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.

This paper proposes a novel sixth-generation (6G) multi-modal intelligent vehicle-to-vehicle (V2V) channel model from light detection and ranging (LiDAR) point clouds based on Synesthesia of Machines (SoM). To explore the mapping relationship between physical environment and electromagnetic space, we construct a new V2V high-fidelity mixed sensing-communication integration simulation dataset with different vehicular traffic densities (VTDs). Based on the constructed dataset, we develop a novel scatterer recognition (ScaR) algorithm utilizing neural network SegNet to recognize scatterer spatial attributes from LiDAR point clouds via SoM. Subsequently, the developed ScaR algorithm is incorporated into channel modeling to recognize scatterers in a physically informed manner. We further distinguish recognized scatterers into dynamic and static scatterers based on LiDAR point cloud features. The aforementioned procedures determine parameters related to dynamic and static scatterers, e.g., distance, angle, and number, enabling the channel model to generate the corresponding channel impulse responses (CIRs). Through the ScaR algorithm, dynamic and static scatterers change with the variation of LiDAR point clouds over time, which precisely models channel non-stationarity and consistency under different VTDs. Some important channel statistical properties, such as time-frequency correlation function (TF-CF) and Doppler power spectral density (DPSD), are obtained and analyzed. Simulation results match well with ray-tracing (RT)-based results, thus demonstrating the necessity of exploring the mapping relationship and the utility of the proposed model.

With the evolution of 6G networks, modern communication systems are facing unprecedented demands for high reliability and low latency. However, conventional transport protocols are designed for bit-level reliability, failing to meet the semantic robustness requirements. To address this limitation, this paper proposes a novel Semantic Information Transport Protocol (SITP), which achieves TCP-level reliability and UDP level latency by verifying only packet headers while retaining potentially corrupted payloads for semantic decoding. Building upon SITP, a cross-layer analytical model is established to quantify packet-loss probability across the physical, data-link, network, transport, and application layers. The model provides a unified probabilistic formulation linking signal noise rate (SNR) and packet-loss rate, offering theoretical foundation into end-to-end semantic transmission. Furthermore, a cross-image feature interleaving mechanism is developed to mitigate consecutive burst losses by redistributing semantic features across multiple correlated images, thereby enhancing robustness in burst-fade channels. Extensive experiments show that SITP offers lower latency than TCP with comparable reliability at low SNRs, while matching UDP-level latency and delivering superior reconstruction quality. In addition, the proposed cross-image semantic interleaving mechanism further demonstrates its effectiveness in mitigating degradation caused by bursty packet losses.

Dynamic multi-robot systems face the intricate multi-agent pathfinding (MAPF) challenge as a pivotal hurdle. It has been uncovered through recent research that tackling MAPF issues can be effectively approached through reinforcement learning, offering a fully decentralized solution. Nonetheless, the escalation in the scale of the multi-robot system introduces sample inefficiency, posing a significant barrier for learning-based methods. We introduce a novel hierarchical reinforcement learning architecture aimed at addressing large-scale MAPF by leveraging spatial and temporal abstraction. This approach enhances exploration efficiency by recognizing intermediate rewards. The framework employs an upper-tier controller that segments the map into linked regions, thereby streamlining the optimization of agents' paths on a regional basis to foster improved global outcomes. To tackle each segmented problem, a subordinate-level controller is designed, which integrates heuristic directions and an inter-agent communication strategy. The merit of our methodology is confirmed by empirical experiments, showcasing advancements over prevailing methods in success rates and reduction in completion time across test scenarios of various magnitudes.

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.

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.

In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-uncrewed aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model.

In this paper, a novel adaptive near-field channel model with an extremely large-scale multiple-input multiple-output (XL-MIMO) uniform planar array (UPA) is proposed for sixth generation (6G) multiple-uncrewed aerial vehicle (multi-UAV) cooperative communications. In the proposed model, a novel selective near-field area (SNA) of the XL-MIMO UPA, where the transmission is regarded as spherical wavefront, is proposed to balance complexity and accuracy of near-field channel modeling. To jointly model the non-stationarity on the array, and in the space, time, and frequency domains, an adaptive UPA-UAV-time-frequency non-stationary algorithm is developed, which mimics the non-stationarity on the XL-MIMO UPA for the first time. The channel parameters related to the three-dimensional (3D) continuously arbitrary trajectory and self-rotation of multi-UAVs are also taken into account in the proposed model and the developed algorithm. To explore the channel statistics and validate the proposed model, a new XL-MIMO-UPA-to-multi-UAV channel dataset at low-terahertz (low-THz) frequency band under National Stadium scenario is built. Key UPA-to-multi-UAV channel statistics, such as the array-space-time-frequency correlation function (ASTF-CF), time stationary interval (TSI), Doppler power spectral density (DPSD), and singular value spread (SVS), are obtained. The close agreement between the simulation results and ray-tracing results in National Stadium scenario is achieved, demonstrating the accuracy of proposed channel model.

Integrated sensing and communications (ISAC) has become increasingly crucial in next-generation wireless networks. Leveraging the reliable line-of-sight (LoS) links and mobility of unmanned aerial vehicles (UAVs), UAV-assisted ISAC has attracted significant attention. Different from the previous UAV-ISAC scenarios with single target or overlapping users and targets, we investigate ISAC in a more general multi-UAV network with independent multiple communication users and multiple sensing targets, where the UAVs provide downlink communications to the users while sensing the targets. Additionally, we consider the complicated interference management among the UAVs to further enhance the network’s practicality. Such a scenario presents a new challenge for the joint optimization problem in terms of the UAV trajectories, the user association, the target association, and the power control. Furthermore, since the existing single-objective and weighted optimization approaches may result in potential performance loss and optimization biases, we propose a dual-objective model to further optimize ISAC, aiming for a better tradeoff between the communication and sensing performance. Specifically, we propose an efficient sensing and communication dual-objective multi-UAV optimization algorithm (SC-DO-MUOA) to maximize communication rate and simultaneously minimize sensing Cramér-Rao bound (CRB). Simulation results demonstrate that our proposed SC-DO-MUOA outperforms various baselines in both communication and sensing performance.

To support cooperative perception (CP) of networked mobile agents in dynamic scenarios, the efficient and robust transmission of sensory data is a critical challenge. Deep learning-based joint source-channel coding (JSCC) has demonstrated promising results for image transmission under adverse channel conditions, outperforming traditional rule-based codecs. While recent works have explored to combine JSCC with the widely adopted multiple-input multiple-output (MIMO) technology, these approaches are still limited to the discrete-time analog transmission (DTAT) model and simple tasks. Given the limited performance of existing MIMO JSCC schemes in supporting complex CP tasks for networked mobile agents with digital MIMO communication systems, this paper presents a Synesthesia of Machines (SoM)-based task-driven MIMO system for image transmission, referred to as SoM-MIMO. By leveraging the structural properties of the feature pyramid for perceptual tasks and the channel properties of the closed-loop MIMO communication system, SoM-MIMO enables efficient and robust digital MIMO transmission of images. Experimental results have shown that compared with two JSCC baseline schemes, our approach achieves average mAP improvements of 6.30 and 10.48 across all SNR levels, while maintaining identical communication overhead.

Wireless image transmission underpins diverse networked intelligent services and becomes an increasingly critical issue. Existing works have shown that deep learning-based joint source-channel coding (JSCC) is an effective framework to balance image transmission fidelity and data overhead. However, these studies oversimplify the communication system as a mere pipeline with noise, failing to account for the time-varying dynamics of wireless channels and concrete physical-layer transmission process. To address these limitations, we propose a Synesthesia of Machines (SoM)-empowered Dynamic Channel Adaptive Transmission (DCAT) scheme, designed for practical implementation in real communication scenarios. Building upon the Swin Transformer backbone, our DCAT demonstrates robust adaptability to time-selective fading and channel aging effects by effectively utilizing the physical-layer transmission characteristics of wireless channels. Comprehensive experimental results confirm that DCAT consistently achieves superior performance compared with baseline approaches across all conditions. Furthermore, our neural network architecture exhibits high scalability due to its interpretable design, offering substantial potential for cost-efficient deployment in practical applications.

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.

High complexity in precoding design for frequency division duplex systems necessitates streamlined solutions. Guided by Synesthesia of Machines (SoM), this paper introduces a heterogeneous multi-vehicle, multi-modal sensing aided precoding scheme within a vertical federated learning (VFL) framework, which significantly minimizes pilot sequence length while optimizing the system's sum rate. We address the challenges posed by local data heterogeneity due to varying on-board sensor configurations through a meticulously designed VFL training procedure. To extract valuable channel features from multimodal sensing, we employ three distinct data preprocessing methods that convert raw data into informative representations relevant for precoding. Additionally, we propose an online training strategy based on VFL framework, enabling the scheme to adapt dynamically to fluctuations in user numbers. Numerical results indicate that our approach, utilizing short pilot sequences, closely approximates the performance of traditional optimization methods with perfect channel state information.

We propose a continuous gathering scheme based on the swept volume to address the challenges involved in planning a tethered robot duo to efficiently collect marine debris. Specifically, we model the tethered robot duo by constructing a double-layer U-shape, and then apply an object-aware optimization approach that leverages the swept volume signed distance field (SVSDF) to guide trajectory optimization, promoting complete object collection while maintaining a continuous and collision-free gathering motion. Existing algorithms either fail to fully address key challenges, such as assuming an unrealistically infinite tether length or incurring high computational costs. In contrast, our proposed method, by adopting the double-layer U-shape technique, effectively manages tether length constraints and preserves the tether shape, ensuring feasible collection. By utilizing the SVSDF technique to guide the trajectory optimization process, we maximize the swept coverage of objects while minimizing that of obstacles. This enables complete object coverage, avoids collisions, and prevents the tether from becoming trapped by obstacles during the collection process. Moreover, we propose a set of metrics for this gathering planning problem and validate the generated trajectories in simulation, using a collision-free multi-UAV information-gathering approach to efficiently estimate the target area. Simulations demonstrate that our proposed method achieves superior, resolution-independent gathering performance compared to existing algorithms.

