modeling, infrastructure supporting, data management, and knowledge discovery aspects.

This paper investigates the resource allocation (RA) problem for the downlink multiple input multiple output-based non-orthogonal multiple access (MIMO-NOMA) system, which is usually solved through a beamforming (BF) process and a user selection (US) process. In this paper, we propose the multiple-user channel state information (CSI)-based singular value decomposition (MU-CSI-SVD) BF scheme and the minimization of power (Min-Power) US scheme. Under perfect CSI scenarios, the proposed MU-CSI-SVD achieves near optimal sum rate performance with the high-complexity BF scheme while has the same level computational complexity as the low-complexity BF scheme. For imperfect CSI scenarios, the proposed MU-CSI-SVD also achieves better outage probability performance but requires the same level computational complexity as the existing low-complexity BF scheme. Moreover, MU-CSI-SVD is helpful to decrease the computational complexity for solving the Min-Power US problem. Compared with existing US schemes, the proposed Min-Power scheme can achieve larger sum rate or lower outage probability with limited computational complexity increment. Therefore, in this paper, we develop a complete RA scheme for 5G MIMO-NOMA systems, which has excellent performance while with low computational complexity for both perfect and imperfect CSI scenarios.

This article is focused on secure relay beamformer design with a correlated channel model in the relay-eavesdropper network. In this network, a single-antenna source-destination pair transmits secure information with the help of an amplify-and-forward (AF) relay equipped with multiple antennas, and the legitimate and eavesdropping channels are correlated. The relay cannot obtain the instantaneous channel state information (CSI) of the eavesdropper, and has only the knowledge of correlation information between the legitimate and eavesdropping channels. Depending on this information, we derive the conditional distribution of the eavesdropping channel. Two beamformers at the relay are studied for the approximate ergodic secrecy rate: (1) the generalized match-and-forward (GMF) beamformer to maximize the legitimate channel rate, and (2) the general-rank beamformer (GRBF). In addition, one lower-bound-maximizing (LBM) beamformer at the relay is discussed for maximizing the lower bound of the ergodic secrecy rate. We find that the GMF beamformer is the optimal rank-one beamformer, that the GRBF is the iteratively optimal beamformer, and that the performance of the LBM beamformer for the ergodic secrecy rate gets close to that of the GRBF for the approximate secrecy rate. It can also be observed that when the relay has lower power or the channel gain of the second hop is low, the performance of the GMF beamformer surpasses that of the GRBF. Numerical results are presented to illustrate the beamformers’ performance.

Cooperative localization has emerged as a promising technique which can complement or even replace global positioning systems (GPS) in many practical scenarios, such as GPS-denied environments. In this paper, we concentrate on the distributed cooperative localization design. The conventional distributed cooperative localization approaches usually yield high computational complexity and communication overhead, due to the lack of an efficient link selection mechanism. For this purpose, we propose a novel concept named opportunistic cooperative localization, based on which each agent is able to select the most informative links rather than utilize all the possible links in a distributed, self-organized, and self-optimized manner. To achieve effective opportunistic selection, overlapping coalition formation (OCF) game is employed. In addition, we also provide an optimized terminating criterion, based on which the agents will be able to know whether and when they are well localized, and thus can terminate their localization procedure efficiently. Through simulations, we observe that by virtue of the proposed OCF game based opportunistic cooperative localization scheme along with the provided terminating criterion, the limitations of conventional distributed cooperative localization can be alleviated at the cost of negligible performance degradation.

An optimized relaying method for multi-antennas cooperative networks, estimate-and-forward (EF) strategy for MIMO relay, is proposed and analyzed in this paper. According to our theoretical analysis, it performs like amplify-and-forward (AF) for the low signal noise ratio (SNR) region and behaves like detect-and-forward (DF) for the high SNR region. For the relay networks with a large number of antennas and/or high order constellations, two approximate methods are proposed to reduce the complexity of the signal estimation at the relay. The first one uses a list sphere decoder to generate an estimate list and to obtain the approximate minimum mean squared error (MMSE) estimate based on the reduced list. The proposed list EF retains the advantages of the exact EF relay strategy in large MIMO relay networks at a negligible performance loss. The second algorithm computes the estimate of the symbols using Gaussian approximation to approximate the summation to be integral. We find the proposed EF scheme performs better than both AF and DF across all SNRs without switching algorithms for different SNRs.

In recent years, the capacity and charging speed of batteries have become the bottleneck of mobile communications systems. With the development of wireless power transfer technologies, simultaneous wireless information and power transfer (SWIPT) becomes a possible alternative. Due to the limitations of practical circuit implementation, the energy harvesting circuit cannot directly decode the information carried by the same signal. Hence, the problem of optimal power splitting at the receiver arises in addition to the conventional problem of power allocation in communication systems. In this paper, the joint power allocation and splitting (JoPAS) for SWIPT over a timevariant channel is proposed with the objective of maximizing the achievable data rate with constraints on the delivered power. Simulations show significantly improved performance compared with the existing dynamic power splitting scheme. A suboptimal algorithm, named decoupled power allocation and splitting (DePAS), is also proposed with dramatically reduced computational complexity and simulations demonstrate its near optimum performance.

In this paper, we investigate the optimization of the power splitting factor and the relay selection problem in two-way full-duplex (FD) relay networks, where the relays are wirelessly powered by harvesting a portion of the received signal power from the sources. To the best of the authors' knowledge, this is the first time that the two-way FD relays with simultaneous wireless and information transfer (SWIPT) capabilities are investigated. For each relay, we prove the quasi- convexity of the power splitting (PS) factor optimization and obtain the optimal PS factor in terms of the outage probability by linear search. We propose two relay selection schemes that minimize the outage probability and maximize the sum capacity respectively. The performance improvement over the random selection scheme and the all- participate scheme is demonstrated by simulations.

This paper investigates the performance of bidirectional dynamic networks (BDN) with massive multiple input multiple output (MIMO) systems. In BDN, dynamic allocation of the number of uplink and downlink remote radio heads (RRHs) is proposed, which offers a flexible solution to balance the data traffic asymmetry without requiring the time synchronization. Intuitively, the interference between the downlink and uplink RRHs is one of the main challenges in BDN. However, we prove that the massive MIMO strategy can effectively reduce a certain portion of the downlink-to-uplink interference. We derive the approximations of the achievable uplink and downlink rates using a maximum ratio transmission (MRT) precoder and a maximum ratio combination (MRC) receiver. Based on the asymptotic analysis, we exploit the power scaling laws that both the uplink and downlink power should scale down to 1/M (M is the antenna number) to ensure a desirable uplink or downlink rate. Furthermore, simulations show that BDN outperforms traditional time division duplex (TDD) systems in terms of the spectral efficiency.

In this paper, we investigate the flexible power transfer among electric vehicles (EVs) from a cooperation perspective in an energy Internet based EV system. First, we introduce the concept of cooperative EV-to-EV (V2V) charging, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, we propose a flexible energy management protocol, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, we propose a max-weight V2V matching algorithm, which can lead to an optimized V2V matching in terms of the network social welfare. Simulation results verify the efficiency of our proposed cooperative V2V charging based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs.

The efficient design of evolving smart grid faces serious challenges of a variety of malicious attacks. In this paper, the complex decision making processes between a network of electricity users that perform wireless meter data aggregation via other users and multiple sophisticated wireless attackers that are able to act as eavesdroppers and as jammers are investigated. We model the interactions among users and attackers as a hybrid network formation-nonzero sum game. On the one hand, each user seeks to choose the next-hop user that can minimize the electricity use cost which reflects the security and reliability of its meter data transmission. On the other hand, the objective of the attackers is to choose whether to eavesdrop, jam, or use a combination of both strategies, in a way to increase the total network costs. To solve this game, we devised an algorithm based on fictitious play to reach a mixed-strategy Nash equilibrium. Simulation results suggested that the proposed meter data aggregation scheme enables the electricity users to significantly decrease their expected costs as well as adapt to sophisticated wireless attacks in the smart grid.

In this paper, an effective generalized spatial modulation (GenSM) scheme with transmit antenna grouping is proposed to overcome the performance degradation caused by correlated channels. In the proposed scheme, the transmit antennas are divided into several equal-sized groups, and spatial modulation (SM) is carried out to select one active antenna in each group independently. It is quite different from the conventional GenSM which jointly selects active antenna set. Apart from the straightforward block grouping method, which collects the adjacent antennas to the same group, interleaved grouping is also introduced. It can maximize the average distance between the antennas in the same group, since the channel correlation depends on it. To evaluate the performance, a closed-form expression of the average bit error probability (ABEP) upper bound is derived for all proposed grouping methods and Monte-Carlo simulations are conducted to verify the analysis and reveal the performance gain of the proposed scheme in terms of bit error rate (BER) in comparison with conventional GenSM and SM.

