The attack surface of Industrial IoT (IIoT) is enlarged by the interconnected devices and systems. Although many works have facilitated advanced approaches to help industrial entities against possible cyber threats, they may overlook the rich operational context of manufacturing processes, leaving the security evaluation context-agnostic. Recognizing that cyber attacks and production activities are increasingly intertwined, this paper introduces Meta-KadaSec, a novelMeta-Knowledge-drivenadaptiveSecurity provisioning approach that embeds security context within the natural operational fabric of manufacturing environments. Our approach integrates: (1) STKG-PPO, a reinforcement learning model that leverages manufacturing contextual knowledge through Knowledge Graphs with Spatial-Temporal associations; and (2) Reptile-CMDPs, a meta-reinforcement learning approach that enables rapid adaptation across diverse manufacturing contexts with theoretical guarantees for convergence. Evaluations are driven by realistic attack vectors from the Edge-IIoTset dataset and an open source factory simulator, demonstrating that our context-embedded model STKG-PPO improves production efficiency by 52.5% and reduces convergence time by 6.7% compared to context-agnostic baselines. Furthermore, our meta-learning approach Reptile-CMDPs accelerates adaptation, achieving 95.2% higher average rewards compared to training from scratch.

Large Artificial Intelligence Models (LAMs) possess powerful learning capabilities and are regarded as key technologies for addressing communication challenges in the future sixth-generation (6G) wireless networks. However, their massive parameters make them difficult to deploy on computation resource-constrained end nodes. Recently, large-small model collaboration has been extensively studied, but most works assume homogeneous computational resources across end nodes. This assumption neglects the heterogeneity among nodes, potentially causing significant performance degradation or even system failures due to improper resource allocation and task partitioning. To address this challenge, we propose a large-small model collaboration framework that accounts for heterogeneous computational resources and limited wireless communication bandwidth. In this proposed framework, end nodes are responsible for data collection and local inference using small models. They also cooperate with the edge server that provides large model inference and model update. We design a joint optimization strategy that considers data transmission optimization and transmission resource allocation. The primary objective of this strategy is to enhance the inference accuracy of the framework by maximizing the mean average precision (mAP). Furthermore, we derive a closed-form lower bound for the mAP of the proposed framework. Simulations based on object detection experiments demonstrate that the proposed framework significantly outperforms existing frameworks under different communication bandwidths and data scales.

A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained three-dimensional (3D) radio map construction involves inferring a high-resolution radio map for the two-dimensional (2D) area at an arbitrary target height within a 3D region of interest, using radio samples collected by sensors sparsely distributed in that 3D region. Solutions to the problem are crucial for efficient spectrum management in 3D spaces, particularly for drones in the rapidly developing low-altitude economy. However, this problem is challenging due to ultra-sparse sampling, where the number of collected radio samples is far fewer than the desired resolution of the radio map to be estimated. In this paper, we design RadioLAM, a fine-grained 3D radio map construction paradigm built on generative Large Artificial Intelligence Models (LAMs). RadioLAM employs the creative power and the strong generalization capability of LAM to address the ultra-sparse sampling challenge. It consists of three key blocks: 1) an augmentation block, using the radio propagation model to project the radio samples collected at different heights to the 2D area at the target height; 2) a generation block, leveraging a diffusion-based LAM under an Mixture of Experts (MoE) architecture to generate a candidate set of fine-grained radio maps for the target 2D area; and 3) an election block, utilizing the radio propagation model as a guide to find the best map from the candidate set. Extensive simulations show that RadioLAM is able to solve the fine-grained 3D radio map construction problem efficiently from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art (SOTA). Furthermore, real-world experiments also confirm that RadioLAM achieves superior performance compared to SOTA.

Carrier Aggregation (CA) is investigated to address spectrum scarcity in 6G Integrated Sensing and Communication (ISAC) systems. However, inter-band phase offsets impose high complexity on achieving coherent gain across multiple bands. Furthermore, typical CA focuses on multi-frequency-band expansion, while multi-time-duration expansion has received limited attention. To address the above issues, this paper proposes a novel high-accuracy Multi-Band Joint Carrier Phase Sensing (MB-JCPS) framework that utilizes carrier phase information from multiple time-frequency bands to improve range and velocity sensing accuracy. A carrier phase extraction method is introduced based on oversampling Range-Doppler phase spectrum. The impact of non-ideal factors—including time, frequency, and phase offsets, as well as phase noise—on carrier phase is modelled, analyzed, and eliminated via an inter-path difference method. A Real-valued Multi-Carrier Ambiguity Resolution (RMCAR) algorithm is designed to resolve integer ambiguities in the range and velocity carrier phase models. The time-frequency selection problem is formulated as a constrained Rayleigh-quotient optimization problem and solved via successive convex approximation. The Cramér-Rao lower bound (CRLB) for the range and velocity estimation under phase offsets is derived, demonstrating the performance gain of CA in both frequency and time domains. Numerical simulations show that the proposed method achieves higher sensing accuracy with lower complexity than benchmark algorithms in 3GPP Uma-AV environments.

Recently, reconfigurable holographic surfaces (RHSs) have been proposed as a cost-efficient approach for achieving ultra-massive multiple-input multiple-output to satisfy the growing data rate requirements. Unlike existing antennas, the RHS follows a serial transmission characteristic where the electromagnetic (EM) wave propagates along the RHS element and is radiated outward one after another, concurrently with the gradual decrease of the EM wave energy. Thus, there exists a minimum RHS size that satisfies the data rate requirement in the RHS-enabled communication system without excessive antenna elements. In this paper, we first propose a serial propagation model of RHS to depict the coupling effect among RHS elements that is induced by the excitation of EM waves carrying signals one after another. Based on such a model, we derive the closed-form expression of the minimum required RHS size by estimating the upper and lower bounds of the achievable rate. Simulation results verify the theoretical analysis and reveal the relationship between the radiation characteristics of the RHS and the required minimum size.

Radio simultaneously localization and mapping (SLAM) is indispensable for a wide range of wireless applications owing to its ability to provide both location and mapping information. Traditional radio SLAM systems use fixed-aperture antennas with invariant beamwidths, which cannot adapt to the varying requirements for beam gain and coverage in complex environments. In this paper, we propose an aperture-changeable reconfigurable holographic surface (RHS)-assisted SLAM system. By deactivating different numbers of RHS elements, the equivalent antenna aperture is changeable, leading to adaptive beamwidths across detection directions. However, the RHS should follow the leakage power constraint, which stipulates that the total radiated power of RHS elements cannot exceed the input power. Its impact on the elements’ radiated power makes the optimization of RHS equivalent aperture challenging. To address this issue, we formulate a detection probability maximization problem and then adopt a two-step decomposition method to solve it. In the first step, we relax the leakage power constraint to obtain a relaxed solution. In the second step, we refine the relaxed solution by incorporating the leakage power constraint. We analyze the impact of RHS equivalent aperture and leakage power constraint on the SLAM system and evaluate the complexity of the proposed algorithm. Simulation results demonstrate that the proposed SLAM system can effectively reduce agent localization error compared with benchmark schemes.

Radio map presents communication parameters of interest, e.g., received signal strength, across a geographical region in a specific frequency band. It can be leveraged to improve the efficiency of spectrum utilization. With the rapid development of radio technology and the proliferation of radioenabled devices, there is an increasing demand for finer granularity (higher resolution) in radio maps. However, the problem of fine-grained radio map construction is uniquely challenging, as it requires to utilize an extremely small number of radio samples collected by sparsely distributed sensor devices to infer the map. To address the challenge, we propose a hybrid device-edge-cloud collaboration paradigm called CollaboRadio. CollaboRadio first groups sensor devices into multiple clusters, with cluster locations optimized based on principles of radio propagation. Next, it leverages a small AI model on each edge server to generate a local radio map for the respective cluster region from radio samples collected by intra-cluster sensors. Finally, it employs a large AI model in the cloud to construct a global radio map for the entire region from the local maps produced by edge servers of different clusters. For the implementation of CollaboRadio, we develop an UNet-based small model for the edge server and a Transformerbased large model for the cloud. Extensive simulations show that CollaboRadio is capable of constructing fine-grained radio maps from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art.

The fourth industrial revolution drives AI-powered smart manufacturing through cloud-edge computing, enabling intelligent production processes and data-driven automation. To handle security concerns arising from massive IoT deployments, attribute-based access control (ABAC) has become essential for smart factories. It offers flexibility in dynamic environments by utilizing attributes of users, devices, and contextual conditions to decide whether an access request should be permitted or denied. However, the proliferation of IoT devices drastically increases the number of attributes, causing exponential growth in policy complexity and severe decision latency at resource-constrained edge nodes. To address this issue, we propose PRUNE, a cloud–edge collaborative framework for ABAC policy pruning. The framework adaptively determines pruning strategies based on the real-time security state of the factory. Specifically, it employs deep reinforcement learning (DRL) for coarse-grained control in highly dynamic environments, while switching to a Deterministic Policy Optimizer (DPO) for fine-grained adjustment under quasi-static conditions. The pruned lightweight ABAC policy subset is then deployed on edge nodes for real-time access decisions. By continuously monitoring factory conditions and analyzing historical access requests, PRUNE dynamically adjusts pruning strategies. Simulation results on our containerized digital-twin testbed show that PRUNE reduces security response latency by 22% and improves operational efficiency by 30%, while maintaining robust security with anomaly rates consistently below 10%.

In response to the escalating communication demand, transceivers are transforming from a data-oriented to an artificial intelligence (AI)-driven semantic-aware paradigm. However, current semantic-aware transceivers fail to simultaneously adapt to unseen data without labels and guarantee low data processing latency because strong generalization ability requires large-scale models with robust semantic understanding, while low latency leads to small model size and simple structure. To this end, we propose a feature-foundation model evolution framework deployed at cloud, edge, and users, where models with different scales can cooperate to address these issues. Specifically, the transmitter at edge sends images to the users by performing real-time semantic feature extraction and data encoding, and the small feature model is evolved with the aid of a large foundation model at cloud when unseen data occurs. To simultaneously update the feature model and avoid loss of previously acquired semantic understanding, we design a feature-foundation model evolution scheme where outputs of both foundation and feature models are leveraged. Additionally, to tackle coupled communication-computation resources for evolution, we formulate the resource scheduling problem and design algorithms to minimize the evolution latency. We conduct rigorously-designed simulation to validate the effectiveness of our framework.

Amodal sensing enables the shape reconstruction of occluded objects, facilitating a wide range of sensing applications in complex environments. However, traditional amodal sensing methods based on cameras or LiDAR suffer from privacy issues and performance degradation under poor weather conditions. In this demo, we present a wireless amodal sensing system that leverages mmWave signals to improve robustness and protect privacy. The system first segments the obtained mmWave point clouds into individual object instances, and then reconstructs their complete shapes. Unlike camera and LiDAR-based methods, it is challenging to realize wireless amodal sensing due to the measurement errors caused by wireless channel noise and the data sparsity. To address these challenges, we first design an RCS-enhanced error suppression module to mitigate measurement errors by leveraging the negative correlation between radar cross-section (RCS) values and noise. For the data sparsity, we utilize a modified Transformer architecture to extract diverse geometric features at multiple scales, and incorporate a fine-tuned vision-language model (VLM) to generate semantic features that describe object classes. The geometric and semantic features are finally fused to reconstruct complete object shapes using pre-trained generative models. The effectiveness of the proposed system is demonstrated through extensive experiments in real-world scenarios.

The highly dynamic Low Earth Orbit (LEO) environment poses significant challenges for efficient and heterogeneous service provision. A promising approach is to use the communication links of LEO satellites as signals of opportunity, where opportunistic offloading techniques appear to improve the overall performance. However, current solutions often overlook the sophisticated predictive capabilities to exploit spatio-temporal correlation across multiple dimensions (e.g., link quality, node capacity, task requirements), failing to evaluate the quality of transient opportunities for offloading decisions. This paper proposes a Spatio-Temporal Transformer-based Opportunistic Task Offloading (STOTO) approach for heterogeneous tasks in dynamic LEO. The core of STOTO is a spatio-temporal Transformer prediction model, designed to achieve superior awareness of evolving service requirements and resource availability. Then, the scheme dynamically evaluates the situation and resource availability, allocating tasks to the optimal nodes in real time. Experimental results demonstrate that our approach significantly outperforms existing methods, achieving 8.4% higher task completion rates on average.

Reconfigurable intelligent surface (RIS) has emerged as a promising solution to enable ultra-massive multiple-input multiple-output (MIMO) for 6G wireless communications. To reduce pilot overhead, multi-resolution codebook-based beam training has been proposed for RIS-aided communication systems. However, the hardware constraints of the RIS such as the limited control capability, mutual coupling, and manufacturing tolerances have not been fully considered, which may result in non-ideal beam patterns and misleading beam search directions, thereby degrading the overall beam training performance. To address this issue, in this paper, we revisit the multi-resolution codebook design and beam training scheme by taking the RIS hardware constraints into account. First, we propose a wide-beam generation method via multi-beam superposition, where the RIS reflection coefficients are optimized to achieve high beam gain with minimal fluctuations. Then, a posterior-based beam training scheme is proposed to adaptively correct erroneous beam search directions, thereby improving the beam training accuracy. We implement a prototype RIS with 32x32 reflective elements and deploy a 26 GHz millimeter-wave RIS-aided wireless communication testbed to experimentally evaluate the proposed scheme. The results indicate that our approach achieves low training overhead and a data rate close to the exhaustive search method.

With the development of the Internet of Things (IoT), mobile edge computing (MEC) and energy harvesting (EH) technologies are applied to handle numerous computing-intensive and latency-sensitive IoT applications by offloading data to MEC servers as well as achieving sustainable operation. In this paper, we investigate a distributed EH-enhanced MEC system consisting of multiple MEC servers and user mobile devices (MDs). The MEC servers, as service providers, inherently control the price and availability of resources. MDs reactively optimize their task offloading and battery energy management strategies. Considering the hierarchical structure and large optimization dimension of MEC, a Stackelberg game-based bi-level multi-agent deep deterministic policy gradient (BL-MADDPG) algorithm is proposed which treats the MD and joint MEC servers as follower and leader, respectively. Different from the parallel decision of conventional multi-agent deep reinforcement learning, the BL-MADDPG algorithm establishes a two-stage decision process and can achieve an energy efficient offloading strategy by taking MDs’ limited EH power into account. Simulation results are carried out to validate the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) are increasingly deployed for real-time intelligent tasks requiring large model inference, but their limited onboard computing power brings the necessity of model collaboration between the edge UAVs and cloud servers on the ground. Existing frameworks of edge-cloud collaborations often assume homogeneous data distribution among the UAVs, which fail to address real-world diversities. This paper presents a novel edge-cloud collaborative inference framework for aerial networks with distributionally diverse data. The framework introduces a joint optimization of confidence thresholds and quantization strategies to balance inference ac-curacy and bandwidth usage. We conduct probability density function (PDF) estimations on the confidence levels of different scenario datasets, which guide decisions on data uploading and quantization. Simulation results on the CIFAR-10 dataset demonstrate that our approach significantly enhances system accuracy compared to frameworks for independent identically distributed data, validating its effectiveness in diverse UAV scenarios.

Radio map presents communication parameters of interest, e.g., received signal strength, across a geographical region. It can be leveraged to improve the efficiency of spectrum utilization. With the rapid development of radio technology and the proliferation of radio-enabled devices, there is an increasing demand for finer granularity (higher resolution) in radio maps. However, the problem of fine-grained radio map construction is uniquely challenging, as it requires to utilize an extremely small number of radio samples collected by sparsely distributed sensors to infer the map. To address the problem, we propose a novel edge-cloud model collaboration paradigm. This paradigm first groups sensors into multiple clusters, with cluster locations optimized based on principles of radio propagation. Next, a small model on each edge device generates a local radio map for the respective cluster region using intra-cluster radio samples. Finally, a large model in the cloud constructs a global radio map for the entire region from the local maps produced by edge devices of different clusters. To evaluate the proposed paradigm, we also develop an UNet-based small model for the edge device and a Transformer-based large model for the cloud. Extensive simulations show that our paradigm is capable of constructing fine-grained radio maps from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art.

The widespread adoption of large artificial intelligence (AI) models has enabled numerous applications of the Internet of Things (IoT). However, large AI models require substantial computational and memory resources, which exceed the capabilities of resource-constrained IoT devices. End-edge collaboration paradigm is developed to address this issue, where a small model on the end device performs inference tasks, while a large model on the edge server assists with model updates. To improve the accuracy of the inference tasks, the data generated on the end devices will be periodically uploaded to edge server to update model, and a distilled model of the updated one will be transmitted back to the end device. Subjected to the limited bandwidth for the communication link between the end device and the edge server, it is important to investigate whether the system should allocate more bandwidth to data upload or to model transmission. In this paper, we characterize the impact of data upload and model transmission on inference accuracy. Subsequently, we formulate a bandwidth allocation problem. By solving this problem, we derive an efficient optimization framework for the end-edge collaboration system. The simulation results demonstrate our framework significantly enhances mean average precision (mAP) under various bandwidths and datasizes.

Modern satellite networks are transitioning from monolithic designs to microservice architectures, introducing complex spatio-temporal patterns, varied on-board processing capabilities, and high mobility with dynamic topologies. These features require accurate prediction of inter service dependencies for optimal resource allocation and system management. To address these challenges, this paper introduces the Spatio-Temporal Graph Neural Network with Orbital contextual features (STGNN-O). This model incorporates orbital information as contextual features, processes spatial dependencies through multi-head graph attention networks, and captures temporal patterns at three different timescales to complete satellite service performence metrics prediction, which refers to forecasting key performance indicators of microservices running on satellite platforms. Also, due to the lack of real-world relavent dataset, a comprehensive satellite service benchmark dataset is created based on real-world parameters and service patterns across multiple orbital configurations. Experiments demonstrate that STGNN-O significantly outperforms state-of-the-art baselines, achieving substantial improvements in prediction accuracy. Ablation studies confirm that the integration of orbital information and multi-scale temporal features significantly contributes to prediction accuracy across all orbital regimes.

Recently, holographic integrated sensing and communication (ISAC) has been proposed as a promising paradigm to support dual functionalities of sensing and communications. It employs reconfigurable holographic surfaces (RHSs), a cost-effective solution for extremely large-scale antenna arrays, to provide unprecedented spatial degrees of freedom for beam management in ISAC. However, as a type of leaky-wave antenna, the radiation power of an RHS is inherently featured by the leakage power constraint, i.e., the sum of leaky wave power radiated by the serially-fed metamaterial elements cannot exceed the input power of the RHS. To this end, we consider a holographic ISAC system with leakage power constraints in this paper, where two RHSs are applied for signal transmission and reception. In such a system, it is challenging to optimize the RHS beamformers because the radiation amplitudes of all the elements are coupled under the leakage power constraint. To tackle these challenges, we formulate the holographic beamforming optimization problem with leakage power constraints and develop a joint transmit and receive beamforming optimization algorithm to solve the formulated problem. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme and demonstrate that the leakage power constraints on the transmit and receive RHSs at the BS have different impacts on the overall ISAC performance.

In smart factories, the integration of cyber-physical systems and IoT devices has revolutionized manufacturing processes, enabling agile and highly customized production. However, the hyper-connected nature of these environments introduces significant security challenges, particularly in access control. Attribute-based access control (ABAC) is widely adopted for its flexibility in handling dynamic and complex access policies. Yet, ABAC suffers from rule explosion, where the exponential growth of policy combinations leads to high computational overhead and delays, adversely impacting production efficiency. To address these, we propose ABAC-Prune, an environment-adaptive DRL-rule fused ABAC pruning framework, where deep reinforcement learning (DRL) drives coarse-grained adaptation and rule-based policies enable fine-tuning. ABAC-Prune continuously monitors factory conditions, analyzes historical access patterns, and adjusts pruning strategies in real-time to balance security and production efficiency. Our evaluation in a simulated smart factory environment demonstrates that ABAC-Prune achieves a 22% reduction in security response latency and high improvement in production yield, while maintaining robust security by keeping the anomaly rate (illicit requests allowed) below a predefined threshold.

Millimeter-wave (mmWave) radar has emerged as a promising sensing technology for various applications due to its capabilities of all-weather operation and direct velocity measurement. However, existing mmWave radar schemes exhibit significant shape reconstruction errors when the target is partially occluded by obstacles. To address this issue, we propose a wireless amodal sensing paradigm that supports the shape reconstruction of the occluded target using a single mmWave radar. The basic idea is to first extract the features from the mmWave point cloud of the occluded target, and then leverage pre-trained generative models to complete the whole shape based on these features. New challenges have arisen that mmWave radar point clouds are inherently sparse, containing measurement noise that reduces the accuracy of feature extraction, thus resulting in degraded shape reconstruction quality. To tackle these challenges, we propose WASNet that incorporates a radar cross section (RCS)-enhanced geometric feature extraction module to suppress measurement noise and a Vision-Language Model (VLM)-based semantic injection module to enhance the shape reconstruction accuracy by extracting semantic features. Experiments demonstrate the effectiveness and robustness of the proposed scheme, which achieves a 70.5% reduction in point cloud reconstruction error compared with baselines.

The growth of the Internet of Things (IoT) has reshaped the way devices, systems, and applications connect, leading to an enormous surge in data generation across various domains. This expansion, paired with the exponential increase in IoT devices, requires advanced data analysis capabilities to manage the multimodal sensory data collected by the massive IoT devices in real time, such as sensor outputs, visual data, audios, and videos. To address this challenge, large generative artificial intelligent (AI) models are designed, showing promise in processing multimodal data. However, deploying these models on IoT devices is constrained by limited computational power, memory, and energy resources, preventing full realization of their potential for real-time IoT systems. To address these limitations, we propose an innovative end-edge collaborative model framework between end nodes and edge servers, designed to balance computational load and optimize resource use. This approach transmits both extracted features and residual mapping data from end nodes to edge servers, allowing for spectrum efficient data handling across the network. Our work formulates an optimization strategy to enhance mean average precision (mAP) by adjusting task distribution, bandwidth, and data quantization in response to real-time network and device conditions. Comprehensive simulations demonstrate the proposed approach’s superiority over conventional centralized edge model computing and distributed end model computing frameworks, achieving enhanced efficiency across various communication rates in real time.

The radio map presents communication parameters of interest, e.g., received signal strength, at every point across a geographical region. It can be leveraged to improve the efficiency of spectrum utilization in the region, particularly critical for unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sensors sparsely distributed in the region to infer a high-resolution radio map. This problem is challenging due to the ultra-low sampling rate, i.e., because the number of available samples is far fewer than the high resolution required for radio map estimation. We propose WiFi-Diffusion – a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model; 2) a generation block, leveraging the diffusion model to generate a candidate set of fine-grained radio maps; and 3) an election block, utilizing the radio propagation model as a guide to find the best fine-grained radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate; and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA.

The evolving Industrial Internet of Things (IIoT) is shifting towards decentralized collaborative manufacturing, posing heightened network security issues within interconnected value chains, thus requiring advanced Network Intrusion Detection (NID) systems to identify potential threats. In this context, traditional centralized NID systems are insufficient due to cross-industrial privacy concerns and interconnected secure threats. Federated Learning (FL) has emerged as a promising solution to enable the sharing of security insights without compromising privacy across participants. However, establishing an FL-based NID framework in realistic IIoT scenarios faces several hurdles, including the limited availability of large-scale devices and heterogeneous attack data distributions. The former leads to inconsistent client participation and degraded performance, while the latter hinders model convergence. To address these, we propose a novel Federated Learning-based Industrial Network Intrusion Detection (FedIn-NID) framework, incorporating a multidimensional client selection strategy and a dynamic global aggregation strategy. The selection strategy synergistically considers multidimensional factors including client availability, local dataset distribution, and dataset size. This approach accommodates clients with varying availability and avoids the selection of biased clients with data concentrated in a few categories. During model aggregation, the proposed strategy leverages the concept of exponential moving average to dynamically balance the holistic yet slightly older knowledge in the global model with the partial but relatively newer knowledge in local models, ensuring effective aggregation and convergence of the global NID model. Experiments demonstrate that FedIn-NID outperforms baselines by 10% to 30%, showcasing remarkable robustness with increasing data distribution heterogeneity and device count.

The highly variable nature of cellular networks challenges end-to-end network transmissions in achieving low-latency and high-throughput performance. In high-speed rail (HSR) networks, the intermittent connectivity and capacity dynamics imposed by high client mobility further add complexity and difficulty in providing seamless service. While congestion control algorithms (CCAs) play an essential role in ensuring optimal network performance, prior works on congestion control have predominantly concentrated on enhancing network performance within stationary or low-mobility mobile networks without considering frequent disconnections and highly dynamic network capacities imposed by HSR networks, resulting in severe RTT inflation and slow loss recovery. In this paper, we argue that a dedicated transport layer protocol is necessary for high-mobility scenarios. We propose an end-to-end low-latency congestion control algorithm HiMo for HSR networks that reacts to abrupt bandwidth changes quickly, handles frequent handovers, and is immediately deployable. Our trace-driven emulation on real-world datasets demonstrates that HiMo can reduce 51.3% 95th-percentile latency with comparable throughput on high-speed rail networks, compared to state-of-the-art CCAs.

In this paper, we propose an efficient model training scheme, namedGroup-basedHierarchicalSplitLearning (GHSL), which can accelerate the artificial intelligence (AI) training process in edge networks in a “first-sequential-then-parallel” manner. Specifically, the proposed scheme hierarchically splits an AI model into a user-side and server-side model, while dividing a number of users into multiple groups. Users in each group train user-side models with the interaction of the shared server-side model sequentially; different groups perform the above training process parallelly; the AI models of each group are aggregated into a global model. We also carry out the convergence analysis for the proposed scheme over non-independent and identically distributed data, which reveals that the convergence rate depends on user grouping. Furthermore, we propose a data-driven two-stage user grouping algorithm to minimize the overall training delay, taking user resource heterogeneity and the black-box training process into account. The proposed algorithm first utilizes the Gaussian process regression approach to determine the number of groups, and then employs the coalition game theory to determine the optimal user grouping decision. Comprehensive simulation results demonstrate that the proposed scheme can reduce training delay, user-side computational workload, and communication overhead by up to 19%, 53%, and 54%, respectively, comparing to state-of-the-art benchmarks.

Network slicing is the key enabler for the 5G Industrial Internet of Things (IIoT), allowing tailored services and security guarantees for vertical industries. With the advent of 5G-Advanced (5G-A) and 6G era, the number of slices will increase significantly, leading to more diverse security requirements given different slice features. To provide adaptive security management spanning multiple slices in IIoT, this paper proposes a novel slice-specific secure IIoT (SSIOT) architecture with an AI-enabled solution. The SSIOT architecture separates the control and data planes, where the control plane orchestrates the Security Service Function Chains (SSFC) across network slices and the data plane analyzes the slice-specific features like traffic patterns, resource SLA guarantees, and Virtual Security Network Function (VSNF) dependencies. To extract these spatial-temporal features from the dynamic IIoT environments, we facilitate the powerful deep reinforcement learning (DRL) methods and propose a structural GS2L approach. GS2L is maliciously designed with the core principles of graph convolutional network (GCN) and Gated Recurrent Unit (GRU), enabling a thorough understanding of physical resource distribution and the request dynamics across slices. Extensive experiments are conducted in diverse IIoT slices with the real-world USNet and fat-tree topologies. Simulation results demonstrate that GS2L outperforms state-of-the-art learning and heuristic benchmarks, showcasing an overall 15.2% improvement with efficient and stable resource utilization.

Due to the openness of wireless channels, wireless transmission is susceptible to malicious jamming attacks, posing a severe threat to wireless communications. Existing studies on anti-jamming mainly considered enhancing desired signals or mitigating jamming, leading to limited performance. To address this issue, intelligent omni-surface (IOS) is a promising solution, which can simultaneously nullify jamming and enhance desired signals by jointly manipulating its reflective and refractive properties. In this paper, we consider an IOS-aided multi-user anti-jamming communication system. We aim to improve desired signals and nullify jamming by joint IOS phase shifts and transmit beamforming optimization, which is challenging due to the coupled and discrete nature of IOS reflection and refraction phase shifts, unknown jammer’s beamformer, and imperfect jammer-related channel state information. To tackle this, we relax IOS phase shifts to continuous states and develop a coupling-aware algorithm using Cauchy-Schwarz inequality and S-procedure, followed by a local search to recover discrete states. Simulation results show that the proposed scheme significantly improves the sum rate in the presence of jamming attacks.

Radio map presents communication parameters of interest, e.g., radio power, across a geographical region in a specific frequency band. It can be leveraged to improve the efficiency of spectrum utilization. The problem of constructing a radio map involves utilizing measurements from sparsely distributed sensors to infer the parameter of interest at every point in the region. One major challenge of solving this problem is ensuring a strong generalization capability for the solution, i.e., ensuring that the solution is able to make accurate inferences even in the presence of unknown system variables that can affect radio propagation, such as obstacle layouts and weather conditions. This paper focuses on analyzing and optimizing the generalization capability of radio map construction, an aspect that has been neglected in prior research as far as we know. We design RobUNet—a UNet-based algorithm that captures multi-scale features of radio maps. It further leverages mechanisms of residual connection, channel attention, and pixel attention to enhance its generalization capability. Simulations based on real-world dataset show that (i) the radio maps constructed by RobUNet are much more accurate than those constructed by many existing solutions in the presence of unknown system variables, and (ii) the accuracy of the radio maps constructed by RobUNet when system variables are unknown is close to that of the radio maps constructed by RobUNet when system variables are known. As a result, the generalization capability of RobUNet is much stronger than that of state-of-the-art.

The Industrial Internet of Things (IIoT) continues to evolve alongside advancements in 5G and beyond 5G communication technologies, and network slicing has emerged as a promising technique to offer isolated slices and tailored services across industrial use cases, which profoundly affects the traditional security solution. As the future IIoT evolves towards the post-5G era, the anticipated growth in network slices will unavoidably exacerbate challenges in security service management, but developing an adaptive and effective security strategy remains a significant challenge. This study introduces a novel slice-specific IIoT (SSIOT) architecture, meticulously crafted to address the unique security needs of each slice based on network function virtualization. To adapt to the SSIOT, an AI-driven GS2L model is presented, which combines graph convolutional network (GCN) with sequence-to-sequence (Seq2Seq) deep reinforcement learning (DRL). The GS2L offers the network topology explanation module, service request analysis module, and slice-specific distribution extraction module, and adeptly navigates the multifaceted Security Service Function Chain (SSFC) embedding conundrum and resource allocation in slice-specific systems. Comprehensive experimental evaluations underscore GS2L's superiority, showcasing its proficiency in delivering augmented QoS satisfaction while ensuring prudent resource utilization over the learning-based and heuristic benchmark.

As a crucial facilitator of future autonomous driving applications, wireless simultaneous localization and mapping (SLAM) has drawn growing attention recently. However, the accuracy of existing wireless SLAM schemes is limited because the antenna gain is constrained given the cost budget due to the expensive hardware components such as phase arrays. To address this issue, we propose a reconfigurable holographic surface (RHS)-aided SLAM system in this paper. The RHS is a novel type of low-cost antenna that can cut down the hardware cost by replacing phased arrays in conventional SLAM systems. However, compared with a phased array where the phase shifts of parallel-fed signals are adjusted, the RHS exhibits a different radiation model because its amplitude-controlled radiation elements are series-fed by surface waves, implying that traditional schemes cannot be applied directly. To address this challenge, we propose an RHS-aided beam steering method for sensing the surrounding environment and design the corresponding SLAM algorithm. Simulation results show that the proposed scheme can achieve more than there times the localization accuracy that traditional wireless SLAM with the same cost achieves.

The rise of collaborative manufacturing, driven by the rapid proliferation of Industrial Internet of Things (IIoT) technologies, has markedly enhanced agility and productivity in industrial environments. However, this advancement has also significantly broadened the attack surface and uncovered unique vulnerabilities intrinsic to these interconnected systems. This paper introduces a novel Hierarchical Federated Incremental Learning Network Intrusion Detection (HFIN) approach. To our knowledge, this is the first attempt to address the ever-evolution network intrusion detection (NID) challenges in IIoT landscapes from the continuous attack-defense perspective. Our proposed HFIN capitalizes on decentralized model training across multifarious IIoT devices, ensuring data privacy and empowering continuous learning capabilities. It utilizes distributed data sources for secure experience sharing, collaboratively enhancing the continuous detection performance of the global model. Furthermore, regarding the inherent resource constraints of IIoT devices, we proposed a novel edge-client Weighted Transmission Optimization strategy (WTO). This strategy adeptly balances effective intrusion detection with the operational constraints of IIoT devices. By holistically considering detection capabilities and data volume across different attack types, it prioritizes the transmission of more critical and scarce attack data for training within bandwidth constraints. This maintains the comprehensive detection capability of the global model against various network attacks. To validate the effectiveness of HFIN, we conduct extensive experiments using the NF-UQ-NIDS-v2 and NF-ToN-IoT-v2 datasets. Experimental results demonstrate that our method outperforms baselines by approximately 10% in terms of accuracy and F1-score, highlighting the applicability and effectiveness of HFIN in enhancing security against sophisticated industrial environments and ever-evolving cyber threats.

