In resource constrained satellite-terrestrial integrated networks (STINs), satellite downlinks supporting earth stations often have to share the same band with terrestrial networks. Under this scenario, achieving secure and timely communication in STINs could be challenging due to the presence of co-channel interference, imperfect CSI, and potential eavesdroppers. Existing security techniques in STINs e.g. robust secure beamforming (BF) often overlook another key performance indicator of STINs, i.e. communication timeliness in terms of Age of Information (AoI). To overcome this issue, this paper proposes a new joint AoI and security-oriented optimization in enhancing the performance of STINs. Specifically, two schemes are developed to achieve low latency and secure BF, including a single-slot scheme and a multi-slot scheme. Specifically, we first establish a continuous-time AoI evolution model and derive a closed-form secrecy margin for the wiretap channel, which enables a unified characterization of information freshness and transmission security. Building on this, we incorporate imperfect channel state information (CSI) to formulate a single-slot joint optimization problem that explicitly targets both AoI-aware freshness and physical-layer security. The objective is to minimize the total transmit power, while meeting the required secrecy-margin constraints, quality of service (QoS) constraints, eavesdropping probability constraints, and pertransmitter power budget constraints. To handle this nonconvex problem, we first apply Bernstein inequalities to convert the probabilistic constraints into deterministic forms. Subsequently, an iterative difference-of-convex programming algorithm is proposed to derive the BF vectors. Furthermore, to capture multi-slot temporal dynamics, we extend the design to a multi-slot framework that considers the impact of random data arrivals on system performance and establishes queue stability conditions based on the data queue. We apply the Lyapunov optimization technique to transform the multi-slot stochastic problem into a per-slot penalized power minimization problem, after which each slot can be solved in the same manner as the single-slot design. Finally, experimental results show that the proposed single-slot scheme satisfies both security and timeliness requirements while significantly reducing system power consumption, whereas the multi-slot optimization retains these performance advantages and further enables a favorable trade-off between power consumption and queue stability.

Non-orthogonal multiple access (NOMA) enables multiple terminal devices to simultaneously share wireless resources, providing efficient computing offloading services for wireless devices in networks that integrate unmanned aerial vehicles (UAVs) with mobile edge computing (MEC). However, the broadcast characteristics of UAV line-of-sight (LoS) communication introduce serious security issues for NOMA-based UAV-MEC systems, especially when facing an aerial warden. To address this issue, we propose a covert communication scheme for NOMA-based UAV-MEC systems against an aerial warden, where the aerial warden monitors the task offloading behavior of terminal devices. In the proposed scheme, the average computing capacity is maximized by jointly optimizing the UAV trajectory and system resources while ensuring the covert performance requirements. Firstly, considering the terminal devices have a fixed number of computing tasks, a block coordinate descent (BCD)-based algorithm is proposed, which decomposes the non-convex original problem into several subproblems and solves them iteratively. Secondly, considering the case of dynamic tasks arrival at terminal devices, we propose a double-deep Q-learning (DDQN)-based algorithm, where the optimal strategy for trajectory planning and resource allocation is obtained. Simulation results demonstrate that the proposed scheme using two algorithms outperform their respective baselines.

Uncrewed Aerial Vehicles (UAVs) have become key enabling platforms for low-altitude economic networks, yet achieving efficient and adaptive optimization under resource-constrained and dynamic environments remains challenging. This paper investigates language models for UAV-enabled Wireless Power Transfer (WPT) systems. First, a lightweight small language model (SLM)-based solution is developed using a pre-trained BERT backbone, enhanced UAV embeddings and contextual features, a geometry-aware path decoder, and ensemble inference to achieve low complexity, low latency, and high energy efficiency. Second, an Agentic AI-based framework is designed to exploit the reasoning and interactive capabilities of large language models (LLMs). It integrates four collaborative agents—Initializer, Actor, Critic, and Reflector—to form a closed loop of generation, optimization, evaluation, and reflection for iterative UAV path and energy optimization. Finally, simulations compare the SLM-, LLM-, and Agentic AI-based approaches.

Aerial wardens could pose significant security threats to satellite-ground communications due to their stronger received signals than legitimate ground users. To address this issue, the signals from all jamming satellites in low Earth orbit satellite networks, i.e., full jamming strategy (FJS), are utilized to counter the detection of the aerial warden. However, this worsens the communication quality of ground users. To improve it, we utilize the difference in visible spherical crowns between the ground user and the aerial warden due to the Earth blockage to propose the safeguard-zone strategy (SGS) via merely muting the jamming satellites visible to the ground user. To evaluate the effectiveness of the proposed strategies, we propose a stochastic geometry-based analytical framework to derive the covert probability and connection probability. To capture the trade-off between covertness and reliability, the effective covert rate, defined as the product of transmission rate, covert probability, and connection probability, is also analyzed and optimized. The results validate the accuracy of the analytical expressions and illustrate that SGS outperforms the FJS in the connection probability and effective covert rate with a small loss in covert probability, which can be compensated by increasing the transmit power or the number of jamming satellites.

This paper focuses on the design and performance analysis of faster-than-Nyquist (FTN) signaling employing enhanced 5G low-density parity-check (LDPC) codes, oriented toward the requirements of future 6G systems. We propose the extrinsic information transfer (EXIT) chart analysis for the LDPC-coded FTN system based on the Ungerboeck observation model, where the input-output mutual information function of the detector is approximated using least squares fitting. With the proposed EXIT chart analysis, we explore the thresholds and decoding performance of different LDPC codes (regular codes, irregular codes and protograph codes) in both Nyquist and FTN systems, revealing two important observational findings for FTN signaling: 1) Unlike Nyquist systems, where certain 5G New Radio (NR)-like information puncturing can enhance the decoding threshold and performance, we observe that in the FTN setting considered in this paper such puncturing leads to performance degradation; 2) Unlike Nyquist systems, the paritycheck matrix of LDPC codes optimized for FTN signaling tends to be relatively sparser within comparable ensembles, due to the intentionally introduced inter-symbol interference (ISI). Based on these findings, we develop tailored LDPC codes for FTN signaling by applying the masking operation to the base matrix of the standard 5G LDPC codes, aiming to achieve a lower decoding threshold and thereby better decoding performance. Moreover, the raptor-like structure and rate compatibility are preserved in the proposed LDPC codes, and the encoder and decoder are reused with only minor modifications. Numerical results show that: 1) All simulation results align with the decoding thresholds obtained by the proposed EXIT chart analysis, confirming the effectiveness of the analysis; 2) For the FTN system, the tailored LDPC codes outperform standard 5G LDPC codes, achieving over 0.4 dB coding gain and approaching (slightly exceeding) the constrained Nyquist capacity; 3) Under the same spectral efficiency, FTN with tailored LDPC codes performs better than standard 5G LDPC codes with Nyquist signaling, demonstrating a coding gain of up to 0.6 dB; 4) The proposed LDPC codes with the FTN signaling achieve better performance compared to existing high-performance codes specifically designed for FTN signaling.

The success of collaborative task completion among networked devices hinges on the effective selection of trustworthy collaborators. However, accurate task-specific trust evaluation of multi-hop collaborators can be extremely complex. The reason is that their trust evaluation is determined by a combination of diverse trust-related perspectives with different characteristics, including historical collaboration reliability, volatile and sensitive conditions of available resources for collaboration, as well as continuously evolving network topologies. To address this challenge, this paper presents a graph neural network (GNN)-aided distributed agentic AI (GADAI) framework, in which different aspects of devices’ task-specific trustworthiness are separately evaluated and jointly integrated to facilitate multi-hop collaborator selection. GADAI first utilizes a GNN-assisted model to infer device trust from historical collaboration data. Specifically, it employs GNN to propagate and aggregate trust information among multi-hop neighbours, resulting in more accurate device reliability evaluation. Considering the dynamic and privacy-sensitive nature of device resources, a privacy-preserving resource evaluation mechanism is implemented using agentic AI. Each device hosts a large AI model-driven agent capable of autonomously determining whether its local resources meet the requirements of a given task, ensuring both task-specific and privacy-preserving trust evaluation. By combining the outcomes of these assessments, only the trusted devices can coordinate a task-oriented multi-hop cooperation path through their agents in a distributed manner. Experimental results show that our proposed GADAI outperforms the comparison algorithms in planning multi-hop paths that maximize the value of task completion.

Due to the growing diversity of vertical applications, current integrated sensing and communications (ISAC) technologies in wireless networks remain insufficient to support complex services beyond communications. To this end, future networks are evolving toward an integrated heterogeneous service provisioning (IHSP) platform, which aims to integrate a broad range of heterogeneous services beyond the dual-function scope of ISAC. Nevertheless, this trend intensifies the conflicts among concurrent heterogeneous services under constrained resource sharing. In this paper, we overcome this resource constraint by the joint use of two novel elastic design strategies: compromised service value assessment and flexible multi-dimensional resource sharing. Consequently, we propose a value-prioritized elastic multi-dimensional multiple access (MDMA) mechanism for IHSP. First, we define the compromised Value-of-Service (VoS) metric by incorporating elastic parameters to characterize user-specific tolerance and compromise in response to various performance degradations under constrained resources. This VoS metric serves as the foundation for prioritizing resource sharing among IHSP services with fairness among concurrent competing demands. Next, we adapt the MDMA to elastically multiplex services using appropriate multiple access schemes across different resource domains. This protocol leverages user-specific interference tolerances and cancellation capabilities across different domains to reduce resource-demanding conflicts and co-channel interference within the same domain. Then, we maximize the system’s VoS by jointly optimizing MDMA design and power allocation. Since this problem is non-convex, we propose a monotonic optimization-aided dynamic programming (MODP) algorithm to obtain its optimal solution. Additionally, we develop the VoS-prioritized successive convex approximation (SCA) algorithm to efficiently find its suboptimal solution. Finally, simulations are presented to validate the effectiveness of the proposed designs.

Concurrently supporting heterogeneous services, e.g., sensing and communication (S&C), presents a significant challenge for future wireless networks due to the increasing number of connected devices, limited resources, and the complexity of integrated service provisioning. Furthermore, dynamic network conditions, along with varying heterogeneous needs from coexisting devices, further exacerbate the challenges of traditional rigid system operation, where heterogeneous network services are treated as either entirely independent or fully integrated. This rigid operation neglects the fluctuating gains and costs of the integrated heterogeneous service provisioning. To transform isolated operations into a highly integrated paradigm, this paper proposes a progressive scheme for integrated sensing and communication (ISAC). The scheme elastically adjusts the integration level based on continuously accumulated system state observations, including user demand, resource conditions, and environmental changes, to regulate resource utilization dynamically. Specifically, we present a unified Value of Service (VoS) metric, which adaptively incorporates user service experiences, resource costs, and gains from S&C coupling to guide efficient resource allocation. In addition, building on this progressive integration scheme, we develop a dynamic stage-dependent resource optimization algorithm for bandwidth allocation. Simulation results demonstrate the effectiveness of the proposed integrated framework and algorithm in optimizing resource allocation and maintaining system performance under stringent resource constraints.

To empower diverse Internet of Things applications, collaboration among resource-constrained devices becomes essential for effective task completion with complex service requirements, time constraints, and resource conditions. Under such complexities, trust becomes a key mechanism for effective collaborator selection, by leveraging both direct experience and peer recommendations. However, selecting reliable collaborators for new tasks remains challenging due to the changing task requirements blocking reuse of previous trust evaluations (TEs). While indirect trust from recommendations saves evaluation time, unattended recommender-specific characteristics such as capability and reliability adversely affect its usefulness. These challenges motivate us to develop disruptive trust fusion mechanisms for accurate and efficient TEs under changing conditions. For this purpose, we propose an original concept of trust permeability for new task-specific TEs, effectively integrating historical TEs and recommendations for new task-specific TEs. By decoding task-specific trust into task success parameters (TSPs), we develop permeable pathways fully exploiting existing TEs and enable collaborator selections for new tasks. Specifically, we propose a task permeable trust-based collaborator selection (TPTCS) mechanism which decodes existing TEs of potential collaborators from old tasks, forming sets of TSPs, and adaptively combines them with all previous task-specific TEs and quality recommendations for new tasks with new requirements. Eliminating the impacts of changing task requirements, the proposed TPTCS creates permeable pathways for rapid task-specific TEs under new task requirements, accelerating collaborator selection. During the combination, TEs are weighted between the decoding process and quality recommendations. Numerical results demonstrate TPTCS outperforms comparison mechanisms in selecting reliable collaborators for new tasks.

While physical layer authentication can mitigate wireless channel vulnerabilities, its reliability is often compromised by inherent noise and variability of observed physical layer attributes. As a solution, collaborative physical layer authentication (CPLA) introduces multiple nodes to enhance performance, but incurs additional computational and communication costs for collaborators. Without incentive, desired collaborators may act selfishly and withdraw, and involving unreliable collaborators could degrade performance. Therefore, this paper proposes an incentive mechanism with a new centralized governance approach to coordinate CPLA, engaging reliable collaborators to optimize authentication accuracy. Specifically, we model the interaction between the center and collaborators as a Stackelberg game to establish. To reduce redundant computations in equilibrium solving, we first construct a candidate pool containing potential trainable combinations. Subsequently, we design incentive and training schemes for each candidate combination. Moreover, a quality-driven combination selection scheme is proposed to maximize incentive effectiveness. Based on the candidate pool and strategies, it integrates a deep Q-network as collaborator quality manager and a combination-level evaluation module, and via “filter-then-verify” identifies optimal incentive targets with low complexity while improving authentication accuracy. Simulations demonstrate that the proposed scheme successfully incentivizes selfish collaborators and achieves 99% authentication accuracy in unreliable collaborative environments.

Future 6G-enabled industrial systems will rely on distributed sensing and control over communication networks to manage concurrent processes, collaboratively achieving system-level operational objectives. However, various physical constraints, including excessive communication delays, complex interprocess dependencies, and dynamic system objectives, inevitably cause deteriorated operational outcomes compared to ideal conditions. To minimize this operational loss, we propose a goal-oriented digital twin (GDT) framework that overcomes these physical constraints through system orchestration in the virtual domain for dynamic objective fulfillment. Based on operational goals, the proposed GDT selectively integrates distributed sensing information into system digital twins, which then map system-level objectives into executable control tasks for individual devices. Specifically, by continuously evaluating the goal relevance of sensing data from individual devices, distributed observations are selected and prioritized, enabling control-aware communication resource allocation that balances control performance and communication efficiency. Moreover, delay-compensated control commands are accurately derived within the GDT framework, where the sensed temporal synchrony and interprocess dependencies are intentionally considered for coordinated task execution across distributed devices. Through this cohesive joint sensing and control design in the virtual domain, system-level objectives are fulfilled with minimized operational loss. Extensive simulations validate that GDT significantly improves control accuracy and resource efficiency in large-scale industrial systems.

Ensuring safe, robust, and scalable motion planning for multiagent systems in dynamic and uncertain environments is a persistent challenge, driven by complex interagent interactions, stochastic disturbances, and model uncertainties. To overcome these challenges, particularly the computational complexity of coupled decision-making and the need for proactive safety guarantees, we propose a reachability-enhanced dynamic potential game (RE-DPG) framework, which integrates game-theoretic coordination into reachability analysis. This approach formulates multiagent coordination as a dynamic potential game, where the Nash equilibrium (NE) defines optimal control strategies across agents. To enable scalability and decentralized execution, we develop a neighborhood-dominated iterative best response scheme, built upon an iterated$\varepsilon$-BR process that guarantees finite-step convergence to an$\varepsilon$-NE. This allows agents to compute strategies based on local interactions while ensuring theoretical convergence guarantees. Furthermore, to ensure safety under uncertainty, we integrate a multiagent forward reachable set mechanism into the cost function, explicitly modeling uncertainty propagation and enforcing collision avoidance constraints. Through both simulations and real-world experiments in 2-D and 3-D environments, we validate the effectiveness of RE-DPG across diverse operational scenarios.

Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, uncrewed aerial vehicles (UAVs) serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks. Existing security approaches often assume eavesdroppers follow predefined trajectories, which fail to capture the dynamic mobility patterns of eavesdroppers in realistic maritime environments. To address this challenge, we consider a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers with uncertain positions to enhance the physical layer security. Since such a system requires balancing the conflicting performance metrics of the secrecy rate and energy consumption of UAVs, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP). To solve this dynamic and long-term optimization problem, we first reformulate it as a partially observable Markov decision process (POMDP). We then propose a novel soft actor-critic with conditional variational autoencoder (SAC-CVAE) algorithm, which is a deep reinforcement learning algorithm improved by generative artificial intelligence. Specifically, the SAC-CVAE algorithm employs advantage-conditioned latent representations to disentangle and optimize policies, while enhancing computational efficiency by reducing the state space dimension. Simulation results demonstrate that our proposed intelligent jamming approach achieves secure and energy-efficient maritime communications. Furthermore, comparison results show that the proposed SAC-CVAE algorithm outperforms baseline methods across various eavesdropper movement patterns, simultaneously maximizing the secrecy rate and minimizing the energy consumption of UAVs.

Due to the massive computational demands of large generative models, AI-Generated Content (AIGC) can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content generation for resource-constrained users. However, such a paradigm faces two significant challenges: i) raw prompts (i.e., the task description from users) often lead to poor generation quality due to users' lack of experience with specific AIGC models, and ii) static service provisioning fails to efficiently utilize computational and communication resources given the heterogeneity of AIGC tasks. To address these challenges, we propose an intelligent mobile AIGC service scheme. Firstly, we develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation through small-scale expert demonstrations. Secondly, we formulate a dynamic mobile AIGC service provisioning problem that jointly optimizes the number of inference trials and transmission power allocation. Then, we propose the Diffusion Enhanced Deep Deterministic Policy Gradient (D3PG) algorithm to solve the problem. By incorporating the diffusion process into Deep Reinforcement Learning (DRL) architecture, the environment exploration capability can be improved, thus adapting to varying mobile AIGC scenarios. Extensive experimental results demonstrate that our prompt engineering approach improves single-round generation success probability by 6.3×, while D3PG increases the user service experience by 50.3% compared to baseline DRL approaches.

Designing effective incentive mechanisms in mobile crowdsensing (MCS) networks is crucial for engaging distributed mobile users (workers) to contribute heterogeneous data for various applications (tasks). In this paper, we propose a novel stagewise trading framework to achieve efficient and stable task-worker matching, explicitly accounting for task diversity (e.g., spatio-temporal limitations) and network dynamics inherent in MCS environments. This framework integrates both futures and spot trading stages. In the former, we introduce the futures trading-driven stable matching and pre-path-planning mechanism (FT-SMP3), which enables long-term taskworker assignment and pre-planning of workers' trajectories based on historical statistics and risk-aware analysis. In the latter, we develop the spot trading-driven DQN-based path planning and onsite worker recruitment mechanism (ST-DP2WR), which dynamically improves the practical utilities of tasks and workers by supporting real-time recruitment and path adjustment. We rigorously prove that the proposed mechanisms satisfy key economic and algorithmic properties, including stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Extensive experiements further validate the effectiveness of our framework in realistic network settings, demonstrating superior performance in terms of service quality, computational efficiency, and decision-making overhead.

Federated Learning (FL) offers a pioneering distributed learning paradigm that enables devices/clients to build a shared global model that can be obtained through frequent model transmissions between clients and a central server, causing high latency, energy consumption, and congestion over backhaul links. To overcome these drawbacks, Hierarchical Federated Learning (HFL) has emerged, which organizes clients into multiple clusters and utilizes edge nodes (e.g., edge servers) for intermediate model aggregations between clients and the central server. Current research on HFL mainly focus on enhancing model accuracy, latency, and energy consumption in scenarios with a stable/fixed set of clients. However, addressing the dynamic availability of clients – a critical aspect of real-world scenarios – remains underexplored. This study delves into optimizing client selection and client-to-edge associations in HFL under intermittent client participation so as to minimize overall system costs (i.e., delay and energy), while achieving fast model convergence. We unveil that achieving this goal involves solving a complex NP-hard problem. To tackle this, we propose a stagewise methodology that splits the solution into two stages, referred to as Plan A and Plan B. Plan A focuses on identifying long-term clients with high chance of participation in subsequent model training rounds. Plan B serves as a backup, selecting alternative clients when long-term clients are unavailable during model training rounds. This stagewise methodology offers a fresh perspective on client selection that can enhance both HFL and conventional FL via enabling low-overhead decision-making processes. Through evaluations on diverse datasets, we show that our methodology outperforms existing benchmarks on crucial factors such as model accuracy and system costs.

Future 6 G networks are envisaged to tightly integrate communication, sensing, and computing, demanding real-time, intent-driven intelligence at the edge. Whilelargelanguagemodels (LLMs) excel in intent recognition and semantic reasoning, their application to real-time network lifecycle management at the edge is limited by heterogeneousapplicationintents (APPIs), dynamic network conditions, and severe resource constraints. This paper proposes a novel lightweight LLM architecture, KGLlama-KD, that synergizes knowledge graphs (KGs) withknowledgedistillation (KD) to enable intent-driven networking and enhance 6 G edge intelligence. Specifically, a KG is constructed to formally describe the relationships among application scenarios, functional primitives, performance requirements within APPIs, and the correspondences between APPIs andnetworkservicerequests (NSRs), thereby producing a structured intent training dataset. Building upon the Llama 3 foundation model, a two-phase optimization framework is designed to support lightweight edge deployment while preserving translation fidelity. The LLM is first fine-tuned with KG guidance and compressed via KD in the cloud, and then deployed on resource-constrained edge nodes to perform real-time, accurate, and efficient APPIs interpretation. Experiments validate that KGLlama-KD achieves 95% accuracy for APPI understanding, surpassing DeepSeek and Qwen by an average of 8%. The distilled model reduces inference latency by 60% compared to full-scale LLMs, fulfilling the sub-100 ms requirement for 6 G latency-sensitive services.

Effective provision of integrated sensing, communication, and computation (ISCC) services in future networks will inevitably increase their operational complexity. The distinct requirements of diverse tasks for tailored ISCC devices further exacerbate the challenge of adaptively allocating constrained resources among concurrent tasks. To address these difficulties, a task-specific joint resource orchestration scheme is proposed in this paper to enhance the effectiveness of ISCC operation and heterogeneous tasks completion. Specifically, the completion of concurrent heterogeneous tasks by different devices relies on the task-specific sharing of limited resource among sensing, real-time data computing and delay-tolerant data processing. Consequently, a value of multi-task completion (VoC) indicator is designed to connect and balance among the diverse demands from concurrent tasks, including computing rate, time delay, and sensing performance. The VoC is then maximized by collaborative optimization of multi-dimensional resources, including transmit beamformer, local and offloading CPU-cycle frequency, data factor assignment and computation capacity. To solve this challenging optimization problem with the lack of close-form solution and coupling of multi-variables, we first transform it into an equivalent form that is tractable to handle. Next, the problem is decomposed into several subproblems, which can be approximately solved by iterative updates. Simulation results demonstrate the performance enhancement of the proposed scheme is superior to the benchmarks.

Mobile edge computing (MEC) has emerged as a promising technology for marine Internet of Things (IoT) networks, supporting diverse application requirements that could be both computationally intensive and delay-sensitive. However, most existing studies assume access to pre-existing network information and rely on single-layer MEC frameworks to provide services from an offline perspective, struggling to ensure low latency. To overcome the related issues, we first consider an online hierarchical computation offloading framework in this paper for marine IoT networks with aerial, offshore, and onshore devices. We further develop a hybrid transmission strategy combining non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) to enhance the computation offloading efficiency within the framework. Considering the time-varying capacity of wireless channels, we thus minimize the hierarchical computation offloading delay by jointly optimizing the offloading strategy and network resource allocation in the marine IoT networks online. To solve the formulated mixed-integer nonlinear programming (MINLP) problem, we design a problem-solving framework based on a decomposition structure. Specifically, we decompose the formulated MINLP problem into two subproblems. For the bottom subproblem, we design a successive convex approximation (SCA)-based algorithm to optimize the hierarchical transmission durations and the offloaded workload with a given user association scheme. For the top subproblem, we propose a deep reinforcement learning (DRL)-based algorithm to realize online optimization of the user association scheme under the time-varying channels. Finally, numerical results demonstrate that the proposed algorithms, including the SCA-based algorithm and the DRL-based algorithm, can reach near-optimal results. Furthermore, the proposed hierarchical computation offloading framework significantly outperforms traditional benchmarks.

Low altitude uncrewed aerial vehicles (UAVs) are expected to facilitate the development of aerial-ground integrated intelligent transportation systems and unlocking the potential of the emerging low-altitude economy. However, several critical challenges persist, including the dynamic optimization of network resources and UAV trajectories, limited UAV endurance, and imperfect channel state information (CSI). In this paper, we offer new insights into low-altitude economy networking by exploring intelligent UAV-assisted vehicle-to-everything communication strategies aligned with UAV energy efficiency. Particularly, we formulate an optimization problem of joint channel allocation, power control, and flight altitude adjustment in UAV-assisted vehicular networks. Taking CSI feedback delay into account, our objective is to maximize the vehicle-to-UAV communication sum rate while satisfying the UAV's long-term energy constraint. To this end, we first leverage Lyapunov optimization to decompose the original long-term problem into a series of per-slot deterministic subproblems. We then propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm, which innovatively integrates diffusion models to determine optimal channel allocation, power control, and flight altitude adjustment decisions. Through extensive simulations using real-world vehicle mobility traces, we demonstrate the superior performance of the proposed D3PG algorithm compared to existing benchmark solutions.

In reconfigurable intelligent surface (RIS)-assisted communication systems, the amplitudes and phases of reflected electromagnetic waves are modified to enhance signal power and provide additional communication paths, particularly in scenarios lacking a line-of-sight (LOS) propagation path. However, conventional RIS-aided communication systems reflect all incident signals indiscriminately, without distinguishing between desired signals and interference. In this paper, we propose a novel RIS-assisted communication system capable of selectively reflecting only the desired signal by performing interference suppression directly at the RIS. By exploiting the degree of freedom (DOF) in spatial domain of RIS, the design of RIS coefficients is formulated as a minimum variance distortionless response (MVDR) problem, where the optimal RIS coefficients are obtained by estimating the interference subspace and the desired signal. Then, we propose a two-step iterative approach based on atomic norm minimization (ANM) to estimate the subspace and desired signal simultaneously. The resulting ANM problem is solved with alternative optimization by decomposing it into two subproblem. Each subproblem is formulated as semidefinite programming (SDP) problem and efficiently solved using the alternating direction method of multipliers (ADMM). Theoretical analysis is conducted to verify the estimations are consistent and bounded. Simulation results validate its effectiveness and superiority over existing methods.

In this paper, we investigate an air-to-ground (A2G) wireless network system where multiple uncrewed aerial vehicles (UAVs) provide downlink communication coverage for mobile ground users (GUs). This system accounts for UAVs progressively depleting their energy during coverage provision, ceasing operations when their energy reserves fall below a predefined threshold. We aim to maximize cumulative system throughput over the task period while satisfying the minimum fairness requirement through joint trajectory control and resource allocation (JTCRA) optimization. To meet the fairness requirement, enhancing system robustness is critical; energy-sufficient UAVs must autonomously assist GUs that lose connectivity when their serving UAVs terminate operations. Therefore, we propose a multi-agent deep reinforcement learning (MADRL) framework with a parameter-sharing architecture to solve this problem. As conventional parameter sharing is restricted to homogeneous agents with identical observation-action spaces, we design a dual-agent structure: a trajectory agent (Traj-agent) and a communication agent (Comm-agent) are deployed for each UAV. This separation organizes the heterogeneous tasks of trajectory control and resource allocation into distinct homogeneous agent groups, facilitating effective parameter sharing within each type. Based on this framework, we apply two alternative algorithms: an MAPPO-based JTCRA algorithm and a QMIX-based JTCRA algorithm. Simulation results demonstrate the superiority and effectiveness of our proposed JTCRA algorithms, which maintain service continuity for GUs through intelligent trajectory control, thereby minimizing the adverse impact of coverage gaps.

Optimizing unmanned aerial vehicle (UAV)-assisted wireless networks to serve mobile users (MUs) via beamforming presents significant challenges, mainly due to the dynamic and complex environments. Traditional single-modal data-based modeling methods are often insufficient for capturing the varying environmental characteristics, leading to inaccurate UAV trajectory design and beamforming. To address these issues, we propose a multi-UAV-assisted integrated sensing, communication, and computation (ISCC) framework that processes multi-modal data to enhance environmental awareness and improve communication performance. We then formulate an optimization problem to maximize the average sum rate by jointly optimizing the UAV trajectory and beamforming vectors. Given the non-convex nature of the problem, traditional optimization techniques are inadequate. To this end, we introduce a fine-tuned multi-modal large language model (M2LLM)-driven deep reinforcement learning (DRL)-based joint optimization framework. Specifically, a pre-trained M2LLM is first fine-tuned to predict future MU positions by leveraging historical multi-modal data, including texts, images, and wireless sensing data. The fine-tuned M2LLM is then employed to extract environmental features, where the output of the fine-tuned M2LLM’s last hidden layer is regarded as the environment state vector to eliminate the output uncertainty of the M2LLM. Subsequently, we use a DRL agent to optimize the UAV trajectory and beamforming in a coordinated manner. Extensive simulation results demonstrate that the proposed framework can significantly enhance network performance by enabling environment-aware and adaptive trajectory design and beamforming. The code is available in https://huggingface.co/blYin/MmllmDrlUavTdBf.

The integration of network function virtualization (NFV) with mobile edge computing (MEC) fosters a more agile service provisioning in a network operational cost-efficient manner. However, some challenges exist in adapting to the unpredictable network stochastics and resource restrictiveness, when placing virtualized network functions (VNFs) or service function chains (SFSs) appropriately onto MEC networks. In this work, we study the cost-efficient online SFC deployment in MEC networks, where each service is translated as an SFC flow and traverses through networks to meet service demands. First, we formulate a long-term time-averaged network operational cost minimization problem, by optimizing both SFC mapping and flow routing, to keep the system stability. Then, to deal with the non-trivial mixed-integer programming (MIP) and stochasticity properties in the SFC deployment, we use both Lp(0 <p< 1) norm-based relaxation and penalization, and learn-and-adapt techniques, to obtain an improved performance-stability tradeoff. Finally, both theoretical analyses and numerical simulations are conducted to demonstrate the proposed method’s superiority, in terms of its asymptotic optimality and reduced queue backlog.

Effective task-oriented semantic communications relies on perfect knowledge alignment between transmitters and receivers for accurate recovery of task-related semantic information, which can be susceptible to knowledge misalignment and performance degradation in practice. To tackle this issue, continual knowledge updating and sharing are crucial to adapt to evolving task and user related demands, despite the incurred resource overhead and increased latency. In this paper, we propose a novel collaborative knowledge sharing-empowered semantic transmission mechanism in a two-tier edge network, exploiting edge cooperations and bit communications to address KB mismatch. By deriving a generalized effective semantic transmission rate (GESTR) that considers both semantic accuracy and overhead, we formulate a mixed integer nonlinear programming problem to maximize GESTR of all mobile devices by optimizing knowledge sharing decisions, extraction ratios, and BS/subchannel allocations, subject to task accuracy and delay requirements. The joint optimum solution can be obtained by proposed fractional programming based branch and bound algorithm and modified Kuhn-Munkres algorithm efficiently. Simulation results demonstrate the superior performance of proposed solution, especially in low signal-to-noise conditions.

Conventional wireless communication techniques and performance indicators are becoming inadequate for the designs of integrated sensing, positioning, and communication (ISPAC) systems, due to their inability to balance diverse competing demands from concurrent heterogeneous services under constrained resources. In this paper, we overcome these challenges with two new elastic design strategies: compromised service value assessment and flexible multi-dimensional service multiplexing. Accordingly, we propose an elastic value-prioritized service provisioning based on multi-dimensional multiple access (MDMA) for ISPAC systems. First, we modify our previous value-of-service (VoS) metric by incorporating elastic parameters to capture user-specific tolerance and compromise in response to various performance degradations under constrained resources. The modified VoS metric can serve as a foundation for prioritizing service and enabling effective service provisioning among competing services. Then, we adapt the MDMA to elastically multiplex services using appropriate multiple access schemes across various resource domains. This protocol leverages user-specific interference tolerances and cancellation capabilities across different resource domains to reduce resource-demanding conflicts and co-channel interference within the same domain. Finally, we formulate a system VoS maximization problem by jointly optimizing the MDMA design and power allocation, and then propose a sub-optimal algorithm to solve it efficiently.

The exponential increase in concurrent demands, driven by diverse intelligent applications, poses substantial challenges for emerging beyond-communication networks, such as integrated sensing and communication (ISAC). Specifically, this growing demand necessitates the development of efficient resource allocation strategies and unified performance metrics to effectively manage heterogeneous service requirements in dynamic environments. To realize 'sensing with communication' and ultimately enhance service quality, this paper proposes an adaptive resource allocation scheme aimed at maximizing integration gain. To achieve this, we introduce an integration gain evaluation metric that leverages the inherent similarities between sensing and communication (S&C) channels to quantify information sharing, thereby continuously strengthening the correlation and integration of both functions within the ISAC system. To enable unified system operation across heterogeneous services, an integration gain-guided adaptive grained search algorithm is developed for bandwidth resource allocation. Simulation results demonstrate improved performance of the proposed integration framework and resource allocation algorithm compared with other benchmarks.

Federated Learning (FL) has gained significant attention in recent years due to its distributed nature and privacy-preserving benefits. However, a key limitation of conventional FL is that it learns and distributes a common global model to all participants, which fails to provide customized solutions for diverse task requirements. Federated meta-learning (FML) offers a promising solution to this issue by enabling devices to fine-tune local models after receiving a shared meta-model from the server. In this paper, we propose a task-oriented FML framework over non-orthogonal multiple access (NOMA) networks. A novel metric, termed value of learning (VoL), is introduced to assess the individual training needs across devices. Moreover, a task-level weight (TLW) metric is defined based on task requirements and fairness considerations, guiding the prioritization of edge devices during FML training. The formulated problem—to maximize the sum of TLW-based VoL across devices—forms a non-convex mixed-integer non-linear programming (MINLP) challenge, addressed here using a parameterized deep Q-network (PDQN) algorithm to handle both discrete and continuous variables. Simulation results demonstrate that our approach significantly outperforms baseline schemes, underscoring the advantages of the proposed framework.

With the increasing demand for secure communication in 5G and beyond, authentication of wireless devices has become a crucial task for communication security. Radio frequency fingerprint identification (RFFI) leverages the hardware-specific features in radio frequency (RF) signals, known as radio frequency fingerprints (RFF), to achieve highprecision device identification. However, the dependence of RFFI on the physical characteristics of devices makes it vulnerable to physical spoofing attacks. This paper proposes an innovative physical spoofing attack framework that combines spoofed transmitter and legitimate transmitter models. It performs RFF modeling, RFF concealment (RFFC), and RFF spoofing (RFFS) sequentially to achieve precise spoofing of the original baseband signal. We validate the effectiveness of the proposed physical spoofing mechanism through simulations of seven types of transmitters using MATLAB Simulink. The performance is further evaluated on an RFFI model based on complexvalued convolutional neural networks (CVCNN). Experimental results show that neural networks (NN) significantly outperform the generalized memory polynomial (GMP) model in nonlinear data fitting and temporal relationship modeling. Consequently, NN-based physical spoofing methods exhibit superior attack effectiveness. Specifically, under the signal-to-noise ratio (SNR) of 15 dB, the NN-based physical spoofing method achieves a target attack success rate (TSR) as high as 98%, which is superior to adversarial attack methods. NN-based methods also enhanced performance in terms of stealthiness metrics.

Integrated sensing and communication (ISAC) can boost the spectrum efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, it may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning aided transceiver design for ISAC. Particularly, the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio is minimized subject to the constraints of constant modulus signal and waveform similarity by transceiver design. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to solve this non-convex optimization problem. To reduce the complexity, we propose a deep unfolding neural network (NN), which can unfold the underlying ADMMbased iterative algorithm to a lightweight NN with some learnable parameters and circumvent the bisection method using the projected gradient descent. Simulation results demonstrate the effectiveness of our proposed deep unfolding NN.

Social Internet-of-Things (IoT) enhances collaboration between devices by endowing IoT systems with social attributes. However, calculating trust between devices based on complex and dynamic social attributes—similar to trust formation mechanisms in human society—poses a significant challenge. To address this issue, this paper presents a new hypergraph-enabled selfsupervised contrastive learning (HSCL) method to accurately determine trust values between devices. To implement the proposed HSCL, hypergraphs are first used to discover and represent high-order relationships based on social attributes. Hypergraph augmentation is then applied to enhance the semantics of the generated social hypergraph, followed by the use of a parametersharing hypergraph neural network to nonlinearly fuse the high-order social relationships. Additionally, a self-supervised contrastive learning method is utilized to obtain meaningful device embeddings by conducting comparisons among devices, hyperedges, and device-to-hyperedge relationships. Finally, trust values between devices are calculated based on device embeddings that encapsulate high-order social relationships. Extensive experiments reveal that the proposed HSCL method outperforms baseline algorithms in effectively distinguishing between trusted and untrusted nodes and identifying the most trusted node.

Trust is emerging as an effective tool to ensure the successful completion of collaborative tasks within collaborative systems. However, rapidly and continuously evaluating the trustworthiness of collaborators during task execution is a significant challenge due to distributed devices, complex operational environments, and dynamically changing resources. To tackle this challenge, this paper proposes a Siamese-enabled rapid and continuous trust evaluation framework (SRCTE) to facilitate effective task collaboration. First, the communication and computing resource attributes of the collaborator in a trusted state, along with historical collaboration data, are collected and represented using an attributed control flow graph (ACFG) that captures trust-related semantic information and serves as a reference for comparison with data collected during task execution. At each time slot of task execution, the collaborator's communication and computing resource attributes, as well as task completion effectiveness, are collected in real time and represented with an ACFG to convey their trust-related semantic information. A Siamese model, consisting of two shared-parameter Structure2vec networks, is then employed to learn the deep semantics of each pair of ACFGs and generate their embeddings. Finally, the similarity between the embeddings of each pair of ACFGs is calculated to determine the collaborator's trust value at each time slot. A real system is built using two Dell EMC 5200 servers and a Google Pixel 8 to test the effectiveness of the proposed SRCTE framework. Experimental results demonstrate that SRCTE converges rapidly with only a small amount of data and achieves a high anomaly trust detection rate compared to the baseline algorithm.

A robust secure beamforming scheme for the IRS-aided ISAC with imperfect channel state information (CSI) is investigated in this paper, where a device-to-device (D2D) pair is utilized as a cooperative jammer to interfere with the eavesdropping target. Based on a statistical CSI error model, an optimization problem is formulated to minimize the transmit power by jointly optimizing the transmit beamforming and IRS phase shifts, subject to the constraints on the secrecy rate, the D2D communication rate, and the echo signal-to-noise ratio. To address this non-convex problem, we first utilize the Bernstein-type inequality to convert the robust probabilistic constraints into linear matrix inequality forms. Then, it is decomposed into two subproblems, and an alternating optimization algorithm based on the semi-definite relaxation is developed to solve them iteratively. Numerical results verify the effectiveness and robustness of the proposed scheme for secure ISAC.

Radio Frequency Fingerprinting (RFF) is a critical technology for enhancing physical-layer security by leveraging the unique RF characteristics of hardware, enabling authentication and anti-counterfeiting for wireless communication devices. In recent years, Deep Learning (DL) has been extensively applied in$R$FF, significantly improving identification accuracy and efficiency. However, DL- based RFF methods still encounter challenges regarding robustness, particularly in cross-channel scenarios. To address these challenges, we propose a channel-robust RFF method based on a Multi-Scale Convolutional Attention Network (MSCAN) with Random Overlay Augmentation (ROA). Specifically, MSCAN extracts and fuses features at different scales, allowing it to capture more comprehensive signal characteristics. Additionally, ROA is a combinatorial data augmentation (DA) strategy designed to simulate diverse characteristics of wireless propagation environments, thereby enhancing the adaptability and robustness of RFF in complex channel conditions. Experiments conducted on the ORACLE dataset demonstrate that our proposed method achieves over 92 % accuracy in cross-channel scenarios, outperforming the previously proposed DA strategy. The codes will be published in GitHub11https://github.com/BeechburgPieStar/SDG-for-Robust-SEI

Given the limited onboard resources and operational time constraints, dynamic collaboration among moving intelligent machines, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) through task offloading has become essential for effective task completion. However, the growing offloading complexity and mismatch between task specifics and distributed resources inevitably lead to resource wastage and potential task failures. Furthermore, malicious collaborators may sneak into offloading processes, which undermines collaborative system reliability. To tackle these challenges collectively, a goal-driven trusted task offloading strategy is proposed, which efficiently matches diverse tasks to optimal distributed resources. Specifically, multidimensional goals of complex tasks are modeled as distinct task completion metrics, jointly termed Value of Service (VoS). Moreover, we define task-specific trust as a goal-achieving mechanism that enables the construction of a reliable collaborator group for a given task with diverse VoS. Based on the task-specific trust evaluation of all potential collaborators, the task offloading process is transformed into a trust-guided bipartite graph matching problem. To mitigate the matching complexity in large-scale collaborative systems, decomposed subtasks with similar goals are initially clustered into limited categories and subsequently arranged by priorities. Simulation results show the proposed strategy efficiently selects capable and reliable collaborators who complete tasks as expected in unreliable dynamic environments.