In this paper, a novel multi-modal uncrewed aerial vehicle (UAV)-to-ground channel model is proposed for sixth-generation (6G) intelligent sensing-communication integration, where communication information and sensing data, i.e., light detection and ranging (LiDAR) point cloud data, are integrated. To thoroughly explore the mapping relationship between the electromagnetic space and the physical environment in UAV-to-ground scenarios, a new intelligent sensing-communication integrated UAV-to-ground dataset with channel information and LiDAR point clouds is constructed in an urban scenario under low, medium, and high vehicular traffic density (VTD) conditions. By detecting dynamic and static objects in the physical environment with LiDAR point clouds, scatterers in the electromagnetic space are divided into dynamic and static scatterers. In addition, the parameters of dynamic and static scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different VTDs in the physical environment. In the proposed model, time non-stationarity and consistency along time axis and the frequency non-stationarity at different frequencies are simultaneously mimicked. The channel statistical properties, such as time-frequency correlation function and Doppler power spectral density (DPSD), are derived. Since simulation results match ray-tracing (RT) results well, the accuracy of the proposed multi-modal UAV-to-ground channel model in urban scenarios is validated.

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.

Point cloud-based 3-D object detection is a significant and critical issue in numerous applications. While most existing methods attempt to capitalize on the geometric characteristics of point clouds, they neglect the internal semantic properties of point and the consistency between the semantic and geometric clues. We introduce a semantic consistency (SC) mechanism for 3-D object detection in this article, by reasoning about the semantic relations between 3-D object boxes and its internal points. This mechanism is based on a natural principle: the semantic category of a 3-D bounding box should be consistent with the categories of all points within the box. Driven by the SC mechanism, we propose a novel SC network (SCNet) to detect 3-D objects from point clouds. Specifically, the SCNet is composed of a feature extraction module, a detection decision module, and a semantic segmentation module. In inference, the feature extraction and the detection decision modules are used to detect 3-D objects. In training, the semantic segmentation module is jointly trained with the other two modules to produce more robust and applicable model parameters. The performance is greatly boosted through reasoning about the relations between the output 3-D object boxes and segmented points. The proposed SC mechanism is model-agnostic and can be integrated into other base 3-D object detection models. We test the proposed model on three challenging indoor and outdoor benchmark datasets: ScanNetV2, SUN RGB-D, and KITTI. Furthermore, to validate the universality of the SC mechanism, we implement it in three different 3-D object detectors. The experiments show that the performance is impressively improved and the extensive ablation studies also demonstrate the effectiveness of the proposed model.

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.

The performance of the sixth-generation (6G) vehicle-to-vehicle (V2V) communication systems will be significantly improved, but they are also confronted with many technical challenges like massive terminal access and low transmission delay. A fundamental and difficult problem is how to establish an intelligent 6G V2V channel model with high accuracy, low complexity, and generality. In this paper, we propose a dynamic V2V channel model in complicated urban scenarios driven by effective environment information, channel data, and physical statistics. To begin with, the bimodal features representing the environment information are extracted from vector maps by a set of fully automatic algorithms. Heuristic graph datasets are constructed using features coupled with locations and ground-truth large-scale parameters (LSPs), i.e., the channel data reflecting realistic statistical properties. Then, we design a novel network based on attention-assisted graph convolution and pooling layers, which enables us to perform prediction for path loss, delay spread, and angular spreads. Compared with convolutional neural networks-based methods, the proposed LSPs prediction model can reduce both the number of trainable parameters and the FLOPs by two orders of magnitude with higher accuracy. Moreover, the predicted LSPs are next fed into multi-link V2V simulations based on physical statistics. Dynamic channel impulse response generation is implemented based on a spatially consistent geometrical modeling methodology. Eventually, we validate our model by comparing key channel characteristics with those of the ground-truth values, and better agreements are shown compared with existing methods.

Integrated sensing and communication (ISAC) technology is essential for supporting vehicular networks. However, the communication channel in this scenario exhibits time variations, and the potential targets may move rapidly, resulting in double dynamics. This nature poses a challenge for real-time precoder design. While optimization-based solutions are widely researched, they are complex and heavily rely on perfect channel-related information, which is impractical in double dynamics. To address this challenge, we propose using constrained deep reinforcement learning to facilitate dynamic updates to the ISAC precoder. Additionally, the primal dual-deep deterministic policy gradient and Wolpertinger architecture are tailored to efficiently train the algorithm under complex constraints and varying numbers of users. The proposed scheme not only adapts to the dynamics based on observations but also leverages environmental information to enhance performance and reduce complexity. Its superiority over existing candidates has been validated through experiments.

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.

While autonomous intersection management (AIM) emerges to facilitate signal-free scheduling for connected and autonomous vehicles (CAVs), several challenges arise for planning secure and swift trajectories. Existing works mainly focus on addressing the challenge of multi-CAV interaction complexity. In this context, multi-agent reinforcement learning-based (MARL) methods exhibit higher scalability and efficiency compared with other traditional methods. However, current AIM methods omit discussions on the practical challenge of CAV heterogeneity. As CAVs exhibit different dynamics features and perception capabilities, it is inappropriate to adapt identical control schemes. Besides, existing MARL methods that lack heterogeneity adaptability may experience a performance decline. In response, this paper exploits MARL to model the decision-making process among CAVs and proposes a novel heterogeneous-agent attention gated trust region policy optimization (HAG-TRPO) method. The proposed method can accomplish more effective and efficient AIM with CAV discrepancies by applying a sequential update schema that boosts the algorithm adaptability for MARL tasks with agent-level heterogeneity. In addition, the proposed method utilizes the attention mechanism to intensify vehicular cognition on disordered ambience messages, as well as a gated recurrent unit for temporal comprehension on global status. Numerical experiments verify that our method results in CAVs passing at the intersection with fewer collisions and faster traffic flow, showing the superiority of our method over existing benchmarks in terms of both traffic safety and efficiency.

Integrated Sensing and Communication (ISAC) emerges as a promising technology for BSG/6G, particularly in the millimeter-wave (mmWave) band. However, the widespread adoption of hybrid architecture in mmWave systems compromises multiplexing gain due to limited radio-frequency chains, resulting in mediocre performance when embedding sensing functionality. To avoid sacrificing the spectrum efficiency in hybrid structures while addressing performance bottlenecks in its extension to ISAC, we present an optimized beam pattern modulation-embedded ISAC (BPM-ISAC). BPM-ISAC applies index modulation over beamspace by selectively activating communication beams, aiming to minimize sensing beampattern mean squared error (MSE) under communication MSE constraints through dedicated hybrid transceiver design. Optimization involves the analog part through a min-MSE-based beam selection algorithm, followed by the digital part using an alternating optimization algorithm. Convergence and asymptotic pairwise error probability (APEP) analyses accompany numerical simulations, validating its overall enhanced ISAC performance over existing alternatives.

Navigating challenging conditions characterized by stringent bandwidth constraints and noisy channels, Distributed Sensor Networks (DSNs) demand robust feature compression techniques for accurate data fusion. The advent of Supervised Contrastive Learning with Mask-Sparsity (SCL-MS) presents a neural approach to feature compression, offering compact and semantically aligned continuous representations tailored for DSNs. However, the unconventional structure of SCL-MS poses a challenge for traditional neural scalar quantization. This paper introduces an innovative quantization method, Doubly Progressive Quantization, specifically crafted for SCL-MS. Experiments on distribution image classification tasks show that with marginal reduction in accuracy, the proposed method can achieve a compression gain of 37 times compared with the baseline without quantization. In addition, the proposed method is shown to outperform decision-level data fusion in terms of noise-resilience and node-scaling.

Semantic communications is a key enabler of the Internet of Things (IoT). By focusing on the semantic meaning of data rather than bit-level recovery, it allows intelligent agents to communicate necessary information at much lower rates. A promising technique for semantic communications is discrete neural representation learning (DNRL). The main idea is to learn discrete symbols from low-level, high dimensional sensory data, such that each symbol is grounded to a meaningful pattern in the sensory domain. This paper proposes a DNRL scheme that integrates three mechanisms into a coherent framework: contrastive learning, sparse coding, and neural index quantization. The proposed scheme is applied to public image datasets for lossy image compression with a downstream classification task. Results show that the proposed approach produces a highly compact continuous latent representation and a semantic discrete representation, with marginal degradation to the classification accuracy. The interpretability and consistency of the learned sub-symbolic discrete representations are validated by experiments of neural-net dissection, neural-net visualization, and MaxAmp-K classification test, a concept that we propose to evaluate classification performance of extremely compressed signals. Finally, the discrete representations are shown to be useful in rate-adaptive distributed sensing applications at the low-to-medium signal-to-noise ratios (SNR).

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.

In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed LA-GBSM approach, a new millimeter wave (mmWave) channel model for sixth-generation (6G) vehicular intelligent sensing-communication integration is proposed, which can support the design of intelligent transportation systems (ITSs). The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time. Specifically, by detecting dynamic vehicles and static buildings/trees through LiDAR point clouds via machine learning, scatterers are divided into static and dynamic scatterers. Furthermore, statistical distributions of parameters, e.g., distance, angle, number, and power, related to static and dynamic scatterers are quantified under high, medium, and low VTD conditions. To mimic channel non-stationarity and consistency, based on the quantified statistical distributions, a new visibility region (VR)-based algorithm in consideration of newly generated static/dynamic scatterers is developed. Key channel statistics are derived and simulated. By comparing simulation results and ray-tracing (RT)-based results, the utility of the proposed LA-GBSM is verified.