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

Internet of meta-material things (meta-IoT) is a network of sensors composed of meta-materials with the advantages of low cost, ultra-low power consumption, and robust, showing great potential for the coming 6G communications. However, existing meta-IoT systems assume specular reflection on meta-IoT sensors, which limits their applications. For example, in chemical factories with harsh environments, receivers are often deployed on mobile robots. Due to their mobility, when measuring the signals, it is infeasible to ensure that the receivers are located at a certain angle relative to the meta-IoT sensors. Therefore, it is necessary to broaden the angle range of reflected signal coverage. In this paper, we propose a meta-IoT system capable of supporting receivers deployed at arbitrary angles in a broadened angle range. To be specific, we first propose an inhomogeneous structural design for meta-IoT sensors to achieve reflection coverage broadening. Then, we establish the signal transmission model from the transmitter to the receiver, going through the proposed meta-IoT sensor. To maximize the reflection coverage while ensuring accurate sensing results, we formulate a joint meta-IoT structure and sensing function optimization problem and propose efficient algorithms to solve it. Simulation results verify the effectiveness of the design method for the proposed meta-IoT system to achieve reflection coverage broadening.

Pattern reconfigurable multiple-input multiple-output (PR-MIMO) can manipulate the wireless channel according to different communication requirements. In this paper, we discuss the potential of constructive interference (CI)-based symbol-level precoding (CI-SLP) in PR-MIMO communication systems. The joint design problem that optimizes the SLP strategy and the radiation pattern of PR-MIMO for phase-shift keying (PSK) modulation is formulated to maximize the worst serviced user’s communication quality. Since the optimization variables are softly-coupled, we employ the alternating optimization framework to decompose the joint design problem into the SLP design sub-problem and the pattern design sub-problem. We simplify the pattern design sub-problem and propose an interior-point algorithm, where a sequential optimization-based scheme is further proposed as a sub-optimal solution with low complexity. Furthermore, the discussion is extended to quadrature amplitude modulation (QAM) modulated systems, where a special stopping criterion is proposed to guarantee the performance gain of the proposed scheme. The practical realization of the designed reconfigurable antenna array is also discussed, where we propose a design scheme using programmable metasurface antennas based on time-division switching to enable quick and adaptive pattern reconfiguration. Numerical results demonstrate that the radiation pattern configurability can further enhance the benefit of SLP over conventional precoding approaches.

Holographic massive-input-massive-output (HMIMO) is expected to play an important role in 6G, which integrates numerous antennas or reconfigurable elements into a compact surface to form a continuous aperture. However, it is not energy efficient to implement the HMIMO with conventional phased arrays, since hundreds of energy-intensive phase shifters are required, leading to inevitably huge power consumption and degraded energy efficiency. Compared with the phased array, metasurface-based antennas, also referred to as reconfigurable refractive surface (RRS), can significantly improve the energy efficiency, since they are free of those energy-hungry phase shifters. In this paper, we consider an RRS-enabled multi-user HMIMO system, where the energy efficiency of the system is maximized by optimizing the size of the RRS. However, different from the traditional metasurfaces that locate far from the base station (BS) and work as relays, the RRS is much closer to the BS such that the BS antennas cannot be assumed to locate in the far field of the RRS. Therefore, it is challenging to maximize the energy efficiency of the RRS-aided system. To cope with this issue, the capacity and power consumption of this system are analyzed first, based on which the energy efficiency is maximized by optimizing the number of RRS elements. The maximized energy efficiency is then compared against that obtained by the phased array. Through theoretical analysis and simulations, we verify that the RRS is a more energy efficient solution to HMIMO than the phased array when the power consumption per RRS element is lower than a derived closed-form threshold.

The Internet of Meta-material Things (Meta-IoT) has significant potential for future IoT applications due to its ability to function without a battery and its ease of maintenance. Specially, the environmental information can be obtained by analyzing the frequency response of Meta-IoT sensor from the reflected signals. Through the reflection, these Meta-IoT sensors can also provide additional paths for improving communication performance while performing sensing. In this paper, we design a dual-functional Meta-IoT network for integrated sensing and communication (ISAC), i.e., the Meta-IoT sensors can sense environmental conditions and facilitate communication simultaneously. However, ensuring the quality of both sensing and communication for multiple users is not trivial since the impact of the sensor structure on sensing and communication at the same time. Moreover, a customized beamforming scheme is required to balance the performance of two functions. To address these challenges, we optimize the sensor structure and the transmit beamforming. Simulation results show that the communication performance can be significantly improved with the proposed scheme while the sensing performance is guaranteed.

Holographic communication is recently envisioned to be a promising technology to handle the exponentially increasing data transmission demands, utilizing a large number of compact and tunable antenna elements. In this paper, we consider a holographic communication system where the beam-forming scheme is developed by the codebook design and beam training to avoid the high overhead of acquiring perfect channel state information. Given the large-scale antenna array, users are expected to be distributed in both the near and far fields of the base station, and thus, we design a near-far field codebook to apply to all users in unknown locations. However, the fine-grained beam training using narrow beams capable of enhancing received signal power at the expense of a high overhead which occupies significant time resources. To tackle such a conflict, we propose the adaptive beam training that leverages user feedback to train user-densely distributed regions at a fine-grained level and user-sparsely distributed ones in a coarse manner, thereby improving the throughput. Under a general setting including both the near-field and far-field users, simulation results show that the proposed scheme achieves a higher sum rate and throughput compared to the state-of-the-art schemes.

Edge computing has been viewed as a powerful technology to realize the vision of network services. However, due to the limited capabilities and insufficient security systems, edge computing is vulnerable to distributed denial of service (DDoS) attacks which may exhaust the resources of edge servers with excessive requests and degrade their service capabilities. Setting detection thresholds for DDoS detection indicators can effectively prevent DDoS attacks, but existing thresholding methods fail to update detection thresholds in time to guarantee the detection performance whenever the system settings vary. In this paper, we propose a multi-dimensional thresholding method against DDoS attacks in edge computing. We design three detection indicators based on the behavior features of DDoS attackers. By solving a threshold optimization problem, we obtain closed-form solutions and numerical solutions of the optimal detection thresholds, which adapt to dynamic system settings. Simulations show that the proposed thresholding method has a superior detection performance in terms of both the accuracy and robustness.

The Internet of Things (IoT) is a major driving force for future sixth-generation (6G) wireless systems. With the emergence of various novel IoT applications, more data should be collected and transmitted. However, IoT devices are constrained by battery, transmit power, and processing capacity. Featured by line-of-sight communication links, favorable channels, and better coverage, aerial access networks have been proposed to facilitate data transmission from IoT devices. In parallel, by shifting the computing and storage resources from the cloud to the edge of the network, edge computing [e.g., fog and mobile-edge computing (MEC)] can better support various computing-intensive and low-latency IoT applications. The integration of aerial access networks and edge computing, so-called aerial computing, is anticipated to provide not only traditional communication services but also advanced services for the IoT on a global scale.

Ultra-massive multiple-input multiple-output (MIMO) is expected to be one of the key enablers in the forthcoming 6G networks to handle various user demands by exploiting spatial diversity. In this paper, a new paradigm termed holographic radio is considered for ultra-massive MIMO via integrating numerous antenna elements into a compact space, thereby achieving a spatially quasi-continuous aperture and realizing high beampattern gain. We propose a practical path to implement holographic radio by a novel metasurface-based antenna called a reconfigurable holographic surface (RHS). Specifically, the RHS is capable of holographic beamforming over the spatially quasi-continuous apertures by incorporating densely packed tunable metamaterial elements with low power consumption. To enhance the performance of the RHS as an antenna array for achieving ultra-massive MIMO, a holographic beamforming optimization algorithm is developed for beampattern gain maximization based on the hardware design and full-wave analyses of RHSs. We then implement a prototype of an RHS and build an RHS-aided communication platform to further substantiate the feasibility of RHS-enabled holographic radio. Both simulation and experimental results verify the effectiveness of the proposed holographic beamforming optimization algorithm. It is also proved that the RHS-aided communication platform is capable of supporting real-time transmission of high-definition video.

Using widely deployed Internet of Things (IoT) sensors to perceive the environmental distribution is crucial in many IoT applications, such as intelligent healthcare and smart home. As using traditional sensors will lead to high costs and maintenance, it is important to design the next-generation IoT sensor to reduce the cost of ubiquitous deployment. For this purpose, we propose a novel IoT system based on low-cost and fully passive meta-material sensors. Specifically, the meta-material sensors can sense multiple environmental conditions such as temperature and humidity levels, and transmit back the information by signal reflection, simultaneously. With the information contained in the received signals, a wireless receiver can obtain detailed environmental distributions. However, it is not trivial to achieve high sensing accuracy in the meta-material sensor based IoT system because the structure of the meta-materials, the deployment positions of sensors, and the reconstruction function for environmental distributions need to be jointly optimized. To handle this challenge, we propose an algorithm to design the meta-material based IoT system with the help of a deep learning approach. Simulation results verify that the proposed algorithm effectively maximizes the sensing accuracy. Experimental evaluations also show that the proposed scheme can obtain humidity distribution with an accuracy of over 93%.

Reconfigurable multiple-input multiple-output can provide performance gains over traditional MIMO by reshaping the channels, i.e., introducing more channel realizations. In this paper, we focus on the achievable rate maximization pattern design for reconfigurable MIMO systems. Firstly, we introduce the matrix representation of pattern reconfigurable MIMO (PR-MIMO), based on which a pattern design problem is formulated. To further reveal the effect of the radiation pattern on the wireless channel, we consider pattern design for both the single-pattern case where the optimized radiation pattern is the same for all the antenna elements, and the multi-pattern case where different antenna elements can adopt different radiation patterns. For the single-pattern case, we show that the pattern design is equivalent to a redistribution of gains among all scattering paths, and an eigenvalue optimization based solution is obtained. For the multi-pattern case, we propose a sequential optimization framework with manifold optimization and eigenvalue decomposition to obtain near-optimal solutions. Numerical results validate the superiority of PR-MIMO systems over traditional MIMO in terms of achievable rate, and also show the effectiveness of the proposed solutions.

Wireless simultaneous localization and mapping (SLAM) has attracted much attention as a promising technique to empower location based services. However, the accuracy of traditional wireless SLAM systems is limited as the wireless signals are easily disturbed by the uncontrollable radio environments. To mitigate this issue, in this paper, we propose a MetaSLAM system where multiple reconfigurable intelligent surfaces (RISs) are deployed to customize the wireless environments. To be specific, through adjusting the phase shifts of these RISs, the strength of reflected signals can be enhanced in order to resist the variance of radio environments. However, it is challenging to coordinate multiple RISs and optimize their phase shifts especially when their locations are unknown to the agent. In order to address these challenges, we formulate a MetaSLAM optimization problem, and design a two-stage optimization algorithm based on the genetic and particle filter algorithms to solve the formulated problem. Analysis of the complexity and the positioning error bound of the proposed SLAM system are provided. Simulation results show that compared with the benchmark schemes, the positioning error obtained by the MetaSLAM system is reduced by at least 31%.

Different from traditional reflection-only reconfigurable intelligent surfaces (RISs), simultaneously transmitting and reflecting RISs (STAR-RISs) represent a novel technology, which extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. STAR-RISs provide new degrees-of-freedom (DoF) for manipulating signal propagation. Motivated by the above, a novel STAR-RIS assisted non-orthogonal multiple access (NOMA) (STAR-RIS-NOMA) system is proposed in this paper. Our objective is to maximize the achievable sum rate by jointly optimizing the decoding order, power allocation coefficients, active beamforming, and transmission and reflection beamforming. However, the formulated problem is non-convex with intricately coupled variables. To tackle this challenge, a suboptimal two-layer iterative algorithm is proposed. Specifically, in the inner-layer iteration, for a given decoding order, the power allocation coefficients, active beamforming, transmission and reflection beamforming are optimized alternatingly. For the outer-layer iteration, the decoding order of NOMA users in each cluster is updated with the solutions obtained from the inner-layer iteration. Moreover, an efficient decoding order determination scheme is proposed based on the equivalent-combined channel gains. Simulation results are provided to demonstrate that the proposed STAR-RIS-NOMA system, aided by our proposed algorithm, outperforms conventional RIS-NOMA and RIS assisted orthogonal multiple access (RIS-OMA) systems.

Holographic Multiple Input Multiple Output (HMIMO), which integrates massive antenna elements into a compact space, has been considered as a promising enabling technique for future wireless networks. For the HMIMO implemented by traditional phased arrays, the system capacity is insufficient to satisfy the requirement of future networks since the phased array requires energy-consuming phase shifters. Compared with the phased array, reconfigurable refractive surface (RRS), which is free of phase shifters, can significantly improve the system capacity given the same power budget. In this paper, we consider a multi-user RRS-based HMIMO system. Unlike traditional metasurfaces working as passive relays, the RRS is used as transmit antennas, indicating that the RRS is much closer to the feeds. Therefore, the far-field approximation no longer holds, urging a new performance analysis framework. To address the above challenge, we first derive the system capacity, which is then compared against that obtained by the phased array. Simulation results verify our analysis and show that the RRS can bring higher system capacity.

Holographic multiple-input multiple-output (HMI-MO) has recently motivated its potential use to handle the exponentially increasing data transmission demands by achieving a spatially continuous aperture. With densely-packed metamaterial elements, the reconfigurable refractive surfaces (RRSs) serving as antennas emerge to enable HMIMO. In this paper, we consider a multi-user system where an RRS is employed as the transmit antenna array at the base station (BS). To avoid the high overhead of acquiring perfect channel state information (CSI), a codebook and beam training mechanism is required to develop the beamforming scheme. Given the large physical dimension of the RRS, users are likely to distribute in both the near field and the far field of the BS, making the codebook design more difficult. To address this issue, we design a hybrid near-far field codebook which applies to all users in any location with low overhead. To further reduce the training overhead, each codeword is designed to cover multiple spatial areas, enabling a multi-user beam training mechanism which can be performed for all users simultaneously. Under a general setting including both the near-field and far-field users, simulation results show that the proposed scheme significantly reduces the overhead and achieves a higher sum rate compared to the state-of-the-art codebooks, which performs very close to that of the perfect CSI case.

Obtaining fine-grained structural information about building through ubiquitous sensors is crucial for assessing their aging and damage. However, due to the energy requirements, traditional sensors deployed in the building structure need frequent maintenance works which are easy to produce irreversible damage to the building. Besides, the larger volume of sensors also brings the difficulty of deployment in buildings with complex structures. To solve these problems, we propose a novel sensing system to obtain fine-grained structural information based on ubiquitous deployed meta-material sensors. Specifically, meta-material sensors are small pieces of PCB printed with metal structure, which work without a power supply and suit wide deployment. The experiment realizes the humidity sensing with a spatial resolution of 0.5m, while existing methods for dispersing sensors achieve space intervals of 10m at the same cost. With this framework, the need of providing information support for assessing structural failure can be met.

Visible Light Backscatter Communication (VLBC) presents an emerging ultra-low-power IoT connectivity solution with high spatial-spectral efficiency and intrinsic human-perceivable privacy advantages. However, research progress on enhanced data rate and sophisticated device coordination of state-of-the-art VLBC systems still cannot meet the low-latency requirement (sub-second level for an IoT network) for massive connections.

In this paper, we focus on the pattern reconfigurable multiple-input multiple-output (PR-MIMO), a technique that has the potential to bridge the gap between electro-magnetics and communications towards the emerging Electro-magnetic Information Theory (EIT). Specifically, we focus on the pattern design problem aimed at maximizing the channel capacity for reconfigurable MIMO communication systems, where we firstly introduce the matrix representation of PR-MIMO and further formulate a pattern design problem. We decompose the pattern design into two steps, i.e., the correlation modification process to optimize the correlation structure of the channel, followed by the power allocation process to improve the channel quality based on the optimized channel structure. For the correlation modification process, we propose a sequential optimization framework with eigenvalue decomposition to obtain near-optimal solutions. For the power allocation process, we provide a closed-form power allocation scheme to redistribute the transmission power among the modified subchannels. Numerical results show that the proposed pattern design scheme offers significant improvements over legacy MIMO systems, which motivates the application of PR-MIMO in wireless communication systems.

Recently, the intelligent omni-surface (IOS) has been proposed as a novel instance of metasurface to achieve full-dimensional communications by jointly engineering its reflective and refractive properties. However, optimal beamforming scheme for the IOS is hard to obtain due to the difficulty in acquiring perfect channel state information (CSI). To address this issue, in this paper, we consider an IOS aided system where the beamforming scheme is designed via beam training with codebooks at the base station (BS), the IOS, and users. Given that the refractive/reflective signals are closely related to both incident signals from the BS and phase shifts of IOS elements, the codebooks at the BS and the IOS are designed jointly. Based on the joint BS-IOS codebook, a multi-lobe beam training mechanism is proposed to perform beam training for multiple users simultaneously, thereby reducing the training overhead. Simulation results indicate that our proposed scheme achieves a higher sum rate than the state-of-the-art beam training schemes and performs close to the perfect CSI case.

Ultra-dense low-Earth-orbit (LEO) satellite communication networks have significant potential for providing high-speed data services. To compensate the severe path loss in satellite communications, a key conceptual enabler is the holographic multiple input multiple output (HMIMO) with a spatially continuous aperture which can achieve a high directive gain with a small antenna size. In this paper, we consider a novel metamaterial antenna called a reconfigurable holographic surface (RHS) integrated with a user terminal (UT) to support LEO satellite communications. Composing of densely packing sub-wavelength metamaterial elements, the RHS can realize continuous or quasi-continuous apertures and provide a practical way towards the implementation of HMIMO. To obtain the desired beam directions towards the satellites, we propose a LEO satellite tracking scheme based on the temporal variation law such that frequent satellite positioning can be avoided. A holographic beamforming algorithm for sum rate maximization is then developed where a closed-form for the optimal holographic beamformer is derived. The robustness of the algorithm against the tracking errors of the satellites’ positions is also proved. Simulation results verify the theoretical analysis and show that the RHS outperforms the traditional phased array of the same physical dimension in terms of the sum rate when the compact element spacing of the RHS leads to much more RHS elements. Moreover, the RHS also provides a more cost-effective solution for pursuing high data rate compared with the phased array.

The next generation wireless communications aiming at enhancing capacity and massive connectivity significantly over high-frequency bands urge the development of novel multiple access technologies. In this paper, we propose a new type of space-division multiple access (SDMA), called holographic-pattern division multiple access (HDMA). We develop the principle for HDMA with the main idea of mapping the intended signals for receivers to a superposed holographic pattern. The multi-user holographic beamforming scheme for HDMA is then presented. Based on the theoretical analysis, we find that there exists an optimal holographic pattern such that the sum rate with simple zero-forcing precoding can achieve the asymptotic capacity of the HDMA system. Simulation results verify the theoretical analysis and show that the HDMA scheme outperforms the traditional SDMA scheme in terms of both the cost-efficiency and the sum rate.

To overcome spectrum congestion, a promising approach is to integrate sensing and communication (ISAC) functions in one hardware platform. Recently, metamaterial antennas, whose tunable radiation elements are arranged more densely than those of traditional multiple-input-multiple-output (MIMO) arrays, have been developed to enhance the sensing and communication performance by offering a finer controllability of the antenna beampattern. In this paper, we propose a holographic beamforming scheme, which is enabled by metamaterial antennas with tunable radiated amplitudes, that jointly performs sensing and communication. However, it is challenging to design the beamformer for ISAC functions by taking into account the unique amplitude-controlled structure of holographic beamforming. To address this challenge, we formulate an integrated sensing and communication problem to optimize the beamformer, and design a holographic beamforming optimization algorithm to efficiently solve the formulated problem. A lower bound for the maximum beampattern gain is provided through theoretical analysis, which reveals the potential performance enhancement gain that is obtained by densely deploying several elements in a metamaterial antenna. Simulation results substantiate the theoretical analysis and show that the maximum beamforming gain of a metamaterial antenna that utilizes the proposed holographic beamforming scheme can be increased by at least 50% compared with that of a traditional MIMO array of the same size. In addition, the cost of the proposed scheme is lower than that of a traditional MIMO scheme while providing the same ISAC performance.

A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication system over fading channels is investigated, where both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are employed. In particular, the time overhead for configuring the RIS reflective elements at the beginning of each fading channel is considered. The optimization goal is maximizing the effective throughput of the entire transmission period by jointly optimizing the phase shift of the RIS and the power allocation of the AP for each channel block. In an effort to solve the formulated problem and fill the research vacancy of the performance comparison between different machine learning tools in wireless networks, a deep learning (DL) approach and a reinforcement learning (RL) approach are proposed and their representative superiority and inferiority are investigated. The DL approach can locate the optimal phase shifts with the deep neural network fitting as well as the corresponding power allocation for each user. From the perspective of long-term reward, the phase shift control with configuration overhead can be regarded as a Markov decision process and the RL algorithm is proficient in solving such problems with the assistance of the Bellman equation. The numerical results indicate that: 1) From the perspective of the wireless network, NOMA can achieve a throughput gain of about 42% compared with OMA; 2) The well-trained RL and DL agents are able to achieve the same performance in Rician channel, while RL is superior in the Rayleigh channel; 3) The DL approach has lower complexity and faster convergence, while the RL approach has preferable strategy flexibility.

In future cellular systems, wireless localization and sensing functions will be built-in for specific applications, e.g., navigation, transportation, and healthcare, and to support flexible and seamless connectivity. Driven by this trend, the need for fine-resolution sensing solutions and centimeter-level localization accuracy arises, while the accuracy of current wireless systems is limited by the quality of the propagation environment. Recently, with the development of new materials, reconfigurable intelligent surfaces (RISs) provide an opportunity to reshape and control the electromagnetic characteristics of the environment, which can be utilized to improve the performance of wireless sensing and localization. In this tutorial, we will first review the background and motivation for utilizing wireless signals for sensing and localization. Next, we will introduce how to incorporate RIS into applications of sensing and localization, including key challenges and enabling techniques, and then, some case studies will be presented. Finally, future research directions will also be discussed.

For many applications envisioned for the Internet of Things (IoT), it is expected that the sensors will have very low costs and zero power, which can be satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their constituent meta-materials can reflect wireless signals with environment-sensitive reflection coefficients, meta-IoT sensors can achieve simultaneous sensing and transmission without any active modulation. However, to maximize the sensing accuracy, the structures of meta-IoT sensors need to be optimized considering their joint influence on sensing and transmission, which is challenging due to the high computational complexity in evaluating the influence, especially given a large number of sensors. In this paper, we propose a joint sensing and transmission design method for meta-IoT systems with a large number of meta-IoT sensors, which can efficiently optimize the sensing accuracy of the system. Specifically, a computationally efficient received signal model is established to evaluate the joint influence of meta-material structure on sensing and transmission. Then, a sensing algorithm based on deep unsupervised learning is designed to obtain accurate sensing results in a robust manner. Experiments with a prototype verify that the system has a higher sensitivity and a longer transmission range compared to existing designs, and can sense environmental anomalies correctly within 2 meters.

The intelligent omni-surface (IOS) is a dynamic metasurface that has recently been proposed to achieve full-dimensional communications by realizing the dual function of anomalous reflection and anomalous refraction. Existing research works provide only simplified models for the reflection and refraction responses of the IOS, which do not explicitly depend on the physical structure of the IOS and the angle of incidence of the electromagnetic (EM) waves. Therefore, the available reflection-refraction models are insufficient to characterize the performance of full-dimensional communications. In this paper, we propose a complete and detailed circuit-based reflection-refraction model for the IOS, which is formulated in terms of the physical structure and equivalent circuits of the IOS elements, as well as we validate it with the aid of full-wave EM simulations. Based on the proposed circuit-based model for the IOS, we analyze the asymmetry between the reflection and transmission coefficients. Moreover, the proposed circuit-based model is utilized for optimizing the hybrid beamforming of IOS-assisted networks and hence improving the system performance. To verify the circuit-based model, the theoretical findings, and to evaluate the performance of full-dimensional beamforming, we implement a prototype of IOS and deploy an IOS-assisted wireless communication testbed to experimentally measure the beam patterns and to quantify the achievable rate. The obtained experimental results validate the theoretical findings and the accuracy of the proposed circuit-based reflection-refraction model for IOSs.

The number of devices connected to the Internet of Things (IoT) is growing at an enormous rate globally. In the next generation networks, distributed fog computing deployments at the network edge can provide computing resources to the users, especially for latency-sensitive applications. Further, the heterogeneous needs of the fifth generation (5G) networks demand the virtualization of network functions, termed as network function virtualization (NFV). Therefore, an integrated NFV and fog computing resource allocation framework for IoT is of prime importance. Accordingly, in this paper, we model the interactions between the data service operators (DSOs) and the authorized data service subscribers (ADSSs) as an equilibrium problem with equilibrium constraints (EPEC), and utilize the alternating direction method of multipliers (ADMM) as a large-scale optimization tool to obtain solutions. This results in the optimization of resource pricing for the DSOs and the amount of resources to be purchased by the ADSSs. Moreover, we propose a many-to-many matching based model to allocate the fog node (FN) resources according to the VNF resource requirements of the ADSSs. Simulation results show the effectiveness of our proposed approach in achieving efficient resource allocation in NFV enabled IoT fog computing.

Indoor localization has drawn much attention owing to its potential for supporting location based services. Among various indoor localization techniques, the received signal strength (RSS) based technique is widely researched. However, in conventional RSS based systems where the radio environment is unconfigurable, adjacent locations may have similar RSS values, which limits the localization precision. In this paper, we present MetaRadar, which explores reconfigurable radio reflection with a surface/plane made of metamaterial units for multi-user localization. By changing the reflectivity of metamaterial, MetaRadar modifies the radio channels at different locations, and improves localization accuracy by making RSS values at adjacent locations have significant differences. However, in MetaRadar, it is challenging to build radio maps for all the radio environments generated by metamaterial units and select suitable maps from all the possible maps to realize a high accuracy localization. To tackle this challenge, we propose a compressive construction technique which can predict all the possible radio maps, and propose a configuration optimization algorithm to select favorable metamaterial reflectivities and the corresponding radio maps. The experimental results show a significant improvement from a decimeter-level localization error in the traditional RSS-based systems to a centimeter-level one in MetaRadar.

The future sixth generation (6G) networks look forward to providing revolutionary mobile connectivity and high-throughput data services through low-cost communication devices. Benefiting from the programmability and tunability of metamaterials, the reconfigurable holographic surface (RHS) inlaid with numerous metamaterial radiation elements shows its great potential to achieve such a bold vision. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight antenna integrated with the transceiver to generate desirable directional beams with low hardware cost and power consumption. In this paper, we consider a downlink RHS-aided multi-user communication system where a base station (BS) equipped with an RHS transmits signals to users. We propose an RHS-based hybrid beamforming scheme where the digital beamforming and the holographic beamforming are performed at the BS and RHS, respectively, together with the receive combining at each user. We formulate a sum-rate maximization problem for RHS-based hybrid beamforming and decompose it into three sub-problems. A joint sum-rate maximization algorithm is then developed to solve the sub-problems in an alternating manner. Simulation results show that based on the proposed RHS-aided hybrid beamforming scheme, a moderate-sized RHS is enough to achieve a satisfactory sum-rate, which is also higher than a same-sized phased array can achieve.

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).

Semantic segmentation is a process of partitioning an image into segments for recognizing regions of humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring. To avoid privacy violation, using radio frequency (RF) signals instead of photos for semantic segmentation has gained increasing attention. However, traditional human and object recognition by using RF signals is a passive signal collection and analysis process without changing the radio environment. The recognition accuracy is restricted significantly by unwanted multi-path fading, and/or the limited number of independent channels between RF transceivers. This paper introduces MetaSketch, a novel RF-sensing system that performs semantic recognition and segmentation for humans and objects by making the radio environment reconfigurable. A metamaterial-based reconfigurable intelligent surface is incorporated to diversify the information carried by RF signals. Using compressive sensing techniques, MetaSketch reconstructs a point cloud consisting of the reflection coefficients of humans and objects at different spatial points, and recognizes the semantic meaning of the points by using symmetric multilayer perceptron groups. Our evaluation results show that MetaSketch is capable of generating favorable radio environments, extracting exact point clouds, and labeling the semantic meaning of the points with an average error rate of less than 1% in an indoor space.

In the coming 6G communications, the internet of things (IoT) will be a fundamental enabler for ubiquitous environment perception, which requires the IoT sensors to consume near-zero power and have the lowest cost. For this purpose, the IoT sensors are expected to perform simultaneous sensing and transmission, so that energy and hardware costs due to signal modulation can be saved. In this paper, we propose the concept of meta-IoT, i.e., the IoT with sensors composed of specially designed meta-materials, which can achieve simultaneous sensing and transmission without supplied power. The basic idea of meta-IoT sensors is that the signal reflection on the sensors is sensitive to environmental conditions, which can be captured by a wireless receiver. In order to optimize the sensing systems with meta-IoT sensors, we establish the mathematical model of meta-IoT sensors’ sensing and transmission and then jointly optimize the sensors’ structure and the environment estimation at the receiver. We design and implement a practical meta-IoT sensing system for monitoring temperature and humidity levels. Simulation results show that the proposed technique can obtain the optimal sensor structure, and the experimental results verify that the designed meta-IoT sensing system achieves low measurement errors.

In this paper, we study the cellular communications over unlicensed millimeter-wave (mmWave) bands to satisfy the extra-high transmission rate demands. To guarantee the harmonious coexistence of cellular and WiGig users over 60 GHz bands, we design a frame structure for the unlicensed mmWave spectrum sharing to reduce the interference caused by cellular data transmission to WiGig networks. By joint frequency and spatial resource allocation, an iterative channel allocation and hybrid beamforming algorithm is designed to maximize the sum rate of all cellular users while minimizing the interference to WiGig networks. Simulation results show that our proposed scheme outperforms the SVD-based hybrid beamforming method. The fairness between the cellular and the WiGig users is achieved by setting an optimal transmit power of the gNB for unlicensed mmWave spectrum sharing. The influence of the beamwidth and beam direction of the gNB is evaluated in terms of the system performances of both the unlicensed cellular users and WiGig users.

Recently, the intelligent omni-surface (IOS) has been proposed as a novel instance of metasurface to achieve full-dimensional communications by jointly engineering its reflective and refractive properties. However, optimal beamforming scheme for the IOS is hard to obtain due to the difficulty in acquiring perfect channel state information (CSI). To address this issue, in this paper, we consider an IOS aided system where the beamforming scheme is designed via beam training with codebooks at the base station (BS), the IOS, and users. Given that the refractive/reflective signals are closely related to both incident signals from the BS and phase shifts of IOS elements, the codebooks at the BS and the IOS are designed jointly. Based on the joint BS-IOS codebook, a multi-lobe beam training mechanism is proposed to perform beam training for multiple users simultaneously, thereby reducing the training overhead. The training/feedback overhead of the proposed beam training and the impact of the codebook size are then analyzed theoretically. Simulation results indicate that the proposed scheme achieves a higher sum rate than the state-of-the-art beam training schemes and performs close to the perfect CSI case.

Recently, reconfigurable intelligent surfaces (RISs) have been proposed as a novel solution to enhance wireless communications such as suppressing inter-cell interference. However, signals arriving at a conventional reflecting-type RIS can only be reflected towards one side, leading to a limited service coverage, especially in an indoor environment involving potential obstacles. In this paper, we consider an intelligent omni-surface (IOS) which can provide services for users on both sides by enabling simultaneous signal reflection and transmission. Specifically, we propose an IOS aided indoor communication system where an IOS is embedded in a wall between two independent access points (APs) to suppress inter-cell interference. Due to the independence of the APs, we design a distributed hybrid beamforming scheme consisting of digital beamforming at APs and IOS-based analog beamforming to maximize the sum rate without any exchange of channel state information (CSI) between APs. Simulation results indicate that the proposed system performs very close to an optimal centralized scheme, and has a better sum rate performance compared to existing schemes.

