Temporal knowledge graph (TKG) reasoning involves inferring future unknown facts based on historical data. Current approaches to temporal reasoning can be broadly categorized into two main paradigms: embedding-based methods and symbolic methods. While embedding-based methods excel at capturing time by representing temporal facts as vector, symbolic methods exploit temporal dependencies using techniques such as random walks for inference purposes. However, existing methods often fail to fully exploit both the inherent time and intricate temporal relationship patterns simultaneously. To address this limitation, we propose Temporal neural probabilistic logic learning (TNPLL), an innovative framework that seamlessly integrates symbolic logic with neural embeddings for robust temporal reasoning. Our approach incorporates two key components, a set of temporal logic rules equipped with explicit temporal relationships and a scoring module implemented through a novel temporal memory network architecture. The proposed method effectively combines time and temporal relationship patterns to predict future facts. We conducted experiments on several benchmark datasets, demonstrating that TNPLL achieves improved performance while fully leveraging time information. Specifically, our framework excels in scenarios where prior knowledge is available, but data samples are sparse. The experimental outcomes show that TNPLL outperforms state-of-the-art models in such cases.

Federated learning (FL) over wireless networks has become a key enabler for privacy-preserving distributed artificial intelligence (AI). However, high learning latency remains a critical bottleneck due to the presence of stragglers, limited wireless resources, and frequent model uploads. While model quantization can mitigate this issue by reducing communication overhead, its effectiveness is sensitive to device heterogeneity and time-varying channel conditions. To address this issue, we proposeFedDamQu, a communication-efficient FL framework with doubly-adaptive model quantization, which dynamically adjusts quantization bit-widths across devices and communication rounds to balance latency and accuracy. Our objective is to maximize the model performance under learning latency constraints. The main contributions are summarized as follows. 1) Convergence Analysis under Unreliable Channels: We derive a novel convergence error upper bound forFedDamQu, which explicitly quantifies the impact of device selection, unreliable transmission, and quantization error on the global model performance, under both fixed and dynamic quantization gain settings. 2) Joint Optimization Framework: Based on the knowledge from the proposed theoretical bound, we formulate a joint mixed integer nonlinear programming (MINLP) problem that integrates device selection, quantization bit-width configuration, and bandwidth allocation to minimize the convergence error under latency constraints. 3) Efficient Solution Design: The MINLP problem is decomposed into three subproblems, where closed-form solutions for quantization bit-width configuration and bandwidth allocation subproblems are derived, and a lightweight yet effective iterative algorithm is developed to obtain a suboptimal solution for the device selection subproblem. Extensive experiment results validate the theoretical analysis and demonstrate thatFedDamQuconsistently outperforms existing methods in terms of convergence rate and model accuracy, while significantly reducing the overall learning latency.

The development of Large AI Models (LAMs) for wireless communications, particularly for complex tasks like spectrum sensing, is critically dependent on the availability of vast, diverse, and realistic datasets. Addressing this need, this paper introduces the ChangShuoRadioData (CSRD) framework, an open-source, modular simulation platform designed for generating large-scale synthetic radio frequency (RF) data. CSRD simulates the end-to-end transmission and reception process, incorporating an extensive range of modulation schemes (100 types, including analog, digital, OFDM, and OTFS), configurable channel models featuring both statistical fading and site-specific ray tracing using OpenStreetMap data, and detailed modeling of realistic RF front-end impairments for various antenna configurations (SISO/MISO/MIMO). Using this framework, we characterize CSRD2025, a substantial dataset benchmark comprising over 25,000,000 frames (approx. 200TB), which is approximately 10,000 times larger than the widely used RML2018 dataset. CSRD2025 offers unprecedented signal diversity and complexity, specifically engineered to bridge the Sim2Real gap. Furthermore, we provide processing pipelines to convert IQ data into spectrograms annotated in COCO format, facilitating object detection approaches for time-frequency signal analysis. The dataset specification includes standardized 8:1:1 training, validation, and test splits (via frame indices) to ensure reproducible research. The CSRD framework is released at https://github.com/Singingkettle/ChangShuoRadioData1The dataset is designed to be fully reproducible using the provided framework, configurations, and configurable fixed random seeds to accelerate the advancement of AI-driven spectrum sensing and management.