Index modulated orthogonal frequency division multiplexing (IM-OFDM) is a newly proposed technique, which achieves significantly improved error performance and peak-to-average power ratio (PAPR) in comparison with classical OFDM due to the activation of partial subcarriers. However, in the presence of inter-carrier interference (ICI), error detection of the subcarrier indices may easily occur, such that the performance of IM-OFDM is severely degraded and can be even worse than classical OFDM. To solve this problem, in this paper, we propose to tailor the ideas of classical ICI self-cancellation and two-path cancellation to IM-OFDM with two mapping methods, i.e., symmetric mapping and mirror mapping. Thanks to the joint design of the grouping and mapping methods, the proposed schemes inherit the IM-OFDM virtue of partially activated subcarriers but with reduced ICI. Monte Carlo Simulations validate that the proposed schemes significantly outperform conventional OFDM with ICI cancellation in additive white Gaussian noise (AWGN) channels with frequency deviation without sacrifice of the spectral efficiency and increase of the computational complexity.

In this paper, we investigate the physical layer security issue in Device-to-Device (D2D) communications underlaying cellular networks. In order to optimize the system secrecy rate of the cellular secure communication, we derive the optimal joint power control solutions of both the cellular communication links and D2D pairs in terms of the secrecy capacity. Furthermore, we propose a secrecy-based joint power and access control (JPAC) scheme with optimum D2D pair selection mechanism that can achieve an improved network secrecy performance with very low computational complexity. Simulation results validate the efficiency of the proposed secrecy-based JPAC scheme.

Orthogonal frequency division multiplexing (OFDM) is prone to frequency offset which gives rise to intercarrier interference (ICI). Recently, a novel OFDM transmission scheme called OFDM with index modulation (IMOFDM) is proposed, which outperforms conventional OFDM in the absence of frequency offset. As in IM-OFDM, partial subcarriers are set to be idle by spatial modulation, the potential of IM-OFDM in ICI reduction is expected. In this paper, we find that this potential vanishes in some real scenarios with ICI, which leads to IM-OFDM exhibiting even worse performance than conventional OFDM. To improve this situation, a novel ICI cancellation scheme is designed, which integrates the ICI self-cancellation technique into the IM-OFDM framework. Via careful designs, the proposed scheme not only provides a solution to ICI problems of IM- OFDM, but also achieves an attractive tradeoff between the spectral efficiency and ICI cancellation performance of the system. Simulations validate that in the presence of carrier frequency offset (CFO), the proposed scheme significantly outperforms existing IM-OFDM and more importantly shows better performance than the conventional OFDM with ICI self-cancellation.

Underwater acoustic communications (UAC) feature frequency‐dependent signal attenuation, long propagation delay and doubly‐selective fading. Thus the design of reliable UAC protocols is challenging. On the other hand, cooperative relay communications, which have been extensively studied in terrestrial environments, are promising paradigms for reliable communications. However, their application to UAC has not been thoroughly explored. In this study, the authors will design an asynchronous relaying protocol to achieve reliable underwater communications. This new scheme accounts for and takes advantage of the unique characteristics of UAC channels. To avoid time synchronisation difficulty in UAC, and facilitate energy‐efficient relay processing, asynchronous amplify‐and‐forward relaying is adopted in the protocol. In addition, precoded orthogonal frequency division multiplexing is chosen to address the frequency selectivity issue in UAC, while collecting ample multipath diversity provided by the channel and enabled by the asynchronous relaying design. To demonstrate the performance of the protocol, the end‐to‐end signal‐to‐noise ratio is derived, the average pair‐wise error probability is evaluated and the maximum collectable diversity is also proven. Simulations and comparisons are presented to corroborate the analyses and design.

Forward error correction codes are commonly adopted in dual-hop relay communications, among which Luby transform (LT) codes are favorable because of their low-complexity decoder and rate adaptability to erasure channels. To alleviate the high computational cost in the primitive LT-based cooperative communications, hybrid decomposed LT (h-DLT) codes are proposed recently. By dispersing the computational cost of LT codes into the source and the relay, the computational cost of both nodes can be reduced considerably. However, there are some practical limitations. First, the nonnegative decomposition algorithm developed for h-DLT codes construction has no control of decomposition accuracy. Second, the cooperative relay communication protocol based on the original h-DLT codes can induce high communication cost. In this paper, we propose a stochastic nonnegative polynomial decomposition algorithm, which achieves robust decomposition and higher decomposition accuracy for h-DLT codes construction. Based on the new algorithm, a new type of h-DLT codes is proposed for cooperative relay communications with higher energy efficiency. Simulations are conducted to manifest the performance of the new h-DLT codes and benefits of the corresponding cooperative relay communication system. In addition, multiple design factors are investigated.

Big data for social transportation brings us unprecedented opportunities for resolving transportation problems for which traditional approaches are not competent and for building the next-generation intelligent transportation systems. Although social data have been applied for transportation analysis, there are still many challenges. First, social data evolve with time and contain abundant information, posing a crucial need for data collection and cleaning. Meanwhile, each type of data has specific advantages and limitations for social transportation, and one data type alone is not capable of describing the overall state of a transportation system. Systematic data fusing approaches or frameworks for combining social signal data with different features, structures, resolutions, and precision are needed. Second, data processing and mining techniques, such as natural language processing and analysis of streaming data, require further revolutions in effective utilization of real-time traffic information. Third, social data are connected to cyber and physical spaces. To address practical problems in social transportation, a suite of schemes are demanded for realizing big data in social transportation systems, such as crowdsourcing, visual analysis, and task-based services. In this paper, we overview data sources, analytical approaches, and application systems for social transportation, and we also suggest a few future research directions for this new social transportation field.

Small cell is an attractive and promising technology for improving capacity in traffic hotspots using cell densification. We consider the traffic load-balancing problem for a macro-assisted small cell architecture exploiting the flexible 3-D beamforming facilitated by the adoption of the active antenna system at base stations. Specifically, 3-D user equipment (UE)-specific beamforming is utilized for traffic load balancing due to its unique feature that the received signal is maximized, while the interference is limited with narrower beamwidth. We propose a novel traffic load-balancing algorithm based on cell association and cell sectorization using 3-D UE-specific beamforming, and the performance of the proposed algorithm is evaluated via system-level simulations. Results show that cell edge user throughput and cell average throughput both improve significantly with the proposed load-balancing algorithm compared with the conventional sectorization with fixed antenna down-tilt scheme. In addition, in denser small cell scenarios, our proposed scheme can further increase the load-balancing gain with reduced cell planning efforts compared with the conventional scheme.

In this paper, we investigate the cooperation issue via spectrum sharing when employing physical layer security concept into the device-to-device (D2D) communications underlaying cellular networks. First, we derive the optimal joint power control solutions of the cellular communication links and D2D pairs in terms of the secrecy capacity under a simple cooperation case and further propose a secrecy-based access control scheme with the best D2D pair selection mechanism. Then, we consider a more general case that multiple D2D pairs can access the same resource block (RB) and one D2D pair is also permitted to access multiple RBs, and provide a novel cooperation mechanism in the investigated network. Furthermore, we formulate the provided cooperation mechanism among cellular communication links and D2D pairs as a coalitional game. Then, based on a newly defined max-coalition order in the constructed game, we further propose a merge-and-split-based coalition formation algorithm for cellular communication links and D2D pairs to achieve efficient and effective cooperation, leading to improved system secrecy rate and social welfare. Simulation results indicate the efficiency of the proposed secrecy-based access control scheme and the proposed merge-and-split-based coalition formation algorithm.

In this paper, we propose a novel virtual multi-input-multi-output (VMIMO) architecture to convey information from the source node (SN) to the destination node (DN) via multiple relay nodes (RNs). As it is built on dual-hop networks with differential spatial modulation (DSM), we call it dual-hop hybrid DSM (DH-HDSM). In order to harvest the diversity provided by the SN and reduce the complexity at the RNs, DH-HDSM forms a precoding aided DSM pattern in the first hop and applies incoherent detection in a centralized or distributed manner at the RNs. Based on the decode-and-forward protocol, DSM transmission is carried out in the second hop and achieves high energy efficiency via statistical single RN activation at any time instant. We analyze the performance of DH-HDSM in terms of the average bit error probability and make comparisons with dual-hop hybrid spatial modulation (DH-HSM) and differential VMIMO systems with either a single relay or best relay selection. Theoretical results and Monte Carlo simulations confirm that DH-HDSM outperforms DH-HSM and other differential relay schemes in terms of error performance when the DN is equipped with multiple antennas.

Motivated by the concept of spatial modulation (SM), a differential scheme has been recently proposed. This scheme, termed as differential (D-)SM, dispenses with channel estimation while maintains a similar bit error rate (BER) performance to SM. The conventional optimal DSM detector based on a maximum likelihood (ML) criterion, however, gives rise to prohibitive computational complexity when either the space-domain or signal-domain constellation size is large. In this paper, we propose a low-complexity yet optimal detection algorithm for DSM by utilizing sphere decoding (SD). The complexity analysis concerning Euclidian distance equation for DSM-SD is derived. Simulation results show that the proposed DSM-SD algorithm maintains an identical BER performance and achieves a significant reduction of computational complexity compared with the DSM-ML algorithm. The DSM- SD algorithm is especially efficient when the number of receive antennas is large. Moreover, compared with SD applied to SM, its application to DSM is verified to be more useful and attractive.