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.

Scene graph generation is a significant and challenging task for scene understanding. Most existing methods are confined to the 2D space (i.e. images) or additional use of segmentation information, while neglecting the richer spatial and geometric information of 3D space. In this paper, we propose a novel method to generate scene graphs from 3D point clouds. Specifically, our model consists of three parts: a point feature extraction backbone, a box head, and a relation head. The feature extraction backbone extracts base features directly from raw point clouds, and the box head produces detected 3D bounding boxes. Final 3D scene graphs are obtained from the relation head which takes the extracted features and 3D boxes as inputs. We also design a point RoI module which sequentially processes points inside 3D boxes with a bidirectional LSTM. To further leverage the geometric characteristics of point clouds, we propose a location attention module which learns the influence of relative locations between objects. We introduce the RelationScanNet dataset with densely annotated semantic and geometric relationships, which extends one of the most widely used dataset ScanNetV2 in 3D indoor scene understanding. We test the proposed method on the RelationScanNet dataset and 3DSSG dataset. The results prove the strength of our method.

This paper proposes a novel three-dimensional (3D) non-stationary multiple-unmanned aerial vehicle (multi-UAV) cooperative channel model for sixth generation (6G) wireless communication systems. In the proposed multi-UAV cooperative channel model, the channel impulse response (CIR) of sub-channel between each UAV and ground station (GS) is calculated, including line-of-sight (LoS), non-LoS (NLoS), as well as ground reflection transmissions. The relative distance and transmission relationship among sub-channels between multi-UAVs and GS are further modeled and analyzed. To simultaneously capture the cooperative non-stationarity of multi-UAV cooperative channels in space and time domains, a novel cooperation-based space-time (S-T) non-stationary algorithm is developed based on the birth-death (BD) process for the first time. Meanwhile, the UAV-related parameters, e.g., the height and 3D movement of UAVs, are considered. Some important channel statistical properties, such as space-time-frequency correlation function (STF-CF), Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are obtained. Based on the simulation results, multi-UAV cooperative channel statistical properties are analyzed. Finally, the simulation results match well with the ray-tracing results, which validates the accuracy of the proposed multi-UAV channel model.

In this paper, a mixed-bouncing based channel model with cooperative space-array-time (S-A-T) consistency and space-array-time-frequency (S-A-T-F) non-stationarity is proposed for sixth generation (6G) multiple-unmanned aerial vehicle (multi-UAV) cooperative communication systems with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies. To model the transmission propagation in multi-UAV integrated channels more accurately, the single-bouncing transmissions and multi-bouncing transmissions in multi-UAV integrated channels are simultaneously modeled and quantified by a cooperative cluster density index for the first time. Meanwhile, the transmissions through line-of-sight (LoS), ground reflection, single-bouncing, and multi-bouncing are captured. To jointly mimic cooperative S-A-T consistency and S-A-T-F non-stationarity in the integrated scattering environment (SE), a new cooperative consistent and non-stationary modeling algorithm is developed based on the frequency-dependent path gain, visibility region (VR), and birth-death (BD) survival probability. The channel parameters related to multi-UAVs are also taken into account in the developed algorithm. The corresponding multi-UAV cooperative channel statistical properties are derived by taking mixed-bouncing transmission into account. Meanwhile, the accuracy of the mixed-bouncing based multi-UAV integrated channel model with cooperative S-A-T consistency and S-A-T-F non-stationarity is validated as simulation results match well with ray-tracing results.

Unmanned aerial vehicle (UAV) communications have emerged as a promising solution for future full coverage networks. To further meet the massive connection demands in beyond-fifth-generation (B5G) systems, in this paper, we propose to employ non-orthogonal multiple access (NOMA) in both uplink and downlink relaying hops in an amplify-and-forward (AF) based UAV relaying network with multiple source-destination (SD) user pairs. Specifically, taking NOMA design in both hops into a joint consideration in relaying networks is investigated for the first time and presents a new challenge for the joint optimization problem in terms of the deployment of the UAV relay, the two-hop NOMA user grouping, and the transmit power control for both the source users and the UAV. To maximize the system sum rate, we propose an efficient joint uplink and downlink NOMA-based relay (JUDNR) scheme to decompose the problem into three sub-problems and adopt the alternating optimization (AO) method to iteratively obtain a promising solution. In detail, we provide a joint NOMA groups design of both the uplink and downlink and propose a novel recursive two-hop NOMA grouping (RTNG) algorithm to efficiently group users. Simulation results demonstrate that our proposed JUDNR scheme outperforms various baselines in terms of sum rate.

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.

In this paper, a novel three-dimensional (3D) irregular shaped geometry-based stochastic model (IS-GBSM) is proposed for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) channels. To investigate the impact of vehicular traffic density (VTD) on channel statistics, clusters are divided into static clusters and dynamic clusters, which are further distinguished into static/dynamic single/twin-clusters to capture the mixed-bouncing propagation. A new method, which integrates the visibility region and birth-death process methods, is developed to model space-time-frequency (S-T-F) non-stationarity of V2V channels with time-space (T-S) consistency. The continuously arbitrary vehicular movement trajectory (VMT) and soft cluster power handover are modeled to further ensure channel T-S consistency. From the proposed model, key channel statistics are derived. Simulation results show that S-T-F non-stationarity of channels with T-S consistency is modeled and the impacts of VTD and VMT on channel statistics are analyzed. The generality of the proposed model is validated by comparing simulation results and measurement/ray-tracing (RT)-based results.

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.

Unmanned aerial vehicle (UAV) relays are increasingly playing a significant role with their advantages of quick establishment for emergency communication, expansion of communication coverage, and increase of system capacity. As demand for massive simultaneous connections is ever-increasing, UAV relaying communication aided by non-orthogonal multiple access (NOMA) can more efficiently utilize spectrum resources to further improve capacity. To fully unlock the potential of NOMA in complex and practical relaying networks, we investigate a pioneering two-hope NOMA UAV relaying network with multi-pair separated users, where the uplink and downlink resources are jointly considered. For the purpose of enhancing the efficiency of our challenging UAV relaying, a developed joint uplink and downlink NOMA relay (JUDNR) scheme is proposed, which jointly optimizes UAV location, user grouping, and power control. Specifically, based on alternating-optimization (AO) method, we iteratively solve non-convex sub-problems by successive-convex-approximation (SCA) algorithm, a novel recursive reformulation grouping (RRG) algorithm based on fractional programming, and difference-of-convex (DC) algorithm, respectively. Simulation results demonstrate that our scheme can significantly enhance the system sum rate performance compared with reference strategies.

State-of-the-art multi-agent collision avoidance algorithms face limitations when applied to cluttered public environments, where obstacles may have a variety of shapes and structures. The issue arises because most of these algorithms are agent-level methods. They concentrate solely on preventing collisions between the agents while the obstacles are handled merely out-of-policy. Obstacle-aware policies output an action considering both agents and obstacles. Current obstacle-aware algorithms, mainly based on Lidar sensor data, struggle to handle collision avoidance around complex obstacles. To resolve this issue, this paper investigates how to find a better way to travel around diverse obstacles. In particular, we present a global map assisted collision avoidance algorithm which, following the lead of a high-level goal guide and using an obstacle representation called distance map, considers other agents and obstacles simultaneously. Moreover, our model can be loaded into each agent individually, making it applicable to large maps or more agents. Simulation results indicate that our model outperforms the state-of-the-art algorithms, showing in scenarios with complex obstacles. We present a notion for incorporating global information in decentralized decision-making, along with a method for extending agent-level algorithms to cluttered environments in real-world scenarios.

The Multi-Agent Path Finding (MAPF) problem is a critical challenge in dynamic multi-robot systems. Recent studies have revealed that multi-agent reinforcement learning (MARL) is a promising approach to solving MAPF problems in a fully decentralized manner. However, as the size of the multi-robot system increases, sample inefficiency becomes a major impediment to learning-based methods. This paper presents a hierarchical reinforcement learning (HRL) framework for large-scale multi-agent path finding, featuring applying spatial and temporal abstraction to capture intermediate reward and thus encourage efficient exploration. Specifically, we introduce a meta controller that partitions the map into interconnected regions and optimizes agents' region-wise paths towards globally better solutions. Additionally, we design a lower-level controller that efficiently solves each sub-problem by incorporating heuristic guidance and an inter-agent communication mechanism with RL-based policies. Our empirical results on test instances of various scales demonstrate that our method outperforms existing approaches in terms of both success rate and makespan.

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.

Abstract While the terrestrial base stations (TBSs) in the fifth‐generation (5G) network provide high throughput for the conventional terrestrial users (TUs), it is still challenging for the network to support massive TUs and unmanned aerial vehicles (UAVs) simultaneously due to the complicated air–ground channel and severe interference. In this paper, the deployment of a high‐altitude platform (HAP) as a supplement for the terrestrial networks, in which the HAP and TBSs serve TUs and UAVs simultaneously in a joint manner, is studied. The novel network has two challenges. First, the deployment of the HAP, which is a new degree of freedom, should be optimized considering the terrestrial network. Second, the channel of the joint HAP and TBS network that serves multiple TUs and UAVs concurrently is complicated, and the resource allocation of the network should be designed. To tackle the above two challenges, a joint resource allocation and HAP deployment problem are formulated, and a gradient‐and‐matching‐based algorithm is proposed to solve it efficiently. Simulation results show that the HAP and the proposed algorithm enhance the sum‐rate of the network by over 30%, and the average data rate of both the TUs and the UAVs can be effectively improved.