The growing demands for high-precision sensing and ultra-reliable low-latency communications in Internet of Vehicles (IoV) networks, coupled with increasingly congested spectrum resources, have driven integrated sensing and communication (ISAC) technologies toward millimeter-wave (mmWave) frequency bands. However, apart from the inherent openness of wireless channels, this transition exposes critical security gaps that the conventional physical layer security methods featured by communication interference struggle to mitigate: the cross-domain coupling interference between communication and sensing subsystems among vehicles, forming an emergent threat landscape in ISAC-IoV networks. Based on the fact that conventional forward-facing vehicular mmWave radars are typically equipped with horizontally oriented narrow beam, and have limitations in vertical resolution due to the utilization of 1D horizontally arranged antenna arrays, in this paper, we propose a 3D communication-2D sensing (Com3DSen2D) enabled spatial separation and interference decoupling framework. Furthermore, under the proposed Com3DSen2Dframework, to make a balance between sensing accuracy, communication reliability and security, we formulate a non-convex optimization problem of secrecy rate maximization through jointly optimizing 3D-BF design of communication subsystems and radar sensing power allocation of sensing subsystems, subject to the expected levels of sensing accuracy and communication reliability constraints. Experimental evaluations demonstrate that our joint optimization approach achieves significant performance gains over individual optimization benchmarks, yielding 25.3% and 57.8% improvements in secrecy rate compared to isolated 3D-BF optimization and radar sensing power allocation, respectively. Moreover, experimental results obtained from the hardware platform further validate the effectiveness of our proposed framework. This work establishes a comprehensive solution for coupling interference management and security enhancement in next-generation ISAC-IoV networks.

Integrated sensing and communication (ISAC) has gained traction in academia and industry. Recently, multipath components (MPCs), as a type of spatial resource, have the potential to improve the sensing performance in ISAC systems, especially in richly scattering environments. In this paper, we propose to leverage MPC and Khatri-Rao space-time (KRST) code within a single ISAC system to realize high-accuracy sensing for multiple dynamic targets and multi-user communication. Specifically, we propose a novel MPC-enhanced sensing processing scheme with symbol-level fusion, referred to as the “SL-MSP” scheme, to achieve high-accuracy localization of multiple dynamic targets and empower the single ISAC system with a new capability of absolute velocity estimation for multiple targets with a single sensing attempt. Furthermore, the KRST code is applied to flexibly balance communication and sensing performance in richly scattering environments. To evaluate the contribution of MPCs, the closed-form Cramér-Rao lower bounds (CRLBs) for location and absolute velocity estimation are derived. Simulation results illustrate that the proposed SL-MSP scheme is more robust and accurate in localization and absolute velocity estimation compared with the existing state-of-the-art schemes.

Integrated sensing and communication (ISAC) has been recognized as a key technology in sixth-generation wireless networks, and the additional spatial degrees of freedom obtained by movable antenna (MA) technology can significantly improve the performance of ISAC systems. This paper considers an ISAC-assisted vehicle-to-infrastructure (V2I) network, where extended kalman filter-based prediction is combined with real-time optimization to jointly optimize transmit antenna positions and beamforming and power allocation vectors in dynamic environments. We propose two algorithms: a preprocessing-schur complement-projected gradient ascent algorithm for scenarios without sensing quality of service (QoS) constraints, which explores the potential range of sensing performance to provide reference and warm-starting for subsequent constrained optimization; and a heuristic reflective projected dynamic particle swarm optimization algorithm for sensing QoS-constrained scenarios, which achieves substantial performance gains under non-convex constraints with a small number of iterations. Simulation results demonstrate that these approaches enhance both the communication sum-rate and the lower of the Cram´er-Rao lower bound of motion parameter estimation, validating the effectiveness of MA-assisted beamforming in dynamic V2I ISAC networks.

Movable antenna (MA) technology provides a promising avenue for actively shaping wireless channels through dynamic antenna positioning, thereby enabling electromagnetic radiation reconstruction to enhance physical layer security (PLS). However, its practical deployment is hindered by two major challenges: the high computational complexity of real-time optimization and acritical temporal mismatch between slow mechanical movement and rapid channel variations. Although data-driven methods have been introduced to alleviate online optimization burdens, they are still constrained by suboptimal training labels derived from conventional solvers or high sample complexity in reinforcement learning. More importantly, existing learning-based approaches often overlook communication-specific domain knowledge—particularly the asymmetric roles and adversarial interactions between legitimate users and eavesdroppers, which are fundamental to PLS. To address these issues, this paper reformulates the MA positioning problem as a predictive task and introduces RoleAware-MAPP, a novel Transformer-based framework that incorporates domain knowledge through three key components: role-aware embeddings that model user-specific intentions, physics-informed semantic features that encapsulate channel propagation characteristics, and a composite loss function that strategically prioritizes secrecy performance over mere geometric accuracy. Extensive simulations under 3GPP-compliant scenarios show that RoleAware-MAPP achieves an average secrecy rate of 0.3606 bps/Hz and a Secrecy Performance Coverage Probability (SPSC) of 79.26%,outperforming the state-of-the-art predictive baseline by 35.5% and 6.73 percentage points, respectively, while maintaining robust performance across diverse user velocities and noise conditions.