In this paper, we investigate the cooperation issue via spectrum sharing when employing the physical layer security concept into the Device-to-Device (D2D) communications underlaying cellular network. Different from previously related works, we consider a more general interference case that multiple D2D pairs can access the same resource block (RB) and one D2D pair is also permitted to access multiple RBs, and provide a novel cooperation mechanism in the investigated D2D communications underlaying cellular network. Furthermore, we formulate the provided cooperation mechanism among cellular communication links and D2D pairs as a coalitional game. Then, based on a newly defined Max-Coalition order in the constructed game, we further propose a merge-and-split based coalition formation algorithm for cellular communication links and D2D pairs to achieve efficient and effective cooperation, leading to both improved system secrecy rate and social welfare. Simulation results indicate the efficiency of the designed cooperation mechanism and the proposed merge-and-split based coalition formation algorithm.

In this paper, we propose a novel Multiple Input Multiple Output (MIMO) scheme based on Spatial Modulation (SM) and termed as Pre-coding aided Differential Spatial Modulation (PDSM). By mapping information to the receiver side space-time transmit blocks and conducting differential modulation, the PDSM scheme achieves further reduced receiver side complexity in comparison with Pre-coding aided Spatial Modulation (PSM). A general upper bound on the Average Bit Error Probability (ABEP) achieved by the PDSM architecture with an arbitrary number of transmit antennas and two receive antennas is derived, and an exact closed-form ABEP expression is provided for BPSK signaling in Rayleigh fading environment. The PSM architecture with the same system configuration is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a less-than-3dB Signal-to- Noise power Ratio (SNR) penalty of the considered system in comparison with the benchmark.

Underwater acoustic communications (UAC) features limited bandwidth, distance-dependent attenuation, long and variable propagation delay, and doubly-selective fading. Therefore, the design of reliable and efficient UAC protocols is very challenging. Cooperative relay communications is promising for future UAC as it can improve its reliability and extend the data transmission ranges. In this work, we consider the adaptive relay-aided OFDM UAC (RA-UAC) using the amplifyand- forward (AF) protocol. We first present the results of the optimal power allocation between the source and the relay as well as the power distribution over all subcarriers based on instantaneous channel state information (CSI). However, the adaptive power allocation is very challenging. The fast timevarying UAC channels and the long propagation delay can make the CSI feedback outdated and therefore degrade the performance of the RA-UAC system using the optimal power allocation. To overcome this problem, we implement channel prediction to compensate the channel variation during the CSI signal propagation. The recursive least square (RLS) adaptive filter is utilized to predict the future channel impulse response (CIR) due to its low computational cost and small storage size. The approximate mean square error (MSE) of the RLS filter is derived. Simulation results confirm the necessity of channel prediction through MSE and overall system performance comparison.

An effective self-interference (SI) cancellation scheme operated in the digital domain is proposed for the newly-emerging spatial modulated full duplex (SMFD) system. In the proposal, the SMFD receiver performs the SI cancellation through a combination of signals received in successive time slots by resorting to the properties of the SMFD system, i.e., the SI channel is reciprocal and the transmitted data is known by both the transmitter and the receiver. To achieve the ultimate performance of the proposal, however, the transmitted symbols need to be rotated and the number of time slots involved in the SI cancellation has to be carefully chosen to balance the receiver complexity and the system performance. These issues are also discussed in great detail. Monte-Carlo simulations on the error-free information transmission capability of the SMFD system with the proposed SI cancellation scheme over Rayleigh fading channels are conducted and advantages are revealed.

Differential spatial modulation (DSM) is a newly proposed differential modulation technique tailored to spatial modulation (SM), which requires no channel state information (CSI) at the receiver. DSM can offer flexible tradeoff between the reception reliability and the system complexity. In this paper, we are the first to study the adoption of DSM in a dual-hop amplify-and-forward (AF) relaying system, which consists of a two-antenna source, a single-antenna relay, and a single-antenna destination, so as to reduce the burden of channel tracking on both the relay and the destination. We derive a general upper bound on the average bit error probability (ABEP) achieved by the system. Moreover, an exact closed-form ABEP expression and the asymptotic result are provided for BPSK signaling in Rayleigh fading environment. The same system setup with the adoption of SM at the source is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a 3dB signal-to-noise power ratio (SNR) penalty of the considered system compared with the benchmark.

The rapid progress in the research and development of electronics, sensing, signal processing, and communication networks has significantly advanced the state of applications of intelligent transportation systems (ITSs). However, efficient and low-cost methods for gathering information in large-scale roads are lacking. Consequently, wireless sensor network (WSN) technologies that are low cost, low power, and self-configuring are a key function in ITS. The potential application scenarios and design requirements of WSN for urban transportation (WSN-UT) are proposed in this work. A customized network topology is designed to meet the special requirements, and WSN-UT is specifically tailored for UT applications. WSN-UT enables users to obtain traffic and road information directly from the local WSN within its wireless scope instead of the remote ITS data center. WSN-UT can be configured according to different scenario requirements. A three-level subsystem and a configuration and service subsystem constitute the WSN-UT network frame, and the service/interface and protocol algorithms for every subsystem level are designed for WSN-UT.

Dynamic allocation of subframes to uplink (UL) or downlink (DL) in time division duplex (TDD), termed `Dynamic TDD,' has been studied by the 3rd Generation Partnership Project (3GPP) since the Long Term Evolution (LTE) Release 11 timeframe. At the same time, 3GPP is also standardizing macrocell-assisted small cell heterogeneous architectures for inclusion in LTE Release 12 as a solution offering high data rate to user terminals (UEs) along with high system capacity through spatial reuse of spectrum. In this paper, we focus on a particular small cell architecture proposed by DOCOMO, known as the Phantom Cell architecture, which provides the option to support dynamic TDD. For an arbitrarily-located UE in a small cell network, we apply results from stochastic geometry to derive expressions for the distribution of DL signal to interference plus noise ratio (SINR) at an arbitrary UE and the distribution of UL SINR at its serving base station (BS). The analytical results are verified by system level simulations. These results can be used to study aspects of system design for small cells, and the sensitivity of SINR to the extent of synchronization across small cells employing dynamic TDD. In order to deal with the severe inter-cell interference (ICI) problem in dynamic TDD, we further propose a frequency domain interference coordination technique. Finally, system level simulations based on more realistic system models and assumptions are conducted to evaluate the performance of dynamic TDD systems and the proposed interference coordination technique.

Full-duplex transmission is a promising technique to enhance the capacity of relay communications. In this paper, we investigate the power and location optimization for full-duplex decode-and-forward (DF) relaying systems. The outage probability is adopted as the optimization criterion and a two-dimensional (2D) optimization problem is formulated for systems with and without the direct source-destination link. Analytical and numerical results are provided to demonstrate advantages of the power and location optimization. More interestingly, the effect of residual self-interference (RSI) introduced by full-duplex transmission is investigated in terms of the optimal power allocation and relay location deployment and the outage probability performance improvement. Furthermore, we show that the minimal outage probability can be achieved via joint power-location optimization. The effect of power optimization on the outage performance is also compared between full-duplex and half-duplex relaying.

Intelligent transportation systems (ITS) are becoming a crucial component of our society, whereas reliable and efficient vehicular communications consist of a key enabler of a well-functioning ITS. To meet a wide variety of ITS application needs, vehicular-to-vehicular and vehicular-to-infrastructure communications have to be jointly considered, configured, and optimized. The effective and efficient coexistence and cooperation of the two give rise to a dynamic spectrum management problem. One recently emerged and rapidly adopted solution of a similar problem in cellular networks is the so-termed device-to-device (D2D) communications. Its potential in the vehicular scenarios with unique challenges, however, has not been thoroughly investigated to date. In this paper, we for the first time carry out a feasibility study of D2D for ITS based on both the features of D2D and the nature of vehicular networks. In addition to demonstrating the promising potential of this technology, we will also propose novel remedies necessary to make D2D technology practical as well as beneficial for ITS.

In this paper, we propose a novel centralized time-division multiple access (TDMA)-based scheduling protocol for practical vehicular networks based on a new weight-factor-based scheduler. A roadside unit (RSU), as a centralized controller, collects the channel state information and the individual information of the communication links within its communication coverage, and it calculates their respective scheduling weight factors, based on which scheduling decisions are made by the RSU. Our proposed scheduling weight factor mainly consists of three parts, i.e., the channel quality factor, the speed factor, and the access category factor. In addition, a resource-reusing mode among multiple vehicle-to-vehicle (V2V) links is permitted if the distances between every two central vehicles of these V2V links are larger than a predefined interference interval. Compared with the existing medium-access-control protocols in vehicular networks, the proposed centralized TDMA-based scheduling protocol can significantly improve the network throughput and can be easily incorporated into practical vehicular networks.