Compared with the conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting intelligent omini-surfaces (STAR-IOSs) are able to achieve 360° coverage “smart radio environments”. By splitting the energy or altering the active number of STAR-IOS elements, STAR-IOSs provide high flexibility of successive interference cancellation (SIC) orders for non-orthogonal multiple access (NOMA) systems. Based on the aforementioned advantages, this paper investigates a STAR-IOS-aided downlink NOMA network with randomly deployed users. We first propose three tractable channel models for different application scenarios, namely the central limit model, the curve fitting model, and the M-fold convolution model. More specifically, the central limit model fits the scenarios with large-size STAR-IOSs while the curve fitting model is extended to evaluate multi-cell networks. However, these two models cannot obtain accurate diversity orders. Hence, we figure out the M-fold convolution model to derive accurate diversity orders. We consider three protocols for STAR-IOSs, namely, the energy splitting (ES) protocol, the time switching (TS) protocol, and the mode switching (MS) protocol. Based on the ES protocol, we derive closed-form analytical expressions of outage probabilities for the paired NOMA users by the central limit model and the curve fitting model. Based on three STAR-IOS protocols, we derive the diversity gains of NOMA users by the M-fold convolution model. The analytical results reveal that the diversity gain of NOMA users is equal to the active number of STAR-IOS elements. Numerical results indicate that 1) in high signal-to-noise ratio regions, the central limit model performs as an upper bound of the simulation results, while a lower bound is obtained by the curve fitting model; 2) the TS protocol has the best performance but requesting more time blocks than other protocols; 3) the ES protocol outperforms the MS protocol as the ES protocol has higher diversity gains.

As a widely used localization and sensing technique, radars will play an important role in future wireless networks. However, the wireless channels between the radar and the targets are passively adopted by traditional radars, which limits the performance of target detection. To address this issue, we propose to use the reconfigurable intelligent surface (RIS) to improve the detection accuracy of radar systems due to its capability to customize channel conditions by adjusting its phase shifts, which is referred to as MetaRadar. In such a system, it is challenging to jointly optimize both radar waveforms and RIS phase shifts in order to improve the multi-target detection performance. To tackle this challenge, we design a waveform and phase shift optimization (WPSO) algorithm to effectively solve the multi-target detection problem, and also analyze the performance of the proposed MetaRadar scheme theoretically. Simulation results show that the detection performance of the MetaRadar scheme is significantly better than that of the traditional radar schemes.

Intelligent reflecting surfaces (IRSs), which are capable of adjusting radio propagation conditions by controlling the phase shifts of the waves that impinge on the surface, have been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, an IOS is an important instance of a reconfigurable intelligent surface (RIS) that can provide service coverage to mobile users (MUs) in a reflective and a refractive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS jointly perform beamforming, for improving the received power of multiple MUs on both sides of the IOS, through different reflective/refractive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to efficiently solve the resulting non-convex program. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.

In this paper, we investigate the traffic-sensitive multi-layer low Earth orbit (LEO) satellite-terrestrial network. Massive terrestrial user access to the core network is realized via the backhaul supported by multi-layer LEO satellites. The ultra-dense satellite topology enables a promising solution for the high-capacity backhaul data transmission for terrestrial users. Jointly considering the backhaul capacity requirement and traffic dynamics of terrestrial satellite terminals, we analyze their average backhaul capacity using both stochastic geometry and queueing theory. Aiming to minimize the total required satellite number for fulfilling the backhaul capacity and seamless global coverage requirements, we propose a multi-layer LEO satellite constellation deployment scheme considering the satellite mobility. Simulation results verify the backhaul capacity analysis and the advantage of multi-layer constellation for saving satellites. The optimized multi-layer LEO satellite constellation with any coverage requirement and traffic rate is presented.

In the coming 6G communications, the internet of things (IoT) serves as a key enabler to collect environmental information and is expected to achieve ubiquitous deployment. However, it is challenging for traditional IoT sensors to meet this expectation because of their requirements of power supplies and frequent maintenance, which are due to their power-demanding sense and transmit modules. To address this challenge, we propose a meta-IoT sensing system, where the IoT sensors are based on specially designed meta-materials. The meta-IoT sensors achieve simultaneous sensing and transmission by physical reflection and require no power supplies. In order to design a meta-IoT sensing system with optimal sensing accuracy, we jointly consider the sensing and transmission of meta-IoT sensors and propose efficient algorithms to optimize the meta-IoT structure and the sensing function at the receiver. As an example, we apply the meta-IoT system to sensing environmental temperature and humidity levels. Simulation results show that by using the proposed algorithm, the sensing accuracy can be largely increased.

The federated learning scheme enhances the privacy preservation through avoiding the private data uploading in cloud-edge computing. However, the attacks against the uploaded model updates still cause private data leakage which demotivates the privacy-sensitive participating edge devices. Facing this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the edge devices are motivated to actively contribute to the updated model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We formulate the incentive design problem as a three-layer Stackelberg game, where the server-device interaction is further formulated as a contract design problem. Extensive numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.

In this paper, we propose a Meta-IoT system to achieve ubiquitous deployment and pervasive sensing for future Internet of Things (IoT). In such a system, sensors are composed of dedicated passive meta-materials whose frequency response for wireless signals is sensitive to environmental conditions. Therefore, we can remove the energys-suppliers in the future IoT by obtaining sensing results from the reflected signals of Meta-IoT devices. Nevertheless, it remains a challenge to reconstruct 3D environmental condition distributions by using the Meta-IoT system. Because of the interferences among the reflected signals, it requires the optimization of the deployment of the meta-IoT devices to ensure the sensing accuracy. To handle this challenge, we establish a mathematical model of Meta-IoT devices' sensing and transmission to calculate the interference between Meta-IoT devices. Then, an algorithm is proposed to minimize the interference and reconstruction error by optimizing the Meta-IoT devices' position and the estimation function. The simulation results verify that the proposed system can obtain a 3D environmental conditions' distribution with high accuracy.

Indoor wireless simultaneous localization and mapping (SLAM) is considered as a promising technique to provide positioning services in future 6G systems. However, the accuracy of traditional wireless SLAM system heavily relies on the quality of propagation paths, which is limited by the uncontrollable wireless environment. In this paper, we propose a novel SLAM system assisted by a reconfigurable intelligent surface (RIS) to address this issue. By configuring the phase shifts of the RIS, the strength of received signals can be enhanced to resist the disturbance of noise. However, the selection of phase shifts heavily influences the localization and mapping phase, which makes the design very challenging. To tackle this challenge, we formulate the RIS-assisted indoor SLAM optimization problem and design an error minimization algorithm for it. Simulations show that the RIS assisted SLAM system can decrease the positioning error by at least 31% compared with benchmark schemes.

In this paper, we investigate the close contact detection for COVID-19 patients based on the heterogeneous mobile edge computing (MEC) framework. Collecting the spatial-temporal data of a large number of mobile users, the base stations equipped with MEC servers organize these data via the R-tree structure. The cloud center (CC) aggregates the spatial-temporal data from all MEC servers. Considering the mobility of users as well as various positions of MEC servers, the CC then partitions and assigns the close contact detection tasks to different servers for faster processing. Aiming to minimize the system latency, we propose a Deep Deterministic Policy Gradient-based task and resource allocation scheme, where the computing loads are balanced among different servers. Simulation results show that a minimum system latency is reached while maintaining the load balance among all servers. Up to 37% detection accuracy enhancement is achieved compared with an existing task allocation scheme without load balance.

In this paper, a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) system is proposed, where the STAR-RIS can simultaneously transmit and reflect the incident signals. Our objective is to maximize the achievable sum rate by jointly optimizing the decoding order, power allocation coefficients, active beamforming, transmission and reflection beamformings. However, the formulated problem is non-convex with intricately coupled variables. To tackle this challenge, a suboptimal two-layer iterative algorithm is proposed. Specifically, in the inner-layer iteration, for a given decoding order, the power allocation coefficients, active beamforming, transmission and reflection beamformings are optimized alternatively. For the outer-layer iteration, the decoding order of NOMA users in each cluster is updated with the solutions obtained from the inner-layer iteration. Simulation results are provided to demonstrate that the proposed STAR-RSI-NOMA system outperforms conventional RIS assisted systems.

Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous reflection, have appeared as a promising method to enhance wireless systems. In this paper, we propose to use an RIS as a spatial equalizer to address the well-known multi-path fading phenomenon. By introducing some controllable paths artificially against the multi-path fading through the RIS, we can perform equalization during the transmission process instead of at the receiver, and thus all the users can share the same equalizer. Unlike the beam-forming application of the RIS, which aims to maximize the received energy at receivers, the objective of the equalization application is to reduce the inter-symbol interference (ISI), which makes phase shifts at the RIS different. To this end, we formulate the phase shift optimization problem and propose an iterative algorithm to solve it. Simulation results show that the multi-path fading effect can be eliminated effectively compared to benchmark schemes.

In this paper we propose a cluster-based platoon handoff protocol (CPHP), in which the platoon is divided into clusters to minimize its handoff delay between two adjacent cells. Via the vehicle-to-vehicle (V2V) communications, multiple clusters are constructed within one platoon and only the cluster head (CH) communicates with the base station (BS) via the vehicle-to-infrastructure (V2I) communications. In this way, the number of V2I links can be greatly saved and the signaling overhead is reduced, thereby shortening the handoff delay. Specifically, we formulate a delay minimization problem based on a discrete-time Markov chain and then optimize the number of clusters and spectrum resource allocation. Simulation results demonstrate a significant decrease in the handoff delay of the platoon. The optimal expected delay under a platoon with 50 vehicles by utilizing the proposed CPHP is 15% smaller than that of the traditional handoff scheme.

Emerging unmanned aerial vehicles (UAVs) are playing an increasingly important role in military, public, and civilian applications. More recently, UAVs have become a topic of central research interest in the wireless communication community. For example, the 3rd Generation Partnership Project (3GPP) standardisation body has recently worked on a study item to facilitate seamless integration of UAVs into future cellular networks, which is called the cellular Internet of UAVs. UAVs can be exploited in different ways to enhance cellular communications. On the one hand, dedicated UAVs can be used as airborne wireless access points or relay nodes to further improve terrestrial communications, which is referred to as UAV-assisted cellular communications. On the other hand, UAVs may be exploited for sensing purposes by leveraging their advantages such as on-demand deployment, larger service coverage compared with the conventional fixed sensor nodes, and flexible spatial network architecture. We refer to this category of UAV applications as cellular-assisted UAV sensing. Unlike terrestrial cellular networks, UAV communications have many distinctive features such as high dynamic network topologies and weakly connected communication links. Besides, they also suffer from some practical constraints such as battery power, no-fly zones, and sensing requirements. Therefore, it is essential to develop novel communication and signal-processing techniques in support of ultra-reliable and real-time sensing applications. This special issue aims to create a platform for researchers from both academia and industry to disseminate state-of-the-art results and to advance the integration of UAVs into cellular networks. In total, 12 excellent papers were accepted after a rigorous multi-round review process. These papers can be divided into two topics: UAV-assisted cellular communications and cellular-assisted UAV sensing. In the following, we will introduce these papers and highlight their contributions. In their survey paper 'A survey on unmanned aerial vehicle relaying networks', Li et al. comprehensively summarise UAV relaying communications, which is an important paradigm of UAV-assisted cellular communications, and introduce its application scenarios. Key challenges are presented and corresponding technologies to address these challenges are discussed. Furthermore, they also show the research opportunities of UAV relaying communications. Yuan et al., in their paper 'Interference coordination and throughput maximisation in an unmanned aerial vehicle-assisted cellular: User association and three-dimensional trajectory optimisation', consider a UAV as an aerial base station (BS) to serve ground users. To reduce the interference between the UAV and terrestrial BSs, they propose a joint user association and 3D trajectory optimisation method. An improved block successive upper-bound minimisation based penalty algorithm is proposed. In 'Age-optimal path planning for finite-battery UAV-assisted data dissemination in IoT networks', Changizi and Emadi consider using UAVs to assist wireless sensor networks to deliver information with the aim to explore the freshness of data. An UAV trajectory planning for data dissemination is proposed, taking into account both maximal use of energy and the freshness of data. The effect of limited energy for UAVs is also discussed. In 'metaheuristic-based optimal 3D positioning of UAVs forming aerial mesh network to provide emergency communication services', Gupta and Varma study the optimal placement of UAVs to facilitate post-disaster emergency communication services. Coverage, quality-of-services, energy consumption, equal load distribution over UAVs, and fault tolerance are all considered for improving network connectivity and lifetime. Two metaheuristic-based hybrid optimisation algorithms are proposed to integrate these objectives together. Sun et al., in their paper 'An efficient data collection framework in the sky: An affine transformation approach based on Internet of unmanned aerial vehicles', use a UAV as a data collector to collect data from sensors. An efficient data collection framework is proposed and a min-maximum data processing strategy is adopted based on data value to store the collected time-series data. Moreover, an efficient affine transformation method is proposed to improve the efficiency of the system. In their paper 'Advanced squirrel algorithm-trained neural network for efficient spectrum sensing in cognitive radio-based air traffic control application', Eappen et al. utilise a cognitive radio manner to establish a connection between the UAV and the ground controller. A neural network trained by Advanced Squirrel Algorithm (ASA) is proposed for efficient spectrum sensing. Simulation-based evaluation shows that the proposed method is capable of efficiently detecting the spectrum holes with high convergence rate. In 'Blockchain-assisted secure UAV communication in 6G environment: Architecture, opportunities, and challenges', Gupta et al. investigate the security and privacy issues in UAV sensing applications. They propose an Interplanetary File System and blockchain-based secure UAV communication scheme. The proposed scheme ensures data security and privacy, reduces data storage cost, and enhances network performance. Wu et al., in their paper 'Optimisation of virtual cooperative spectrum sensing for UAV-based interweave cognitive radio system', consider UAVs equipped with spectrum sensing for data transmission. Based on a virtual cooperative spectrum sensing model, the authors propose an energy-efficient virtual cooperative spectrum sensing with the sequential 0/1 fusion rule to reduce the average number of decisions without any loss in the detection performance. Moreover, the optimisation problem of virtual cooperative spectrum sensing for UAV-based interweave cognitive ratio systems is formulated and solved. In 'Cellular UAV-to-device communications: Joint trajectory, speed, and power optimisation', Liu et al. study two communication modes for the UAV sensing applications, that is, UAVs can transmit through the BS or to the corresponding mobile devices directly. The authors propose a joint sensing and transmission protocol to schedule UAV sensing and transmission, and formulate an energy utility maximisation problem. A joint trajectory, speed, and transmit power optimisation algorithm is proposed to obtain a suboptimal solution. Ren et al., in their paper 'Computation offloading game in multiple unmanned aerial vehicle-enabled mobile edge computing networks', use mobile edge computing to offload the computation tasks for UAVs. To obtain the minimum computing time, the offloading percentage and the transmission power is optimised through a game theory modelling. Numerical results verify that the proposed schemes can effectively decrease the computing time and energy consumption, especially for a large number of UAVs. In 'An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling', Yuan et al. examines a scheduling problem in consideration of the loading capacity, the maximum flight time, and the flight speed. A genetic-based algorithm framework is presented for solving the scheduling problem. Moreover, in order to reduce the search space and accelerate the execution of this algorithm, a weight-based loading method is adopted. Finally, in their paper 'Multi-channel underdetermined blind source separation for recorded audio mixture signals using an unmanned aerial vehicle', Xie et al. apply UAVs for locating sound-emitting targets and study the source separation problem when the number of sources is more than the number of sensors. An underdetermined blind source separation algorithm to separate the multi-channel audio mixture signals recorded by an unmanned aerial vehicle is proposed. As a result, the frequency-domain sources are estimated through Wiener filtering and time-domain sources are obtained via inverse short-time Fourier transform. We would like to express our sincere thanks to all the authors for submitting their papers and to the reviewers for their valuable comments and suggestions that significantly enhanced the quality of these articles. We are also grateful to Prof. Liuqing Yang, the Editor-in-Chief of the IET Communications, for her great support throughout the whole review and publication process of this special issue, and, of course, to all the editorial staff. Hongliang Zhang received the B.S. and Ph.D. degrees at the School of Electrical Engineering and Computer Science at Peking University, China, in 2014 and 2019, respectively. Currently, he is a postdoctoral associate in the Department of Electrical Engineering at Princeton University, USA. His current research interest includes reconfigurable intelligent surfaces, aerial access networks, and game theory. He received the best doctoral thesis award from the Chinese Institute of Electronics in 2019. He is an exemplary reviewer for IEEE Transactions on Communications in 2020. He has served as a TPC Member for many IEEE conferences, such as Globecom, ICC, and WCNC. He is currently an associate editor for IET Communications and Frontiers in Signal Processing. He also serves as a Guest Editor for IEEE IoT-J special issue on Internet of UAVs over cellular networks. Walid Saad received the Ph.D. degree from the University of Oslo, Norway, in 2010. He is currently a professor with the Department of Electrical and Computer Engineering, Virginia Tech, USA, where he leads the Network science, Wireless, and Security (NEWS) Laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a recipient of the NSF CAREER Award in 2013, the AFOSR Summer Faculty Fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He has authored or co-authored 10 conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He is also a recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2017 IEEE ComSoc Best Young Professional in Academia Award, the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and the 2019 IEEE ComSoc Communication Theory Technical Committee. He has also co-authored the 2019 IEEE Communications Society Young Author Best Paper. From 2015 to 2017 he was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and in 2017 he was named College of Engineering Faculty Fellow. He received the Dean's award for research excellence from Virginia Tech in 2019. He currently serves as an editor for IEEE Transactions on Mobile Computing and IEEE Transactions on Cognitive Communications and Networking. He is an Editor-at-Large of IEEE Transactions on Communications. He is an IEEE Distinguished Lecturer. Mérouane Debbah received the M.Sc. and Ph.D. degrees from Ecole Normale Supérieure Paris-Saclay, France. In 1996, he joined Ecole Normale Supérieure Paris-Saclay. He was with Motorola Labs, France, from 1999 to 2002, and also with the Vienna Research Center for Telecommunications, Austria, until 2003. From 2003 to 2007, he was an assistant professor with the Mobile Communications Department, Institut Eurecom, France. From 2007 to 2014, he was the director of the Alcatel-Lucent Chair on flexible radio. Since 2007, he has been a full professor with CentraleSupelec, France. He has managed eight EU projects and more than 24 national and international projects. His research interests include fundamental mathematics, algorithms, statistics, information, and communication sciences research. He was a recipient of the ERC Grant MORE (Advanced Mathematical Tools for Complex Network Engineering) from 2012 to 2017. He received 20 best paper awards, among which the 2015 IEEE Communications Society Leonard G. Abraham Prize, the 2016 IEEE Communications Society Best Tutorial Paper Award, and the 2018 IEEE Marconi Prize Paper Award. He is an associate editor-in-chief of the journal Random Matrix: Theory and Applications. He was an associate area editor and a senior area editor of IEEE Transactions on Signal Processing from 2011 to 2013 and from 2013 to 2014, respectively. Lingyang Song received the Ph.D. from the University of York, UK, in 2007, where he received the K.M. Stott Prize for excellent research. He worked as a postdoctoral research fellow at the University of Oslo, Norway, and Harvard University, USA, until rejoining Philips Research UK in March 2008. In May 2009, he joined the School of Electronics Engineering and Computer Science, Peking University, China, as a full professor. His main research interests include cooperative and cognitive communications, physical layer security, and wireless ad hoc/sensor networks. He has published extensively, writing six textbooks, and is co-inventor of a number of patents (standard contributions). He received nine paper awards in IEEE journals and conferences including IEEE JSAC 2016, IEEE WCNC 2012, ICC 2014, Globecom 2014, and ICC 2015. He is currently on the editorial board of IEEE Transactions on Wireless Communications and Journal of Network and Computer Applications. He served as the TPC co-chairs for the International Conference on Ubiquitous and Future Networks (ICUFN2011/2012), symposium co-chairs in the International Wireless Communications and Mobile Computing Conference (IWCMC 2009/2010), IEEE International Conference on Communication Technology (ICCT2011), and IEEE International Conference on Communications (ICC 2014, 2015). He is the recipient of the 2012 IEEE Asia Pacific (AP) Young Researcher Award. Dr. Song is a fellow of IEEE and IEEE ComSoc distinguished lecturer since 2015.

The Emerging unmanned aerial vehicles (UAVs) have been widely exploited for sensing purposes due to the larger service coverage compared with the conventional fixed sensor nodes. However, due to the limited computation capability of UAVs, real-time sensory data needs to be transmitted to the BS/server for real-time data processing. In this regard, the cellular networks are necessary to support the data transmission for UAVs, which is called the Internet of UAVs. Very recently, 3GPP has approved a study item on the enhanced support to seamlessly integrate UAVs into future cellular networks.

Driven by the increasingly serious air pollution problem, nowadays different systems can be used to achieve the monitoring task of air quality index (AQI) in urban areas. In this article, we design a novel unmanned aerial vehicle-aided (UAV-aided) AQI monitoring system, called AQ360, which detects the air quality level from the 360-degree aerial panoramic images taken by the onboard camera. Specifically, we first present our own AQI recognition approach based on the physical form of the haze pictures, where the AQI is jointly decided by the images captured along six directions over the target location. Then, we study the UAV placement problem of selecting UAV's flight altitude and 2-D coordinates during the monitoring process. The objective is to save the system energy consumption while maintaining the accuracy of estimating AQI distribution. For practical considerations, we implement and evaluate the proposed system in real-world scenarios. The results show that our system can provide a lower AQI recognition error compared with existing vision-based monitoring approaches, and energy consumption is also reduced when applying for large-area tasks.

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.

In this article, we consider a downlink ultra-dense LEO-based multi-terminal satellite system where multiple satellites fly in formation to serve a number of ground terminal stations for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the massive virtual antenna array formed by multiple satellites. We aim at exploring how the satellite distribution and formation size influence the channel capacity in this case. We first derive the upper bound of the channel capacity in the general multi-antenna satellite networks. The single-antenna satellite case is considered where we present the specific forms of the derived capacity bounds to prove that the capacity first increases linearly then increases more and more slowly with the satellite formation size, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. Simulation results verify our theoretical analysis and show that such statements also hold for the general multi-antenna satellite case.

In recent years, unmanned aerial vehicles (UAVs) have unlocked numerous sensing applications, which are expected to add billions of dollars to the world economy in the next decade. To further improve the Quality-of-Service in these applications, the 3rd Generation Partnership Project has considered the use of terrestrial cellular networks to support UAV sensing services, also known as the cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device (U2D) communications. To evaluate the freshness of the sensory data, the concept of age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we investigate the AoI minimization problem for UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus we utilize multi-agent deep reinforcement learning to approximate the state-action space. Then, we propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a lower AoI than a greedy algorithm, policy gradient algorithm, and overlay U2D scheme.

Grant-free access, in which channels are accessed without undergoing assignment through a handshake process, is a promising solution to support massive connectivity needed for Internet-of-Things (IoT) networks. In this paper, we consider uplink grant-free massive access for an IoT network with multiple channels. To be specific, the IoT devices generate short packets and have grant-free non-orthogonal access to a channel to transmit the generated packets to a base station (BS). With the aim of keeping the information fresh at the BS, we first derive the age of information (AoI) for grant-free short-packet communications, and then formulate the AoI minimization problem. However, the problem is challenging as the number of users involved is large, and to tackle this problem efficiently, we propose a mean-field evolutionary game-based approach. In this approach, the average behavior of the IoT devices is considered rather than their individual behaviors, and the dynamics of the strategies of the IoT devices are modeled by an evolutionary process. Simulation results verify the effectiveness of the proposed mean-field evolutionary game-based approach.

Wireless network virtualization is basically the abstraction, isolation, and sharing of wireless resources among different entities. Consequently, virtualization provides great flexibility and higher network efficiency, and enables easier migration to new technologies in wireless networks. Traditionally, a wireless network virtualization controller manages the virtual resources (including radio resources and infrastructure resources) known as slices which are available to the Service Providers (SPs). The SPs then allocate their purchased resources to serve their subscribed mobile users. Such a centralized allocation decouples the Quality-of-Service (QoS) management by the SPs from the virtual resource management by the controller. In this paper, we propose a matching based wireless network virtualization resource allocation mechanism: a distributed three-sided (3D) matching between radio resources, physical infrastructure and mobile users. The Restricted Three-sided Matching with Size and Cyclic preference model (R-TMSC) is implemented to obtain a stable solution. Simulation results show that our proposed spectrum-oriented and user-oriented algorithms outperform the traditional resource allocation schemes. The spectrum-oriented algorithm enhances the user throughput and the system performance, within a lesser run time. Furthermore, for an increasing number of users, the proposed algorithms serve more users than traditional methods.

The 360-degree video streaming system delivers a monocular panoramic video surrounding the user, and the user can change the viewing direction of mobile devices to see different parts of the video through the “viewport”. Due to the limited network bandwidth, playbacks of high-resolution 360-degree videos often suffer from rebuffering, while too much bandwidth is wasted in delivering those out-of-viewport parts that the user never watches. In this article, we present an Ensemble Prediction and Allocation based Streaming System, named as EPASS360, for delivering high Quality of Experience (QoE) 360-degree videos. The prediction model takes advantages of ensemble learning, providing high accuracy on the prediction of viewports. The allocation model divides a video into tiles, and allocates high resolution to tiles where a user's viewpoint may appear in the future by solving the QoE-aware optimization problem. Trace-driven emulation on real-world datasets shows that EPASS360 enhances the QoE in various scenarios compared to state-of-the-art streaming approaches. Experiments on the head-mounted device and the hand-held device over real-world Internet confirm the high user experience of EPASS360.

With the evolution of 5G, 6G and beyond, device-to-device (D2D) communications have been developed as an energy-, and spectrum-efficient solution. However, D2D links are allowed to share the same spectrum resources with cellular links, which will bring significant interference to those cellular links. Fortunately, an emerging technique called reconfigurable intelligent surface (RIS), can mitigate aggravated interference caused by D2D links by adjusting phase shifts of the surface to create favorable beam steering. In this paper, we study an RIS-assisted single cell uplink communication scenario, where a cellular link and multiple D2D links share the same spectrum and an RIS is adopted to mitigate the mutual interference. The problem of maximizing total system rate is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of the surface. To obtain practical solutions, we capitalize on alternating maximization and the problem is decomposed into two sub-problems. For the power allocation, the problem is a difference of concave functions (DC) problem, which is solved with the gradient descent method. For the phase shift optimization, a local search algorithm is utilized. Simulation results show that deploying the RIS with optimized phase shifts can effectively eliminate the interference in D2D networks.

Recently, the ultra-dense low Earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the constellation while satisfying the backhaul requirement of each user terminal (UT). We first derive the average total backhaul capacity of each UT, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage ratio requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm. Given the same number of deployed LEO satellites, the average coverage ratio of the proposed LEO satellite constellation is at least 10 percentage points higher than that of Telesat constellation.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.

In this paper, we consider a three-layer distributed multi-access edge computing (MEC) network where multiple clouds, MEC servers, and edge devices (EDs) are deployed at the top layer, middle layer, and bottom layer, respectively. Each cloud center (CC) is associated with an independent service provider and publishes an application-driven computing task. To deliver the tasks, CCs rely on EDs to generate the raw data and offload part of the computing tasks to both EDs and MEC servers such that their computing and transmission resources can be fully utilized to reduce the system latency. However, in such a three-layer network, the distributed deployment of tasks leads to inevitable resource competition among CCs. To address this issue, we propose a distributed scheme based on multi-agent reinforcement learning, where each CC jointly determines the task offloading and resource allocation strategy based on its inference of other CCs' decisions. Simulation results indicate that a lower system latency is achieved via our proposed scheme compared with the existing schemes. In addition, the influence of the number of CCs, MEC servers, and EDs on latency performance is also discussed.

The received signal strength (RSS) based technique is extensively utilized for localization in the indoor environments. Since the RSS values of neighboring locations may be similar, the localization accuracy of the RSS based technique is limited. To tackle this problem, in this paper, we propose to utilize reconfigurable intelligent surface (RIS) for the RSS based multi-user localization. As the RIS is able to customize the radio channels by adjusting the phase shifts of the signals reflected at the surface, the localization accuracy in the RIS aided scheme can be improved by choosing the proper phase shifts with significant differences of RSS values among adjacent locations. However, it is challenging to select the optimal phase shifts because the decision function for location estimation and the phase shifts are coupled. To tackle this challenge, we formulate the optimization problem for the RIS-aided localization, derive the optimal decision function, and design the phase shift optimization (PSO) algorithm to solve the formulated problem efficiently. Analysis of the proposed RIS aided technique is provided, and the effectiveness is validated through simulation.

In this paper, we study the harmonious coexistence of cellular and WiGig users around 60 GHz unlicensed millimeter-wave bands. To guarantee the fairness of two types of users, we propose a sensing based adaptive unlicensed channel sharing protocol. Jointly considering the frequency and spatial resource allocation, an iterative channel allocation and hybrid beamforming algorithm is designed to maximize the sum rate of all cellular users while minimizing the interference to WiGig networks. Simulation results show that our proposed scheme achieves a significant sum rate enhancement compared with that when utilizing only the licensed band.

In this paper, we focus on the reconfigurable intelligent surface (RIS) assisted single-cell uplink communication network scenario. In the network, one cellular link and multiple device-to-device (D2D) links sharing the same spectrum resources combine direct and reflective channel transmissions with the assistance of the RIS, which is adopted to alleviate the interference by fully using the beamforming capability. Subjected to qualityof-service (QoS) and total power constraints, a system sumrate maximization problem is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of all RIS elements. Since it is a mixed integer non-convex non-linear problem, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution efficiently. For the power allocation sub-problem, it is a difference of concave functions (DC) problem, which is transformed to a convex one by the multivariate Taylor expansion and then solved with the gradient descent method. For the phase shift subproblem, a local search algorithm is utilized. Simulation results verify that our proposed scheme can eliminate the interference of D2D networks better than the scheme without RIS and other benchmark schemes.

Recently, the ultra-dense low earth orbit (LEO)satellite communication networks over high-frequency band have served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the LEO satellite constellation while satisfying the backhaul requirement of each terrestrial-satellite terminal (TST). We first derive the average total backhaul capacity of each TST, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the LEO satellite constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage percentage requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm.

In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device communications. To evaluate the freshness of the sensory data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we aim to minimize the AoIs of UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus, we propose a multi-UAV trajectory design algorithm by leveraging multi-agent deep reinforcement learning to solve it. Simulation results show that our proposed algorithm outperforms both a greedy algorithm and a policy gradient algorithm.

User profiles are not always visible in E-commerce scenarios, in which case the recommender systems can only summarize users' preferences through sessions of historical records. However, the items in a session might be irrelevant to users' preferences or become the disturbances for modelling the users' portraits, and thus degrade the performance of the recommender systems. In this paper, we propose the preference-aware mask to capture user preferences over the items within the sessions, which adapts to the preference-irrelevant items within the sessions and provides explainable evidence for the recommendation. Evaluation over three real-world datasets verifies that MBTREC performs well on the new-item recommendation task, and outperforms several state-of-the-art recommender systems on the general metrics.

In this paper, we consider the distributed energy saving for heterogeneous multi-layer mobile edge computing (HetMEC), where the edge devices (EDs) upload computing tasks to the mobile edge computing (MEC) servers and the cloud center (CC) for processing. To reduce the energy consumption, task offloading and resource allocation are performed by each device independently to distribute the computation load. However, due to the partial information, the offloading strategies of devices on different layers can hardly be aligned, which may lead to network congestion. To address this problem, we propose a smart pricing mechanism to coordinate the strategies of multi-layer devices, where the CC charges the MEC servers and EDs for computing services and network congestion. The pricing based distributed task offloading and resource allocation algorithm is designed to minimize the total energy consumption subject to latency requirements. Simulation results indicate that our algorithm achieves lower energy consumption and congestion probability compared with the traditional distributed method.

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

In this paper, we consider the cellular Internet of unmanned aerial vehicles (UAVs), where UAVs sense data for multiple tasks and transmit the data to the base station (BS). To quantify the “freshness” of the data at the BS, we bring in the concept of the age of information (AoI). The AoI is determined by the time for UAV sensing and that for UAV transmission, and gives rise to a trade-off within a given period. To minimize the AoI, we formulate a joint sensing time, transmission time, UAV trajectory, and task scheduling optimization problem. To solve this problem, we first propose an iterative algorithm to optimize the sensing time, transmission time, and UAV trajectory for completing a specific task. Afterwards, we design the order in which the UAV performs data updates for multiple sensing tasks. The convergence and complexity of the proposed algorithm, together with the trade-off between UAV sensing and UAV transmission, are analyzed. Simulation results verify the effectiveness of our proposed algorithm.