While being able to avoid single point failures, emerging decentralized security techniques are facing new challenges of reliability, robustness, and privacy preservation in blockchain-enabled Internet of Things (IoT) systems. To circumvent these issues, a zero-trust security scheme is proposed through distributed access control, enhanced authentication, dynamic authorization, and privacy preservation enabled by the consortium blockchain. The proposed scheme integrates three key components, i.e., a distributed recommendation mechanism, where multiple authorized nodes are utilized as referrers to efficiently confer their trust on a new public entity for enhanced authentication; an anonymous credential generation strategy, which is developed for the new entity to further protect its privacy from linking attacks; and an adaptive reputation update strategy, which is proposed for evaluating the nodes’ behaviors in the system for accountability and dynamic multiple-level authorization. The proposed scheme is implemented in a Hyperledge Fabric and the results show that it significantly enhances security and protects private information.

Efficient use of scarce communication and computing resources is critical for meeting the diverse requirements of vertical industrial applications in the Industrial Internet of Things (IIoT). However, the randomness of concurrent service arrivals and the diversity of service demands result in dynamic competition for limited resources at a specific time, presenting significant challenges for effective service provisioning in IIoT systems with changing conditions. To address this challenge, this article investigates a collaborative service provisioning scheme that incorporates service urgency and situation-adaptive system goals to meet diverse vertical applications. Specifically, a novel concept, urgency of service, is introduced to characterize the sensitivity of service to limited resources, thereby mitigating resource competition by prioritizing concurrent services. To cope with system uncertainty, we design a situation-adaptive system goal that enables a dynamic tradeoff between service demands and system energy consumption. For this purpose, we develop a comprehensive metric that integrates the Value of Service (VoS) and system energy requirements, termed VoSE, to customize the time-varying system goals. The overall goal is to maximize the long-term VoSE, which is formulated as a mixed-integer nonlinear programming (MINLP) problem. Since it is NP-hard, we decompose it into a service provisioning subproblem (SPP) and a dynamic system goal subproblem (DGP). A reverse auction-based and urgency-driven service provisioning algorithm is first developed to solve the SPP. Furthermore, a dynamic system operation goal determination algorithm based on the VoSE ratio is proposed for the DGP. Extensive simulation results validate the effectiveness of the proposed algorithm in various performance parameters and demonstrate its significant superiority over baseline schemes.

Despite its popularity, federated learning (FL) in heterogeneous networks faces two critical challenges, i.e., the straggler problem due to devices with limited capabilities and low resource utilization rate of the FL server. The straggler problem arises when devices with limited computational capabilities delay the convergence of the global model. On the other hand, the computational resources of the FL server are often underutilized, mainly due to its relatively simple involvement for model aggregation. To tackle the issues in diverse scenarios, we propose a new joint computational resource allocation and layer partitioning (JCRALP) scheme to improve the overall FL performance by leveraging the capabilities and resources of both FL server and all clients. In the scenario where system parameters regarding the computational capabilities of the clients and the task burden can be accurately measured, we propose an optimization-based approach that leverages our proposed multi-step water-level equalization algorithm and the incremental ceiling adjustment algorithm. In the scenario where parameters cannot be measured accurately, we propose a reinforcement learning-based method using a modified twin delayed deep deterministic policy gradient algorithm. Extensive simulation results demonstrate that JCRALP efficiently and effectively mitigates the straggler problem and inclusively enables more client participation in FL. By including more datasets, the global model becomes more representative, while server computational resources are utilized more efficiently, significantly reducing convergence latency.

Secure communication is crucial in many emerging systems enabled by uncrewed aerial vehicle (UAV) communication networks. To protect legitimate communication in a chaotic UAV environment, where both eavesdropping and jamming become straightforward from multiple adversaries with line-of-sight signal propagation, a new reliable and integrated physical-layer security mechanism is proposed in this article for a massive multiple-input-multiple-output (MIMO) UAV system. Particularly, a physical-layer fingerprint, also called a tag, is first embedded into each message for authentication purpose. We then propose to reuse the tag additionally as a reference to encode each message to ensure secrecy for confidentiality enhancement at a low cost. Specifically, we create a new dual-reference symmetric tag generation mechanism by inputting an encoding-insensitive feature of plaintext along with the key into a hash function. At a legitimate receiver, an expected tag, reliable for decoding, can be symmetrically regenerated based on the received ciphertext, and authentication can be performed by comparing the regenerated reference tag to the received tag. However, an illegitimate receiver can only receive the fuzzy tag which can not be used to decode the received message. Additionally, we introduce artificial noise (AN) to degrade eavesdropping to further decrease message leakage. To verify the efficiency of our proposed tag-based encoding (TBE) scheme, we formulate two optimization problems, including ergodic sum secrecy rate maximization and authentication fail probability minimization. The power allocation solutions are derived by difference-of-convex (DC) programming and the Lagrange method, respectively. The simulation results demonstrate the superior performance of the proposed TBE approach compared to the prior AN-aided tag embedding scheme.

To effectively support diverse services and applications, operation of future Wi-Fi networks has to be highly intelligent. AI/ML has been considered an important component of the next-generation Wi-Fi standard (i.e., Wi-Fi 8). In addition, the distributed and relatively stable Wi-Fi operational environment bring more realistic AI/ML applications than other wireless networks. By opportunistically leveraging distributed characteristics of Wi-Fi, this paper focuses on the service-differentiated joint optimization of communication and computing resource allocation with varying privacy sensitivity. By extending multi-agent deep reinforcement learning (MADRL), a new semi-centralized and fully distributed joint resource optimization structure is created. Our purpose is to maximize the service quality of the DRL model for privacy-insensitive users while minimizing private information exchange during the training period of privacy-sensitive users during the DRL interaction process. The proposed approach leverages the varying service requirements of different stations (STAs), facilitates real-time optimization of local communication resource allocation, and enables concurrent decision-making for computing resources. In addition, we explored the heterogeneous differences in channel states between communication nodes and utilized the federated weighting (FedWgt) method to address this issue, further improving the stability of the distributed model in solving the service-differentiated joint optimization problem of resource allocation. Extensive simulation experiments demonstrate that the proposed scheme outperforms baseline methods significantly in terms of throughput, calculation latency, and energy consumption improvement.

By harnessing the delay-Doppler (DD) resource domain, orthogonal time-frequency space (OTFS) substantially improves the communication performance under high-mobility scenarios by maintaining quasi-time-invariant channel characteristics. However, conventional multiple access (MA) techniques fail to efficiently support OTFS in the face of diverse communication requirements. Recently, multi-dimensional MA (MDMA) has emerged as a flexible channel access technique by elastically exploiting multi-domain resources for tailored service provision. Therefore, we conceive an elastic multi-domain resource utilization mechanism for a novel multi-user OTFS-MDMA system by leveraging user-specific channel characteristics across the DD, power, and spatial resource domains. Specifically, we divide all DD resource bins into separate subregions called DD resource slots (RSs), each of which supports a fraction of users, thus reducing the multi-user interference. Then, the most suitable MA, including orthogonal, non-orthogonal, or spatial division MA (OMA/ NOMA/ SDMA), will be selected with each RS based on the interference levels in the power and spatial domains, thus enhancing the spectrum efficiency. Then, we jointly optimize the user assignment, MA scheme selection, and power allocation in all DD RSs to maximize the weighted sum-rate subject to their minimum rate and various practical constraints. Since this results in a non-convex problem, we develop a dynamic programming and monotonic optimization (DPMO) method to find the globally optimal solution in the special case of disregarding rate constraints. Subsequently, we apply a low-complexity algorithm to find sub-optimal solutions in general cases.

In task-oriented semantic communications, the transmitters are designed to deliver task-related semantic information rather than every signal bit to receivers, which alleviates the spectrum pressure by reducing network traffic loads. Effective semantic communications depend on the perfect alignment of shared knowledge between transmitters and receivers, however, the knowledge alignment cannot always be guaranteed in practice. In multi-cell networks, due to heterogeneous transceivers with distinct knowledge bases and limited computation capabilities, and random channel conditions in between, it is challenging for mobile devices (MDs) to access the best small base station (SBS) to perform effective semantic communications and complete requested tasks. To address the knowledge mismatch issue, we propose a novel task-oriented semantic transmission mechanism, leveraging knowledge sharing and bit communications to guarantee the effective target task execution. To maximize the derived semantic-based performance metric, i.e., generalized effective semantic transmission rate of all MDs under the designed mechanism, a mixed integer nonlinear programming problem is formulated to jointly optimize knowledge sharing decisions, semantic extraction ratios, and SBS associations while satisfying the semantic accuracy and delay requirements of target tasks. By decomposing the formulated problem into multiple subproblems equivalently, an optimum algorithm is proposed and another efficient algorithm is further developed using hierarchical class partitioning and monotonic optimization. A variety of simulation results demonstrate the validity and excellent performance of proposed solutions over a wide range of system parameters.

Coordinated beamforming across distributed base stations (BSs) in cell-free wireless infrastructure can efficiently support integrated sensing and communication (ISAC) users by enhancing resource sharing and suppressing interference in the spatial domain. However, intensive coordination among distributed BSs within the ISAC-enabled network poses risks of generating substantial interference to other coexisting networks sharing the same spectrum, while also incurring elevated costs from energy consumption and signaling exchange. To address these challenges, this paper develops an interference-suppressed and cost-efficient cell-free ISAC network, which opportunistically and cooperatively orchestrates distributed radio resources to accommodate the competing demands of sensing and communication (S&C) services. Specifically, we conceive a radiation footprint control mechanism that autonomously suppresses interference across the entire signal propagation space to safeguard other networks without exchanging channel knowledge signaling. Then, we propose joint BS activation and beamforming coordination to dynamically activate appropriate BSs and orchestrate their spatial beams for service provisioning. Building upon this framework, we formulate a cost-efficient utility maximization problem that considers individual S&C demands and location-dependent radiation footprint constraints. Since this results in a non-convex optimization problem, we develop a monotonic optimization embedded branch-and-bound (MO-BRB) algorithm to find the optimal solution. Additionally, we apply a low-complexity iterative method to obtain near-optimal solutions. Finally, simulation results validate the effectiveness of the proposed algorithms.

Semantic Communication (SemCom) has attracted significant attentions due to its potential to enhance communication efficiency and support human-centric services in 6G networks. However, the presence of mismatched background knowledge and dynamic communication channels decreases the performance of SemCom. These issues ultimately lead to a degradation in users’ quality of experience (QoE). To overcome this challenge, a hybrid semantic and bit communication framework is proposed to effectively improve communication performance under mismatched knowledge constraints. Specifically, we design a time division duplex (TDD) SemCom scheme, where the transmitter and the receiver synchronize background knowledge through the uplink transmission to eliminate mismatch constraints. To guide subframe configuration and communication mode selection in the TDD system, a novel QoE model including perceived quality and energy consumption is proposed, and a long-term average QoE maximization problem is further formulated. To solve the proposed NP-hard problem, a joint subframe configuration and communication mode selection algorithm (JSCA) is designed, and the original problem is decomposed into two subproblems. Firstly, the subframe configuration subproblem is transformed into a quasi-concave problem, and the optimal solution is obtained by the bisection method. Secondly, a deep reinforcement learning (DRL)-based approach is designed to select the communication mode for each service. The numerical results validate the effectiveness of JSCA and demonstrate that the proposed hybrid semantic and bit communication scheme can achieve higher QoE compared with fixed schemes, especially in long-term service scenarios.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission, which enables grant-based users to share their spectrum with grant-free (GF) users, becomes an effective solution to the challenge of massive connections. However, this improvement is limited as most of the existing schemes can only admit one GF user. In this paper, we propose a novel NOMA-assisted SGF transmission scheme to support the access of multiple GF users in unmanned aerial vehicle networks. Moreover, in order to further enhance the advantages of the multi-user SGF scheme, two SGF schemes with power matching are devised to further improve the performance by employing the benefits of distributed contention. For ease of performance evaluation, the theoretical expressions of achievable sum rate and average age of information of the three SGF schemes are derived by applying order statistics. We also investigate the high signal-to-noise approximation expressions of sum rate for these schemes to give more insight. Finally, simulation results are provided to demonstrate the performance improvement of three proposed SGF schemes and validate the correctness of the theoretical analysis expressions.

Achieving both a holistic and in-depth understanding of network dynamics through accurate modeling is essential for orchestrating future 6G networks, considering their increasing complexity and service diversity. However, traditional situation-agnostic data collection and network modeling approaches often undermine the efficacy and timeliness of network orchestration in such complex environments. Furthermore, temporal misalignments caused by varying modeling delays across distributed networks further impair centralized decision-making. To address these challenges, this paper proposes a hierarchical digital twin framework with an adaptive layered architecture designed for problem-oriented 6G network modeling and orchestration. At higher layers, we introduce an adaptive attribute selection mechanism that efficiently evaluates network situations and identifies problematic areas. This mechanism prioritizes critical attributes by jointly considering their relevance to current network objectives and modeling complexity. At lower layers, these prioritized attributes and critical users are selectively incorporated into scalable network modeling. More detailed digital twins are then created to deliver targeted solutions for optimizing user association and power allocation. Additionally, we implement a multi-level synchronization mechanism to ensure temporal alignment among the digital twins, thereby enhancing the effectiveness of model-based orchestration. Extensive simulations validate the efficient identification of pressing operational issues and the effective orchestration of complex 6G networks.

Integrated sensing and communication (ISAC) has evolved as a critical paradigm to enhance the dual functions concurrently. However, ISAC may encounter performance limitations, due to undesired channel conditions, small target size, and security threats. In this paper, we investigate intelligent reconfigurable surface (IRS)-aided bi-static ISAC networks, where the IRS is mounted directly on the target surface, and analyze the signal enhancing and suppressing effects of the target-mounted IRS, respectively. First, we maximize the sensing signal-to-noise ratio (SNR) while satisfying the users’ communication requirements by jointly optimizing the transmit beamforming and IRS reflection. To solve this optimization problem, an alternating optimization algorithm is employed to decouple the optimization variables, followed by the application of successive convex approximation and penalty dual decomposition to solve the subproblems. Second, we consider two threatening scenarios where two adversarial base stations (BSs) intend to capture the information reflected by the target. In the first scenario where the adversarial receiving BS attempts to exploit the reflected ISAC signal, we minimize its received power via optimizing the transmit beamforming and the IRS reflection alternately. In the second scenario where the adversarial transmitting BS emits a dedicated signal to detect the target, we focus on optimizing the IRS reflection. Simulation results are presented to show the effectiveness of the proposed schemes.

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times.

Semantic communications, which focus on transmitting the semantic meaning of data, have been proposed as a novel paradigm for achieving efficient and relevant communication. Meanwhile, non-orthogonal multiple access (NOMA) enhances spectral efficiency by allowing multiple users to share the same spectrum. However, semantic communications are unlikely to fully replace conventional bit-level communications in the near future, as the latter remain dominant. Therefore, integrating semantic users into a NOMA network alongside conventional bit-based users becomes a meaningful approach to improve both transmission and spectrum efficiency. Nonetheless, due to the lack of a mathematical model that accurately characterizes the relationship between the performance of semantic transceivers and wireless resource allocation, enhancing performance through resource optimization remains a challenge. Moreover, successive interference cancellation (SIC), a key technique in NOMA, introduces additional complexity in system design and implementation. To address these challenges, this paper first improves the deep semantic communication (DeepSC) transceiver to make it adaptive to varying wireless transmission conditions. Subsequently, a data-driven regression approach is employed to develop a mathematical model that captures the impact of wireless resources on semantic transceiver performance. In parallel, a multi-cluster hybrid NOMA (H-NOMA) framework is proposed, where each cluster consists of one semantic user and one bit-based user, to mitigate the complexity introduced by SIC. A total transmit power minimization problem is then formulated by jointly optimizing the beamforming design, bandwidth allocation, and semantic symbol factor. The formulated problem is non-convex and challenging to solve directly. To tackle this, a closed-form optimal solution for the beamforming vectors is first derived. Then, a block coordinate descent (BCD)-based algorithm is developed to determine the bandwidth allocation, while an exhaustive search method is used to optimize the semantic symbol factor. Simulation results illustrate the advantages of the semantic communication over the conventional bit-level communication and verify the superior performance of the proposed framework compared with existing benchmark schemes.

This paper characterizes the physical layer security (PLS) in a network utilizing massive multiple-input multiple-output (MIMO) for over-the-air computation (AirComp). When the direct links between the access point (AP) and the sensors are blocked, an intelligent reflecting surface (IRS) is employed to establish communication. Furthermore, the AP sends artificial noise (AN) to the eavesdropper to prevent wiretapping. We study the problem of minimizing the mean-square-error (MSE) between the original and intercepted signals subject to the transmit power constraints at the AP and the sensors, as well as how the MSE threshold hinders the eavesdropper under both perfect and imperfect channel state information (CSI). In the case of perfect CSI, obtaining a globally optimal solution for the investigated non-convex problem is challenging due to the optimization variables’ couple nature. Hence, we convert the problem into two sub-problems to obtain locally optimal solutions. One sub-problem can be solved by an exact penalty-based algorithm, while the other has a closed-form solution using the popular majorization-minimization (MM) algorithm. For the imperfect CSI, the robust beamforming optimization problem formulated is still non-convex. To address this, we harness the block coordinate descent (BCD) algorithm for alternately optimizing the variables to solve it. The results of our simulations demonstrate that the superior MSE performance exhibited by the proposed scheme.

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks.

Radio frequency fingerprint (RFF) is a promising solution for realizing secure and efficient device identification. However, the accuracy of currently existing solutions suffer from multipath effects in practical scenarios. In this paper, we provide a robust RFF identification method that leverages channel state information (CSI) feedback to counteract the effect of the channel on the extracted RFF features. A straightforward zero-forcing (ZF) equalization fails to fully decouple RF impairments from the channel, making conventional approaches ineffective. To overcome this challenge, we utilize the potential of multi-antenna and introduce a new device-specific feature called Relative-RFF (R-RFF), which represents the relation between different RF chains in a multi-antenna transmitter. We propose an enhanced ZF post-equalization algorithm to eliminate the multipath channels and preserve the users’ R-RFF to the greatest extent. We evaluate the robustness of R-RFF under various channel conditions and noise levels and the performance of R-RFF in terms of identification accuracy under different channel scenarios. The results show that the proposed R-RFF method can achieve an identification accuracy of 91.2% for 70 devices in tapped delay line channel with a signal-to-noise ratio (SNR) of 30 dB.

Visible light communication (VLC) technology has emerged as a promising solution to address the stringent requirements of indoor industrial communication scenarios, such as the dynamic capacity requirements of smart factory. However, the inevitable deployment of ultra-dense VLC access points introduces new challenges for VLC user equipments, including difficulties related to interference control, resource allocation, and intercell handover. Motivated by these, this article proposes a user-centric networking strategy tailored for indoor VLC systems. The proposed algorithm initiates by tackling system-wide interference mitigation through the use of spectral clustering to partition the network, thereby minimizing intersubnetwork interference. Subsequently, orthogonal subchannel allocation within each subnetwork is employed, along with subchannel multiplexing across subnetworks. Simulations demonstrate the efficacy of our proposed methods, showcasing superior performance in terms of achievable rates compared to benchmarks.

Due to its unique signal propagation environment, the introduction of uncrewed aerial vehicles (UAVs) for 6G communications has brought many new challenges. These new challenges, particularly increased resource constraints and signal interference among UAVs, necessitate optimal UAV deployment through trajectory planning. Unfortunately, existing two-dimensional (2D) UAV trajectory planning techniques with pre-determined heights can hardly meet the demands of ground user equipment (UE) through opportunistic use of limited radio resources. To overcome the related issues, a new multi-UAV three-dimensional (3D) trajectory planning strategy enabled by multi-agent deep reinforcement learning (MADRL) is proposed in this work. First, the position of each UAV at every timeslot can be adaptively adjusted in 3D to boost the agility of on-demand deployment. Furthermore, more comprehensive system performance metrics, including UE coverage rate, downlink sum rate, and energy consumption, are jointly considered as the optimization objectives, formulating a multi-objective optimization problem for multi-UAV 3D trajectory planning. To support the diverse demands of UAVs autonomously, a MADRL algorithm, multi-agent proximal policy optimization (MAPPO), is further developed as the solution. Simulations have been conducted based on practical scenario settings. The results indicate that the improved MAPPO can empower the 3D movements of UAVs based on their local observations while optimizing the system performance.

This paper explores an interesting worker recruitment challenge where the mobile crowd sensing and computing (MCSC) platform hires workers to complete tasks with varying quality requirements and budget limitations, amidst uncertainties in worker participation and local workloads. We propose an innovative hybrid worker recruitment framework that combines offline and online trading modes. The offline mode enables the platform to overbook long-term workers by pre-signing contracts, thereby managing dynamic service supply. This is modeled as a 0-1 integer linear programming (ILP) problem with probabilistic constraints on service quality and budget. To address the uncertainties that may prevent long-term workers from consistently meeting service quality standards, we also introduce an online temporary worker recruitment scheme as a contingency plan. This scheme ensures seamless service provisioning and is likewise formulated as a 0-1 ILP problem. To tackle these problems with NP-hardness, we develop three algorithms, namely,i)exhaustive searching,ii)unique index-based stochastic searching with risk-aware filter constraint,iii)geometric programming-based successive convex algorithm. These algorithms are implemented in a stagewise manner to achieve optimal or near-optimal solutions. Extensive experiments demonstrate our effectiveness in terms of service quality, time efficiency, etc.

Task scheduling as an effective strategy can improve application performance on computing resource-limited devices over distributed networks. However, existing evaluation mechanisms for application completion fail to depict the complexity of diverse applications and time-varying networks, which involve dependencies among tasks, computing resource requirements, multi-dimensional quality of service (QoS) constraints, and limited contact duration among devices. Furthermore, traditional QoS-oriented task scheduling strategies struggle to meet the performance requirements without considering differences in satisfaction and acceptance of the application, leading to application failures and resource wastage. To tackle these issues, a quality of experience (QoE) cost model is designed to evaluate application completion, depicting the relationship among application satisfaction, communications, and computing resources over the time-varying distributed networks. Specifically, considering the sensitivity and preference of QoS, we model the different dimensional QoS degradation cost functions for dependent tasks, which are then integrated into the QoE cost model. Based on the QoE model, the dependent task scheduling problem is formulated as the minimization of overall QoE cost, aiming to improve the application performance over the time-varying distributed networks, which is proven Np-hard. Moreover, a heuristic Hierarchical Multi-queue Task Scheduling (HMTS) algorithm is proposed to address the QoE-oriented task scheduling problem among multiple dependent tasks, which utilizes hierarchical multiple queues to determine the optimal task execution order and location according to different dimensional QoS priorities. Finally, extensive experiments demonstrate that the proposed algorithm can significantly improve the satisfaction of applications.

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, calledtemplates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, wepropose ahybrid graphtaskscheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies.

To facilitate responsive and cost-effective computing service delivery over edge networks, this paper investigates a novel two-stage double auction methodology via discovering an interesting idea of resource overbooking to overcome dynamic and uncertain nature of supply of edge servers (sellers) and demand generated from mobile devices (as buyers). The proposed auction integrates multiple essential goals such as maximizing social welfare as well as accelerating the decision-making process from both short-term and long-term perspectives (e.g., the time required to determine winning seller-buyer pairs), by introducing a stagewise strategy: an overbooking-driven pre-double auction (OPDAuction) for determining long-term cooperations between sellers and buyers before practical resource transactions as Stage I, and a real-time backup double auction (RBDAuction) for quickly coping with residual resource demands during actual transactions. In particular, by embedding a proper overbooking rate, OPDAuction helps with facilitating trading contracts between appropriate sellers and buyers as guidance for future transactions, by allowing the booked resources to exceed theoretical supply. Then, since pre-auctions may cause risks, our RBDAuction adjusts to real-time market changes, further enhancing the overall social welfare. More importantly, we offer an interesting view to show that our proposed two-stage auction can support significant design properties such as truthfulness, individual rationality, and budget balance. Extensive experiments demonstrate that our TwoSAuction achieves up to 76.8% reduction in decision-making time compared to conventional double auctions when considering 150 buyers and 25 sellers, while maintaining superior performance in social welfare and computational scalability over dynamic edge settings.

Hierarchical Federated Learning (HFL) extends conventional Federated Learning (FL) by introducing intermedi ate aggregation layers, enabling distributed learning in geograph ically dispersed environments, particularly relevant for smart IoT systems, such as remote monitoring and battlefield operations, where cellular connectivity is limited. In these scenarios, UAVs serve as mobile aggregators, dynamically connecting terrestrial IoT devices. This paper investigates an HFL architecture with energy-constrained, dynamically deployed UAVs prone to communication disruptions. We propose a novel approach to minimize global training costs by formulating a joint optimization problem that integrates learning configuration, bandwidth allocation, and device-to-UAV association, ensuring timely global aggregation before UAV disconnections and redeployments. The problem accounts for dynamic IoT devices and intermittent UAV con nectivity and is NP-hard. To tackle this, we decompose it into three subproblems: (i) optimizing learning configuration and bandwidth allocation via an augmented Lagrangian to reduce training costs; (ii) introducing a device fitness score based on data heterogeneity (via Kullback-Leibler divergence), device-to UAV proximity, and computational resources, using a TD3-based algorithm for adaptive device-to-UAV assignment; (iii) developing a low-complexity two-stage greedy strategy for UAV redeployment and global aggregator selection, ensuring efficient aggregation despite UAV disconnections. Experiments on diverse real-world datasets validate the approach, demonstrating cost reduction and robust performance under communication disruptions.

Supporting rapidly growing industrial applications with complex heterogeneous service requests exacerbates the radio resource shortage, posing a perpetual challenge for future networks. Emerging beyond-communication technologies for providing concurrent services, such as integrated sensing and communication (ISAC), further complicate resource allocation due to the uneven and non-uniform distribution of heterogeneous service demands as well as the growing network conflict among concurrent services. To tackle these issues, this paper propose an opportunistic spatiotemporal resource reuse scheme that optimally improve resource utilization efficiency by leveraging the disparities in resource utilization capabilities among users across both spatial and temporal domains. To enhance the heterogeneous service provisioning for different users, a Value of Service (VoS) metric, adopted to evaluate resource allocation performance per user per unit space, is optimized through a clustering-based resource-sharing strategy. To reduce mutual interference among users, the base station utilizes a clustering process that considers each user’s physical location and service request. In each cluster, we derive an analytical solution for communication resource allocation and use a many-to-many matching-based algorithm for assigning the sensing subchannels. The numerical simulation results demonstrate enhanced resource utilization efficiency of our proposed scheme compared with other benchmarks.

By extracting radio frequency (RF) fingerprints from received signals, specific emitter identification (SEI) becomes a promising technique for physical layer identification of wireless devices. Recently, channel-robust SEI has attracted increasing attention due to the weak robustness exhibited by deep learning (DL)-based SEI methods in cross-channel conditions. To address these limitations, we propose a novel channel-robust SEI framework based on single-source domain generalization (SDG). Initially, we analyze the weak robustness of existing SEI methods from the perspective of the “shortcut learning” phenomenon in DL. Shortcut learning may lead traditional SEI methods to prioritize easily-mined, yet transient, channel characteristics in signal samples, rather than focusing on the more stable RF fingerprints derived from hardware differences. Next, from the perspective of SDG, we outline the optimization goal to rectify the shortcut learning in SEI. Inspired by this optimization goal, we then propose a channel-robust SEI method. This method consists of feature embedding through a multi-scale convolutional attention network (MSCAN), domain expansion using random overlay augmentation (ROA) to generate multiple virtual domains, and dual alignment strategy based on contrastive learning. Specifically, supervised contrastive learning is implemented for category-wise alignment, while supervised contrastive adversarial learning is utilized for domain-wise alignment. This dual alignment strategy can optimize the MSCAN to learn discriminative and domain-invariant feature representations, thereby enhancing the robustness of SEI. Simulation experiments on the ORACLE dataset and the WiSig dataset have demonstrated the superiority of our method compared to state-of-the-art techniques. The codes can be downloaded from GitHub (https://github.com/BeechburgPieStar/SDG-for-Channel-Robust-SEI).

Despite the advantage of preserving data privacy, federated learning (FL) still suffers from the straggler issue due to the limited computation resources of distributed clients and the unreliable wireless communication environment. By effectively imitating the distributed resources, digital twin (DT) shows great potential in alleviating this issue. In this paper, we leverage DT in the FL framework over non-orthogonal multiple access (NOMA) network to assist FL training process, considering malicious attacks on model updates from clients. A reputation-based client selection scheme is proposed, which accounts for client heterogeneity in multiple aspects and effectively mitigates the risks of poisoning attacks in FL systems. To minimize the total latency and energy consumption in the proposed system, we then formulate a Stackelberg game by considering clients and the server as the leader and the follower, respectively. Specifically, the leader aims to minimize the energy consumption while the objective of the follower is to minimize the total latency during FL training. The Stackelberg equilibrium is achieved to obtain the optimal solutions. We first derive the strategies for the follower-level problem and include them in the leader-level problem which is then solved via problem decomposition. Simulation results verify the superior performance of the proposed scheme.

To achieve sustainable communication and sensing, simultaneous wireless information and power transfer (SWIPT) has been introduced into integrated sensing and communication (ISAC). However, this combination brings significant security challenges due to signal multiplexing and spectrum sharing. In this paper, an active intelligent reflecting surface (IRS) assisted secure integrated sensing and SWIPT system is proposed with the power splitting (PS) model adopted. To maximize the harvested power while satisfying the constraints of sidelobe level ratio and secrecy rate, a problem is formulated to jointly optimize the transmit beamforming, artificial noise (AN) vectors, PS ratios, and amplification factors and phase shifts of active IRS, which is difficult to solve due to the coupled variables. To this end, we decompose it into two sub-problems, and propose two alternating optimization (AO) algorithms to solve them. First, an AO algorithm based on semi-definite relaxation (SDR) is developed. Specifically, we develop a two-layer algorithm to obtain the transmit beamforming matrix, AN covariance matrix and PS ratios, and utilize the penalty-based method to design the coefficients of active IRS. To reduce the complexity caused by the high-dimensional matrix operation of SDR, an AO algorithm based on successive convex approximation (SCA) is proposed, which can approximate the original problem as a sequence of convex counterparts via the first-order Taylor expansion. Simulation results show that the SCA-based AO algorithm can achieve the performance close to that of SDR with lower complexity.

In an integrated sensing and communication (ISAC) system, it is often necessary to sense both the state of individual targets and the overall situation of a group target (GT) simultaneously, but the latter is more challenging due to the limited sensing capabilities and resources of a single base station. Owing to the recent rapid development of artificial intelligence (AI) and unmanned aerial vehicle (UAV) technologies, it is feasible to acquire the high-performance situation awareness of the GT by using AI to process sensing data under the cooperation of multiple UAV aerial base stations. However, due to many force majeure factors, such as power depletion, and disruptive actions, some UAVs may be disabled, which affects the situation awareness of the GT. Therefore, it is important to improve the robustness of the situation awareness. To achieve that, we consider a multi-UAV-enabled integrated sensing, communication and computation (ISCC) network, where more than one UAVs complete the group target sensing task (GTST) cooperatively while providing communication services for users. Furthermore, we deploy a pre-trained AI model to process the sensing data for improving the performance of the GTST. To enhance the robustness of the GTST, we apply split learning (SL) to divide the inference task of disabled UAVs into multiple neural network (NN) blocks that are cooperatively inferred by working UAVs. Then, we formulate a GTST completion rate maximization problem in which the trajectory, resource allocation, sensing target scheduling, beamforming, and NN layer split policy are optimized. Due to the non-convexity of the problem, we propose a collaborative multi-agent reinforcement learning scheme. The simulation results show that the proposed scheme can effectively improve the GTST performance while the minimum communication and sensing performance are guaranteed, and its performance is better than that of some benchmark schemes.

Integrated sensing and communication (ISAC) can enhance spectral efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, effective operation of ISAC may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning-aided transceiver design scheme for ISAC in a cluttered environment. In particular, we optimize the transmit waveform and receive filtering to minimize the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio (SINR), while adhering to the constraints of a constant modulus signal and waveform similarity. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to address this non-convex optimization problem with both equality and inequality constraints. To further reduce the computational complexity, we develop two deep unfolding neural networks (NNs), termed ADMM-DL-NET and ADMM-PGD-NET, to handle this problem, which can unfold the underlying ADMM-based iterative algorithm to a lightweight neural network with learnable parameters and eliminate the need for the bisection method by adopting the Uzawa’s method and projected gradient descent, respectively. Simulation results demonstrate that our proposed deep unfolding NNs can achieve comparable performance to the ADMM-based iterative algorithm with significantly reduced complexity, and outperform the unsupervised learning benchmarks in performance and number of learnable parameters.

Modern vehicular networks encounter a multitude of computation-intensive tasks that have unique processing topologies represented by graph structures. The integration of edge computing and vehicular networks has provided a unique platform for handling these tasks at the network edge. However, the complex structure of these tasks makes their scheduling and execution challenging. This paper proposes a Vehicular Computing-assisted Edge Network (VCEN) architecture, where graph tasks are scheduled over a Vehicle-Edge Collaborative Cloud (VECC) for parallel execution. Our goal is to obtain feasible mappings between task components and computing nodes in the VECC while minimizing task execution latency and energy consumption. We show that achieving this goal requires solving an NP-hard optimization problem with complex constraints related to task structure and VECC topology. We then propose a fast and lightweight approach for graph task scheduling over VECC that comprises two key phases. In the former phase, we introduce a preprocessing algorithm that reduces the graph task's dimensionality by merging important components and cutting redundant edges. In the latter phase, we deploy a cost-reduction-preferred mapping algorithm to obtain feasible mappings between task components and VECC. Through simulations, we demonstrate our superior performance in different network settings.

The rapid convergence of wireless infrastructure and vertical applications has brought the growing needs for integrated sensing and communications. Due to competing purposes and limited radio resources, an effectively designed integrated sensing and communication (ISAC) system has to precisely adjust its resource allocation to communication and sensing. To maximize the value of service (VoS) for ISAC operation with varying concurrent demands and resource conditions, a successive resource allocation scheme for multi-user ISAC systems is proposed. Specifically, the bandwidth and power allocation are formulated as a mixed integer optimization problem by considering the varying user requirements and resource availability. To solve this problem, a deep-reinforcement learning (DRL) based adaptive resource allocation algorithm is utilized for successive ISAC operational gain maximization. Simulation results demonstrate the adaptiveness and effectiveness of the proposed resource allocation scheme under dynamic scenarios.

Wideband DOA estimation has been applied in various signal source location scenarios, e.g., in wireless communication systems to improve the capacity of communication. Existing wideband DOA methods often require prior knowledge such as the number of sources as well as pre-estimations. Moreover, they may suffer from model-mismatch problem. In this paper, we employ the manifold separation technique and Jacobi-Anger expansion to allow multi-frequency joint processing of wideband DOA, which alleviates the challenge of model-mismatch and leads to a much higher DOA resolution. The proposed method is further formulated to be a multi-convex rank-minimization problem to facilitate the analysis of the problem and to improve the convergence performance. The superior performance of the proposed multi-frequency joint processing method has been demonstrated by several numerical studies.

Integrated sensing and communication (ISAC), is emerging as a promising technology for future mobile networks. This paper studies the robust secure transmission for intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV)-ISAC networks without eavesdropping channel state information. Particularly, the UAV, as a dual-functional ISAC base station, serves$K$communication users and senses$J$targets with an IRS. Furthermore, an eavesdropper aims at eavesdropping the private information from the UAV to$K$users. Without eavesdropping channel state information, a secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, and the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization, the successive convex approximation and the manifold optimization is proposed to obtain a sub-optimal solution. Simulation results verify the effectiveness of the proposed scheme.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission is a favorable solution to tackle the challenges of massive access in the Internet of Things (IoT), however, only one grant-free user is permitted to access in most of the existing schemes. In this paper, we propose a new NOMA-assisted SGF transmission scheme by artfully employing the benefits of the distributed contention, which can support the access of multiple grant-free (GF) users and hence effectively improve the spectrum efficiency and connectivity of the IoT network. Moreover, we theoretically derive the closed-form expressions of the achievable sum rate and the high signal-to-noise ratio approximation expressions to get some insights. In addition, we also derive the average age-of-information to provide a comprehensive performance evaluation. Finally, simulation results are provided to demonstrate the performance improvement of the new SGF scheme.

Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.

Despite its advantage of preserving data privacy, federated learning (FL) could suffer from the limited computation resources of the distributed clients particularly when they are connected by wireless networks. By imitating the distributed resources effectively, digital twin (DT) shows great potential in eliminating the straggler issue in FL. In this paper, we leverage DT in the FL framework over non-orthogonal multiple access (NOMA) network, where DT deployed at the server can assist FL training process. To minimize the total latency and energy consumption in the proposed system, we formulate a Stackelberg game by considering clients and the server as the leader and the follower, respectively. Specifically, the leader aims to minimize the energy consumption via the optimization of DT mapping data ratio and resource allocation, while the objective of the follower is to minimize the total latency during FL training by optimally allocating DT computation resource. The Stackelberg equilibrium is considered to obtain the optimal solutions. We first derive the closed-form solution for the follower-level problem and include it in the leader-level problem which is then solved through the deep reinforcement learning (DRL) method. Simulation results verify the superior performance of the proposed scheme.

Unmanned aerial vehicle (UAV) assisted wireless communication is essential for the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the design of control information transmission is essential, requiring ultra reliability, low latency, and high security. In this paper, considering both the large-scale path loss and the Nakagami-m small-scale fading, we investigate the secrecy performance of UAV control information transmission in a NOMA ground-air network with an external flying eavesdropper. A spherical secrecy protection zone is set, and the closed-form expressions for average secure BLER and average achievable secrecy throughput are derived. After that, the asymptotic performance in the high SNR regime is analyzed to get more insights. Ultimately, simulation results verify the accuracy of analysis.

Multiple description coding (MDC) is a type of error-resilient source coding that is advantageous for communications over channels with high loss and long delays. While neural network-based MDC promises higher compression efficiency and better semantic awareness, the aspect of semantic awareness is under-investigated. This paper is among the first efforts to study MDCs with competing performance goals known as the distortion-classification tradeoff. A new design concept called semantic polarization is proposed to contradict the traditional philosophy of balanced side encoder designs. We present a simple conceptual model to demonstrate the advantage of polarized MDC in having higher probabilities of satisfying at least one performance goal. We also propose a detailed implementation of polarized MDC based on SRGANs. Experiments on public datasets show that compared with state-of-the-art single description neural image codecs, the proposed MDC has multiple benefits in terms of enlarged rate range, superior semantic protection, and better perception quality, at the cost of slightly reduced but competitive distortion performance.

A critical component for various interactive visual Internet of Things (IoT) applications is to reconstruct 3-D scenes from RGB images, i.e., 3-D visualization. When multiple cameras are involved, the visualization outcome mainly depends on the quality of input images, which carry correlated and complementary visual information from different camera perspectives. One main challenge to improve visualization performance is how to efficiently coordinate multiple cameras under complex environmental conditions. To overcome this challenge, we propose a situation-aware multicamera collaboration scheme based on the maximization of correlated information among different inputs. First, the information gain of a single camera is modeled by quantifying the effect of view direction, resolution and signal-to-noise ratio (SNR) on image quality. A spherical Gaussian is then designed to model the mutual information among neighboring viewpoints and further calculate the total correlated information of the camera group by considering their information redundancy and complementarity. An adaptive coarse-to-fine algorithm is proposed to maximize the correlated information, which achieves effective decision making of optimal multicamera collaboration strategy, including cameras’ location, direction, and focal length configurations. Simulation and realistic experiments demonstrate the accuracy of the correlated information model and the efficacy of the scheme to improve reconstruction quality.