Nowadays, more and more communication systems are deployed under mobile environments with very high velocity. This paper focuses on the channel estimation problem in vehicular scenarios, which is very challenging in view of the extremely time-varying characteristics of mobile channels. Specifically, we propose a novel channel estimator named constructed data pilot (CDP) estimator for the current communication standards by fully exploiting the channel correlation characteristics across two concatenated symbols. On the basis of the CDP estimator, we further resort to two efficient techniques to improve its performance over the entire signal-to-noise-ratio (SNR) region. For the first technique, the time-variant mobile channel is modeled as a first-order Markov process so that the exact autocorrelation value of the two adjacent symbols can be derived. For the second technique, the SNR is estimated and serves as a priori information. Simulation results reveal that our proposed channel estimators outperform existing alternatives with lower computational complexity.

In this paper, we propose a two-layer game-theoretic framework to solve the network selection problem in heterogeneous wireless networks (HetNets). At the intra-network layer, a hierarchical game among the SP and its admitted users is employed, and the closed-form equilibrium solutions for the pricing and transmission rate are provided. Meanwhile, at the inter-network layer, all service providers (SP) and the active users are engaged in a dynamic hedonic game and finally self-organized into a Nash-stable coalition structure. The key feature of our proposed approach is the inclusion of social-behavioral (SoBe) constraints capturing the real-world user and SP preferences, hierarchy and membership. Not only that SoBe more accurately models practical scenarios, it also leads to markedly reduced unnecessary handovers, and well maintained call blocking rate. Simulations confirm the superior performance of SoBe in comparison with the widely adopted user-driven alternative in terms of nearly all critical performance criteria.

Small cell is an attractive and promising technology for improving capacity in traffic hotspots using cell densification. In this paper, we propose the capacity enhancement for small cells under macrocell-assisted architecture utilizing the flexible 3-dimensional (3D) beamforming facilitated by the adoption of the active antenna system (AAS) at base stations (BSs). In contrast to conventional macrocell network, more dynamic and flexible 3D beamforming with narrow beamwidth is feasible in the small cell layer because the service coverage and mobility robustness are basically supported in the macrocell layer. This dynamic beam adaptation in full dimensions can improve the received signal quality and at the same time control the interference more effectively, which is especially useful for the dense small cell deployment scenario where the interference issue is one of the major concerns. In particular, performance comparison between the conventional sectorization with fixed down-tilt scheme and UE (user equipment)-specific 3D beamforming is studied. Furthermore, a novel UE group-specific 3D beamforming is proposed as a more realistic operation compared to UE-specific beamforming. System level simulations demonstrate the significant gain of capacity enhancement with 3D beamforming over the conventional sectorization with fixed down-tilt in terms of both the cell average capacity (up to 124.8% gain) and the cell edge user throughput (up to 454.3% gain). It is also shown that UE group-specific beamforming can achieve performance comparable to that of UE-specific beamforming.

Recently, localization has become an indispensable technique for wireless applications. In view of the limitation of global position system (GPS) in certain environments, alternative approaches are in demand. In this paper, we consider a cooperative localization approach named sum-product algorithm over a wireless network (SPAWN). Although SPAWN theoretically facilitates cooperative localization, it has several practical limitations. Specifically, SPAWN results in high computational complexity and increased network traffic. The main complexity of SPAWN lies in the selection of agents/anchors involved in the cooperative localization. To this end, we formulate the agent/anchor selection problem into a network formation game. Together with a practical limit on the number of agents/anchors used for cooperative localization, our proposed approach can markedly reduce the computational complexity and the resultant network traffic. Simulations show that these advantages come with a slight degradation in the localization mean squared error (MSE) performance.

Small cell is an attractive and promising technology for improving capacity in traffic hotspots using cell densification. We consider the traffic load balancing problem for small cells under the enhanced Local Area (eLA) architecture utilizing the flexible 3-dimensional (3D) beamforming facilitated by the adoption of the active antenna system (AAS) at base stations (BSs). Different load balancing methods based on combinations of cell association algorithms and antenna tilt adjustment schemes are proposed and evaluated via system level simulations. Results show the potential gain of traffic load balancing with 3D beamforming in terms of cell edge user throughput. In particular, for the enhanced load balancing cell association combined with UE (user equipment)-specific tilting scheme, significant cell edge user throughput improvements can be achieved while good average cell throughput performance can still be maintained.

Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.

Data dissemination is a promising application for the vehicular network. Existing data dissemination schemes are generally built upon some random-access protocol, which results in the unavoidable collision problem. To address this problem, in this paper we design a novel data dissemination strategy from the scheduling perspective. A data dissemination scheduling framework is then proposed. In the proposed framework, the main challenge is how best to assign the transmission opportunity to nodes with maximum dissemination utility and to avoid the collision problem. We then propose a novel and practical relay selection strategy and adopt the space-time network coding (STNC) with low detection complexity and space-time diversity gain to improve the dissemination efficiency. Compared with the random-access dissemination such as CodeOn-Basic and the noncooperative transmission, our proposed data dissemination strategy performs better in terms of the dissemination delay. In addition, the proposed strategy works even better in the dense network than the sparse scenario, benefitting from the space-time diversity gain of STNC and no-collision transmissions. This is in sharp contrary to the CodeOn-Basic method.

To ensure resilient and reliable meter data collection that is essential for the smart grid operation, we propose a QoS-oriented wireless routing scheme. Specifically tailored for the heterogeneity of the meter data traffic in the smart grid, we first design a novel utility function that not only jointly accounts for system throughput and transmission latency, but also allows for flexible tradeoff between the two with a strict transmission latency constraint, as desired by various smart meter applications. Then, we model the interactions among smart meter data concentrators as a mixed-strategy network formation game. To avoid potential information exchange which is not always practical in meter data collection scenario, a stochastic reinforcement learning algorithm with only private and incomplete information is proposed to solve the network formation problem. Such a problem formulation, together with our proposed stochastic learning algorithm on graph, results in a steady probabilistic route. Both contributions are novel and unique in comparison with existing work on this topic. Another distinct feature of our approach is its capability of effectively maintaining the QoS of smart meter data collection, even when the network is under fault or attack, as verified by simulations.

Spectrum sensing is an important building block to realize the cognitive radio concept. In order to combat fading in the wireless environment, cooperation among the sensing users is usually employed. In this paper, we develop a closed-form optimal local decision threshold for cooperative spectrum sensing in cognitive radio systems via large deviation analysis. The resultant strategy is independent of the total number of cooperating users. We show that it is not only asymptotically optimal when the number of sensing users approaches infinity, but also can achieve the maximum diversity. Numerical results are provided to verify our analysis.

In this paper, we for the first time consider the space-time network coding (STNC) to propose a novel cooperative data dissemination strategy in the time-division multiple access (TDMA) manner for the vehicle network, to overcome the shortcomings of the traditional network coding (NC) techniques with carrier sense multiple access/collision avoidance (CSMA/CA) protocol, i.e, high signal detection complexity and channel collision problem. An optimal relay selection strategy in the procedure of cooperative data dissemination is proposed to maximize the average system capacity for the data dissemination. In a addition, a suboptimal relay selection strategy is put forward to reduce the computation complexity and thus makes the developed data dissemination strategy more practical. Compared with the non-cooperative data dissemination, the proposed cooperative data dissemination via STNC has better performance in terms of the average system capacity, the average user equipment (UE) capacity, and the average data dissemination delay.

Two-path transmission schemes have attracted much attention due to its effectiveness in suppressing intercarrier interference (ICI) in orthogonal frequency-division multiplexing (OFDM) systems. This paper proposes a general implementation framework for the existing two-path transmission schemes, which not only reveals the underlying relationship between the existing two-path transmission schemes and the mirror-subcarrier-mapping based ICI self-cancellation technique but also owns the advantage of backward compatibility with normal OFDM systems. Moreover, using the proposed framework, new two-path transmission schemes can be easily developed. With insights provided by this framework, the carrier-to-interference power ratio (CIR) and the bit error probability (BEP) achieved by the existing two-path transmission schemes are further analytically investigated. Monte Carlo simulations validate the analysis.

Full duplex transmission is a promising technique to enhance the capacity of relay communications. In this paper, we investigate the optimum transmit power allocation for full duplex decode-and-forward (DF) relaying systems. The outage probability is adopted as the optimization criterion and a transmit power optimization problem is formulated for systems with and without total power constraint. Analytical and numerical results are provided to show the advantages of the power optimization. More interestingly, the effect of residual self-interference introduced by full duplex transmission is examined in terms of optimal power allocation and outage probability performance improvement.