Electric vehicles (EVs), one of the most effective solutions to reduce gas emission and realize fossil fuels replacement, are enjoying growing popularity from governments to customers. The development of EVs leads to significant advances in vehicle automation and electrification, but meanwhile poses additional heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing (VFC) and smart EV charging for joint optimization and propose an integrated charging and computing (IC2) architecture for EV-included smart grid. In the proposed IC2 architecture, charging stations are profit-driven third-party power prosumers that also help compute tasks offloaded by smart grid while EVs act as both energy consumers and computation providers. We employ the contract theory to provide a multiattribute contract-based charging protocol for EVs and charging stations in an information asymmetry scenario. To obtain the optimal contract, we derive KKT conditions and design a convex–concave-procedure-based contract optimization algorithm. We also design a heuristic offloading algorithm to assign heterogeneous tasks toward different EVs. Numerical results indicate that the proposed multiattribute contract-based charging-computing scheme can effectively benefit both the charging stations and EVs, and meanwhile improves the task computation capability in EV-integrated smart grid.

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.

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proven to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI).

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.

In this paper, a novel unmanned aerial vehicle (UAV) space-time-frequency (S-T-F) non-stationary channel model with time-space consistency for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. In the proposed model, the line-of-sight (LoS) transmission and non-LoS (NLoS) transmission through ground reflection, single-clusters, and twin-clusters are modeled. Meanwhile, the three-dimensional (3D) continuously arbitrary trajectory and the self-rotation of UAV are imitated. To capture the time-space consistency and S-T-F non-stationarity simultaneously, a new UAV-related non-stationary modeling algorithm that integrates the visibility region (VR), frequency-dependent path gain, and survival probability is developed for the first time. In this algorithm, the UAV-related parameters are considered, including UAV’s height, 3D moving velocity, and self-rotation angles. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the cluster density index influenced by communication scenarios, frequency, UAV’s height, and the distance between transceivers. Some important channel statistical properties, such as S-T-F correlation function (STF-CF), Doppler power spectral density (DPSD), and stationary interval, are derived. Finally, simulation results match well with ray-tracing-based results, which verifies the utility of the proposed model.

In this paper, a novel cooperative array-space-time-frequency (A-S-T-F) non-stationary multi-unmanned aerial vehicle (UAV) channel model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. The channel impulse response (CIR) of the proposed multi-UAV cooperative channel model is derived, including line-of-sight (LoS), ground reflection, and non-LoS (NLoS) transmissions. To capture the 3D continuously arbitrary trajectories of multi-UAVs, the 3D time-varying accelerations of multi-UAVs, clusters, and ground station (GS) and the multi-UAVs’ self-rotations are taken into account. In addition, to model the non-stationarity of massive MIMO mmWave multi-UAV cooperative channels in the A-S-T-F domain, a new cooperative A-S-T-F non-stationary algorithm based on the birth-death (BD) process and the$K$-Means clustering algorithm is developed. Important cooperative channel statistical properties, including cooperative array-space-time-frequency correlation function (ASTF-CF), cooperative Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are derived and investigated. The accuracy of the proposed multi-UAV cooperative channel model is verified by the close agreement between ray-tracing-based results and simulation results.

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.

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.

Electric vehicles (EVs) are enjoying growing popularity from governments to customers. However, the development of EVs inevitably poses heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing and EV charging for joint optimization and propose an integrated charging-computing

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.

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.

This paper proposes a novel three-dimensional (3D) mixed-bouncing based unmanned aerial vehicle (UAV) channel model with space-time-frequency (S-T-F) non-stationarity for sixth generation (6G) massive multiple-input-multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems. In the proposed mixed-bouncing based model, the line-of-sight (LoS) transmission, single-bouncing transmission, and multi-bouncing transmission are simultaneously modeled. A new parameter, i.e., cluster density index, is defined and developed. The influence of communication scenarios, frequency, UAV’s height, and distance between transceivers on cluster density index is also analyzed. To capture the S-T-F non-stationarity, a mixed-bouncing based S-T-F non-stationary algorithm is developed based on the birth-death (BD) process and frequency-dependent factor for the first time. In the developed algorithm, the non-stationarity is captured from the perspectives of both single-clusters and twin-clusters. Meanwhile, the impact of UAV-related parameters, such as the UAV’s height and 3D moving velocity, on the non-stationary modeling is considered. Based on the simulation results, the impact of cluster density index on the important channel statistical properties, such as S-T-F correlation function (STF-CF) and Doppler power spectral density (PSD), is explored. Finally, the simulation results match well with the measurement and ray-tracing (RT)-based results, validating the accuracy of the proposed model.

This paper proposes a novel general sixth-generation (6G) irregular-shaped geometry-based stochastic model (IS-GBSM) for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed IS-GBSM can model spherical wavefront propagation, high delay resolution, and three-dimensional (3D) space-time-frequency non-stationarity of channels with time-space consistency, and thus can be applied to integrated communication and sensing systems. To capture the 3D non-stationarity with channel consistency, a new hybrid method combining geometric and parametric methods is developed. Based on the geometric method, i.e., visibility region (VR), the smooth and consistent appearance and disappearance of clusters during array-time evolution are modeled. With the help of the parametric method, a frequency-dependent path gain is introduced, and a visible factor based on uniform continuity theorem and bounded variation function is further proposed to capture the soft cluster power handover during cluster array-time evolution. Key channel statistics of the proposed IS-GBSM are derived and investigated. Simulation results demonstrate that the space-time-frequency non-stationarity is modeled and time-space consistency is captured. Finally, the utility of the proposed IS-GBSM is verified by the excellent agreement between simulation results and measurement.

This article presents a novel event-triggered near-optimal control (ETNOC) method for discrete-time (DT) constrained nonlinear systems. First, the tracking error system is constructed to convert the tracking control problem to the regulation problem. By introducing the tracking error system, the asymmetric control constraints design for the original constrained system can be converted to the symmetric control constraints design for the tracking error system. Second, a novel triggering condition is developed using the time-triggered optimal value function and control law. It is proven that the closed-loop system (CLS) is asymptotically stable under the developed ETNOC method, and there exists a predetermined upper bound for the real performance index. Then, to implement the developed ETNOC method, a parallel control approach with neural networks (NNs) and adaptive dynamic programming techniques is proposed to predict the next state of the system and obtain the optimal value function and control law. The stability analysis of the CLS is provided in the consideration of the estimation errors of the NN weights and state. Finally, the effectiveness of the developed ETNOC method is validated by an application to a boiler-turbine system.

This paper proposes a novel three-dimensional (3D) non-stationary irregular-shaped geometry-based stochastic model (IS-GBSM) for beyond fifth-generation (B5G) and sixth-generation (6G) vehicle-to-vehicle (V2V) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. By distinguishing dynamic clusters and static clusters, the proposed IS-GBSM for the first time explores the impact of vehicular traffic density (VTD) on channel statistics in B5G/6G V2V mmWave massive MIMO scenarios. Furthermore, a novel method is developed to model the channel space-time-frequency non-stationarity. In the developed method, dynamic/static correlated clusters are first generated by an improved$K$-Means clustering algorithm. Then, by employing a birth-death process based on correlated clusters, the consistency in birth and death between dynamic/static correlated clusters during time-array evolution is modeled. Key channel statistical properties, e.g., space-time-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD), are derived. Simulation results demonstrate that the space-time-frequency non-stationarity is captured and the influence of VTDs on channel statistics is explored. Finally, the close agreement is achieved between simulation results and measurements, verifying the utility of proposed IS-GBSM.

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.

In this paper, based on the geometric stochastic modeling method, a space-time-frequency (S-T-F) non-stationary model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation is proposed for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. It is the first 6G massive MIMO mmWave UAV channel model that considers the 3D continuously arbitrary trajectory of UAV in practice and models S-T-F non-stationarity of 6G UAV channels. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the 3D time-varying accelerations and self-rotations of transceivers and clusters. To further model the S-T-F non-stationarity of UAV channels, a novel UAV-related birth-death (BD) algorithm based on correlated clusters is developed. In the developed algorithm, the impact of typical UAV-related parameters, e.g., the UAV’s moving direction, altitude, and time-varying velocity, on the setting of correlated clusters and the BD process is sufficiently considered. Important channel statistical properties are derived and investigated. Some numerical results and interesting observations are given, which can provide some assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and ray-tracing-based results.

This paper proposes a non-stationary three-dimensional (3D) irregular-shaped geometry-based stochastic model (IS-GBSM) for fifth generation (5G) and beyond massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. This is the first sixth generation (6G) massive MIMO mmWave UAV IS-GBSM that can model the UAV channel space-time non-stationarity, and can describe the impact of some unique UAV-related parameters, e.g., the UAV’s moving direction, height, and speed, on channel statistical properties. To better represent the space-time non-stationarity in UAV scenarios, a novel UAV-related space-time cluster evolution algorithm is developed. The developed algorithm considers the characteristics of UAV communications on the modeling of space-time non-stationarity. Based on the proposed model, some channel statistical properties are derived and thoroughly investigated, including the space-time-frequency correlation function, Doppler power spectral density, envelope level crossing rate, and average fade duration. Some numerical results and interesting observations are given, and the impact of UAV-related parameters on channel statistical properties is explored, which can provide assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the applicability of the proposed model is verified by the close agreement between simulation results and measurement.