By enabling connectivity and collaboration among moving vehicles, Internet of Vehicles (IoV) is expected to bring dramatically improved road safety and traffic efficiency. With limited onboard resources and real-time operational constraints, achieving these goals through handling time-sensitive IoV services and tasks inevitably relies on rapid and reliable collaboration among moving vehicles. Due to safety-related considerations, such collaboration always requires complex evaluation of potential collaborative vehicles, resulting in increased latency in time-sensitive IoV task handling. To achieve rapid and reliable IoV collaboration, a comprehensive concept of trust among neighboring vehicles is first conceptualized in this article to maximize Quality of Experience (QoE) by expediting the IoV collaborator selection as well as overall task handling. Specifically, we propose a new concept of indirect trust and the related Rapid and reliable Trust Evaluation (RTE) mechanism by enabling trust transfer from reliable third parties to reduce the trust evaluation latency of potential collaborative peers. Furthermore, capability trust and direct experiential trust are introduced as two additional evaluation factors in RTE to assess the capability and reliability of collaborators and to reduce task computation time. Finally, the different factors of the proposed trust, i.e., indirect trust, direct experiential trust, and capability trust, are integrated and adaptively utilized at different stages of IoV collaboration by a proposed adaptive trust factor aggregation scheme. Simulation results demonstrate that the proposed RTE mechanism achieves higher QoE with reduced task completion latency by swiftly selecting the optimal IoV collaborator compared to existing trust evaluation mechanisms.

Existing physical-layer authentication (PLA) schemes of tag superimposed on message signals (TSM) can achieve high authentication accuracy at the cost of increased latency and reduced communication performance. The schemes of tag superimposed on pilot signals (TSP) achieve desirable communication performance and low latency, but low randomness of the tag results in lower security. To further improve both security and communication performance, we propose a pseudo random watermark hopping-based PLA scheme in this article. The proposed scheme generates a pseudo-random sequence and designs a watermark hopping mechanism, which superimposes a carefully designed tag on the pilot or message signals accordingly. The proposed scheme enhances the security by utilizing the randomness from both tag generation and watermark hopping mechanism. Meanwhile, it decreases the authentication latency and improves the communication performance by superimposing the tag on the pilot signals without the message recovery process before authentication. The theoretical and experimental results demonstrate that the proposed scheme decreases the bit error rate (BER) and outage probability as well as increases the achievable rate of the system compared with the TSM scheme with the same key equivocation. Moreover, the security performance of our scheme is significantly improved compared with both TSM and TSP schemes.

With rapid adoption in vertical industries and further assistance of edge computing, Internet-of-Things (IoT) applications are experiencing phenomenal growth. However, the concurrence of heterogeneous IoT devices, limited system resources, and varying network conditions poses an ultimate challenge to resource scheduling for meeting the increasingly diverse requirements of IoT applications. Most existing resource scheduling techniques are achieved using common performance indicators for all devices as the optimization objective, which may lose effectiveness when dealing with the diverse requirements across heterogeneous IoT devices. Towards this end, we focus on enhancing IoT device-specific Quality of Experience (QoE) through jointly optimizing communication and computation resources. First, a three-layer QoE assessment model is constructed to characterize the general correlation between resource provisioning and device-specific QoE. Then, to maximize the overall QoE amongst IoT devices, a two-stage resource scheduling scheme is proposed to realize the simultaneous optimization of IoT devices and the edge system. Specifically, during stage I, a distributed resource scheduling algorithm with low complexity is designed for each IoT device to optimize the local computing rate by considering its resource-constrained nature. During stage II, a Proximal Policy Optimization (PPO)-based online learning approach is proposed on the edge system to schedule communication bandwidth and optimize computational rate. Finally, extensive experiments demonstrate that our proposal outperforms the existing works from the perspective of QoE performance.

The proliferation of wireless-enabled industrial applications highlights the growing importance of Integrated Sensing and Communication (ISAC) for concurrent provisioning of environment sensing and data transmission capabilities. However, the resource-hungry nature of sensing processes, coupled with competing demands from coexisting users, poses the fundamental challenge of effective and fair resource allocation in multi-user ISAC systems. To address this challenge, we propose a value of service (VoS)-oriented resource allocation scheme for concurrent heterogeneous service provisioning in a multi-user collaborative ISAC system. Specifically, a performance indicator VoS is utilized to guide system-wide effective resource allocation while guaranteeing fairness among all ISAC users. Specifically, we formulate the multi-user resource allocation problem as a bargaining game-based model and tackle it with an iterative algorithm to attain the Nash equilibrium. In each iteration, the allocation of power and bandwidth resources is optimized by solving the Lagrangian dual problem. Numerical simulations are performed under varying resource conditions, service demands, and channel states. The results demonstrate the superiority of the proposed scheme over non-collaborative alternatives and the other two benchmark schemes.

While sensing imagery in space missions has broad applications, the growing image resolution and data volume have caused a major challenge due to limited deep space channel capacities. To address this challenge, semantics-aware image compression becomes a promising direction. This paper is motivated to explore lossy compression at the ultra-low rate regime, which is a deviation from the high-fidelity- oriented tradition. Specifically, we propose an ultra-low rate deep image compression (DIC) codec by synthesizing multiple neural computing techniques such as style generative adversarial network (GAN), inverse GAN mapping, and contrastive disentangled representation learning. In addition, a residual-based progressive encoding framework is proposed to enable smooth transitions from the ultra-low rate regime to near- lossless regime. Experiments on the FFHQ and DOTA dataset demonstrate that compared with existing DICs, the proposed DIC can push the minimum rate boundary by about one order of magnitude while preserving the semantic attributes and maintaining a high perception quality. We further elaborate the design considerations for cross-rate-regime progressive DIC. Our study confirm that a semantically disentangled DIC holds the promise to bridge multiple rate regimes.

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultralow latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next-generation multiple access schemes to facilitate the provisioning of these stringent demands. This inspires the embrace of nonorthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats due to the open nature of the air interface, the broadcast characteristic of radio propagation, and the intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security-aware design; for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

Future wireless networks are expected to support diverse Internet of Things (IoT) applications under dynamic network conditions through effective resource allocation. However, the growing complexity of underlying optimization problems for resource allocation has brought many challenges to traditional centralized network operations due to inherent computational constraints. To overcome these challenges, this paper proposes a Distributed-Optimization with Centralized-Refining (DO-CR) mechanism to achieve more efficient resource allocation by engaging both access point and all devices. Specifically, the new DO-CR mechanism first utilizes the distributed processing capacity of all devices, allowing them to optimize their own resource allocation schemes through a new resource reservation and reporting technique. Then a centralized optimizer generates a graph of resource trading topology based on individual optimization results and achieves the Pareto optimal solution by the graph-based algorithm. This Pareto optimal solution simplifies the overall optimization problem and enables the central optimizer to solve it with smaller feasible regions. The analysis presents that the DO-CR mechanism’s performance is bounded by Pareto optimality as lower limit and global optimality as upper limit. Simulation results demonstrate that the proposed DO-CR mechanism significantly reduces processing time on the centralized optimizer while maintaining near-optimal utility performance compared to conventional optimization methods.

This paper investigates the performance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with Rician fading channels and channel estimation errors. We adopt the two-timescale scheme to design the systems, namely, applying the instantaneous channel state information (CSI) to design the base station (BS) beamforming and leveraging the statistical CSI to design the phase shifts of the STAR-RIS. Specifically, we estimate the overall channels based on the linear minimum mean-squared error (LMMSE) estimator and derive the closed-form expression of the average achievable rate. Based on the derived rate, we analyze the power scaling laws in which the transmit power is respectively reduced inversely proportional to the number of BS antennas and STAR-RIS elements. Besides, we draw insights from the comparison between STAR-RIS and conventional RIS under the same condition and the power scaling laws of STAR-RIS and optimize the phase shifts of the STAR-RIS to maximize the sum rate using an accelerated gradient ascent-based algorithm. Finally, numerical results are provided to validate our theoretical insights. In particular, we also compare the two-timescale scheme with the instantaneous CSI scheme in the simulation. We show that STAR-RIS outperforms conventional RIS, and the two-timescale-based scheme outperforms the instantaneous CSI-based scheme. Furthermore, we draw insight into this phenomenon.

Unmanned aerial vehicle (UAV) assisted wireless communication is a key component of the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the transmission of control information is indispensable, requiring not only ultra reliability and low latency, but also high security. In this paper, we investigate the secrecy performance of the control information in a NOMA ground-air short-packet wireless network with an untrusted internal UAV or an external flying eavesdropper, respectively. Both the large-scale path loss and the Nakagami-m small-scale fading are considered. First, the closed-form expressions of the average secure block error rate (BLER) and the average achievable secrecy throughput in each scenario are derived. Then, the asymptotic performance in the high signal-to-noise ratio (SNR) regime is analyzed to get more insights from both scenarios. Specifically, analytical results show that error floors occur with the increase of SNR. Moreover, a one-dimensional search is applied to maximize the average achievable secrecy throughput by optimizing the blocklength. Simulation results are provided to verify the accuracy of analysis and the effectiveness of optimization.

To effectively meet the diverse Quality of Service (QoS) requirements from proliferating applications, a widely-adopted practical solution in radio access network (RAN) is categorized QoS provisioning, which utilizes virtual networks (i.e., tenants) to support a limited number of service categories. Apparently, one critical issue is RAN resource allocation among coexisting tenants. However, conventional single objective-based approaches cannot ensure fairness among different service categories. Moreover, except for radio resource, computing and storage resources also need to be considered. Besides, appropriate allocation of computing and storage resources could help mitigating backhaul network congestion. Hence, we aim to optimize the key QoS indicators of three main service categories and reduce backhaul bandwidth consumption simultaneously. We formulate the problem of multi-dimensional resource allocation from RAN to tenants as a multi-objective mixed-integer non-linear programming (MINLP) problem, which is challenging to solve directly due to the competing objectives and the mutual-influenced resources. For guaranteeing fairness, this problem is reformulated as a single-objective optimization problem using weighted sum approach. Moreover, a decoupling-based iterative optimization (DBIO) algorithm is proposed to decompose it into three subproblems to solve iteratively. Simulation results demonstrate that DBIO algorithm can achieve superior performance with much less time consumption, compared with three metaheuristic algorithms.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged to support computation-intensive tasks in 6G systems. Since the battery capacity of a UAV is limited, to serve as many users as possible, a joint design on UAV trajectory and offloading strategy with consideration for service fairness is essential to provide energy-efficient computation offloading to the users in UAV-MEC networks. Unfortunately, such a joint decision-making problem is not straightforward due to various task types required from users and various functionalities of different UAVs enabled by different application programs. Considering the above issues, we take energy efficiency and service fairness as the objective, and propose aMulti-AgentEnergy-Efficient jointTrajectory andComputationOffloading (MA-ETCO) scheme. To adapt to dynamic demands of users, we develop an optimization-embedding multi-agent deep reinforcement learning (OMADRL) algorithm. Each UAV autonomously learns the trajectory control decision based on MADRL to adapt to dynamic demands. Then, it will obtain the optimal computation offloading decision by solving a mixed-integer nonlinear programming problem. The computation offloading result, in turn, will be used as an indicator to guide UAVs’ trajectory design. Compared to relying solely on deep reinforcement learning, such an optimization-embedding way reduces action space dimension and improves convergence efficiency.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communications and ultra-high frequency bands are both potential enablers for satisfying extreme performance requirements of future wireless systems. Thanks to low hardware cost and power consumption, holographic metasurface antennas (HMAs) operating at high frequencies have recently emerged as an effective realization of large-scale antenna arrays, leading to greatly enlarged near-field region. In this paper, we investigate a power-efficient HMA-based near-field downlink multiuser system, where three different HMA-based arrays are considered. Specifically, we aim to minimize the total transmit power for each HMA-based array while maintaining the signal to interference plus noise ratio (SINR) constraint of each user by jointly optimizing the digital transmit precoder and the analog HMA weighting matrix. In the special single-user scenario, we validate that the original optimization problem can be decomposed into several independent subproblems each corresponding to a single HMA microstrip, whose optimal solution can be obtained by the successive convex approximation (SCA) based method. It is also revealed that the HMA-based array is capable of achieving near-field beam focusing. In the general multiuser scenario, we develop an efficient SCA-alternating direction method of multipliers (ADMM) based alternating optimization (AO) algorithm to tackle the intractable optimization problem, where the digital precoders and the HMA weighting matrix are iteratively optimized in an alternating manner. Numerical results demonstrate the superior performance of our proposed algorithms over existing benchmark schemes. It is also shown that the HMA-based array attains lower hardware overhead and power consumption as compared to the conventional hybrid array.

Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.

Despite its potential for reducing the detection probability at the warden, the effectiveness of covert communication in practical situations is often hindered by harsh wireless signal propagation environments. Fortunately, intelligent reflecting surface (IRS) can establish programmable wireless channels to tackle this issue. In this paper, we propose two IRS-assisted finite-blocklength covert communication schemes to maximize the effective covert throughput (ECT) with equal and unequal transmit prior probabilities, respectively. First, we analyze the warden’s detection performance with its optimal detection threshold derived, which is the worst situation for the covert transmission. We jointly optimize the transmit power, transmission blocklength, prior transmission probability and IRS’s phase shifts to maximize ECT in the common scenario and packet-generation scenario, respectively, which covers a wide range of practical applications. The designed optimal phase shifts not only maximize the signal-to-noise ratio at the receiver, but also introduce uncertainty to the warden for covertness provisioning. The closed-form expressions of solutions indicate that there exists a non-trivial trade-off between ECT and covertness, and adopting unequal transmit prior probabilities is proved to perform better than its counterpart of equal probabilities. Finally, numerical results demonstrate the superior performance achieved by the proposed covert communication schemes.

In federated learning (FL), distributed clients can collaboratively train a shared global model while retaining their own training data locally. Nevertheless, the performance of FL is often limited by the slow convergence because of poor communications links when FL is deployed over wireless networks. Due to the scarceness of radio resources, it is crucial to select appropriate clients and allocate communication resource accurately for enhancing FL performance. To address these challenges, in this paper, a joint optimization problem of client selection and resource allocation is formulated, aiming to minimize the total time consumption of each round in FL over a non-orthogonal multiple access (NOMA) enabled wireless network. Specifically, considering the staleness of local FL models, we propose an age of update (AoU) based novel client selection scheme. Subsequently, the closed-form expressions for resource allocation are derived by monotonicity analysis and dual decomposition method. In addition, a server-side artificial neural network (ANN) is proposed to predict the FL models of clients who are not selected at each round to further improve FL performance. Finally, extensive simulation results demonstrate the superior performance of the proposed schemes over FL performance, average AoU and total time consumption.

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram’er-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://github.com/chenpengseu/PassiveDOA-ISAC-RIS.git.

Recent advances in distributed machine learning and wireless network technologies are bringing new opportunities for Internet of Things (IoT) systems, where smart devices are often wirelessly connected to collaborate, jointly completing tasks known ascommunication-dependent computing (CDC)tasks. However, due to the dependence of computing on communication and the presence of concurrent tasks, it remains a challenge to optimize CDC task performance and efficiency while fulfilling multi-dimensional requirements, particularly with incomplete system information and dynamic environmental impacts. To overcome these, we present a concurrent CDC task framework to model the correlated communication and computing stages and multi-dimensional requirements of CDC tasks. We then formulate a task orchestration and resource management problem to optimize overall utility, where each task's utility is designed as a joint metric including the cumulative computing deviation and time efficiency of task completion. To solve this, we employ auxiliary graphs to capture the topological information of tasks and resources, and update weights based on the utility in dynamic environments. Subsequently, a multi-agent reinforcement learning algorithm is leveraged to make distributed decisions with incomplete information. Experiments demonstrate the proposed approach outperforms baselines in terms of task performance and efficiency, indicating our solution holds great potential.

Accurate analysis of traffic flow (TF) data is crucial for the vehicular applications. Conventional deep learning models require task-specific training and are susceptible to high-frequency disturbances, degrading the feature representation capability. To overcome these limitations, this paper proposes a Token-based SelfSupervised Network (TSSN) that can learn TF features in both tokenization and task-agnostic manners. It provides a properly bootstrapped pre-training model for various downstream tasks. In support of the edge computing and vehicular cloud computing, the pooled computational resources facilitate real-time inferences of downstream models. In TSSN, TF data are segmented into tokens. A pretext task, named as Masked Token Prediction (MTP), is then developed to allow TSSN to understand the underlying correlations of TF by predicting randomly masked tokens. By utilizing MTP, TSSN is able to extract the high-level intrinsic semantics of TF, and provide general-purpose token embeddings, leading to improved overall performance and enhanced ability to adapt to different tasks. By substituting the last fully-connected layers with a group of untrained new layers and fine-tuning using small-scale task-specific data, TSSN can be utilized for a variety of downstream tasks in vehicular applications. Simulation results indicate that the TSSN enhances overall performance in comparison to state-of-the-art models.

Cloud-aidedmobileedgenetworks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore anoverbooking-drivenaforehandcross-layermatching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services; and anonsitecross-layermatching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare.

With the growing scale and intrinsic heterogeneity of Internet of Things (IoT) systems, distributed device collaboration becomes essential for effective task completion by dynamically utilizing limited communication and computing resources. However, the separated design and situation-agnostic operation of computing, communication and application layers create a fundamental challenge for rapid task-resource matching, which further deteriorate the overall task completion effectiveness. To overcome this challenge, we utilize hypergraph as a new tool to vertically unify computing, communication, and task aspects of IoT systems for an effective matching by accurately capturing the relationships between tasks and communication and computing resources. Specifically, a state-of-the-art task-resource matching hypergraph (TRM-hypergraph) model is proposed in this paper, which is used to effectively transform the process of allocating complex heterogeneous resources to convoluted tasks into a hypergraph matching problem. Taking into account computational complexity and storage, a game-theoretic hypergraph matching algorithm is proposed via considering the hypergraph matching problem as a non-cooperative multi-player clustering game. Numerical results demonstrate that the proposed TRM hypergraph model achieves superior performance in matching of tasks and resources compared with comparison algorithms.

Given the exponential surge in data traffic and the proliferation of connected smart devices, traditional radio frequency (RF)-based wireless communication systems have to confront mounting challenges of spectrum scarcity and access congestion, particularly for networks operated in low-frequency bands. Visible light communication (VLC) technology has emerged as a promising solution, but it has own limitations, including coverage constraints and limited uplink capability, necessitating hybrid systems that leverage VLC and RF. This paper focuses on an indoor hybrid VLC-RF system extending VLC to Wi-Fi’s public spectrum, enabling VLC’s uplink via RF while enhancing system capacity. Yet, integrating VLC-RF with Wi-Fi introduces new challenges due to the coexistence of VLC-RF with existing Wi-Fi systems. To address these challenges, we propose an intelligent coexistence approach, dynamically adjusts duty cycles to ensure fairness and performance optimization between VLC-RF and Wi-Fi. Moreover, a spectrum multiplexing algorithm is introduced in the coexistence approach to enable the hybrid VLC-RF system’s multiplexing transmission on public spectrum, while preserving Wi-Fi system transmission integrity without interference, thereby further optimizing resource utilization. Extensive simulations on a meticulously constructed system-level platform validate our approach, showcasing its efficacy in enhancing system performance while maintaining equitable transmission between hybrid VLC-RF and Wi-Fi systems.

By opportunistically engaging mobile users (workers), mobile crowdsensing (MCS) networks have emerged as important approach to facilitate sharing of sensed/gathered data of heterogeneous mobile devices. To assign tasks among workers and ensure low overheads, we introduce a series of stable matching mechanisms, which are integrated into a novel hybrid service trading paradigm consisting offutures tradingandspot tradingmodes, to ensure seamless MCS service provisioning. In futures trading, we determine a set of long-term workers for each task through anoverbooking-enabledin-advancemany-to-manymatching (OIA3M) mechanism, while characterizing the associated risks under statistical analysis. In spot trading, we investigate the impact of fluctuations in long-term workers' resources on the violation of service quality requirements of tasks, and formalize a spot trading mode for tasks with violated service quality requirements under practical budget constraints, where the task-worker mapping is carried out viaonsitemany-to-manymatching (O3M) andonsitemany-to-onematching (OMOM). We theoretically show that our proposed matching mechanisms satisfy stability, individual rationality, fairness, and computational efficiency. Comprehensive evaluations confirm the satisfaction of these properties in practical network settings and demonstrate our commendable performance in terms of service quality, running time, and decision-making overheads, e.g., delay and energy consumption.

In this paper, we consider deploying multiple unmanned aerial vehicles (UAVs) to provide integrated sensing, communication, and computation (ISCC) services. During serving communication users, each UAV also senses targets and collaborates with the edge server to run a deep neural network (DNN) model to process the obtained sensing data for target classification. Considering that applying the fixed collaborative computation configurations for the UAVs and edge server cannot adapt to various task latency requirements and dynamic network conditions, we propose to adaptively split the DNN into two parts and execute them on the UAV and the edge server separately to realize flexible collaborative computation. We aim to maximize the average sum rate of users by jointly optimizing the user association, target assignment, DNN splitting, transmit beamforming, computation resource allocation, and UAVs’ locations, subject to the latency and accuracy requirements of sensing tasks. We apply alternating optimization algorithm to solve this complicated non-convex optimization problem. Specifically, the problem is decomposed into four subproblems, and the matching-based method, penalty dual decomposition, and successive convex approximation are leveraged to solve them. Finally, simulation results demonstrate the superiority of the proposed adaptive DNN splitting scheme and the effectiveness of the proposed algorithm.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) can provide flexible computing service for terminal-devices (TDs). However, malicious active aerial eavesdroppers can perform air-to-ground eavesdropping and air-to-air attacking, which makes TDs’ tasks offloading computation more vulnerable, posing significantly secure threats to UAV-enabled MEC. To overcome this challenge, we aim to design collaborative communication and computation schemes for the secure UAV-enabled MEC system, where an active aerial eavesdropper is capable of wiretapping the tasks information offloaded from TDs and transmitting attack signals to the legitimate network. The total weighted energy consumption of the system is minimized via optimizing time allocation, transmit power, local and offloading computation bits, as well as UAV trajectory. First, considering the given number of computational tasks of TDs, a block coordinate descent (BCD)-based scheme is proposed to decompose the original multi-variables-coupling and close-form-lacking problem into several tractable subproblems that can be addressed by iterations. Next, considering that there are dynamic and random tasks arriving to TDs’ original tasks, a deep reinforcement learning (DRL)-based scheme is proposed to maintain the stability of tasks, where the solution of computation, communication and trajectory optimization is intelligently obtained by adopting double-deep Q-learning (DDQN). Simulation results demonstrate that the proposed schemes outperform the respective benchmarks for secure UAV-enabled MEC against active aerial eavesdropping.

Given the frequently changing and potentially unreliable environment, the seamless handover authentication is essential to achieve zero-trust Internet of Vehicles (IoV) network with dramatically enhanced communication and transportation safety. The traditional centralized handover authentication schemes may suffer from the excessive latency and situation agnostic limitation, leading to potential interruption of critical services for fast moving vehicles. To overcome the above challenges, this paper proposes a novel decentralized edge collaboration-based handover authentication scheme with the assistance of blockchain for providing continuous protections in zero-trust IoV. A distributed learning process is designed by involving multiple authentication cooperators (ACs) to collect device/location-related features of vehicles at network edge and then to verify their identities. During the movement of vehicles, the access point (AP) could select new ACs by transferring the security information from existing ACs to the new members for seamless handover authentication. A situation-aware AC selection and update algorithm is proposed for maximizing handover authentication accuracy. Moreover, a hierarchical blockchain-assisted security information transfer and reputation management mechanism is designed for reliable collaboration and efficient management in zero-trust IoV. Compared with the existing schemes, our results characterize the outperformance of the proposed scheme in authentication accuracy and time cost of handover.

To address the challenges of device diversity and service heterogeneity in human and machine-type communications, a predominant approach in future networks is to serve users by differentiated quality-of-service (QoS) categories. However, due to exacerbated conflicts among concurrent services for constrained resources, 6G networks call for more inclusive and equitable QoS provisioning strategies. This paper proposes a novel service provisioning framework empowered by demand-aware prioritization mechanism and diversity, equity, inclusion (DEI)-based resource allocation. Particularly, the proposed scheme discerns heterogeneous users’ resource needs by customized utility models according to specific service categories and requirements. By considering demand-aware priorities for individual users, we propose a DEI-based metric evaluated by the weighted mean-variance tradeoff of network-wide user utilities. Our overall objective is to maximize the long-term DEI value in multi-dimensional multiple-access (MDMA) network. To address this NP-hard problem, we design an alternate optimization framework wherein the subchannel and power allocation are solved by matching theory and sequential quadratic programming (SQP) algorithm. Simulations verify the proposed scheme can inclusively support all users of differentiated service categories with higher average utility and smaller inter-user disparity. Furthermore, the DEI method can adaptively accommodate and prioritize diverse QoS demands based on individualized service requirements and dynamic resource conditions.

To achieve accurate awareness of channel condition, the access point (AP) of a Wi-Fi network has to collect channel state information (CSI) from stations (STAs) periodically. However, existing CSI feedback mechanisms in Wi-Fi are situation agnostic, leading to substantial overhead due to the lack of adaptability and intelligence under dynamic and complex environments. To address this challenge, a concurrent dual channel information feedback mechanism with improve situation-awareness is proposed based on need-driven AP-STA coordination, aiming to maintain the accuracy of collected CSI while dramatically reducing the feedback overhead. By analyzing the latency tolerance of the channel information to be gathered, this concurrent dual feedback mechanism consists of both a delayed channel feature information (CFI) feedback by data frame and an immediate CSI feedback via control frame. In the delayed CFI feedback, a STA collaborates with AP and proactively determines when and what content of CFI to be fed back to the AP. Specifically, the CFI represents channel statistical channel features, which are crucial for the AP to learn the evolving channel conditions. Then, CFI is transmitted to the AP by piggybacking it in the uplink data payload at cost of a certain delay. On the other hand, STA can also utilize the existing CSI feedback mechanism for immediate CSI feedback. With the situation-aware CFI updates from both feedbacks, AP can effectively infer the downlink channel pattern and adapt the time-frequency resolution of CSI feedback to reduce the overhead. Accordingly, a deep cooperative multi-agent reinforcement learning algorithm is proposed to enable a closed-loop coordination between STA and AP for feedback. Simulation results confirm the effectiveness of our proposed mechanism.

Location awareness and time consensus, which are two intertwined aspects of distributed systems, have become more important in vertical industrial Internet of Things (IoT) applications. Existing integrated localization and synchronization (ILAS) in a connected system relies on collaborative measurement of time of arrival, as well as exchange of estimated location and clock related states. However, with the growing scale and dynamics of wireless IoT systems, the unselected and excessive information obtained from the collaborating nodes becomes less effective in ILAS. To enhance the performance of ILAS with controlled complexity, we first propose an optimal measurement geometry directed collaborating nodes selection scheme in this paper. Specifically, the optimal measurement geometry evaluated by the dilution of precision is utilized to prioritize the corresponding subset collaborating nodes for the best estimation accuracy with limited complexity. Moreover, to further reduce the computation complexity in increased-scale systems, a sequential state stacking belief propagation algorithm is proposed for the related states estimation, where the matrix inversions and square root calculations reduce to the dimensions of a subset of the overall collaborating states. Numerical simulations demonstrate a significant enhancement in the robustness of the ILAS estimation and reduction in the computational complexity compared to the baseline schemes.

Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client selection policy considering client heterogeneity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.

Owing to the controlling flexibility and cost-effectiveness, fixed-wing unmanned aerial vehicles (UAVs) are expected to serve as flying base stations (BSs) in the air-ground integrated network. By exploiting the mobility of UAVs, controllable coverage can be provided for mobile group users (MGUs) under challenging scenarios or even somewhere without communication infrastructure. However, in such dual mobility scenario where the UAV and MGUs are all moving, both the non-hovering feature of the fixed-wing UAV and the movement of MGUs will exacerbate the dynamic changes of user scheduling, which eventually leads to the degradation of MGUs’ quality-of-service (QoS). In this paper, we propose a fixed-wing UAV-enabled wireless network architecture to provide moving coverage for MGUs. In order to achieve fairness among MGUs, we maximize the minimum average throughput between all users by jointly optimizing the user scheduling, resource allocation, and UAV trajectory control under the constraints on users’ QoS requirements, communication resources, and UAV trajectory switching. Considering the optimization problem is mixed-integer non-convex, we decompose it into three optimization subproblems. An efficient algorithm is proposed to solve these three subproblems alternately till the convergence is realized. Simulation results demonstrate that the proposed algorithm can significantly improve the minimum average throughput of MGUs.

Owing to the abundance of onboard energy and wide coverage, fixed-wing unmanned aerial vehicles (FW-UAVs) have better capabilities to serve as aerial base stations, thereby extending communication coverage and improving the performance of ground wireless communication networks. Therefore, the FW-UAV is regarded as one of the essential components of the sixth-generation (6G) communication networks. However, due to its inability to hover, a single FW-UAV may only serve a few mobile users (MUs) at a given time which introduces challenges in ensuring uninterrupted service. Additionally, the limited communication resource further impacts the quality of service (QoS). In order to improve the QoS and guarantee the uninterrupted services of the MUs that are located in a wide range, we consider a multi-FW-UAV communication network to maximize the cumulative throughput by optimizing the trajectory, power control, user association, and subcarrier allocation policy jointly. Since the above problem is non-convex, we first decompose the optimization problem into two subproblems i.e., the trajectory optimization subproblem and the power control, user association, and subcarrier allocation policy optimization subproblem. Then, a multi-agent deep reinforcement learning (MA-DRL)-based joint optimization scheme is proposed to optimize the two subproblems jointly. Simulation results demonstrate that the proposed scheme can maximize the cumulative throughput and gain superior performance compared to the benchmark schemes.

Despite its advantage of improving the spectrum and hardware efficiency, integrated sensing and communication (ISAC) system is susceptible to eavesdropping due to the open nature of wireless channels. In this paper, we investigate the secure transmission of ISAC aided by an intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV). Moreover, assuming that an aerial target is a potential eavesdropper, the artificial noise is introduced to disrupt the eavesdropping, while enhancing the sensing signal-to-noise ratio and the users’ quality of service. Aiming to maximize the sum secrecy rate, we jointly optimize the UAV deployment, BS transmit beamforming, artificial noise power and passive beamforming. The formulated non-convex problem is decomposed into three subproblems and solved via an iterative alternating optimization algorithm. Specifically, we introduce auxiliary variables to transform the non-convex subproblems into convex ones. For the UAV deployment solution, it can be obtained by successive convex approximation. With the optimal UAV deployment, the BS transmit beamforming, artificial noise power and passive beamforming can be derived by semi-definite relaxation. Finally, we present simulation results to validate the performance improvement of the proposed scheme on the security of ISAC.

Due to its capability of channel reconfiguration and enhancement, intelligent reflecting surface (IRS) can be introduced to improve the secrecy rate of non-orthogonal multiple access (NOMA) networks. However, the cost and hardware complexity of full-digital beamforming in existing related studies are high, especially for the systems with massive antennas. This paper studies the robust secure transmission for IRS-aided NOMA networks with cost-effective hybrid beamforming. Specifically, we deploy an IRS to assist the secure transmission from a base station with cost-effective hybrid beamforming to a cell-center user (U1) and a cell-edge user (U2), with the existence of a potential eavesdropper. Two schemes are proposed for guaranteeing the secure transmission of U1 with the perfect and imperfect channel state information (CSI), respectively. With the perfect CSI, the secrecy rate of U1 is maximized subject to the constant modulus constraint and the quality of service (QoS) constraint of U2 via optimizing the hybrid beamforming and phase shifts of IRS. With the imperfect CSI, the achievable rate at U1 is maximized, satisfying its worst-case eavesdropping rate constraint, the constant modulus constraint and the QoS constraint of U2. Because of the non-convexity, we first decompose each problem into two subproblems, respectively. Then, the subproblems are solved via the penalty-based algorithm and the successive convex approximation. Simulation results verify that the two proposed schemes have higher energy efficiency and can boost the security of IRS-aided NOMA networks with perfect and imperfect CSI, respectively.

Integrated sensing and communication (ISAC), which can make full use of the wireless platform and the spectrum for concurrent sensing and communication purposes, is emerging as a promising technology for future mobile networks. This paper studies the secure transmission for intelligent reflecting surface (IRS) aided unmanned aerial vehicle (UAV)-ISAC networks. Particularly, the UAV, as a dual-functional ISAC base station, servesKcommunication users and sensesJtargets with the help of an IRS. Furthermore, a potential eavesdropper, whose channel state information is not available, aims at eavesdropping the private information from the UAV toKusers. A secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, as well as the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization (AO), the successive convex approximation (SCA) and the manifold optimization (MO) is proposed to obtain a near-optimal solution. Moreover, we also investigate the energy efficiency maximization problem. We develop another iterative algorithm based on the AO, the SCA, the MO and the Dinkelbach’s algorithm to obtain a near-optimal solution to this non-convex fractional programming problem. The effectiveness of the proposed schemes is verified via simulation results.

Continuous authentication is essential to realize the new zero-trust based security provisioning. Conventional authentication techniques often rely on security keys, credentials, or device fingerprints, which may suffer from either high network overhead or low reliability in highly dynamic environments. To concurrently overcome these challenges, we jointly design the multi-dimensional multiple access and lightweight continuous authentication (MDMA-LCA) to explore multiple domains of the users' access channels for both communication and security enhancement. The access time frame, subchannel, and power allocation of multiple users are formulated as a joint optimization problem to maximize the achievable sum rate (ASR) of the users while continuously authenticating their identities assisted by the non-orthogonal multiple access (NOMA). The proposed scheme achieves lightweight continuous authentication by prearranging the access time sequences of multiple users and by verifying them directly and simultaneously at the base station (BS). Then, the joint optimization problem is decomposed and transferred to a maximum flow problem in a designed graph, and a joint MDMA-LCA algorithm is developed. Simulation results demonstrate that, compared with several existing schemes, the proposed scheme achieves an ASR gain while guaranteeing the continuous authentication of the users.

Internet of Things (IoT) systems have become the critical platform to enable a wide variety of smart applications. During IoT data gathering over wireless network, data may be missing due to the constraints of sensors as well as the reliability of communications. From a graph signal processing perspective, recovery of missing data may be strongly affected by the IoT system topology, which can be characterized by a directed adjacency matrix. To guarantee a robust data gathering, we propose a novel method in this paper to design the optimal topology for IoT networks via Bayesian networks, where the designed directed adjacency matrix is with orthogonal graph frequency components. Moreover, the gathering of IoT data becomes sparser in the graph frequency domain using the designed adjacency matrix and may hence improve the recovery performance of missing data. Experimental results show that our proposed methods outperform several existing algorithms.

Covert communication assisted by intelligent reflecting surface (IRS) has been widely investigated. Specifically, IRS can reconfigure wireless propagation environment to introduce uncertainty to the warden for covertness provisioning. In this paper, we propose an IRS-assisted finite-blocklength covert communication scheme with unequal transmit prior probabilities (UTPP) resulting from random packet generation at the transmitter. First, we analyze the warden's detection performance with its optimal detection threshold derived, which is the worst case for covert transmission. Then, we jointly optimize the transmit power, the blocklength, the phase shifts of IRS, and the transmit prior probabilities to maximize the effective covert throughput (ECT). Theoretical analysis reveal that UTPP can perform better tradeoff between ECT and covertness than equal transmit prior probabilities. Finally, numerical results demonstrate the superiority of the proposed covert communication scheme with UTPP.

The secure transmission of integrated sensing and communication signals aided by intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) is investigated in this paper, with another aerial target as a potential eavesdropper. Moreover, artificial noise (AN) is introduced to disrupt the eavesdropping, while enhancing the sensing signal-to-noise ratio. To maximize the sum secrecy rate, we jointly optimize the active and passive beamformings, AN power and UAV deployment. The formulated non-convex problem is decomposed into three subproblems and solved with an efficient algorithm iteratively. Specifically, we first introduce auxiliary variables to transform the non-convex subproblems into convex ones. Then, the UAV deployment and active and passive beamformings can be derived by successive convex approximation and semi-definite relaxation, respectively. Finally, we present simulation results to validate the effectiveness of the proposed scheme.

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support the computation-intensive tasks in the Internet of Things (IoT) networks. As for the operation of UAV-assisted MEC, jointly design of the UAV trajectory control and computation offloading strategies becomes the key for achieving high offloading efficiency, which is extremely challenging due to the uncertain and dynamic demands in the network. In this paper, aiming at maximizing the offloading task amount, we propose an Multi-Agent joint TrAjectory and Computation Offloading (MA-TACO) scheme, where all related factors including task type variety, quality of service (QoS) guarantee, and service fairness are taken into account. To facilitate each UAV to obtain the best joint strategy under dynamic network environment, considering the complex decisions with both continuous and discrete variables, we develop an Optimization-oriented Multi-Agent Deep Reinforcement Learning approach (OMADRL), where each UAV could autonomously learn the trajectory decision to adapt to the dynamic demands, and the offloading decision would be made by solving a mixed-integer programming problem based on the observations, which would be utilized to guide the trajectory learning. Comparing with solely relying on learning, such an optimization-oriented way could reduce the action space dimension and make each UAV achieve the best strategy faster. The simulation results indicate the effectiveness of the proposed scheme.

Radio frequency fingerprinting (RFF) is a promising solution for realizing secure and efficient device authentication. The multipath channel overshadows and disrupts the RFF extraction, which causes difficulties in training new models in the presence of fading. Existing approaches attempt to deal with this challenge by traversing channels through simulated channel models. However, this solution requires a large amount of data for training and it is difficult to guarantee that the training covers all possible channels. To mitigate the multipath channel effect on RFF with less training data, we propose a new method in a multi-antenna system, named Relative-RFF (R-RFF), which utilizes channel state information (CSI) feedback to counteract the multipath channel. The RFF imperfection relation between the different antenna chains of the device is proved to be retained after the counteraction of the multipath channel. Numerical results demonstrate that the proposed R-RFF can achieve an identification accuracy of 95.9% for 30 UEs in Tapped Delay Line channel with a signal-to-noise ratio of 20 dB.

In this paper, we propose a secure transmission scheme for aerial intelligent reflecting surface (IRS) assisted wireless networks. A multi-antenna access point (AP) serves multiple legitimate users in the presence of multiple eavesdroppers, whose precise positions are unknown. An IRS is carried by the unmanned aerial vehicle (UAV) to help establish virtual line-of-sight links between the AP and ground users, as well as ensuring the secure transmission. The hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS are jointly optimized to maximize the worst-case sum secrecy rate, subject to the minimum rate requirement of legitimate users. The non-convex optimization problem is decomposed into three subproblems, each of which is transformed into a convex one by utilizing successive convex approximation. An alternating optimization algorithm is applied to tackle the subproblems iteratively. Simulation results validate the effectiveness of the proposed scheme and the security enhancement by the joint optimization.

Due to the ability of channel reconfiguration, intelligent reflecting surface (IRS) can be used to boost the secrecy rate of cognitive non-orthogonal multiple access (NOMA) systems. However, the cost and hardware complexity of full-digital beamforming in existing related studies is high, especially for the systems with massive antennas. In this paper, we investigate the secure transmission for IRS-aided cognitive NOMA systems with cost-effective analog beamforming. The secrecy rate of primary user is maximized subject to the quality of service constraint of secondary user via joint analog and passive beamforming optimization. Owing to the non-convexity, we first transform the problem into two subproblems. Then, each subproblem is tackled via the penalty-based algorithm and the successive convex approximation. Simulation results demonstrate that the proposed transmission scheme has higher energy efficiency and can boost the security of IRS-aided cognitive NOMA systems.

This paper aims to address the challenge of wireless authentication without security server support by proposing a novel dynamic encryption scheme based on the shared experiential history at different layers between communicating peers for enhanced peer-to-peer authentication. By utilizing an initial physical layer observation and less noisy cross-layer attributes over time as shared historical secrets and source of dynamicity, an ever growing historical log is established, enabling dynamic encryption key generation for passive authentication. The proposed peer-to-peer authentication scheme is further enhanced by a channel-aware k-means quantization and mapping method which reduces the effect of observation noise from the physical layer attributes while increasing the efficiency and entropy by finding the optimal k-value. Both mathematical analysis and numerical simulation results are provided to evaluate the scheme’s performance.