The multi-path transmission techniques enable multiple paths to maximize resource usage and increase throughput in transmission, which have been installed over mobile devices in recent years. For video streaming applications, compared to the single-path transmission, the multi-path techniques can establish multiple subflows simultaneously to extend the available bandwidth for streaming high-quality videos in mobile devices. Existing adaptive video streaming systems have difficulty in harnessing multi-path scheduling and balancing the tradeoff between the quality of experience (QoE) and quality of service (QoS) concerns. In this paper, we propose an actor-critic network based on Periodical Experience Replay for Multi-path video streaming (PERM). Specifically, PERM employs two actor modules and a critic module: the two actor modules respectively assign the path usage of each subflow and select bitrates for the next chunk of the video, while the critic module predicts the overall objectives. We conduct trace-driven emulation and real-world testbed experiment to examine the performance of PERM, and results show that PERM outperforms state-of-the-art multi-path and single path streaming systems, with an improvement of 10%- 15% on the QoE and QoS metrics.

Given high-speed mobile Internet access today, audiences are expecting much higher video quality than before. Video service providers have deployed dynamic video bitrate adaptation services to fulfill such user demands. However, legacy video bitrate adaptation techniques are highly dependent on the estimation of dynamic bandwidth, and fail to integrate the video quality enhancement techniques, or consider the heterogeneous computing capabilities of client devices, leading to low quality of experience (QoE) for users. In this paper, we present a super-resolution based adaptive video streaming (SRAVS) framework, which applies a Reinforcement Learning (RL) model for integrating the video super-resolution (VSR) technique with the video streaming strategy. The VSR technique allows clients to download low bitrate video segments, reconstruct and enhance them to high-quality video segments while making the system less dependent on estimating dynamic bandwidth. The RL model investigates both the playback statistics and the distinguishing features related to the client-side computing capabilities. Trace-driven emulations over real-world videos and bandwidth traces verify that SRAVS can significantly improve the QoE for users compared to the state-of-the-art video streaming strategies with or without involving VSR techniques.

In this paper, we consider an uplink ultra-dense LEO-based multi-terminal satellite system where each ground terminal station connects to multiple satellites for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the spatial diversity of multiple satellites. To evaluate the multi-satellite channel capacity performance, we first derive the lower bound and upper bound of the channel capacity in uplink LEO-based multi-terminal systems with multiple single-antenna satellites. Based on the capacity bounds, we theoretically prove that the capacity grows almost linearly with the number of satellites, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. We then illustrate that such statements also hold for the multi-antenna satellite case where the upper bound of the channel capacity and the lower bound of the achievable rate after receive beamforming are derived separately. Simulation results verify our theoretical analysis.

Confronted with the severe indoor air pollution nowadays, we propose the usage of multiple robots to detect the indoor air quality (IAQ) cooperatively for fewer sensors and larger sensing area. To acquire the complete real-time IAQ distribution map, we exploit the real statistical data to construct the IAQ data model and adopt Kalman Filter to obtain the estimation of the unmeasured area. Since the movement of the robots affects the estimation accuracy, a proper movement strategy should be planned to minimize the total estimation error. To solve this optimization problem, we design a deep Q-learning approach, which provides sub-optimal movement strategies for real-time robot sensing. By simulations, we verify the adopted IAQ data model and testify the effectiveness of the proposed solution. For application considerations, we have deployed this system in Peking University since Dec. 2018 and developed a website to visualize the IAQ distribution.

Indoor air pollution has become a growing health risk, but it is challenging to provide low-cost air quality monitoring for the indoor environment. In this article, we present “AirScope,” a mobile sensing system that employs cooperative robots to monitor the indoor air quality. Since the wireless coverage can be incomplete in some indoor areas, AirScope allows the robots to defer uploading the data to the central server by utilizing their own data buffers. In order to guarantee the timeliness of the data in the server, AirScope aims to minimize the average data latency by properly planning the routes of the robots. Such a route planning strategy has to be implemented in a distributed way since the robots that are out of wireless coverage can only make plans on their own. In addition, the cooperation of the robots is also necessary because the aggregation of the robots in a small area increases the average data latency of the other unattended areas. To solve this distributed and cooperative routing planning problem, we propose a solution based on distributed deep Q-learning (DDQL). We evaluate the system performance by simulations and real-world experiments. The results show that AirScope is effective to reduce data latency, where the proposed DDQL is 8% better than the greedy algorithm and 24% better than the random strategy.

Reconfigurable intelligent surfaces (RISs) have drawn considerable attention from the research community recently. RISs create favorable propagation conditions by controlling the phase shifts of reflected waves at the surface, thereby enhancing wireless transmissions. In this paper, we study a downlink multi-user system where the transmission from a multi-antenna base station (BS) to various users is achieved by an RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only a limited number of discrete phase shifts can be realized by a finite-sized RIS. A hybrid beamforming scheme is proposed and the sum-rate maximization problem is formulated. Specifically, continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively, and an iterative algorithm is designed to solve this problem. Both theoretical analysis and numerical validations show that the RIS-based system can achieve good sum-rate performance by setting a reasonable size of the RIS and a small number of discrete phase shifts.

Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems, and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.

As distributed energy resources (DERs) are widely deployed, DC packetized power microgrids have been considered as a promising solution to incorporate DERs effectively. In this paper, we consider a DC packetized power microgrid, where the energy is dispatched in the form of power packets with the assistance of a power router. However, the benefits of the microgrid can only be realized when energy subscribers (ESs) equipped with DERs actively participate in the energy market. Therefore, peer-to-peer (P2P) energy trading is necessary in the DC packetized power microgrid to encourage the usage of DERs. Different from P2P energy trading in AC microgrids, the dispatching capability of the router needs to be considered in DC microgrids, which will complicate the trading problem. To tackle this challenge, we formulate the P2P trading problem as an auction game, in which the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. Analysis of the proposed scheme is provided, and its effectiveness is validated through simulation.

Due to the advantages of flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) have significant potential for sensing applications in the next generation of cellular networks, which will give rise to a cellular Internet of UAVs. In this article, we consider a cellular Internet of UAVs, where the UAVs execute sensing tasks through cooperative sensing and transmission to minimize the age of information (AoI). However, the cooperative sensing and transmission is tightly coupled with the UAVs' trajectories, which makes the trajectory design challenging. To tackle this challenge, we propose a distributed sense-and-send protocol, where the UAVs determine the trajectories by selecting from a discrete set of tasks and a continuous set of locations for sensing and transmission. Based on this protocol, we formulate the trajectory design problem for AoI minimization and propose a compound-action actor-critic (CA2C) algorithm to solve it based on deep reinforcement learning. The CA2C algorithm can learn the optimal policies for actions involving both continuous and discrete variables and is suited for the trajectory design. Our simulation results show that the CA2C algorithm outperforms four baseline algorithms. Also, we show that by dividing the tasks, cooperative UAVs can achieve a lower AoI compared to non-cooperative UAVs.

In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed spectrum. However, the interference from surrounding terminals is uncontrollable due to the opportunistic channel access. In this paper, we consider a cellular Internet of UAVs to guarantee the Quality-of-Service (QoS), where the sensory data can be transmitted to the mobile devices either by UAV-to-Device (U2D) communications over cellular networks, or directly through the base station (BS). Since UAVs' sensing and transmission may influence their trajectories, we study the trajectory design problem for UAVs in consideration of their sensing and transmission. This is a Markov decision problem (MDP) with a large state-action space, and thus, we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space, and then propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than policy gradient algorithm and single-agent algorithm.

Multi-access edge computing (MEC) has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in an MEC network (MECN) is computation offloading in which computing nodes, with limited capabilities and performance, can offload computation-intensive tasks to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable from other decision makers and have a non-negligible effect on the total utility of the network. Since MECNs are implemented through finite number of computing nodes and the computing capability of a computing node can affect the state (i.e., the number of computation tasks) of the network, we propose non-cooperative and cooperative MFTG approaches to formulate computation offloading problems. In these scenarios, the goal of each computing node is to offload a portion of the aggregate computation tasks from the network that minimizes a specific cost. Then, we utilize a direct approach to calculate the optimal solution of these MFTG problems that minimizes the corresponding cost. Finally, we conclude the paper with simulations to show the significance of the approach.

Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul. In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access. However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO's original users. Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing. Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently. The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed.

In this paper, we consider the cellular Internet of unmanned aerial vehicles (UAVs), where UAVs sense data with onboard sensors for multiple sensing tasks and transmit the data to the base station (BS). To quantify the “freshness” of the data at the BS, we bring in the concept of the age of information (AoI). The AoI is determined by the time for UAV sensing and that for UAV transmission, which gives rise to a trade-off within a given period. To minimize the AoI, we formulate a joint sensing time, transmission time, UAV trajectory, and task scheduling optimization problem. This NP-hard problem can be decoupled into two subproblems. We first propose an iterative algorithm to optimize the sensing time, transmission time, and UAV velocity for completing a specific task. Afterwards, we design the order in which the UAV performs data updates for multiple sensing tasks. The convergence and complexity of the proposed algorithm, together with the trade-off between UAV sensing and UAV transmission, are analyzed. Simulation results show that the AoI with the proposed algorithm is about 15% lower than that of the greedy algorithm, and over 40% lower than that of the random algorithm.

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing~(MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages of limited computing capacity, preventing computation-intensive tasks to be processed on time. To relief this issue, we propose a heterogeneous multi-layer MEC (HetMEC) network where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We design the latency minimization algorithm by jointly coordinating the task assignment, computing and transmission resources among the EDs, multi-layer MEC servers, and the CC. Simulation results indicate that our proposed algorithm can achieve a lower latency and stronger robustness than the conventional MEC schemes.

In this paper, we consider an ultra-dense LEO-based satellite-terrestrial network where the traditional operator (TO) cannot satisfy the increasing data demand of its subscribed users due to the limited backhaul capacity of traditional small cells. Therefore, the TO offloads its users to LEO-based small cells owned by the satellite operator (SO) for satellite-backhauled network access. To motivate both operators for data offloading and maximize their revenues, we propose a pricing mechanism for data offloading based on the Stackelberg game. An iterative optimization algorithm framework is developed by jointly considering user association, Ka-band spectrum allocation and pricing to achieve the Stackelberg equilibrium. The user association scheme and pricing scheme to maximize the TO and the SO's revenues are designed separately. The closed-form optimal solution for user association problem is derived, and the iterative pricing mechanism is also designed. Simulation results show that our proposed pricing scheme can motivate two operators for offloading efficiently. The influence of frequency resources and the number of LEO satellites is also discussed.

In this paper, we consider a cellular Internet of UAVs, where UAVs execute multiple sensing tasks continuously and cooperatively through sensing and transmission with the objective to minimize the age of information (AoI). However, the cooperative sensing and transmission is coupled with the trajectories of the UAVs, which makes the trajectory design a challenging problem. To tackle this challenge, we first propose a distributed sense-and-send protocol to coordinate the UAVs. Based on this protocol, we formulate the trajectory design problem for AoI minimization and propose a deep reinforcement learning algorithm to solve it, which we refer to as the compound-action actor-critic (CA2C) algorithm. Simulation results show that the CA2C algorithm outperforms two baseline algorithms for AoI minimization.

In this paper, we consider a cellular Internet of unmanned aerial vehicles (UAVs) where the sensory data can be transmitted to the mobile devices directly by overlaying UAV-to-Device (U2D) communications, or through the base station (BS) by cellular communications. Since the transmission modes of UAVs may influence their trajectories, we study the trajectory design problem for UAVs aiming to maximize the total utility in consideration of their transmission modes. This problem is a Markov decision problem (MDP) with a large state-action space, and thus, we propose a multi-UAV trajectory design algorithm using multi- agent deep reinforcement learning (DRL) to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than the single-agent method.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing user fairness, we formulate a joint mode selection, subchannel allocation, trajectory optimization, and power allocation problem, which is NP-hard. To solve the problem efficiently, we first decompose it into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation. Then we propose a joint mode selection and subchannel allocation, trajectory optimization, and power allocation (JMS-T-P) algorithm where these subproblems are solved iteratively. Simulation results show that the JMS- T-P algorithm outperforms the random algorithm and the cellular scheme.

In this work, we consider a heterogeneous multi-cloud mobile edge computing (Het-MEC) network, where multiple independent cloud centers (CCs) publish tasks to edge devices (EDs) and MEC servers, and compete for their computing and transmission resources. To minimize the system latency, we propose a distributed scheme for each CC to determine its data offloading and resource allocation strategy independently. Competition among multiple CCs in this distributed scheme is depicted by our designed multi-agent reinforcement learning (MARL) based algorithm, where each CC is self-motivated to learn the explicit models of other CCs and adjusts their behaviors. Simulation results indicate that multiple clouds are self-organized to take full advantage of computing and transmission resources to minimize their own task latency, while a lower system latency can be achieved compared with the cloud computing and local computing schemes.

As distributed energy resources (DERs) are widely deployed, the DC packetized power microgrid is a promising solution to incorporate DERs effectively and steadily. In this paper, we consider a DC microgrid, where the energy is dispatched by a power router in the form of power packets. Since energy subscribers (ESs) with DERs in the microgrid can generate surplus electricity, the peer-to-peer (P2P) energy trading is an effective way in order to balance the energy. Different from the P2P trading in AC smart grids, the dispatching capability of the router in the DC microgrid needs to be considered, which will make the trading problem more complicated. To tackle this challenge, we formulate the P2P trading problem as an auction game, where the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. The effectiveness of the proposed scheme is validated through simulations.

In this paper, the energy efficient virtual machine (VM) placement and virtual resource allocation problem for the mobile edge computing (MEC) system is explored. Particularly, we develop an optimization framework of energy consumption minimization for computing and offloading by jointly optimizing the VM placement matrix and the number of physical machines (PMs). To resolve this problem, we transform the optimization problem into a non-uniform weighted hypergraph model. In this model, the weight of hyperedge is defined as the negative of the accumulated energy consumption for computing at VM instances hosted by one PM. Based on the hypergraph model, a hypergraph matching algorithm by utilizing the local search policy is proposed for finding the maximum-weight subset of vertex-disjoint hyperedges, aiming to obtain an optimal VM placement, i.e., (M*)-perfect matching. Furthermore, the virtualized resources are further allocated to user equipments (UEs) in the form of multiple VM instances via the optimal VM placement to meet the requirement of workloads. Simulation results are presented to demonstrate the effectiveness of the proposed hypergraph matching algorithm over the alternative benchmark algorithm.

Due to the significant air pollution problem, monitoring and prediction for air quality have become increasingly necessary. To provide real-time fine-grained air quality monitoring and prediction in urban areas, we have established our own Internet-of-Things-based sensing system in Peking University. Due to the energy constraint of the sensors, it is preferred that the sensors wake up alternatively in an asynchronous pattern, which leads to a sparse sensing dataset. In this paper, we propose a novel approach to predict the real-time fine-grained air quality based on asynchronous sensing. The sparse dataset and the spatial-temporal-meteorological relations are modeled into the correlation graph, in which way the prediction procedures are carefully designed. The advantage of the proposed solution over existing ones is evaluated over the dataset collected by our air quality monitoring system.

In the past few years, virtual reality (VR) has become an enabling technique, not only for enriching our visual experience but also for providing new channels for businesses. Untethered mobile devices are the main players for watching 360-degree content, thereby the precision of predicting the future viewpoints is one key challenge to improve the quality of the playbacks. In this paper, we investigate the image features of the 360-degree videos and the contextual information of the viewpoint trajectories. Specifically, we design ladder convolution to adapt for the distorted image, and propose LadderNet to transfer the knowledge from the pre-trained model and retrieve the features from the distorted image. We then combine the image features and the contextual viewpoints as the inputs for long short-term memory (LSTM) to predict the future viewpoints. Our approach is compared with several state-of-the-art viewpoint prediction algorithms over two 360-degree video datasets. Results show that our approach can improve the Intersection over Union (IoU) by at least 5% and meeting the requirements of the playback of 360-degree video on mobile devices.

In this paper, we study a multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything communication. The platoon formation is important and difficult in multi-platoon scenario, for the platoon size, power and resource allocation of each platoon interact with each other and influence those of other platoons. To this end, we propose a two-step resource allocation strategy in consideration of platoon formation, including the resource allocation at the base station and within each platoon, respectively. A branch and bound algorithm is utilized for the resource allocation at the BS. We design a distributed dynamic programming based subchannel allocation and power control algorithm for the joint optimization of platoon size, power and intra-platoon resource allocation. Simulation results evaluate the influence of the penalty factor and latency constraint on the system performance.

Mobile edge computing has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in a mobile edge computing network is computation offloading in which computing nodes with limited capabilities and performance can offload a computation-intensive task to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable and have a non-negligible effect on the total utility of the network. Since mobile edge computing networks have a finite number of computing nodes where the computing capability of a computing node can affect the state (i.e., the amount of computation task) of the network, we propose a MFTG approach to formulate and solve a computation offloading problem. In this scenario, the goal of each computing node is to compute the portion of the aggregate computation task it can offload from the network that minimizes its cost. Then, we utilize a direct approach to solve for the optimal portion of the aggregate computation task that minimizes the cost incurred by a computing node. Finally, we conclude the paper with simulations to show the significance of the approach.

Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the implementation and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. The objective of our optimization problem is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy a limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

In this paper, we consider a device-to-device (D2D) communications underlaying cellular network where Long Term Evolution (LTE) and D2D users are allowed to communicate over both licensed and unlicensed bands for spectrum efficiency improvement. LTE users utilize spectrum orthogonally and share it with D2D users. To maximize the total throughput of this D2D system, we leverage stochastic geometry to derive the throughput for each kind of users by modeling the deployment of users as Poisson point processes (PPPs), and investigate the mode selection problem for D2D users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP) based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.

The DC power packet transmission contributes to a reliable integration of distributed energy resources (DERs) in power grids and reduces the load fluctuation. In this paper, we consider a DC packetized-power microgrid for the integration of DERs and propose a power packet dispatching protocol to regulate the peer to peer power interchange within the microgrid. We formulate the joint subscriber matching and energy allocation problem to optimize the subscribers' benefits, which is proved to be NP-hard. Based on the matching theory, we associate the problem equivalent to a many-to-many matching problem and design a two-sided matching algorithm to solve it. We then design a graph coloring based algorithm to schedule the energy transmissions of the matched energy subscribers. Simulation results validate the effectiveness of the proposed protocol in achieving a steady and efficient microgrid power dispatching.

Game AI is of great importance as games are simulations of reality. Recent research on game AI has shown much progress in various kinds of games, such as console games, board games and MOBA games. However, the exploration in RTS games remains a challenge for their huge state space, imperfect information, sparse rewards and various strategies. Besides, the typical card-based RTS games have complex card features and are still lacking solutions. We present a deep model SEAT (selection-attention) to play card-based RTS games. The SEAT model includes two parts, a selection part for card choice and an attention part for card usage, and it learns from scratch via deep reinforcement learning. Comprehensive experiments are performed on Clash Royale, a popular mobile card-based RTS game. Empirical results show that the SEAT model agent makes it to reach a high winning rate against rule-based agents and decision-tree-based agent.

Given the increasingly serious air pollution problem, air quality index (AQI) monitoring in urban areas has drawn considerable attention. This paper presents ImgSensingNet, a vision guided aerial-ground sensing system, for air quality monitoring and forecasting by the fusion of haze images taken by the unmanned-aerial-vehicle (UAV) and the AQI data collected by an on-ground wireless sensor network. Specifically, ImgSensingNet first leverages the computer vision technique to tell the AQI scale in different regions from the haze images, where haze-relevant features and a deep convolutional neural network (CNN) are designed for direct learning between haze images and corresponding AQI scale. Based on the learnt AQI scale, ImgSensingNet determines whether to wake up on-ground wireless sensors for small-scale AQI monitoring and inference, which can greatly reduce the energy consumption of the system. An entropy-based model is employed for accurate real-time AQI estimation at un-measured locations and future air quality distribution forecasting. We implement and evaluate ImgSensingNet on two university campuses since Feb. 2018, and has collected 17,630 photos and 2.6 millions of AQI data samples. Experimental results confirm that ImgSensingNet can achieve high estimation accuracy while greatly reduce the battery consumption, compared to other state-of-the-art AQI monitoring approaches.

360-degree videos have gained more popularity in recent years, owing to the great advance of panoramic cameras and head-mounted devices. However, as 360-degree videos are usually in high resolution, transmitting the content requires extremely high bandwidth. To protect the Quality of Experience (QoE) of users, researchers have proposed tile-based 360-degree video streaming systems that allocate high/low bit rates to selected tiles of video frames for streaming over the limited bandwidth. It is challenging to determine which tiles should be allocated with a high/low rate, because (1) the video playbacks include too many features that dynamically change over time when making the rate allocation; (2) most of the state-of-the-art systems focus on a fixed set of heuristics to optimize a specific QoE objective, while users may have various QoE objectives that need to be optimized in different ways. This paper presents a Deep Reinforcement Learning (DRL) based framework for 360-degree video streaming, named DRL360. The DRL360 framework helps improve the system performance by jointly optimizing multiple QoE objectives across a broad set of dynamic features. The DRL-based model adaptively allocates rates for the tiles of the future video frames based on the observations collected by client video players. We compare the proposed DRL360 to the existing systems by trace-driven evaluations as well as conducting a realworld experiment over a wide variety of network conditions. Evaluation results reveal that DRL360 can adapt to all considered scenarios, and outperform the state-of-the-art approaches by 20%-30% on average given different QoE objectives.

Due to the limited backhaul capacity of traditional small cells (TSC), it is non-trivial for the traditional operator (TO) to satisfy the increasing data demand of users. To relieve this issue, we propose a data offloading scheme, where the TO offloads TSC users to LEO-based small cells owned by the satellite operator for satellite-backhauled network access. An iterative optimal algorithm based on the Stackelberg game is developed to maximize both operators' revenues. Simulation results show the effectiveness of our scheme.

In this poster, we consider the Peer-to-Peer (P2P) energy trading in a local area packetized power network (LAPPN), where demander energy subscribers (ESs) can buy energy from supplier ESs or the utility grid (UG). Since the energy is transmitted in the form of power packets in the power channels in a time division multiplex (TDM) manner, the limited channel resources should be considered in the trading. The trading problem is formulated, in which selfish demander ESs compete for power packets and channels to maximize their own utilities, while the controller maximizes the total revenue. To tackle this problem, an iterative auction scheme is proposed, and its effectiveness is validated by simulation.

Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation, and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q -learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks. The performance of a UAV is determined by both its sensing and transmission processes, which are influenced by the trajectory of the UAV. However, it is challenging for the UAV to determine its trajectory, since it works in a dynamic environment, where other UAVs determine their trajectories dynamically and compete for the limited spectrum resources in the same time. To tackle this challenge, we adopt the reinforcement learning to solve the UAV trajectory design problem in a decentralized manner. To coordinate multiple UAVs performing real-time sensing tasks, we first propose a sense-and-send protocol, and analyze the probability for successful valid data transmission using nested Markov chains. Then, we propose an enhanced multi-UAV Q-learning algorithm to solve the decentralized UAV trajectory design problem. Simulation results show that the proposed algorithm converges faster and achieves higher utilities for the UAVs, compared to traditional singleand multi-agent Q-learning algorithms.

In this paper, we propose a multilayer data flow processing system, i.e., EdgeFlow, to integrally utilize the computing capacity throughout the whole network, i.e., the cloud center (CC) on the top layer, the mobile edge computing (MEC) servers on the middle layer, and the edge devices (EDs) on the bottom layer. To realize the efficient data processing in EdgeFlow, we optimally assign the tasks to multiple layers, and allocate the wireless transmission resources between the MEC servers and EDs as well as the wired transmission resources between the CC and MEC servers. We prove that the system is naturally classified into two states, the nonblocking state and the blocking state, according to various data generation speed at the EDs. The system latency is minimized for the nonblocking state even though the problem is nonconvex. As for the blocking state, the recovery time is minimized through solving a min-max problem. Based on the analytical results, the EdgeFlow system is implemented on the universal software radio peripheral and the Intel next units of computing. A typical Internet of Things application, photo recording and face recognition, is used for the simulation and the experiment, and indicates that the EdgeFlow can achieve a low latency and recovery time than the previous distributed frameworks, e.g., the Cloudlet and the Markov decision process.

In this paper, we consider a cellular controlled unmanned aerial vehicle (UAV) network in which multiple UAVs cooperatively complete each sensing task. We first propose a sense-and-send protocol where the UAVs collect sensory data of the tasks and transmit the collected data to the base station. We then formulate a joint trajectory, sensing location, and UAV scheduling optimization problem that minimizes the completion time for all the sensing tasks in the network. To solve this NP-hard problem efficiently, we decouple it into three subproblems: 1) trajectory optimization; 2) sensing location optimization; and 3) UAV scheduling. An iterative trajectory, sensing, and scheduling optimization (ITSSO) algorithm is proposed to solve these subproblems jointly. The convergence and complexity of the ITSSO algorithm, together with the system performance are analyzed. Simulation results show that the proposed ITSSO algorithm saves the task completion time by 15% compared to the noncooperative scheme.

Small-cell architecture is widely adopted by cellular network operators to increase spectral spatial efficiency. However, this approach suffers from low spectrum temporal efficiency. When a cell becomes smaller and covers fewer users, its total traffic fluctuates significantly due to insufficient traffic aggregation and exhibits a large “peak-to-mean” ratio. As operators customarily provision spectrum for peak traffic, large traffic temporal fluctuation inevitably leads to low spectrum temporal efficiency. To address this issue, in this paper, we advocate device-to-device (D2D) load-balancing as a useful mechanism. The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency. We provide theoretical modeling and analysis to characterize the benefit of D2D load balancing, in terms of total spectrum requirements and the corresponding cost, in terms of incurred D2D traffic overhead. We carry out empirical evaluations based on real-world 4G data traces and show that D2D load balancing can reduce the spectrum requirement by 25% as compared to the standard scenario without D2D load balancing, at the expense of negligible 0.7% D2D traffic overhead.

In this paper, we consider device-to-device (D2D) communications as an underlay to cellular networks over both licensed and unlicensed spectrums, where long-term evolution (LTE) users utilize the spectrum orthogonally while D2D users share the spectrum with LTE users. In the system, each LTE and D2D user can access the licensed or unlicensed band for communications. To maximize the total throughput of the system, we leverage stochastic geometry to derive the throughput for each kind of user by modeling the deployment of users as Poisson point processes (PPPs), and investigate the spectrum access problem for these users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP)-based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra-dense low earth orbit (LEO) networks and the terrestrial networks to achieve efficient data offloading. In TSN, each ground user can access the network over C-band via a macro cell, a traditional small cell, or a LEO-backhauled small cell (LSC). Each LSC is then scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate and the number of accessed users while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite-based backhaul links. The optimization problem is then decomposed into two closely connected subproblems and solved by our proposed matching algorithms. The simulation results show that the integrated network significantly outperforms the non-integrated ones in terms of the sum data rate. The influence of the traffic load and LEO constellation on the system performance is also discussed.

In this paper, we study the platoon cooperation in the multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything (V2X) communication. The platoon cooperation is important for the interference management and communication reliability enhancement, yet it is difficult, since the platoon formation, subchannel allocation, and power control of each platoon interact with each other and influence those of other platoons. To increase the number of vehicles in the platoon and reduce the power consumption, we propose a two-step resource allocation strategy in consideration of platoon formation, i.e., the resource allocation at the base station (BS) and within each platoon. A branch and bound algorithm is utilized for the resource allocation at the BS. We then design a distributed dynamic programming-based subchannel allocation and power control algorithm for the joint optimization of platoon formation, subchannel allocation, and power control. The simulation results evaluate the impact of the penalty factor and the latency requirement on the system performance.

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing (MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages such as limited computing capacity, preventing and computation-intensive tasks to be processed on time. To relief this issue, we propose the heterogeneous multi-layer MEC (HetMEC) where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We aim to minimize the system latency, i.e., the total computing and transmission time on all layers for the data generated by the EDs. We design the latency minimization algorithm by jointly coordinating the task assignment, computing, and transmission resources among the EDs, multi-layer MEC servers, and the CC. The simulation results indicate that our proposed algorithm can achieve a lower latency and higher processing rate than the conventional MEC scheme.

In this paper, we consider an uplink cellular Internet-of-Things (IoT) network, where a cellular user (CU) can serve as the mobile data aggregator for a cluster of IoT devices. To be specific, the IoT devices can either transmit the sensory data to the base station (BS) directly by cellular communications, or first aggregate the data to a CU through machine-to-machine (M2M) communications before the CU uploads the aggregated data to the BS. To support massive connections, the IoT devices can leverage the unlicensed spectrum for M2M communications, referred to as IoT unlicensed (IoT-U). Aiming to maximize the number of scheduled IoT devices and meanwhile associate each IoT device with the right CU or BS with the minimum transmit power, we first introduce a single-stage formulation that captures these objectives simultaneously. To tackle the NP-hard problem efficiently, we decouple the problem into two subproblems, which are solved by successive linear programming and convex optimization techniques, respectively. The simulation results show that the proposed IoT-U scheme can support more IoT devices than that only using the licensed spectrum.

In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two transmission modes are considered to support the multi-UAV communications, i.e., UAV-to-network (U2N) and UAV-to-UAV (U2U) communications. Specifically, the UAV with a high signal-to-noise ratio (SNR) for the U2N link uploads its collected data directly to the BS through U2N communication, while the UAV with a low SNR for the U2N link can transmit data to a nearby UAV through underlaying U2U communication for the sake of quality of service. We first propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and then formulate the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2N and cellular user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed optimization. An iterative subchannel allocation and speed optimization algorithm (ISASOA) is proposed to solve these sub-problems jointly. The simulation results show that the proposed ISASOA can upload 10% more data than the greedy algorithm.

Rapid development of online social networks further facilitates the expansion of information over the Internet. In many scenarios, several pieces of different or even opposite information could diffuse competitively at the same time. Recently, some messenger APPs such as WeChat arose, where users can send links in their "WeChat Moments (WM)", which is called the messenger-based social network (Msg-SN). The unique characteristics in Msg-SN may lead to great difference on the network topology compared to conventional social networks. It is impossible to find the key opinion leaders with millions of followers to help block the rumors or to locate the source of rumors. Thus, the business often tries to select and inject a set of users in the network with the truth to block negative influence diffusion. We call this problem as the competitive influence blocking (CIB) problem. In this paper, we study the CIB problem in Msg-SN under the competitive linear threshold (CLT) model. We propose a fast heuristic algorithm based on eigenvector centrality to optimize the negative influence reduction by selecting a positive seed set. The experimental results using real-world WeChat Moments data show that our algorithm performs better than the effective CLDAG algorithm and also runs faster.

In this paper, we consider the 3 dimensional (3D) small cell basestations (SBSs) placement problem for hetergeneous users with incomplete QoS requirements. We propose a learning embedded hypergraph construction algorithm to jointly predict the heterogeneous users' QoS requirements and deploy the SBSs to cover as many users as possible. Specifically, we predict and leverage users' QoS requirements with a real-world dataset, China Family Panel Studies (CFPS), and design a human behavior based learning framework (HBLF) to anticipate users' QoS requirements with users' profiles. Then, based on HBLF, we propose a hypergraph construction algorithm to deploy the SBSs. Simulation results indicate that the learning embedded hypergraph construction can use the least number of SBSs to cover all users compared with a number of algorithms, such as K-means. The HBLF can achieve a higher coverage ratio of users compared with other learning algorithms, such as decision tree. Simulation results also corroborate that the more accurately we can predict the users' QoS requirements, the more users we can satisfy their QoS requirements.

The downlink coordinated multiple points transmission (CoMP) has addressed a greet number of attentions in recent years. However, an open problem is still remaining that the QoS requirements from the edge users are not guaranteed to be fully achieved due to the low data rate and the high frequency handoff between cells. In this paper, we discuss the spectrum allocation in the downlink CoMP with incomplete QoS requirements. Based on a real world dataset, China Family Panel Studies (CFPS) dataset, we use a number of learning algorithms to anticipate the users’ QoS requirements based on their profiles before the CoMP implementation. Based on the learning results, we further propose a learning embedded three-side matching to do the spectrum allocation. Simulation results show that the human behavior based learning framework can achieve a larger coverage ratio than other learning approaches. Simulation results also show that the learning embedded three-side matching can obtain a larger coverage ratio than the naive matching and random allocation.