The Third Generation Partnership Project (3GPP) has been studying dynamic allocation of sub-frames to uplink or downlink in Time Division Duplex (TDD), since the Long Term Evolution (LTE) Rel. 11 timeframe. At the same time, 3GPP is also standardizing Enhanced Local Area (eLA) small-cell heterogeneous architectures for inclusion in 3GPP Rel. 12 as a solution offering high data rate to user terminals (UEs) along with high spatial reuse of spectrum. In this paper, we focus on a particular eLA architecture proposed by DOCOMO, called the Phantom Cell architecture, that has the option to support dynamic TDD. We evaluate the system performance of dynamic TDD in phantom cells and the corresponding inter-cell interference issues. Then we propose a frequency domain interference coordination technique for dynamic TDD in phantom cells. The superior performance of the proposed scheme over conventional frequency reuse technique and no interference coordination scenario is verified by system level simulations.

Device-to-device (D2D) communications underlaying cellular networks have recently been considered as a promising means to improve the resource utilization of the cellular network and the user throughput between devices in proximity to each other. In this paper, we investigate the resource sharing problem to optimize the system performance in such a scenario. Specifically, we formulate the interference relationships among different D2D communication links and cellular communication links as a novel interference-aware graph, and propose an interference-aware graph based resource sharing algorithm that can effectively obtain the near optimal resource assignment solutions at the base station (BS) but with low computational complexity. Simulation results confirm that, with markedly reduced complexity, our proposed scheme achieves a network sum rate that approaches the one corresponding to the optimal resource sharing scheme obtained via exhaustive search.

A generalized version of orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) referred to as grouped OFDM-IDMA (G-OFDM-IDMA) is introduced in this paper. By dividing users into groups and transmitting each group's data only on some (as opposed to all) subcarriers, G-OFDM-IDMA can have much lower decoding complexity compared with conventional OFDM-IDMA while preserving the bit error probability (BEP) performance and the bandwidth efficiency. The user grouping problem is formulated into an integer linear programming problem whose suboptimal solution is proposed and compared with the lower bound. The optimization complexity issue is also addressed. Simulations are carried out to test the performance of G-OFDM-IDMA under various system configurations. It is observed that up to 80% complexity could be saved when the conventional OFDM-IDMA is substituted by G-OFDM-IDMA configured according to the suboptimal grouping solution.

In this paper, we present a novel user grouping method for grouped OFDM-IDMA systems. Aiming at maximizing the system capacity, we adaptively distribute users among the pre-allocated subcarrier groups according to their respective channel conditions. Using the notion of SNR evolution function, we first analyze the achievable capacity of the system and formulate the optimization problem as a weighted bipartite matching problem. Due to the analytically intractability of the SNR evolution function, we opt to use either the worst case or best case of the achievable capacity to approximate the original problem. Then Kuhn-Munkres method is employed to solve the approximated problems. The performance of the proposed scheme is evaluated by both theoretical analyses and simulations. Results show that with our proposed algorithm, the system capacity is markedly improved and is very close to the theoretical capacity upper bound.

The bit error rate (BER) performance of interleave division multiple access (IDMA) based systems can be predicted by a semi-analytical method referred to as signal-to-noise ratio (SNR) evolution. SNR evolution tracks the average symbol SNR at each iteration and provides a faster solution than brute-force simulation. In this paper a revised SNR updating formula is proposed for orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) systems in Rayleigh fading channels. By alternating the order of expectations and division of random variables when updating the average symbol SNR, a more accurate approximation of the expected SNR is obtained compared with the existing formula. Hence improved BER prediction performance can be achieved, which is verified by simulations.

Cognitive radio is gaining increasingly interest as a promising solution to current spectrum resource shortage. Within various tasks of cognitive radio, spectrum sensing is the fundamental one, but is challenged by wireless channel fading. By collecting diversity among different users, cooperative sensing can overcome the fading problem very well. Usually, only the local binary decisions are available for sensing cooperation due to limitation of the channel bandwidth. However, in our previous work [1], we have shown that this strategy will either sacrifice diversity or signal-to-noise ratio (SNR) gain. In this paper, we will study cooperative sensing with ternary local decisions. Compared with the binary cooperative sensing, this strategy will can regain diversity and recover the extra SNR loss by appropriate threshold selection, without increasing the decision forwarding bandwidth.

Forward error correction (FEC) is commonly adopted in cooperative relay communications to ensure link-layer communication reliability. Among those schemes, rateless fountain codes, such as Luby Transform (LT) codes, are favorable for their low complexity and rate adaptability to channel fading dynamics. However, the cooperative transmission schemes based on primitive fountain codes induce either heavy computation cost or large end-to-end latency. To address these issues, we explore decomposed LT (DLT) codes, which comprise of two layers of random encoding but only a single layer of decoding. By implementing the two layers of encoding at the source and the relay(s) respectively, the cooperative system can ensure communication reliability on both source-relay and relay-destination links with reduced computation cost and latency. In this work, we first develop a general decomposition technique for the DLT code construction. Based on this, we further propose a hybrid decomposition algorithm tailored for LT codes with robust Soliton distribution (RSD). The resultant hybrid DLT (h-DLT) codes facilitate flexible computation cost allocation. The h-DLT codes based cooperative relay communication protocol is then developed and analyzed in terms of the transmission latency and energy consumption.

Cooperative networks provide enhanced system performance by exploiting spatial diversity in a distributed manner. Optimum resource allocation can help improve the performance of cooperative networks and increase the efficiency of resource usage. In the literature, various system performance and optimization results have been reported for different systems and with different optimization metrics. However, there lacks a unifying framework delineating the effects of different factors on resource optimization and the resultant benefit. In this paper, we investigate the relative effects of optimization metric (error rate versus outage probability), modulation type (coherent versus differential) and relaying protocol (amplify-and-forward (AF) versus decode-and-forward (DF)). To facilitate such a case study, we provide a comprehensive set of system performance for four commonly adopted cooperative systems: coherent amplify-and-forward (CAF), coherent decode-and-forward (CDF), differential amplify-and-forward (DAF), and differential decode-and-forward (DDF). A resource optimization problem that minimizes the total transmit energy is formulated. Since energy optimization has been intensively studied in the literature, location optimization will be investigated. The analyses and simulations suggest that: i) The error rate and outage probability metrics yield similar optimization results for AF relaying systems; ii) The relaying protocol determines the optimization results while the modulation type has no effect; and iii) The difference between different relaying protocols diminishes when the number of relays increases.

Forward error correction (FEC) is an effective means of reliable communications in wireless networks. Among all error-correcting codes, the recently developed fountain codes are known for their low complexity and rateless features. In the literature, fountain codes are mostly adopted in point-to-point communications. In this paper, we will investigate decomposed fountain codes for distributed dual-hop systems. In this type of codes, two layers of random XOR encoding are performed, but only a single layer of decoding is needed. By implementing each layer of encoding at one hop, the dual-hop systems can ensure end-to-end communication reliability with significantly reduced computation cost. Since Luby Transform (LT) codes are the first class of practical fountain codes and the core of more recent fountain codes, we will focus our study on decomposed LT (DLT) codes. To construct the DLT codes, we first analyze general LT code decomposition, and then propose a unique decomposition algorithm tailored for the LT code with robust Soliton distribution (RSD). The performance of the resultant DLT code will be evaluated in terms of the decoding probability and computation cost.

Gaussian frequency shift keying (GFSK) is a promising digital modulation scheme. The design of simple and high-performance receivers for GFSK systems is a challenging task. In this letter, we develop an optimized differential GFSK demodulator and investigate the phase wrapping issue in its implementation. Simulation results show bit-error-rate (BER) performance improvement in comparison with conventional differential demodulators in both AWGN and flat fading channels. We also compare our proposed demodulator with other existing alternatives in terms of BER performance.

The battery nonlinearity has never been considered for energy analysis in relay networks. In this letter, we adopt the realistic nonlinear battery model, apply the battery energy consumption results in to general relay networks, investigate the optimum and suboptimum energy allocation solutions for relaying transmission, and establish the relay selection criterion from the battery energy efficiency perspective. Our analyses and comparisons show that relaying does not always increase the system energy efficiency. We further establish closed-form conditions that can be easily checked to determine whether the relay transmission is preferable to the direct transmission. Numerical examples are also presented to verify these results.

As an alternative to the transmitted reference (TR-) ultra-wideband (UWB), the dual-carrier frequency shifted reference (FSR-) UWB and its high-rate multi-carrier counterparts have been recently studied. In this paper, we consider a lowrate multi-carrier differential modulation and its low-complexity receiver designs. We prove that such a multi-carrier system actually results in a pulse position modulation, where the peak of the transmitted waveform changes location according to the value of the data symbol. Based on this discovery, we develop a simple energy detector by detecting the pulse position in time domain. In addition, the multi-carrier nature of this modulation also enables a differential demodulator in frequency domain. We derived the analytical performance for this receiver. Simulations are carried out to evaluate the performance of these time- and frequency-domain low-complexity receivers.