The recent emergence of unmanned aerial vehicles (UAVs) technology has brought up enormous potential applications. However, operating the underlying UAV networks requires an accurate understanding of the dynamic 3D wireless spectrum conditions in real-time. To achieve this, Opportunistic mobile crowdsensing (OCS) has been considered as a cost-effective solution by recruiting mobile devices for spectrum information gathering. Due to a lack of sensing and transmission coordination, existing OCS approaches cannot gather spectrum information with sufficient quality in the data acquisition process, leading to deteriorated UAV network operation performance. To address this challenge, we propose a novel dynamic scheduling mechanism that maximizes the quality of information (QoI) in the location-aware OCS scheme. By jointly allocating sensing and transmission resources, this approach achieves QoI maximization by reducing the OCS communication and sensing overhead. The problem is formulated as a stochastic network optimization problem and uses the Lyapunov optimization theory for solution development. We propose a decentralized sensing scheduling and a centralized dynamic transmission scheduling mechanism to solve the sub-problems. To evaluate the performance of our proposed solution, we conduct extensive simulations and compare our results against other crowdsensing schemes. The simulation results demonstrate the convergence and performance of the proposed approach and show that it outperforms other crowdsensing schemes. The proposed approach maximizes the QoI by dynamically allocating resources while minimizing the communication and sensing overhead.

Distributed edge clouds (ECs) have been recently shown with remarkable advantages in enhancing customized service provisioning by leveraging user proximity and edge resources. However, operating a massive EC network would inevitably incur a huge amount of energy cost to EC providers, which would offset their operating revenue without proper energy cost management. In this paper, we focus on conserving energy cost of ECs by taking advantage of both electricity price-aware geographical task dispatching and dynamic central processing unit (CPU) provisioning according to the spatiotemporal diversities of electricity prices and user task demands. Due to the significant switching cost of turning CPUs and services on/off, we formulate a multi-timescale energy cost minimization problem that integrates both large-timescale CPU provisioning and service placement, and small-timescale geographical task dispatching and CPU resource allocation. The Lagrange dual decomposition theory is exploited to deal with the spatio-temporal variable couplings. A distributed and online mini-batch learning (MBL) algorithm that relies on parameter approximation for large-timescale decision makings is proposed to learn the optimal Lagrange multipliers. Simulation results show the outstanding performance of the MBL algorithm.

In this paper, we propose a new full-duplex transmission reconfigurable-intelligence-surface (RIS)-assisted vehicular communication framework aimed at enhancing vehicular communications among connected vehicles under congested urban areas. By integrating power-domain non-orthogonal multiple access (P-NOMA) and enabling spectrum reuse between the RIS-assisted vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, the proposed framework facilitates concurrent data transmission to multiple vehicles via a single sub-channel, effectively enhancing data rate performance without requiring additional bandwidth. To further improve the spectrum efficiency, we incorporate NOMA as a cross-tier interference coordination mechanism and employ successive interference cancellation (SIC) for mitigating interference originating from V2V communication, rendering our solution highly suitable for vehicular applications. We derive optimal values for transmit power and NOMA pair coefficients, and employ a successive convex approximation (SCA) technique to optimize RIS phase shifts. The superiority of the proposed approach is evaluated in comparison to the dominant interference scenario and orthogonal multiple access (OMA) technique with respect to the data rate.

Ensuring data privacy and mitigating the potential impact of malicious clients are crucial considerations in split learning frameworks. To improve the security and efficiency of split learning, we propose a novel approach that integrates the hashcash algorithm with a deep neural network based malicious client detection mechanism. Specifically, we first design a supervised deep neural network based algorithm to detect potentially malicious clients by analyzing an array of client attributes, such as IP address, region, and behavioral patterns. This serves as a risk ranking system to evaluate each client, indicating the likelihood of malicious behavior. Subsequently, we design a hashcash based algorithm to verify the clients’ legitimacy and computing capabilities for completing the split learning task, using puzzles with different difficulty levels. By using open source datasets and machine learning models, the simulation results demonstrate the effectiveness of the proposed method in identifying and mitigating the impact of malicious clients while enhancing the overall security and efficiency of split learning scenarios.

While edge collaborations play more and more important roles in the sixth-generation (6G) network, the authentication among devices for trusted collaborations is more challenging. The existing authentication mechanisms may suffer from the long latency and high computation overhead in such application scenarios, especially when the collaboration group is large. In this paper, a lightweight group authentication scheme based on multi-dimensional historical information is proposed and implemented in a specific federated learning-based collaborative outdoor localization scenario. To further illustrate, the multi-dimensional historical information consists of the learning parameters and environment sensing data collected by lidar from the latest round of collaboration. Then, we design a key generation strategy based on the historical information and develop a group authentication protocol. In the proposed scheme, every device in the group can identify the others at once based on their broadcasting keys, which will be renewed automatically before every round of collaboration. Hence, the proposed scheme achieves lightweight group authentication and high security. Both implementation and simulation results demonstrate the validity and superior performance of the proposed scheme compared with the existing schemes.

Due to the flexible mobility and agility, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future air–ground-integrated wireless networks, providing temporary and controllable coverage and additional computation capabilities for ground Internet of Things (IoT) devices with or without infrastructure support. Meanwhile, with the breakthrough of artificial intelligence (AI), more and more AI applications relying on AI methods such as deep neural networks (DNNs) are expected to be applied in various fields, such as smart homes, smart factories, and smart cities, to improve our lifestyles and efficiency dramatically. However, AI applications are generally computation intensive, latency sensitive, and energy consuming, making resource-constrained IoT devices unable to benefit from AI anytime and anywhere. In this article, we study mobile-edge computing (MEC) for AI applications in air–ground-integrated wireless networks. Our goal is to minimize the service latency while ensuring the learning accuracy requirements and energy consumption. To achieve that, we take DNN as the typical AI application and formulate an optimization problem that optimizes the DNN model decision, computation and communication resource allocation, and UAV trajectory control, subject to the energy consumption, latency, computation, and communication resource constraints. Considering the formulated problem is nonconvex, we decompose it into multiple convex subproblems and then alternately solve them till they converge to the desired solution. Simulation results show that the proposed algorithm significantly improves the system performance for AI applications.

Accurate and efficient digital twin construction through real-time multi-attribute sensing and remote concurrent data analysis is essential in supporting complex connected industrial applications. Given the unsynchronized nature and heterogeneous sampling rates of distributed sensing processes, the varying time misalignment among different attributes will inevitably deteriorate the remote correlation analysis and digital twin construction. Furthermore, application-agnostic digital twin construction approaches could potentially involve high communication and computation overhead for comprehensive digital twin construction. In this article, a concurrent end-to-end time synchronization and multi-attribute data resampling scheme is proposed to enable accurate and efficient digital twin construction at the remote end. Specifically, digital clocks are concurrently established at the remote end, with each of them associated with a sampling rate of a unique sensing attribute. To tackle the temporal misalignment among multiple sensing attributes, raw data are accurately resampled according to the same reference frequency, with attribute-specific synchronized digital clocks providing cohesively aligned time information. An edge-centric platform is established to efficiently guide the multidimensional data processing during digital twin construction. Simulation results demonstrate that the proposed scheme can achieve more accurate and efficient digital twin construction than existing modeling methods. In the end, the digital twin-driven predictive maintenance is presented as a case study, aiming at illustrating the potential applications and benefits expected of the proposed scheme in industrial environments.

Radio frequency fingerprint (RFF) identification is an emerging device authentication technique that exploits the hardware imperfections resulting from the manufacturing process. Due to the varying impact of the wireless channel during RFF training and test stages, it is challenging to design channel-independent RFF techniques. This article designs a channel robust RFF identification scheme by leveraging the different spectrum of adjacent signal symbols, named the Difference of the Logarithm of the Spectrum (DoLoS), which does not rely on a single RFF feature or requires additional manipulation of the devices under test. Specifically, DoLoS exploits the fact that two different symbols in a packet exhibit different RFF features but have a similar channel response during the channel coherence time. We implemented the DoLoS with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system as a case study. We carried out extensive experiments using seven Wi-Fi devices of the same model in different wireless channel environments, including 12 data collection positions in two completely different environments. Compared with conventional RFF identification schemes that do not eliminate channel effects, our scheme is robust to channel variations and the highest identification accuracy is 99.02% in the single-environment evaluation and 97.05% in the cross-environment evaluation.

Dynamically associating distributed mobile users with proper base stations in 6G heterogeneous networks (HetNets) becomes critical to achieve both diverse quality of service (QoS) requirements of all users and entire network performance. However, the significantly increased complexity of matching the irregularly distributed users and base stations as well as highly dynamic network traffic often cause unbalanced spatial-temporal loads for multi-tier base stations during user association. To overcome this challenge, we propose a new virtual network topology-based digital twin to reduce the complexity of load-balanced user association in 6G HetNets. During the digital twin construction stage, instead of using highly dynamic low-level physical layer attributes (e.g., channel conditions and SINR), we intentionally consider more stable and relevant communication performance indicators and physical statistics to effectively reflect both real-time link quality and overall network dynamics. To assist overall network operation, fast update of the digital twin for HetNets is achieved by adopting principal component analysis to discover specific network areas with changes. To improve the overall QoS provisioning and network performance, the proposed virtual topology-based digital twin is further utilized to predict the spatial-temporal dynamics of HetNets for more balanced user association by bipartite graph matching. Simulation results show that the proposed method can construct effective digital twins and support load-balanced user association with maximized network-wide QoS satisfaction.

The inherent nonlinear behavior exhibited by power amplifiers (PAs) in saturation mode is a major impediment in wireless systems to achieve higher power efficiency, and higher spectral efficiency. Although PA behavioral modeling and digital predistortion (DPD) techniques are widely utilized at transmitter to characterize and linearize such distorted nonlinear output of PAs, the nonlinear distortions with memory effects have become more severe due to simultaneous strong crosstalk between multiple PA branches in multiple-input multiple-output (MIMO) arrays. Furthermore, current multi-input behavioral models suffer from a sharp increase in number of coefficients as the number of transmitter path increases in MIMO. In overcoming these challenges, we propose a decomposed cross-correlation based single-input-single-output (CC-SISO) behavioral modeling architecture. The proposed solution utilizes a low-cost, novel cross-correlation based method to estimate and cancel the simultaneous nonlinear and reverse crosstalk from multiple PA branches. Once the crosstalk is mitigated, the MIMO DPD can be implemented with single-input DPD blocks which significantly reduces the complexity. Furthermore, CC-SISO DPD eliminates the requirement for signal feedback paths before and after the PA, and thus reduces overall hardware implementation complexity. Through simulations, we demonstrate that the proposed CC-SISO architecture can reduce the overall complexity of state-of-art multi-input DPD models for MIMO systems.

With more computational intensive applications deployed involving mobile edge computing (MEC), the collaboration among mobile devices, edge and cloud servers becomes an effective mechanism to fully utilize all available distributed computing resources. However, two main challenges have yet to be addressed to enable this three-way collaboration for securing necessary computational resources and further guaranteeing the quality of service (QoS) of task handling. The first challenge is related to the partitioning of an application task into several dependent subtasks and schedule them among the collaborating device-edge-cloud (DEC). The second is focused on the allocation of necessary computing resources of device, edge and cloud servers for effective subtask handling. To this end, we study the joint task offloading and resource allocation for DEC collaboration in this paper by formulating a new optimization problem with the objective of minimizing the task handling latency. To solve this problem, we decompose the original problem into two subproblems, which include the first one of calculating the optimal task partitioning ratio by mathematical analytical method, as well as the second on using the Lagrangian dual (LD) method for obtaining the optimal task offloading and resource allocation policy. Finally, we conduct simulation experiments on a real-life dataset obtained from the central business district (CBD) of Melbourne, Australia, and the experimental results validate the efficacy of our approach in minimizing latency.

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated estimations cause significant training overhead and dramatically degrade the system performance, especially for applications with dynamic sensing/communication demands and limited radio resources. In this paper, we leverage the channel aging characteristics to reduce training overhead and to design a situation-dependent channel re-estimation interval optimization-based resource allocation in a multi-target tracking DFRC system. Specifically, we exploit the channel temporal correlation to predict radar and communication channels for reducing the need for training preamble retransmission. Then, we characterize the channel aging effects on the Cramer-Rao lower bounds (CRLBs) for radar tracking performance analysis and achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) transmit beamforming for communication performance analysis. In particular, the aged CRLBs and achievable rates are derived as closed-form expressions with respect to the channel aging time, bandwidth, and power. Based on the analyzed results, we optimize these factors to maximize the average total aged achievable rate subject to individual target tracking precision demand, communication rate requirement, and other practical constraints. Since the formulated problem belongs to a non-convex problem, we develop an efficient one-dimensional search based optimization algorithm to obtain its suboptimal solutions. Finally, simulation results are presented to validate the correctness of the derived theoretical results and the effectiveness of the proposed allocation scheme.

Benefiting from the deployment flexibility and the line-of-sight (LoS) channel conditions, unmanned aerial vehicle (UAV) has gained tremendous attention in data collection for wireless sensor networks. However, the high-quality air-ground channels also pose significant threats to the security of UAV-aided wireless networks. In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the remote ground base station (BS). First, during the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective of maximizing the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate of transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

Simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) can provide expanded coverage compared with the conventional reflection-only RIS. This paper exploits the energy efficient potential of STAR-RIS in a multiple-input and multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) system. Specifically, we mainly focus on energy-efficient resource allocation with MIMO technology in the STAR-RIS assisted NOMA network. To maximize the system energy efficiency, we propose an algorithm to optimize the transmit beamforming and the phases of the low-cost passive elements on the STAR-RIS alternatively until the convergence. Specifically, we first decompose the formulated energy efficiency problem into beamforming and phase shift optimization problems. To efficiently address the non-convex beamforming optimization problem, we exploit signal alignment and zero-forcing precoding methods in each user pair to decompose MIMO-NOMA channels into single-antenna NOMA channels. Then, the Dinkelbach approach and dual decomposition are utilized to optimize the beamforming vectors. In order to solve non-convex phase shift optimization problem, we propose a successive convex approximation (SCA) based method to efficiently obtain the optimized phase shift of STAR-RIS. Simulation results demonstrate that the proposed algorithm with NOMA technology can yield superior energy efficiency performance over the orthogonal multiple access (OMA) scheme and the random phase shift scheme.

Existing device authentication techniques may suffer from heavy communication, computation, and storage overhead for identifying a growing number of devices in collaborations. This paper proposes a novel group authentication (GA) method for decentralized edge collaboration by exploiting the historical collaboration process information, i.e., the distributed learning parameters and results from the previous round of collaboration. Two strategies are developed to generate tokens locally at the edge devices’ side for mutual authentication, named random token generation (R-TG) and privacy-preserving token generation (PP-TG). Specifically, the R-TG strategy randomly selects several historical learning parameters as tokens, while the PP-TG strategy designs a one-way function to defend against privacy leakage by concealing the historical information. A GA protocol is proposed, where each device simultaneously authenticates the others in the same group by repeating the learning process using their tokens. If the process converges to an expected result, all the devices are authenticated as legitimate group members at once. The proposed scheme provides a lightweight flexible solution without pre-generating and distributing any keys/secrets operating on top of a standardized security protocol, and protects the collaboration continuously. The simulation results demonstrate the viability of our scheme and its superior performance compared to several benchmark schemes.

The rapid proliferation of smart devices and Internet of Things (IoT) applications have brought significantly increased demands for concurrent sensing, localization and communication services. To achieve multiple functions concurrently, new unified wireless systems including integrated localization and communication (ILAC) and integrated sensing and communication (ISAC) are facing the fundamental challenge of integrative resource allocation among coexisting functions and services. In addressing this challenge, an ILAC system based on the efficient allocation of the common hardware and radio resource pool for localization and communication is proposed. A novel concept, termed Value of Service (VoS), is coined to maximize the unified performance of ILAC system for diverse service provisioning including localization accuracy and communication data rate. Furthermore, the bandwidth and temporal resource allocation problem is formulated for ILAC to maximize its VoS. Specifically, the problem is treated as a mixed-integer nonlinear problem solved by an iterative joint resource allocation (JRA) strategy. In each iteration, the resource allocation is decomposed into two steps. Firstly, the bandwidth resource is optimized with a Kelly mechanism-based continuous allocation method followed by discretization. Secondly, the temporal resource is assigned with the aid of an adaptive particle swarm optimization (PSO)-based approach. Simulation results demonstrate the significant superiority of our proposed VoS evaluation metric and JRA method in ILAC system under limited resources.

Conventional centralized authentication techniques based on both digital cryptography and physical-layer attributes are prone to single-point failure due to either compromised digital security keys or an abrupt change in the physical communication environment. Although these particular challenges could be mitigated by the joint use of decentralized authentication and physical-layer attributes, such schemes often exhibit unpredictable performance. Simultaneously, the necessary involvement of multiple parties and the imperfect observation of the physical communication environment can also significantly increase the latency and computational complexity. As a remedy, a decentralized authentication scheme is proposed in this paper to achieveGuaranteed Authentication Level at Minimized Complexity(GALAMC) based on the intelligent use of distributed collaboration and available distributive physical-layer attributes. Specifically, we aim for minimizing the complexity of the proposed collaborative authentication process by harnessing the minimum number of collaborative nodes and the selected authentication attributes at each node across the different environments while guaranteeing the required authentication level. The related physical-layer authentication scheme is implemented at each collaborative node where different physical-layer attributes can be selected based on their usefulness which is time-varying. The simulation results demonstrate that our scheme maintains the target level of authentication and it is more immune to sudden environmental changes than the conventional centralized physical-layer authentication scheme. It can also be observed that our proposed scheme can adaptively select the minimum number of collaborative nodes for adaptively minimizing the computational cost.

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

We investigate the performance of massive ultra-reliable and low-latency communications (mURLLC) under massive active users, and non-uniform small-scale and shadow fading in the uplink (UL) of a next-generation multiple access (NGMA) system that integrates massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques. We first derive new closed-form expressions to accurately approximate the probability density function (PDF) and cumulative distribution function (CDF) of the channel gains in MIMO systems under the$\kappa $-$\mu $shadowed fading. Then, we derive the post-processing signal-to-noise ratio (SNR) and its closed-form PDFs and CDFs in the NGMA system, under both perfect and imperfect channel state information of the$\kappa $-$\mu $shadowed fading channel. Given the post-processing SNRs and their PDFs, the general expressions are established for the error probability (EP) to analyze the mURLLC of NGMA by applying finite blocklength information theory. Corroborated by extensive simulations, our analysis reveals that with the increasing reliability requirements of the users, the relative gaps in EPs enlarge between users experiencing different fading channels, and the feasible system configurations (i.e., the transmit powers of the users, and the numbers of antennas, active users, and subcarriers) also increasingly differ between the users. The impact of different fading on mURLLC implementations cannot be overlooked, and the research of mURLLC under the$\kappa $-$\mu $shadowed fading model is indispensable. The NGMA system considered in this paper is capable of achieving mURLLC under non-uniform small-scale and shadow fading.

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes.

Ultra-wideband (UWB) ranging techniques can provide accurate distance measurement under line-of-sight (LOS) conditions. However, various walls and obstacles in indoor non-LOS (NLOS) environments, which obstruct the direct propagation of UWB signals, can generate significant ranging errors. Due to the complex through-wall UWB signal propagation, most conventional studies simplify the ranging error model by assuming that the incidence angle is zero or the relative permittivities for different walls are the same to improve the through-wall UWB localization performance. Considering walls are different in realistic settings, this article presents a through-multiple-wall NLOS mitigation method for UWB indoor positioning. First, spatial geometric equilibrium equations of UWB through-wall propagation and a numerical method are developed for the precise modeling of UWB through-wall ranging errors. Then, calculated error maps are determined numerically without field measurements. Finally, the determined error maps are combined with a gray wolf optimization algorithm for localization. The proposed method is evaluated via field experiments with four rooms, three walls, and six penetration cases. The results demonstrate that the method can strongly mitigate the multi-wall. NLOS effects on the performance of UWB positioning systems. This solution can reduce project costs and number of power supplies for UWB indoor positioning applications.

Mobile collaborative computing (MCC) is an emerging platform for effectively improving the quality of mobile service by exploiting the idling computational resources in distributed mobile devices (MDs) through peer-to-peer task offloading. Recently, diverse MCC applications have been developed to provide multiple functional benefits and individualized value to users. In this paper, we propose to use a new concept of value of service (VoS) to represent the total value of all tasks and devices with respect to their performance including latency and energy consumption. To improve service provisioning under fast-varying conditions, a situation-aware offloading scheme is proposed to maximize VoS by opportunistically leveraging the changing resource availability conditions. Specifically, we consider a collaborative computing system where a user can offload input data of computation to other available MDs. VoS maximization for two popular offloading scenarios, i.e., binary and partial offloading, are formulated separately. Decision making of binary offloading is an NP-hard problem and solved by a novel heuristic algorithm which achieves suboptimal solution in polynomial time. Partial offloading is formulated as a non-convex problem involving task partition decision. By exploiting the unique characteristics of the problem, we propose an adapted barrier method (ABM) which achieves significant improvements in convergence efficiency.

Implementing either Federated learning (FL) or split learning (SL) over clients with limited computation/communication resources faces challenges on achieving delay-efficient model training. To overcome such challenges, we investigate a novel distributedClustering-basedHybrid fEdEratedSplit lEarning (CHEESE) framework, consolidating distributed resources among clients by device-to-device (D2D) communications, working in an intra-serial inter-parallel manner. InCHEESE, each learning client can form a cluster with its neighboring helping clients via D2D communications to train an FL model collaboratively. Inside each cluster, the model is split into multiple segments via a model splitting and allocation (MSA) strategy, while each cluster member trains one segment. After completing intra-cluster training, a transmission client (TC) is determined from each cluster to upload a complete model to the base station for global model aggregation under allocated bandwidth. Accordingly, an overall training delay cost minimization problem is formulated, involving the following subproblems: client clustering, MSA, TC selection, and bandwidth allocation. Due to its NP-Hardness, the problem is decoupled and solved iteratively. The client clustering problem is first transformed into a distributed clustering game based on potential game theory, where each cluster further investigates the remaining three subproblems to evaluate the utility of each clustering strategy. Specifically, a heuristic algorithm is proposed to solve the MSA problem under a given clustering strategy, while a greedy-based convex optimization approach is introduced to solve the joint TC selection and bandwidth allocation problem. Extensive experiments on practical models and datasets demonstrate thatCHEESEcan significantly reduce training delay costs.

This article investigates vehicular cloud (VC)-assisted task scheduling in an air-ground integrated vehicular network (AGVN), where tasks carried by unmanned aerial vehicles (UAVs) and resources of VCs are both modeled as graph structures. We consider a scenario in which resource-limited UAVs carry a set of computation-intensive graph tasks, which are offloaded to resource-abundant vehicles for processing. We formulate an optimization problem to jointly optimize the mapping between task components and vehicles, and transmission powers of UAVs, while addressing the trade-off between i) completion time of tasks, ii) energy consumption of UAVs, and iii) data exchange cost among vehicles. We show that this problem is a mixed-integer non-linear programming, and thus NP-hard. We subsequently reveal that satisfying constraints related to graph task structure requires addressing the non-trivial subgraph isomorphism problem over a dynamic vehicular topology. Accordingly, we propose a decoupling approach by segregating template searching from transmission power allocation, where atemplatedenotes a mapping between task components and vehicles. For template search, we introduce a low-complexity algorithm for isomorphic subgraphs extraction. For power allocation, we develop an algorithm using$p$-norm and convex optimization techniques. Extensive simulations demonstrate that our approach outperforms baseline methods in various network settings.

Unmanned aerial vehicle (UAV) has high flexibility and controllable mobility, therefore it is considered as a promising enabler for future integrated sensing and communication (ISAC). In this paper, we propose a novel adaptable ISAC (AISAC) mechanism in the UAV-empowered system, where the UAV performs sensing on demand during communication and the sensing duration is flexibly configured according to the application requirements rather than keeping the same with the communication duration. Our designed mechanism avoids the excessive sensing and waste of radio resources, therefore improving the resource utilization and system performance. In the UAV-empowered AISAC system, we aim at maximizing the average system throughput by optimizing the communication and sensing beamforming as well as the UAV trajectory while guaranteeing the quality-of-service requirements of communication and sensing. To efficiently solve the considered non-convex optimization problem, we propose an efficient alternating optimization algorithm to alternately optimize the communication and sensing beamforming as well as the UAV trajectory to obtain a suboptimal solution. Numerical results validate the superiority of the proposed adaptable mechanism and the effectiveness of the designed algorithm.

With the rapid development of 5G, massive machine-type communication is expected to experience significant growth, leading to severe random access collisions. To address this issue, we first adopt deep neural networks to detect random access collisions by learning the features of the received signals. Based on the collision-detection results, we propose a double-contention random access (DCRA) scheme, with which the base station can schedule one more contention process for devices experiencing collisions. To fully harness the collision-resolution capability of the proposed DCRA scheme, we further analyze its performance and illustrate how to tune the backoff parameters to optimize the network throughput. It is revealed that the maximum throughput of the DCRA scheme depends on the number of random access preambles and the collision recognition accuracy. The corresponding optimal backoff parameters are then obtained, which greatly facilitates implementations in practice. Simulation results show that with a high collision recognition accuracy, the proposed scheme can achieve significant throughput improvement.

In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the base station (BS). First, for the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective to maximize the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

Combining non-orthogonal multiple access (NO-MA) and unmanned aerial vehicles (UAVs) can achieve better performance for wireless networks. In this paper, we propose an effective scheme for NOMA-UAV network with multiple clusters. Due to the limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the non-convex sub-problems can be transformed into convex ones by successive convex approximation. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

Joint utilization of orthogonal radio resources from multiple domains such as spatial, time-frequency, and delay-doppler domains has become an important paradigm to support diverse QoS requirements (higher datarate, higher spectral efficiency, and low latency) in beyond 5G, and 6G. However, due to higher carrier frequency (mmWave) communication with closely packed massive MIMO antennas, and high-speed mobility in future wireless channels, severe non-orthogonal interferences are dynamically induced in multiple domains which dramatically deteriorate the communication datarate of current OFDM systems. In high speed mobility scenarios, orthogonal time frequency space (OTFS) modulation scheme achieves better communication performance than OFDM at higher modulation cost. Based on these observations, this paper is motivated to propose a novel, situation-aware, cost efficient, switched modulation in spatial, time-frequency, and delay-doppler domains termed hybrid multi-dimensional modulation (H-MDM) scheme that jointly optimizes the radio resource separation to minimize the non-orthogonality degree in each domain, and thus achieves maximized communication datarate under dynamically varying non-orthogonality conditions in those domains. Simulation results validate that the proposed H-MDM achieves maximized datarate compared to state-of-art MIMO-OFDM, and MIMO-OTFS systems under such randomly varying non-orthogonality conditions. Furthermore, we demonstrate that the proposed H-MDM scheme is highly advantageous for high speed mobility, and massive MIMO communication.

Timestamping accuracy is of the utmost importance to achieve accurate time synchronization of large-scale connected systems. However, the heterogeneity and complexity inherent to Internet of Things (IoT) systems lead to multi-source timestamping uncertainties and significantly deteriorate performance of traditional inflexible synchronization methods. In this paper, a situation-aware hybrid time synchronization protocol is designed based on multi-source timestamping uncertainty modeling and integrated time information exchange mechanism for heterogeneous IoT systems. More specifically, the multi-source timestamping error inherent to the overall synchronization process are accurately modeled by exploring the impact of the multi-faceted operating conditions. By analyzing the real-time timestamping uncertainties, a hybrid time synchronization scheme is actualized, which can achieve optimal synchronization strategy for clock parameters estimation. In addition, an integrated time information exchange mechanism is designed to reduce timestamping redundancy during time synchronization. Simulation results show that the proposed scheme can enhance the synchronization accuracy for heterogeneous operating scenarios.

Cooperative task offloading has emerged as a compelling computing paradigm for balancing spatially uneven task workloads and computational resources in distributed mobile edge computing (MEC) systems. However, enabling cooperation among multiple MEC nodes inevitably requires extra communication and computational energy overheads which might counteract the cooperation gain without energy-efficient offloading mechanisms. This paper presents an adaptive cooperative task offloading algorithm aiming at maximizing the time-averaged energy efficiency for small cell MEC networks enabled by millimeter-wave backhauls. With the considered network dynamics, the proposed algorithm makes a good tradeoff between the harvested cooperation utility and the total energy consumption in the long term. In addition, our algorithm ensures the network stability and fulfills the task admission rate requirement of each individual user equipment, by making slot-based decisions over time without requiring a-priori knowledge of the network dynamics. Simulation results verify the outstanding performance of the proposed algorithm by comparing with the static cooperative and adaptive non-cooperative schemes.

Abstract The 5G and beyond (B5G) networks are expected to provide significantly increased capacity for diverse services with limited spectrum resources. However, new aspects of B5G networks particularly the ultra‐dense network deployment and the heterogeneous network structure, make spectrum resources highly distributed in three‐dimension (3D), which brings unprecedented challenges for highly efficient spectrum utilization, especially in an indoor environment. To tackle the challenges on dynamic spectrum utilization and improving the volume capacity of indoor 3D networks, a collaborated crowdsensing approach is proposed for the coordinated 3D spectrum utilization through integrating the crowdsensing, data analytics, and software defined network (SDN) techniques. The integration of sensing, learning, and intelligent control provides critical capabilities for timely observing 3D radio resources and enabling the coordinated radio resource utilization among co‐existing indoor heterogeneous networks (HetNets). Case study confirms the superiority of the proposed coordinating method on achieved volume capacity.

The link flooding attack (LFA) has emerged as a new category of distributed denial of service (DDoS) attacks in recent years. Along with the massive deployment of low-cost insecure Internet-of-Things (IoT) devices, the fast proliferation of IoT botnets dramatically increases the risk of LFAs. However, how to efficiently defend against LFAs in IoT still remains as an open problem. To overcome this challenge, we model the interaction between an LFA attacker and the network manager as a two-person Bayesian game in this article to precisely characterize the behaviors of both sides. Then, the rational behaviors of the attacker and the optimal strategies of the defender are unveiled by deriving the Bayesian Nash equilibrium (BNE). Inspired by the obtained BNEs, a cost-effective decision framework is proposed for the defender to make defense decisions. Furthermore, we numerically analyze the effect of all the related factors and present feasible suggestions to deter attack motivations fundamentally. Experimental results demonstrate that the proposed method not only consistently outperforms baseline methods in terms of the defender’s utilities under different attack intensities, but also is robust to the changes in important parameters, including the value of benign traffic and the latency of traffic scrubbing.

The proliferation of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) not only threatens the security of digital devices and infrastructure but also severely degrades IoT system performance due to the overly consumed network resources. With the knowledge of identity information of devices and signaling data, Internet service providers (ISPs) can detect and block DDoS traffic by monitoring the upstream IoT packets, and thereby, improve network efficiency. However, inspecting all data packets online for DDoS detection will significantly increase both the network delay and the computational overhead. Therefore, the packet sampling strategy is crucial for the defenders to detect DDoS attacks. To this end, this article formulates a Stackelberg game model to analyze the collaborative IoT packet sampling against DDoS attacks. Through the equilibrium analysis of the DDoS game, we derive the lower bound of packet sampling rate (PSR) that can effectively deter potential attackers. Unlike traditional offline detection, our proposed packet sampling strategy can support both the online detection and proactive prevention of DDoS traffic. As a use case, a multipoint DDoS defense framework is developed to address the IP spoofing in 5G networks based on the proposed packet sampling strategy, which deters DDoS attacks and reduces the packet sampling cost, and thereby, maximizes the IoT utility, compared with existing methods. In typical reflection attacks (in which no more than five packets of response are triggered by a request packet), our proposed scheme not only reduces more than 70% of the sampling rate but also demonstrates superior robustness against boundary condition variation.

Spatially beamformed communication, which is achieved by antenna array and multiple-input and multiple-output (MIMO) technologies, has become the most critical technology to drastically increase spectrum utilization rate of 5G. To guarantee the successful deployment of 5G, different aspects of device design, particularly those related to radio-frequency front end and antenna array for enabling beamformed communication, have to be accurately verified. It is more cost effective to identify design imperfections through lab testing rather than using field testing-based trial and error approaches, especially for the explosive growth of 5G-enabled Internet of Things devices. In this article, a directly connected over-the-air (OTA) test solution for 5G MIMO OTA evaluations with the focus on dynamic beamforming devices is proposed. The proposed solution can mathematically achieve constructions among the S ports of base station (BS) and U ports of the receiving antenna array, which makes it possible for measuring the throughput of terminal with an adaptive beamforming array in an OTA way. All system parameters are emulated, including S elements BS antenna gain, U elements of receiving terminal performance, and 3-D$S{\times }U$propagation channel characteristics. To further validate the theoretical analysis and test procedure, a newly developed 4${\times }$4 5G MIMO device is measured in terms of its throughput. The results exactly reflect the true performance of the proposed solution under realistic operational conditions.

With the emergence of Blockchain-based Internet of Things (BIoT) applications, smart contracts have become one of the most appealing aspects because they reduce the cost and complexity of distributed administration. However, the immaturity of smart contracts may result in significant financial losses or the leakage of sensitive information. This article first investigates the taxonomy of security issues associated with smart contracts considering BIoT scenarios. To address these security concerns and overcome the limitations of existing methods, a tree-based machine learning vulnerability detection (TMLVD) method is proposed to perform the vulnerability analysis of smart contracts. TMLVD feeds the intermediate representations of smart contracts derived from abstract syntax trees (AST) into a tree-based training network for building the prediction model. Multidimensional features are captured by this model to identify smart contracts as vulnerable. The detection phase can be implemented quickly with limited computing resources and the accuracy of the detection results is guaranteed. The experimental evaluation demonstrated the effectiveness and efficiency of TMLVD on a data set comprised of Ethereum smart contracts.

The increasingly diversified Quality-of-Service (QoS) requirements envisioned for future wireless networks call for more flexible and inclusive multiple access techniques in 6G for supporting emerging applications and communication scenarios. To achieve this, we propose a multi-dimensional multiple access (MDMA) protocol to meet individual User Equipment’s (UE’s) unique QoS demands while utilizing multi-dimensional radio resources cost-effectively. In detail, the proposed scheme consists of two novel aspects, i.e., selection of a tailored multiple access mode for each UE while considering the UE-specific radio resource utilization cost caused by non-orthogonal interference cancellation; and multi-dimensional radio resource allocation among coexisting UEs under dynamic network conditions. To reduce the UE-specific resource utilization cost, the base station (BS) organizes UEs with disparate multi-domain resource constraints as UE coalition by considering each UE’s specific resource availability, perceived quality, and utilization capability. Each UE within a coalition could utilize its preferred radio resources, which leads to low utilization cost while avoiding resource-sharing conflicts with remaining UEs. Furthermore, to meet UE-specific QoS requirements and varying resource conditions at the UE side, the multi-dimensional radio resource allocation among coexisting UEs is formulated as an optimization problem to maximize the summation of cost-aware utility functions of all UEs. A solution to solve this NP-hard problem with low complexity is developed using the successive convex approximation and the Lagrange dual decomposition methods. The effectiveness of our proposed scheme is validated by numerical simulation and performance comparison with state-of-the-art schemes. In particular, the simulation results demonstrate that our proposed scheme outperforms these benchmark schemes by large margins.

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated. In contrast to the existing ideal STAR-RIS model assuming an independent transmission and reflection phase-shift control, a practical coupled phase-shift model is considered. Then, a joint active and passive beamforming optimization problem is formulated for minimizing the long-term transmission power consumption, subject to the coupled phase-shift constraint and the minimum data rate constraint. Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy. Inspired by this observation, a pair of hybrid reinforcement learning (RL) algorithms, namely the hybrid deep deterministic policy gradient (hybrid DDPG) algorithm and the joint DDPG & deep-Q network (DDPG-DQN) based algorithm are proposed. The hybrid DDPG algorithm controls the associated high-dimensional continuous and discrete actions by relying on the hybrid action mapping. By contrast, the joint DDPG-DQN algorithm constructs two Markov decision processes (MDPs) relying on an inner and an outer environment, thereby amalgamating the two agents to accomplish a joint hybrid control. Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption. Furthermore, both the proposed algorithms outperform the baseline DDPG algorithm, and the joint DDPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.

Combining non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs) could achieve better performance for wireless networks. However, effective resource allocation for quality of service (QoS) provision among all users still remains as a great challenge for multi-cluster NOMA-UAV networks. In this paper, we propose a NOMA-UAV scheme, where a UAV is deployed as the mobile base station to serve ground users. To meet the QoS requirements of all users with limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the power and location optimizations are also non-convex, which can be transformed into convex ones by successive convex approximation. The duration optimization is a linear programming which can be solved directly. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

Unmanned aerial vehicle (UAV) assisted edge caching has been emerged as a promising solution to alleviate network congestion, which can provide users with their desired contents with reduced latency. For achieving effective UAV-assisted edge caching, how to jointly design the trajectory and caching strategy is critical, which, however, is not straightforward due to the heterogeneous and uncertain demand in the network. In this paper, aiming at maximizing the reduced delay brought by the UAV-assisted caching, we propose a proactive joint strategy on trajectory and caching for the UAV, where the demand uncertainty is particularly studied. Specifically, by regarding the demand on each content as a random variable, we formulate the strategy design as a risk-averse stochastic optimization problem to make the network performance guaranteed under certain confidence level. Different from most existing works assuming the perfect distributional information is available to deal with the uncertainty, we develop a data-driven approach based on the first and second order statistics to achieve a distributionally robust (DR) solution, which can make the strategy trustworthy with guaranteed network performance even though the specific distributional information is unknown. Simulation results have demonstrated the effectiveness of the proposed DR strategy.

The papers in this special section focus on artificial intelligence-enabled software defined industrial networks. With the development of intelligent manufacturing, new manufacturing modes such as personalized customization and networked collaboration have been widely developed. These new manufacturing modes require frequent data exchanges between manufacturing machines and industrial information systems through the networks, and dynamically change according to the variations of orders, business, and environments, which cannot be supported in traditional manufacturing modes that focus on local and fixed processes. The current industrial network architecture cannot meet the needs of the aforementioned upcoming manufacturing mode. For example, there are many industrial network protocols, forming a complex industrial heterogeneous network, which seriously affects the interconnections between the underlying devices and the upper layer application systems. In addition, the layering information technology (IT) networks and the operation technology (OT) networks in the factory have hindered the developments of the industrial networks and intelligent manufacturing. There is an urgent need to build a flat, efficient, and flexible industrial network to support the new manufacturing modes.

Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-varying traffic of Vehicle-to-Vehicle (V2V) services and the fast-changing network topology. By leveraging the widely deployed LTE infrastructures, we propose a semi-decentralized network slicing framework in this paper based on the C-V2X Mode-4 standard to provide customized network slices for diverse V2V services. With only the long-term and partial information of vehicular networks, eNodeB (eNB) can infer the underlying network situation and then intelligently adjust the configuration for each slice to ensure the long-term QoS performance. Under the coordination of eNB, each vehicle can autonomously select radio resources for its V2V transmission in a decentralized manner. Specifically, the slicing control at the eNB is realized by a model-free deep reinforcement learning (DRL) algorithm, which is a convergence of Long Short Term Memory (LSTM) and actor-critic DRL. Compared to the existing DRL algorithms, the proposed DRL neither requires any prior knowledge nor assumes any statistical model of vehicular networks. Furthermore, simulation results show the effectiveness of our proposed intelligent network slicing scheme.

Vehicular cloud computing has been emerged as a promising solution to fulfill users’ demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users’ selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers’ utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as a 0-1 integer programming (0-1 IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the abovementioned 0-1 IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the contrast methods considering various problem sizes.

Conventional computing resource trading over mobile networks generally faces many challenges, e.g., excessive decision-making latency, undesired trading failures, and underutilization of dynamic resources, owing to the constraint of wireless networks. To improve resource utilization rate under dynamic network conditions, this paper introduces a novel computing resource provisioning mechanism empowered by overbooking, that allows the amount of booked resources to exceed the resource supply. Cloud-aided mobile edge networks are considered for the proposed framework, where an edge server can purchase more resources from a cloud server to offer computing services to multiple end-users with computation-intensive tasks. Specifically, the proposed mechanism relies on designing pre-signed forward trading contracts among edge and end-users, as well as between edge and cloud in advance to future practical trading; while encouraging an appropriate overbooking rate to improve resource utilization, via analyzing historical statistics associated with uncertainties such as dynamic resource supply/demand, and varying channel qualities. The contract design is formulated as a multi-objective optimization problem that aims to maximize the expected utilities of end-users, edge, and cloud, via evaluating potential risks; for which a two-phase multilateral negotiation scheme is proposed that facilitates the bargaining procedure among the three parties, to reach the final trading consensus (namely, contract terms). Experimental results demonstrate that the proposed mechanism achieves mutually beneficial utilities of three parties, while outperforming baseline methods on significant indicators such as task completion, trading failure, time efficiency, resource usage, etc., from various analytical angles.

Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.

Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futures-based resource trading approach in edge computing-enabled internet of vehicles (EC-IoV), where a forward contract is used to facilitate resource trading-related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties of the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings mutually beneficial utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.

Three dimensional rendering enabled IoT visualization provides an immersive operation view across large physical environments by contextually aggregating and visualizing numerous data streams from various systems. The massive resource demand to support real-time and high-quality rendering services can be fulfilled by collaborative rendering among resource-constrained wireless devices. To deliver reliable performance, one main challenge is to achieve reliable and sustainable collaboration in a dynamic IoT system with heterogeneous resource capacity and changing user intent. To overcome such issues, we propose a situation-aware orchestration mechanism of resource allocation and task scheduling. The proposed technique achieves objective-driven exploration of collaboration opportunity among heterogeneous resource by three steps: recognizing dynamic condition of resource and task, including resource reliability and computational demand; understanding the mutual impact of resource condition and task performance in the aspect of energy consumption and latency; precise alignment of resource capacity and task demands via a redundant task scheduling scheme. The proposed task scheduling problem is formulated as an optimization model with the objective of maximizing collaboration utility. A genetic algorithm (GA) with adaptive mating-distance is designed to tackle the NP-hard problem, which improves the optimal solution in simulation by approximately 25% and 30% compared to conventional GA and Greedy algorithm, respectively.

Due to the proliferation of mobile devices, provisioning of massive connectivity has become a major challenge for future networks. The combination of millimeter wave (mmWave) with non-orthogonal multiple access (NOMA) provides a promising solution to massive connectivity. However, the security issue therein cannot be ignored due to the openness of wireless channels. To overcome the security challenge in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of randomly located eavesdroppers. According to their channel disparity, the NOMA users with stronger channel gains are deemed as SUs for better secrecy performance, while the remaining ones are served as CUs. To further enhance the security, hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs’ requirements. To solve the intractable non-convex problem, we decompose it into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

Securing necessary resources for edge computing processes via effective resource trading becomes a critical technique in supporting computation-intensive mobile applications. Conventional onsite spot trading could facilitate this paradigm with proper incentives, which, however, incurs excessive decision-making latency/energy consumption, and further leads to underutilization of dynamic resources. Motivated by this, a hybrid market unifying futures and spot is proposed to facilitate resource trading among an edge server (seller) and multiple smart devices (buyers) by encouraging some buyers to sign a forward contract with seller in advance, while leaving the remaining buyers to compete for available resources with spot trading. Specifically, overbooking is adopted to achieve substantial utilization and profit advantages owing to dynamic resource demands. By integrating overbooking into futures market, mutually beneficial and risk-tolerable forward contracts with appropriate overbooking rate can be achieved relying on analyzing historical statistics associated with future resource demand and communication quality, which are determined by an alternative optimization-based negotiation scheme. Besides, spot trading problem is studied via considering uniform/differential pricing rules, for which two bilateral negotiation schemes are proposed by addressing both non-convex optimization and knapsack problems. Experimental results demonstrate that the proposed mechanism achieves mutually beneficial player’s utilities, while outperforming baseline methods on critical indicators, e.g., decision-making latency, resource usage, etc.

The integration of intelligent reflecting surface (IRS) and multiple access provides a promising solution to improved coverage and massive connections at low cost. However, securing IRS-aided networks remains a challenge since the potential eavesdropper also has access to an additional IRS reflection link, especially when the eavesdropping channel state information is unknown. In this paper, we propose an IRS-assisted non-orthogonal multiple access (NOMA) scheme to achieve secure communication via artificial jamming, where the multi-antenna base station sends the NOMA and jamming signals together to the legitimate users with the assistance of IRS, in the presence of a passive eavesdropper. The sum rate of legitimate users is maximized by optimizing the transmit beamforming, the jamming vector and the IRS reflecting vector, satisfying the quality of service requirement, the IRS reflecting constraint and the successive interference cancellation (SIC) decoding condition. In addition, the received jamming power is adapted at the highest level at all legitimate users for successful cancellation via SIC. To tackle this non-convex optimization problem, we first decompose it into two subproblems, and then each subproblem is converted into a convex one using successive convex approximation. An alternate optimization algorithm is proposed to solve them iteratively. Numerical results show that the secure transmission in the proposed IRS-NOMA scheme can be effectively guaranteed with the assistance of artificial jamming.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a promising technology that aids in achieving full-space coverage on both sides of the surface, by splitting the incident signal into transmitted and reflected signals. This paper investigates the resource allocation problem in a STAR-RIS-assisted multi-carrier communication networks. To maximize the system sum-rate, a joint optimization problem comprising of the channel assignment, power allocation, and transmission and reflection beamforming at the STAR-RIS for orthogonal multiple access (OMA) is first formulated. To solve this challenging problem, we first propose a channel assignment scheme utilizing matching theory and then invoke the alternating optimization-based method to optimize the resource allocation policy and beamforming vectors iteratively. Furthermore, the sum-rate maximization problem for non-orthogonal multiple access (NOMA) with flexible decoding orders is investigated. To efficiently solve it, we first propose a location-based matching algorithm to determine the sub-channel assignment, where a transmitted user and a reflected user are grouped on a sub-channel. Based on thistransmission-and-reflectionsub-channel assignment strategy, a three-step approach is proposed, which involves the optimization of decoding orders, beamforming-coefficient vectors, and power allocation, by employing semidefinite programming, convex upper bound approximation, and geometry programming, respectively. Numerical results unveil that: 1) For OMA, a general design that includes the same-side user-pairing for channel assignment is preferable, whereas for NOMA, the proposed transmission-and-reflection scheme can achieve comparable performance to the exhaustive search-based algorithm. 2) The STAR-RIS-aided NOMA network significantly outperforms networks employing conventional RISs and OMA.

A communication enabled indoor intelligent robots (IRs) service framework is proposed, where non-orthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state in order to construct the radio map. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent, which can save training time and hardware costs. Build on the proposed communication model, motions of IRs who need to reach designated mission destinations and their corresponding down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to solve this optimization problem, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation results demonstrate in the following: 1) with the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.

Considering the security issue in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of ran-domly located eavesdroppers. For better secrecy performance, the NOMA users with stronger channel gains are deemed as SUs, and the hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs' requirements. The non-convex problem is decomposed into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

Beamforming and non-orthogonal multiple access (NOMA) are two key techniques for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is mounted with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. We aim to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The considered optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in partial offloading pattern, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we propose an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. A resource allocation algorithm for partial offloading pattern is thereby proposed. Simulation results demonstrate that our designed algorithm yields a significant computation performance enhancement as compared to the benchmark schemes.

In this paper, we develop a joint cooperative localization and synchronization scheme for dense mobile wireless sensor networks (WSNs) using a scalable belief propagation (BP) based method. We consider a distributed time-varying WSN with mobile devices, where message packets are propagated among all devices starting from temporal and spatial anchors. To account for the nonlinear system models and to compute the belief at each device while maintaining low communication and computation complexity, we propose an efficient scalable BP scheme, where a temporary posterior belief is calculated and updated sequentially so that the dimension of measurement covariance matrices is fixed instead of the unlimited dimension augmentation and batch computation in sigma point belief propagation (SPBP). Simulation results demonstrate a significant enhancement on the robustness of the algorithm and reduction of the computational complexity compared to the baseline scheme.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrin-sic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.

Accurately localizing a moving target (MT) assisted with 5G in indoor environment could enable a wide variety of new applications. However, an MT in 3-dimensional space is usually considered as a rigid body with six degrees of freedom for industrial applications. Furthermore, the radio-based localization suffers from the non-line-of-sight (NLOS) condition in indoor scenes due to the uncertain environments, which proves to be a main source of location error. To improve the rigid body localization accuracy as well as unravel useful environmental information from the received signals, a novel rigid body localization and environment sensing scheme is proposed in this paper. The angle of arrivals (AOAs) derived from 5G channel estimation combined with singular value decomposition (SVD) method is adopted to achieve rigid body position and orientation estimation. Also, we propose a reflection point estimation method by leveraging a hierarchical iterative maximum likelihood-DCS-SOMP (HIML-DCS-SOMP) algorithm to extract the angular information of the single-bounce specular reflections. Simulation results demonstrate that the proposed scheme can achieve high accuracy rigid body localization and sketch the environment information in indoor scene.

Accurate network synchronization is crucial to orchestrate distributed infrastructures in Industrial Internet of Things (IIoT) systems for accomplishing network-wide tight temporal collaboration. Traditional clock synchronization can be achieved with extensive exchanges of explicit timestamps for estimating clock offsets, which becomes impractical due to high overhead with the expansion of the network scale. The performance of conventional synchronization will also be dramatically deteriorated due to various uncertainties of IIoT networks. In this article, we propose a passive network synchronization scheme based on concurrent passive observations to calibrate the distributed clocks in IIoT systems while significantly reducing the explicit interactions and network resource consumption during synchronization. By processing the physical phenomena observed concurrently by a group of selected IIoT devices, the local clock offsets of the passive observing devices can be efficiently estimated according to the common time reference linked to the event observed. Multiple relay nodes are further coordinated by the cloud center to disseminate the reference time information throughout the IIoT system. Simulation results demonstrate that by utilizing a series of concurrent observations with efficient coordination, the proposed scheme can achieve accurate and reliable network synchronization for large-scale IIoT systems with significantly reduced network overhead.

Tight cooperation among distributively connected equipment and infrastructures of an Industrial-Internet-of-Things (IIoT) system hinges on low latency data exchange and accurate time synchronization within sophisticated networks. However, the temperature-induced clock drift in connected industry facilities constitutes a fundamental challenge for conventional synchronization techniques due to dynamic industrial environments. Furthermore, the variation of packet delivery latency in IIoT networks hinders the reliability of time information exchange, leading to deteriorated clock synchronization performance in terms of synchronization accuracy and network resource consumption. In this article, a digital-twin-enabled model-based scheme is proposed to achieve an intelligent clock synchronization for reducing resource consumption associated with distributed synchronization in fast-changing IIoT environments. By leveraging the digital-twin-enabled clock models at remote locations, required interactions among distributed IIoT facilities to achieve synchronization is dramatically reduced. The virtual clock modeling in advance of the clock calibrations helps to characterize each clock so that its behavior under dynamic operating environments is predictable, which is beneficial to avoiding excessive synchronization-related timestamp exchange. An edge-cloud collaborative architecture is also developed to enhance the overall system efficiency during the development of remote digital-twin models. Simulation results demonstrate that the proposed scheme can create an accurate virtual model remotely for each local clock according to the information gathered. Meanwhile, a significant enhancement on the clock accuracy is accomplished with dramatically reduced communication resource consumption in networks with different packet delay variations.

The detection of anomalous Internet-of-Things (IoT) sensor data is extremely important in many industrial applications due to the catastrophic consequences of the faulty or unreliable sensor data. Good anomalous data detection performance with high detection efficiency is indeed a dilemma since it is difficult to derive an explicit detection function to characterize the relationships between the anomalous values and the detection indicator. To overcome this difficulty, the location information of the IoT sensors is exploited in this study to characterize and reconstruct the relationships among the sensor data based on a second-order nonlinear polynomial graph filter (NPGF). The analysis of the sensor data reconstruction model is then conducted in the frequency domain based on the 2-D inverse graph Fourier transform (GFT), and the reconstruction error function for the sensor data is analytically derived based on the second-order GFT coefficients. It is shown that the detection efficiency can be greatly improved if the input graph signal is designed to be bandlimited. The anomalous sensor data detection is then conducted in the frequency domain as high-frequency components are more sensitive to the deviation values. An NPGF-based frequency-domain algorithm is proposed for the anomalous sensor data detection, which is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the detection performance and efficiency improvement of the proposed algorithm in anomaly detection.

Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.

To meet the ever-increasing communication services with diverse requirements, situation-aware intelligent utilization of multi-dimensional communication resources is becoming essential. In this paper, considering a time-division-duplex downlink cellular scenario, a deep learning-based framework for multi-dimensional intelligent multiple access (MD-IMA) scheme is developed for beyond 5G and 6G wireless networks to meet the real-time and diverse quality of service (QoS) requirements by fully utilizing the available radio resources in heterogeneous domains. To achieve intelligent operation of MD-IMA, the proposed deep learning scheme is achieved based on the convergence of long short term memory (LSTM) and deep reinforcement learning (DRL). Specifically, an LSTM neural network is used to predict the long-term network dynamics and inference changes in QoS requirements of the MD-IMA. Meanwhile, a deterministic policy gradient (DDPG) algorithm, a model-free DRL technique, is adopted to optimize the multi-dimensional radio resource allocation in real-time by dynamically following the fluctuations of the network situation. With the aid of the DDPG algorithm, radio resource management for MD-IMA can be achieved efficiently with reduced processing latency as compared to the conventional model-based approaches. Furthermore, the effectiveness of our proposed deep learning framework for MD-IMA is validated through real-world cellular traffic data-sets. The experimental results demonstrate that the proposed scheme can outperform state-of-the-art algorithms.

Mobile edge computing (MEC) has emerged as one of the key technical aspects of the fifth-generation (5G) networks. The integration of MEC with resource-constrained unmanned aerial vehicles (UAVs) greatly enables flexible resource provisioning for supporting dynamic and computation-intensive UAV applications. Existing resource trading could facilitate this paradigm with proper incentives, which, however, may often incur unexpected negotiation latency and energy consumption, trading failures and unfair pricing, due to the unpredictable nature of the resource trading process. Motivated by these challenges, an efficient futures-enabled resource trading mechanism for edge computing-assisted UAV network is proposed, where a mutually beneficial and risk-tolerable forward contract is devised to promote resource trading between an MEC server (seller) and a UAV (buyer) with multiple tasks. Two key problems i.e. futures contract design before trading, and transmission power optimization during trading are studied. By analyzing historical statistics associated with future resource supply, demand, and air-to-ground communication quality, the contract design is formulated as a multi-objective optimization problem aiming to maximize both the seller’s and the buyer’s expected utilities, while estimating their acceptable risk tolerance. Accordingly, we propose an efficient bilateral negotiation scheme to help players reach a trading consensus on the amount of resources and the relevant price. For the power optimization problem, we develop a practical algorithm that enables the buyer to determine its optimal transmission power via convex optimization techniques. Comprehensive simulations demonstrate that the proposed mechanism offers mutually beneficial utilities to players, while achieving commendable performance on trading failures and fairness, negotiation latency and cost, comparing with baseline methods.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on the intrinsic hardware characteristics of wireless devices. This paper designs a deep learning-based RFFI scheme for Long Range (LoRa) systems. Firstly, the instantaneous carrier frequency offset (CFO) is found to drift, which could result in misclassification and significantly compromise the stability of the deep learning-based RFFI system. CFO compensation is demonstrated to be effective mitigation. Secondly, three signal representations for deep learning-based RFFI are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, fast Fourier transform (FFT) results and spectrograms, respectively. For these signal representations, three deep learning models are implemented, i.e., multilayer perceptron (MLP), long short-term memory (LSTM) network and convolutional neural network (CNN), in order to explore an optimal framework. Finally, a hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy. The CFO will not change dramatically over several continuous days, hence it can be used to correct predictions when the estimated CFO is much different from the reference one. Experimental evaluation is performed in real wireless environments involving 25 LoRa devices and a Universal Software Radio Peripheral (USRP) N210 platform. The spectrogram-CNN model is found to be optimal for classifying LoRa devices which can reach an accuracy of 96.40% with the least complexity and training time.

Conventional authentication techniques based on cryptography and computational hardness are facing growing challenges for deployment in resource-constrained Internet-of-Things (IoT) devices. The dramatically increased security overhead and latency from the inherent computational processing make these conventional static security techniques undesirable for emerging machine communications. In this paper, we propose a novel lightweight continuous authentication scheme for identifying multiple resource-constrained IoT devices via their pre-arranged pseudo-random access time sequences. A transmitter will be authenticated as legitimate if and only if its access time sequential order is matched with a pre-agreed unique pseudo-random binary sequence (PRBS) between itself and the base station. The seed for generating the PRBS between each transceiver pair is acquired by exploiting the channel reciprocity, which is time-varying and difficult for a third party to predict. Hence, the proposed scheme provides seamless protection for legitimate communications by refreshing the seeds adaptively without incurring long latency, complex computation, and high communication overhead. Our results show that the proposed scheme achieves high entropy and low bit mismatch rate. Finally, we demonstrate the superiority of our scheme over the existing schemes in quantization performance, authentication performance, and computation cost.

Network slicing is a key paradigm in 5G and is expected to be inherited in future 6G networks for the concurrent provisioning of diverse quality of service (QoS). Unfortunately, effective slicing of Radio Access Networks (RAN) is still challenging due to time-varying network situations. This paper proposes a new intelligent RAN slicing strategy with two-layered control granularity, which aims at maximizing both the long-term QoS of services and spectrum efficiency (SE) of slices. The proposed method consists of an upper-level controller to ensure the QoS performance, which enforces loose control by performing adaptive slice configuration according to the long-term dynamics of service traffic. The lower-level controller is to improve SE of slices, by tightly scheduling radio resources to users at the small time-scale. To realize the proposed RAN slicing strategy, we propose a model-free deep reinforcement learning (DRL) framework, which is a hierarchical structure that collaboratively integrating the modified deep deterministic policy gradient (DDPG) and double deep-Q-network algorithm. Specifically, the lower-level control problem is a mixed-integer stochastic optimization problem with multiple constraints. This kind of problem is hard to be directly solved by the exiting DRL algorithms, since it involves searching for the solution in a vast set of mixed-integer action space, which will induce unbearable computational complexity. Thus, we propose a novel action space reducing approach, embedding the convex optimization tools into the DDPG algorithm, to speed up the lower-level control. Furthermore, simulation results confirm the effectiveness of our proposed intelligent RAN slicing scheme.

Although the use of coordinated direct and relay transmission (CDRT) in non-orthogonal multiple access (NOMA) can extend the coverage, its duplicated transmission reduces the spectrum efficiency (SE) of NOMA. To improve the SE, we propose a spectrum-efficient scheme for NOMA-based CDRT over Nakagami-m fading channels. In this scheme, the base station (BS) connects with a cell-center user (CCU) directly while communicating with a cell-edge user (CEU) via a relay and the CCU. Then, the relay and the CCU use network coding to process and retransmit the signals sent by the BS first and the CEU later. Finally, the BS and the relay simultaneously broadcast downlink signals. We derive the closed-form expressions for the average SE, the user fairness index and the energy efficiency (EE) as well as the asymptotic average SE using both perfect and imperfect successive interference cancellation (SIC). Simulations verify the correctness of our theoretical analysis and the superiority of the proposed scheme in SE and EE.

The awareness of physical network topology in a large-scale Internet of Things (IoT) system is critical to enable location-based service provisioning and performance optimization. However, due to the dynamics and complexity of IoT networks, it is usually very difficult to discover and update the physical topology of the large-scale IoT systems in real-time. Considering the stringent latency requirements in IoT systems, while the initial processing time for topology discovery can be tolerated, latency due to real-time topology update constitutes an even higher level of challenge. In this paper, a novel fast hierarchical topology update scheme is proposed for the large-scale IoT systems enabled by using the edge-cloud collaborative architecture. Specifically, an event-driven neighbor update algorithm, termed as TriggerOn, is firstly developed to update the local neighbor table of the end devices when device association or disassociation occurs. Based on the updated neighbor tables, the physical topology update of the subnet is conducted at the coordinated edge device, where a hybrid multidimensional scaling (MDS) based 3D localization algorithm is developed to locate the newly associated devices. Simulation results have indicated that as compared to the benchmark methods, the neighbor discovery latency has been reduced dramatically, and the 3D localization accuracy has been improved. Furthermore, the overall latency incurred by the proposed hierarchical physical topology update scheme is significantly lower than the distributed consensus-based update scheme, especially for the large-scale IoT subnets.

The ever-growing wireless applications and their diverse Quality of Service (QoS) requirements bring the challenge of tailored QoS provisioning with limited radio resources in future cellular networks. While resource constraint is ubiquitous, different communication equipment in cellular networks could experience very different constraints in the multi-dimensional resource domains. To achieve stringent yet diverse QoS with limited resources, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is proposed in this paper to exploit disparate resource constraints among heterogeneous equipment for 5G beyond and 6G networks. With the assist of real-time data analysis, real-time QoS requirements, and resource availability of the related equipment are first determined in the proposed MD-IMA. Based on this, multiple access (MA) scheme is then intelligently adapted accordingly for each equipment in multi-dimensional resource domain to maximize the overall system requirement with operational constraints. The resource allocation in the MD-IMA system is further formulated as an optimization problem. To solve this non-convexity optimization of high computational complexity, the overall optimization is divided into several sub-problems and a joint optimization algorithm is adopted. Simulation results demonstrate the system energy efficiency performance gain of proposed MD-IMA over traditional MA is around 15% - 18%.

Time-division-duplexing massive multiple-input multiple-output (MIMO) systems estimate the channel state information (CSI) by leveraging the uplink-downlink channel reciprocity, which is no longer valid when the mismatch arises from the asymmetric uplink and downlink radio frequency (RF) chains. Existing works treat the reciprocity mismatch as constant for simplicity. However, the practical RF chain consists of nonlinear components, which leads to nonlinear reciprocity mismatch. In this work, we examine the impact and the calibration approach of the nonlinear reciprocity mismatch in massive MIMO systems. To evaluate the impact of the nonlinear mismatch, we first derive the closed-form expression of the ergodic achievable rate. Then, we analyze the performance loss caused by the nonlinear mismatch to show that the impact of the mismatch at the base station (BS) side is much larger than that at the user equipment side. Therefore, we propose a calibration method for the BS. During the calibration, polynomial function is applied to approximate the nonlinear mismatch factor, and over-the-air training is employed to estimate the polynomial coefficients. After that, the calibration coefficients are computed by maximizing the downlink achievable rate. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

While Wireless Local Area Network (WLAN) gains growing popularity during the last two decades, it faces several new challenges in meeting the requirements of emerging applications due to spectrum shortage, complicated network management and inefficient radio resource allocation (RRA) in densely deployed scenarios. The operating band has evolved from microwave band (a.k.a. sub-6 GHz) to millimeter-wave band (mmWave for short) or even multi-band to provide higher data rates. However, the existing distributed WLAN architecture works in different bands separately and doesn't support the coordination among access points (APs), making it difficult for efficient network management and improved quality of service (QoS) provisioning. In overcoming these challenges, a centralized control architecture for WLAN with decoupled control and data planes can be used to orchestrate the RRA within the whole network while achieving improved communication performance. Therefore, a new control/data plane decoupled WLAN architecture is designed in this paper to realize efficient network management and optimized RRA by separating the control plane and data plane into sub-6 GHz and mmWave respectively. Then, we propose the sub-6 GHz assisted mmWave beamforming protocol, which can significantly reduce the overhead of beamforming training (BFT). Last, we investigate joint RRA for the control/data plane decoupled WLAN since the control plane affects the RRA in the data plane. Performance analysis and simulation show that the joint RRA mechanism in the control/data plane decoupled network can significantly improve the number of successfully scheduled users and the sum-rate compared with the traditional RRA.

Distributed Reflection Denial of Service (DRDoS) attack has become one of the most serious threats to Internet security. With the ongoing development of 5G, a massive number of insecure Internet of Things (IoT) devices are connected to the Internet, which brings great challenges to defend against DRDoS attacks. To overcome these challenges, we extend the User Plane Function (UPF) of 5G core network, and propose a new framework accordingly for source IP address validation, so as to suppress the source IP address spoofing behaviors of DRDoS attackers. Under this framework, the packet inspection rate (PIR), i.e., the inspection probability of each packet, is crucial to simplify the validation complexity. To unveil the optimal PIR, we establish a two-player game which models the IP address spoofing and detection behaviors. Analysis on the formulated game implies a lower bound of sufficient PIR, which may be used to set PIR in practice. Simulation results show that the proposed method can efficiently deter IP spoofing behaviors. Thereby the derived PIR could achieve low-cost and effective defense of DRDoS.

The link flooding attack (LFA) arises as a new class of Distributed Denial of Service (DDoS) attacks in recent years. By aggregating low-rate protocol-conforming traffic to congest selected links, LFAs can degrade the connectivity of target servers indirectly. Due to the fast proliferation of insecure Internet of Things (IoT) devices, the deployment of botnets is getting easier, which dramatically increases the risk of LFAs. Since the attacking traffic may not reach the victims directly and seems to be legitimate, LFAs are extremely difficult to detect and defend using traditional methods. In this work, we model the interaction between the LFA attacker and the defender as an extensive form game with incomplete information. By using action space compression and the divide and conquer method, we analyze the Nash equilibrium of the subgame on each link, which reveals the rational behaviors of attackers and the optimal strategies of defenders. Furthermore, we concretely expound how to adopt local optimal strategies in the Internet-wide scenario. Experimental results show the effectiveness and robustness of our proposed decision-making method in explicit LFA defending scenarios.

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial.

As a promising solution of spectrum shortage, spectrum sharing has received tremendous interests recently. However, under different sharing policies of different licensees, the shared spectrum is heterogeneous both temporally and spatially, and is usually uncertain due to the unpredictable activities of incumbent users. In this paper, considering the spectrum uncertainty, we propose a spectrum sharing based delay-tolerant traffic off-loading (SDTO) scheme. To capture the available heterogeneous shared bands, we adopt a mesh cognitive radio network and employ the multi-hop transmission mode. To statistically guarantee the end-to-end (E2E) session request under the uncertain spectrum supply, we formulate the SDTO scheme into a stochastic optimization problem, which is transformed into a mixed integer nonlinear programming (MINLP) problem. Then, a coarse-fine search based iterative heuristic algorithm is proposed to solve the MINLP problem. Simulation results demonstrate that the proposed SDTO scheme can well schedule the network resource with an E2E session guarantee.

Detecting the existence of anomaly and localizing the faulty sensors in the Internet-of-Things (IoT) systems are extremely critical, since the incorrect data could lead to catastrophic consequences in many vertical industry applications. The difficulties of such problems come from deriving an explicit error function for each sensor in IoT, and the data continuity in the temporal domain would also be seriously challenged. To overcome these difficulties, the irregular spatial information of the IoT sensors is utilized by constructing an adjacency matrix using the distances among different sensors, and the nonlinear polynomial graph filter (NPGF) is employed to characterize the relationships among the collected sensor data. The NPGF provides a more accurate model for reconstructing the sensor data by taking the data nonlinear relationships into account. The error functions at each sensor for newly detection data are theoretically derived, and it is demonstrated that the error at the anomalous sensor performs differently from that of the normal sensors if the adjacency matrix is designed appropriately. The proposed NPGF-based algorithm is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the superior performance of our scheme in both anomaly detection and faulty sensor localization when compared with existing algorithms, such as the graph frequency algorithm and oversampling PCA (OS-PCA) method, especially for the case of small sensor data deviations.

Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks. Specifically, we firstly formulate the time domain massive MIMO-OTFS signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path. Correspondingly, with the help of the fast Bayesian inference, one low complex approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully exploit the angle, delay and Doppler reciprocity between the uplink and the downlink and reconstruct the angles, the delays, and the Doppler frequencies for the downlink massive channels at the base station. Furthermore, we examine the downlink massive MIMO channel estimation over the delay-Doppler-angle domain. The channel dispersion of the OTFS over the delay-Doppler domain is carefully analyzed and is utilized to associate one given path with one specific delay-Doppler grid if different paths of any user have distinguished delay-Doppler signatures. Moreover, when all the paths of any user could be perfectly separated over the angle domain, we design the effective path scheduling algorithm to map different users' data into the orthogonal delay-Doppler-angle domain resource and achieve the parallel and low complex downlink 3D channel estimation. For the general case, we adopt the least square estimator with reduced dimension to capture the downlink delay-Doppler-angle channels. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

Conventional authentication mechanisms routinely used for validating communication devices are facing significant challenges. This is mainly due to their reliance on both `spoofable' digital credentials and static binary characteristic, and inevitable misdetection in physical layer authentication using time-varying attributes, leading to the cascading risks of security and trust. To circumvent these impediments, we develop an adaptive trust management based soft authentication and progressive authorization scheme by intelligently exploiting the time-varying communication link-related attribute of the transmitter to improve wireless security. First of all, the trust relationship between the transmitter and receiver is established based on the evaluation of selected physical layer attribute for fast authentication and multiple-level authorization. Through the designed trust model, the transmitter is authorized by the specific level of services/resources corresponding to its trust level, so that soft security is achieved. To dynamically update the trust level of the transmitter, we propose an online conformal prediction-based adaptive trust adjustment algorithm relying on the real-time validation of its attribute estimates at the receiver, thus resulting in progressive authorization. The performance of our scheme is theoretically analyzed in terms of its individual risk and individual satisfaction. Our simulation results demonstrate that the proposed scheme significantly improves the security performance and robustness in time-varying environments, and performs better than the static binary authentication scheme and existing physical layer authentication benchmarker.

Accurate clock synchronization in the industr-ial-Internet-of-Things systems forms the cornerstone of distributed interaction and coordination among various infrastructures and machines in an industrial environment. However, due to the widespread use of wireless networks in industrial applications, constraints inherent to wireless networks including uncertain propagation delays, random packets losses, and unguaranteed communication resources are unavoidable, leading to dramatically increased clock synchronization error and unreliable or even outdated information. Meanwhile, time information transmissions are vulnerable to suffer from malicious attacks, causing unreliable timestamps and insecure synchronization. In this article, we proposed a distributed clock synchronization protocol based on an intelligent clustering algorithm to achieve accurate, secure, and packet-efficient clock synchronization. The varying rate of skew of every clock is collected and utilized for cluster formation as well as malicious node detection. According to established clusters, various synchronization frequencies are assigned, which can avoid excessive network access contention, reduce overall communication resource consumption, and improve synchronization accuracy. Meanwhile, a two-tier fault detection algorithm consists of outlier detection and second-order regressive model prediction is applied to determine potential malicious nodes. The simulation results demonstrate that the proposed protocol overwhelms simultaneous synchronization protocols in terms of synchronization performance and faulty node detection.

Accurately estimating network traffic from the partial measurements plays a crucial role in network management. However, the potential anomaly existing in real networks usually makes this goal difficult to achieve. Existing network traffic estimation methods generally impute network traffic independent of anomaly detection, which incurs significant performance degradation with network anomaly. To address this issue in the realistic network scenario, we propose a novel anomaly-aware network traffic estimation method to recover network traffic data concurrently with network anomaly detection. Specifically, by exploiting the inherent spatio-temporal characteristics, we first formulate the network traffic estimation as a low-rank tensor completion problem. Then, an outlier-robust tensor completion (OrTC) model is constructed by introducing both L2,1-norm regularization and LF-norm regularization, which can not only well fit the intrinsic low-rank property of real traffic data, but also is robust against both the dense noise and the sparse anomaly. Furthermore, an effective optimization algorithm OrTC-AM is designed to solve the non-convex and non-smooth OrTC model based on the popular alternating minimization method. Finally, the extensive experiments performed on the public dataset demonstrate that our proposed OrTC-AM method outperforms the previously widely used network traffic estimation methods.

High quality multimedia streaming over the Internet has proliferated in modern society due to the ever-increasing ties amongst people and businesses, thereby occupying the majority of all exchanged data traffic. The Internet is the primary multimedia exchange medium that is being used beyond its intended design. Using Hypertext Transfer Protocol/Transmission Control Protocol (HTTP/TCP), multimedia can be delivered to virtually all devices connected to the Internet. HTTP-based streaming suffers from the increasing overhead generated as the stream length, quality, and non-deterministic path conditions vary. In this paper, a novel cross-layer signalling reduction scheme is proposed to alleviate resource consumption in networks exchanging multimedia. The proposed scheme is a steganographic-based protocol translator that encodes information within multimedia payloads prior to packet flight to reduce the size of exchanged data. The encoded data is used by node pairs to replace bulky protocols, such as TCP, with lightweight protocols, such as User Datagram Protocol (UDP). In addition to the protocol translator, a routing scheme is given to be used in place of the inflated networking protocols to further reduce the header footprint. A utility function is developed to find the optimal overhead savings where simulations are conducted to verify the designs. Using virtual machines, the proposed translator is implemented where a multimedia file is exchanged and subject to encoding. The simulations and implementation show that the proposed methodologies will decrease the amount of signalling needed while increasing the overall network capacity. Furthermore, the proposed methods are capable of successfully extending existing signalling reduction methods.

Utilizing the idle computing resources from the distributed Internet of Things devices can sustainably increase the computational capacity and thereby effectively alleviate the pressure on resource-constrained devices, which is referred to as collaborative computing. However, extra computing consumption potentially impacts the local computation tasks of collaborative computing devices. Hence, it is essential to design an efficient incentive mechanism for computational resources sharing. Specifically, we consider the collaborative computing system where a user offloads the computation-intensive and latency-sensitive tasks to multiple idle computing devices (ICDs) by a centralized computing sharing platform (CSP). We first propose a computational latency-based pricing mechanism from the perspective of the quality-of-experience performance; then, a game-theoretic computing task allocation approach is developed among the CSP and multiple ICDs to maximize all participants' profit. The CSP first determines the optimal task partition dynamically upon the tasks' arrival; then, the ICDs derive the optimal central processing unit-cycle frequency correspondingly. Simulation results demonstrate that the overall computational latency of our proposed mechanism is significantly decreased, and achieves by at least 13.5 percent improvement compared with the existing schemes. Meanwhile, the profit of all participants is maximum in collaborative computing, which is improved by 41.5 and 27.9 percent for the CSP and the ICDs, respectively.

The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.

In this paper, we propose a novel hierarchical radio-frequency identification (RFID) tag-recognition method based on blind source separation (BSS), graph-based automatic modulation classification (AMC), and direct-sequence spread-spectrum (DSSS). In our proposed method, RFID tags can be modulated using different modulation schemes according to different scenarios (e.g., different users or different tag devices). For each modulation scheme, the direct-sequence spread-spectrum strategy is employed to allow simultaneous transmissions of multiple commands. In the signal separation phase, BSS is employed to separate different transmitted signals. Then in the first hierarchy of the recognition phase, different modulation types are adopted to distinguish different users, the graph-based AMC is built upon the periodicity of the modulated signals: the cyclic spectrum of the received signal is established; the graph representation is then constructed according to the cyclic spectrum. Ultimately, robust features are extracted from the graph representation. In the second hierarchy of the recognition phase, the DSSS scheme is utilized to differentiate the control or sensed data carried by individual tags; the signature sequence set with low cross-correlations can be generated from Kasami sequences. In the third hierarchy of the recognition phase, the information data are thus spread by these signature sequences. In our proposed new RFID framework, multiple tags can transmit signals simultaneously in the same frequency band where each tag signal can still be separated and identified and its carried information can be recovered. Monte Carlo simulation results demonstrate the promising performance of our proposed new RFID scheme.

As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.

With the deployment of large scale antenna array in millimeter wave (mmWave) band, the resolution of beamforming has been dramatically improved. To reduce the long beam-training process using reference signal (RS) in codebook-based high resolution beamforming, hierarchical codebook is often used to reduce the number of beam-training symbols. However, the large beam-training overhead is still the bottleneck for overall system performance improvement in term of the true achievable data rate. In this paper, with the angle reciprocity in frequency duplex division (FDD) system, a neural network based line of sight path angle of arrival (LAoA) estimation algorithm is proposed for beam selection, in order to achieve the non-RS-aided codebook-based beamforming. To further achieve high accuracy LAoA estimation, two-step neural network models are designed to capture the relationship between the receiving signal and the corresponding LAoA. The numerical results show that the proposed algorithm outperforms the benchmark algorithm in terms of sum weighted data rate (SWR) and sum data rate (SR). In the low signal to noise ratio (SNR) environments with a couple of uplink signal snapshots, our algorithm also performs better than MUSIC based beam selection algorithm.

The next generation 5G and beyond network, which necessitates advanced physical layer design utilizing distributed data and computational resources intelligently with improved context awareness, is expected to support multi-service traffics fundamentally different from the traditional ones. For this network, orthogonal frequency division multiplexing (OFDM) is believed to be one of the promising candidate waveforms where its performance depends on the accuracy of estimated CSI coefficients obtained via the discrete Fourier transform (DFT) method. This method first estimates the channel impulse response (CIR) followed by taking the DFT of the CIR coefficients. In practice, however, such an estimator suffers from performance degradation when the number of dominant CIR taps (i.e., taps with non-negligible amplitudes) is very small compared to the total size of CIR taps. This paper addresses this limitation by first examining the dominant CIR tap identification problem, and then using only dominant CIR taps in DFT based CSI estimation. In this regard, we propose a novel approach to formulate this problem as a signal to noise ratio (SNR) maximization convex problem where its global optimal solution can be obtained with a simple integer based bisection search. The formulated problem depends on the SNR of each CIR tap which is estimated from the received samples of the previous OFDM data blocks using a new and computationally manageable adaptive bootstrapping technique. We carry out extensive simulations to validate the analytical expressions and examine the effects of different parameters including channel stationarity duration and number of reference sub-carriers which are not used during data transmission (i.e., null sub-carriers). Numerical simulations corroborate the relevance of identifying dominant CIR taps for CSI estimation. In a typical long-term evolution (LTE) channel environment, the proposed approach can achieve up to 60% improvement in spectrum efficiency.

Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multiaccess edge computing, roadside units (RSUs) could become cache-capable and offer fast caching services to moving vehicles for content providers. On the other hand, deep learning makes it possible to accurately estimate the behavior of vehicles, which enables effective proactive caching strategies. However, caching services considering both the mobility of vehicles and storage could incur increased latency and considerable cost due to the cache size needed in RSUs. In this paper, we model such a problem using Markov decision processes, and propose a heuristic Q-learning solution together with vehicle movement predictions based on a long short-term memory network. The optimal caching strategy which minimizes the latency of caching services can be derived by our heuristic εn-greedy training processes. Numerical results demonstrate that our proposed strategy can achieve better performance compared with several baselines under different prediction accuracies.

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this article, we propose a joint computation off-loading and multiuser scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multiuser resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. The simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size K-means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.

The machine-to-machine (M2M) communication is an emerging technology that is widely utilized in a vast number of industrial Internet-of-Things (IIoT) applications. Due to the diversity of IIoT applications, provisioning of heterogeneous delay requirements of delay-sensitive machine type devices (MTDs) while optimizing the access efficiency of delay-tolerate MTDs becomes a critical challenge for M2M communications. To address this issue, a multigroup analytical framework for massive random access of M2M communications in IIoT is proposed in this article. Specifically, we consider delay-sensitive MTDs and delay-tolerate MTDs coexist in the network, and those MTDs are divided into multiple groups according to their delay requirements. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the throughput and the mean access delay of each group are characterized. It is found that for each group, the mean access delay decreases as the throughput increases and is minimized when the throughput is maximized. To achieve the maximum throughput of delay-tolerate MTDs under delay constraints of delay-sensitive MTDs, the backoff parameters of delay-sensitive MTDs should be tuned according to the delay constraints while that of delay-tolerate MTDs should be tuned further according to the aggregate packet arrival rate and the number of MTDs in each group. It is further demonstrated that the optimal tuning of backoff parameters is robust against the burstiness of input traffic. The analysis sheds important light on the access design of M2M communications in IIoT with delay constraints.

This paper proposes a novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. With the proposed channel estimation and beamforming approach, we determine the number of cells$N_{c}$that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has a maximum of$L$uncorrelated multipath taps (or correlated multipath taps satisfying modest criteria which is valid in most practical scenarios), it is shown that$N_{c}=L$cells can estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination. The proposed approach can also be applied for general correlated multipath taps, and achieves good performance for this scenario as well. In a typical long term evolution (LTE) channel environment having delay spread$T_{d}=4.69\,\,\mu \text{s}$and channel bandwidth$B=5$MHz, we have found that$L=36$cells can use this band. In practice,$T_{d}$is constant for a particular environment and carrier frequency, and hence$L$increases as the bandwidth increases. All the analytical expressions have been validated, and the superiority of the proposed design over the existing ones is demonstrated using extensive numerical simulations both for correlated and uncorrelated channels. The proposed channel estimation and beamforming design is linear and simple to implement.

Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machinelearning as an intelligent process to learn and utilize the complex time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N-dimensional feature space to a single-dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least mean square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.