In this paper, we consider a cooperative autonomous driving system where a vehicle overtakes the one in front based on collective perception. To avoid collisions with vehicles on the other lane, we propose a V2X-based cooperative collision avoidance scheme. The overtaking vehicle estimates its distance with the neighbors via V2V communications and decides whether to overtake or not. Two cases where the distance information is obtained independently and cooperatively are taken into account. We derive the probability of collision avoidance, and analyze the influence of different factors such as speed and density of vehicles on the system performance. Simulation results verify our analysis and show the improvement brought by the cooperative case compared to the independent case.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra- dense low earth orbit (LEO) networks and the terrestrial networks for data offloading. In TSN, each user can access the network over C-band via a macro cell, a traditional small cell, or a LEO- backhauled small cell (LSC). Each LSC is scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite based backhaul links. The optimization problem is then solved by our proposed matching algorithm. Simulation results show that the integrated network significantly outperforms the non-integrated one in terms of the sum data rate.

In this paper, we consider a three-level hierarchical structure for resource allocation in the radio access network (RAN) slicing. The infrastructure provider (InP) allocates the RAN slices to the mobile virtual network operators (MVNOs), and the MVNOs then allocate the radio resources to the users. It is challenging for the InP to determine resource allocation strategy efficiently due to the selfish strategic responses of both the MVNOs and the users. To handle this issue, we propose a tri-level Stackelberg game to jointly solve the frequency and power allocation and payment negotiation problem among the three levels. Simulation results verify a general market principle that the more the MVNOs focus on revenue collecting, the lower payoff the InP and the users will obtain.

Recent advance in Internet-of-Things (IoT) brings consumer- level smart mobile robot to our life. Indoor navigation is one of the most critical challenges for mobile robots. Existing approaches using wireless signal fingerprinting (e.g., WiFi fingerprint), computer vision techniques, require extensive war-driving of the indoor environment to collect sufficient environmental data. In this paper, we present SIGN, a lightweight, visual-tag based, indoor navigation approach that is free of indoor war-driving. The approach deploys a set of coded visual tags in the environment, and allows the robot to autonomously decide the moving direction by recognizing nearby tags and leveraging the geometry information. The proposed approach is robust to the change of the environment such as unexpected obstacles. Experiments in two indoor spaces under various scenarios show that SIGN helps the mobile robot using the off-the- shelf camera to self-navigate in indoor environment with the deployment of coded visual tags.

With the large penetration of distributed energy resources, DC power packet transmission is a potential technique to achieve efficient peer to peer (P2P) power dispatching. In this paper, a multi-router local area packetized power network (LAPPN) is considered, where multiple power routers are employed to dispatch power packets among demander and supplier energy subscribers (ESs) connected to different routers. To achieve the efficient P2P power transmission in the LAPPN, a power packet dispatching protocol is developed, in which the routes of power packets are optimized first to maximize the power packets utilization efficiency. Then the transmission schedule is determined by allowing different power packets to transmit on different power channels concurrently to meet the variety of urgency requirements of demander ESs. Simulation results demonstrate the effectiveness of the proposed LAPPN power dispatching protocols in achieving high power packet utilization.

In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two communication modes are considered to support the multi-UAV communications, i.e., UAV-to-infrastructure (U2I) and UAV-to-UAV (U2U) communications. We then formulate the subcarrier allocation and trajectory design problem to maximize the uplink sum-rate taking the delay of sensing tasks into consideration. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2I and cellular user (CU) subcarrier allocation, U2U subcarrier allocation, and UAV trajectory design. An iterative subcarrier allocation and trajectory design algorithm (ISATCA) is proposed to solve these sub-problems jointly. Simulation results show that the proposed ISATCA can upload 20% more data than the one without U2U communication.

In this paper, we consider a cellular controlled unmanned aerial vehicle (UAV) sensing network in which multiple UAVs cooperatively complete each sensing task. We formulate a joint trajectory, sensing location, and UAV scheduling optimization problem that minimizes the completion time for all the sensing tasks in the network. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: trajectory optimization, sensing location optimization, and UAV scheduling. An iterative trajectory, sensing, and scheduling optimization (ITSSO) algorithm is proposed to solve these sub-problems jointly. The convergence of the proposed algorithm and the dominated factors on the system performance are analysed. Simulation results show that the task completion time obtained by the proposed ITSSO algorithm is 15% less than that by non- cooperative scheme.

Mobile sensing is challenging in 3D space, as there are many inaccessible places where people rarely venture. Unmanned aerial vehicle (UAV), commonly known as drone, has greatly extended the scope of mobile sensing in 3D space, and pushed forward a variety of 3D mobile sensing applications, such as aerial photo- or video-graphy, 3D wireless signal survey, and air quality monitoring. However, the short battery life of drones has largely restricted the wide adoption of these applications. In this paper, we study the trajectory planning problem for optimizing the flight route in a given sensing space. We first divide the 3D space into an infinite three-dimensional network of observation locations (OLs), and model the sensing scope as a finite subgraph of 3D OL network. We formulate the problem as finding the optimal trajectory in the sensing scope. We propose an algorithm that finds trajectory in each divided 3D grid of the sensing scope by generating a nearly optimal dominating path, and finding the minimum dominating set in the dominating path. Then, we concatenate obtained trajectories in 3D grids to a nearly optimal trajectory in the sensing scope. Experimental results show that the proposed algorithm takes 24% less time to complete sensing the given space, and during the battery life it can cover 19% more sensing scope, than existing solutions.

Caching popular files in the storage of edge networks, namely edge caching, is a promising approach for service providers (SPs) to reduce redundant backhaul transmission to edge nodes (ENs). In this paper, an edge network with one SP, a large number of ENs, and mobile users with time-dependent requests is considered. A convergent and scalable Stackelberg game for edge caching is designed. Specifically, the game is decomposed into two types of sub-games, a storage allocation game (SAG) and a number of user allocation games (UAGs). A Stackelberg game-based alternating direction method of multipliers (Stackelberg game-based ADMM) is proposed to solve either the SAG or each UAG in a distributed manner. The convergence speed and the optimum of the entire game are linearly (or sublinearly) related to the network size, which indicates that this framework can potentially cope with large-scale caching problems. It is also seen in the simulation results that this framework requires fewer backhaul resources than existing approaches.

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

WeChat is the largest mobile instant messaging service in China, where users can send messages to friends or post them over their walls (a.k.a. friend circle, or WeChat Moments). Interactive web apps are quite attractive for businesses, institutes, or individuals to promote products or events. In this paper, we analyze the diffusion statistics of interactive web apps in WeChat and conduct an empirical measurement study over a dataset with 54 million users and 20 thousand web apps crawled. We discover the lifecycle of interactive web apps varies drastically, which is largely dependent on the content, date, time upon the first release, and the social influence of viewers and senders. Meanwhile, we develop a model based on the matrix factorization method to extract latent features of interactive web apps and an app lifecycle model that characterizes how the features affect apps' lifecycle, achieving the mean absolute error (MAE) of 2.32 days in predicting app's lifecycle. Our results hold the promise of helping businesses to promote their marketing information dissemination through long-lived interactive web apps at the right timing and with the appropriate content.

Unmanned Aerial Vehicle (UAV) has been recognized as a promising way to assist future wireless communications due to its high flexibility of deployment and scheduling. In this paper, we focus on temporarily deployed UAVs that provide downlink data offloading under a macro base station (MBS), where the MBS allocates some of its spectrum to the UAVs in an exclusive usage mode. Since the manager of the MBS and the operators of the UAVs could be of different interest groups, we formulate the spectrum trading problem by means of contract theory, where the manager of the MBS has to design an optimal contract to maximize its own revenue. Such contract comprises a set of bandwidth-price options, and each UAV operator only chooses the most profitable one from the whole contract. We analytically derive the optimal contract design, and then propose a dynamic programming algorithm to achieve the optimal result in polynomial time. By simulations, we compare the outcome of the MBS optimal contract with that of a socially optimal one, and find that a selfish MBS manager sells less bandwidth to the UAV operators.

With the increasing demand for vehicular data transmission, limited dedicated cellular spectrum becomes a bottleneck to satisfy the requirements of cellular vehicle-to-everything (V2X) users. To address this issue, we study the coexistence problem between cellular V2X users and vehicular ad-hoc network (VANET) users over the unlicensed spectrum. To facilitate the coexistence, we design an energy sensing based spectrum sharing scheme, where cellular V2X users are able to access the unlicensed channels fairly, thereby reducing the collisions of data transmission. In order to maximize the number of active cellular V2X users, we formulate the scheduling and resource allocation problem as a two-sided many-to-many matching with peer effects. A dynamic vehicle-resource matching algorithm (DV-RMA) is then proposed to solve this problem. Simulation results show that the proposed scheme outperforms the existing approaches in supporting the massive connectivity.

Driven by the increasingly serious air pollution problem, the monitoring of fine-grained air quality index (AQI) in urban areas has drawn considerable attention. In this paper, we design a novel algorithm to recommend the placement of sensors for energy- efficient AQI monitoring in urban three-dimensional (3D) space. Specifically, we first propose an entropy- based semi-supervised learning (ESSL) model to estimate the AQI distribution of unobserved locations, using the sparse historical spatial-temporal data and other features, including 3D coordinates, wind speed and weather conditions. Based on ESSL, we then design an entropy minimization ranking (EMR) algorithm to recommend the best sensor locations for AQI monitoring. Through the emulation on a fine-grained AQI dataset, the results demonstrate our scheme can provide energy- efficient solutions by using the least number of sensors to achieve higher accuracy than other existing approaches.

Decentralizing content delivery to edge devices has become a popular solution for saving the bandwidth consumption of CDN when the CDN bandwidth is expensive. One successful realization is the hybrid CDN-P2P VoD system, where a client is allowed to request video content from a number of seeds (seed clients) in the P2P network. However, the seed scarcity problem may arise for a video resource when there are an insufficient number of seeds to satisfy requests to the video. To alleviate this problem, many commercial VoD systems have employed a video push mechanism that directly sends the recent scarce video resources to randomly-chosen seeds to serve more requests. However, the current video push mechanism fails to consider which videos will become scarce in the future, or differentiate the uploading capability of different seeds. In this paper, we propose Proactive-Push, a video push mechanism that lowers the bandwidth consumption of CDN by predicting future scarce videos and proactively sending them to competent seeds with strong uploading capabilities. Proactive-Push trains neural network models to correctly predict 80% of future scarce video resources, and identify over 90% of competent seeds. We evaluate Proactive-Push using a trace-driven emulation and a real-world pilot deployment over a commercial VoD system. Results show that Proactive-Push can further reduce the proportion of direct download from CDN by 21%, and save the CDN bandwidth cost at peak time by 18%.

In this paper, we study satellite secure communications with physical layer security techniques in the presence of multiple eavesdroppers. In order to increase the secrecy rate to the legitimate user, a terrestrial base station operates as a two-stage amplify-and-forward (AF) relay to assist the secure transmission. In the first stage, the legitimate user sends artificially generated noise to the satellite using paired carrier multiple access (PCMA) such that the artificial noise only degrades the eavesdroppers' channels. In the second stage, the base station forwards its received satellite signal to the legitimate user via beamforming. In this two-stage transmission, we consider two scenarios with non- colluding eavesdroppers and colluding eavesdroppers. We design beamforming schemes for both the two scenarios and compare the proposed beamforming schemes with the null-space beamforming scheme, which nulls out the signal leakages at all eavesdroppers. Simulation results demonstrate the proposed beamforming scheme performs better than the null-space beamforming scheme. It is also shown that the proposed terrestrial network aided satellite communication has a significant performance improvement of secure performance as compared to direct transmissions.

Efficient use of energy has recently become an essential research topic for wireless communications. Channel state information (CSI) feedback has been well recognised to have great significances for the energy‐efficiency (EE) of the closed‐loop wireless networks. However, works on EE focus mostly on the feed‐forward data link by performing precoding and upper layer resource allocation and scheduling approaches. Unlike traditional work, the authors propose to study the EE by controlling the CSI feedback, where the mobile transmitter and receiver pairs exchange data by precoding for co‐channel interference reduction. The EE maximisation problem can be formulated in the analytical settings of a game‐theoretic framework by a two‐level Stackelberg‐type CSI feedback control game (SCFC), which can balance the power consumptions and the bandwidth. Specifically, the leader maximises the EE objective by introducing a price factor in the higher level. In lower level, multiple mobile receivers compete for the feedback channel utilisation to maximise their own utilities. The existence of the equilibriums is corroborated and the convergence behaviour is discussed. Simulation results demonstrate that the proposed distributed SCFC game can greatly enhance the EE performance by adjusting the pricing factor which can achieve close optimal performances in comparison with the centralised scheme.

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on the unmanned-aerial-vehicles, called ARMS, to efficiently build fine-grained AQI maps in real-time. Specifically, we first propose the Gaussian plume model on the basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a 2-D open space like a roadside park, and a 3-D space like a courtyard inside a building. The experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm.

This letter investigates an amplify-and-forward two-way relay network (TWRN) for short-packet communications. We consider a classical three-node TWRN consisting of two sources and one relay. Both two time slots (2TS) scheme and three time slots (3TS) scheme are studied under the finite blocklength regime. We derive approximate closed-form expressions of sum-block error rate (BLER) for both schemes. Simple asymptotic expressions for sum-BLER at high signal-to-noise ratio (SNR) are also derived. Based on the asymptotic expressions, we analytically compare the sum-BLER performance of 2TS and 3TS schemes, and attain an expression of critical blocklength, which can determine the performance superiority of 2TS and 3TS in terms of sum-BLER. Extensive simulations are provided to validate our theoretical analysis. Our results discover that 3TS scheme is more suitable for a system with higher differences between the average SNR of both links, and relatively lower requirements on data rate and latency.

Recently, owing to the advances in the self-interference cancellation technology, the in-band full-duplex (FD) capability has been demonstrated at Wi-Fi range. However, the simultaneous uplink (UL) and downlink (DL) transmission may lead to inter-user interference (IUI) and result in decoding failure. Spectrum efficiency should also be considered in the construction process of the FD transmission. In this paper, we propose a hybrid half-duplex/FD MAC protocol based on a two-fold RTS/CTS contention resolution mechanism, in order to fully exploit the channel access opportunities provided by the simultaneous UL and DL transmissions. The noteworthy features of the proposed protocol lie in the following two aspects. First, the protocol provides the flexibility for the AP to decide the probability of constructing FD transmission, and then it adopts a second-fold of RTS/CTS mechanism to prevent the constructed transmission from being affected by the IUI. The second-fold contention and the probability of constructing FD transmission are optimized separately to maximize the spectrum efficiency given different transmission demands. Simulation results show that the proposed MAC protocol achieves higher capacity compared with previous works, and the hybrid characteristic enables the FD Wi-Fi networks to meet with different system requirements.

Unmanned aerial vehicle (UAV) has been recognized as a promising way to assist future wireless communications due to its high flexibility of deployment and scheduling. In this paper, we focus on temporarily deployed UAVs that provide downlink data offloading in some regions under a macro base station (MBS). Since the manager of the MBS and the operators of the UAVs could be of different interest groups, we formulate the corresponding spectrum trading problem by means of contract theory, where the manager of the MBS has to design an optimal contract to maximize its own revenue. Such contract comprises a set of bandwidth options and corresponding prices, and each UAV operator only chooses the most profitable one from all the options in the whole contract. We analytically derive the optimal pricing strategy based on fixed bandwidth assignment, and then propose a dynamic programming algorithm to calculate the optimal bandwidth assignment in polynomial time. By simulations, we compare the outcome of the MBS optimal contract with that of a socially optimal one and find that a selfish MBS manager sells less bandwidth to the UAV operators.

With the increasing demand for vehicular data transmission, limited dedicated cellular spectrum becomes a bottleneck to satisfying the requirements of all cellular vehicle-to-everything (V2X) users. To address this issue, unlicensed spectrum is considered to serve as the complement to support cellular V2X users. In this paper, we study the coexistence problem of cellular V2X users and vehicular ad hoc network (VANET) users over the unlicensed spectrum. To facilitate the coexistence, we design an energy sensing-based spectrum sharing scheme, where cellular V2X users are able to access the unlicensed channels fairly while reducing the data transmission collisions between cellular V2X and VANET users. In order to maximize the number of active cellular V2X users, we formulate the scheduling and resource allocation problem as a two-sided many-to-many matching with peer effects. We then propose a dynamic vehicle-resource matching algorithm and present the analytical results on the convergence time and computational complexity. Simulation results show that the proposed algorithm outperforms existing approaches in terms of the performance of the cellular V2X system when the unlicensed spectrum is utilized.

Load balancing is an effective approach to address the spatial-temporal fluctuation problem of mobile data traffic for cellular networks. The existing schemes that focus on channel borrowing from neighboring cells cannot be directly applied to the future 5G wireless networks, because the neighboring cells will reuse the same spectrum band in 5G systems. In this paper, we consider an orthogonal frequency division multiple access ultra-dense small cell network, where device-to-device (D2D) communication is advocated to facilitate load balancing without extra spectrum. Specifically, the data traffic can be effectively offloaded from a congested small cell to other underutilized small cells by D2D communications. The problem is naturally formulated as a joint resource allocation and D2D routing problem that maximizes the system sum-rate. To efficiently solve the problem, we decouple the problem into a resource allocation subproblem and a D2D routing subproblem. The two subproblems are solved iteratively as a monotonic optimization problem and a complementary geometric programming problem, respectively. Simulation results show that the data sum-rate in the neighboring small cells increases 20% on average by offloading the data traffic in the congested small cell to the neighboring small cell base stations.

Caching popular files in the storage of edge networks, namely edge caching, is a promising approach for service providers (SPs) to reduce redundant backhaul transmission to edge nodes (ENs). It is still an open problem to design an efficient incentive mechanism for edge caching in 5G networks with a large number of ENs and mobile users. In this paper, an edge network with one SP, a large number of ENs and mobile users with time-dependent requests is investigated. A convergent and scalable Stackelberg game for edge caching is designed. Specifically, the game is decomposed into two types of sub-games, a storage allocation game (SAG) and a number of user allocation games (UAGs). A Stackelberg game-based alternating direction method of multipliers (Stackelberg game-based ADMM) is proposed to solve either the SAG or each UAG in a distributed manner. Based on both analytical and simulation results, the convergence speed, the optimum of the entire game, and the amount of information exchange are linearly (or sublinearly) related to the network size, which indicates that this framework can potentially cope with large-scale caching problems. The proposed approach also requires less backhaul resource than the existed approaches.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the latency and to improve the packet reception probability. In the proposed scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a non-orthogonal manner while the vehicles autonomously perform distributed power control. We formulate the centralized scheduling and resource allocation problem as a multi-dimensional stable roommate matching problem and develop a novel rotation matching algorithm to solve it. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the latency and reliability.

Recently, the in-band full-duplex (FD) capability has been demonstrated at Wi-Fi range. However, the simultaneous uplink (UL) and downlink (DL) transmission may lead to inter-user interference (IUI). In this paper, we propose a hybrid MAC protocol, in which the AP decides the probability of constructing FD transmission. The protocol also adopts a second fold of RTS/CTS mechanism to prevent the constructed transmission from being affected by the IUI. The second fold RTS/CTS mechanism and the probability for FD transmission are optimized respectively to maximize the expected spectrum efficiency. Simulation results show that the proposed MAC protocol achieves higher capacity compared to a half-duplex counterpart in terms of both UL and DL throughput.

Fog Computing potentially plays a pivotal role in delivering real-time data services to users, where large number of Fog Nodes (FNs) are deployed by various Data Service Operators (DSOs) to provide efficient services to the Authorized Data Service Subscribers (ADSSs). As a result, there exist tradings between DSOs and ADSSs in providing and purchasing these services, respectively. There also exists competition among all DSOs for providing these services at the prices that can maximize their profits. Moreover, competition exists among all ADSSs for purchasing the required amount of resources at the lowest available prices, and thus minimizing their costs. In this paper, we model the aforementioned competitions in fog computing as an Equilibrium Problem with Equilibrium Constraints (EPEC). In the EPEC, the DSOs provide incentives to the ADSSs and balance the utilities between the DSOs and the ADSSs. At the same time, the ADSSs leverage the incentives provided by the DSOs to their advantage. As the size of a typical fog computing network is large, the Alternating Direction Method of Multipliers (ADMM) algorithm, that has been recognized as a key method in large scale optimization can be employed. Utilizing the fast convergence and decomposition properties of ADMM, we achieve optimum results. Simulation results show that with the proposed framework, optimization of the utility functions of DSOs and ADSSs can be achieved in real-time. It is also shown that compared to the profit in traditional cloud computing and data center services, the total maximum profit of the ADSSs is improved to a great extent in fog computing.

Recently, mobile devices have been used to carry sensors to monitor air quality index (AQI), and help construct an AQI map in 2-dimensional (2D) areas. In this paper, we design a novel 3-dimensional (3D) AQI monitoring system, called Arms (AQI realtime monitoring system), to efficiently build realtime fine-grained 3D AQI maps, with the help of unmanned-aerial- vehicles (UAVs). Based on the data monitored by Arms, a novel dispersion model, namely Adaptive Gaussian Plume Model (AGPM) is proposed to predict the distribution of AQI. Moreover, the adaptive monitoring techniques, i.e., complete and optimized monitoring, are designed to effectively produce and maintain realtime AQI maps, while greatly reducing the measurement efforts. Experimental results verify that Arms can provide higher predicting accuracy of AQI with the proposed AGPM than other existing models. In addition, the whole system's battery consumption can be greatly reduced.

Driving navigation is challenging in enclosed areas, e.g., urban canyon areas, due to loss of GPS signals, or poor availability of landmarks. Many leader-follower navigation systems are independent of GPS signals or landmarks: a leader device pre-collects sensory signals of landmarks along a pathway to build a reference trace; and the follower device compares the signals at his current location against the ones in the reference trace to get navigated. However, such navigation systems are subject to the volatility of the sensory data, e.g., image landmarks or WiFi signals may change over time. This requires labor-intensive updates of sensory signals along the pathway. In this paper, we propose, Trail-Tracker, a leader-follower driving navigation system that is free of the volatility of these sensory data. Specifically, Trail-Tracker creates the trajectory segments by fusing the visual and inertial data captured by smartphone sensors, and it compares one's trajectory segment against the leader's for navigation, instead of directly comparing the series of volatile sensory data recorded in their devices. Trail-Tracker is infrastructure-free and robust to the change of environment. We implemented Trail-Tracker on smartphones, and evaluated it in a street canyon area and on a public dataset. Our experimental results show that the final spatial errors during navigation were 2.9% of the whole journey, with a low latency of about 100ms per frame.

With large coverage area, high data rate, low latency and high spectral efficiency, LTE-V has been considered as a promising communication technology in the vehicular networks. However, as all vehicles share the same wireless resource, in LTE-V, how to design power allocation strategy for each vehicle while motivating other vehicles to forward the data remains challenging. In this paper, we model the data transmission in the uplink scenario of the vehicular network as an equilibrium program with equilibrium constraints (EPEC), where the upper-layer vehicles as receivers (VaRs) provide priced relaying service to vehicle as a transmitter (VaT) in the bottom layer. Observing the prices set by serving VaRs in allocated channels, we adopt multi-level water- filling algorithm at the VaT for power allocation. With joint consideration on the optimal reaction of the VaT and the pricing strategy of other VaRs, each VaR optimizes its setting price by adopting the sub-gradient algorithm such that the equilibrium of the formulated EPEC is finally achieved. Simulation results corroborate our theoretical analysis and demonstrate the performance superiority of our proposal as compared with classic pricing strategies of VaRs.

Device-to-Device (D2D) communication, which enables direct communication between nearby mobile devices, is an attractive technique to improve spectrum efficiency by reusing licensed spectrum. Nowadays, LTE-unlicensed (LTE-U) emerges to extend the cellular network to the unlicensed spectrum to alleviate the spectrum scarcity issue. In this paper, D2D communication is allowed to work in unlicensed spectrum (D2D-U) as an underlay of the cellular network for further booming the network capacity. A sensing-based protocol is designed to support the unlicensed channel access for both LTE and D2D users, based on which we investigate the subchannel allocation problem to maximize the total sum rate while taking into account their interference to the existing Wi-Fi systems. Specifically, we formulate the subchannel allocation as a many-to-many matching problem with externalities, and develop an iterative usersubchannel swap algorithm. Analytical and simulation results show that the proposed D2D-U scheme can significantly improve the network capacity.

In this paper, we propose a general pricing framework, helping the controller promote agents to achieve its objective, for a big data network with one controller and a large number of agents. The convergence of the framework is guaranteed for a general class of objective functions: a separable convex function for the controller and a convex function for each agent. Specially, the proposed framework can converge linearly, when the controller's objective is strongly convex, and the agents' objectives have a uniform Lipschitz gradient. The convergence, and especially the linear convergence is not dependent on the number of agents, which is important for a network with large size. Through numerical results, we apply our pricing framework in a wireless virtualized network to verify its fast convergence, where the pricing framework converges after just a few steps.

Vehicle-to-everything (V2X) communications enable the information exchange between the vehicle and the infrastructure, pedestrian, device, or any other entity that may affect the vehicle. V2X is known as a critical component for the vision of connected and autonomous vehicles in the future, with the goal of improving safety, saving energy, optimizing the traffic, etc. Many stake holders (e.g., 3GPP, IEEE) have announced the draft standards or technical reports that detail new opportunities of V2X-enabled vehicles for the cellular and automotive industries. However, the security issues in V2X communications receive little attention from industry and academia communities. In this paper, we describe how adversaries can exploit the security vulnerabilities to degrade the performance of V2X communications and evaluate the likelihood of V2X specific attacks in undermining the road safety. Our investigation reveals that the existing security mechanisms fall short of addressing the key security threats, and we also discuss countermeasures by adding a security sub-layer for V2X communication protocols to provide V2X users with privacy, authentication, and confidentiality.

Load balancing is an effective approach to address the spatial-temporal fluctuation of mobile traffic in cellular networks. However, existing schemes that rely on channel borrowing from neighboring cells cannot be directly applied to current LTE-A systems where all cells are deployed with the same spectrum band. In this paper, we consider a multi-cell OFDMA network, where Device-to-Device (D2D) communication is opted for load balancing without the need of extra spectrum. Specifically, the traffic can be steered from a congested cell to another underutilized cells by D2D communications. The problem is naturally formulated as a joint resource allocation and D2D routing problem that maximizes the system sum-rate. In particular, we formulate the resource allocation as a Monotonic optimization problem and the D2D routing as a complementary Geometric Programming problem. The two subproblems are solved iteratively to obtain the joint resource allocation and D2D routing solution. Simulation results demonstrate that the D2D load balancing can improve the system sum-rate efficiently.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the access latency and to improve the packet reception probability. In the proposed two-fold scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a nonorthogonal manner while the vehicles autonomously perform distributed power control with iterative signaling control. We formulate the centralized scheduling and resource allocation problem as equivalent to a multi-dimensional stable roommate matching problem, in which the users and time/frequency resources are considered as disjoint sets of objects to be matched with each other. We then develop a novel rotation matching algorithm, which converges to an L-rotation stable matching after a limited number of iterations. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the access latency and reliability.

With the wide adoption of smart mobile devices, there is a rapid development of location-based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivizes a large group of mobile users to participate. In this paper, we investigate the problem of designing a crowdsourcing tournament to maximize the principal's utility in crowdsourcing and provide continuous incentives for users by rewarding them based on the rank achieved. First, we model the user's utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Finally, we present numerical results to show the parameters' impact on the tournament design and compare the system performance under the different proposed incentive mechanisms. We show that by using the tournament, the principal successfully maximizes the utilities, and users obtain the continuous incentives to participate in the crowdsourcing activity.

In cognitive radio networks (CRNs), spectrum trading is an efficient way for secondary users (SUs) to achieve dynamic spectrum access and to bring economic benefits for the primary users (PUs). Existing methods require full payment from SU, which blocked many potential “buyers,” and thus limited the PU's expected income. To better improve PUs' revenue from spectrum trading in a CRN, we introduce a financing contract, which is similar to a sealed non-cash auction that allows SU to do financing. Unlike previous mechanism designs in CRN, the financing contract allows the SU to only pay part of the total amount when the contract is signed, known as the down payment. Then, after the spectrum is released and utilized, the SU pays the rest of payment, known as the installment payment, from the revenue generated by utilizing the spectrum. The way the financing contract carries out and the sealed non-cash auction works similarly. Thus, contract theory is employed here as the mathematical framework to solve the non-cash auction problem and form mutually beneficial relationships between PUs and SUs. As the PU may not have the full acknowledgment of the SU's transmission status, the problems of adverse selection and moral hazard arise in the two scenarios, respectively. Therefore, a joint adverse selection and moral hazard model is considered here. In particular, we present three situations when either or both adverse selection and moral hazard are present during the trading. Furthermore, both discrete and continuous models are provided in this paper. Through simulations, we show that the adverse selection and moral hazard cases serve as the upper and lower bounds of the general case where both problems are present.

Alternating direction method of multipliers (ADMM) has been well recognized as an efficient optimization approach due to its fast convergence speed and variable decomposition property. However, in big data networks, the agents may not feedback the variables as the centralized controller expects. In this paper, we model the problem as a Stackelberg game and design a Stackelberg game based ADMM to deal with the contradiction between the centralized objective of the controller and the individual objectives from the agents. The Stackelberg game based ADMM can converge linearly, which is not dependent on the number of agents. The case study verifies the fast convergence of our game-based incentive mechanism.

In this paper, we study the user pairing in a downlink non-orthogonal multiple access (NOMA) network, where the base station allocates the power to the pairwise users within the cluster. In the considered NOMA network, a user with poor channel condition is paired with a user with good channel condition, when both their rate requirements are satisfied. Specifically, the quality of service for weak users can be guaranteed, since the transmit power allocated to strong users is constrained following the concept of cognitive radio. A distributed matching algorithm is proposed in the downlink NOMA network, aiming to optimize the user pairing and power allocation between weak users and strong users, subject to the users' targeted rate requirements. Our results show that the proposed algorithm outperforms the conventional orthogonal multiple access scheme and approaches the performance of the centralized algorithm, despite its low complexity. In order to improve the system's throughput, we design a practical adaptive turbo trellis coded modulation scheme for the considered network, which adaptively adjusts the code rate and the modulation mode based on the instantaneous channel conditions. The joint design work leads to significant mutual benefits for all the users as well as the improved system throughput.

With the rapid growth of sensor technology, smartphone sensing has become an effective approach to improve the quality of smartphone applications. However, due to time-varying wireless channels and lack of incentives for the users to participate, the quality and quantity of the data uploaded by the smartphone users are not always satisfying. In this paper, we consider a smartphone sensing system in which a platform publicizes multiple tasks, and the smartphone users choose a set of tasks to participate in. In the traditional non-cooperative approach with incentives, each smartphone user gets rewards from the platform as an independent individual and the limit of the wireless channel resources is often omitted. To tackle this problem, we introduce a novel cooperative approach with an overlapping coalition formation game (OCF-game) model, in which the smartphone users can cooperate with each other to form the overlapping coalitions for different sensing tasks. We also utilize a centralized case to describe the upper bound of the system sensing performance. Simulation results show that the cooperative approach achieves a better performance than the non-cooperative one in various situations.

LTE-Unlicensed has recently captured intense attention from both academic and industrial fields. By integrating the unlicensed spectrum with the licensed spectrum, using carrier aggregation, LTE-Unlicensed users can experience enhanced transmission while maintaining the seamless mobility management and predictable performance. However, due to different transmission regulations, the coordination between LTE and Wi-Fi systems requires careful design. It is especially important to understand how to guarantee the transmission quality for LTE users and reduce Wi-Fi users' performance degradation, under the impact of the co-channel interference. In other words, how can we solve the unlicensed resource allocation problem under both LTE and Wi-Fi transmission requirements? In this paper, we propose a matching theory framework to tackle this problem. Specifically, the coexistence between LTE and Wi-Fi systems, i.e., the interaction between LTE and Wi-Fi users, is modeled as a stable marriage game. The coexistence constraints are interpreted as the preference lists. Two semi-distributed solutions, namely, the Gale-Shapley and the random path to stability algorithms are proposed. In addition, to address the external effect in matching, the inter-channel cooperation algorithm is introduced. Last but not least, the resource allocation problem is studied with network dynamics and the proposed mechanisms are evaluated under two typical user mobility models.