In underwater acoustic communications (UAC), frequency-dependent signal attenuation, long propagation delay and doubly-selective fading channels render reliable communications a challenging problem, especially at long distances. To enhance reliability and to extend range, relay communications have been extensively studied in terrestrial environments. However, their application to UAC has not been thoroughly explored. In this paper, we analyze the capacity of relay-aided (RA- )UAC. The result shows a prominent capacity increase in RA-UAC systems, when compared with traditional direct-link UAC. In addition, effects of various system parameters on capacity are also evaluated. These parameters include source-to-destination distance, transmit power allocation and relay location. To realize the benefits of RA-UAC, special considerations are to be taken in practical RA-UAC system designs. To account for and to take advantage of the unique characteristics of UAC channels, we develop a practical asynchronous amplify-and-forward (AF) relay system for UAC. To collect the ample multipath energy and diversity enabled by this relaying protocol, we also employ the precoded orthogonal frequency division multiplexing (OFDM) as the basic physical layer module. Our system resolves both the time synchronization difficulty and frequency selectivity of UAC. Simulations and comparisons are presented to verify our analysis and design.

Remote health monitoring by exploiting wireless communications technologies is an emerging area receiving increasing interests from academia, research labs and industry. This issue has also been brought up in the standardization process of IEEE 802.15 Task Group 6 Wireless Body Area Networks (WBAN). The challenge is to find the appropriate combination of medical data processing techniques and the wireless technology in order to meet the particularly stringent error, latency and power consumption requirements of healthcare applications. In this paper, we present the results of a case study on wireless electrocardiogram (ECG) monitoring over Bluetooth. Based on the special considerations of (processing and transmission) power consumption at wireless ECG sensors, we first propose a low complexity ECG compression method. Comparisons with existing approaches confirm the superior performance of our method. We then study the data reconstruction performance at the Bluetooth receiver and find that: i) the uncompressed ECG data transmission is not necessarily better than the compressed transmission; and ii) there exists an optimum ECG data compression ratio for the wireless link.

In a case study of the waveform design for target estimation in MIMO sensing, [9] developed the optimum waveform design in the presence of colored noise, as well as the joint robust transmitter and receiver designs. Three criteria, namely mutual information (MI), minimum mean square error (MMSE), and normalized MSE (NMSE), are considered. The relationship between the essential measure MI in communications and the commonly adopted MSE indicators for sensing has created a vision of communications-inspired sensing. In this paper, we will further investigate this relationship by analyzing the sensitivity of the optimum design to the overestimation error. For each of the three criteria, we derive the explicit formula for the error mode strength threshold, above which the error mode would consume nonzero transmit power and the original waveform design will be inevitably altered. We also develop a normalized NMSE indicator to measure the estimation performance variation induced by the error mode. Both analytical and numerical results confirm that the optimum waveform designs based on the three criteria do not show significant performance deterioration. While the NMSE-based optimum solution is always more sensitive to the overestimation error than the MI-based one, there is no universal relationship between these two criteria and MMSE.

In this paper, we compare the battery power efficiencies of various pulse-based modulations widely adopted for their low complexity. Taking into account circuit modules and battery imperfectness, we establish simple closed-form analytical formulas which can be used to conveniently determine the relative preference between arbitrary pulse-based modulation pairs in terms of their actual average battery energy consumption.

In recent years, Ultra-Wideband (UWB) technology has emerged as a promising physical layer candidate for a wide range of wireless networks, especially due to its low operation power level and coexistence ability with traditional wireless systems. However, none of these physical layer realizations has specified the compatible operation among them (see, e.g.,). Therefore, the effective interoperability between asymmetric UWB transceivers needs to be considered for ubiquitous wireless communications. In this paper, we investigate the transceiver design for asymmetric UWB links with a single transmitter and a single receiver. We consider factors that can lead to the asymmetry between UWB transmitters and receivers such as different numbers of signal bands and different pulse rates. Our analysis reveals the similarity between the asymmetric UWB links and the conventional multiantenna systems. Then, MIMO signal processing approaches can be readily applied to achieve the optimal design in terms of channel throughput or bit error rate (BER). Analysis and simulations corroborate the effectiveness of our transceiver designs.

Coherent communications over doubly-selective fading channels are attracting increasing research interests because of the performance advantage over their noncoherent counterparts. In this paper, we use a simple windowing and dewindowing technique to improve the accuracy of an existing basis expansion model (BEM) and develop a windowed least-squares (WLS) estimator for doubly-selective fading channels. We also design the optimum pilot pattern for the WLS estimator. Our designs can considerably improve the channel estimation performance. Simulations are provided to corroborate our theoretical analysis.

Rapid and accurate timing synchronization is the first and foremost task in ultra-wideband (UWB) systems. The timing with dirty templates (TDT) algorithm introduced in [10] is a promising method with low complexity and relaxed operation conditions in the presence of unknown time hopping and multipath channel. Its optimality, however, remains unexplored. In this paper, we develop the maximum-likelihood (ML) timing algorithm and obtain its optimum training sequence. We show that the optimum training sequence of the ML timing estimator coincides with that of the TDT algorithm. In addition, we prove that, using this training sequence, the ML algorithm can be simplified, and that the simplified ML (SML) is equivalent to TDT.

In this paper, we propose a novel noncoherent UWB (de)modulation system, which uses a pair of orthogonal pulses to convey information symbols. Same as the original noncoherent UWB, our approach remains operational even without timing and channel estimation. Moreover, due to the orthogonality between the received waveforms of the pulse pair, our approach results in a differential demodulator. This means that our approach does not need Viterbi decoding which is required by, even in the presence of timing error. Simulations are also carried out to corroborate our theoretical analysis.

Optimum resource allocation in cooperative networks has been studied for diverse system setups and with various optimization metrics. However, there lacks a unifying framework delineating the effects of different factors on resource optimization and its resultant benefit. In this paper, we investigate the relative effects of the optimization metric (error rate vs. outage probability), modulation type (coherent vs. differential) and the relaying protocol (amplify-and-forward (AF) vs. decode-and-forward (DF)). To facilitate such a unifying study, we provide a comprehensive set of system performance and optimization results for four commonly adopted cooperative systems: coherent amplify-and-forward (CAF), coherent decode-and-forward (CDF), differential amplify-and-forward (DAF), and differential decode-and-forward (DDF). Our analyses and simulations suggest that: i) The error rate and outage probability metrics yield similar optimization results; ii) The relaying protocol affects the optimization results more than the modulation type when the number of relays (L) is small; and iii) The CAF system is uniquely different from others when L is large.

Sensor nodes in wireless sensor networks (WSNs) are often expected to operate on batteries for a long period of time. Battery power efficiency (BPE) is therefore a critical factor dictating the lifetime of WSNs. In this paper, we aim to select the appropriate modulation scheme from a battery power efficiency perspective. Pulse position modulation (PPM) and on-off keying (OOK), as low-complexity pulse-based modulation schemes, are used for a case study of our methodology. The analysis is based on a general model that integrates typical WSN transmission and reception modules with a realistic nonlinear battery model. We first present the quantitative comparison results under general system design criteria. Then, we illustrate the comparisons with theoretical and numerical results under the bit error rate (BER) system design criterion.

Spectrum sensing is a critical issue in cognitive radio networks. Cooperation among the secondary users is utilized to improve the performance of spectrum sensing. In this paper, we quantify the gain of cooperation in spectrum sensing by introducing the concept of diversity order. With different system performance metrics, we introduce different diversity quantities. We analyze the single-user sensing and the multi-user sensing with soft and hard information fusion strategies using the diversity quantities as our figure of merit. In particular, we discuss the selection of threshold in each sensing scheme with respect to the diversity performance and obtain the quantitative relationship among the diversity performance, threshold, and the number of cooperative users. We also observe and quantify the tradeoff between false alarm and missed detection performance in all spectrum sensing schemes.

In this letter, we apply energy detection based time- of-arrival (ToA) estimation to multi-band orthogonal frequency-division multiplexing signals. We analyze the mistiming performance of the ToA estimator in the Nakagami-m channel. Analysis shows that the slope of the probability of mistiming curve increases with the number of subbands and the Nakagami-m parameter. This is known in communications as diversity. Simulations are carried out in various channels to corroborate our theoretical analysis.

Multi-input multi-output (MIMO) scheme in communication systems enhances the system performance and capacity. In wireless communications, cooperative networks can provide spatial diversity gain by creating virtual antenna arrays. In this paper, we consider cooperative networks adopting the differential unitary space time code (DUSTC) which bypasses the channel estimation at the receiver. With high signal-to-noise ratio (SNR), the codeword error rate (CER) of these systems is analyzed using both decode-and- forward and amplify-and-forward relaying protocols. The effect of link quality on the error performance is also investigated. Using these results, the comparison between the STC-based and conventional cooperative networks, i.e, repetition-based cooperative system, is addressed.

In this paper, we propose a simple model-based time-of-arrival (TOA) estimation technique for multi-band orthogonal frequency division multiplexing (MB-OFDM) signals based on the ECMA-368 standard [1]. The proposed technique does not need oversampling at the receiver. The key idea is to minimize the energy leakage from the first channel path due to mis-sampling. We use realistic channel models and assume that no channel statistical information is known at the receiver. In our approach, the multi-band signals are coherently combined in order to improve the resolution of the TOA estimation. We compare the performance of the proposed scheme with the well-known maximum-likelihood based algorithm, namely, space-alternating generalized expectation-maximization (SAGE). Simulations based on the ECMA-368 standard parameters in both IEEE 802.15.3a and IEEE 802.15.4a standard channel models show that our approach outperforms the SAGE algorithm by avoiding the local maxima. Furthermore, we have also shown that our approach can achieve a root mean square error of TOA estimation much smaller than the receiver sampling interval and it is robust to the narrowband interference.