To achieve mono-static sensing in the integrated sensing and communication network, an additional receiving panel for sensing is needed to cover the blind area around the base station. However, high-resolution estimation requires a wideband signal with large-scale antennas, making the receiver expensive and power hungry with traditional high sampling-rate and bit-depth analog-to-digital converters (ADCs). In this paper, a low complexity sensing receiver with one-bit quantization is proposed. In addition, an adaptive time-varying threshold architecture is designed to solve the dynamic range problem of one-bit ADC. An algorithm combining tensor unfolding and one-bit compressive sensing is proposed first to estimate the parameters of strong signals, based on which they could be reconstructed and the thresholds of one-bit ADCs are tuned adaptively using an adjustable switch network. After that, parameters of the weak target could be estimated in a sequential manner. The Cramér-Rao lower bound of the weak signal is derived to verify the effectiveness of the method. Numerical results show that our method eliminates the dynamic range problem in theory and after adaptive quantization, the weak-to-strong power ratio has an improvement of 27 dB, which is mainly affected by the estimation accuracy of the strong signals.

The task offloading technology plays a vital role in the Internet of Vehicles (IoV) by satisfying diversified vehicular demands, such as energy consumption and processing delay of computing tasks. Unlike the current related works, which not only ignored wireless interference when making information exchange, but also overlooked the available resources of parked and moving vehicles, this paper proposes a comprehensive solution. First, we model vehicle speed using a truncated Gaussian distri bution, replacing simplistic average speed models in prior studies. Wireless interference in V2V/V2I communications significantly impacts communication quality and reliability, leading to packet loss, increased latency, and reduced throughput. For instance, in high-density traffic scenarios, interference can disrupt com munication links, hindering effective task offloading. Next, by incorporating wireless interference and effective communication duration in V2V and RSUs, we propose an analytical framework for task offloading that jointly optimizes energy consumption and processing delay, leveraging resources from parked/moving vehicles and RSUs. Furthermore, inspired by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), we design an Interference-Aware Multi-Agent Deep Deterministic Policy Gradient (IA-MADDPG) algorithm. The algorithm ensures resource load balancing while reducing energy consumption and latency, and improves the task offloading completion rate. Simulations validate the effectiveness of IA-MADDPG, demonstrating supe rior convergence speed, energy efficiency, and latency reduction compared to existing methods.

Unmanned aerial vehicles (UAVs) are poised for explosive growth in the low-altitude economy, causing spectrum congestion and posing a challenge to airspace regulation. Although integrated sensing and communication (ISAC) enables simultaneous communication and sensing, alleviating the spectrum shortage, the capability of one single base station (BS) is generally limited. Therefore, a multi-BS cooperative ISAC system is developed to perceive the status of UAVs at the cell edge. Multiple BSs share the same time-frequency resources and adopt a time-division scheme to avoid mutual interference between communication and sensing functionalities. Specifically, the frame structure of the communication system is modified to accommodate the sensing functionality. A robust interference nulling based beam pattern is first proposed to prevent the line-of-sight (LoS) interference between BSs from overrunning the dynamic range of the analog-to-digital converter (ADC). Moreover, we designed a space-time block codec-based orthogonal frequency division multiplexing (OFDM) to separate echo signals originating from different BSs, which transforms the inter-BS reflected interference into bistatic sensing signals. Furthermore, a data-level fusion method based on the signal-to-interference-plus-noise ratio (SINR) of the range profile is applied to improve the positioning accuracy. The numerical results reveal that the proposed beam pattern greatly avoids LoS interference. The echo signals originating from neighboring BSs can assist in target detection and angle of arrival (AoA) estimation. Compared to soft fusion and single-BS schemes, the proposed fusion method enhances positioning precision by an order of magnitude, and is practically feasible even in the presence of clock synchronization errors.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless networks, enabling simultaneous high-speed communication and precise environmental awareness. This paper presents a novel ISAC imaging method, which leverages sparse multi-view channel state information (CSI) from existing communication infrastructure to reconstruct scattering fields, thereby achieving high-fidelity 3D imaging and environment reconstruction without the need for dedicated sensing hardware. A Neural Scattering Field Network (NSFNet) is designed to accomplish this task. The framework consists of two key components: 1) EdgeFusionNet, which extracts robust geometric features from sparse multi-view CSI using a multi-scale 3D CNN with edge-guided attention, and 2) MLP-based decoder that explicitly regresses view-dependent scattering coefficients, thereby addressing both the limited-view sampling challenge and the physical view-dependency of scattering. A self-supervised training strategy combining reconstruction loss and total variation regularization ensures accurate and smooth reconstructions. Experimental results demonstrate that NSFNet significantly outperforms compressed sensing and ablation deep learning baselines in complex scenarios, achieving superior performance in terms of F1-score (>0.83) and Chamfer Distance (<0.15 m). Furthermore, the method maintains stable performance under practical signal-to-noise ratio conditions and varying user equipment deployment densities, offering a scalable and hardware-efficient solution for ISAC-enabled environmental sensing. The proposed approach bridges the gap between sparse communication channel measurements and high-resolution 3D imaging, paving the way for seamless integration of sensing and communication in next-generation wireless networks.