This article proposes a novel 3-D nonstationary wideband vehicle-to-vehicle (V2V) geometry-based stochastic model (GBSM) with uniform planar antenna arrays (UPAs) for massive multiple-input–multiple-output (MIMO) wireless communication systems. In the proposed GBSM, a novel method, so-called birth–death (BD) process and seed algorithm-based selective cluster evolution, is developed to capture the space nonstationarity of V2V massive MIMO with UPA channels. The time nonstationarity is further mimicked by employing this novel method over the entire timeline. In addition, the proposed GBSM not only models the reflection of the ground, but also divides clusters into static clusters and dynamic clusters to sufficiently investigate the impact of vehicular traffic density (VTD) on channel statics. The channel parameters are properly calculated by 3-D vectors, resulting in the proposed GBSM with high accuracy and low complexity. Important statistical properties, such as the space-time correlation function (S-T CF), spatial cross-correlation function (CCF), temporal auto-correlation function (ACF), and Doppler power spectrum density (PSD) are derived and thoroughly investigated. Simulation results show that the space-time nonstationarity is successfully mimicked and the VTD has a significant impact on channel statistics. Finally, an excellent agreement is achieved between simulation results and measurements, validating the accuracy of the proposed GBSM.

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.

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.

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.

In this paper, a general three-dimensional (3D) space-time-frequency non-stationary model is proposed for sixth generation (6G) channels. From the proposed model, a novel method, so-called correlated cluster based birth-death (BD) process method, is developed to efficiently and jointly mimic the 3D channel space-time-frequency non-stationarity. In this developed method, the frequency non-stationarity is properly captured by correlated clusters, which are obtained via an unsupervised learning algorithm in machine learning, i.e., K-Means clustering algorithm. Additionally, the developed method involves the cluster based space-time non-stationary modeling. Based on the correlation coefficient of clusters, the BD probabilities on the array and time axes are reasonably modified by the linear weight method and matrix iteration algorithm. Therefore, interactions among the space, time, and frequency non-stationary modeling are sufficiently considered. Important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the channel non-stationarity in space-time-frequency domains can be sufficiently characterized. Finally, the excellent agreement between the simulation results and measurements further verifies the accuracy of the proposed model.

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.

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.

As vehicular communications for beyond fifth‐generation (B5G) and sixth‐generation (6G) is picking up interests from academia and industry recently, more and more research and development have been devoted towards the establishment of vehicular communications for B5G and 6G that is capable of supporting the ever more intelligent transportation systems. One key facilitating the design and improvement of vehicular communications for B5G and 6G is channel modelling, which is widely regarded as the foundation of all communication and networking systems. In this paper, the authors focus on the research and analysis of B5G and 6G vehicular channel measurements and modelling. By emphasising the new requirements and challenges that the emerging B5G and 6G technologies and frequency bands bring to vehicular communication channel measurements and modelling, they present an overview of the existing work and identify the limitations therein, and provide guidelines on the channel model development and adoption for various system development and verification objectives. Finally, future challenges related to vehicular channel measurements, modelling, and their application for B5G and 6G are addressed.

In this study, the authors propose a new non‐geometrical stochastic model (NGSM) for non‐stationary wideband vehicular communication channels. To include the line‐of‐sight component, the proposed model first generates a non‐uniformly distributed tap phase, which can be obtained from the widely used uniformly distributed tap phase. Moreover, the proposed model can practically experience variable types of Doppler spectra for different delays by modifying the autocorrelation function used in the existing NGSM. In consideration of the non‐stationarity in the frequency domain of vehicular communication channels, the authors further consider that the amplitude and phase of different taps are correlated. To evaluate the performance of the proposed model, they derive some significant statistical properties in terms of the power delay profile, tap correlation coefficient matrix and Doppler power spectral density. It is demonstrated that in comparison with the existing NGSMs, the proposed model possesses the ability to better mimic characteristics of real vehicular channels. Finally, the excellent agreement is achieved between the simulation results and the corresponding measured data, confirming the accuracy of the proposed model.

Unmanned aerial vehicle (UAV)‐assisted communications have been regarded as a promising technology, which can be used in dynamic heterogeneous networks, since the UAVs can be applied as mobile base stations (MBSs). It is more efficient to set the UAVs as MBSs in different layers according to their functions. Due to the ability in communicating with other vehicles or facilities, vehicle‐to‐everything (V2X) makes the information required by the vehicles be efficiently transmitted and processed, so as to increase the safety of driving. However, because of the rapid change of topology and the huge demand of data transmission, it is hard to guarantee the efficient coverage of all the vehicles only by the support of terrestrial wireless communication networks. Considering this, this study combines UAV and V2X communications together, and proposes a multi‐layer aerial‐road vehicular (MLARV) architecture. The UAVs in the higher layer are in charge of overall monitoring, while the UAVs in the lower layer are responsible for hot‐spot area coverage. To solve the throughput maximisation problem, the authors further propose a density‐aware deployment (DAD) scheme with an iterative three‐dimensional matching resource allocation algorithm. Simulation results show that the proposed DAD scheme with the MLARV architecture outperforms the traditional UAV‐assisted V2X communications with single layer architecture.

In this article, we propose a novel 3-D geometry-based stochastic model (GBSM), i.e., a two-cylinder model, for unmanned aerial vehicle (UAV) multiple-input-multiple-output (MIMO) Ricean fading channels. The received signal is a sum of the Line-of-Sight (LoS) component, single-bounced (SB) rays at the UAV side, SB rays at the ground station side, SB rays on the ground, and double-bounced rays. This makes our model adaptable to a wide variety of UAV communication scenarios. More importantly, the proposed UAV model is the first two-cylinder model that considers ground reflections. Moreover, our model has the ability to investigate the impact of some unique UAV-related parameters (e.g., the UAV's moving direction, UAV's altitude, and antenna orientation) on channel characteristics in a 3-D nonisotropic propagation environment. From the proposed model, we derive and study some significant statistical properties, including the space-time correlation function (CF), Doppler power spectral density (PSD), envelope level crossing rate (LCR), and average fade duration (AFD). Some numerical results and interesting observations are provided, which can be considered useful guidance for the design of UAV-MIMO communication systems. Finally, the utility of our model is verified by the close agreement between the theoretical results and some example measurement data.

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 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.

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.

For the practical application and accurate analysis of unmanned aerial vehicle (UAV) multiple-input multiple-output (MIMO) communication systems, it is significant to propose corresponding simulation models for non-isotropic scattering UAV-MIMO channels. In this paper, we propose new deterministic and stochastic simulation models by using a two-cylinder reference model, where the signal is a superposition of line-of-sight (LoS) component, single-bounced (SB) rays, and double-bounced (DB) rays. Second order statistics of the simulation models have been compared to the reference model, including envelope level crossing rate (LCR), and average fade duration (AFD). The utility of our newly proposed simulation models is verified by the close agreement to the statistical features between theoretical and simulation results. Moreover, the developed stochastic simulation model has similar simulation complexity but performs better approximation than the deterministic model.

For the unmanned aerial vehicle (UAV)‐aided vehicular multiple‐input multiple‐output Ricean fading channel, this study proposes a novel three‐dimensional geometry‐based stochastic model (GBSM). This model is the first GBSM for UAV‐aided vehicular communication channels with the ability to investigate channel characteristics in different vehicular traffic density (VTD) scenarios, which is a combination of a single‐cylinder model and an elliptic‐cylinder model, and the signal is a sum of the line‐of‐sight component, single‐bounced, and double‐bounced rays. In addition, some expressions of channel statistical properties are derived, including space–time correlation function, space‐Doppler spectral power density, envelope level crossing rate, and average fade duration. Based on the reference model, corresponding deterministic and stochastic simulation models are developed. Finally, the authors further explore the impact of some unique UAV‐related parameters and different VTDs on channel statistics, and get some useful conclusions based on numerical results.

In this study, the authors propose a novel three‐dimensional (3D) non‐stationary geometry‐based stochastic model (GBSM) and the corresponding deterministic and stochastic simulation models for non‐isotropic unmanned aerial vehicle (UAV) multiple‐input multiple‐output (MIMO) Ricean fading channels. The proposed GBSM employs a two‐cylinder model, where the signal is a superposition of line‐of‐sight component, single‐bounced rays around the UAV, single‐bounced rays around the ground station (GS), single‐bounced rays on the ground, and double‐bounced rays. The non‐stationarity is reflected on the time‐varying angles of departure and angles of arrival. Since the authors consider the GS to be mobile, the proposed 3D GBSM is general and suitable for UAV‐aided vehicular communication scenarios. Based on the proposed model, some important channel statistical properties are derived and thoroughly investigated, including the space‐time correlation function, Doppler power spectral density, envelope level crossing rate and average fade duration. Moreover, the proposed model has the ability to investigate the influence of some unique UAV‐related parameters on channel characteristics and non‐stationarity. Finally, excellent agreement is achieved between the reference model and simulation models, demonstrating the feasibility of simulation models as well as the correctness of theoretical derivations.

In this paper, we propose a 3-D geometry-based stochastic model for multi-input multioutput wideband nonstationary channels between the unmanned aerial vehicle (UAV) and the ground user. The proposed model uses a single concentric-cylinders model, where the signal is a superposition of a line-of-sight component and single-bounced rays. In order to describe the nonstationarity of the channel, time-varying angels have been properly derived and added in the proposed model. At the same time, based on the reference model, we derive the expressions of the channel statistical properties of the space-time-frequency correlation function and space-Doppler power spectral density. In addition, corresponding deterministic and stochastic simulation models are developed based on the reference model. Moreover, we investigate the influence of some important UAV-related parameters on channel statistical properties as well as the channel nonstationarity. Some interesting observations and conclusions are obtained, which are helpful for the design of UAV communication systems. Finally, the great agreement between simulation models and the reference model demonstrates not only the utility of simulation models but also the correctness of the theoretical derivations and simulations.

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.