Recent improvements in vehicular ad hoc networks are accelerating the realization of intelligent transportation system (ITS), which not only provides road safety and driving efficiency, but also enables infotainment services. Since data dissemination plays an important part in ITS, recent studies have found caching as a promising way to promote the efficiency of data dissemination against rapid variation of network topology. In this paper, we focus on the scenario of roadside unit (RSU) caching, where multiple content providers (CPs) aim to improve the data dissemination of their own contents by utilizing the storages of RSUs. To deal with the competition among multiple CPs for limited caching facilities, we propose a multi-object auction-based solution, which is sub-optimal and efficient to be carried out. A caching-specific handoff decision mechanism is also adopted to take advantages of the overlap of RSUs. Simulation results show that our solution leads to a satisfactory outcome.

In this paper, we study an X-duplex relay system with one source, one amplify-and-forward relay, and one destination, where the relay is equipped with a shared antenna and two radio frequency (RF) chains used for transmission or reception. X-duplex relay can adaptively configure the connection between its RF chains and antenna to operate in either half-duplex (HD) or full-duplex (FD) mode, according to the instantaneous channel conditions. We first derive the distribution of the signal to interference plus noise ratio, based on which we then analyze the outage probability, average symbol error rate (SER), and average sum rate. We also investigate the X-duplex relay with power allocation and derive the lower bound and upper bound of the corresponding outage probability. Both analytical and simulated results show that the X-duplex relay achieves a better performance over pure FD and HD schemes in terms of SER, outage probability and average sum rate, and the performance floor caused by the residual self interference can be eliminated using flexible RF chain configurations.

Explosive demand for wireless internet services has posed critical challenges for wireless networks due to their limited capacity. To tackle this hurdle, wireless Internet service providers (WISPs) take the smart data pricing to manage data traffic loads. Meanwhile, from the users' perspective, it is also reasonable and desired to employ mobile data traffic management under the pricing policies of WISPs to improve the economic efficiency of data consumption. In this paper, we introduce a concept of cost efficiency (CE) for user's mobile data management, defined as the ratio of user's mobile data consumption benefits and its expense. We propose an integrated CE-based data traffic management scheme, including long-term data demand planning, short-term data traffic pre-scheduling, and real-time data traffic management. The real-time data traffic management algorithm is proposed to coordinate user's data consumption to tailor to the pre-scheduled data traffic profile. Numerical results demonstrate the effectiveness of CE framework in indicating and motivating mobile user's data consumption behavior. The proposed management scheme can effectively motivate the user to adjust its data consumption profile to obtain the optimal data consumption CE.

In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward relay allocates the spectrum and power resources to the source-destination (SD) pairs. We aim to optimize the resource allocation to maximize the average sum-rate. The optimal approach requires an exhaustive search, leading to an NP-hard problem. To solve this problem, we propose two efficient many-to-many two-sided SD pair-subchannel matching algorithms, in which the SD pairs and sub-channels are considered as two sets of players chasing their own interests. The proposed algorithms can provide a sub-optimal solution to this resource allocation problem in affordable time. Both the static matching algorithm and the dynamic matching algorithm converge to a pair-wise stable matching after a limited number of iterations. Simulation results show that the capacity of both proposed algorithms in the NOMA scheme significantly outperforms the conventional orthogonal multiple access scheme. The proposed matching algorithms in NOMA scheme also achieve a better user-fairness performance than the conventional orthogonal multiple access.

Device-to-device (D2D) communication, which enables direct communication between nearby mobile devices, is an attractive add-on component to improve spectrum efficiency and user experience by reusing licensed cellular spectrum in 5G system. In this paper, we propose to enable D2D communication in unlicensed spectrum (D2D-U) as an underlay of the uplink LTE network for further booming the network capacity. A sensing-based protocol is designed to support the unlicensed channel access for both LTE and D2D users. We further investigate the subchannel allocation problem to maximize the sum rate of LTE and D2D users while considering their interference to the existing Wi-Fi systems. Specifically, we formulate the subchannel allocation as a many-to-many matching problem with externalities, and develop an iterative user-subchannel swap algorithm. Analytical and simulation results show that the proposed D2D-U scheme can significantly improve the system sum rate.

It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs' interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

The study item: "Feasibility Study on LTE-based V2X Services", approved at 3GPP TSG RAN #68, has aroused the interest in the study of LTE assisted vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communications in the networks of connected vehicles. By deploying the direct device-to-device (D2D) technology of traditional cellular networks into the V2X (including both V2V and V2I) communications, performance improvements can be expected, such as better reliability, lower latency, and more efficient content sharing. This paper investigates the content sharing problem in the D2D based V2X communication networks. With both vehicles and eNBs carrying multiple different classes of data, this work studies how to optimize the information exchanged within the network, and in the mean time to guarantee the system quality of service (QoS) requirements. By jointly considering the data diversity and link quality, the interactions between vehicles/eNBs, or in other words, the V2V and V2I link scheduling, is modeled as the stable fixture (SF) matching game. Different from traditional D2D communications, where each node is limited to one link, we allow multiple V2X connections for each vehicle to further optimize the content sharing. More specifically, the formations of such V2X links are independent from each other, thus more flexible than the conventional clustering formation in the Vehicular ad hoc networks (VANETs). The SF game is solved by the proposed Irving's stable fixture (ISF) algorithm. Its advantages over some heuristics are demonstrated through simulation experiments.

Recently, some CSMA/CD-alike protocols have been proposed for full-duplex (FD) WiFi networks, in which users are able to monitor the channel and transmit data simultaneously so as to avoid data collisions and improve the spectrum efficiency. However, along with its benefits, the residual self- interference (RSI) brought by FD becomes a new challenge for the network. As the RSI increases with the transmit power, the sensing performance degrades. Thus, on the one hand, increased transmit power of individual user will promote the system throughput. On the other hand, as the user who raises its transmit power suffers from more detection failure, its transmit probability will be higher and leads to a suppression of that of other users, so the system fairness may decrease. Consequently, a tradeoff between the system throughput and fairness emerges. In this paper, we provide theoretical analysis of the system throughput and fairness, and reveal their relationship with the power profile of users in FD WiFi networks. We formulate the power control problem in FD WiFi networks as a non-cooperative game, for which we propose a distributed power control mechanism considering both system throughput and fairness. Simulation results validate the fairness-throughput tradeoff for the proposed power control mechanism.

To maintain the interest of both energy supplier and demand side consumers in the electricity market, in this paper, we propose a Nash bargaining demand side management scheme that optimizes the demand loads to achieve a balanced interest distribution. We formulate the power distribution problem by the Nash bargaining framework, where the optimization objective, defined as social benefit, is product of the consumers' interests represented by the cost efficiency of power consumption, and the energy supplier's interest measured by its profit. Simulation results confirm the optimality of the proposed scheme in obtaining the social benefit and provide insights of maximizing the benefit as well as satisfaction level and on how the behaviors of the two sides impact on the interests.

Wireless network virtualization has emerged as a promising technology to provide a variety of services and applications for future wireless network as by enabling a more effective exploitation of network resources. In a mobile virtual network (MVN), both infrastructure provider (InP) and service provider (SP) must have a complementary relationship, as their revenues are mutually dependent. The trading of resources and services between the InP and SP is usually a long-term supply contract, and details of trades are left to be specified in the future. Thus, the returns of the InP and SP depend on their bargaining positions, ex post, and investments, ex ante. As a result, the InP and SP may hesitate to have specific investment, since it may put them at a risk of no return. In this paper, the problems of determining how the ownership of the resources affect the InP and SP's incentives to invest and how to choose the most efficient investments in an MVN are studied. First, a general system model is developed in multiple InPs and SPs engaged in a complementary relationship to exchange multiple physical and virtual resources. Subsequently, for this formulated problem, the optimal investments are derived. Furthermore, we give detailed analysis of a special case and shed light on the problem of ownership and investment efficiency by answering the question on whether the ownership of resources should be integrated or operated separately by the SP and InP. Simulation results assess the parameters that affect the efficiency of investment through simulations.

In this paper, we study the codebook-based resource allocation problem for an uplink sparse code multiple access (SCMA) network. The base station (BS) assigns to each user a set of subcarriers corresponding to a specific codebook, and each user performs power control over multiple subcarriers. We aim to optimize the subcarrier assignment and power allocation to maximize the total sum-rate. To solve the above problem, we formulate it as a many-to-many two-sided matching problem with externalities. A novel swap-matching algorithm is then proposed in which the users and the subcarriers are considered as two sets of players, and every two users can cooperate to swap their matches so as to improve each other's profits. The algorithm converges to a pair-wise stable matching after a limited number of iterations. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a random allocation scheme.

In this paper, a novel X-Duplex relay scheme with one source, one amplify-and-forward (AF) relay and one destination is proposed. The relay is equipped with a shared antenna and two radio frequency (RF) chains used for transmission or reception. The proposed scheme can be reduced to either full-duplex (FD) or half-duplex (HD) with different RF chain configurations. In the proposed scheme, relay adaptively configures the connection between its RF chains and the antenna to optimise the end-to-end system performance according to the instantaneous channel conditions. In this paper, we analyze the system overall performances based on the distribution of the signal to interference plus noise ratio (SINR) of the hybrid mode, including outage probability and average sum rate. Monte-Carlo simulations are used to validate the analytical expressions. Results show that the X-Duplex relay achieves a lower outage probability and a higher average sum rate compared to FD and HD schemes.

Featured by centralized processing and cloud-based infrastructure, cloud radio access network (C-RAN) has emerged as a promising solution to handle the data proliferation in future wireless networks. However, the attractive capacity enhancement brought by large-scale centralized processing comes along with increased computing resource requirement in the baseband unit (BBU) pool. Thus, computing resource as another dimension of manageable resource needs to be considered in resource allocation and C-RAN system design. In this paper, we first characterize the relationship between PHY transmission characteristics and the required computing resource in the BBU pool. Based on this, we propose a feasible algorithm to maximize the network sum-rate under limited computing resource constraint, which is a binary-integer non-linear programming (BINLP) problem with non-convex constraints in nature. Numerical results show the significant impact of computing resource on both user-RRH association strategy and the achievable sum-rate performance.

Caching has shown the success in performance improvement for many wireless communications and networking systems. In this paper, we introduce a caching mechanism for the energy harvesting based Internet of Things (IoT) sensing service. In the service, a sensor harvests energy from an environment. The energy is stored in the battery, and the sensor uses it for sensing and transmitting the reading to the user. A sensing cache can be implemented at a wireless gateway of the sensor to avoid activating the sensor too frequently, hence reducing its energy consumption. We develop an analytical model to investigate the benefit of the proposed caching mechanism. We also introduce the threshold adaptation algorithm that allows the sensing cache dynamically to adjust the parameter of caching to maximize the combined hit rate of the sensing service from multiple sensors. The performance evaluation clearly shows the tradeoff between energy consumption and caching.

In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward (AF) relay allocates the spectrum and power resources to the source-destination (SD) pairs. We aim to optimize the spectrum and power resource allocation to maximize the total sum-rate. This is a very complicated problem and the optimal approach requires an exhaustive search, leading to a NP hard problem. To solve this problem, we propose an efficient many-to-many two sided SD pair-subchannel matching algorithm in which the SD pairs and sub-channels are considered as two sets of rational and selfish players chasing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations with a low complexity compared with the optimal solution. Simulation results show that the sum-rate of the proposed algorithm approaches the performance of the optimal exhaustive search and significantly outperforms the conventional orthogonal multiple access scheme, in terms of the total sum-rate and number of accessed SD pairs.

Device-to-Device (D2D) communication is an important component for 5G networks. Traditional D2D communications are mainly within the same cell. However, due to the reduced cell sizes, and the long communication range among mobile devices, the issues in D2D communication across cells have not yet been well addressed. In this paper, we first introduce an operation protocol to support cross-cell D2D communications underlaying cellular networks, then propose a hypergraph based resource allocation scheme to optimize the sum rate over the shared resource. In a hypergraph, the D2D and cellular users are regarded as vertices, and hyperedges represent mutual interference. For efficient interference coordination, the hypergraph is partitioned into different clusters corresponding to different channels. Simulation results demonstrate that the scheme efficiently leads to a good performance on the sum rate.

Simultaneously information and power transfer in mobile relay networks have recently emerged, where relays can harvest the radio frequency (RF) energy and then use this energy for data forwarding and system operation. However, most of the previous works do not consider that relays may have their own objectives, such as harvesting more residual energy for maximizing its own data transmission instead of reaching a single goal of the network. Therefore, in this paper, we propose a Nash bargaining approach to balance the information transmission efficiency of source-to-destination pairs and the residual harvested energy of relays. We theoretically analyze and prove that this Nash bargaining problem has several desirable properties such as the quasi-concavity when it is decomposed into three sub-problems: energy harvesting power optimization, time division between information transmission and energy harvesting, and the power control for information transmission. Based on the theoretical analysis, we propose an alternating power control and time division scheme to find a sub-optimal solution. Simulation results clearly show and demonstrate the properties of the problem and the convergence of our algorithm.

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

In this paper, the authors study a half‐duplex two‐way relay channel with correlated sources exchanging bidirectional information. In the case, when both sources have the knowledge of correlation statistics, a source compression with physical‐layer network coding scheme is proposed to perform the distributed compression at each source node. When only the relay has the knowledge of correlation statistics, the authors propose a relay compression with physical‐layer network coding scheme to compress the bidirectional messages at the relay. The closed‐form block error rate expressions of both schemes are derived and verified through simulations. It is shown that the proposed schemes achieve considerable improvements in both error performance and throughput compared with the conventional non‐compression scheme in correlated two‐way relay networks.

In a cognitive radio (CR) network, the transmitting signal of a secondary user (SU) is traditionally considered to be harmful for the primary user (PU), since it decreases the capacity of the PU's channel. However, for PU's secrecy capacity, the SUs' interference can be beneficial if it decreases the capacity of the source-eavesdropper channel more than that of the source-destination channel. In this paper, we consider using the SUs' interference to improve the PU's secrecy capacity and providing the SUs the opportunity to access the spectrum as a reward. But, there exists a tradeoff between the SUs' channel capacity and the PU's secrecy capacity. To decide which SUs can share the spectrum with the PU, we present a coalition formation game model with nontransferable utility, and propose a merge and split algorithm. The simulation results verify the efficiency of the proposed algorithm in terms of both the SUs' channel capacity and the PU's secrecy capacity in various scenarios.

In this paper, the resource allocation and scheduling problem for a full-duplex (FD) orthogonal frequency-division multiple-access network is studied where an FD base station simultaneously communicates with multiple pairs of uplink (UL) and downlink (DL) half-duplex (HD) users bidirectionally. In this paper, we aim to maximize the network sum-rate through joint UL and DL user pairing, OFDM subchannel assignment, and power allocation. We formulate the problem as a non-convex optimization problem. The optimal algorithm requires an exhaustive search, which will become prohibitively complicated as the numbers of users and subchannels increase. To tackle this complex problem more efficiently, we formulate the user-pairing and subchannel allocation problem as a three-sided matching problem, and propose a novel low-complexity near-optimal matching algorithm. The algorithm is analyzed, and we prove that it converges to a stable matching. Simulation results show that the FD scheme can significantly improve the spectrum efficiency compared with the HD scheme. The proposed algorithm performs very close to the optimal algorithm, and significantly outperforms other resource allocation schemes.

In this paper, we study the resource allocation and user scheduling problem for a downlink non-orthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm, which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme.

Wireless network virtualization has been well recognized as a way to improve the flexibility of wireless networks by decoupling the functionality of the system and implementing infrastructure and spectrum as services. Recent studies have shown that caching provides a better performance to serve the content requests from mobile users. In this paper, we propose that caching can be applied as a service in mobile networks, i.e., different service providers (SPs) cache their contents in the storage of wireless facilities that are owned by mobile network operators. Specifically, we focus on the scenario of small-cell networks, where cache-enabled small-cell base stations are the facilities to cache contents. To deal with the competition for storage among multiple SPs, we design a mechanism based on multi-object auctions, where the time-dependent feature of system parameters and the frequency of content replacement are both taken into account. Simulation results show that our solution leads to a satisfactory outcome.

Cooperation in resource sharing among wireless users and network operators has been widely studied in wireless communication. However, because of the limited coordination capability or cheating strategies, each participant of the cooperation may cease its cooperative behavior or duties unilaterally during the resource sharing, resulting in unsatisfying quality of services (QoSs) for all other participants. In this paper, we model the resource sharing among participants as an iterated game. Specifically, we first define the participant who is responsible for maintaining the social welfare as an administrator of cooperation (AoC), and other selfish participants as the regular participants of cooperation (PoCs). Then we consider three scenarios, i.e., with two-player applying discrete strategy, two-player applying continuous strategy, and multi-player applying continuous strategy, Finally, we investigate the power control problem in each of scenarios, and apply the zero-determinant strategies for the AoC to find the maximum social welfare that the AoC can achieve with existence of PoCs. Simulation results show that the high and stable social welfare can be maintained by the the AoC with the proposed zero-determinant algorithm.

Device-to-device (D2D) communication has been recognized as a promising technique to offload the traffic for the evolved Node B (eNB). However, D2D transmission as an underlay causes severe interference to both the cellular and other D2D links, which imposes a great technical challenge to radio resource allocation. Conventional graph based resource allocation methods typically consider the interference between two user equipments (UEs), but they cannot model the interference from multiple UEs to completely characterize the interference. In this paper, we study channel allocation using hypergraph theory to coordinate the interference between D2D pairs and cellular UEs, where an arbitrary number of D2D pairs are allowed to share the uplink channels with the cellular UEs. Hypergraph coloring is used to model the cumulative interference from multiple D2D pairs, and thus, eliminate the mutual interference. Simulation results show that the system capacity is significantly improved using the proposed hypergraph method in comparison to the conventional graph based one.

In this paper, we study the resource allocation and scheduling problem for a downlink non- orthogonal multiple access (NOMA) network where the base station (BS) allocates the spectrum resources and power to the set of users. We aim to optimize the sub-channel assignment and power allocation to achieve a balance between the number of scheduled users and total sum-rate maximization. To solve the above problem, we propose a many-to-many two-sided user-subchannel matching algorithm in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations. Simulation results show that the proposed algorithm can approach the performance of the upper bound and greatly outperforms the OFDMA scheme.

LTE, as the advanced mobile telecommunication technology, is serving heavy mobile broadband traffic nowadays. Motivated by the potential boost in performance of LTE utilizing the unlicensed spectrum, significant efforts have been devoted into the commonly referred LTE-Unlicensed technique. In this work, we investigate the carrier aggregation of licensed and unlicensed spectrum by deploying micro-cell base stations, which have access to the unlicensed spectrum, to provide cellular users a more reliable and efficient transmission. We tackle the unlicensed resource allocation problem by modeling it as a student-project allocation matching game. In addition, a postmatching procedure of resource re- allocation is introduced to guarantee unlicensed users' quality of service (QoS), as well as the system-wide stability. The simulation evaluation shows the effectiveness and efficiency of our proposed matching-based approach.

The idea of in-band full-duplex (FD) communications revives in recent years owing to the significant progress in the self-interference cancellation and hardware design techniques, which offers the potential to double spectral efficiency. However, the adaptations from lower to upper layers are highly demanded in the design of FD communication systems. In this paper, we first propose a novel medium access control (MAC) protocol on a single channel using FD techniques that allows transmitters to monitor the channel usage while transmitting, and backoff when collision happens. Specifically, imperfect sensing brought by residual self-interference (RSI) in the physical layer is taken into account in the design of the protocol, and throughput is analytical derived. Then, we extend the protocol to multichannel scenario and propose a multichannel selection mechanism based on the rate and occupancy history of each channel for FD users. Simulation results show the effectiveness of the channel selection protocol and indicate that the total throughput of the proposed FD-MAC protocol can significantly outperforms the greedy channel selection strategy based on the CSMA access mechanism.

Device-to-Device (D2D) is an emerging technology that is typically employed as an underlay of the uplink (UL) cellular networks. The downlink cellular spectrum, however, is rarely reused by D2D links due to the strong interference from the base station to the D2D receivers. To this end, we propose to enable D2D transmissions in the downlink by multiplexing D2D and cellular users using beamforming techniques. In particular, a cross-layer design approach is adopted to jointly optimize beamforming, spectrum allocation, and power control, so that the total system transmission power is minimized. To deal with the non-convex and combinatorial nature of the problem, we transform the formulation into a convex optimization problem by identical deformation and relaxations, and propose a semidefinite relaxation (SDR) based algorithm to approximate the optimal solution. Moreover, we focus on the feasibility of the convex problem, and propose an improved algorithm which reduces the probability of users out of service. The simulation results show that the performance of our proposed algorithm is close to the optimal solution, and the improved algorithm gives a higher efficiency on user admission control.

With the wide adoption of smart mobile devices, there is rapid development of location based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivize a large group of mobile users to participate. In this paper, we investigate the problem of designing a tournament to provide continuous incentives for users by rewarding them based on the rank achieved in crowdsourcing. First, we model the user's utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Furthermore, we show that, the tournament can approximate the optimal contract under full information by step function. Finally, we present numerical results to compare the system performance under the different proposed incentive mechanisms; we show that by using the tournament, the users obtain the continuous incentives to participate in the crowdsourcing activity.

Allowing cellular operators to offload data traffic to unlicensed spectrum has the potential to significantly increase the capacity of the cellular network systems. This paper considers the spectrum sharing among multiple cellular operators in the unlicensed spectrum. One of the main challenges for this system is how to control the interference between the cellular users and the unlicensed users in other networks, e.g., Wi-Fi, and the interference among cellular users of different operators. As such, we develop a hierarchical game where there is a Kalai-Smorodinsky bargaining game among leaders and a Stackelberg game between operators and mobile users (MU). Accordingly, multiple operators can negotiate with each other for the revenue obtained from the unlicensed spectrum and use a pricing mechanism to control the interference caused by each MU to other operators and users in other unlicensed networks. Simulation results show that our proposed strategy significantly increases the revenue and utility for both operators and MUs.

A properly designed incentive mechanism is important for cooperative relay networks, as it will encourage relays assisting sources' data transmissions. However, previous related studies didn't give enough consideration to the topology effect, i.e., how the network topology can substantially influence the relay selection and profit distribution in cooperations. In this paper, we quantify the topology effect in multi-source-multi-relay networks analytically, by using a multi-node Nash bargaining framework based on the network exchange theory. The proposed multinode Nash bargaining outcome guarantees not only the individual satisfaction for each node, but also the social optimality for the entire network (of all nodes). Then, we propose a distributed incentive mechanism, named as natural algorithm, which enables each node to take advantage of the network topology to reach a multi-node Nash bargaining outcome through proper source/relay selection and payment bargaining. Simulation results illustrate the profit distribution among relays and sources under different network topologies.

In the dynamic spectrum access (DSA) paradigm for cognitive radio networks (CRNs), one of the commonly used Medium Access Control (MAC) schemes is designed on basis of the popular carrier sensing multiple access with collision avoidance. However, this proposal suffers from two major problems that may significantly decrease the system performance: (1) collision among the secondary users (SUs) can hardly be detected, thus leading to the secondary transmission failures, and (2) SUs cannot abort transmission when collision occurs, making the long collision duration possible. In this paper, we propose a new cognitive MAC protocol for efficient DSA based on full-duplex CRNs (FD-CRNs), where SUs are able to perform simultaneous spectrum sensing and data transmission owing to full-duplex techniques. Specifically, SUs can detect the collision during transmission, so as to reduce the collision time and improve secondary network performance. Analytical results include the derivations of key design parameters such as the collision ratio with the PU, spectrum usage ratio, optimal contention window size, and the performance comparisons with the conventional DSA in half-duplex CRNs (HD-CRNs), which are further confirmed by simulation results.

Recently, the development in full-duplex communications has offered a great opportunity to perform simultaneous spectrum sensing and spectrum access in cognitive radio networks. In this paper, we consider a cognitive cellular network, in which the secondary base station (SBS) is a full-duplex device that can simultaneously sense the primary spectrum and transmit to the secondary users. We show that the power allocation of the SBS can affect both the sensing performance and the transmission capacity, and thus, we jointly consider the power allocation problem in the spectrum management process. First, we formulate the considered problem as a 3-dimensional matching problem and prove its NP-hardness. Then, we propose an approximate solution by extending a 2-dimensional matching algorithm. The simulation results show that the proposed algorithm can highly increase the secondary throughput of the SBS, compared with the greedy algorithm and the random algorithm.

Small-cell architecture is widely adopted by cellular network operators to increase network capacity. By reducing the size of cells, operators can pack more (low-power) base stations in an area to better serve the growing demands, without causing extra interference. However, this approach suffers from low spectrum temporal efficiency. When a cell becomes smaller and covers fewer users, its total traffic fluctuates significantly due to insufficient traffic aggregation and exhibiting a large "peak to-mean" ratio. As operators customarily provision spectrum for peak traffic, large traffic temporal fluctuation inevitably leads to low spectrum temporal efficiency. In this work, we first carryout a case-study based on real-world 3G data traffic traces and confirm that 90% of the cells in a metropolitan district are less than 40% utilized. Our study also reveals that peak traffic of adjacent cells are highly asynchronous. Motivated by these observations, we advocate device-to-device (D2D) load-balancing as a useful mechanism to address the fundamental drawback of small-cell architecture. The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency. We provide theoretical modeling and analysis to characterize the benefit of D2D load balancing, in terms of sum peak traffic reduction of individual cells. We also derive the corresponding cost, in terms of incurred D2D traffic overhead. We carry out empirical evaluations based on real-world 3G data traces to gauge the benefit and cost of D2D load balancing under practical settings. The results show that D2D load balancing can reduce the sum peak traffic of individual cells by 35% as compared to the standard scenario without D2D load balancing, at the expense of 45% D2D traffic overhead.

Roadside units (RSUs) with caching abilities are becoming an important part for the future transportation system, enabling both Internet accesses and local caching services for vehicular users. In this paper, we address the caching problem which involves the coexistence of multiple service providers who intend to cache their own contents into the RSUs by competitions to improve the data disseminations. And we propose a mechanism based on multi-object auctions, which can achieve a sub-optimal outcome. Simulation results also show the effectiveness of our solution.

With the booming exploitation of WiFi networks based on the conventional CSMA/CA access scheme, the frequent long collision period becomes unbearable. To better utilize the WiFi spectrum, in this poster, we propose a novel WiFi protocol with the assistance of full-duplex (FD) technique that allows users to sense the spectrum while transmitting, and stop transmission once collision is detected. Analytical and simulated results show that the average collision length is sharply reduced and the normalized throughput can be significantly improved by the proposed FD-WiFi protocol compared with the conventional CSMA/CA.

WiFi Direct is a new technology proposed by WiFi Alliance to enable direct device to device communications using the same spectrum as WiFi. In some scenarios, communication distance between two devices cannot be covered by one hop, and thus, multihop Device to Device (D2D) communication is required to solve this problem. However, existing WiFi Direct protocol doesn't provide multihop message routing. In this paper, we propose a multihop D2D communication protocol based on WiFi Direct, and implement it on Android smartphone. Devices will form a group in consideration of power and topology condition, delay-tolerant routing between two groups is supported by the protocol.

In this paper, we study the resource allocation problem in a small cell Orthogonal Frequency Division Multiple Access (OFDMA) network consisting of multiple access points (APs) and subscribed users. We formulate the subchannel allocation and the user assignment as a joint optimization problem, and solve it utilizing a novel three-sided matching algorithm. In the proposed algorithm, a unified cooperative framework is constructed in which we discuss both the non-overlapping and the overlapping coalitions formed by the APs, as well as the traditional non-cooperative case.

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

Driven by both personal and commercial interests, fast popular content delivery, as one of the key services offered by vehicular ad-hoc networks (VANETs), has recently received considerable attention. Most existing work mainly focuses on the resource allocation such as transmit power or subcarrier assignment from the on-board units (OBUs) to the roadside units (RSUs). However, due to the limited backhaul capacity, great efforts still need to be taken for delivering large-size files such as videos and music to the high speed vehicles. Motivated by the recent work of pre-storing files in the cell-edge base stations, in this paper, we address the efficient content delivery problems in VANET by caching popular files in the RSUs with large storage capacity. The main objective is to minimize the average time that an OBU downloads a file. We propose three algorithms of allocating files to RSUs, in the optimal, sub-optimal, and greedy ways respectively, where the first one can achieve the best performance, and the greedy one has the lowest complexity. We also analyze the average downloading time performance in terms of the number of RSUs, storage capacity, and vehicle speed. Simulation results indicate that the proposed RSU caching methods can significantly reduce the file-downloading time, and thus, improve the content delivery efficiency.

The emergence of smart meters has enabled the new energy efficiency services in an automatic fashion. With the information and communication technology, the smart meters are devised to gather and communicate the information of electricity suppliers and residential electricity consumers to ameliorate the efficiency of power distribution as well as the sustainability of the power resources. Due to the enormous amount of electricity consumers, the analysis of the big data produced by the smart meters is a crucial challenge faced by the electricity companies and researchers. In this paper, we analyze the big data based on the smart meter readings collected in the Houston area. The statistical properties of the data is investigated such that the behaviors of the consumers can be better understood. Moreover, the kernel PCA analysis and non-parametric clustering of the data gives a comprehensive guidance on what are the potential clusters of the customers and how to allocate the power more efficiently.

Femtocaching is a caching system to assist the popular content downloading services in heterogenous networks, in which femto base stations (FBSs) utilize their storage capabilities to cache popular files for mobile users (MUs). When the requested files are cached, the content can be downloaded directly from the FBSs through high-rate wireless links, which avoids the backhaul bottleneck to the core network. Previous studies focus on the optimal caching strategy for a given network topology, which is referred to as static femtocaching. However, due to the mobility of MUs, the topology of a practical network rarely stays unchanged and the FBSs need periodically refreshing their caches to adapt to the current network. Limited by the weak backhaul of FBSs, the cache refreshing rate may not catch up with the changing topology, which makes dynamic femtocaching essentially different from the static scenario. In this paper, we first formulate dynamic femtocaching as an optimization problem which is proved to be NP-hard. Then, we propose two dynamic algorithms, centralized and decentralized, to give suboptimal solutions. Simulation results show that the mobility of MUs degrades the performance of dynamic femtocaching for all algorithms, while the proposed algorithms perform 18% ~ 24% better than the traditional algorithm proposed for static scenarios, and 22% ~ 25% better than a simple popular caching system.

Game theory is a powerful tool to deal with the interaction of decision makers with conflicting interests. However, for certain game models such as Chicken-Dare games, traditional strategies in game theory cannot achieve stable and high social welfare because of the competition between players. In this paper, we suppose one player in the game as an administrator, who concerns about the performance of the whole network, and the other player aims to improve its own utility based on the behavior of its opponent. Then we propose a zero-determinant strategy for the administrator so as to reach an equilibrium where the social welfare is satisfying. Such equilibrium can be widely applied in resource management of wireless network, and simulation results show the correctness and superiority of the proposed strategy, compared with other equilibrium concepts such as the correlated equilibrium.

Device-to-device (D2D) communication is viewed as one promising technology for boosting the capacity of wireless networks and the efficiency of resource management. D2D communication heavily depends on the participation of users in sharing contents. Thus, it is imperative to introduce new incentive mechanisms to motivate such user involvement. In this paper, a contract-theoretic approach is proposed to solve the problem of providing incentives for D2D communication in cellular networks. First, using the framework of contract theory, the users' preferences toward D2D communication are classified into a finite number of types, and the service trading between the base station and users is properly modeled. Next, necessary and sufficient conditions are derived to provide incentives for users' engagement in D2D communication. Finally, our analysis is extended to the case in which there is a continuum of users. Simulation results show that the contract can effectively incentivize users' participation, and increase capacity of the cellular network than the other mechanisms.

Recently, cellular networks have become severely overloaded by social-based services, such as YouTube, Facebook, and Twitter, in which thousands of clients subscribe to a common content provider (e.g., a popular singer) and download his/her content updates all the time. Offloading such traffic through complementary networks, such as a delay tolerant network formed by device-to-device (D2D) communications between mobile subscribers, is a promising solution to reduce the cellular burdens. In the existing solutions, mobile users are assumed to be volunteers who selflessly deliver the content to every other user in proximity while moving. However, practical users are selfish and they will evaluate their individual payoffs in the D2D sharing process, which may highly influence the network performance compared to the case of selfless users. In this paper, we take user selfishness into consideration and propose a network formation game to capture the dynamic characteristics of selfish behaviors. In the proposed game, we provide the utility function of each user and specify the conditions under which the subscribers are guaranteed to converge to a stable network. Then, we propose a practical network formation algorithm in which the users can decide their D2D sharing strategies based on their historical records. Simulation results show that user selfishness can highly degrade the efficiency of data offloading, compared with ideal volunteer users. Also, the decrease caused by user selfishness can be highly affected by the cost ratio between the cellular transmission and D2D transmission, the access delays, and mobility patterns.