This paper studies a wireless control system at network edge, in which a base station (BS) wirelessly coordinates the closed-loop control of multiple subsystems each consisting of a plant, a sensor, and an actuator. In this system, the BS first collects the state information from the sensors of plants, then processes the information via edge computing, and finally sends the obtained command signals back to the actuators for controlling the plants. In particular, we consider the hyper-reliable and low-latency communications (HRLLC) for the state and command signal transmission, by using the rate formulas based on short-packet communication. Under this setup, we first present a time-division-multiple-access (TDMA) protocol for coordinating the sensing, communication, and computation among the multiple plants. Then, we jointly optimize the task scheduling as well as the communication and computation resource allocations to minimize the closed-loop control latency while ensuring the stability of the multiple control subsystems. The considered problem is a highly non-convex combinatorial optimization problem that is difficult to solve. To resolve this issue, we present efficient algorithms by first optimizing the communication and computation resource allocations under given task scheduling via the techniques of alternating optimization and successive convex approximation, and then designing the task scheduling based on the exhaustive search or the low-complexity flow-shop scheduling. Numerical results show that the proposed joint resource allocation design with exhaustive search based task scheduling significantly outperforms other benchmark schemes without such joint optimization, and the proposed low-complexity task scheduling based on flow-shop scheduling achieves performance close to the upper bound by exhaustive search.

Emerging applications such as low-altitude economy and intelligent transportation hold the promise of significant economic and social benefits, requiring the support of technologies that integrate robust communication with precise sensing. Integrated sensing and communication (ISAC), as a technology enabled seamless connection between communication and sensing, is regarded a core enabling technology for these applications. However, the accuracy of single-node sensing in ISAC systems is limited, prompting the emergence of multi-node cooperative sensing. In multi-node cooperative sensing, the synchronization error limits the sensing accuracy, which can be mitigated by the architecture of cell-free massive multi-input multi-output (CF-mMIMO), whose fiber-optic interconnections ensure high synchronization accuracy. However, the multi-node cooperative sensing in CF-mMIMO ISAC systems faces the following challenges: 1) The joint optimization of placement and resource allocation of distributed access points (APs) to improve the sensing performance in multi-target detection scenario is difficult; 2) The fusion of the sensing information from distributed APs with multi-view discrepancies is difficult. To address these challenges, this paper proposes a joint placement and antenna resource optimization scheme for distributed APs to minimize the sensing Cramér-Rao bound for targets’ parameters within the area of interest. Then, a symbol-level fusion-based multi-dynamic target sensing (SL-MDTS) scheme is provided, effectively fusing sensing information from multiple APs. The simulation results validate the effectiveness of the joint optimization scheme and the superiority of the SL-MDTS scheme. Compared to state-of-the-art grid-based symbol-level sensing information fusion schemes, the proposed SL-MDTS scheme improves the accuracy of localization and velocity estimation by 41.8% and 38.7%, respectively.