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 this paper, we propose an innovative spatial-modulation (SM) based full-duplex (FD) decode-and-forward (DF) relaying protocol where the energy-constrained dual-antenna relay is powered by the radio frequency (RF) energy from the single-antenna source using the time-switching (TS) architecture. In this system, either one or both of the relay antennas receive the energy signal from the source in the energy harvesting phase. In the information transmission phase, one of the two relay antennas is selected to be active to decode and forward the information transmitted from the source and the other relay antenna receives the information from the source at the same time. In this way, the throughput of the information transmission between the relay and the destination can be significantly improved by the additional information mapped to the active antenna index which consequently leads to the improvement of the overall system throughput. Since the current SM capacity solution is not in a closed-form, we propose two tight SM capacity upper bounds and present the solution of the optimal time split ratio for the maximum system throughput according to the proposed upper bound. Monte-carlo simulations are conducted to verify the analysis and reveal the throughput gain of the proposed SM-FD relaying protocol in comparison with conventional FD relaying protocol.

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.

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.

Index modulated orthogonal frequency division multiplexing (IM-OFDM) is a novel multicarrier transmission scheme, which provides considerable performance improvement compared with classical OFDM by conveying information via the active subcarrier indices in conjugation with the constellation symbols. In this paper, we extend the idea of IM-OFDM to multi-input multi-output (MIMO) systems and propose precoded MIMO-OFDM (PIM-MIMO-OFDM). Based on the channel state information at the transmitter, PIM-MIMO-OFDM selects the active elements of the receiver-side space-frequency subblocks via linear precoding. The spectral efficiency enhancement method of in-phase/quadrature index modulation is also employed to construct PIM-MIMO-OFDM with in-phase quadrature modulation by selecting the active space-frequency elements in the in-phase and quadrature components of the constellation symbol. Thanks to the carefully designed precoding, the co-channel interference among received antennas can be completely eliminated. Consequently, low-complexity maximum likelihood and suboptimal detectors are devised with linear detection complexity. Both analytical and numerical results show that, with the help of precoding, PIM-MIMO-OFDM can achieve better bit error rateperformance than traditional precoded MIMO-OFDM (P-MIMO-OFDM) in various system configurations. In addition, the spectral efficiency can be also enhanced compared with the P-MIMO-OFDM under certain configurations.

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.

Recently, the use of unmanned aerial vehicle (UAV) is going to receive great interest in various areas. As a newly emerging area, UAV-aided communications encounter unique communication scenarios and thus need corresponding UAV channel models for better design of such UAV communication systems. In this paper, we propose a three-dimensional (3D) sphere UAV air-to-ground (A2G) multiple-input-multiple-output (MIMO) channel model. Based on the proposed channel model, the space-time (ST) correlation function of this model is derived and analytically studied in terms of various parameters. Some interesting and useful observations are obtained. Finally, we verify the usefulness of the proposed channel model by comparing it with measured data. This model provides a new and practical approach to investigate A2G MIMO channels and gives guidelines for UAV communication system design.

This paper proposes a novel generalised pre-coding aided spatial modulation (GPSM) scheme, called generalised virtual spatial modulation (GVSM), which performs index modulation on the virtual parallel channels obtained by the singular value decomposition (SVD) of multiple-input multiple-output (MIMO) channels. To improve the diversity performance, the coordinate interleaving technique is further introduced into GVSM, resulting in so-called coordinate interleaved GVSM (CI-GVSM), in which the real and imaginary parts of two complex symbols are interleaved and transmitted over strong and weak eigen-modes. A closed-form upper bound on the average bit error probability (ABEP) applicable to both GVSM and CI-GVSM is derived, followed by the asymptotic performance analysis that reveals the error floor and diversity order under imperfect and perfect channel estimation, respectively. Numerical and simulation results corroborate our analysis and show that the proposed GVSM and CI-GVSM outperform the existing GPSM schemes at the same spectral efficiency.

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.

This paper proposes a resource allocation policy which enhances physical layer security in vehicular communication networks. Due to the high mobility in vehicular networks, the feedback channel state information (CSI) can easily get outdated, especially when it takes non-negligible time to obtain the resource-allocation solution. Under the assumption that only outdated CSI is available, we formulate the problem as joint power and subcarrier allocation in order to optimize the uses' secrecy rate based on maximum-minimum (max-min) fairness criterion. The formulated optimization problem is a mixed integer nonlinear programming problem. To reduce the complexity, we further propose a two-step suboptimal algorithm that performs power and subcarrier allocation separately. For a given subcarrier assignment, the optimal power allocation is solved by developing an algorithm of polynomial computational complexity. Numerical results show that the performance of our proposed algorithm can approximate to the optimal one.

A three-dimensional (3D) theoretical model for unmanned aerial vehicle (UAV) communications is proposed in this paper. From the theoretical model, the envelope level crossing rate (LCR) and average fade duration (AFD) are derived under a 3D propagation environment. Based on the derived expressions, we for the first time investigate the LCR and AFD for UAV channels with different UAV- related parameters. The close agreement between the theoretical results and measured data demonstrates the utility of the proposed model.

In this paper, we extend the idea of Index modulated orthogonal frequency division multiplexing (IM-OFDM) to the MIMO scenario and propose the precoded index modulation for multi-input multi-output OFDM (PIM- MIMO-OFDM) for downlink transmission. Based on the channel state information (CSI) at the transmitter, PIM-MIMO-OFDM jointly selects the active elements of the receiver-side space-frequency OFDM subblocks. The co-channel interference (CCI) among receive antennas therefore can be completely eliminated. Low-complexity maximum likelihood (ML) and suboptimal detectors are proposed to achieve linear detection complexity. Both analytical and numerical results are provided to show that PIM-MIMO-OFDM achieves significantly improved bit error rate (BER) performance when compared with precoded MIMO-OFDM (P-MIMO-OFDM).

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.

Unmanned Aerial Vehicles (UAVs) have been a promising platform in realizing high-speed wireless networks. As an emerging scenario, the UAV communication is distinct from widely used cellular systems or vehicular networks, requiring the development of practical yet easy-to-use channel models. In this paper, for the first time we introduce the geometry-based stochastic model (GBSM) to UAV channel modeling, and propose a new three- dimensional (3D) GBSM for UAV Multi-Input Multi- Output (UAV-MIMO) channels. Based on the proposed model, we derive and investigate the space-time correlation function (STCF) under a 3D moving and scattering environment. The usefulness of this model is verified by the comparison between the theoretical results and some measurement data.

In this study, the authors investigate the energy‐efficient resource allocation in a multiuser downlink multiple‐input multiple‐output (MIMO) orthogonal frequency‐division multiple access system with block diagonalisation. Unlike the existing works in which one subcarrier is exclusively used, they consider the multiple user MIMO case on each subcarrier. An optimisation problem involving user selection, receive‐signal subspace selection and power allocation is formulated to maximise the system energy efficiency (EE). As optimising the orientation of the receive‐signal subspace is difficult, they considered two simpler resource allocation schemes, subspace selection scheme and user selection scheme. The two optimisation problems for the subspace and user selection schemes can be rewritten as a unified problem involving set selection and power allocation. By relaxing the combinatorial variables, they obtain an optimal joint set selection and power allocation solution, which can be directly applied to the original problem in most cases. To further reduce the complexity of the algorithm, they also develop a preselection technique for the user and received‐signal subspace on each subcarrier. Through simulations, they discuss the impact of different parameters on the EE for the two schemes and show that the proposed suboptimal algorithm can achieve a close performance to the optimal algorithm.

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.

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.

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.

Recently, a measurement campaign for characterizing the channels in underground subway environments was conducted in Shanghai, China. Downlink signals transmitted by 46 universal mobile telecommunication system cells deployed along a 34-km-long subway were collected. Channel impulse responses are extracted from the data received in the common pilot channels, based on which parameters of multipath components are estimated by using a high-resolution parameter algorithm derived using the space-alternating generalized expectation-maximization principle. Multiple time-evolving clusters are obtained, each representing the channel from a remote-radio-unit of a base station to the receiver. Based on a total of 98 time-evolving clusters, channels observed in the station scenario and the tunnel scenario are modeled separately for their distinctive behaviors in many aspects, particularly in the variations of clusters’ trajectories. Intracluster characteristics parameterized by cluster delay and Doppler frequency spreads,$K$-factor, and dependences among these parameters are investigated. Intercluster parameters, including coexisting cluster number, delay offset, power offset, and cross correlations, are investigated for the station scenario. A path loss model is established for the tunnel scenario.

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.

Channel measurements and modelling have been long considered as the foundation for effective and efficient wireless communication system designs. Recently, there has been an explosive growth of research work dedicated to the so‐called device‐to‐device (D2D) communications. In the mean time, however, measurements and modelling of D2D channels seem to somewhat fall behind. To promote research on these aspects, in this study, the authors provide a critical overview of the current state of research on D2D channels, and comprehensively discuss future trends and research directions.

Differential spatial modulation (DSM) is a newly-emerging differential scheme tailored to the spatial modulation technique, which selects only one among a group of antennas for transmission at any time instant. DSM, however, gives rise to prohibitive search complexity when the number of transmit antennas is large. In this letter, a low-complexity suboptimal detector is proposed for DSM. It is designed based on the maximum-likelihood criterion but takes more candidates for the antenna activation orders into account. The detection is performed in two steps: the first step is to confine the number of candidates for the modulated symbols to a small portion by exploiting the symmetry of the signal constellation; the second step is to select the most likely modulated symbols from the output of the first step according to the determined antenna activation order via a Viterbi-like algorithm. Analyses and simulations show that the proposed detector achieves near-optimal performance yet largely reduces the search complexity.