More network operators have recently provided content delivery network (CDN) services, where traffic engineering techniques, such as the base station (BS) association, are jointly employed with content delivery. This deployment attempts to reduce the network operating cost and enhance the quality of service (QoS) of end users. Toward this end, we study the BS association and file caching problem considering the spatial diversity of the file popularity and the realistic time-scale separation between the file caching and the BS association decisions in this paper. Our design aims to minimize the file delivery latency and operating power consumption in the cellular networks, where the tradeoff between these conflicting objectives is controlled by a single parameter. The short time-scale BS association problem is solved by using the convex optimization technique for a given file caching solution. However, the long time-scale file caching problem considering the varying BS association decisions taken at the short time-scale is difficult to tackle. To solve this file caching problem, we prove and leverage the submodularity property of the underlying objective function to develop a greedy content caching algorithm that guarantees a constant approximation ratio of the optimal objective value. Via simulations using real-world datasets, we show that the proposed algorithms outperform file caching and BS association algorithms that do not consider the spatial diversity of the file popularity in terms of the power consumption and delay performance in the geographically heterogeneous file popularity scenario.

To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-grid bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.

Multimedia streams consume a significant chunk of the consumer Internet traffic exchanged and will continue to do so due to the ever-increasing connection among people, businesses, and industries. To cope with the deviation of the Internet's intended use, unreliable underlying infrastructure, and best effort protocols while leveraging existing technologies, Hypertext Transfer Protocol Adaptive Streaming is utilized by numerous multimedia services. Performance of HAS-based streaming services is limited by the growing control overhead generated by the Transmission Control Protocol/Internet Protocol (TCP/IP) stack as the stream length, multimedia fidelity, and network conditions vary. In this paper, a novel cross-layer steganographic-enabled signaling scheme is proposed to reduce service provider costs while improving multimedia session performance and maintaining expected Quality-of-Service (QoS). The proposed scheme is designed to encode control stream messages from any TCP/IP layer within payload messages to reduce the total amount of overhead exchanged, thereby decreasing resource utilization within source and intermediate nodes. Furthermore, the encoding scheme probes network conditions and session statistics for adaptive decision-making to enable real-time pliability of the proposed process. A utility function is developed to find the optimal cost savings where simulations are conducted to verify the designs. The proposed solution is then implemented using VideoLan Media Player transceivers residing in linux containers virtual machines, where a multimedia file is exchanged in the popular Advanced Video Coding (H.264) format. The results show a decrease in bandwidth and average queue waiting time costs of 4.71% and 29.61%, respectively, with a throughput increase of 5.77%.

The antagonistic game between a cognitive transmitter and a smart jammer is considered. The cognitive transmitter divides its transmitting power between several concurrent channels trying to maximize their total capacity. The goal of the game is to adapt the cognitive transmission strategy to the changing strategy of the smart jammer. We propose the modification of the classical Q- learning algorithm by adding memory component for determining the strategy of the cognitive transmitter power allocation among several channels under different scenarios of the jammer behavior. The proposed algorithm successfully handles different jammer strategies and adapts to their changes.

Advanced intrusion detection systems are beginning to utilize the power and flexibility offered by Complex Event Processing (CEP) engines. Adapting to new attacks and optimizing CEP rules are two challenges in this domain. Optimizing CEP rules requires a complete framework which can be ported to stream processors because a CEP rule cannot run without a stream processor. External dependencies of stream processors make CEP rule a black box which is hard to optimize. In this paper, we present a novel adaptive and functionally autoscaling stream processor: “Wisdom” with a built-in hybrid optimizer developed using Particle Swarm Optimization, and Bisection algorithms to optimize CEP rule parameters. We show that an adaptive “Wisdom” rule tuned by the proposed optimization algorithm is able to detect selected attacks in CICIDS 2017 dataset with an average precision of 99.98% and an average recall of 93.42% while processing over 2.5 million events per second. The proposed distributed functionally autoscaling deployment mode consumes significantly fewer system resources than the monolithic deployment of CEP rules.

This paper proposes a novel approach for designing channel estimation, beamforming and scheduling jointly for wideband massive multiple input multiple output (MIMO) systems. With the proposed approach, we first quantify the maximum number of user equipments (UEs) that can send pilots which may or may not be orthogonal. Specifically, when the channel has a maximum of ℒ multipath taps, and we allocate $\tilde{M}$ sub-carriers for the channel state information (CSI) estimation, a maximum of $\tilde{M}$ UEs' CSI can be estimated (ℒ times compared to the conventional CSI estimation approach) in a massive MIMO regime. Then, we propose to schedule a subset of these UEs using greedy based scheduling to transmit their data on each sub-carrier with the proposed joint beamforming and scheduling design. We employ the well known maximum ratio combiner (MRC) beamforming approach for the uplink channel data transmission. All the analytical expressions are validated via numerical results, and the superiority of the proposed design over the conventional orthogonal frequency division multiplexing (OFDM) transmission approach is demonstrated using extensive numerical simulations in the long term evolution (LTE) channel environment. The proposed channel estimation and beamforming design is linear and simple to implement.

Mobile edge computing (MEC) is an emerging paradigm that mobile devices can offload the computation-intensive or latency-critical tasks to the nearby MEC servers, so as to save energy and extend battery life. Unlike the cloud server, MEC server is a small-scale data center deployed at a wireless access point, thus it is highly sensitive to both radio and computing resource. In this paper, we consider an Orthogonal Frequency-Division Multiplexing Access (OFDMA) based multi-user and multi-MEC-server system, where the task offloading strategies and wireless resources allocation are jointly investigated. Aiming at minimizing the total energy consumption, we propose the joint offloading and resource allocation strategy for latency- critical applications. Through the bi-level optimization approach, the original NP-hard problem is decoupled into the lower-level problem seeking for the allocation of power and subcarrier and the upper-level task offloading problem. Simulation results show that the proposed algorithm achieves excellent performance in energy saving and successful offloading probability (SOP) in comparison with conventional schemes.

In this paper, a coordinated multiple-relays-based cooperative communication scheme is proposed to improve the physical-layer security. In order to benefit the relays in forwarding the signals for defending against the eavesdropping attacks, the interactions between the source and the multiple relays are modeled as a single-leader multiple-followers Stackelberg game. The source plays as the leader to coordinate the relays, including the phase of signals forwarded by the relays and the transmit power of the relays, for maximizing the secrecy capacity of the system. An algorithm is developed for the relays to find an optimal price allocation to achieve the fairness among the multiple relays based on the egalitarian welfare solution, and an approximate optimal strategy of source (i.e., phase coordinated vector and power allocation) is studied. The closed-form intercept probability of the proposed scheme is derived. Numerical studies demonstrate that the proposed scheme can greatly improve the utilities of both the source and multiple relays over that resulted from the Nash equilibrium scheme and rand scheme, which means that the relays are more willing to participate in the cooperative communication, and the source can achieve better secure transmission based on the proposed scheme. It is also shown that the proposed scheme performs much better in defending against the eavesdropping attacks than those existing schemes, for example, the single relay selection scheme, the opportunistic relay selection scheme, the optimal relay scheme, and cooperative jamming scheme.

Wireless sensor networks (WSNs) have been rapidly integrated into Internet of Things (IoT) systems, empowering rich and diverse applications such as large-scale environment monitoring. However, due to the random deployment of sensor nodes (SNs), physical topology of the WSNs cannot be controlled and typically remains unknown to the IoT cloud server. Therefore, in order to derive the physical topology at the cloud for effective real-time event detection, a cloud-orchestrated physical topology discovery scheme for large-scale IoT systems using unmanned aerial vehicles (UAVs) is proposed in this paper. More specifically, the large-scale monitoring area is first split into a number of subregions for UAV-enabled data collection. Within the subregions, parallel Metropolis-Hastings random walk (MHRW) is developed to gather the information of WSN nodes, including their IDs and neighbor tables. The collected information is then forwarded to the cloud through UAVs for the initial generation of logical topology. Thereafter, a network-wide 3-D localization algorithm is further developed based on the discovered logical topology and multidimensional scaling method (Topo-MDS), where the UAVs equipped with global positioning system are served as mobile anchors to locate the SNs. Simulation results indicate that the parallel MHRW improves both the efficiency and accuracy of logical topology discovery. In addition, the Topo-MDS algorithm dramatically improves the 3-D location accuracy, as compared to the existing algorithms in the literature.

By leveraging direct device-to-device interaction, LTE vehicle-to-vehicle (V2V) communication becomes a promising solution to meet the stringent requirements of vehicular communication. In this paper, we propose jointly optimizing the radio resource, power allocation, and modulation/coding schemes of the V2V communications, in order to guarantee the latency and reliability requirements of vehicular user equipments (VUEs) while maximizing the information rate of cellular user equipment (CUE). To ensure the solvability of this optimization problem, the packet latency constraint is first transformed into a data rate constraint based on random network analysis by adopting the Poisson distribution model for the packet arrival process of each VUE. Then, utilizing the Lagrange dual decomposition and binary search, a resource management algorithm is proposed to find the optimal solution of joint optimization problem with reasonable complexity. Simulation results show that the proposed radio resource management scheme can reduce the interference from V2V communication to CUEs and ensure the latency and reliability requirements of V2V communication.

This paper proposes a novel Wi-Fi based indoor localization system. Specially designed Wi-Fi beacons are set up to detect the real time signal strengths of Wi-Fi access points and send this data to a server. By establishing the distances along flat planes between beacons and a mobile tag, the location of the mobile tag is estimated by finding the most likely intersection between the planes corresponding to the tag. This proposed approach eliminates several bottlenecks that affect time and cost efficiency, as the localization technique developed in this project uses the relative signal strengths of the smartphone compared to signal strengths recorded at beacon devices. This eliminates the requirements of having control of and location knowledge of Wi-Fi access points. Preliminary experimentation results show that the proposed approach can achieve a localization accuracy comparable to a fingerprint approach.

Future Internet of Things (IoT) networks are expected to support a massive number of heterogeneous devices/sensors in diverse applications ranging from eHealthcare to industrial control systems. In highly-dense deployment scenarios such as industrial IoT systems, providing reliable communication links with low-latency becomes challenging due to the involved system delay including data acquisition and processing latencies at the edge-side of IoT networks. In this regard, this paper proposes a priority-based channel access and data aggregation scheme at the Cluster Head (CH) to reduce channel access and queuing delays in a clustered industrial IoT network. First, a prioritized channel access mechanism is developed by assigning different Medium Access Control (MAC) layer attributes to the packets coming from two types of IoT nodes, namely, high-priority and low-priority nodes, based on the application-specific information provided from the cloud-center. Subsequently, a preemptive M/G/1 queuing model is employed by using separate low-priority and high- priority queues before sending aggregated data to the Cloud. Our results show that the proposed priority-based method significantly improves the system latency and reliability as compared to the non-prioritized scheme.

In this paper, we develop a content caching and flow level BS-user association framework in a network environment with the spatial variation of content popularity. Because the studied content caching and BS-user association functions are tightly intertwined with each other, and their decision time scales can be very different in practice, our design considers the time-scale separation of these network functions to tackle and develop the BS-user association and content caching policies. Specifically, we propose an optimal BS-user association algorithm, namely OptUA, operating in the short time scale for a given content caching solution, and a greedy content caching algorithm, namely GCC, operating in the long time scale. The GCC algorithm exploits the submodularity characteristics of the objective function which ensures that the GCC algorithm achieves a constant fraction of the optimal performance for most feasible caching sets. Via extensive numerical studies in heterogeneous cellular networks, we demonstrate that proposed OptUA and GCC algorithms outperform other algorithms which do not consider spatial variations of content popularity in terms of average end-to-end delay per content request and average system load per content at each BS.

In order to exploit the potential capacity of 5G, the deployment of ultra-dense small cells is an approach that can dramatically increase the radio resource reuse factor and network capacity. However, network densification with a large number of small cells brings challenges due to increased network complexity, deployment cost and inter-cell interference. In this paper, a new 5G architecture with virtual small cells (VSCs), which are dynamically formed by grouping a number of user devices in close proximity and adapted according to traffic condition, is proposed to improve the cost and energy efficiency compared with the traditional fixed deployment of small cells. In each virtual small cell, one mobile device is selected as a cell head (CH) to aggregate intra- cell traffic using unlicensed band transmissions and then communicates with its macro-cell base station in a licensed band through beamformed transmission, which reduces the inter-cell interference and improves spectrum efficiency. In this paper, a highly directional beamforming technique is employed to enable a dedicated inband fronthaul link for VSC. Our work focuses on how to design adaptive beamforming to minimize the transmit power under throughput requirements and power constraints. Both the mathematical analysis and simulation results demonstrate that VSCs can increase power efficiency dramatically while providing flexibility and reduced cellular load, when compared with macrocell only deployment and traditional fixed small cells scenario.

The recent advancement of Internet of Things (IoT) technologies has enabled many emerging applications, including smart building and connected vehicles. These advanced applications generate massive amount of data at the edge of IoT networks, which usually need to be relayed to a remote data center for further real-time processing. However, uploading all these IoT data to the cloud platform imposes a heavy burden on the underlying network. The unavoidable long delay from data exchange and processing significantly reduces the time-responsiveness of real-time IoT applications. Recently, fog computing has been introduced to IoT applications as an intermediate between end devices and cloud for primary IoT data processing. In this paper, a temporal IoT data reduction scheme through fog computing is proposed to reduce the total amount of IoT data uploaded to the cloud. More specifically, IoT data are first modeled as multivariate normal distribution by the cloud. Dual Kalman filters (KF) with identical parameters are then deployed at both the cloud and fog platforms. The same predictions are simultaneously triggered by the dual KFs at both platforms. Only the measured IoT data out of predicted range are further uploaded from fog to cloud. Otherwise, predicted values at both platforms are used instead of measurements. A simple prototype IoT system is developed for performance evaluation. Experimental results indicate that the proposed scheme significantly reduces the number of packets uploaded to the cloud platform with high data accuracy.

Cognitive Radio (CR) communication has been considered as one of the promising technologies to enable dynamic spectrum sharing in the next generation of wireless networks. Among several possible enabling techniques, Spectrum Sensing (SS) is one of the key aspects for enabling opportunistic spectrum access in CR Networks (CRN). From practical perspectives, it is important to design low-complexity wideband CR receiver having low resolution Analog to Digital Converter (ADC) working at a reasonable sampling rate. In this context, this paper proposes a novel spatio-temporal wideband SS technique by employing multiple antennas and one-bit quantization at the CR node, which subsequently enables the use of a reasonable sampling rate. In our analysis, we show that for the same sensing performance requirements, the proposed wideband receiver can have lower power consumption than the conventional CR receiver equipped with a single-antenna and a high-resolution ADC. Furthermore, the proposed technique exploits the spatial dimension by estimating the direction of arrival of Primary User (PU) signals, which is not possible by the conventional SS methods and can be of a significant benefit in a CRN. Moreover, we evaluate the performance of the proposed technique and analyze the effects of one-bit quantization with the help of numerical results.

In order to support dramatically increased traffic from diverse network services, deployment of ultra dense networks to improve the overall capacity of the fifth generation (5G) wireless networks becomes inevitable. However, network densification with increased number of small cells brings significant challenges in terms of quality of service provisioning and deployment cost due to increased network complexity, signalling overhead and inter-cell interference. In this paper, virtual small cell (VSC), which is formed adaptively according to traffic condition and service requirements, is investigated as a solution for cost-effective and reliable service provisioning in 5G ultra dense networks. A K-means clustering based VSC formation scheme is proposed in this paper, and the corresponding protocol conversion for data transmission across unlicensed and licensed networks at cell head (CH) is developed. Based on the VSC architecture design, a new resource allocation algorithm is also proposed for VSC scenario in order to improve the system throughput with comparable fairness.

This paper is devoted to the investigation of different properties of dynamic vehicular ad hoc network on a road. In our model each moving vehicle on the road communicates with several neighbouring cars. We derive important characteristics of such network including distributions of number of clusters, cluster size, biggest cluster size distribution and distribution of cars not being able to communicate with any other car in the network as well as probability of graph being fully connected. Understanding of clustering is an important issue in management of virtual cells organization, distributed data collection and processing. One of advantages of the considered model is that it can be used for arbitrary intervehicle distribution model.

Dynamic Spectrum Access (DSA) is considered as a promising solution to address the problem of spectrum scarcity in future wireless networks. However, the main challenges associated with this approach are to acquire accurate spectrum usage information in a timely manner and to deal with the dynamicity of channel occupancy. Although Cooperative Sensing (CS) can provide significant advantages over individual device-level sensing in terms of sensing efficiency and the achievable throughput, the acquired channel occupancy information may become outdated in dynamic channel conditions due to the involved latency. In this regard, we propose to utilize a collaborative cloud-edge processing framework to minimize the CS delay in DSA networks. In this framework, the cloud-center can estimate channel occupancy parameters such as duty cycle based on the available historical sensing data by using a suitable spectrum prediction technique, and subsequently this prior knowledge can be utilized to adapt the sensing mechanism employed at the edge-side of a DSA network. Motivated by this, we formulate and solve the problem of minimizing CS delay in cloud-assisted DSA networks. A two-stage bisection search method is employed to solve this CS delay minimization problem. Our results show that the proposed cloud-assisted CS scheme can significantly reduce the CS delay in DSA networks.

This paper investigates the impact of radio frequency (RF) cochannel interference (CCI) on the performance of dual-hop free-space optics (FSO)/RF relay network. The considered FSO/RF system operates over mixed Malaga-M/composite fading/shadowing generalized-K (GK) channels with pointing errors. The H-transform theory, wherein integral transforms involve Fox's H-functions as kernels, is embodied into a unifying performance analysis framework that encompasses closed-form expressions for the outage probability, the average bit error rate (BER), and the ergodic capacity. By virtue of some H-transform asymptotic expansions, the high signal-to-interference-plus-noise ratio (SINR) analysis culminates in easy-to-compute expressions of the outage probability and BER.

One of the important use-cases of 5G network is the vehicle to infrastructure (V2I) communication which requires accurate understanding about its dynamic propagation environment. As 5G base stations (BSs) tend to have multiple antennas, they will likely employ beamforming to steer their radiation pattern to the desired vehicle equipment (VE). Furthermore, since most wireless standards employ an OFDM system, each VE may use one or more sub-carriers. To this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without the training signal and channel impulse response (CIR) model. In this regard, first we utilize the well known adaptive recursive least squares (RLS) technique for predicting the next block CIR from the past and current block received signals (a block may have one or more OFDM symbols). Then, we jointly design the beamforming and VE scheduling for each sub- carrier to maximize the uplink channel average sum rate by utilizing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution is guaranteed. And, the VE scheduling design is formulated as an integer programming problem which is solved by employing a greedy search. The superiority of the proposed channel prediction and scheduling algorithms over those of the existing ones is demonstrated via numerical simulations.

In this paper we study properties of the network formed by a number of randomly distributed cars on a straight road. Various system level characteristics are investigated, i. e. the average channel capacity between vehicles, the average value of the adjacency matrix and distribution of the eigenvalues of it. A connectivity graph corresponding to the vehicle network is considered. Communication quality of connection is determined by parameter introduced in the article. Two theorems are proven establishing a close correspondence between value of this parameter and the connectivity of the graph. These results can be used in practice to determine the degree of connectivity of the vehicle network.

This paper proposes a novel physical-layer-aided security technique for protecting a social Internet of things (SIoT) architecture-based healthcare system. Exploiting the social relationship link between healthcare user (e.g., patients and elderly people) and healthcare provider (e.g., physicians), social networks can play the role of a trusted online platform to establish service application interfaces between healthcare user (HU) and healthcare provider (HP). This enables the Internet of things (IoT) medical devices (e.g., IoT body sensor) to timely share the bio-data of HU with remote HP via the both storage-rich and computational resource-rich social networks. Given the high security requirement on SIoT data sharing and the fact that resource-constrained IoT devices cannot efficiently execute complicated cryptography, a robust and cost-effective two-phase security method is proposed by exploiting the device-specific physical-layer (PHY) attributes. Specifically, the PHY carrier frequency offset and in-phase/quadrature-phase imbalance of an IoT device are practically estimated to generate the PHY-ID. Using our PHY-ID, the SIoT HU authentication and the bio-data confidentiality are simultaneously enhanced without posing any additional implementation overhead at IoT body sensors, which is especially applicable for the resource-constrained IoT devices.

With the capacity of overcoming radio spectrum shortages for wireless communications in smart grids, cognitive radio enabled Advanced Metering Infrastructure (CR-AMI) networks are expected to enhance the efficiency and practicability of future smart grids. As an integral component of the smart grid ecosystem, CR-AMI networks are practically deployed as a static multi-hop wireless mesh network. This paper focuses on the investigation of an novel RPL-based routing protocol for enhancing the energy efficiency in CR-AMI networks. In accordance with practical requirements of green communications in smart grids, the proposed routing protocol adopts the energy efficiency over virtual distance as the core of routing mechanism such that the energy-efficient route can be achieved. In addition, the protocol has the mechanism for primary (licensed) users protection whilst meeting the utility requirements of cognitive radio users. System-level evaluation shows that the proposed routing protocol has better performances compared with existing routing protocols for cognitive radio- enabled AMI networks.

Establishing direct communication between cellular devices in proximity brings benefits for the overall system sum-rate while generating interference. To deal with this additional interference efficiently, a low complexity uplink resource allocation algorithm for device-to-device (D2D) communication has been proposed in the underlying cellular networks by considering application-level requirements and request time-out. The related resource allocation is formulated as a non-linear optimization problem to maximize the system weighted sum-rate. An explicitly distributed coordination between different sources of channel state information (CSI) is introduced to avoid gathering all CSI information at the Base Station (BS) thus leading to significantly reduced overhead. Also, users' data-rate probability density function (pdf) is theoretically derived to determine approximately, for how long a D2D pair should wait to get the requested service. To assist the BS in collecting D2D requests, a frame structure has been proposed. Through simulation, it has been demonstrated that the proposed scheme outperforms the scheme with random allocation. Also, it has been confirmed that the proposed algorithm and the Exhaustive search strategy performs very close to the Exhaustive search strategy that exhibits optimal performance when the D2D pairs are fewer in number.

While multiple-input multiple-output (MIMO) is considered as the key enabling technology for high data rate wireless communications, it faces several major challenges in the 5th generation MIMO systems due to limited number of radio frequency (RF) chains and space restrictions. In light of this, generalized-spatial modulation (GSM) and dual- polarized (DP) antenna arrays are two potential technologies to tackle these challenges. In this paper, we propose a novel two-stage optimum antenna grouping scheme in GSM with DP antennas. In the first stage of the proposed scheme, we select antennas with their polarizations as group indicators followed by the second stage, which determines the potential antennas and polarizations that can be selected within each group. The proposed algorithm directly chooses the activated antennas and therefore, completely eliminates the necessity of search over an extensive space. We use the average bit error probability (ABEP) to analyze the performance of the system and validate them by extensive Monte Carlo simulations.

Wireless fidelity (Wi-Fi) has become a key access technology, which offloads significant percentage of mobile Internet traffic. Nevertheless, QoS provisioning over dense WiFi faces many challenges due to the nature of its contention-based protocol and poor inter-network coordination. In light of this, long-term evolution (LTE) technology, which benefits from centralized coordination, has been proposed to exploit unlicensed spectrum through LTE-U for data offloading. However, the prosperity of deploying LTE in very crowded unlicensed band relies on the effective coexistence among LTE-U and increasingly dense Wi-Fi networks. In this paper, we propose to use the space dimension and beamforming to facilitate the effective coexistence and inter-network coordination. Two distinct approaches to evaluate the proposed coexistence mechanism, namely, Wi-Fi received power minimization and LTE user signal to noise ratio (SNR) maximization, have been investigated. The two proposed algorithms are simulated to show the potential and effective feasibility of coexistence between Wi-Fi and LTE in unlicensed spectrum.

Low‐energy critical infrastructure monitoring (LECIM) networks is essential for the monitoring of infrastructure facilities in smart cities. One critical requirement of an LECIM network is its wide coverage of up to several kilometres by using a star topology instead of the tree or mesh networks. In meeting this requirement, this study develops a system with a transceiver of extremely high receiver sensitivity based on the IEEE 802.15.4k physical layer specifications. To reduce the energy consumption, the modulation schemes suitable for low complexity detection are chosen for the data transmission in the design. Also, an efficient parallel preamble and payload data detection are adopted at the access point of the proposed LECIM to acquire concurrent packets from respective nodes. Meanwhile, a data‐aided dynamic timing adjustment scheme is proposed for data field detection to rapidly and adaptively synchronise to the long duration of data packet. Furthermore, a testbed is implemented using a software‐defined radio to demonstrate the effectiveness of the proposed system design.

Internet of Things (IoT) is emerging as the underlying technology of our connected society, which enables many advanced applications. In IoT-enabled applications, information of application surroundings is gathered by networked sensors, especially wireless sensors due to their advantage of infrastructure-free deployment. However, the pervasive deployment of wireless sensor nodes generate massive amount of sensor data, and data outliers are frequently incurred due to the dynamic nature of wireless channels. As operation of IoT systems relies on sensor data, data redundancy and data outliers could significantly reduce the effectiveness of IoT applications or even mislead systems into unsafe conditions. In this paper, a cluster-based data analysis framework is proposed using recursive principal component analysis (R-PCA), which can aggregate the redundant data and detect the outliers in the meantime. More specifically, at a cluster head, spatially correlated sensor data collected from cluster members are aggregated by extracting the principal components (PCs), and potential data outliers are determined by the abnormal squared prediction error score, which is defined as the square of residual value after extraction of PCs. With R-PCA, the parameters of PCA model can be recursively updated to adapt to the changes in IoT systems. Cluster-based data analysis framework also releases the computational and processing burdens on sensor nodes. Practical databases-based simulations have confirmed that the proposed framework efficiently aggregates the correlated sensor data with high recovery accuracy. The data outlier detection accuracy is also improved by the proposed method compared to other existing algorithms.

Non-orthogonal multiple access (NOMA) has been considered as a highly efficient communication technology in the fifth generation (5G) networks by serving multiple users concurrently through non-orthogonal sharing communication resources. NOMA can be combined with both massive multiple input multiple output (MIMO) and relaying technologies to further improve 5G system efficiency at the cost of increased complexity. These combinations rely on the efficient utilization of 3-D communication resources. In the first part of this paper, we investigate highly efficient 3-D resource allocation for massive MIMO-NOMA systems. Due to hardware complexity constraints and channel variation in the massive MIMO-NOMA system, efficient antenna selection and user scheduling algorithms are proposed for sum rate maximization. In the second part of this paper, a collaborative NOMA-assisted relaying (CNAR) system is proposed to serve multiple cell-edge users by 3-D resource utilization. To reduce the relaying complexity in CNAR system, a simplified-CNAR (S-CNAR) system is proposed as an alternative NOMA-enabled relaying strategy. Numerical results show that our antenna selection and user scheduling algorithms achieve similar performance to existing methods with reduced complexity. Under high target rate, CNAR obtains better performance over other transmission strategies and S-CNAR reaches similar performance by simplified relaying scheme.

Secrecy rate achievability in three-node amplify-and-forward cooperative systems at the presence of passive eavesdroppers without location information is statistically analyzed and optimized. First, the effective vulnerability area, the area in which no secrecy rate is achievable when an eavesdropper resides in that area, is introduced as the performance metric to measure the security of the cooperative system. Then, it is shown analytically that the best location of the relay to minimize the effective vulnerability region is on the source-destination crossing line. In addition, it is proved that the optimum relay with the best location and power allocation is located on the line segment bounded by the source and destination. Exact and some special closed-form expressions for the probability of secrecy rate achievability are obtained for the Rayleigh fading environment. Using this, the impact of the location and the relay power on the effective vulnerability area are analyzed and the best cases minimizing this area are numerically computed. It is shown that the effective vulnerability area can be reduced more than 50% in comparison with the case where no relay is used if the optimum relay location and the optimum relay power allocation are adopted.

This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN) data transmission and detection approach for a single input multiple output system. It is assumed that the signal having a bandwidth B is transmitted through a wireless channel with L multipath components. Under this assumption, this paper provides a novel and simple OFTN transmission and symbol-by-symbol detection approach that exploits the multiplexing gain obtained by the multipath characteristic of wideband wireless channels. It is shown that the proposed design can achieve a higher transmission rate than the existing one [i.e., orthogonal frequency division multiplexing (OFDM)]. Furthermore, the achievable rate gap between the proposed approach and that of the OFDM increases as the number of receiver antennas increases for a fixed value of L. This implies that the performance gain of the proposed approach can be very significant for a large-scale multi-antenna wireless system. The superiority of the proposed approach is shown theoretically and confirmed via numerical simulations. Specifically, we have found upper-bound average rates of 15 and 28 bps/Hz with the OFDM and proposed approaches, respectively, in a Rayleigh fading channel with 32 receive antennas and signal-to-noise ratio of 15.3 dB. The extension of the proposed approach for different system setups and associated research problems is also discussed.

Physical layer authentication techniques developed in conventional macrocell wireless networks face challenges when applied in the future fifth-generation (5G) wireless communications, due to the deployment of dense small cells in a hierarchical network architecture. In this paper, we propose a novel physical layer authentication scheme by exploiting the advantages of amplify-and-forward (AF) cooperative relaying, which can increase the coverage and convergence of the heterogeneous networks. The essence of the proposed scheme is to select the best relay among multiple AF relays for cooperation between legitimate transmitter and intended receiver in the presence of a spoofer. To achieve this goal, two best relay selection schemes are developed by maximizing the signal-to-noise ratio (SNR) of the legitimate link to the spoofing link at the destination and relays, respectively. In the sequel, we derive closed-form expressions for the outage probabilities of the effective SNR ratios at the destination. With the help of the best relay, a new test statistic is developed for making an authentication decision, based on normalized channel difference between adjacent end-to-end channel estimates at the destination. The performance of the proposed authentication scheme is compared with that in a direct transmission in terms of outage and spoofing detection.

The Internet of Things (IoT) envisions the seamless interconnection of the physical world and the cyber space. This provides a promising opportunity to build powerful services and applications for manufacturing. This paper provides an overview of key research issues to be addressed and the latest advances in the area of IoT-enabled manufacturing. We first introduce the core technologies of IoT, such as Radio Frequency Identification, Wireless Sensor Networks, Cloud computing, and Big Data. Then we discuss some key research issues of IoT-enabled manufacturing in term of architecture, deployment and business model, data acquisition and processing, model-based decision-making, dynamic service composition, user-centric pervasive environment and latency reduction with state-of-the-art reviews. Finally, we point out some potential application areas of IoT in manufacturing.

Cloud Manufacturing can provide mass manufacturing resources and capabilities as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, etc. The ability to leverage ample services hosted in MCs has direct relation to the success or failure of a manufacturer. Meanwhile, various uncertainties in today's highly-dynamic business environment can easily disrupt manufacturing activities, rendering original schedules ineffective or even obsolete. IoT's real-time sensing ability can be used to detect those uncertainties. However, little work has been done to take advantage of abundant services from MCs and to effectively deal with uncertainties. In order to address this issue, we propose a mathematical formulation for IoT-enabled dynamic Service Selection (SS) across multiple MCs. We consider three kinds of uncertainties (fluctuation of completion time, choices of manufacturing services, and runtime changes made by users) that come from both the user and market sides. The formulation can guide the dynamic SS and enable users to continuously adjust SS to be more effective and efficient.

This paper presents our recent work on human activity detection based on smart phone sensors and incremental clustering algorithms. The proposed unsupervised (clustering) activity detection scheme works in an incremental manner, which contains two stages. In the first stage, streamed sensor data will be processed. A single-pass clustering algorithm is used in order to generate pre-clustered results for the next stage. In the second stage, pre-clustered results will be refined to form the final clusters, which means the clusters are built incrementally adding one cluster at a time. Experiments on phone sensors data of five basic human activities show that the proposed scheme could get comparable results with traditional clustering algorithms but working in a streaming and incremental manner, which is promising for automatic annotated data collection.

This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN) data transmission and detection approach for a single input multiple output (SIMO) system. It is assumed that the signal having a bandwidth is transmitted through a wireless channel having multipath components. Under this assumption, the current paper provides novel OFTN transmission and symbol-by-symbol detection approach that exploits the multiplexing gain obtained by the inherent characteristics of multipath components of wideband channels. In doing so, the proposed design achieves a higher transmission rate than the existing orthogonal frequency division multiplexing (OFDM) approach. It is also shown that the capacity gap between the proposed approach and that of OFDM increases as the number of receiver antennas increases for fixed . The superiority of the proposed approach has been shown theoretically and confirmed via numerical simulations.

Wireless sensor networks (WSNs) are widely considered as the most important information gathering platform in enabling Internet of Things (IoT). In order to evolve the traditional WSNs for low-power and low-loss IoT applications, time slotted channel hopping (TSCH) MAC protocol has been proposed to tackle the single channel and inefficient medium access drawbacks through improved network topology awareness. However, the problem of maintaining the physical topology of a WSN at the server end remains unresolved. In this paper, we propose a novel physical topology discovery scheme for WSNs by exploitation of random walk process and iterative multilateration localization algorithm. Explicitly, information specific to the sensor nodes, including IDs and neighbor tables, are collected in the random walk process. The physical topology is then reconstructed at the server end based on the collected information and the iterative multilateration localization algorithm. Simulation results indicate that the average location offset between the established topology and the ground- truth topology can be as low as 1.26m.

Future fifth generation (5G) wireless infrastructure tends to be highly heterogeneous, with dense small cells deployed overlay to cellular networks. Along with extremely high capacity and stringent latency requirements, security provisioning is becoming challenging in 5G Heterogeneous Networks (HetNets). Security key management could be difficult in small cells where users join and leave frequently, not to mention the limited capability of simplified access points (APs). On the other hand, frequent handovers and authentications in small cells also introduce unnecessary latency. Therefore in this article, we propose a software defined networking (SDN) enabled fast authentication scheme using weighted secure-context-information (SCI) transfer in order to improve authentication efficiency during handover and meet 5G latency requirement. The proposed algorithm is then applied in Neyman Pearson (NP) hypothesis test to authenticate users, which shows enhanced authentication accuracy and reduced latency in MATLAB simulations. Furthermore, we first analyze the SDN structure using priority queuing theory, and prove the performance of SDN enabled authentication handover.

Wireless sensor networks have already been pervasively utilized due to the rapid deployment of information and communication technology (ICT) in many industrial applications, which generate massive amount of sensor data. This development has brought several technical challenges in sensor data processing, e.g., data fault and data redundancy. Principal component analysis (PCA) has been used recently to process the massive but correlated sensor data. However, the conventional PCA method is difficult to be adapted in following the dynamic conditions of wireless sensor networks. In this paper, recursive principal component analysis (R-PCA) method is exploited to progressively update the transformation basis for extracting principal components. Furthermore, a novel R-PCA based algorithm is proposed to address data fault and data redundancy problems. Different from conventional PCA-based algorithms, the proposed algorithm is cluster-based so that the network efficiency can be further improved. Simulations based on a practical dataset have been conducted to evaluate the performance of algorithms. Simulation results show that the proposed algorithm improves the fault detection accuracy by about 20% and reduces the data restoration error by about 28%.

Thin-client computing is being adopted by many industries to bring services to users through a unique platform over wide area networks (WAN). To improve thin-client communication sessions, some authors have proposed using the thin-client protocol with enhanced image compression techniques while others have proposed using drawing commands instead. To take full advantage of the capabilities of thin-client systems, an advance communication protocol is needed. In this paper, a lightweight multicasting protocol for thin-client systems, Thin- Cast, is proposed where the Quality of Service (QoS) and Quality of Experience (QoE) perceived by a user are maintained while meeting the latency constraints. ThinCast establishes a peer-to-peer (P2P) overlay network using the underlying Internet protocol (IP) infrastructure. A central server explicitly requests a peer to forward a payload to a list of defined neighboring peers. While using ThinCast, server costs are reduced since the required throughput to achieve the same quality of service is lowered. On average, the throughput decreased by 11% under different simulation scenarios. The decrease in throughput values allows the server to temporarily increase the packet generation rate, thereby increasing the quality of the received video by the users.

Customized/personalized products are gaining more shares in today's product market. Such products need collective efforts from consumers, manufacturers and third parties. On the other side, the Internet of Things (IoT) with pervasive sensing/actuating/networking ability greatly facilitates remote operation of manufacturing activities and efficient collaboration among stakeholders. This provides great opportunities to the above demand. Thus we propose a full-connection model of product lifecycle in the IoT-enabled cloud manufacturing environment. The model uses social networks to connect multiple parties and facilitate open innovations, IoT to glue physical space to cyber space and cloud manufacturing to provide various elastic services, so that the on-demand workspace, interaction, information sharing or collective problem solving are enabled. We also propose a supporting infrastructure for this model using the latest information and communication technologies. Finally, we present a RFID (Radio-frequency identification) enabled production system for customized/personalized products with the ability to enable a new paradigm of “dynamic processes and close collaborations among different roles” and secure robust production.

This paper presents our recent work on the analyses of smart phone sensor data collected for the human activity recognition (HAR), with the objective to develop more accurate activity recognition systems independent of smart phone models. We identify the multi-device scenario and present the impairments of different smartphone embedded sensor models on HAR applications. Outlier removal, interpolation, and filters in the preprocessing stage are proposed as mitigating techniques. Based on datasets collected from four distinct smartphones, the proposed mitigating methods show positive effects on 10-fold cross validation, device-to-device validation, and leave-one-out validation. Improved performance for smartphone based human activity recognition is observed.

Nowadays, self-driving vehicles which would shoulder the burden of driving and set free human on board are gradually becoming a reality. Consequently, the supporting of growing in-vehicle data traffic will be challenging in future 5G and vehicular networks, due to the high mobility nature of vehicles and the densified irregular distribution on road especially during rush time. Therefore in this paper, a Software-Defined Networking (SDN) enabled integrated 5G-VANET architecture is proposed to improve heterogeneous network (HetNet) management and aggregate vehicle traffic through IEEE 802.11p; a novel vehicle clustering method and dual cluster head design are then introduced to reduce signaling overhead and enhance the overall communication quality in 5G-VANET HetNet under the coordination of SDN. It is also proved by simulation that the proposed design reduced 5G users' blocking probability to the operators services with a back-up cluster head (CH) in each cluster, and also realized adaptive clustering without excessive SDN's processing delay.

To achieve extremely high spectral efficiency in the 5- th generation (5G) communication network, the combination of massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technologies becomes a promising solution. However, due to limited radio frequency (RF) chains and channel condition variation, it is important to develop efficient antenna selection and user scheduling algorithms in complex MIMO-NOMA system. In this paper, efficient antenna selection and user scheduling algorithms are investigated to maximize the sum rate in two MIMO-NOMA scenarios. In the first simple single- band two-user scenario, the proposed antenna selection algorithm achieves higher search efficiency by limiting the candidate antennas to those are beneficial to the relevant users. In the multi-band multi-user scenario, the proposed joint antenna and user (AU) contribution algorithm considers the contribution of each antenna's and user's channel gain to total channel gain jointly. Numerical results show that proposed antenna selection algorithm achieves near-optimal performance, and joint AU contribution algorithm achieves similar performance to existing methods with reduced complexity.

Nowadays, data traffic in-car communication is increasing dramatically, due to the emerging technology of self-driving and on-board infotainment applications. The direct connections between vehicles and cellular infrastructures will introduce significant signalling overhead and excessive energy consumption, especially for congested and fast moving traffic. In order to improve energy efficiency and achieve green networking, an heterogeneous network, 5G-Vehicular Ad Hoc Network (5GVANET) is presented in this paper, which coupling the high data rates of VANET and the wide coverage area of 5G. In this integrated architecture, vehicles are clustered accordingly, and one vehicle in each cluster is selected as a gateway to support aggregated traffic. To ensure the capacity of the trunk link between the gateway and base station, a Non- orthogonal Multiplexed Modulation (NOMM) scheme is proposed in this paper to effectively aggregate the Vehicle-to-Infrastructure (V2I) traffic and further improve energy efficiency. NOMM splits data stream of each user into multi-layers and modulate them simultaneously. Sparse spreading code is also applied in partially superposing the modulated symbols on several resource blocks. Furthermore, we analyzed the energy efficiency of proposed NOMM scheme and traditional M-QAM theoretically. It was also validated by simulation results that NOMM provides less power consumption than M-QAM modulation.

This paper proposes using cooperative relays to improve the ergodic secrecy capacity (ESC) of a multi-hop decode-and-forward (DF) relaying system where communication takes place in the presence of multiple non-colluding eavesdroppers. The proposed scheme is based on a recent approach to generate artificial noise in which transmitter of each hop allocates a portion of its power for generating an intentional interference at the eavesdroppers. Under the assumption that the transmitters can only use limited transmit power, a power allocation strategy has been developed which maximizes the secrecy capacity by optimally distributing the power between the original signal and the artificial noise. Since the optimal solution depends on the channel state information (CSI) of the eavesdroppers, which is difficult to obtain in practice, a sub-optimal solution is also presented in which the CSI of the eavesdroppers is not needed.