A near-field motion parameter estimation method is proposed. In contrast to far-field sensing systems, the near-field sensing system leverages spherical-wave characteristics to enable full-vector location and velocity estimation. Despite promising advantages, the near-field sensing system faces a significant challenge, where location and velocity parameters are intricately coupled within the signal. To address this challenge, a novel subarray-based variational message passing (VMP) method is proposed for near-field joint location and velocity estimation. First, a factor graph representation is introduced, employing subarray-level directional and Doppler parameters as intermediate variables to decouple the complex location-velocity dependencies. Based on this, the variational Bayesian inference is employed to obtain closed-form posterior distributions of subarray-level parameters. Subsequently, the message passing technique is employed, enabling tractable computation of location and velocity marginal distributions. Two implementation strategies are proposed: 1) System-level fusion that aggregates all subarray posteriors for centralized estimation, or 2) Subarray-level fusion where locally processed estimates from subarrays are fused through Guassian product rule. Cramér-Rao bounds for location and velocity estimation are derived, providing theoretical performance limits. Numerical results demonstrate that the proposed VMP method outperforms existing approaches while achieving a magnitude lower complexity. Specifically, the proposed VMP method achieves centimeter-level location accuracy and sub-m/s velocity accuracy. It also demonstrates robust performance for high-mobility targets, making the proposed VMP method suitable for real-time near-field sensing and communication applications.

In this paper, we incorporate physical knowledge into learning-based high-precision target sensing using the multi-view channel state information (CSI) between multiple base stations (BSs) and user equipment (UEs). Such kind of multi-view sensing problem can be naturally cast into a conditional generation framework. To this end, we design a bipartite neural network architecture, the first part of which uses an elaborately designed encoder to fuse the latent target features embedded in the multi-view CSI, and then the second uses them as conditioning inputs of a powerful generative model to guide the target’s reconstruction. Specifically, the encoder is designed to capture the physical correlation between the CSI and the target, and also be adaptive to the numbers and positions of BS-UE pairs. Therein the view-specific nature of CSI is assimilated by introducing a spatial positional embedding scheme, which exploits the structure of electromagnetic(EM)-wave propagation channels. Finally, a conditional diffusion model with a weighted loss is employed to generate the target’s point cloud from the fused features. Extensive numerical results demonstrate that the proposed generative multi-view (Gen-MV) sensing framework exhibits excellent flexibility and significant performance improvement on the reconstruction quality of target’s shape and EM properties.

For integrated sensing and communication (ISAC) systems in complicated propagation scenarios, achieving precise multi-target sensing still faces many challenges. In particular, dynamic target perception is severely affected by clutter generated from static environmental factors, making accurate target distinction difficult. To address this issue, we propose an effective ISAC scheme designed for clutter suppression and efficient multi-target sensing. Unlike conventional methods that depend on prior information and complex computations, the proposed method eliminates long-term dependencies and reduces computational complexity. Specifically, we first construct a hybrid channel model that jointly considers static environments and dynamic targets. Upon receiving the ISAC signal, the base station (BS) estimates the static channel and applies an efficient spatial-domain filter to extract the effective dynamic channel. Subsequently, a reduced-complexity perception algorithm is employed to estimate key parameters, including distance, velocity, and angle. Simulation results demonstrate the feasibility and performance benefits of the proposed method in detecting numerous targets within complex cluttered environments.

To address the latency bottleneck in wireless federated learning (FL) systems, we propose FedDamQu, a communication-efficient framework that adaptively adjusts model quantization bit-width across both devices and communication rounds to cope with device heterogeneity and dynamic wireless conditions. Our objective is to maximize global model performance under strict latency constraints. The key contributions are threefold. First, we derive a novel convergence error upper bound that explicitly characterizes the effects of device selection, unreliable transmission, and quantization error. Second, we formulate a joint mixed-integer nonlinear programming (MINLP) problem that integrates device selection, quantization bit-width configuration, and bandwidth allocation to minimize the convergence error under latency constraints. 3) Third, we decompose the MINLP into three tractable subproblems, obtain closed-form solutions for quantization bit-width configuration and bandwidth allocation, and develop a lightweight iterative algorithm for device selection. Extensive experiments demonstrate that FedDamQu consistently outperforms existing methods in terms of convergence speed and model accuracy, while significantly reducing the overall learning latency.