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, a recently conducted measurement campaign for high-speed-train (HST) channels is introduced, where the downlink signals of an in-service Universal Mobile Terrestrial System (UMTS) deployed along an HST railway between Beijing and Shanghai were acquired. The channel impulse responses (CIRs) are extracted from the data received in the common pilot channels (CPICHs). Within 1318 km, 144 base stations (BSs) were detected. Multipath components (MPCs) estimated from the CIRs are clustered and associated across the time slots. The results show that, limited by the sounding bandwidth of 3.84 MHz, most of the channels contain a single line-of-sight (LoS) cluster, and the rest consists of several LoS clusters due to distributed antennas, leaking cable, or neighboring BSs sharing the same CPICH. A new geometry-based random-cluster model is established for the clusters' behavior in delay and Doppler domains. Different from conventional models, the time-evolving behaviors of clusters are characterized by random geometrical parameters, i.e., the relative position of BS to railway, and the train speed. The distributions of these parameters, and the per-cluster path loss, shadowing, delay, and Doppler spreads, are extracted from the measurement data.

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.

In this paper, we investigate the pilot position selection (PPS) problem in the frequency domain pilot multiplexing technique (FDPMT) for channel estimation of single-carrier block transmission with frequency domain equalization (SC-FDE). Unlike the widely accepted point of view that the conventional PPS technique is the optimal one, this paper for the first time questions this viewpoint and demonstrates the suboptimal property of the conventional PPS technique. To further improve the performance of the FDPMT, an optimal PPS technique is proposed based on the minimization of the average bit error rate (BER). Compared with the conventioanl PPS technique, the proposed PPS technique exhibits better performance with similar complexity.

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.

Spatial modulation (SM) is a relatively new multiple-input multiple-output (MIMO) technology that can provide high data rate with reasonable spectral efficiency. In this paper, the bit error rate (BER) performance of SM systems under vehicle-to-vehicle (V2V) channel models is investigated. The theoretical BER expression is given. The impact of some V2V channel model parameters on the underlying space-time correlation function (STCF) and the BER performance of SM systems are also studied. Simulation results indicate that modulation schemes, maximum Doppler frequency, the distance between the transmitter (Tx) and receiver (Rx), and antenna element spacings can affect the performance of SM systems.

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.

High-speed railway (HSR) brings convenience to peoples' lives and is generally considered as one of the most sustainable developments for ground transportation. One of the important parts of HSR construction is the signaling system, which is also called the “operation control system,” where wireless communications play a key role in the transmission of train control data. We discuss in detail the main differences in scientific research for wireless communications between the HSR operation scenarios and the conventional public land mobile scenarios. The latest research progress in wireless channel modeling in viaducts, cuttings, and tunnels scenarios are discussed. The characteristics of nonstationary channel and the line-of-sight (LOS) sparse and LOS multiple-input-multiple-output channels, which are the typical channels in HSR scenarios, are analyzed. Some novel concepts such as composite transportation and key challenging techniques such as train-to-train communication, vacuum maglev train techniques, the security for HSR, and the fifth-generation wireless communications related techniques for future HSR development for safer, more comfortable, and more secure HSR operation are also discussed.

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.

This paper proposes a generic geometry-based stochastic model for nonisotropic scattering vehicle-to-vehicle (V2V) Ricean fading channels. With the proposed model, the level crossing rate (LCR) and average fade duration (AFD) are derived. The resultant expressions are sufficiently general and subsume many well-known existing LCRs and AFDs as special cases. The derived LCR and AFD are further investigated in terms of some important parameters, e.g., the shape of the scattering region (two-ring or ellipse), mean angle, angle spread, and directions of movement of the Tx and Rx (same or opposite direction). More importantly, in this paper, the impact of the vehicular traffic density on the LCR and AFD for nonisotropic scattering V2V Ricean fading channels is investigated for the first time. Excellent agreement is observed between the theoretical LCRs/AFDs and corresponding measured data, thus demonstrating the validity and utility of the proposed model.

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.

This paper proposes a novel three-dimensional (3D) theoretical regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels. The proposed RS-GBSM, combining line-of-sight (LoS) components, a two-sphere model, and an elliptic-cylinder model, has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics, and jointly considers the azimuth and elevation angles by using the von Mises Fisher distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding two-dimensional (2D) model. It is demonstrated that the 3D model is more accurate to characterize real V2V channels, in particular for pico cell scenarios. Finally, close agreement is achieved between the theoretical model, SoS simulation model, and simulation results, demonstrating the utility of the proposed models.

In this paper, by considering the case of two transmit antennas, we propose a novel approach for introducing differential signaling into Spatial Modulation (SM)/Space-Shift Keying (SSK) and thus develop a differential SM/SSK (DSM/DSSK) scheme, where neither the transmitter nor the receiver has access to channel state information. The proposed approach can be applied to any equal energy signal constellations. Performance analysis of the proposed DSM is developed analytically and demonstrated via simulations, leading to some interesting observations and useful conclusions. It is shown that the performance degradation of DSM over SM can be less than 3dB and that the loss decreases as the number of receive antennas decreases.

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.

Dual-polarized antenna systems have received more and more attention due to its potential to double the channel capacity compared with omnidirectional single-input single-output (SISO) systems. However, the implementation of dual-polarized systems into real three-dimensional (3-D) environments may result in the power loss from the third polarization direction. Therefore, the channel capacity of dual-polarized systems in real 3-D scenarios will be significantly affected by the power loss. Based on the newly proposed 3-D two-spheric double-bounced geometry-based stochastic channel model, this paper for the first time analyzes this impact of the power loss on the channel capacity and demonstrates the critical maximum elevation spread, where the channel capacity of dual-polarized systems is equal to that of omnidirectional SISO system. The work in this paper is significant for the guidance of the proper application of dual-polarized systems in the future.

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.

This paper for the first time considers a low complexity multiple-input multiple-output (MIMO) mode switching technique between the conventional MIMO and distributed MIMO. By considering the conventional space-time block coding (STBC) and the distributed STBC (DSTBC), we propose a novel mode switching scheme between them to maximize the capacity performance. Since the difference between the STBC and D-STBC mainly comes from the three-directional (3D) distances, the proposed mode switching scheme is based on the jointly consideration of the received signal-to-noise ratio (SNR) at the user equipment (UE) and the 3D distances. Compared with non-adaptive MIMO modes, the proposed adaptive scheme has better capacity performance and thus is more energy-efficient.

In this paper, we investigate the resource sharing problem to optimize the system performance in device-to-device (D2D) communications underlaying cellular networks from a distributed and cooperative perspective. Specifically, we formulate a coalitional game with transferable utility, in which each user intends to maximize its own utility and has the incentive to cooperate with other users to form a strengthened user group that can increase the opportunity to win its preferred spectrum resources. Furthermore, we propose a distributed merge-and-split based coalition formation algorithm based on a new defined Max-Coalition order to effectively process the resource allocation problem. Simulation results confirm that, with much lower computational complexity, the proposed scheme achieves an approaching performance in terms of network sum-rate compared with the centralized optimal resource allocation scheme obtained via exhaustive search.

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.

In this paper, we propose a new regular-shaped geometry-based stochastic model (RS-GBSM) for non-isotropic scattering wideband multiple-input multiple-output vehicle-to-vehicle (V2V) Ricean fading channels. By correcting the unrealistic assumption widely used in current RS-GBSMs, the proposed model can more practically study the impact of the vehicular traffic density on channel statistics for different time delays. From the proposed model, we derive the Doppler power spectral density (PSD) and find that highly dynamic Doppler spectrum appears for V2V channels. Excellent agreement is achieved between the derived Doppler PSD and measured data, demonstrating the utility of the proposed model. To combat the intercarrier interference (ICI) caused by highly dynamic Doppler spectrum in real orthogonal frequency division multiplexing based V2V systems, this paper proposes a new type of ICI cancellation scheme, named as precoding based cancellation (PBC) scheme. The proposed scheme can be easily implemented into real V2V systems with the same ICI mitigation performance as the current best ICI cancellation scheme that has high complexity. To further improve the performance of the proposed PBC scheme, a new phase rotation aided (PRA) method, namely constant PRA (CPRA) method, is proposed. Compared with the existing PRA method, the CPRA method has better performance and much less implementation complexity. Therefore, the proposed PBC scheme with the CPRA method is the best ICI cancellation scheme for real V2V systems.

Peer-to-peer communication has been recently considered as a popular issue for local area services. An innovative resource allocation scheme is proposed to improve the performance of mobile peer-to-peer, i.e., device-to-device (D2D), communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which as bidders compete to obtain business while the packages of the D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit, as a basis of the proposed auction. And then a detailed non-monotonic descending price auction algorithm is explained depending on the utility function that accounts for the channel gain from D2D and the costs for the system. Further, we prove that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds. We explain non-monotonicity in the price update process and show lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.

In this paper, we present an analytical study of the bit error rate (BER) for interleaved single-carrier frequency-division multiple access (SC-FDMA) systems over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels with fading parameters {m} being integers when minimum mean-square error frequency-domain equalization (MMSE-FDE) is applied. Under the assumption of independent fading characteristics among channel frequency responses (CFRs) at the allocated subcarriers for a specific user, accurate numerical BER computation for square M-ary quadrature amplitude modulation (M-QAM) is developed by exploiting the statistics of the equalized noise. More importantly, the BER derivation is based on the real distribution of the CFRs without applying the widely used approximation of the CFRs in previous literature, resulting in a more accurate BER analysis. Monte-Carlo simulations are conducted to validate the analysis.