Device-to-device (D2D) communication underlaying cellular networks is expected to bring significant benefits for utilizing resources, improving user throughput, and extending the battery life of user equipment. However, the allocation of radio and power resources to D2D communication needs elaborate coordination, as D2D communication can cause interference to cellular communication. In this paper, we study joint channel and power allocation to improve the energy efficiency of user equipments. To solve the problem efficiently, we introduce an iterative combinatorial auction algorithm, where the D2D users are considered bidders that compete for channel resources and the cellular network is treated as the auctioneer. We also analyze important properties of D2D underlay communication and present numerical simulations to verify the proposed algorithm.

In this paper, we propose a joint relay and transmit/ receive (Tx/Rx) antenna mode selection scheme (RAMS) in the general full-duplex (FD) relay networks consisting of one source, one destination, and N FD amplify-and-forward (AF) relays. Each FD relay is equipped with two antennas, one for receiving and the other for transmitting. In the proposed scheme, each antenna of the FD relay is able to transmit/receive the signal. Each relay adaptively selects its Tx antenna and Rx antenna based on the instantaneous channel conditions, and the optimal single relay with the optimal Tx/Rx antenna configuration is selected to maximize the end-to-end signal to interference and noise ratio (SINR) of the FD relay system. The performance of the proposed scheme is analyzed. The closed-form expressions of the outage probability, average symbol error rate, and the ergodic capacity are derived. The analytical results are verified by the simulations. To reduce the error floor and capacity ceiling caused by the self-loop interference in FD relay, we propose a RAMS scheme with adaptive power allocation (RAMS-PA). We provide an upper bound and a lower bound of the end-to-end SINR for RAMS-PA scheme, and prove that the error floor can be removed in the RAMS-PA scheme. Results show that the proposed scheme achieves an extra spatial diversity in the medium SNR region due to the FD antenna selection at the relay nodes and considerably improve the system performance compared to the conventional FD relay selection scheme with fixed relay Tx and Rx antennas.

Device-to-device (D2D) communication is seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, a novel social-aware approach for optimizing D2D communication by exploiting two layers, namely the social network layer and the physical wireless network layer, is proposed. In particular, the physical layer D2D network is captured via the users' encounter histories. Subsequently, an approach, based on the so-called Indian Buffet Process, is proposed to model the distribution of contents in the users' online social networks. Given the social relations collected by the base station, a new algorithm for optimizing the traffic offloading process in D2D communications is developed. In addition, the Chernoff bound and approximated cumulative distribution function (cdf) of the offloaded traffic are derived and the validity of the bound and cdf is proven. Simulation results based on real traces demonstrate the effectiveness of our model and show that the proposed approach can offload the network's traffic successfully.

In this paper, we consider a point to point full duplex (FD) MIMO communication system. We assume that each node is equipped with an arbitrary number of antennas which can be used for transmission or reception. With FD radios, bidirectional information exchange between two nodes can be achieved at the same time. In this paper, we design bidirectional link selection schemes by selecting a pair of transmit and receive antenna at both ends for communications in each direction to maximize the weighted sum rate or minimize the weighted sum symbol error rate (SER). The optimal selection schemes require exhaustive search, so they are highly complex. To tackle this problem, we propose a Serial-Max selection algorithm, which approaches the exhaustive search methods with much lower complexity. In the Serial-Max method, the antenna pairs with maximum “obtainable SINR” at both ends are selected in a two-step serial way. The performance of the proposed Serial-Max method is analyzed, and the closed-form expressions of the average weighted sum rate and the weighted sum SER are derived. The analysis is validated by simulations. Both analytical and simulation results show that as the number of antennas increases, the Serial-Max method approaches the performance of the exhaustive-search schemes in terms of sum rate and sum SER.

In traditional cognitive radio networks, secondary users (SUs) typically access the spectrum of primary users (PUs) by a two-stage "listen-before-talk" (LBT) protocol, i.e., SUs sense the spectrum holes in the first stage before transmit in the second stage. In this paper, we propose a novel "listen-and-talk" (LAT) protocol with the help of the full-duplex (FD) technique that allows SUs to simultaneously sense and access the vacant spectrum. Analysis of sensing performance and SU's throughput are given for the proposed LAT protocol. And we find that due to self-interference caused by FD, increasing transmitting power of SUs does not always benefit to SU's throughput, which implies the existence of a power-throughput tradeoff. Besides, though the LAT protocol suffers from self-interference, it allows longer transmission time, while the performance of the traditional LBT protocol is limited by channel spatial correction and relatively shorter transmission period. To this end, we also present an adaptive scheme to improve SUs' throughput by switching between the LAT and LBT protocols. Numerical results are provided to verify the proposed protocol and the theoretical results.

Cooperation of resource sharing among wireless users and network operators has been widely studied in wireless communication. However, during the resource sharing, because of the weak communication signals or cheating strategies, each participant of the cooperation may sometimes stop its cooperative behavior unilaterally. Such behavior causes non-cooperation, resulting in unsatisfying quality of services for all participants. In this paper, we model the resource sharing between two participants as an iterated prisoner's dilemma game. Based on the applications of wireless cooperations, we define the participant who is responsible to maintain the high social welfare as the administrator of cooperation (AoC), and the other rational selfish participant as the regular participant of cooperation (PoC). Then, we propose a zero-determinant strategy for the AoC, and find the maximum social welfare that the AoC can maintain regardless of the strategy of PoC. Simulation results show that when the AoC applies the proposed zero-determinant strategy, the high social welfare can be maintained, and both AoC and PoC receive better performances than those of noncooperation.

This paper studies the joint transmit and receive antennas selection (JTRAS) in bidirectional MIMO communication systems consisting of two full duplex (FD) nodes. We assume that each node is equipped with N antennas which can be used for transmission or reception. For this bidirectional FD system, we select one transmit antenna and one receive antenna from all the possible antenna configurations at each node to achieve the minimum sum symbol-error-rate (Min-SER). The optimal Min-SER based selections is performed by exhaustively searching, so that it is difficult to analyze and has a high complexity. To tackle this problem, we propose a low-complexity near-optimal Serial-Max method which selects the antenna pairs with maximum SINR in a two-step serial way. The performance of the proposed Serial-Max method is analyzed, and the closed-form expression of the average sum SER is derived. The analysis is validated by simulations. It is shown that the diversity order of the Serial-Max method is (N - 1)2with perfect self-interference cancelation, or zero with residual self interference. Both analytical and simulation results show that as N increases, the Serial-Max method approaches the optimal performance in terms of average sum SER.

With the fast development of mobile terminals and wireless communication networks, mobile social networks (MSNs) play an important role in everyday lives to access social activities. However, most research on MSNs typically focuses on the relations of the users' physical location, but not make sufficient use of social ties. Consequently, in this paper, we consider a scenario of MSNs with online social networks and offline Device-to-Device (D2D) communication underlaying cellular networks, and study the problem of data dissemination to the mobile users under the constraint of limited spectrum resources. We first present a novel approach to formulate the social relationships for the offline mobiles by comparing the similarity of mobile users' social activities with the Bayesian model. And then we realize efficient data propagation using coalitional graph game. Finally, we provide simulation results to verify effectiveness of our studies.

In this paper, we study the point-to-point E-band millimeter wave wireless channel with uniform linear antenna arrays (ULAs) deployed at both link ends and present an analytical approach to characterize the channel behavior. We first derive explicit expressions for some channel eigenvalues at certain discrete system settings. The asymptotic behavior and the effective multiplexing distance (EMD) of the E-band channel are then investigated, where the latter is defined as the end-to-end distance at which the channel can support a certain number of spatially independent streams at finite signal-to-noise ratios (SNRs). We analytically show that the EMD for a given number of parallel signal transmissions is mainly determined by the product of the aperture sizes of the transmit and receive ULAs. This finding provides useful insights into the design of practical multi-gigabits wireless communication systems over E-band.

An auction-based joint subcarrier and power allocation approach is investigated to improve the performance of device-to-device (D2D) communication underlay cellular networks with uplink (UL) resource sharing. To maximize the system sum rate over the resource reuse of multiple D2D pairs, we introduce a reverse iterative combinatorial auction to formulate the optimization problem. In the auction, cellular channels are viewed as bidders competing to obtain rate increase while packages of D2D pairs and the corresponding transmit power are auctioned as goods in each round. We first give the evaluation of bidders' optimal value for packages, and then explain a descending price auction in detail, also give properties of convergency and low-complexity. The simulation results are finally provided to indicate the efficiency of the proposed auction-based algorithm.

In this paper, we propose a joint relay and antenna selection scheme in general full-duplex (FD) relay networks with one source, one destination and N FD amplify-and-forward (AF) relays. Each FD relay is equipped with two antennas, one for receiving and one for transmitting. We consider a joint antenna and relay selection scheme to optimize the end-to-end error performance. In the proposed scheme, each relay adaptively selects the transmit antenna and receive antenna based on the instantaneous channel conditions, and the optimal single relay with the optimal Tx/Rx antenna configuration is selected to optimize the end-to-end performance of the system transmission. This is in contrast to the conventional pure FD relay selection, where the Tx and Rx FD antenna of each relay are fixed. The proposed scheme achieves an extra space diversity due to the antenna selection at the relay nodes, and considerably improves the system performance compared to the conventional FD relay selection. Furthermore, closed-form expressions for the outage probability and average symbol error rate (SER) are derived. The analytical results are verified by the computer simulations. Results show that the proposed scheme outperforms the conventional full-duplex relay selection scheme with fixed relay Tx and Rx antennas.

Device-to-Device (D2D) communications have been proposed recently to improve the spectral efficiency. In this paper, we consider physical-layer security in D2D communication as an underlay to cellular networks with an eavesdropper. Benefiting from the underlaid spectrum reuse, D2D users can contribute to the system secrecy capacity, while D2D users may interfere the cellular users and decrease their secrecy capacity. We formulate this problem as a matching problem in the weighted bipartite graph and introduce the Kuhn-Munkres (KM) algorithm to provide the optimal solution. Simulation results show that the system secrecy capacity can be greatly improved by introducing D2D communications underlaying cellular networks.

Smart meters aided by wireless communications have been widely used to collect the information of the electrical appliances. In this paper, we consider a two-layer heterogeneous smart building network, consisting of a number of cluster-organized smart meters, and a base station (BS). The communication takes two phase: 1) periodical data collection via cluster heads in the first layer, and 2) data transmission from the cluster heads to the BS in the second layer. But, due to the irregular topology of smart meter networks and various types of data traffic of electrical appliances, the data aggregation to the heads and associated spectrum allocation become quite challenging. To solve these problems, we first analyze the relationship between the system performance and the cost of network construction. By using coalition formation game theory, we propose a practical strategy in optimizing channel allocation and cluster-heads deployment. The proposed algorithms are verified through computer simulations.

In this study, we consider the selective combining in hybrid cooperative networks (SCHCNs scheme) with one source node, one destination node and N relay nodes. In the SCHCN scheme, each relay first adaptively chooses between amplify‐and‐forward protocol and decode‐and‐forward protocol on a per frame basis by examining the error‐detecting code result, and N c (1 ≤ N c ≤ N ) relays will be selected to forward their received signals to the destination. We first develop a signal‐to‐noise ratio (SNR) threshold‐based frame error rate (FER) approximation model. Then, the theoretical FER expressions for the SCHCN scheme are derived by utilising the proposed SNR threshold‐based FER approximation model. The analytical FER expressions are validated through simulation results.

Most researches on relay selection (RS) in bidirectional relay network typically assume perfect channel state information (CSI). However, outdated CSI, which is caused by the the time-variation of channel, cannot be ignored in the practical system, and the performance of the conventional bidirectional RS scheme degrades greatly with outdated CSI. In this paper, to improve the performance of RS with outdated CSI, we propose a weighted bidirectional RS scheme, in which a deterministic weight factor decided by the correlation coefficient of outdated CSI, is introduced in the selection process. The outage probability bound of the weighted bidirectional RS is derived and verified, along with the asymptotic expression in high signal-to-noise ratio (SNR). Based on the analytical expressions, the optimal weight factor and the optimal power allocation scheme in minimizing the outage probability are obtained. Simulation results reveal that when the CSI is outdated, the diversity order reduces from full diversity to one. Furthermore, the weighted bidirectional RS scheme with the optimal weight factor yields a significant performance gain over the conventional bidirectional RS scheme, especially in high SNR.

In this paper, we perform transmit antenna selection to improve the energy efficiency of large scale multiple antenna systems. We derive a good approximation of the distribution of the mutual information in this antenna selection system. It shows that channel hardening phenomenon is still retained as full complexity with antenna selection. Then, we use this closed-form expression to assess the energy efficiency performance. Specifically, we evaluate the performance of the energy efficiency in two different cases: 1) the circuit power consumption is comparable to or even dominates the transmit power, and 2) the circuit power can be ignored due to relatively much higher transmit power. The theoretical analysis indicates that there exists an optimal number of selected antennas to maximize the energy efficiency in the first case, whereas in the second case, the energy efficiency is maximized when all the available antennas are used. Based on these conclusions, two simple but efficient antenna selection algorithms are proposed to obtain the maximum energy efficiency. All the analytical results are verified through computer simulations.

Cooperative spectrum sensing has been shown to yield a significant performance improvement in cognitive radio networks. In this paper, we consider distributed cooperative sensing (DCS) in which secondary users (SUs) exchange data with one another instead of reporting to a common fusion center. In most existing DCS algorithms, the SUs are grouped into disjoint cooperative groups or coalitions, and within each coalition the local sensing data is exchanged. However, these schemes do not account for the possibility that an SU can be involved in multiple cooperative coalitions thus forming overlapping coalitions. Here, we address this problem using novel techniques from a class of cooperative games, known as overlapping coalition formation games, and based on the game model, we propose a distributed DCS algorithm in which the SUs self-organize into a desirable network structure with overlapping coalitions. Simulation results show that the proposed overlapping algorithm yields significant performance improvements, decreasing the total error probability up to 25% in the Qm+ Qfcriterion, the missed detection probability up to 20% in the Qm/Qfcriterion, the overhead up to 80%, and the total report number up to 10%, compared with the state-of-the-art non-overlapping algorithm.

The full duplex (FD) technique, which allows the communication node to transmit and receive signals over the same frequency band simultaneously, has the potential to double the spectral efficiency in comparison with the traditional half duplex (HD) technique. However, self-interference, leaking from the FD node's transmission to its own reception, has the detrimental impact on the performance of FD communication. In this paper, we analyze and optimize the two-way FD relay system using amplify-and-forward protocol, when the multi-relay scenario is considered. The optimal relay selection scheme in maximizing the effective signal-to-interference and noise ratio is proposed, which significantly improves the system performance than a single relay network. Furthermore, to facilitate the comparisons with the traditional two-way HD relay, the analytical expressions of the two-way FD relay are derived in a closed form, including bit error rate (BER), ergodic capacity, and outage probability. Based on the analytical expressions, the optimal power allocation and the optimal choice of duplex mode, i.e., FD and HD, are obtained by minimizing the outage probability. Monte-Carlo simulations are fulfilled to verify the analytical expressions. The results reveal that the residual self-interference after interference suppression limits the performance of two-way FD relay: when the residual interference is small, the FD mode has lower BER/outage probability and higher ergodic capacity since it utilizes the resources effectively; otherwise, the HD mode achieves lower BER/outage probability and higher ergodic capacity since it can completely cancel the self-interference at the cost of lower resource utilization.

In this paper, we investigate the performance of multi-pair two-way relaying, in which multiple pairs of users exchange information within pair, with the help of a shared relay. Each user has a single antenna, and the relay is equipped with very large number of antennas. The relay adopts the amplify-and-forward protocol, and the beamforming matrixes of maximum-ratio combining/maximum ratio transmission and zero-forcing reception/zero-forcing transmission are both considered. Due to array gain of antenna array, the power of each user or the relay (or both) can be made inversely proportional to the number of relay antennas, without compromising the performance. Thus, three power-scaling schemes are studied. Furthermore, the asymptotic spectral and energy efficiencies of the system are obtained analytically, when the number of relay antennas approaches to infinity. The asymptotic results are beneficial to provide more insightful understandings for the fundamental limits of the very large antenna system, and verified by the Monte-Carlo simulations. The analytical and simulation results reveal that very large antenna arrays in such system can average the small-scale fading, eliminate the inter-pair interference, and reduce the total power consumption.

This paper studies the fundamental characteristics of point-to-point E-band channels with uniform linear antenna arrays (ULAs) deployed at both the transmitter and receiver. We model the channels as line-of-sight (LoS) multiple-input multiple-output (MIMO) ones and focus on the channel eigenvalue characterization when theRayleigh distance criterioncannot be fulfilled due to limited physical sizes of the transmitter and receiver. We first derive explicit expressions for some channel eigenvalues at certain discrete system settings. Asymptotic analyses are then developed when the antenna numbers at the transmitter and receiver or the distance between them goes to infinity. Based on these analytical results, the maximum eigenvalue and theeffective multiplexing distance(EMD) of the E-band channel are investigated, where EMD is defined as the end-to-end distance at which the channel can support a certain number of simultaneous spatial streams at a given signal-to-noise ratio (SNR). We analytically show that the EMD for a given number of parallel signal transmissions is mainly determined by the product of the aperture sizes of the transmit and receive ULAs. Numerical results are provided to validate the analyses.

In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a novel approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing work which assumes that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as a coalition formation game with overlapping coalitions. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed overlapping coalition formation game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions.

With the rapid growth of sensor technology, smartphone sensing has become an effective approach to help improve the quality of applications in smartphones. However, the quality and quantity of the sensed data uploaded by the users are not always satisfying due to the lack of incentives for users to participate. In this paper, we design an incentive mechanism in which the users can get satisfying rewards from the platform by efficiently allocating their resources, and meanwhile, the platform can achieve a relatively high social welfare. Specifically, to solve the resource allocation problem in the incentive mechanism, we consider a cooperative game model with overlapping coalitions, in which the smartphone users can self-organize into the overlapping coalitions for different sensing tasks. Then, we propose a distributed algorithm that converges to a stable outcome in which no user has the motivation to change its current resource allocation so as to increase its individual payoff unilaterally. Simulation results show that the proposed scheme achieves a better performance than the average distribution case and the single-task distribution case in various conditions.

In this paper, we propose a novel multihop transmission scheme using selective network coding (NC) and differential modulation (SNC-DM) for two-way relay networks (TWRNs) when neither the source nodes nor the relay nodes know the channel state information (CSI). We first develop a bidirectional transmission scheme using NC where the information exchange in a two-way multihop relay network with the arbitrary number of hops can be completed in four transmission phases. As a result, the maximum achievable throughput does not decrease as the number of hops increases. To overcome the error propagation in the multihop transmission with decode-and-forward (DF) protocol in wireless fading channels, a selective NC scheme is proposed. In addition, we apply differential modulation in the proposed scheme to avoid channel estimation in the multihop networks. The performance of the proposed scheme is analyzed, and a closed-form frame error rate (FER) expression is derived. It is shown that the proposed scheme achieves significant improvements in both FER performance and network throughput compared to the conventional multihop DF scheme in TWRNs. The analytical results are verified through numerical simulations.

In this paper, we perform transmit antenna selection to improve the energy efficiency of large scale MIMO systems. We first derive a good approximation of the distribution of the mutual information in this antenna selection system. It shows that channel hardening phenomenon is still retained as full complexity with a large number of available antennas, though the number of actually used antennas can be small. Then, we use this closed-form expression to assess the energy efficiency performance. Specifically, we evaluate the performance of the energy efficiency in two different cases: 1) the circuit power consumption is comparable to or even dominates the transmit power, and 2) the circuit power can be ignored due to high transmit power. The theoretical analysis indicates that there exists an optimal number of selected antennas less than the number of the total available antennas to maximize the energy efficiency in the first case, whereas in the second case, the energy efficiency is maximized when all the available antennas are used. All the analytical results are verified through computer simulations.

It is well recognized that channel state information (CSI) feedback plays a key role in the performance of closed-loop wireless networks. However, most work on energy efficiency (EE) typically studies this problem from the downlink data transmission point of view. In this paper, we propose an alternative approach to investigate the EE for wireless communication networks through controlling the channel state information (CSI) in the feedback link, in which a number of multiple-antenna mobile transmitters exchange information with their corresponding mobile receivers using linear precoding for interference reduction. Specially, we formulate this EE maximization problem in the analytical setting of a game theoretic framework, and propose a two-level Stackelberg-type CSI feedback control game (SCFC) to balance the bandwidth and power consumptions in a distributed manner. The existence of the equilibriums of such games is proved, and the convergence behavior is investigated. Simulation results show that by adjusting the pricing factor, the proposed distributed SCFC game effectively improves the EE performance.

In this paper, we address the popular content distribution (PCD) problem in a highway scenario, in which popular files are distributed to a group of on-board units (OBUs) driving through a single roadside unit (RSU). Due to the high speeds, the OBUs may not finish downloading a large file within the limited time for vehicle-to-roadside (V2R) communication and a peer-to-peer (P2P) network consisting of OBUs out of the RSU coverage can be constructed for completing the file delivery process. However, due to fast and unpredictable topological changes of the vehicular ad hoc network (VANET), the static methods in traditional P2P networks can be inefficient. We model this problem as a coalition formation game with transferable utilities, and propose a coalition formation algorithm that converges into a Nash-stable partition adapting to environmental changes. Based on this algorithm, we further propose a distributed scheme for the overall PCD problem. Simulation results show that our scheme presents a considerable performance improvement relative to the non-cooperative case using the carrier sense multiple access with collision avoidance (CSMA/CA).

Device-to-device (D2D) communication as an underlay to cellular networks brings significant benefits to users' throughput and battery lifetime. The allocation of power and channel resources to D2D communication needs elaborate coordination, as D2D user equipments (UEs) cause interference to other UEs. In this paper, we propose a novel resource allocation scheme to improve the performance of D2D communication. Battery lifetime is explicitly considered as our optimization goal. We first formulate the allocation problem as a non-cooperative resource allocation game in which D2D UEs are viewed as players competing for channel resources. Then, we add pricing to the game in order to improve the efficacy, and propose an efficient auction algorithm. We also perform simulations to prove efficacy of the proposed algorithm.

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.

Cell selection is an important issue in femtocell networks, which can balance the utilization of the whole network. In this paper, we investigate cell selection problem in a two-tier femtocell network that contains a micro base station (MBS) and several femtocells with different access methods and coverage areas. We propose the evolutionary game model to describe the dynamics of the cell selection process and consider the evolutionary equilibrium as the solution. In order to achieve the evolutionary equilibrium, we introduce the reinforcement learning algorithm that can help distributed individual users make selection decisions independently. With their own knowledge of the past, the users can learn to achieve the evolutionary equilibrium without complete knowledge of other users. Finally, the performance of the evolutionary game and reinforcement learning algorithm is analyzed, and simulation results show the convergence and effectiveness of the proposed algorithm.

Collaborative spectrum sensing (CSS) has been shown to be able to highly improve the performance of spectrum sensing in cognitive radio networks. However, most existing works focused on either centralized approaches that rely on a global fusion center, thus requiring significant overhead, or on distributed approaches that rely on disjoint coalitions of secondary users (SUs) in which an SU can only cooperate with a single, selected coalition, hence limiting the performance gains of CSS. In this paper, a novel, coalition-based approach to CSS is proposed in which an SU can share its sensing results with more than one coalition. The problem is formulated using a novel class of cooperative games, known as overlapping coalitional games, which enables the SUs to decide, in a distributed manner, on the number of coalitions in which they wish to cooperate, depending on the associated benefit and cost tradeoffs. To solve this game, a novel, distributed algorithm is proposed using which the SUs can self-organize into a stable overlapping coalitional structure. Simulation results show that our proposed algorithm significantly improves the performance in terms of both the average probability of misdetection and the convergence time, relative to the noncooperative case and the state-of-art cooperative CSS with non-overlapping coalitions.

Device-to-device (D2D) communication as an underlay to cellular networks can bring significant benefits to users' throughput. However, as D2D user equipments (UEs) can cause interference to cellular UEs, the scheduling and allocation of channel resources and power to D2D communication need elaborate coordination. In this paper, we propose a joint scheduling and resource allocation scheme to improve the performance of D2D communication. We take network throughput and UEs' fairness into account by performing interference management. Specifically, we develop a Stackelberg game framework in which we group a cellular UE and a D2D UE to form a leader-follower pair. The cellular user is the leader, and the D2D UE is the follower who buys channel resources from the leader. We analyze the equilibrium of the game, and propose an algorithm for joint scheduling and resource allocation. Finally, we perform computer simulations to study the performance of the proposed algorithm.

In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a new approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing works which assume that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as an overlapping coalition formation game. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions.

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.

Driven by both safety concerns and commercial interests, vehicular ad hoc networks (VANETs) have recently received considerable attentions. In this paper, we address popular content distribution (PCD) in VANETs, in which one large popular file is downloaded from a stationary roadside unit (RSU), by a group of on-board units (OBUs) driving through an area of interest (AoI) along a highway. Due to high speeds of vehicles and deep fadings of vehicle-to-roadside (V2R) channels, some of the vehicles may not finish downloading the entire file but only possess several pieces of it. To successfully send a full copy to each OBU, we propose a cooperative approach based on coalition formation games, in which OBUs exchange their possessed pieces by broadcasting to and receiving from their neighbors. Simulation results show that our proposed approach presents a considerable performance improvement relative to the non-cooperative approach, in which the OBUs broadcast randomly selected pieces to their neighbors as along as the spectrum is detected to be unoccupied.

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.

To ensure security in data transmission is one of the most important issues for wireless relay networks, and physical layer security is an attractive alternative solution to address this issue. In this paper, we consider a cooperative network, consisting of one source node, one destination node, one eavesdropper node, and a number of relay nodes. Specifically, the source may select several relays to help forward the signal to the corresponding destination to achieve the best security performance. However, the relays may have the incentive not to report their true private channel information in order to get more chances to be selected and gain more payoff from the source. We propose a Vickey-Clark-Grove (VCG) based mechanism and an Arrow-d'Aspremont-Gerard-Varet (AGV) based mechanism into the investigated relay network to solve this cheating problem. In these two different mechanisms, we design different "transfer payment" functions to the payoff of each selected relay and prove that each relay gets its maximum (expected) payoff when it truthfully reveals its private channel information to the source. And then, an optimal secrecy rate of the network can be achieved. After discussing and comparing the VCG and AGV mechanisms, we prove that the AGV mechanism can achieve all of the basic qualifications (incentive compatibility, individual rationality and budget balance) for our system. Moreover, we discuss the optimal quantity of relays that the source node should select. Simulation results verify efficiency and fairness of the VCG and AGV mechanisms, and consolidate these conclusions.

Application of cyber technologies improves the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the prices of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for attacker and defender, respectively. This situation is modeled as a zero-sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5-Bus test system, we can show the effectiveness and properties of the studied game.

In this paper, we consider general two-way relay networks (TWRNs) with two source and N relay nodes when neither the source nodes nor the relay nodes have access to channel-state information (CSI). A distributed differential space time coding with analog network coding (DDSTC-ANC) scheme is proposed. A simple blind estimation and a differential signal detector are developed to recover the desired signal at each source. The pairwise error probability (PEP) and block error rate (BLER) of the DDSTC-ANC scheme are analyzed. Exact and simplified PEP expressions are derived, which can be used for power allocation between the source and relay nodes. The analytical results are verified through simulations.

An innovative auction-based allocation scheme is proposed to improve the performance of 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 compete to obtain business as bidders while packages of D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit for packages of D2D links. And then a detailed non-monotonic descending price auction algorithm is explained. Further, we prove that the proposed scheme is cheat-proof, converges in a finite number of iteration rounds, and has 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.

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.

An innovative resource allocation scheme is proposed to improve the performance of 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 sequential second price auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which are auctioned off by groups of D2D pairs in sequence. We first formulate the value of each resource unit for each D2D pair, as a basis of the proposed auction. And then a detailed auction algorithm is explained using a N-ary tree. The equilibrium path of a sequential second price auction is obtained in the auction process, and the state value of the leaf node in the end of the path represents the final allocation. The simulation results show that the proposed auction algorithm leads to a good performance on the system sum rate, efficiency and fairness.

Interference alignment (IA) has been well recognized as an efficient approach to reduce interference at high signal to noise ratio (SNR). However, it demands global channel state information (CSI) at both transmitters and receivers for preceder design in order to maximize the multiplexing gain. In this paper, we discuss IA with delayed differential CSI feedback for time-correlated multiple input multiple output (MIMO) block fading channels. We consider the impact of distortion caused by channel estimation errors and quantized CSI feedback delay, and thus, find an optimal feedback interval to minimize this distortion, as well as the sum rate performance. Specifically, we derive the minimum differential feedback rate. And with the feedback-channel capacity constraint, we further study the relationship between the average sum rate and the feedback interval. Analytical results are verified by simulations.

In a smart grid, the strong coupling between cyber and physical operations makes power systems vulnerable to cyber attacks. Changing the traditional structure of power systems and integrating communication devices are beneficial for better monitoring and decisionmaking by System Operators but increases the chance of being maliciously attacked. The communication links can be hacked so that the attacker can alter the power flow and power injection measurements, which are used to estimate the states of power system. In this paper, we formulate an attack strategy that can change the congestion of transmission lines without being detectable. Moreover, the financial benefit in an Ex-Post market is also investigated. Simulation results on an IEEE 30-Bus test system shows both the changes of congestion and the potential financial benefit gained by an attacker.

In this study, the superimposed training strategy is introduced into orthogonal frequency division multiplexing-modulated amplify-and-forward two-way relay network (TWRN) to perform two-hop transmission-compatible individual channel estimation. Through the superposition of an additional training vector at the relay under power allocation, the separated source–relay channel information can be directly obtained at the destination and then used to estimate the channels. The closed-form Bayesian Cramér-Rao lower bound (CRLB) is derived for the estimation of block-fading frequency-selective channels with random channel parameters, and orthogonal training vectors from the two source nodes are required to keep the Bayesian CRLB simple because of the self-interference in the TWRN. A set of optimal training vectors designed from the Bayesian CRLB are applied in an iterative linear minimum mean-square-error channel estimation algorithm, and the mean-square-error performance is provided to verify the Bayesian CRLB results.

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.

It has been well recognized that channel state information (CSI) feedback is of great importance for downlink transmissions of closed-loop wireless networks. However, the existing work typically researched the CSI feedback problem for each individual mobile station (MS), and thus, cannot efficiently model the interactions among self-interested mobile users in the network level. To this end, in this paper, we propose an alternative approach to investigate the CSI feedback-rate control problem in the analytical setting of a game theoretic framework, in which a multiple-antenna base station (BS) communicates with a number of co-channel MSs through linear precoder. Specifically, we first present a non-cooperative feedback-rate control game (NFC), in which each MS selects the feedback-rate to maximize its performance in a distributed way. To improve efficiency from a social optimum point of view, we then introduce pricing, called the non-cooperative feedback-rate control game with price (NFCP). The game utility is defined as the performance gain by CSI feedback minus the price as a linear function of the CSI feedback-rate. The existence of the Nash equilibrium of such games is investigated, and two types of feedback protocols (FDMA and CSMA) are studied. Simulation results show that by adjusting the pricing factor, the distributed NFCP game results in close optimal performance compared with that of the centralized scheme.

The hybrid relay selection (HRS) scheme, which adaptively chooses amplify-and-forward (AF) and decode-and-forward (DF) protocols based on the decoding results at the relay, is very effective to achieve robust performance in wireless relay networks. This paper analyzes the frame error rate (FER) of the HRS scheme in general wireless relay networks without and with utilizing error control coding at the source node. We first develop an improved signal-to-noise ratio (SNR) threshold-based FER approximation model. Then, we derive an analytical average FER expression as well as a high SNR asymptotic expression for the HRS scheme and generalize to other relaying schemes. Simulation results exhibit an excellent agreement with the theoretical analysis, which validates the derived FER expressions.

In this paper, we investigate the differential channel state information (CSI) feedback problem for a general multiple input multiple output (MIMO) system over time-correlated Rayleigh block-fading channels. Specifically, we derive the closed-form minimum differential feedback rate in the presence of channel estimation errors and quantization distortion. With the feedback-channel capacity constraint, we further study the ergodic capacity in a periodic feedback system in terms of the minimum differential feedback rate and the feedback interval. Through theoretical analysis, we find that there exists an optimal differential feedback interval to achieve the maximum ergodic capacity. Analytical results are verified by simulations in a practical periodic differential feedback system employing water-filling precoder and Lloyd's quantization algorithm.