Ultra-Wideband (UWB) link emerges as a promising physical layer candidate for a wide range of wireless networks. In addition to the potential of very high data rate, the ultra-wide bandwidth also provides enhanced flexibility for transceiver designs with variable complexity, which is particularly suitable for networks with heterogeneous nodes. However, to establish physical communication links between nodes with distinct complexity requirements, asymmetric UWB transceivers need to be designed. Towards this objective, we investigate the communications between devices with different ADC/DAC sampling rates and reveal their similarity with multi-input multi- output (MIMO) systems. Then, the signal processing approaches can be readily applied to achieve optimality in terms of both channel throughput and bit error rate (BER). Analysis and simulations corroborate the effectiveness of our transceiver designs.

In this paper, we investigate the resource allocation in a differentially modulated relay network. In addition to the energy optimization, we also consider location optimization to minimize the average symbol error rate (SER). The closed- form solution is derived for the single-relay case, and formulas allowing numerical search are provided for multiple-relay cases. Analytical and simulated comparisons confirm that the optimized systems provide considerable improvement over the unoptimized systems, and that the minimum SER can be achieved via the joint energy-location optimization.

The optimum resource allocation in relay networks has been treated as an important problem to improve the error performance and increase the energy efficiency. In this paper, a two-dimensional resource allocation, i.e., the energy optimization and location optimization, is carried out based on the average symbol error rate (SER) for the system with and without a direct link. Differential modulation which bypasses the channel estimation at the transceiver is investigated using amplify-and- forward protocol for the system with multiple relays. The benefits of optimization are validated by the analytical and simulated comparisons. We also show that the minimum error rate can be achieved via the joint energy-location optimization.

The optimum resource allocation in communication systems is critical to enhance their performance and efficiency. In wireless networks, relay transmissions can enable cooperative diversity by forming virtual antenna arrays. In this paper, we consider resource allocation which minimizes the average system error rate not only by the power optimization, but also by the location optimization for systems with arbitrary number of relays. Differential modulation which bypasses the channel estimation at the receiver is investigated using the decode-and-forward protocol. Analytical and simulated comparisons confirm that the optimized systems provide considerable improvement over un-optimized ones, and that the minimum error rate can be achieved via joint energy-location optimization.

Timing acquisition is one of the major challenges in ultra-wideband (UWB) communications. The timing with dirty template (TDT) approach is an attractive technique for UWB systems, which is characterized by its low complexity and fast acquisition in the data-aided (DA) mode. However, in the non-data-aided (NDA) mode, the performance of this approach degrades due to the random symbol effect. In this paper, we propose to overcome this issue by adopting orthogonal pulse-shape modulation (PSM) proposed in L.B. Michael et al. (2002), (M. Pinchas and B.Z. Bobrovsky, 2003). Our algorithm uses a train of alternating orthogonal Hermite pulses to modulate the transmitted symbols. The application of the TDT approach to the proposed scheme shows an enhancement of synchronization speed in the NDA mode. Simulations confirm performance improvement of TDT with the proposed modulation relative to the original TDT in terms of the mean square error (MSE) and the acquisition probability.

Sensor nodes in wireless sensor networks (WSNs) are often expected to operate on batteries for a long period of time. Battery power-efficiency is a critical factor dictating the lifetime of WSNs. In this paper, we compare two pulse-based modulations, namely pulse position modulation (PPM) and on-off keying (OOK), both of which are suitable for WSNs due to their low complexity transceivers. The comparison is based on a general model that integrates typical WSN transmission and reception modules with realistic nonlinear battery models. We analyze and compare the battery power-efficiency of PPM and OOK using coherent detection, and with bit error rate (BER) and cutoff rate criteria. Our results reveal that in sparse WSNs, PPM is more battery power-efficient. In dense WSNs, OOK outperforms PPM. In addition, the battery power-efficiency of OOK increases as the required cutoff rate decreases.

Ultra-wideband (UWB) radios have received increasing attention recently for their potential to overlay legacy systems, their low-power consumption and low-complexity implementation. Because of the pulsed or duty-cycled nature of the ultra-short transmitted waveforms, timing synchronization and channel estimation pose major, and often conflicting, challenges and requirements. In order to address (or in fact bypass) both tasks, we design and test noncoherent UWB (de)modulation schemes, which remain operational even without timing and channel information. Relying on integrate-and-dump operations of what we term "dirty templates," we first derive a maximum likelihood (ML) optimal noncoherent UWB demodulator. We further establish a conditional ML demodulator with lower complexity. Analysis and simulations show that both can also be applied after (possibly imperfect) timing acquisition. Under the assumption of perfect timing, our noncoherent UWB scheme reduces to a differential UWB system. Our approach can also be adapted to a transmitted reference (TR) UWB system. We show that the resultant robust-to-timing TR (RTTR) approach considerably improves performance of the original TR system in the presence of timing offsets or residual timing acquisition errors

Due to its robustness against channel frequency selectivity and the low-complexity implementation using fast Fourier transform (FFT) circuits, multi-carrier code division multiple access (MC-CDMA) systems are well suited for high data rate wireless multimedia services. However, the multi-user interference (MUI) emerges when the mutual orthogonality among users' codes is violated by the frequency-selective channel propagation, and in the presence of the so-termed near-far effects. In order to mitigate MUI, we present a joint algorithm which combines transmitter power control, receiver array processing and multiuser detection. The joint algorithm exploits both the multipath diversity and the spatial diversity, where the former is provided by the frequency selectivity and the latter is provided by appropriate spacing among the receiver antenna array elements. Simulations confirm the outstanding performance of the joint algorithm in MUI suppression. In addition, we observe that the algorithm provides the best performance when the propagation channel is frequency-selective and the channel fading is independent across different receiver antenna array elements.

In this paper, we introduce a novel differential signaling approach for ultra-wideband (UWB) communications using multiple digital carriers. Unlike the transmitted reference (TR), differential and noncoherent UWB that also bypass explicit channel estimation, our scheme avoids the analog delay element whose on-chip implementation is challenging. Compared with the frequency-shifted reference (FSR) UWB, our multi-carrier differential signaling captures the signal energy more effectively and can achieve the full diversity gain, even in the presence of inter-frame interference. In addition, our approach relies on digital carriers that do not incur any spectrum expansion and can be realized with standard discrete-cosine transform (DCT) or fast Fourier transform (FFT) circuits operating at the frame-rate. Simulations are also carried out to corroborate our theoretical analysis.

In cooperative wireless networks, virtual antenna arrays formed by distributed network nodes can provide cooperative diversity. Obviating channel estimation, differential schemes have long been appreciated in conventional multi-input multi-output (MIMO) communications. However, distributed differential schemes for general cooperative network setups have not been thoroughly investigated. In this paper, we develop and analyze two distributed differential schemes using both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols. For each scheme and relaying protocol combination, we derive the optimum maximum likelihood (ML) decision rule and its low-complexity suboptimum alternative. Simulations confirm that both schemes provide full diversity gain with either DF or AF relaying protocols. In addition, we carry out performance and rate comparisons between the two distributed differential schemes and preliminary investigations on the optimal relay positioning for the DF and AF relaying protocols

In this paper we apply the structural risk minimization (SRM) principle to derive a blind single-input multiple-output (SIMO) channel estimation algorithm, which is robust to channel order overestimation. Specifically, the blind estimation is formulated as a support vector regression (SVR) problem in which the channel coefficients are the Lagrange multipliers of the dual problem. In this paper, we show that the SRM principle pushes to zero the small leading and trailing terms of the channel impulse response even when its order is highly overestimated. The main drawback of this approach is the high computational cost of the resulting quadratic programming (QP) problem. To alleviate this, in this paper we propose to use a simple and fast algorithm called the Adatron to solve the QP problem. Simulation results are provided to demonstrate the performance of our channel estimator.

Low-duty-cycle Ultra-wideband (UWB) radios have the potential to provide low-probability of detection (LPD) communications with low-power and low-complexity implementation. Pulse position modulation (PPM) is a prevalent scheme for UWB radios since it can further lower the transmitter complexity by avoiding pulse negation. However, the position shifts of impulse-like UWB waveforms, together with the severe frequency-selectivity of the propagation channels, aggravate the difficulty and complexity of timing synchronization and channel estimation. To circumvent both of these challenging tasks, we develop a differential encoder and its corresponding noncoherent demodulator for PPM-UWB signals. Relying on integrate-and-dump operations of "dirty" templates, our designs are operational when the timing offset and channel information both remain unknown.