In this paper, we propose an interference-avoidance pilot design scheme using Zero-Correlation Zone (ZCZ) sequences for multi-cell MIMO-OFDM systems. In the proposed scheme, the ZCZ sequences from the same family set are designed as the time-domain (TD) pilot symbols and are Code-Division Multiplexing (CDM) for different transmit antennas in adjacent cells. Due to the perfect auto-correlation and cross-correlation properties of the ZCZ sequences within a certain correlation zone that is designed equal to or a little larger than the Cyclic Prefix (CP) length, the inter-cell interference of the pilot symbols can be effectively eliminated by utilizing a time-domain correlation-based channel estimation method. Simulation results show that the proposed scheme achieves near optimal normalized Mean Square Error (MSE) performance of channel estimation in a multi-cell environment.

We consider a cognitive radio network in which multiple pairs of secondary users (SUs) communicate by a one-way relay node over orthogonal channels with the existence of an eavesdropper close to the destination. The transmit power of the relay needs efficient distribution for maximizing the sum secrecy rate of the SU pairs, meanwhile satisfying the interference constraint at the single primary user (PU). Specifically, we introduce two multi-object auctions, i.e. the Vickrey auction and the sequential first-price auction, to perform this power allocation problem. We prove the existence and give the general form of the only equilibrium for each auction. We also propose two algorithms based on the equilibriums, respectively. From the simulation results, we see that the system secrecy rate curve of the Vickrey auction gradually coincides with that of the optimal allocation with increasing power units, while the sequential first-price auction reflects more fairness.

In this paper, propagation channel measurement data obtained by using the wide-band multiple-input-multiple-output (MIMO) channel sounder - PROPsound - is applied to analyzing the cross-correlation behavior of spatial-polarimetric (SP) channels in indoor environments. Four antennas are selected from the transmitter (Tx) array and from the receiver (Rx) array to form a 2×2 spatial MIMO channel, with each spatial element consisting of co-located ±45°-polarized antennas. The characteristics of the resultant 16 × 16 narrow-band SP Channel Correlation Matrix (CCM) are studied for five indoor environments of different types. Results show that the Kronecker model proposed in the 3rd Generation Partnership Project (3GPP) TS36.101, which describes the SP CCM as the Kronecker product of the separate Tx and Rx spatial CCMs, and the 4×4 polarimetric CCM, is inapplicable in indoor scenarios. As an alternative, we propose to consider the cross-correlation coefficient of SP channels as a random variable, and apply its empirical statistics for stochastic modeling. Results also show that the empirical distribution of the off-diagonal elements of SP CCM is distinctive with respect to the types of environments.

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

Orthogonal Frequency Division Multiplexing (OFDM) systems may suffer from both carrier frequency offset (CFO) and in-phase/quadrature-phase (I/Q) imbalance at the receiver front end. In this paper, a novel type of intercarrier interference (ICI) self-cancellation method using mirror mapping is proposed to combat these impairment. Based on the proposed novel method and by adopting two widely used mapping operations, we altogether derive two different mirror mapping schemes. Analysis and simulation results verify that the proposed mirror mapping schemes not only inherit the efficiency of the existing self-cancellation schemes in suppressing ICI caused by CFO but also have the ability of compressing or even eliminating IQ imbalance.

In this paper, a group-wise joint detection (JD) algorithm is proposed for dual rate time division-synchronization code division multiple access (TD-SCDMA) systems. The proposed algorithm first groups users based on their data rates and then deals with them group by group starting from the group of high data rate users (HRUs). Simulation results illustrate that compared to conventional JD algorithms, the proposed algorithm can improve the performance of low data rate users (LRUs) significantly while having similar performance for HRUs, with negligible computational complexity increase. It is worth mentioning that the proposed group-wise JD algorithm can be easily extended for the application in multi-rate TD-SCDMA systems.

Abstract For the practical simulation and performance evaluation of mobile‐to‐mobile (M2M) communication systems, it is desirable to develop accurate M2M channel simulation models for more realistic scenarios of non‐isotropic scattering. In this paper, by using a ‘double‐ring’ concept to describe M2M non‐isotropic scattering environments, we propose new deterministic and stochastic sum‐of‐sinusoids (SoS) based simulation models. The proposed simulation models extensively consider the distributions of the angle of arrival (AoA) and the angle of departure (AoD), and thus provide a good approximation to the desired statistical properties of the reference model. Copyright © 2009 John Wiley & Sons, Ltd.

This paper proposes a novel three-dimensional (3D) two-sphere regular-shaped geometry-based stochastic model (RS-GBSM) with only double-bounced rays for non-isotropic scattering narrowband multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) channels. The proposed 3D model has the ability to investigate the joint impact of both the azimuth angle and elevation angle on channel statistics. Based on the proposed model, the space-time (ST) correlation function (CF) is derived and the impact of some important parameters on the resulting ST CF is investigated. Numerical results show that the 3D model results in lower ST correlations than the corresponding 2D model.

This paper proposes a novel three-dimensional (3D) regular-shaped geometry-based stochastic model (RS-GBSM) for non-isotropic multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) Ricean fading channels. The proposed model, combining a two-sphere model and an elliptic-cylinder model, is the first 3D RS-GBSM that has the ability to investigate the impact of the vehicular traffic density (VTD) on channel statistics and jointly consider the azimuth angle and elevation angle. From the proposed model, the space-time (ST) correlation function (CF) and the corresponding space-Doppler (SD) power spectral density (PSD) are derived. Finally, some numerical results and interesting observations are given.

In this paper, based on the tapped delay line (TDL) structure, we propose a geometry-based stochastic model (GBSM) for wideband multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) Ricean fading channels. The proposed wideband model is the first GBSM that has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics for different time delays, i.e., for every tap in our model. From the proposed model, the space-time (ST) correlation function (CF) and the corresponding space-Doppler (SD) power spectral density (PSD) are derived. Excellent agreement is achieved between the theoretical Doppler PSDs and measured data, demonstrating the utility of the proposed model.

In this paper, based on the concept of the tapped delay line (TDL) structure, we first propose a new wideband multiple-ring multiple input multiple output (MIMO) channel reference model for macro-cell scenarios. It is shown that the proposed model can easily match any given or measured power delay profile (PDP) and has the ability to jointly consider the angle of arrival (AoA), angle of departure (AoD), and time of arrival (ToA). From the proposed model, we derive the closed-form expression of the 3-dimensional (3-D) space-time-frequency (STF) correlation function (CF) for each tap, considering the interaction of the temporal, spatial, and frequency correlations. Finally, based on the proposed reference model, a deterministic simulation model is then proposed.

This paper develops a generic geometry-based stochastic model for mobile-to-mobile (M2M) Ricean fading channels. From the generic model, the level crossing rate (LCR) and average fade duration (AFD) are derived. Based on the derived expressions, we for the first time investigate the LCR and AFD for M2M channels with different vehicular traffic densities (VTDs). Excellent agreement is achieved between the theoretical results and measured data, demonstrating the utility of the proposed model.

For realistic simulation of multiple-input multiple -output (MIMO) channels under more realistic scenario of non-isotropic scattering, we propose new deterministic and stochastic sum-of-sinusoids (SoS) based simulation models. The proposed models comprehensively consider the distributions of the angle of arrival (AoA), and thus show a good approximation to the desired statistical properties of the reference model.

To simulate mobile-to-mobile (M2M) Rayleigh fading channels under more realistic scenario of non-isotropic scattering, we propose one deterministic and one stochastic sum-of-sinusoids (SoS) based simulation models. The proposed models extensively consider the distributions of the angle of arrival (AoA) and the angle of departure (AoD), and thus show a good approximation to the desired statistical properties of the reference model.

In this paper, a generic and adaptive geometrybased stochastic model (GBSM) is proposed for non-isotropic multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) Ricean fading channels. The proposed model employs a combined two-ring model and ellipse model, where the received signal is constructed as a sum of the line-of-sight, single-, and doublebounced rays with different energies. This makes the model sufficiently generic and adaptable to a variety of M2M scenarios (macro-, micro-, and pico-cells). More importantly, our model is the first GBSM that has the ability to study the impact of the vehicular traffic density on channel characteristics. From the proposed model, the space-time-frequency correlation function and the corresponding space-Doppler-frequency power spectral density (PSD) of any two sub-channels are derived for a non-isotropic scattering environment. Based on the detailed investigation of correlations and PSDs, some interesting observations and useful conclusions are obtained. These observations and conclusions can be considered as a guidance for setting important parameters of our model appropriately and building up more purposeful measurement campaigns in the future. Finally, close agreement is achieved between the theoretical results and measured data, demonstrating the utility of the proposed model.

With the fast development of web 2.0, user-centric publishing and knowledge management platforms, such as Wiki, Blogs, and Q & A systems attract a large number of users. Given the availability of the huge amount of meaningful user generated content, incremental model based recommendation techniques can be employed to improve users' experience using automatic recommendations. In this paper, we propose an incremental recommendation algorithm based on Probabilistic Latent Semantic Analysis (PLSA). The proposed algorithm can consider not only the users' long-term and short-term interests, but also users' negative and positive feedback. We compare the proposed method with several baseline methods using a real-world Question & Answer website called Wenda. Experiments demonstrate both the effectiveness and the efficiency of the proposed methods.

A number of turbo equalization (TE) methods with reduced complexity have recently been introduced, in which the maximum a posteriori probability (MAP) equalizer is replaced by suboptimal and low complexity ones. These methods can save the computational complexity significantly, but with sacrificed system performance. In this paper, the authors first use the extrinsic information transfer (EXIT) chart tool to visually explain why these suboptimal methods have the worse performance. Then, the authors propose a novel iterative method for TE which takes advantage of both parallel and serial concatenation turbo-like schemes. In the novel iterative method, the EXIT chart switches between the two schemes as a balance. The transmitter side is also changed correspondingly by utilizing the concept of repetition code to acquire a structure similar to turbo codes. It is shown from both analytical and simulation results that the proposed iterative method for TE results in the excellent system performance while its realization complexity is kept relatively low.