In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with the constraints of physical-layer security. Specifically, the proposed algorithms select two or three intermediate nodes to enhance security against the malicious eavesdropper. The first selected node operates in the conventional relay mode and assists the sources to deliver their data to the corresponding destinations using an amplify-and-forward protocol. The second and third nodes are used in different communication phases as jammers in order to create intentional interference upon the malicious eavesdropper. First, we find that in a topology where the intermediate nodes are randomly and sparsely distributed, the proposed schemes with cooperative jamming outperform the conventional nonjamming schemes within a certain transmitted power regime. We also find that, in the scenario where the intermediate nodes gather as a close cluster, the jamming schemes may be less effective than their nonjamming counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and nonjamming modes. Simulation results validate our theoretical analysis and show that the hybrid switching scheme further improves the secrecy rate.

In this paper, we investigate joint relay and jammer selection in a two-way relay network with secrecy constraints, in which there are two sources, a cluster of intermediate nodes, and one eavesdropper. Under a decode-and-forward (DF) protocol, the considered two-way relay network implements a complete transmission in three phases. An intermediate node is selected as conventional relay and another two are selected as jammers during these phases, in order to improve security against the malicious eavesdropper. We find that when the jamming power is higher than that of source nodes, the proposed scheme has a better and more stable secrecy performance than the conventional non- jamming schemes. In addition, the system's security status gets increasingly better when more intermediate nodes are added into the relay/jamming cluster.

Interference alignment (IA) has been well recognized as a promising technique to obtain large multiplexing gain in multiple input multiple output (MIMO) interference channels. Most of the existing IA schemes require global channel state information (CSI) at the transmitter to design precoding vectors, which causes high amount of feedback bits. To reduce the feedback overhead, in this paper, we investigate the IA scheme employing differential CSI feedback over time-correlated MIMO channels. Specifically, we analyze the achievable sum-rate performance, and derive the minimum differential feedback rate to preserve the maximum multiplexing gain. In addition, we use the general alternating-minimization precoder design scheme to verify the analytical results.

In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with secrecy constraints. Specifically, the proposed algorithms select two or three intermediate nodes to enhance security against the malicious eavesdropper. The first selected node operates in the conventional relay mode and assists the sources to deliver their data to the corresponding destinations via the amplify-and-forward protocol. The second and third nodes are used in different communication phases as jammers in order to create intentional interference upon the eavesdropper node. Firstly, we find that in a topology where the relay and jamming nodes are randomly and sparsely distributed, the proposed schemes with cooperative jamming outperforms the conventional non-jamming schemes within a certain transmitted power regime. We also find that, in the scenario in which the intermediate nodes gather as a close cluster, the jamming schemes may be less effective than their non-jamming counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and non-jamming modes. Simulation results validate our theoretical analysis that the hybrid switching scheme further improves the secrecy rate.

In this paper, we propose a relay selection amplify and-forward (RS-AF) protocol in general bi-directional relay networks with two sources and N relays. In the proposed scheme, the two sources first transmit to all the relays simultaneously, and then a single relay will be selected to broadcast the received signals back to both sources to achieve a minimum sum symbol error rate (SER). To facilitate the selection process, we propose a sub-optimal Max-Min criterion, where a single relay which minimizes the maximum SER of two source nodes will be selected. Simulation results show that the proposed Max-Min selection approaches can obtain comparable performance as the optimal selection with a reduced-complexity. We also present simple asymptotic SER expressions and make comparison with the conventional all-participate amplify-and-forward (AP-AF) relaying scheme. The analytical results are verified through simulations. To improve the system performance, optimum power allocation (OPA) between the sources and the relay is determined based on the analytical results. Simulations indicate that the pro posed RS-AF scheme with OPA yields considerable performance improvement over an equal power allocation (EPA) scheme.

It has been well recognized that channel state information (CSI) feedback is important for dowlink transmissions of closed-loop wireless networks. In this paper, we investigate the CSI feedback rate control problem in the analytical setting of a game theoretic framework, where a multiple-antenna base station (BS) communicates with a number of co-channel mobile stations (MS) through a minimum mean square error (MMSE) precoder. Specifically, we present a non-cooperative feedback-rate control game with price (NFCP) over orthogonal feedback channels with a total bandwidth constraint. The game utility is defined as the performance gain by CSI feedback minus the price as a linear function of the CSI feedback rate, subject to an overall bandwidth constraint. The existence of the Nash equilibrium of such a game is investigated. Simulation results show that the distributed game approach results in close optimal performance compared with the centralized scheme.

In this paper, we investigate cooperation of conventional relays and friendly jammers subject to secrecy constraints for cooperative networks consisting of one source node, one corresponding destination node, one malicious eavesdropper node, and several intermediate nodes. In order to obtain a higher secrecy rate, the source selects one conventional relay and several friendly jammers from the intermediate nodes to assist message transmission, and in return, it needs to make a payment. Each intermediate node here has two possible identities to choose, i.e., to be a conventional relay or a friendly jammer, which results in a direct impact on the final utility of the intermediate node. After the intermediate nodes determine their identities, they seek to find optimal partners forming coalitions, which improves their chances to be selected by the source and thus to obtain the payoffs in the end. We formulate this cooperation as a coalitional game with transferable utility and also study its properties. Furthermore, we define a Max-Pareto order for comparison of the coalition value, based on which we employ the merge-and-split rules. We also construct a distributed merge-and-split coalition formation algorithm for the defined coalition formation game. The simulation results verify the efficiency of the proposed coalition formation algorithm.

In this paper, we study the reliable packet forwarding in Wireless Sensor Networks (WSNs) with the multiple-input multiple-output (MIMO) and orthogonal space time block codes (OSTBC) techniques. The objective is to propose a cross-layer optimized forwarding scheme to maximize the Successful Transmission Rate (STR) while satisfying the given end-to-end power consumption constraint. The channel coding, power allocation, and route planning are jointly considered to significantly improve the transmission quality in terms of STR. The joint optimization design is formulated as a global deterministic optimization and also a local stochastic optimization issues. It is found that the stochastic optimization approach can effectively model, analyze, and solve the routing problem. In order to substantially reduce the implementation complication of the global optimization, we propose a low-complexity distributed scheme. The determination of relaying nodes and power budgets are decoupled, i.e. performing route planning and power allocation separately. We have shown that the result in the distributed scheme is able to provide sufficiently accurate predication of the global optimization. In addition, the proposed scheme can clearly reduce the Symbol Error Rate (SER) and achieve higher STR compared with two existing energy-efficient routing protocols, in which no joint design is considered.

Welcome to this special issue of Wiley Journal of Wireless Communications and Mobile Computing (WCMC).To date, wireless vehicular communications have attached much attention for improving road safety, intelligent management and data exchange services, and providing Internet access on the move to ensure wireless pervasive and ubiquitous connectivity.The field of wireless vehicular communications can be typically identified as vehicle-to-person communications, vehicle-to-infrastructure communications, vehicle-to-vehicle communications, and vehicular communication networks.The combination of unique features of wireless vehicular communications and networking issues opens new opportunities for many interesting research areas, for example, real time safety applications, and intelligent diver information services.The goal of this special issue is to present good-performance, highly scalable, robust, and secure vehicular technologies for universal realization of wireless vehicular communications.To guarantee the quality, in this special issue, we have selectively collected 15 papers out of over 60 submissions, and clustered them in four groups: three are survey papers, four papers deal with channel modeling and physical layer issues, six papers investigating MAC and network layer issues, and two papers focusing on applications.Detailed overview of the selected works is given below.The first group includes three papers, which survey recent advances in wireless vehicular communications.The first paper, by Chen et al., provides recent results for data dissemination in vehicular ad hoc networks (VANETs).This paper also discusses security challenges in this field and the need of supporting technologies to enable efficient data dissemination for automotive applications.The paper by Leng et al. gives a global review of media access control techniques for VANETs.This paper summarizes recent work in this domain including MAC standardization activities, wireless channel resource management, QoS capability enhancement in MAC layer, and reliable MAC broadcasting strategies.The third paper, by Martinez et al., makes a survey of several publicly available mobility generators, network simulators, and VANET simulators.This paper contrasts their software characteristics, graphical user interface, popularity, ease of use, input requirements, output visualization capability, and accuracy of simulation.

Abstract Wireless sensor network (WSN) is an important instrument to realize wireless monitoring and control in various application fields. Energy conservation are crucial for WSNs to prolong the network lifetime. In this paper, we study sleeping management schemes which can efficiently control the network topology and significantly reduce the energy consumption by selectively turning off the radios of abundant sensor nodes. Based on the observation that a Matérn Hard‐core Process (MHP) could thin out evenly distributed nodes from a dense graph, we propose Backbone Energy Efficient Sleeping (BEES) management scheme, whose central idea is to generate and maintain the backbone by simulating MHPs. There are three attractive features of BEES: (i) the backbone size could be conveniently scaled according to the practical requirements; (ii) the backbone is energy efficient in the point of view of packet forwarding (or routing); (iii) the construction of backbone is robust to possible ranging errors, which ensures the feasibility and reliability in practice. Theoretical analysis and simulation experiments are carried out to demonstrate the correctness and effectiveness of BEES. Numerical results indicate that, compared with two existing sleeping management schemes, BEES provides about 30% wider range of scalability, consumes about 13% less routing energy, and achieves 11% ∼ 20% longer network lifetime under different traffic loads. Copyright © 2009 John Wiley & Sons, Ltd.

The incremental hybrid relay selection (IHS) scheme has recently attracted great attention for its high spectrum efficiency and robust performance. In this paper, we study the analytical frame error rate (FER) performance of the IHS scheme with and without applying channel coding at the source node. Specifically, we consider a multiple-relay cooperative network with a direct source-destination link, under dissimilar network settings, i.e., the channels of different relay branches experience Rayleigh block fading with different variances. We first develop an improved SNR threshold-based FER model for the general MIMO system. Using this model, we then derive the closed-form FER expression, and further make simplification at high SNRs. Monte- Carlo simulations are performed to validate the analytical results.

Sensor nodes in Cognitive Sensor Networks (CSNs) can work on different frequency bands (or channels) according to dynamically available wireless resources. This paper proposes a spectrum-aware routing scheme for CSNs, which jointly considers traffic balance, route configuration and power control for reliable end-to-end communications. Bayesian learning method is used to estimate the number of neighboring Primary Users (PUs) and Secondary Users (SUs). The estimated results effectively reflect the spectrum utilization and provide important information for route configuration. Multiple Attribute Decision Making (MADM) method is employed to combine the routing objectives of reliability, energy efficiency and path delay into a single target function. Randomized route selection strategy is adopted for traffic balance. The simulation results demonstrate that the proposed spectrum-aware routing scheme significantly improves the communication reliability, and simultaneously has satisfying performances in energy efficiency and end-to-end delay.

In this paper, we consider a two-way relay network where two sources can communicate only through an unauthenticated intermediate relay node. We investigate secure communications of this two-way relay scenario using physical layer security. Specifically, we treat the relay node as an eavesdropper from whom the information transmitted by the sources needs to be kept secret, despite the fact that its cooperation in relaying this information is essential. We first find that a non-zero secrecy rate is indeed achievable in this two-way relay network even without external jammers. Further still, with the help of friendly jammers who can transmit jamming signals to distract the malicious relay, a positive gain of the secrecy rate can be realized. In order to obtain the maximum secrecy rate, we define and then analyze an optimization problem. Finally, an optimal solution of jamming power allocation is provided for the system with friendly jammers.

In this paper, we investigate the differential channel state information (CSI) feedback problem for a general multiple input multiple output(MIMO) system over time-correlated Rayleigh block-fading channels. Specifically, we first derive the analytical minimum differential feedback rate in the presence of channel estimation errors and quantization distortion. With the feedback-channel capacity constraint, in the periodic feedback system, we further study the relationship between the ergodic capacity and the feedback interval. We find that there exists an optimal feedback interval to maximize the ergodic capacity. Analytical results are verified by simulations in a practical differential feedback system employing Lloyd's quantization algorithm.

In this paper, we review some results on the multiplexing gain of of the sum rate of the Gaussian multi-antenna compound broadcast channel in the high SNR regime. The transmitter transmits to each user one private message. The channel realization for each user is arbitrarily chosen from a finite set known to the transmitter. To achieve the multiplexing gain region of the channel, we discuss the methods of interference alignment and subspace coding. We interpret the interference alignment scheme for compound MIMO broadcast channel as a special case of subspace coding. We also point out that the subspace coding method could be applied in wireless multihop network to improve the efficiency of intra-cluster broadcasting.

In this paper, we consider a general bi-directional relay network with two sources and N relays when neither the source nodes nor the relays know the channel state information (CSI). A bi-directional relay selection scheme is proposed using differential analog network coding (BRS-DANC), and a simple linear detector is given to recover the received signals. In the proposed scheme, we provide an optimal and a sub-optimal methods to select the relay node from a single source by minimizing the average symbol error rate (SER). The performance of the proposed BRS-DANC scheme is analyzed, and a simple asymptotic SER expression is derived. It is shown that the SER performance of the proposed differential scheme is about 3 dB away from that of the coherent detection scheme.

The presence of carrier offsets in the multiple-input multiple-output (MIMO) channels is an important practical and theoretical problem. Double-differential coding is a technique which allows the receiver to decode the data without any channel or carrier offset knowledge. We propose a double-differential (DD) coding scheme which is applicable to square orthogonal space-time block codes (OSTBC) using M-PSK constellation. The main advantages of our proposed DD coding scheme are: 1) The previously proposed DD codes are applicable only to the specific class of space-time block codes which follow the diagonal unitary group property, whereas our DD coding is applicable to any square OSTBC. 2) We propose a suboptimal decoder which preserves the linear decoding property of the OSTBC. 3) A theoretical analysis is performed to find a pairwise error probability (PEP) upper bound of the proposed double-differential orthogonal space-time block codes (DDOSTBC). 4) In order to improve the performance of DDOSTBC over the arbitrarily correlated Rayleigh channels we propose a precoder which minimizes an upper bound of the PEP. The proposed DDOSTBC are able to achieve higher coding gain than the similar rate existing DD coding scheme. In addition, the proposed precoded DDOSTBC achieves performance gain for correlated channels as compared to the unprecoded DDOSTBC.

By Lingyang Song, Yan Zhang, Nirwan Ansari, Jianwei Huang and Bechir Hamdaoui, Guest Editors Welcome to this special issue of Wiley Journal of Wireless Communications and Mobile Computing (WCMC). The title of this special issue literally adopts the theme of the 2010 International Wireless Communications and Mobile Computing Conference (IWCMC 2010), as it attempts to represent the ‘best’ of IWCMC 2010 by soliciting representative quality research works presented at the conference for inclusion in this issue via a rigorous selection and review process. This special issue covers a quite broad range of topics of wireless networks, wireless communications, and mobile computing, from the physical layer through application and system design. The goal of this special issue is to create a great opportunity for high impact research from both the mobile communications industry and academia to present and discuss new trends, developments, emerging technologies, and new industrial standards. To guarantee the quality, in this special issue, we have selectively collected 11 expanded papers from the proceedings of IWCMC 2010, and clustered them in three groups: three papers dealing with physical layer aspects, six papers investigating MAC and network layer issues, and two papers focusing on applications as well as prototypes. Detailed overview of the selected works is given below. The first group includes three papers, which provide physical layer results for wireless communications and mobile computing. The first paper, by Takeda et al., studies joint transmit and iterative receive frequency-domain equalization for DS-CDMA. In the proposed scheme, simple one-tap frequency-domain equalization at the transmitter and iterative one-tap FDE at the receiver are jointly performed using the common knowledge of channel state information, and at the same time taking channel estimation constraints into account. The paper by Fan et al. investigates the relay position selection problem for the diamond network over Nakagami-m fading channels. This paper discusses the impact on the performance of diamond network caused by the relays' position for a general Nakagami-m fading channel, which extends the previous work for a special Rayleigh fading case, and gives clear restrictions of their positions based on the requirement of the throughput improvement and network stability. The third paper, by Stuber et al., studies outage probability for cooperative diversity with selective combining in cellular networks. The analysis mainly focuses on outage probability for amplify-and-forward and decode-and-forward cooperative diversity systems with selective combining, for the case of a log-normal Nakagami faded desired signal and log-normal Rayleigh faded co-channel interferers. The second group of papers mainly investigates MAC and network layer issues. The first paper, by Wong et al., deals with switching cost minimization in the IEEE 802.16e mobile WiMAX sleep mode operation in order to improve the battery lifetime of the mobile station. The paper proposes a novel approach to resolve this issue by making a heuristic decision during the listening interval to minimize the switching frequency for better energy efficiency. The second paper by Lin et al. studies Multicast Broadcast Service (MBS) zone configuration for wireless multicast and broadcast service. Two schemes, the overlapping scheme and the enhanced overlapping scheme, are provided for more flexible MBS zone configuration to achieve better performance for MBS in terms of QoS and radio resource utilization. The third paper, by Kumar et al., investigates the issue of trust advisory and its establishment in mobile networks, with application to ad hoc networks, including DTNs. The authors utilized encounters in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. The fourth paper by Znati et al. proposes robust multicast routing algorithms for mobile wireless networks by considering more practical challenges, e.g., the mobility of nodes, the tenuous status of communication links, limited resources, and indefinite knowledge of the network topology. This paper addresses these difficulties by providing a framework and architecture with proactive and reactive components to support multicasting to guarantee reliability and efficiency of end-to-end packet delivery. The fifth paper, by Pu et al., redefines the fairness concept regarding the application utility for time-constraint flows and then presents novel utility-based fair bandwidth sharing approaches in vehicular networks. Accordingly, two practical bandwidth-sharing schemes are provided for transferring data by fast-moving wireless nodes such as vehicles in order to guarantee QoS. The sixth paper, by Ali et al., provides a MAC protocol for cognitive wireless sensor body area networks to increases the critical traffic throughput. The proposed cognitive radio based MAC protocol prioritizes the critical packets access to the transmission medium by transmitting them with higher power while transmitting lower priority packets using lower transmission power. The third group consists of two papers focusing on applications and prototypes. The first paper by Fantacci et al. introduces a novel communication infrastructure for emergency management to interconnect several heterogeneous systems and provide multimedia access to groups of people involved in emergency operations as foreseen by the In.Sy.Eme. (Integrated System for Emergency) project. The main scope of the In.Sy.Eme system is to facilitate functional integration of new technologies with actual or off-the-shelf technologies to provide fast responses to any emergency situations and efficient use of all available resources. The second paper, by Manfrin et al., demonstrates the CalRAdio-Based advanced Spectrum Scanner, an open platform developed to monitor the ISM 2.4-2.499 GHz band, and reveals opportunities for a better utilization of the available spectrum resources. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it capitalizes on the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs. In conclusion, this issue of WCMC offers a state-of-the-art view of recent advances in wireless network, wireless communications, and mobile computing. It also offers both academic and industry appeal—the former as a basis toward future research directions and the latter toward viable commercial applications. In the long term, innovative wireless communications and mobile computing techniques will be characterized by their criticalness in consumer, business, and government applications to enhance the development of the whole world in realizing a better future. Finally, we would like to thank all the authors who have submitted their papers for consideration for publishing their work in this issue. We would like to extend our gratitude to the anonymous reviewers who spent much of their precious time reviewing all the papers. Their timely reviews and comments greatly helped us select the best papers in this special issue. We also would like to thank the devoted staff of Wiley for their high level of professionalism, and particularly express our gratitude to the Editor-in-Chief of WCMC, Professor Mohsen Guizani, for his advice, patience, and encouragement from the beginning until the final stage. We hope you will enjoy reading the great selection of papers in this issue.

Abstract Multiple‐input multiple‐output (MIMO) enabled wireless sensor networks (WSNs) are becoming increasingly important since significant performance enhancement can be realized. In this paper, we propose a packet forward strategy for MIMO sensor networks by jointly considering channel coding, rate adaptation, and power allocation. Each sensor node has multiple antennas and uses orthogonal space time block codes (OSTBC) to exploit both spatial and temporal diversities. The objective is to determine the optimal routing path that achieves the minimum symbol error rate (SER) subject to the source‐to‐destination (S‐D) energy consumption constraint. This SER‐based quality‐of‐service (QoS) aware packet forwarding problem is formulated into the framework of dynamic programming (DP). We then propose a low‐complexity and near‐optimal approach to considerably reduce the computation complexity, which includes state space partition and state aggregation techniques. Simulations indicate that the proposed protocol significantly outperforms traditional algorithms. Further still, the performance gain increases with tighter S‐D energy constraint. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we study the joint optimization problem on channel coding, power allocation, and route planning in wireless sensor networks (WSN) using dynamic programming (DP). Each sensor node has multiple antennas and applies orthogonal space time block codes (OSTBC) in order to improve the transmission reliability. A decode-and-forward protocol is adopted to relay the signals. The objective function is to determine the packet forwarding route that has the maximum successful transmission rate (STR) subject to the source-to-destination (S-D) energy consumption constraint. Specifically, we cast this energy and quality-of-service (QoS) aware packet forwarding problem into the framework of DP, such that adaptive power allocation can be jointly realized at each sensor node. State space partition techniques and state aggregation approximation architecture are introduced to derive the value function. Simulation results show that the proposed protocols significantly outperform classical routing algorithms, especially when the energy constraint becomes stringent.

In this paper, we propose a hybrid relay selection (HRS) scheme in a general cooperative network using differential modulation. In the HRS scheme, when the destination decodes successfully, the relay nodes will remain silent. Otherwise, optimal relay node has to be determined to make an additional transmission. In this process, all the relays are divided into two groups, referred to as an amplify-and-forward (AAF) relay group and a decode-and-forward (DAF) relay group depending on whether they can decode correctly or not. The relay, which has the maximum signal-to-noise ratio (SNR) at the destination, will be selected from both AAF and DAF relay groups. Simulation results show that the proposed relay selection scheme significantly outperforms the conventional AAF selection in terms of both frame error rate (FER) and throughput, and these performance gains considerably grow as the number of relay nodes increases.

Joint transmitter and receiver design problem for frequency-selective, time-invariant fading channels are studied. The authors first propose a simple approximate maximum likelihood serial decision-feedback equaliser (A-ML-SDFE) through DFE, a Gaussian approximation, a pre-whitening filter and a matched filter. Secondly, assuming full channel knowledge available at the transmitter side, the authors perform pre-equalisation in a downlink scenario by moving the decision-feedback part of the A-ML-SDFE to the transmitter. The proposed A-ML-SDFE achieves much better performance than linear minimum mean square error (MMSE) and MMSE-DFE with a lower complexity. The pre-equaliser further improves the system performance at low signal-to-noise ratio with a reduced receiver complexity.

Failure detection (FD) is an important issue for supporting dependability in distributed healthcare systems to guarantee continuous, safe, secure, and dependable operation, and often is an important performance bottleneck in the event of node failure. FD can be used to manage the health status of communication for delivering telemedicine services, and then to help distributed healthcare system reduce fatal accident rate and increase the reliability and safety of systems. Ensuring acceptable quality of service (QoS) is made difficult by the relative unpredictability of the network environment. In this paper, first, we compare QoS metrics of several adaptive FDs, discuss their properties and their relation, and then propose one optimization over the existing methods, called tuning adaptive margin failure detector (TAM FD), which significantly improves QoS, especially in the aggressive range and when the network is unstable. Second, we address the problem of most adaptive schemes, namely their need for a large window of samples. So we also analyze the impact of memory size on the performance of FDs, and then prove that the presented scheme is designed to use a fixed and very limited amount of memory for the distributed system. Our experimental results over several kinds of networks (Cluster, WiFi, LAN, Intercontinental WAN) show that the properties of the existing adaptive failure detectors, and demonstrate that the optimization is reasonable and acceptable. Furthermore, the extensive experimental results show what is the effect of memory size on the overall QoS of each adaptive failure detector. For our TAM FD, the effect of window size on their QoS is very small and can be negligible.

In this paper, we propose a maximum likelihood (ML) decoder for differentially encoded full-rank square nonorthogonal space-time block codes (STBCs) using unitary or non-unitary signal constellations, which is also applicable to full-ranked orthogonal STBC (OSTBC). As the receiver is jointly optimized with respect to the channel and the unknown data, it does not require any knowledge of channel power, signal power, or noise power to decode the signal, and the decision is purely based on two consecutively received data blocks. We analyze the effect of channel correlation on the performance of the proposed system in Rayleigh fading channels. Assuming a general correlation model, an upper bound of the pair-wise error probability (PEP) of the differential OSTBCs is derived. An approximate bound of the PEP for the differential nonorthogonal STBCs is also derived. We propose a precoder designing criterion for differential STBC over arbitrarily correlated Rayleigh channels. Precoding improves the system performance over the correlated Rayleigh MIMO channels. Our precoded differential codes differ from the previously proposed precoder designs for differential OSTBC in the following ways: 1) We propose a precoder design for arbitrarily correlated Rayleigh channels, whereas the previous work considers only for transmit correlation. 2) The previous work is only applicable to the OSTBCs with PSK constellations, whereas our precoder is applicable to any type of full-rank square STBCs with unitary and non-unitary signal constellations.

Communications over multiple-input multiple-output (MIMO) channels with carrier offsets is an important practical and theoretical problem. Double-differential coding is a technique, which allows the receiver to decode the data without any channel or carrier offset knowledge. We propose a double-differential (DD) coding scheme which is applicable to any square orthogonal space-time block codes (OSTBC) using M-PSK constellation. The main advantages of the proposed DD coding scheme are: 1) The previously proposed DD codes are applicable only to the specific class of space-time block codes which follow the diagonal unitary group property, whereas our DD coding is applicable to any square OSTBC. 2) We propose a suboptimal decoder which preserves the linear decoding property of the OSTBC. We derive an upper bound of the pairwise error probability (PEP) of the proposed double-differential orthogonal space-time block codes (DDOSTBCs). The proposed DDOSTBC is able to achieve better performance than the similar rate existing DD coding scheme. In addition, the proposed DDOSTBC outperforms the conventional training based system.

In this paper, we consider the joint transmitter and receiver design problem based on a novel approximate maximum likelihood decision feedback equalizer (A-ML-DFE) over frequency-selective, time- invariant fading channels. By applying decision feedback equalization, Gaussian approximation, a pre-whitening filter, and a matched filter, the proposed A-ML-DFE can realize single symbol detection. The proposed scheme achieves near-optimal performance with a complexity lower than the linear MMSE and the MMSE-DFE. By assuming full channel knowledge, we perform pre-equalization and precoding in a downlink scenario at the transmitter side. The pre-equalizer can achieve perfect decision feedback and thus, provide better performance at low signal- to-noise ratio (SNR) with a reduced receiver complexity. The precoder designed to minimize the analytical symbol error rate (SER) of this system can further improve the performance.

In this paper, we propose a novel and simple approximate maximum likelihood detector (A-ML-D) for single input and single output (SISO) systems over frequency-selective fading channels based on Tomlinson-Harashima pre-equalizer. By assuming full channel state information (CSI) at the transmitter side, the pre-equalizer can remove some inter-symbol interference (ISI) at the transmitter. At the receiver, we implement a Gaussian approximation, a pre-whitening filter and a matched filter to realize a set of parallel SISO schemes, and thus, facilitate single symbol detection. The proposed scheme can obtain full multi-path diversity and achieve near-optimal performance with a complexity lower than linear MMSE and MMSE-DFE. Analytical symbol error rate (SER) is derived to further justify the proposed detector.

In this paper, we propose double-differential (DD) modulation for an amplify-and-forward protocol based on cooperative communication over Nakagami-m fading channels. The proposed scheme is able to achieve performance gain in the presence of random carrier offsets and without channel knowledge at relay or destination. The proposed scheme reflects its utility for the Nakagami-m type flat fading channels with carrier offsets, where the conventional single differential scheme breaks down. In addition, the proposed scheme outperforms the conventional direct transmission double-differential system.

Most research on differential MIMO is based on space-time block codes, aiming to achieve maximum transmit diversity and thus make the transmission more robust by the aid of the special orthogonal or quasi-orthogonal code structures. However, a differential scheme based on a spatial multiplexing approach such as the Bell-Labs layered space time (BLAST) wireless architecture would be likely to provide a much greater capacity. To this end, in this paper, we derive a simple differential modulation scheme based on BLAST for any number of transmit antennas and receive antennas. A special symbol mapping method is developed to avoid amplitude variation of the transmitted signals, which can also improve the system performance. This differential scheme can significantly reduce the system complexity, since it avoids the need for channel estimation. Moreover, an improved sub-optimal detection algorithm based on a Gaussian approximation is applied to greatly reduce computational complexity at the receiver, otherwise prohibitive, with only very slight performance loss.

A general and simple differential modulation scheme that can be applied to both partial-diversity quasi-orthogonal space–time block codes and full-diversity quasi-orthogonal space–time block codes is reported. A new class of quasi-orthogonal coding structures is presented for various number of transmit antennas. Differential encoding and decoding can be simplified to differential Alamouti codes by grouping the signals in the transmitted matrix and decoupling the detection of data symbols, respectively. For the codes with partial transmit diversity, the new scheme can achieve constant amplitude of transmitted signals, and avoid signal constellation expansion; in addition, it has a linear signal detector with very low complexity. Simulation results show that these partial-diversity codes can provide very useful results at low signal-to-nose ratio for current communication systems. For codes with full transmit diversity achieved by constellation rotation, the proposed scheme has performance equal to the best full-rate quasi-orthogonal schemes previously described in the literature with the benefit of a simpler detector. Moreover, a simple linear detector is also presented for the case when two orthogonal ASK constellations are used. Extension to more than four transmit antennas is also considered.

In this letter, we propose a simple differential space-time block code with a quasi-orthogonal structure. A simple and general encoding procedure is presented, which differentially encodes the signal transmission matrix as a whole at the transmitter end. A novel power estimator is derived at the receiver to allow for the non-constant amplitude of the received signals. Simulation results show that our scheme has performance equal to the best full rate quasi-orthogonal schemes previously described, along with a simpler decoder

The aim of this paper is to explore techniques to achieve spatial diversity based on per-antenna rate control (PARC) and develop more flexible schemes that can effectively achieve easy switching between spatial multiplexing and transmit diversity for future mobile systems. The basic idea is to input the same data in each PARC sub-stream and de-correlate these sub-streams via scrambling and/or interleaving techniques with the aim of diversity improvement. After comparing variants of PARC, it has been shown that transmitting the same data in each sub-stream and subsequently de-correlating the sub-streams with different interleaving and/or scrambling not only obtains very promising performance, but also has extremely low computational complexity.

In this paper, the short-term time variation of wideband channels is investigated based on some extensions to the interim beyond-3G (IB3G) spatial channel model (SCM), which was developed for a multi-input multi-output (MIMO) system and used within the European WINNER project. These extensions mainly include some modifications of the generation of the wideband fast fading channel matrix, such as the consideration of the time variant sub-path phases at the mobile station (MS), the consideration of the powers of the time variant multi-paths, and the generation of different last bounce distances (LBDs) for the mid-paths within each path. In order to measure the short-term time variation of the wideband channels, this paper redefines the correlation matrix distance (CMD) metric, which was originally for the narrowband fast fading scenario. The simulated CMD generated from the newly extended Third Generation Partnership Project (NE-3GPP) spatial channel model (SCM) compares better to the measurements taken in the FLOWS project than that of the IB3G SCM.

In this paper, we propose a simple differential spacetime block code with a quasi-orthogonal structure, which can offer full rate and full diversity. At the transmitter, we divide the codes into sub-coding blocks, and combine the sub-coding blocks at the receiver to employ differential decoding by channel power estimation instead of decoding each sub-coding block separately. Our general approach can be extended to the recently-proposed quasi-orthogonal codes with full rate and half diversity. Moreover, this scheme is not limited to PSK constellations but can also utilize QAM constellations, so that additional coding gain can be obtained.

One major assumption in most existing space-frequency (SF) OFDM systems is that neighboring OFDM sub-channels (SC) have the same channel transfer functions (CTF). However, this assumption might be not feasible for small number of SC or long code block length because in such cases the channel gains between adjacent SC will not be approximately constant. Even if the number of SC is large enough, in some parts of the OFDM spectrum, or in a severe multipath environment, the variation of the channel gain cannot be ignored. Because of this the linear ML decoder in Tarokh et al. (1999) cannot be readily used to achieve maximum diversity, and it will cause an irreducible error floor in the high signal-to-noise ratio (SNP) region. To this end, this paper proposes an interference cancellation (IC) signal detector to reduce the error floor. While the computational complexity of the IC detector is a little higher than that of the conventional ML linear detector, the new IC detector provides much better performance by subtracting the interference resulting from variations between the adjacent SC.