Among several potential applications of Full- Duplex (FD) technology, FD Cognitive Radio (CR) communication is one important area where FD can provide several advantages and possibilities such as concurrent sensing and transmission, improved sensing efficiency and the secondary throughput. However, the main challenge is to mitigate the harmful effects of the residual Self-Interference (SI) which depends on the SI mitigation capability of the employed technique. One way to mitigate this effect is to control the transmit power of the CR node, however, this power control over the entire frame duration results in a power- throughput tradeoff. In this context, we propose a novel Two-Phase Concurrent Sensing and Transmission (2P-CST) framework in which a CR performs concurrent sensing and transmission for a certain fraction of the frame duration by employing a power control mechanism and for the remaining fraction of the frame duration, the CR only transmits with the full power. The proposed framework allows the flexibility to optimize the sensing time and the transmit power in order to maximize the achievable throughput of the FD-CR system. Our results demonstrate that the proposed 2P-CST FD transmission strategy provides better performance in terms of the achievable throughput than the conventional Periodic Sensing and Transmission (PST) and CST techniques.

Three potential interference cancellation schemes are compared for the application to a non-orthogonal multiple access communication system. One is the conventional hard successive interference cancellation (SIC) scheme based on independent single-user decodings. The other two, proposed in this paper, are a soft-in soft-out parallel interference cancellation (SISO-PIC) and a hybrid interference cancellation (HIC). The SISO-PIC is an improved joint iterative multi-user detection scheme, which has lower complexity than the prevalent multi-user detection. The HIC combines the advantages of the above two schemes to permit users to be successively processed by a SISO-PIC window according to their receive power levels. A comprehensive comparison is given for these three schemes in aspects of error propagation, detection delay, and complexity when a practical channel code, repeat-accumulate code, is employed. Numerical results show that HIC is a trade-off scheme of the three aspects.

In a dual‐hop relaying system without a direct link between the source and the destination, the source broadcasts information signal to the relay and relay broadcasts it to the destination, thus it uses two phases to transmit one symbol. This study proposes a novel scheme to incorporate signal space diversity into a dual‐hop relaying system with multiple decode‐and‐forward (DF) relays to enhance its spectral efficiency. The proposed dual‐hop signal space cooperative relaying scheme transmits two symbols in three phases while the conventional dual‐hop DF relaying system uses four phases to transmit the same two symbols. Therefore, the proposed scheme improves the spectral efficiency without additional complexity, bandwidth or transmit power. The proposed scheme is analysed over Rayleigh fading channel and error probability performance is derived. Moreover, an asymptotic approximation for the error probability is obtained to illustrate the impact of different system parameters and diversity gain. In addition, this study discusses the power allocation optimisation, relay position optimisation and the joint optimisation of both. Furthermore, closed‐form expression for the average channel capacity is derived. In the end, analytical results are compared and validated through Monte Carlo simulations.

Due to the broadcast nature of radio propagation, the wireless air interface is open and accessible to both authorized and illegitimate users. This completely differs from a wired network, where communicating devices are physically connected through cables and a node without direct association is unable to access the network for illicit activities. The open communications environment makes wireless transmissions more vulnerable than wired communications to malicious attacks, including both the passive eavesdropping for data interception and the active jamming for disrupting legitimate transmissions. Therefore, this paper is motivated to examine the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity, and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state of the art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. Several physical-layer security techniques are reviewed and compared, including information-theoretic security, artificial-noise-aided security, security-oriented beamforming, diversity-assisted security, and physical-layer key generation approaches. Since a jammer emitting radio signals can readily interfere with the legitimate wireless users, we also introduce the family of various jamming attacks and their countermeasures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer, and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.

A novel physical layer authentication enhancement scheme is proposed in this paper by integrating multipath delay characteristics of wireless channels into the channel impulse response (CIR)-based physical layer authentication framework. In order to simplify the decision rule for authentication, a two-dimensional (2-D) quantization method is developed to preprocess the channel variations. More specifically, two one-bit quantizers are used to quantize the temporal channel variations in the dimensions of channel amplitude and path delay, respectively. Under a simple hypothesis testing, a new test statistic is developed based on the sum of outputs of the two quantizers. For performance analysis, false alarm rate (FAR) and probability of detection (PD) are defined based on the developed test statistic, and their closed-form expressions are derived as well. An optimization problem is defined for finding optimal parameters of the proposed scheme based on exhaustive search method. Monte Carlo simulations are utilized to evaluate the performance of the proposed scheme. Compared with other existing method in the literature, the proposed scheme outperforms significantly in spoofing detection.

Relay communications have attracted increasing research attentions as a cost-effective technique to improve spatial diversity, service coverage, and energy efficiency in wireless networks. However, existing relay schemes e.g., amplify-and-forward and decode-and-forward DF schemes still face several major challenges, particularly the accumulation of multipath channels effect in AF and long processing latency in DF. To address these issues, we propose a novel equalize-and-forward EF relay scheme to enhance the retransmission reliability while maintaining low processing delay at the relay node. In particular, the proposed EF relay estimates and equalizes the channel between source and relay to eliminate the channel accumulation effect without signal regeneration. To further reduce the relay processing time, the channel estimation and equalization in the proposed EF design are performed in parallel. The proposed equalization is realized by presetting the equalizer coefficients with the current channel response that is predicted in parallel using multiple past channel responses. Numerical results show that the proposed EF relay scheme can achieve comparable symbol error rate performance as the DF relay with much less relay latency. In addition, the EF relay exhibits low outage probability at the same data rate as compared with traditional amplify-and-forward and DF schemes. schemes. Copyright © 2015 John Wiley & Sons, Ltd

This paper presents our recent work on human activity detection based on smart phone embedded sensors and learning algorithms. The proposed human activity detection system recognizes human activities including walking, running, and sitting. While walking and running can be recorded as daily fitness activities, falling will also be detected as anomalous situations and alerting messages can be sent as needed. Embedded sensors including a tri-axial accelerometer, tri-axial linear accelerometer, gyroscope sensor, and orientation sensors are used for motion data collection. A two-stage data analysis approach is used for prediction model generation: short period statistical analysis (max, min, mean, and standard deviation) and long period data analysis using machine learning. The system is implemented in an Android smart phone platform.

This paper proposes novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. Using our channel estimation and beamforming approach, we determine the number of cells Nc that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has L multipath taps, it is shown that Nc≤ L cells can reliably estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination. For example, in a long term evolution (LTE) channel environment having delay spread Td= 4.69μ second and channel bandwidth B = 2.5MHz, we have found that L = 18 cells can use this band. In practice, Tdis constant for a particular environment and carrier frequency, and hence L increases as the bandwidth increases. The proposed channel estimation and beamforming design is linear, simple to implement and significantly outperforms the existing designs, and is validated by extensive simulations.

In this paper, we propose a random phase shifting scheme applied to modulated data symbols of a dual-hop decode-and-forward (DF) relaying system to enhance its secrecy capacity in the presence of an eavesdropper, which can wiretap the communication signals of both hops. The random phase shift used in the proposed scheme is generated using a shared secret between transmitter and receiver of each hop, such as the reciprocal channel between the communicating devices, which is not accessible to the eavesdropper. Through theoretical analysis of the secrecy capacity, we have proven that the proposed scheme improves the ergodic secrecy capacity of the system. Notably, the proposed scheme is energy efficient so it can be applied to the systems with limited power resources at source and relay nodes.

While achieving localization using wireless networks, the positioning accuracy of a target device is highly sensitive to the placement of reference nodes. As a result, an in-depth analysis on the optimal placement of reference nodes is extremely useful in order to improve deployment outcome. In this paper, we propose an optimum reference node deployment scheme for Time of Arrival (TOA)-based localization by minimizing the Cramer-Rao Bound (CRB) of localization error. In order to find the global minima of the CRB which is highly nonlinear, a novel method is developed to solve the corresponding optimization problem. The essence of our method is to express the CRB in complex coordinates, and then to minimize the CRB with respect to the angles of reference nodes. The mathematical solution provides an interesting result indicating that the highest localization accuracy is achieved when the reference nodes have uniform angular distribution around the service area where the target is expected. We compare several different reference node deployment schemes through simulations, and the results show our derived optimum deployment provides the best performance.

Using the Global Positioning System (GPS) for indoor localization is challenging as the GPS signal is significantly attenuated or completely blocked by the building. In achieving indoor localization, a set of transmitters from wireless communication networks are often used as reference nodes to localize target nodes with unknown locations. The accuracy of such localization process is considerably affected by the spatial distribution of the reference nodes with respect to the target node to be localized. Hence, deploying the reference nodes at best locations will notably increase the localization precision. In this paper, a novel reference nodes deployment scheme is introduced which minimizes the average Mean Square Error (MSE) of the localization over the area of interest with certain shape. The proposed scheme is evaluated for deploying the reference nodes in the circular, square, and hexagonal localization regions. The proposed scheme is validated and illustrated by numerical simulations.

Performance of multihop cooperative sensor networks depends on relaying candidate selection, optimal relay assignment, and cooperative communication. In this paper, we first propose a novel relaying candidate selection scheme (KCN-selection) to choose k-cooperative nodes (KCN) at each hop based on geographic information, while the certain number of k is initially determined based on an on-demand end-to-end (ETE) reliability in the presence of unreliable communication links. However, the pre-assigned KCN cannot ensure an optimal performance due to wireless channel dynamics. To prolong the lifetime of wireless sensor network (WSN), we schedule some part of KCN to sleep while the on-demand ETE reliability still can be guaranteed with wireless channel variations. A probabilistic ETE reliability model is built to compute optimal duty cycle for KCN in an online manner. Furthermore, a KCN based optimal relay assignment and cooperative data delivery (KCN-delivery) scheme is presented, which can provide fully stateless, energy-efficient sensor-to-sink data delivery at a low communication overhead without the help of prior neighborhood knowledge. Simulation results show that our scheme significantly outperforms existing protocols in wireless sensor networks with highly dynamic wireless channel.

Spectrum Sensing (SS) is one of the fundamental mechanisms required by a Cognitive Radio (CR). Among several SS techniques, cyclostationary feature detection is considered as an important technique due to its robustness against noise variance uncertainty and its capability to distinguish among different systems on the basis of their cyclostationary features. However, one of the main limitations of this detector in practical scenarios is its performance degradation in the presence of cyclic frequency mismatch, which mainly arises due to the lack of knowledge about the transmitter clock/oscillator errors at the detector. In this context, this paper proposes a novel solution to address the cyclic frequency mismatch problem utilizing the Slepian basis expansion instead of the widely used Fourier basis expansion. It is shown that the proposed approach captures the deviation in the cyclic frequency caused by the aforementioned imperfections and hence provides a significant improvement in the sensing performance in the presence of cyclic frequency mismatch.

Cloud Manufacturing (CMfg) adopts and extends the concept of cloud computing to make mass Manufacturing Resources and Capabilities (MR/Cs) more widely integrated and accessible to users through the Internet. However, a single manufacturing cloud (MC) only has relatively limited scalability and elasticity. Using the aggregated MR/Cs from multiple MCs is a natural evolution. To address this requirement, we propose an integration framework for multiple MCs, so that MCs can collaboratively cope with peak user demands for MR/Cs. The key functional modules and the business model of the proposed framework are presented to guide future integration of multiple MCs. The enabling technologies, such as semantic web and ontologies, intelligent agents, service oriented architecture, and material handling and logistics technologies are also discussed. An application example is given, showing the feasibility and rationality of the proposed approach.

Energy efficiency is one of the most critical issues in wireless sensor networks, since the sensor nodes are usually battery powered. These energy-constrained sensor nodes are usually densely distributed in indoor environments, which leads to spatially correlated sensor data and low network efficiency. Thus, one way to improve energy efficiency is to reduce the redundancy caused by the correlated data. In this paper, a new sensor scheduling algorithm, based on data correlation, is proposed. The sensor nodes are clustered into groups by a new adaptive dual-metric K-means (DK-means) algorithm. Within each group, the sensor nodes take turns to work as a group representative and transmit data to the sink. Thus, the energy consumed by the redundant transmissions of the correlated sensor data is saved. Performance evaluation of the proposed mechanism is conducted through OPNET simulations. The simulation results show that the adaptive DK-means algorithm significantly improves data reliability, as compared to the adaptive K-means algorithm. Furthermore, this improvement in reliability is achieved with minimal cost in terms of complexity. Finally, it is shown that the proposed sensor scheduling algorithm achieves energy savings of up to 58%, as compared to the baseline ZigBee protocol.

The inherent properties of channel impulse response CIR, which are considered as location-specific characteristics of the physical link, have been exploited for the authentication purpose at the physical layer in the wireless communications. Unfortunately, the reliability of CIR-based physical layer authentication is challenged by the noise present in the CIR estimates, the rapid channel variation induced by the mobility of terminals, and the weak authentication decision by exploiting single CIR difference under the hypothesis testing. In this paper, three CIR-based authentication schemes are proposed to enhance the authentication reliability. Specifically, the noise components of the CIR estimates are mitigated in order to derive an adaptive threshold to form the authentication decision. Additionally, because of the rapid variation of the fading channel, channel prediction technique is employed to predict future CIR, and which is exploited to derive the CIR difference for the authentication analysis. Furthermore, to form the final decision in the authentication process, multiple CIR differences are observed by the receiver in a long range based on the channel predictor. In order to optimize the number of CIR differences, an optimization algorithm is developed by minimizing the total error rate under a false alarm constraint. Finally, the false alarm rate and the probability of detection are theoretically derived for performance evaluation, and the performance of proposed schemes is compared with that of a traditional channel-based authentication method using computer simulation. Copyright © 2014 John Wiley & Sons, Ltd.

In a wireless network, while the signal attenuation due to the distance-related path loss is minimum between a node and its nearest neighbor, this is not the case for the overall attenuation when the fading effect is also taken into account. Hence, the communication best neighbor (CBN) of a node is defined as the neighbor of that node with which it has the highest channel power gain. Recently, the statistics of the physical neighborhood index of the CBN have been derived for a general fading environment in which the nodes have two-dimensional Poisson distribution. In this paper, the statistics of the distance between a node and its CBN as well as the joint CBN distance-index statistics are obtained for the same scenario. Then, the analytical expressions are specialized for the case where the communication channels follow the generalized gamma fading model. Also, the results are verified and illustrated by computer simulations and numerical results. Further, it is shown how the obtained analytical results can be used to identify the CBN more efficiently by limiting the search region and the number of neighbors that are examined.

This paper develops hybrid analog-digital channel estimation and beamforming techniques for multiuser massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF) chains. For these systems, first, we design novel minimum-mean-squared error (MMSE) hybrid analog-digital channel estimator by considering both cases with perfect and imperfect channel covariance matrix knowledge. Then, we utilize the estimated channels to enable beamforming for data transmission. When the channel covariance matrices of all user equipments (UEs) are known perfectly, we show that there is a tradeoff between the training duration and throughput. Specifically, we exploit the fact that the optimal training duration that maximizes the throughput depends on the covariance matrices of all UEs, number of RF chains, and channel coherence time (Tc). We also show that the training time optimization problem can be formulated as a concave maximization problem where its global optimal solution can be obtained efficiently using existing tools. The analytical expressions are validated by performing extensive Monte Carlo simulations.

In this paper, we present a novel eavesdropping-resilient OFDM system through sorted subcarrier interleaving. The transmitter interleaves subcarriers in each OFDM signal according to its dynamic channel state information (CSI) to the legitimate receiver. More specifically, subcarriers are interleaved according to the sorted order of their instantaneous channel gains that are observed at the transmitter. Based on channel reciprocity, the legitimate receiver can derive the interleaving pattern initiated by the transmitter through its local channel estimate, and then de-interleave the received signals. In contrast, since spatially separated wireless channels in rich multipath environments are independent of each other, an eavesdropper at a third location cannot follow the dynamic subcarrier interleaving permutation, and thus fails to eavesdrop this transmission. Considering the imperfect reciprocity of noisy channel estimates at the legitimate terminals, only a subset of subcarriers in each OFDM signal is involved in the interleaving. A subcarrier selection algorithm is investigated to realize a trade-off between the eavesdropping resilience and transmission reliability. Theoretical analysis and Monte Carlo simulations have been provided to validate the proposed system. Compared with prior security enhancement schemes, the proposed approach requires only minor modifications to off-the-shelf systems and avoids additional resource consumption.

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.

Although cooperative relaying has been widely utilized in wireless communications, it simultaneously introduces a new source of security vulnerabilities to the wireless networks such as denial of service attacks. In order to minimize the potential security risks from relays, reliable relay authentication schemes become necessitated. In this paper, a novel relay authentication scheme is proposed to secure amplify-and-forward relay systems through utilizing the device-dependent hardware imperfection in-phase/quadrature (I/Q) imbalance. In this scheme, the I/Q imbalance associated with the receiving and transmission of the relaying process is considered as a unique device fingerprint. This fingerprint is then utilized to develop a two-parameter hypothesis testing based authentication. To enhance the performance in differentiating delicate difference between I/Q imbalances, the generalized likelihood ratio test for classical linear model is used in our hypothesis decision algorithm. The performance of the proposed authentication scheme is assessed and validated by numerical simulations. The results show significantly enhanced authentication accuracy of our new method in comparison with other I/Q imbalance based hypothesis decision algorithms.

Although cooperative relaying is generally considered as an attractive technique to improve system performances, the additional power consumption of a relaying link becomes a consistent concern. In order to characterize the power consumption of the dual-hop opportunistic relaying with power control, an exact expression for the cumulative distribution function of the total required transmission power is derived in this paper. Our analysis is based on the scenario that the best relay is selected among a set of decode-and-forward relays with homogeneous spatial Poisson distribution, such that the source-plus-relay power consumption is minimized. The required transmission power is evaluated by including the randomness of both path loss related to the locations of relays and the channel fading caused by multipath propagation. The analytical solution is verified by computer simulations and is used to compare the required power of relaying and direct links under different channel conditions. The results show that the dual-hop relaying is advantageous in long-term power saving by requiring a bounded average transmission power, while the power for the direct link communication is unbounded in some fading environments. Moreover, the required transmission power for the opportunistic relaying is less than that of the direct link with a reasonable relay density, therefore the cooperative communications can provide the power efficient transmission when the relay network is not sparse.

I/Q imbalance commonly exists in direct conversion architecture based wireless transceivers due to the mismatches of analog components between in-phase (I) and quadrature (Q) branches. In this paper, the device-dependent I/Q imbalance is utilized as transmitter fingerprint to accomplish improved wireless authentication through the collaboration of multiple receivers. In precise, two hypothesis testing based authentication methods are proposed to identify transmitter by using an I/Q imbalance related matrix. To enhance the authentication performance of these two methods, a multi-receiver collaboration scheme is proposed in our study. Simulation results validate our proposed authentication scheme and show a significant enhancement comparing to the scenario without receiver collaboration.

Wireless positioning systems rely on a minimum number of transmitters with known locations as reference nodes to locate a target device. However, in harsh environments where the number of available reference nodes is inadequate, wireless positioning systems may suffer from significant performance degradation. In this paper, we propose a wireless positioning technique utilizing transmitting nodes from other co-existing networks to improve the locationing performance in terms of accuracy and coverage. The procedure of the proposed location technique can be divided into two steps. During the first step, locations of extended reference nodes from other co-existing networks - i.e. additional active transmitters external to the original positioning network - are collaboratively estimated by the original reference nodes. The positioning of the target node can then be improved by a combined use of the original and newly discovered extended reference nodes. Maximum likelihood (ML) principle is applied in both estimation steps. The position estimation performance is analyzed and compared with the Cramer-Rao Lower Bound (CRLB). Computer simulations show that the accuracy of the position estimation using this approach can be improved by the joint effort of the extended and original reference nodes.

The proliferation of mobile services and the explosive growth of data traffic has created new challenges in cellular networks. Mobile data offloading has attracted significant attention, since it has the ability to alleviate cellular burden by using complementary resources and thus, offers better services to end users. In this paper, we introduce intelligence into heterogeneous network management and propose a Software-Defined Networking based module framework, which includes Wi-Fi based partial data offloading and load balancing. Our objective is to make real time decisions for selectively offloading traffic and balancing loads, while taking network conditions and quality of service (QoS) into consideration. The proposed mechanisms are subject to system-level simulations which shows an improvement in load balancing, in terms of equilibrium extent and network stability. We also prove that with the proposed Wi-Fi partial data offloading algorithm, quality of service can be satisfied, while saving a significant amount of cellular resources through smart resource allocation.

During the past decade, marine environment monitoring has attracted more and more researchers around the world and various marine environment monitoring systems have been developed. Traditionally, an oceanographic research vessel is used to monitor marine environments, which is very expensive and time-consuming and has a low resolution both in time and space. Wireless Sensor Networks (WSNs) have recently been considered as a promising solution for this purpose since they have a number of advantages such as easy deployment, unmanned operation, real-time monitoring, and relatively low cost. This paper first describes a common architecture of WSN-based oceanographic monitoring systems and a general architecture of an oceanographic sensor node. Then, it presents a detailed review of some related projects, systems, and technologies. It also highlights major challenges and research opportunities on the development and deployment of wireless sensor networks for marine environment monitoring.

The well-established low-cost broadband OFDM based wireless systems in license-exempt bands, including worldwide interoperability for microwave access (WiMAX) systems, have drawn considerable attention for radar related applications over the past decade. This paper explores an idea of utilizing Commercial Off-The-Shelf (COTS) WiMAX base stations (BSs) in unlicensed band for monostatic airborne synthetic aperture radar (SAR) application. The OFDM PHY of the WiMAX base station has been redesigned to meet the requirements of airborne stripmap SAR applications. The pulse repetition frequency (PRF) offered by standard WiMAX BSs can neither satisfy Doppler bandwidth nor expand SAR slant range. To increase PRF, we propose an RF front modification by employing WiMAX BSs together with a microwave double-pole-double-throw (DPDT) switch and two absorbing loads. Further, a standard WiMAX BS receiver can be directly used to acquire raw data of targets. Simulation results show the proposed scheme can significantly increase the range with limited transmission power.

Providing extra security in wireless communication networks using channel impulse response as a source of common randomness is an appealing direction in prospective systems. While some theoretical and experimental work has demonstrated its potential, a number of issues remain unexplored. This paper is concerned with the reliability of secret key generation under practical estimation requirements. We provide an analytical expression which relates the channel SNRs and degree of reciprocity to probability of key mismatch. In the next step we show how this affects key reconciliation and secrecy leaking. Finally, we consider how quantization with a protective band can help to trade the secret key rate for increased reliability and security of the scheme.

Received Signal Strength (RSS)-based ranging techniques have recently attracted a lot of attention because of their advantages in terms of low cost and easy implementation. The received signal strength highly depends on the path loss effect of radio wave propagation. When there is obstruction between transmitter and receiver, the signal power can drop significantly on the corresponding obstructed link, which degrades the accuracy of distance estimation. In this paper, we propose a novel RSS-based localization algorithm in obstructed environments with unknown Path Loss Exponent (PLE) based on Maximum Likelihood Estimation (MLE). The proposed algorithm can automatically detect the obstructed links between transmitter and receiver, and reduce the localization error caused by obstruction effect. According to the simulation results, our proposed method shows higher localization accuracy in obstructed environments as compared to other existing schemes.

A novel physical layer authentication scheme is proposed in this paper by exploiting the time-varying carrier frequency offset (CFO) associated with each pair of wireless communications devices. In realistic scenarios, radio frequency oscillators in each transmitter-and-receiver pair always present device-dependent biases to the nominal oscillating frequency. The combination of these biases and mobility-induced Doppler shift, characterized as a time-varying CFO, can be used as a radiometric signature for wireless device authentication. In the proposed authentication scheme, the variable CFO values at different communication times are first estimated. Kalman filtering is then employed to predict the current value by tracking the past CFO variation, which is modeled as an autoregressive random process. To achieve the proposed authentication, the current CFO estimate is compared with the Kalman predicted CFO using hypothesis testing to determine whether the signal has followed a consistent CFO pattern. An adaptive CFO variation threshold is derived for device discrimination according to the signal-to-noise ratio and the Kalman prediction error. In addition, a software-defined radio (SDR) based prototype platform has been developed to validate the feasibility of using CFO for authentication. Simulation results further confirm the effectiveness of the proposed scheme in multipath fading channels.

Spectrum sensing is defined as the task of detecting the presence of licensed users and is an essential prerequisite for opportunistic spectrum access in cognitive radio. Motivated by the infeasible assumptions of perfect synchronization and prior noise knowledge in most of the existing spectrum sensing algorithms, a robust orthogonal frequency division multiplexing OFDM signal sensing scheme, with the use of a noise power insensitive threshold, is investigated in this paper. Identification of primary OFDM signals is achieved by sliding the local pilot reference over the received signals and measuring the frequency domain correlations. The necessity of prior noise power knowledge for the sensing threshold determination is removed by employing the proposed interference insensitive test metric, which is a ratio of uniformly distorted correlations. As a result, no noise power information is required in the sensing process. In addition, the effects of both timing and frequency offsets are mitigated with a novel extended time domain segmentation as well as multiple frequency domain correlations via a frequency sliding window. Numerical results are provided to validate the theoretical analysis and estimate the performance of the proposed algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we investigate security issues in a collaborative wireless network in the presence of eavesdropping attacks, where multiple amplify-and-forward (AF) relays are exploited to secure the message transmission between legitimate users. We first consider the multiple AF relays all participating in assisting the transmission from source to destination, which is called all-relay based collaborative transmission scheme as denoted by all-relay scheme for notational convenience. We also propose the best-relay transmission scheme in which only the single “best” relay is selected to help the source transmit messages to destination. We then analyze the intercept behavior in wireless networks and evaluate intercept probabilities of the proposed all-relay and best-relay schemes as well as the conventional direct transmission without relay in a Rayleigh fading environment. Numerical results show that the best-relay transmission scheme always outperforms the all-relay and direct transmission schemes in terms of intercept probability. It is also shown that as the number of eavesdroppers increases, the intercept probabilities of both all-relay and best-relay schemes increase. Moreover, the intercept probability performance of all-relay and best-relay schemes significantly improves with an increasing number of relays, implying the advantage of exploiting multiple relays against eavesdropping attacks.

In cooperative amplify-and-forward (AF) relay networks, the system performance over multipath channels is impacted by both frequency-selective fading and delay spread. However, most relay selection (RS) schemes choose the best relay node only based on the channel gain while ignoring the delay spread effect on the performance. If orthogonal frequency division multiplexing (OFDM) is used in AF relay networks, the cyclic prefix (CP) length has to be extended to tolerate the accumulated delay spread from source via relay to destination due to the lack of the channel compensation at relay nodes. A long CP, which is the transmission overhead, redeces both the effective data transmission throughput and the overall system transmission efficiency. Therefore, an appropriate RS scheme in cooperation multiple-relay networks should not only enhance the overall transmission reliability but also minimize the relay overhead. To this end, we propose a variable-CP based RS scheme for AF relay networks to maximize the transmission efficiency by dynamically choosing the most suitable relay node. In the proposed scheme, a normalized effective throughput is defined as the selection criterion which depends on both the end-to-end channel gain and the accumulated delay spread. Based on this criterion, the best relay link is selected by achieving the tradeoff between the transmission reliability and overhead. Both the theoretical analysis and simulation results show that when the channel delay spread varies, the proposed scheme can dramatically improve the the effective data transmission throughput compared to the maximum signal-to-noise-ratio schemes with variable/fixed CP.

In cooperative communications, spatial channel diversity introduced by scattered locations of cooperating nodes can be exploited to enhance transmission security. In this paper, we propose a secure cooperative orthogonal frequency division multiplexing (OFDM) transmission scheme with the help of multiple relay nodes. For each relay, the wireless channels related to the destination and the eavesdropper are independent of each other, leading to different achievable rates over each subcarrier. In the proposed scheme, relay nodes with high rate differences between the intended receiver and the eavesdropper are selected for transmission. Different cooperation strategies ranging from single-relay selection to per-subcarrier-based multiple-relay selection are studied and compared. As a result of relay selection, each cooperative relay node only forwards a subset of subcarriers that exhibits high capacity differences, thereby enhancing transmission security of the whole system. Secrecy outage probabilities for the proposed scheme with different relay selection strategies are formulated. Numerical simulation results demonstrate the validity of the scheme for security enhancement.

The number of active users in a network is crucial for understanding the security level of wireless operating environments, since any node in a network could perform malicious attacks and be a potential threat. In this paper, we propose a novel estimation technique for the number of active users by exploiting a typical device fingerprint - I/Q imbalance, which has been identified as a device-specific hardware impairment and can be utilized to distinguish different wireless devices. In the design, I/Q imbalance of a transmitter is first estimated from its transmitting signals. The estimate is then compared with the observed I/Q imbalances of previously identified users through a hypothesis testing, where the distances between the new estimate and previous observations are adopted as the test metric. If all the distances are larger than a properly selected threshold, a new active user is claimed. Finally, the number of active users is determined by counting all the distinct I/Q imbalances. Simulation results are provided to validate the proposed estimation scheme.

The success of cooperative communications hinges on the reliability of cooperating nodes between the communicating parties, which may not be guaranteed in realistic scenarios. To protect cooperative networks from selfish misbehaviors and malicious forwarding, a security mechanism monitoring various abnormal behaviors during cooperation is necessary because of the constantly changing network topology and variability of cooperating nodes. In this paper, based on the orthogonal time division protocol commonly used in cooperation, a misbehavior detection scheme is proposed for amplify-and-forward (AF) cooperative orthogonal frequency division multiplexing (OFDM) systems by introducing the time division duplexing (TDD) feature into the source node and exploiting the correlation properties between the transmitted and received signals. With the estimated amplification gain and noise power, two binary hypothesis tests are employed to detect power-reducing selfish behaviors and malicious jamming attacks respectively. Simulation results demonstrate the effectiveness of the proposed scheme in detecting different misbehaviors of cooperating nodes.

Recently, channel impulse response (CIR) based physical layer authentication has been studied to enhance the security of wireless communications. However, the reliability of CIR-based authentication is substantially reduced at low signal-to-noise ratio (SNR) conditions due to the presence of communications noise, channel estimation error and mobility induced channel variation. To this end, we integrate additional multipath delay characteristics into the CIR-based physical layer authentication and propose a two dimensional quantization scheme to tolerate these random errors of CIRs for reduced false alarm rate and more reliable spoofing detection. Instead of directly comparing the estimated CIRs from different transmitters for authentication purpose, we first quantize the CIR estimates in two dimensions (i.e., the amplitude dimension and multipath delay dimension) and then differentiate transmitters based on the quantizer outputs with a binary hypothesis testing. More specifically, the quantization intervals are determined by using a searching algorithm based on a guaranteed miss probability of detection of the presence of spoofing attack. A logarithmic likelihood ratio test (LLRT) is used to evaluate the authentication performance, and a threshold with a constant value is used for the decision-making of authentication under the binary hypothesis testing. To verify the effectiveness of proposed algorithm, an orthogonal frequency division multiplexing (OFDM) system is considered in our simulation.

Due to the broadcast nature of wireless medium, wireless communication is extremely vulnerable to eavesdropping attack. Physical-layer security is emerging as a new paradigm to prevent the eavesdropper from interception by exploiting the physical characteristics of wireless channels, which has recently attracted a lot of research attentions. In this paper, we consider the physical-layer security in cooperative wireless networks with multiple decode-and-forward (DF) relays and investigate the best relay selection in the presence of eavesdropping attack. For the comparison purpose, we also examine the conventional direct transmission without relay and traditional max-min relay selection. We derive closed-form intercept probability expressions of the direct transmission, traditional max-min relay selection, and proposed best relay selection schemes in Rayleigh fading channels. Numerical results show that the proposed best relay selection scheme strictly outperforms the traditional direct transmission and max-min relay selection schemes in terms of intercept probability. In addition, as the number of relays increases, the intercept probabilities of both traditional max-min relay selection and proposed best relay selection schemes decrease significantly, showing the advantage of exploiting multiple relays against eavesdropping attack.

In amplify-and-forward (AF) relay systems, multi-hop transmissions over multiple frequency selective fading channels result in an accumulation of delay spread, which could lead to strong intersymbol interference (ISI) at the destination since there is no proper channel compensation at the relay nodes. If orthogonal frequency division multiplexing (OFDM) with fixed-length guard interval (GI) is used in these systems, then the length of the GI has to be extended to the longest possible channel delay spread to avoid ISI. However, using such a long GI lowers the effective data transmission throughput. To this end, we propose a novel OFDM system with adaptive GI for AF relay networks to reduce the overall transmission overhead by dynamically choosing the suitable GI length. More specifically, the GI is adaptively selected at the transmitter from a variable-length orthogonal code set, which provides a GI sequence with proper length, depending on the accumulated channel duration. By exploiting the orthogonal property between different GI sequences, the proposed adaptive GI scheme can be implemented without any extra control signal transmitted by the source to notify the destination about the GI used. This differs from existing adaptive GI studies which require such an additional control signal and thus introduce extra system overhead. Numerical results show that the proposed adaptive scheme (without additional control signal) can achieve the same symbol error rate (SER) performance as the conventional adaptive GI approaches (with control signal). This implies that the proposed scheme can further save the control signaling overhead without any SER performance loss.

In order to improve the confidentiality and security of orthogonal frequency division multiplexing (OFDM) systems, a physical layer security enhancement scheme by using time domain scrambling techniques is proposed in this paper. The approach is based on secretly scrambling the sample sequence within each time domain OFDM symbol, which is equivalent to constellation transformation over each subcarrier in the frequency domain. Consequently, the unique signal characteristics of the standardized OFDM transmission can be removed. The efficacy of the security enhancement is analyzed by means of the secrecy capacity. Moreover, related simulation results under the AWGN and the Rayleigh channel models indicate that our proposed scheme can improve the system secrecy significantly and the corresponding system performance will not be degraded.

In this paper, we propose a simple and effective eavesdropping-resilient OFDM system achieved by dynamic subcarrier allocation, exploiting the independent frequency selectivities of different wireless channels. The transmitter utilizes the channel state information (CSI) between the legitimate receiver and itself for the OFDM subcarrier allocation. The highly faded subcarriers are dropped for the data transmission and the constellation size of subcarriers with excellent channel conditions is increased in order to retain the overall throughput. Based on channel reciprocity, the channel behaves in the same manner at each pair of users. The subcarrier allocation scheme is thus shared by the transmitter and the legitimate receiver without additional signaling. In contrast, with an independent multipath channel, the eavesdropper at a separate location cannot derive an identical subcarrier allocation scheme. Consequently, mismatched demodulation is carried out at the eavesdropper so that disrupts the information recovery for eavesdropping. Moreover, due to the time-varying nature of wireless channels, the subcarrier allocation is frequently updated which further enhances the security. Theoretical analysis and simulation results are provided to evaluate the performance of the proposed secure OFDM system. It is validated that the proposed system is much more resilient to eavesdropping compared to the conventional OFDM system.

In this paper, we explore the physical-layer security in cooperative wireless networks with multiple relays where both amplify-and-forward (AF) and decode-and-forward (DF) protocols are considered. We propose the AF and DF based optimal relay selection (i.e., AFbORS and DFbORS) schemes to improve the wireless security against eavesdropping attack. For the purpose of comparison, we examine the traditional AFbORS and DFbORS schemes, denoted by T-AFbORS and T-DFbORS, respectively. We also investigate a so-called multiple relay combining (MRC) framework and present the traditional AF and DF based MRC schemes, called T-AFbMRC and T-DFbMRC, where multiple relays participate in forwarding the source signal to destination which then combines its received signals from the multiple relays. We derive closed-form intercept probability expressions of the proposed AFbORS and DFbORS (i.e., P-AFbORS and P-DFbORS) as well as the T-AFbORS, T-DFbORS, T-AFbMRC and T-DFbMRC schemes in the presence of eavesdropping attack. We further conduct an asymptotic intercept probability analysis to evaluate the diversity order performance of relay selection schemes and show that no matter which relaying protocol is considered (i.e., AF and DF), the traditional and proposed optimal relay selection approaches both achieve the diversity order M where M represents the number of relays. In addition, numerical results show that for both AF and DF protocols, the intercept probability performance of proposed optimal relay selection is strictly better than that of the traditional relay selection and multiple relay combining methods.

In this paper, we consider a cognitive radio network that consists of one cognitive base station (CBS) and multiple cognitive users (CUs) in the presence of multiple eavesdroppers, where CUs transmit their data packets to CBS under a primary user's quality of service (QoS) constraint while the eavesdroppers attempt to intercept the cognitive transmissions from CUs to CBS. We investigate the physical-layer security against eavesdropping attacks in the cognitive radio network and propose the user scheduling scheme to achieve multiuser diversity for improving the security level of cognitive transmissions with a primary QoS constraint. Specifically, a cognitive user (CU) that satisfies the primary QoS requirement and maximizes the achievable secrecy rate of cognitive transmissions is scheduled to transmit its data packet. For the comparison purpose, we also examine the traditional multiuser scheduling and the artificial noise schemes. We analyze the achievable secrecy rate and intercept probability of the traditional and proposed multiuser scheduling schemes as well as the artificial noise scheme in Rayleigh fading environments. Numerical results show that given a primary QoS constraint, the proposed multiuser scheduling scheme generally outperforms the traditional multiuser scheduling and the artificial noise schemes in terms of the achievable secrecy rate and intercept probability. In addition, we derive the diversity order of the proposed multiuser scheduling scheme through an asymptotic intercept probability analysis and prove that the full diversity is obtained by using the proposed multiuser scheduling.

ABSTRACT In cyclic delay diversity orthogonal frequency division multiplexing systems, the excessive channel delay spread and corresponding high frequency selectivity makes channel estimation a challenging task. In this paper, we propose a two‐stage scheme to estimate and track the highly frequency selective channel. At the preamble reception stage, least squares channel estimation withL0norm regularization is proposed to exploit the channel sparsity. At the data demodulation stage, an expectation–maximization algorithm with the most significant tap selection is developed to track channel variations by using the channel order obtained from the first stage. Compared with other estimation methods, the proposed scheme requires no prerequisite knowledge of delay parameter settings, which leads to more flexibility. Furthermore, the scheme can exploit the channel sparse structure by detecting the nonzero taps and, consequently, has better mean squared error performance. Simulation results show that the proposed estimation scheme can retain the provided diversity gain of cyclic delay diversity effectively in time‐varying fading channels. Copyright © 2011 John Wiley & Sons, Ltd.

Wireless Sensor Network (WSN) has emerged as a dependable technology to improve the quality of life in smart homes through offering various automated, interactive and comfortable services. Sensors integrated at different places in homes, offices, and even in clothes, equipment, and utilities are used to sense and monitor occupants' positions, movements, vital signs, utility usage, temperature and humidity levels of rooms, etc. Along with sensing and monitoring capabilities, sensors cooperate and communicate with themselves to deliver, share and process sensed information and assist real-time decision-making procedures through triggering appropriate alerts and actions. However, ensuring privacy and providing adequate security in these crucial services provided by WSNs is a major issue in smart home environments. In this paper, we examine the privacy and security challenges of WSNs and survey its practicality for smart home environments. We discuss the unique characteristics that distinguish a smart environment from the rest, elaborate on security and privacy issues and their respective solution measures. A number of challenges and interesting research issues emerging from this study have been reported for further investigation.

This paper considers the classification of digital modulation schemes in the presence of multipath fading channels and additive noise. A novel modulation recognition approach is proposed based on Gaussian Mixture Models (GMM). Our basic procedure involves parameter estimation using GMM to set up an offline database and then to classify the received signal into different modulation schemes based on the database by using Kullback-Leibler (K-L) Divergence. In order to mitigate the negative impact from multipath fading channels, an iterative Maximum A Posteriori (MAP)-based channel estimation is used in conjunction with the Expectation-Maximization (EM) algorithm. Furthermore, Gaussian approximation is carried out to decrease the computational complexity. Monte Carlo simulations are conducted to evaluate the performance of individual modulation scheme classification. Numerical results show that the proposed approach is capable of recognizing various modulated signals with improved performance under AWGN and multipath fading channels.

Information security is becoming a critical challenge in wireless communications due to the open nature of wireless channels and the transparency of standardized transmission schemes. Among the various wireless security techniques, user authentication is one essential measure to identify legitimate users and protect the integrity of transmissions. In this paper, a novel physical layer authentication scheme is proposed to enhance the communication security by exploiting the unique characteristics of oscillator in each communication device. In realistic scenarios, radio frequency (RF) oscillators in each transmitter and receiver pair always present some bias to the nominal carrier frequency due to manufacturing limitations and operating conditions. This bias is characterized by a device-dependent carrier frequency offset (CFO), which can be used to identify a specific wireless transmitter. In the proposed authentication scheme, the CFO at different time of the received signal is first estimated. It is then examined by a hypothesis testing to determine whether the signal has the consistent CFO for authentication purpose. Adaptive thresholds of CFO variation are derived for user discrimination based on the received signal-to-noise ratio (SNR). Simulation results further confirm the effectiveness of the proposed scheme in multipath fading environments.