To synchronize ultra-wideband (UWB) signals with pulse position modulation (PPM), we develop and test timing algorithms in both data-aided and nondata-aided modes based on a novel synchronization criterion that we term timing with dirty templates (TDT). Using symbol-rate integrate-and-dump operations, our TDT-based algorithms for PPM-UWB signals remain operational in practical UWB settings. In addition, our algorithms ensure rapid synchronization by collecting multipath energy. Moreover, for the data-aided mode, we design a simple training pattern which not only expedites the synchronization, but also enables timing in a (possibly ad hoc and asynchronous) multiuser environment. Simulations and comparisons are also performed to corroborate our theoretical analysis.

Emerging indoor technologies including wireless multimedia and personal area networks (WPANs) entail high-rate systems capable of supporting multiple users (piconets) with variable rates. These requirements motivate the design of multiband (MB) ultra-wideband (UWB) radios for their simplicity in handling pronounced frequency selectivity, agility in coping with interference, scalability in providing multirate operation, and their potentially low cost. Relative to baseband UWB radios, MB-UWB systems have gained popularity in the IEEE standards for short-range wireless links. However, multiple-access (MA) schemes must be designed carefully to harness the diversity benefits provided by the MB-UWB propagation, in a spectrally efficient manner. To this end, we introduce a crossband flexible UWB MA scheme for multipiconet WPANs. The resultant design that we term FLEX-UWB offers resilience to multiuser interference, can conveniently accommodate various spreading alternatives, enables full multipath diversity, and can effect scalable spectral efficiency (from low to medium and high data rates). Simulations confirm the merits of FLEX-UWB radios in comparison with various alternatives

The desire for untethered communications spurs increasing interests in wireless sensor networks (WSN) and multimedia communications, including wireless personal area networks (WPAN) with variable rates over a short range. Emerging as a promising candidate for these applications, ultra-wideband (UWB) is gaining increasing attention. However, existing research on UWB overlooks one critical issue in enabling seamless network communications: the heterogeneity among network nodes. This paper addresses this issue by designing simple transmission and reception schemes between network nodes with different sampling rates. Our novel communication schemes designed for asymmetric transceiver pairs are readily applicable to achieve seamless communications among heterogeneous network nodes with multi-access capability. We also establish a general system model, which facilitates further delineation and optimization of complexity-performance-rate tradeoffs.

Ultra-wideband (UWB) technology for indoor wireless communications promises high data rates with low-complexity transceivers. Rapid timing synchronization constitutes a major challenge in realizing these promises. In this paper, we establish a novel synchronization criterion that we term "timing with dirty templates" (TDT), based on which we develop and test timing algorithms in both data-aided (DA) and nondata-aided modes. For the DA mode, we design a training pattern, which turns out to not only speed up synchronization, but also enable timing in a multiuser environment. Based on simple integrate-and-dump operations over the symbol duration, our TDT algorithms remain operational in practical UWB settings. They are also readily applicable to narrowband systems when intersymbol interference is avoided. Simulations confirm performance improvement of TDT relative to existing alternatives in terms of mean square error and bit-error rate.

High data rates give rise to frequency-selective propagation effects. Space-time multiplexing and/or coding offer attractive means of combating fading and boosting capacity of multi-antenna communications. As the number of antennas increases, channel estimation becomes challenging because the number of unknowns increases, and the power is split at the transmitter. Optimal training sequences have been designed for flat-fading multi-antenna systems or for frequency-selective single transmit antenna systems. We design a low-complexity optimal training scheme for block transmissions over frequency-selective channels with multiple antennas. The optimality in designing our training schemes consists of maximizing a lower bound on the ergodic (average) capacity that is shown to be equivalent to minimizing the mean square error of the linear channel estimator. Simulation results confirm our theoretical analysis that applies to both single- and multicarrier transmissions.

Ultra-wideband (UWB) radio is gaining increasing attention thanks to its attractive features that include low-power low-complexity baseband operation and ample multipath diversity. Realization of its potential, however, faces the challenge of low-complexity high-performance timing acquisition. In this paper, we develop a blind timing acquisition algorithm for frame-level synchronization. Relying on simple integrate-and-dump operations over one symbol duration, our algorithm exploits the rich multipath diversity enabled by UWB transmissions. It outperforms existing blind algorithms and has comparable performance to data-aided ones. Equally attractive is its applicability to UWB links with or without time hopping (TH), over frequency-flat or multipath channels. It is also worth stressing that our "dirty" template based scheme is able to achieve timing synchronization at any desirable resolution and is readily applicable to non-UWB systems, so long as intersymbol interference is absent.

Demand for high data rate leads to frequency-selective propagation effects, whereas carrier frequency-offsets and Doppler effects induced by mobility introduce time-selectivity in wireless links. These fading channels, once acquired, offer joint multipath-Doppler diversity gains. In addition, space-time multiplexing and/or coding offer attractive means of combating fading, and boosting capacity of multi-antenna communications. As the number of antennas increases, channel estimation becomes challenging because the number of unknowns increases, and the power is split at the transmitter. Optimal training sequences have so far been designed for flat-fading and frequency-selective multi-antenna systems. In this paper, we design a low complexity optimal training scheme for block transmissions over time-and frequency (a.k.a. doubly)- selective channels with multiple antennas. The optimality in designing our training schemes consists of maximizing a lower bound on the ergodic (average) capacity that is shown to be equivalent to minimizing the mean-square error of the linear channel estimator. Simulation results confirm our theoretical analysis which applies to both single- and multi-carrier transmissions.

Ultra-wideband (UWB) transmissions have well-documented advantages for low-power, peer-to-peer, and multiple-access communications. Space-time coding (STC), on the other hand, has gained popularity as an effective means of boosting rates and performance. Existing UWB transmitters rely on a single antenna, while ST coders have mostly focused on digital linearly modulated transmissions. In this paper, we develop ST codes for analog (and possibly nonlinearly) modulated multiantenna UWB systems. We show that the resulting analog system is able to collect not only the spatial diversity, but also the multipath diversity inherited by the dense multipath channel, with either coherent or noncoherent reception. Simulations confirm a considerable increase in both bit-error rate performance and immunity against timing jitter, when wedding STC with UWB transmissions.

Ultrawideband (UWB) transmissions induce pronounced frequency-selective fading effects in their multipath propagation. Multipath diversity gains can be collected to enhance performance, provided that the underlying channel can be estimated at the receiver. To this end, we develop a novel pilot waveform assisted modulation (PWAM) scheme that is tailored for UWB communications. We select our PWAM parameters by jointly optimizing channel estimation performance and information rate. The resulting transmitter design maximizes the average capacity, which is shown to be equivalent to minimizing the mean-square channel estimation error, and thereby achieves the Crame/spl acute/r-Rao lower bound. Application of PWAM to practical UWB systems is promising because it entails simple integrate-and-dump operations at the frame rate. Equally important, it offers a flexible UWB channel estimator, capable of striking desirable rate-performance tradeoffs depending on the channel coherence time.

Rapid timing acquisition with low complexity constitutes a major challenge in realizing the high potential ultra wideband (UWB) technology promises for indoor wireless communications. We design and test such a timing acquisition algorithm based on training symbols. Relying on a judiciously designed preamble, our algorithm achieves clock synchronization at the receiver using simple integrate-and-dump operations over the symbol duration. Analysis and simulations confirm that with a small number of training symbols, our scheme brings bit-error-rate (BER) performance close to that corresponding to the case with perfect timing.

Low-complexity rapid timing acquisition constitutes a major challenge in realizing the high potential that ultra-wideband (UWB) wireless technology promises for indoor communications. We derive and test two such timing acquisition algorithms which capitalize on the cyclostationarity that is naturally present in UWB transmissions. Our novel schemes are blind, they do not require multiple antennas or oversampling, and rely on frame-rate sampling which reduces complexity and acquisition delay considerably.

Transmitting digital information using ultra-short pulses, impulse radio (IR) has received increasing interest for multiple access (MA). Analog IRMA utilizes pulse-position modulation (PPM) and random time-hopping codes to mitigate inter-symbol interference (ISI) and suppress multiuser interference (MUI) statistically. We develop an all-digital IRMA scheme that relies on block-spreading and judiciously designed transceiver pairs to eliminate MUI deterministically, and regardless of ISI multipath effects.

Transmitting digital information using ultra-short pulses, impulse radio (IR) has received increasing interest for multiple access (MA). When IRMA systems have to operate in dense multipath environments, the multiple user interference (MUI) and intersymbol interference (ISI) induced, adversely affect system capacity and performance. Analog IRMA utilizes pulse position modulation (PPM) and random time-hopping codes to mitigate ISI and suppress MUI statistically. We develop an all-digital IRMA scheme that relies on multistage block-spreading (MS-BS), and judiciously designed transceiver pairs to eliminate MUI deterministically, and regardless of ISI multipath effects. Our proposed MS-BS-IRMA system can accommodate a large number of users and is capable of providing different users with variable transmission rates, which is important for multimedia applications. Unlike conventional IRMA systems, MS-BS-IRMA exhibits no degradation in bit-error rate performance, as the number of users increases.