Aiming at solving the problem of frequency scarcity in dense IEEE 802.11 wireless local area networks (WLANs), a novel channel assignment scheme is proposed in this paper where we explore the partially overlapped channels for additional frequency resources. In our proposed algorithm, we first introduce a user number estimation algorithm at physical layer where the number of users is determined by the number of different channel impulse responses (CIRs). Then, the IEEE 802.11 channels are allocated to the users in a distributed way with the purpose of maximizing system capacity using the information of the number of users for each channel. The interferences caused by the channel partial overlap are mathematically evaluated and involved in the channel assignment. Simulations verify that the proposed algorithm can estimate the number of users accurately while at the same time, significantly improving the system performance.

Recent development in wireless communication-based positioning systems using time-of-arrival (TOA) methods poses a significant challenge for the estimation of signal propagation time in indoor environments. Due to the possible obstruction of the direct path, the signal component from direct propagation can be very weak and therefore, the performance of TOA estimation will be significantly degraded, especially when the received signal experiences severe multipath propagation effects. An optimal direct path detection algorithm using multipath interference cancellation is proposed in this paper to improve the accuracy of communication-based positioning systems. By maximizing the probability of correct identification between the strong interfering paths and noise-only paths, an optimal threshold is derived for interfering multipath component selection. The interference of multipath components is reconstructed and then subtracted from the received signal to enhance the accuracy of direct path detection. The performance of the proposed algorithm is verified by simulations.

Equalization of fast time-varying channels is impacted by short coherence time and deep signal fading, especially in vehicle-to-vehicle communications due to the high mobility of user terminals. Furthermore, the stringent latency requirement of safety applications, such as collision avoidance, cannot tolerate high-complexity operations and long processing time. In order to achieve both requirements of equalization accuracy and short latency for fast varying channels, we propose a new model-based time-domain equalizer. In this equalizer, estimation and equalization are performed in two parallel parts to shorten the processing time. In main path, data symbols pass through an equalizer preset with the up-to-date channel impulse response. Since the channel variation model remains invariable for sufficiently long time, the current channel is estimated in parallel path from a number of past channel impulse responses to improve the accuracy. However, the channel variation during the long processing time of estimation leads to equalization error. Therefore, a predictor is used to update the channel response related to the processing delay, and perform the channel estimation beyond the channel coherence time. Thus, high accuracy and delay-free equalization can be achieved through this parallel structure.

An iterative design method for Orthogonal Frequency Division Multiplexing (OFDM) system parameter estimation and synchronization under a blind scenario for cognitive radio systems is proposed in this paper. A novel envelope spectrumbased arbitrary oversampling ratio estimator is presented first, based on which the algorithms are then developed to provide the identification of other OFDM parameters (number of subcarriers, cyclic prefix (CP) length). Carrier frequency offset (CFO) and timing offset are estimated for the purpose of synchronization with the help of the identified parameters. An iterative scheme is employed to increase the estimation accuracy. To validate the proposed design, the performance is evaluated under an experimental propagation environment and the results show that the proposed design is capable of adapting blind parameter estimation and synchronization for cognitive radio with improved performances.

Industries can benefit a lot from integrating sensors in industrial plants, structures, machinery, shop floors, and other critical places and utilizing their sensing and monitoring power, communicating and processing abilities to deliver sensed information. Proper use of wireless sensor networks (WSNs) can lower the rate of catastrophic failures, and improve the efficiency and productivity of factory operations. Ensuring reliability and providing adequate security in these crucial services provided by WSNs will reinforce their acceptability as a viable and dependable technology in the factory and industrial domain. In this paper, we examine the reliability and security challenges of WSNs and survey their practicality for industrial adoption. We discuss the unique characteristics that distinguish the factory environment from the rest, elaborate on security and reliability issues with their respective solution measures, and analyze the existing WSN architectures and standards. A number of challenges and interesting research issues have emerged from this study and have been reported for further investigation.

Diverse quality of service (QoS) guarantee is critical in wireless multimedia communications to fulfill the requirements of various applications. QoS with conventional single queue scenario has been very much explored but few studies were done on multiple queue system. In this paper, we propose a new multiple queue finite-state Markov chain model where multiple queues are employed at medium access control (MAC) layer and the system is modeled by combining the multiple queues with the finite-state Markov channel (FSMC) at physical (PHY) layer. We also introduce queue control parameters at MAC layer to determine the different priorities of different queues for the provision of diverse QoS, which can further be adjusted dynamically according to users' real-time requirements by configuring queue control parameters. The stationary distribution of the Markov chain is then obtained to derive the closed-form expression of the system QoS performance and finally we validate the proposed multiple queue algorithm by simulations.

Mutual interference plays decisive role in successful operation of a cognitive radio (CR) network. In order to evaluate the interference experienced by a primary user, we have considered a realistic communication scenario where the transmission of any secondary user is not only constrained by the primary network but also constrained from the secondary network itself so that harmful interference to other secondary users can be avoided. The transmission probability for each secondary user has been derived using joint density function of the distance from secondary user to the primary receiver and the distance between adjacent secondary users. Based on this analysis, a closed-form expression for expected mutual interference experienced by a primary receiver is derived. In addition, a cognitive network is simulated to evaluate the aggregated interference experienced by the primary receiver along with constraints from both primary and secondary networks. The proposed interference modeling is validated by comparing theoretical and simulated results of aggregated interference experienced by the primary receiver.

To fully exploit frequency diversity in cyclic delay diversity orthogonal frequency division multiplexing (CDD-OFDM) system, accurate channel estimation is crucial. Due to the excessive channel delay spread in CDD, traditional in-band pilot assisted channel estimation with limited frequency resolution fails to track the considerable variation in the frequency domain. Alternatively, increasing pilot overhead will degrade the system throughput significantly. In this paper, we propose to use superimposed pilots to estimate highly frequency selective channels in CDD-OFDM systems. Compared to in-band pilot based channel estimation, the proposed scheme with pilot symbols superimposed over each subcarrier has full frequency resolution, hence it is more robust to severe frequency selectivity from the excessively long CDD channel. Expectation-Maximization (EM) algorithm is employed to estimate the channel iteratively based on superimposed pilots and tentative soft decisions. At the end of each iteration, to exploit the inherent channel sparsity and refine the estimate, channel taps are sorted and selected according to power. Simulation results show that the performance of the proposed scheme is promising in time varying fading channels without an increase in pilot overhead.

Traditional authentication techniques for wireless communications are facing great challenges, due to the open radio propagation environment and limited options of transmission techniques. A new continuous physical layer authentication technique with time-varying transmission parameters is investigated to enhance the security of orthogonal frequency division multiplexing (OFDM) system. A preceded cyclic prefix (PCP) sequence, which introduces an additional signaling link to carry the time-varying transmission parameters, is employed in each OFDM symbol for physical layer authentication. The new PCP sequences are generated with the same time and frequency domain characteristics as data-carrying OFDM signals to reduce the interception probability. With the proper recovery of system parameters and interference cancellation, only legitimate users can successfully decode the PCP sequence and obtain necessary parameters to decode OFDM data. In addition, a cross layer design approach, which addresses the dependency among PCP configurations, authentication performance and transmitting performance, is introduced to continuously generate optimal PCPs according to dynamic communication conditions. Numerical simulations confirmed that the system performance, in terms of system robustness, security and stealth, can be significantly improved by using the proposed continuous authentication.

IEEE 802.11 wireless local area networks (WLANs) are widely deployed nowadays in home and urban areas. To solve the problem of radio frequency scarcity, interference minimization-oriented channel assignment has been very much explored at PHY layer. However, the effect of time domain simultaneous transmission on the neighboring interference is rarely considered. In this paper, we propose a cross-layer channel assignment algorithm for newly deployed access point (AP) initial setting up in high-density WLANs. Compared to the conventional algorithms which only focus on PHY layer, our proposed algorithm jointly analyzes the time domain overlap from MAC layer and frequency domain overlap at PHY layer to minimize the neighboring interference, which is the fatal reason of network Quality of Service (QoS) reduction. We also modify the beacon frame to support real-time information collection for channel assignment. Simulation results are provided to validate the proposed cross-layer channel assignment algorithm.

A distributed network self-optimization algorithm is proposed in this paper to support the automation of network reconfiguration in overlapping heterogenous access networks. As a large number of reconfigurable network elements coexist heterogeneously and network condition changes dynamically, distributed self-optimization in such complex environments can bring high system performance with minimum operation expenditure but also great challenge. This is because both quick respondence to the environment variation and global coordination among the heterogeneous reconfigurations are highly desired at the same time. In the proposed algorithm, the distributed optimization problem is mapped into a Mixed Strategy Game. According to equilibrium-oriented Mixed Strategy, the operating parameters of every network element can be refined efficiently without suffering impact from the heterogeneous neighboring reconfiguration within overlapping area. Simulation results show that both system blocking probability and average session bandwidth can be significantly improved using the proposed algorithm.

Multiuser OFDM (MU-OFDM) is widely applied nowadays to provide diverse Quality of Service (QoS) for multiple users. Subcarrier allocation according to the instantaneous channel state information (CSI) in MU-OFDM system has been well studied while the research of allocating subcarriers to each user constrained by diverse QoS requirements still remains large space undisclosed. In this paper, in order to meet users' diverse QoS requirements in MU-OFDM system, we propose a cross-layer dynamic subcarrier allocation algorithm where users' MAC layer diverse QoS requirements and the subcarrier allocation at PHY layer are jointly considered. The MAC layer queue status is modeled as a finite-state Markov chain (FSMC), using which the QoS constraints are transformed to the minimal PHY layer data rate requirement of each user. A sub-optimal dynamic subcarrier allocation algorithm is then proposed not only to satisfy the PHY layer data rate but also to significantly reduce the computational burden, aiming at maximizing system capacity. Finally, we verify the proposed cross-layer algorithm by simulations.

In order to improve the transmit power efficiency at the Mobile Station (MS), the transmit power is usually adjusted based on feedback information from the Base Station (BS) or Channel State Information (CSI) estimated at the MS. In fast-varying channels, because of the propagation and estimation delays, and the short channel coherence time, both the feedback information and the estimated CSI may become outdated at the transmit instant, leading to a reduction in power control performance. In this paper, a new channel prediction-based adaptive power control technique is proposed for uplink transmission in Time Division Duplex (TDD) Orthogonal Frequency Division Multiplexing (OFDM) systems. Based on the predicted Channel Impulse Responses (CIRs) provided by a cluster-based time- domain channel predictor, the transmit power is allocated to each OFDM subcarrier and then, a global gain is applied to compensate for the propagation path loss. In doing this, the power control process does not rely on the feedback information from the BS nor the estimated CSI at the MS and thus, the system responsiveness, adaptivity, and power savings are improved.

A novel adaptive Orthogonal Frequency-Division Multiplexing (OFDM) system with multi-layered (ML) transmission is proposed for mobile multimedia networks to support transmission of multiple data streams with flexible Quality-of-Service (QoS) adjustment capability and therefore can accommodate users with different QoS requirements or link conditions. The enhanced layers (ELs), formulated by parallel modulated orthogonal pseudo-random sequences, are superimposed onto OFDM data in frequency domain prior to multicarrier modulation and used to transmit different data information without the requirement of additional links. In this paper, the transceiver design particularly the frequency domain correlation detector and ELs induced interference cancellation is presented. A power distribution scheme for ELs is further proposed to optimize the overall system performance. The performance of the proposed ML-OFDM system is evaluated through numerical simulations.

Adaptive transmission in IEEE 802.11 requires rapid rate adaptation according to the variation of wireless environments. Existing rate adaptation schemes such as Automatic Rate Fallback (ARF) have been commercially implemented due to its simplicity. However, collision detection, which is an essential requirement to avoid network congestion and maintain the quality of service (QoS), is not well investigated in these traditional IEEE 802.11 link adaptation schemes. In this paper, we propose a fast collision detection scheme based on tracking the changes of the signal to interference and noise ratio (SINR). With the help of a nonparametric order-based cumulative sum (CUSUM) algorithm, collisions can be detected within a delay of a few OFDM symbols. Simulation results show the effectiveness of our proposed scheme and demonstrate its flexible implementation in existing IEEE 802.11 systems to assist current rate adaptation.

Recent development in cognitive radio (CR) communications brings significant technical challenges in the design of adaptive and robust transmission techniques in hostile communication environments due to the dynamic channel transitions and strong co-channel interference. A novel adaptive Orthogonal Frequency Division Multiplexing (OFDM) system with a precoded cyclic prefix (PCP) is proposed in this paper to provide a dynamic CR communication platform with fast transmitter-receiver interaction capability. Besides the basic function as a guard interval for the OFDM system, the PCP, combined from two oversampled precoded Kasami sequences, provides an efficient way of sending the system parameters without the requirement of additional signaling or feedback channels. Overall system and network efficiency is substantially improved due to the simplification of the preambles and handshaking signaling required when there is any change in the CR transmission parameters. Meanwhile, the real part of the PCP is uniquely assigned to each CR transceiver as identification label for spectrum sensing. The receiver design particularly the hybrid domain equalization and PCP induced interference cancellation for the proposed OFDM system is presented in this paper. Implementation related issues including exploitation of the PCP structure and complexity reduction are investigated. The performance of the proposed PCP-OFDM system is analyzed and verified through numerical simulations.

Various terrestrial digital television broadcasting standards have been developed in the last two decades for both fixed residential and mobile receivers. Since personal mobile terminal integrated with multiple functionalities is becoming more popular, it is highly desirable to come up with a universal platform for mobile TV reception which can detect the system parameters of DTV signals automatically and adjust its receiving mechanism correspondingly. In this paper, we propose a progressive automatic method for parameter extraction of OFDM-based mobile broadcasting signals. Classification of ATSC and OFDM based DTV signals can be easily achieved with the unique pilot of ATSC signals. Based on a combination of cyclostationarity test and conventional correlation, the proposed algorithm can estimate system parameters of the received OFDM signal, including the sampling rate, the number of subcarriers and the CP length (CP ratio), without any prior information.

A novel, reduced complexity iterative channel estimation algorithm for OFDM systems using superimposed pilots is proposed. It utilizes past channel estimations of double correlated channel as a side information to reduce number of iterations. Since pilots are available at all positions of the time-frequency OFDM grid in superimposed technique, the performance of the channel estimation does not degrade because of the variations of the fast fading channel between two pilots. On the other hand since no subcarrier is reserved for channel estimation purpose, superimposed pilot technique leads to improved spectral efficiency comparing to in-band OFDM pilots. However interference from data carrying signals made channel estimation more complex. In this paper, Least Square (LS) channel estimation followed by two dimensional Wiener filter for reducing OFDM symbol interference is done iteratively to achieve the Minimum Mean Square Error (MMSE). Small variations of the channel over each OFDM symbol duration are neglected due to a high data rate, but the values between different OFDM symbols are assumed correlated. The channel is modeled as a double selective, i.e. both frequency selectivity channel and Doppler shift are taken into consideration. Past channel estimates are used as side information for the present channel estimation to improve the forthcoming channel estimation at the first iteration and reduce the total number of iterations required.

To ensure proper operations of primary users and a fair spectrum sharing among the secondary users, spectrum sensing and user identification at cognitive radio (CR) have to be achieved with high accuracy under low signal- to-noise ratio (SNR). Motivated by the unavailability of perfect synchronization and prior noise knowledge at the CR sensing device, a robust sensing and user identification technique using noise insensitive threshold under low SNR is proposed in this paper for signals from precoded cyclic prefix Orthogonal Frequency Division Multiplexing (PCP-OFDM) system, which is proposed recently to provide a fast-adapting CR communication platform using a PCP enabled signaling link for dynamic system interaction. The proposed spectrum sensing and user identification are achieved with time domain cyclic correlations based on the inherent PCP feature in the received signals. Different users are distinguished in the sensing process with their unique PCPs. The impact of unknown noise statistics on the sensing threshold determination is removed with the proposed correlation ratio metric. The effect of unknown timing offset is also mitigated with the cyclic correlations. Numerical results are provided to verify the analysis and evaluate the performance of the proposed algorithm.

We proposed a precoded in-band pilots design for OFDM signal (PIP-OFDM) in [1]. With a special design, user identification in cognitive network is easily achieved by demodulating unknown information on identification pilot subcarriers embedded in the transmitted PIP-OFDM signal. In this paper, we discuss synchronization issues of PIP-OFDM system. Different from conventional OFDM system, synchronization in PIP-OFDM system is realized at low SNR. Benefit from the redundant information on pilots, performance of synchronization in PIP-OFDM system is greatly improved by processing multiple OFDM symbols. Coarse timing and frequency synchronization are briefly introduced using cyclic prefix correlation and pilots' energy correlation in frequency domain. Fine timing and frequency synchronization are realized with modified maximum likelihood estimator based on phase shift estimation in frequency and new method using the similar principle, respectively. System performance is evaluated by theoretical formulation and computer simulations in terms of mean square error. Results show that synchronization algorithms in this paper work effectively at low SNR from -5dB to 5dB.

We propose a first arriving path (FAP) detection algorithm using multipath interference cancellation techniques for indoor positioning systems. In indoor environments, the FAP is very often less significant in magnitude as compared with later arriving paths (LAPs) and therefore the received signal component of FAP is severely interfered by those of LAPs. As a result, it is difficult to identify the FAP in the presence of strong LAPs in indoor environments. In the proposed algorithm, we reconstruct the interference of LAPs based on channel estimation and data detection results. The interference of LAPs is subsequently reconstructed and cancelled from the received signals to improve the accuracy of FAP detection. Simulation results are provided to verify the performance of the proposed algorithm.

The ever-increasing demand for precious radio spectrum along with the inefficient usage of licensed band has led to the advent of the cognitive radio (CR) technology, which aims to provide opportunistic spectrum usage to unlicensed users and thus lead to the co-existence and interference control problem among heterogeneous systems. In this paper, an interference minimization and subcarrier power allocation approach for orthogonal frequency division multiplexing (OFDM)-based cognitive network is proposed. A transmission power negotiation signaling between CR transmitter and receiver is established through the use of encoded cyclic prefix (CP). Therefore, mutual interference to primary and other cognitive users can be minimized with reduced unnecessary transmission power. In addition, system performance of the receiver can be guaranteed in the process of mutual interference minimization. Beside this, subcarrier power allocation profile can be chosen from a set of predefined profiles and can be sent at the same time without additional signaling link and extra delay to the network. The transceiver structure and control signal encoding and decoding algorithm are investigated. The performance of the proposed signaling links are also analyzed and evaluated through simulations in different channel scenarios. In addition, interference minimization technique is validated through the simulation of probability density function (PDF) and then in a cognitive radio network with randomly distributed nodes to assess the overall perceived interference.

Reliable spectrum sensing in low signal-to-noise ratio (SNR) condition is one of the major technical challenges in cognitive radio network. In this paper, we propose a robust spectrum sensing algorithm for unsynchronized low SNR OFDM signal by mitigating the impacts of time and frequency offsets with multiple processing time window and sliding frequency correlator, respectively. In addition, considering that the noise's statistics are unknown to the devices and varying over time, our proposed algorithm is based on a ratio threshold and hence it is not sensitive to the noise power level. Our theoretical and simulation results show that this algorithm works effectively at very low SNR, while being insensitive to time and frequency offsets, and requires no information of the noise's statistics.

An adaptive Orthogonal Frequency Division Multiplexing (OFDM) system, having precoded cyclic prefix (PCP) was proposed earlier as a way to carry the control signaling parameters to provide much needed flexible transmission techniques in cognitive radio (CR) communications. The ultimate goal of the PCP-OFDM system is to achieve concurrent transmission of CR control signaling parameters with data information. Therefore, hardware and computational efficient demodulation of PCP is of huge importance to recover the signaling parameters and for the design of fair spectrum sharing mechanism in cognitive radio scenario. But due to the hardware and computational complexity of the conventional optimal matched filter, recently a demodulator was proposed in paper which has reduced complexity than the conventional approach. However, the complexity is still high which is often very difficult to implement and also not cost-effective. In this paper, we propose a low complexity demodulator for the efficient demodulation of the signaling information carried by the PCPs. Design of this demodulator is presented and performance is evaluated through the simulations in different channel scenarios. Along with a peak combining technique, impact of the multipath impairment can be mitigated to a reasonable extent. More importantly, the hardware and computational complexity is reduced substantially compared with the other techniques proposed earlier. It is also found from the numerical simulation results that this demodulation scheme provides the same performance as the conventional optimal matched filter, with significantly reduced hardware and computational complexity, therefore reducing the overall complexity and cost of the PCP-OFDM receiver.

Spectrum sensing and signal identification at low SNR, where synchronization and demodulation are not achievable, have emerged as two of the most critical challenges in cognitive radio network. A precoded in-band pilots design for OFDM signal (PIP-OFDM) is proposed in this paper to simplify the spectrum sensing and signal identification for dynamic spectrum sharing. The actual design of pilot signal in this OFDM system contains two groups of pilot tones, i.e. uniform pilot tones and identification pilot tones. Combining the two groups of pilot tones embedded in the OFDM signal, the spectrum sensing based on detecting the energy on pilot tones achieves an exceptionally good performance. In addition, the unique identification pilot code design for each transmitter in this network makes the signal identification easy and reliable. Theoretical analysis and simulation show that this design works well in spectrum sensing and signal identification at low SNR.

Sensing of orthogonal frequency division multiplexing (OFDM) signals at low signal-to-noise ratio (SNR) is of great importance for cognitive radio (CR) communications due to the wide applications of OFDM in many existing and evolving broadband wireless communications. In-band pilots, multiplexed with the data-carrying subcarriers, provides one distinct feature of OFDM signals. The objective of this paper is to investigate the application of in-band pilots in OFDM signal spectrum sensing, particularly when synchronization is not achievable at very low SNR. In this paper, the proposed sensing technique is to match the received OFDM signals with a self-defined pilot tone filter. Due to the extremely low requirement on sensing SNR for CR applications, synchronization of the sensing receiver and OFDM signals usually is not achievable. As a result, carrier frequency offset (CFO) and timing offset have to be dealt with during the sensing process. With the hybrid domain signal processing, the proposed sensing technique is very robust to both timing and frequency offset. We also apply the proposed technique to DVB-T to verify our proposal and subsequent analysis. Robust performance with very low false alarm probability and short sensing time were achieved under various channel conditions including Rician and Rayleigh channels, in the presence of random timing offset and CFO.

An adaptive Orthogonal Frequency Division Multiplexing (OFDM) system, with a preceded cyclic prefix (PCP), was proposed earlier to address the recent need of robust and flexible transmission technique in cognitive radio (CR) communications [1]. Identification of PCP-OFDM signals is therefore of great importance for the design of fair spectrum sharing mechanism, particularly at very low signal-to-noise ratio (SNR)when synchronization is not achievable. The preceded cyclic prefix, multiplexed with the data-carrying OFDM signals, provides one unique and recognizable feature of PCP-OFDM signals. In this paper, a robust PCP-OFDM signal identification technique is proposed under very low SNR in the presence of unknown timing and carrier frequency offset (CFO). Robust performance with very low false alarm probability and short sensing time was achieved under various channel conditions including Rician, Rayleigh and the additive white Gaussian noise (AWGN) channels.

An adaptive orthogonal frequency division multiplexing (OFDM) system, with a preceded cyclic prefix (PCP) as a signaling link was proposed earlier to address the recent need of flexible transmission techniques in cognitive radio (CR) communications. The flexibility of the PCP-OFDM system relies on the concurrent transmission of OFDM signal and PCP signaling which represents OFDM system parameters including bandwidth, modulation, coding schemes etc. Efficient demodulation of PCP signaling is therefore of great importance for OFDM data recovery and reduce the delay from the system adaption. In this paper, we propose to use a three-stage demodulator to recover the signaling information carried by the PCPs. Design of this three-stage demodulator are analyzed and performance is evaluated through the simulations in different channel conditions. In addition, with proposed peak combining technique, impact of the multipath impairment is mitigated to a great extent. It is observed from the simulation results that this signaling demodulation scheme provides the same performance as the conventional optimal matched filter but with significantly reduced hardware and computational complexity.

Channel estimation and equalization techniques are crucial for the ubiquitous wireless communication systems. Conventional receivers for most wireless standards preset the channel length to the maximal expected duration of the channel impulse response for the adopted channel estimation and equalization algorithms. The excessive channel length often significantly increases the implementational complexity of the wireless receivers and leads to the redundant information which would induce the additional estimation errors. Moreover, such a scheme does not allow the dynamic memory allocation for variable channel lengths. This could further increase the power consumption and reduce the battery life of a mobile device. The knowledge of the actual channel length would, in principle, help the system designers decrease the complexity of the channel estimators using maximum likelihood (ML) and minimum-mean-square-error (MMSE) algorithms. In this paper, we address this important channel length estimation problem and propose a novel algorithm to estimate the channel length without the need of pilots or training sequence. In addition, we provide the analysis on the effectiveness of the proposed non-pilot-aided channel length estimator through Monte Carlo simulations.

Recent development in cognitive radio (CR) brings significant technical challenges in the design of robust and flexible transmission technique in hostile communication environment with varying channel condition. An adaptive orthogonal frequency division multiplexing (OFDM) system, with a precoded cyclic prefix (PCP), is proposed in this paper to address these challenges. Besides the basic function as a guard interval for the OFDM systems, the PCP provides an efficient way of sending the transmission system parameters of the cognitive radio simultaneously with the data carrying OFDM signal. These parameters may include the bandwidth, total number of OFDM subcarriers, modulation and coding schemes used. Overall spectrum efficiency can be improved due to the elimination of the preambles and handshaking signaling required when there is any change in the CR transmission parameters. The receiver design particularly a hybrid domain equalizer for the PCP-OFDM system is presented in this paper. Implementation related issues including exploitation of the PCP structure, interference cancellation and complexity reduction are investigated. The performance of the proposed OFDM systems and the channel estimators are analyzed and verified through numerical simulations.

Generalized selection combining (GSC), whereby the receiver selects M out of N received replicas of the same signal for combining, is an effective mean to achieve reliable transmission in fading channel. However, in order to perform coherent combining, the selected signals must be individually tracked for sufficiently long time before accurate channel estimates can be produced for combining purpose. This tracking operation, unfortunately, is incompatible with the inherently antenna switching that takes place inside the GSC receiver. In this paper, we extend the switching rate analysis of M out of N GSC receivers in (J. Cavers et al., 2007) to the case of independent but statistically non-identical branches. Despite the fact that non-identical branches introduces a correlation between the difference of the M-th and the M+1-th strongest signals, u'(t) , and its derivative, u'(t) , we were able to derive an analytical expression for the switching rate of the GSC receiver under this condition (independency between u'(t) and u'(t) is crucial in obtaining the simple results in (J. Cavers et al., 2007)). Our numerical results agree with the intuition that having non- identical branches reduces the switching rate. The more dissimilar the branches are, the larger the reduction. While this lowering of the switching rate allows the GSC receiver more time to dwell on the selected signals and hence producing more accurate channel estimates for coherent combining, the bit-error rate of GSC, unfortunately, is higher when the branches are not identical.

The limited availability of spectrum and the inefficiency of its usage necessitate new research on spectral opportunistic communication technologies including cognitive radio (CR). Such new systems are characterized by the coexistence of the heterogeneous wireless systems. In this paper, a radiation footprint minimization technique is proposed through encoded in-band pilot tones for Orthogonal frequency division multiplexing (OFDM) system. A transmission power negotiation signaling between the transmitter and receiver is established through the encoded pilot tones. Electromagnetic interference to the primary and other cognitive radios can be minimized with an automatically reduced radiation footprint. In addition, system performance of the receiver can be guaranteed in the process of mutual interference minimization. The transceiver structure and the inband pilot tone detection algorithm are investigated. The principle and performance of the system are validated through numerical simulations.

A new multicarrier system, termed multi-symbol encapsulated orthogonal frequency division multiplexing (MSE-OFDM), was proposed earlier, in which one cyclic prefix (CP) is used for multiple OFDM symbols. The motivation of MSE-OFDM is to address the disadvantages of OFDM system, i.e., the sensitivity to carrier frequency offset and high peak to average power ratio at the same time. In this paper, we propose a new time slicing technique by replacing the cyclic prefix with preceded pseudo random sequence for MSE-OFDM system. With the proposed time slicing technique, multiple multimedia data streams with dynamic data rates can be easily multiplexed. In addition, this system overcomes the high sensitivity to frequency offset inherited with OFDMA system and can be used for both uplink and downlink for mobile multimedia communications with variable data rates. The proposed time slicing system also enables the power saving for mobile receivers, through which the batter life can be substantially extended.

Wideband source localization using acoustic sensors has been drawing a lot of research interest recently in wireless communication applications, such as cellular handset localization, global positioning systems (GPS), and land navigation technologies, etc. The maximum-likelihood is the predominant objective which leads to a variety of source localization approaches. However, the appropriate optimization (search) algorithms are still in pursuit by researchers since different aspects about the effectiveness of such algorithms have to be addressed on different circumstances. In this paper, we focus on the two popular source localization methods for wideband acoustic signals, namely the alternating projection (AP) algorithm and the expectation maximization (EM) algorithm. We explore the respective limitations of these two methods and design a new hybrid approach thereupon. Through Monte Carlo simulations, we demonstrate that the trade-off can be achieved between the computational complexity and the localization accuracy using our newly proposed scheme.

The intercarrier interference (ICI) due to the Doppler frequency shift, sampling clock offset, time-varying multipath fading and local oscillator frequency offset becomes the major difficulty for the data transmission via the wireless orthogonal frequency division multiplexing (OFDM) systems. The existing ICI mitigation schemes involve the frequency-domain channel estimation/equalization or the additional coding and therefore require the pilot symbols which reduce the throughput. The frequency-domain channel estimation/equalization relies on the huge matrix inversion with high computational complexity especially for the OFDM technologies possessing many subcarriers such as digital video broadcasting (DVB) systems and wireless metropolitan-area networks (WMAN). In our previous work, we proposed a semi-blind ICI equalization scheme using the joint multiple matrix diagonalization (JMMD) algorithm and empirically showed that the proposed method significantly improved the symbol error rates for QPSK- and 16QAM-OFDM systems. In this paper, we discuss the sufficient condition for the theoretical ICI equalizability and also propose an alternative semi-blind ICI equalization method based on the joint approximate diagonalization of eigen-matrices (JADE) algorithm, which is much more computationally efficient than our previous method.

Kurtosis minimization has been applied for the existing blind equalization schemes but the corresponding optimization procedures are very sensitive to the channel conditions and the initial conditions. In this paper, we introduce a new cognitive receiver front-end, which includes a novel switching blind equalizer and an automatic modulation classifier. We design a switching criterion based on the kurtosis/normalized moment ratio threshold to select the better signal between the raw data and the equalized sequence. Simulations demonstrate that our proposed robust switching blind equalization scheme can significantly outperform the existing blind equalizer and would not degrade the subsequent modulation classification accuracy.

A new cross-layer signaling interface using overlay watermarks for orthogonal frequency division multiplexing (OFDM) system is proposed in this paper. The major advantage of the new proposal is the multiple functionalities the overlay watermark provides, which includes a cross-layer signaling interface, a transceiver identification for position-aware cross-layer design, as well as its basic role as a training sequence for channel estimation. Location information of a mobile can be easily derived for efficient routing when a unique watermark is associated with each individual mobile transceiver. In addition, a new data pipe can be created by modulating the overlay watermarks for the transmission of cross-layer signaling and other interlayer interactive information. We also study the channel estimation and watermark removal techniques at the physical layer for the proposed overlay OFDM. Our channel estimator iteratively estimates the channel impulse response and the combined signal vector from the overlay OFDM signal. Cross-layer design that leads to low power consumption and more efficient routing are investigated.

We propose a new Multi-Symbol Encapsulated Orthogonal Frequency Division Multiplexing (MSE-OFDM) system with reduced implementation complexity in this paper, by replacing the traditional cyclic prefix (CP) with one pseudo random sequence. Although robust performance and high bandwidth efficiency are achieved, the receiver implementation of the previous MSE-OFDM system is substantially more complicated than traditional OFDM. In this paper, we propose a new cyclic prefix for MSE-OFDM using pseudo random sequences. With the new introduced cyclic prefix, inter-symbol interference (ISI) between OFDM symbols within one MSE-OFDM frame can be easily canceled in time domain. An iterative intercarrier interference (ICI) cancellation algorithm is also proposed in this paper. Equalization of the OFDM signal can therefore be achieved on every OFDM symbol basis, based on our new hybrid domain equalizer. It is shown that the implementation complexity of the new MSE- OFDM receiver with the pseudo random sequence cyclic prefix is comparable to the traditional OFDM. The performance of the proposed MSE-OFDM is also analyzed and verified through numerical simulations.

High peak to average power ratio (PAPR) of the transmitted signal is a major drawback of multicarrier transmission such as orthogonal frequency division multiplexing (OFDM). This work considers the problem of PAPR reduction in a multi-symbol encapsulated OFDM (MSE-OFDM) system. This paper employs the discrete cosine transform (DCT) to the data sequence. Not only can this method have the same bandwidth efficiency as that of the CP-reduced MSE-OFDM system, but also reduce the PAPR in the MSE-OFDM. Simulation shows that the proposed scheme can significantly reduce the PAPR in MSE OFDM system without increasing the symbol error rate.

We propose a new orthogonal frequency division multiplexing (OFDM) system embedded with overlay training watermarks in this paper. The major advantage of the proposed overlay watermark is the elimination of the in-band OFDM pilots while keeping the peak to average power ratio (PAPR) low for the overlay OFDM signal. As a result, the corresponding spectral efficiency and the transmitter amplifier efficiency are both improved over the existing OFDM systems. The adopted Kasami sequence encrypted overlay watermark is known to the receiver and has very minimal impact on the data detection performance once the channel impulse response is estimated. The proposed channel estimation technique is based on an iterative estimation of the channel impulse response, and the combined signal vector from the overlay pilot sequence and the desired OFDM signal. We also investigate the crucial interfering effect of the overlay pilot on the OFDM signal demodulation. The PAPR of the overlay OFDM signal is analyzed and it is shown that the PAPR of our proposed new scheme is less than the OFDM systems using the frequency domain overlay sequences. Moreover, according to our PAPR analysis, a new PAPR reduction technique via the selective watermark insertion is also designed in this paper.

Since the modulation type is unknown at a cognitive receiver front-end, the training sequence or pilot symbols are not available and the robust blind equalization is in demand to combat the fading channel problem and improve the symbol detection performance. Kurtosis minimization has been applied for the existing blind equalization schemes but the corresponding optimization procedures are very sensitive to the channel conditions and the initial conditions. In this paper, we introduce a new cognitive receiver front-end, which includes a novel switching blind equalizer and an automatic modulation classifier. We design a switching criterion based on the kurtosis/normalized moment ratio threshold to select the better signal between the raw data and the equalized sequence. Monte Carlos simulations show that our proposed robust switching blind equalization scheme can significantly outperform the existing blind equalizer and would not degrade the latter modulation classification accuracy.

Performance of a new position location system using the transmitter identification (TxID) RF watermark in the digital TV (DTV) signals is analyzed in this paper. Compared to the Global Positioning System (GPS), DTV signals are received from transmitters at relatively short distances, while the broadcast transmitters operate at levels up to a few megawatts of effective radiated power (ERP), which makes the new position location system very robust even inside buildings. Practical receiver implementation issues including non-ideal correlation function and frequency synchronization are analyzed and discussed. New algorithms of removing the bandlimitation effect and frequency synchronization are proposed. Performances of the proposed techniques are evaluated through analysis and Monte Carlo simulations. Possible ways to improve the accuracy of the new position location system are discussed.

Recently, the growing popularity of low-complexity multi-carrier modulation techniques, such as orthogonal frequency division multiplexing (OFDM), has led researchers to consider equalization of single carrier (SC) transmissions in the frequency domain. However, existing frequency domain equalizers for single carrier modulation rely on the introduction of a cyclic prefix (CP), which are usually not available in the conventional SC based communications standards. In this paper, a frequency domain equalizer (FEQ) without cyclic prefix for single carrier system is proposed. An interference analysis is presented for the block equalization of SC system without CP. The cancellation of the inter-block interference and inter-carrier interference are based on the proposed iterative interference cancellation. The proposed equalizer and the subsequent analysis are also verified through numerical simulations.

The developmental aspects and technical characteristics of the ATSC RF transmission standard ("8-VSB") are presented. An exposition is given of the planning and allocation methods that were developed, which are generally applicable to the introduction of a simulcast DTV service independent of the type of modulation used. Additional modulation enhancements (E-VSB)are explained. Techniques for implementation of distributed networks of on-channel transmitters are introduced along with references to some specific applications of these techniques.

A new multicarrier system, termed multi-symbol encapsulated orthogonal frequency division multiplexing (MSE-OFDM), was proposed, in which one cyclic prefix (CP) is used for multiple OFDM symbols. The motivations for this new OFDM system are either to reduce the redundancy caused by the CP or to increase the system robustness to frequency offset, depending on the two different proposed implementations for the MSE-OFDM systems. The corresponding frequency offset and channel estimation algorithms are investigated. Possible ways to reduce the complexity of the joint maximum likelihood (ML) estimator, including the approximation of the joint ML estimator and FFT pruning, are discussed. The performance of the proposed estimators is also analyzed and verified through numerical simulations.

A feature-suppressed orthogonal frequency-division multiplexing (OFDM) system and the corresponding channel estimation and intersymbol interference (ISI) mitigation techniques are investigated in this paper. Cyclic prefix (CP) and pilot tones, which are commonly used in civilian OFDM systems for ISI mitigation and channel estimation, create distinctive waveform features that can be easily used for synchronization and channel estimation purposes by intercepting receivers. As a result, CP and pilot tones are eliminated in the proposed feature suppressed OFDM system to reduce the interception probability. Instead, a set of specially designed OFDM symbols, driven by different pseudorandom sequences, are employed as preambles to avoid unique spectral signature. These preambles are inserted into the OFDM data symbol stream periodically and in a round-robin manner. In addition, a random frequency offset is introduced to each preamble to further mask the multicarrier signature. New challenges arising from these feature suppression efforts are studied, including robust channel estimation and demodulation techniques in the presence of frequency offset and severe interference. Based on our interference analysis, an iterative ISI and intercarrier interference (ICI) estimation-cancellation-based technique is proposed for both channel estimation and OFDM data demodulation. Our channel estimator performs joint frequency offset and channel impulse response estimation based on the maximum-likelihood (ML) principle. To reduce its complexity, we employ a number of techniques, which include approximation of the ML metrics, as well as fast Fourier transform pruning. The performances and feasibility of the proposed feature suppressed OFDM system and the channel estimator are analyzed and verified through numerical simulations.

A new multi-symbol encapsulated orthogonal frequency division multiplexing (MSE-OFDM) system is proposed, in which one cyclic prefix (CP) is used for a frame of multiple OFDM symbols. A system level comparison between a conventional OFDM and the proposed MSE-OFDM system is presented in this study. Two different realizations of the MSE-OFDM, i.e., CP-reduced and FFT size-reduced MSE-OFDM systems, are investigated for different system requirement. An analysis on the bandwidth efficiency, impact of synchronization errors on the system performance and peak to average power ratio (PAPR) of these systems is presented. Comparisons are made on two different assumptions, i.e., either keeping the symbol size of the MSE-OFDM (i.e., number of the subcarriers) unchanged to increase the bandwidth efficiency, or keeping the bandwidth efficiency unchanged (ratio between CP and useful data transmission time) for system robustness to synchronization errors and a lower peak-to-average power ratio. In the first case for CP-reduced MSE-OFDM, bandwidth efficiency is improved due to a reduced number of CPs inserted between OFDM symbols. For the latter case of FFT size-reduced MSE-OFDM, robustness to synchronization errors is improved considerably due to the smaller number of subcarriers. The proposed system is of particular interest for fixed wireless systems and digital subscriber loops (DSL). Large frame size can be used in these applications, due to the static nature of the channel conditions. Implementation complexity of the MSE-OFDM system is also discussed.

Orthogonal frequency division multiplexing (OFDM) has established itself as a very appealing technique for high-rate data transmission over dispersive channels. The main disadvantage of OFDM is that the signal exhibits Gaussian-like time-domain waveform with high peak-to-average power ratio, which limits the efficiency of the transmitter amplifier and degrades the received signal to noise ratio. The companding technique can be employed to mitigate quantization noise and the peak-to-average-power ratio of OFDM system. The symbol error rate of the companded OFDM systems is investigated. The performances of the systems with and without companding are compared.