A subarray-based near-field joint location and velocity estimation framework is proposed for sensing a moving target using extremely large-scale antenna arrays. To tackle the intricate near-field non-linear phase, the piecewise-far-field channel model is adopted, approximating the near-field channel by partitioning the transmit and receive arrays into subarrays and applying the near-field assumption between subarrays and the far-field assumption within each subarray. Based on this model, the complex near-field estimation problem can be transformed into a far-field joint multiple bistatic radar parameter estimation problem, enabling separable location and velocity estimation. An efficient three-stage algorithm is developed, exploiting joint sparsity across transmit-receive subarray pairs. In the first stage, a mixed-norm minimization method is employed to obtain coarse estimates of the location and complex channel gain, which are refined using the gradient descent method in the second stage. Finally, the velocity is estimated using the multiple signal classification spectrum estimation method, based on the refined location estimate. Simulation results demonstrate the effectiveness of the proposed framework and reveal a trade-off in system design: location estimation accuracy improves with increased subarray size, while velocity estimation benefits from a greater number of smaller subarrays.

The increasing demand for delay-sensitive services in industrial manufacturing, the Internet of Vehicles, and smart logistics imposes stringent delay requirements on intelligent machine (IM) networks. To reduce latency, short packet transmissions are widely used. However, their impact on network delay performance remains underexplored, particularly in large-scale deployments prone to packet collisions and queuing congestion. This paper develops a theoretical framework for modeling downlink communication and derives analytical expressions for three key delay metrics: transmission success probability, expected delay, and delay jitter. By incorporating finite blocklength constraints, we accurately characterize the effects of IM density and packet length on delay performance. Simulation results validate our model, offering valuable insights for optimizing IM network design and improving real-time communication efficiency.

Interference cancellation has always been a crucial task in wireless communications, especially in the presence of nonlinear distortions caused by power amplifier. However, when considering the ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) systems, this task becomes more challenging as the channel estimation will be severely impacted by the nonlinearity, thus leading to significant performance degradation. Driven by solving this problem, this paper proposed a novel nonlinear signal processing method, referred to as log-sum-minimization sparse channel estimation based nonlinearity cancellation (LSMSCE-NC). In detail, an optimization model based on the log-sum norm minimization and nonlinearity cancellation is first established and then its iterative solution is also presented. The numerical results reveal that the proposed LSMSCE-NC method achieves significant bit error rate (BER) and normalized mean square error (NMSE) advantages compared to the state-of-the-art algorithm.

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for sixth-generation (6G) mobile communication system. The ISAC research in dense urban areas has been plaguing by severe multipath interference, propelling the thorough research of ISAC multipath interference elimination. However, transforming the multipath component (MPC) from enemy into friend is a viable and mutually beneficial option. In this paper, we preliminarily explore the MPC-aided ISAC signal processing and apply a space-time code to improve the ISAC performance. Specifically, we propose a symbol-level fusion for MPC-aided localization (SFMC) scheme to achieve robust and high-accuracy localization, and apply a Khatri-Rao space-time (KRST) code to improve the communication and sensing performance in rich multipath environment. Simulation results demonstrate that the proposed SFMC scheme has more robust localization performance with higher accuracy, compared with the existing state-of-the-art schemes. The proposed SFMC would benefit highly reliable communication and sub-meter level localization in rich multipath scenarios.

Automatic Modulation Classification (AMC) using deep learning techniques has become a prominent area of research, showcasing considerable practical applications. However, the present AMC deep learning network, trained on a specific channel model, performs poorly in a new channel scenario. To deal with this, an adversarial semi-supervised domain adaptation method is proposed. Specifically, classification accuracy, cluster sensitivity, and distribution distance are optimized together, utilizing a three-stage iterative training approach. As a result, the proposed model has achieved robust performance with a limited amount of labeled data when shifting to a new channel.

Jamming attack is a critical technique in communication countermeasures. This paper proposes a novel jamming method that employs Denoising Diffusion Proba-bilistic Models (DDPM) to generate distorted signals, which effectively increase the bit error rate (BER). The underlying mechanism functions similarly to a parroting technique, where the attacker replicates the transmission behavior during the communication process and transmits nonsensical information to cause interference. Compared to additive white Gaussian noise (AWGN) jamming, the proposed method demonstrates a more favorable energy efficiency ratio.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Emerging applications, such as autonomous driving and Internet of Things (IoT) services put forward the demand for simultaneous sensing and communication functions in the same system. Integrated sensing and communication (ISAC) has the potential to meet the demands of ubiquitous communication and high-precision sensing due to the advantages of spectrum and hardware resource sharing, as well as the mutual enhancement of sensing and communication. However, the ISAC system faces severe interference requiring effective interference suppression, avoidance, and exploitation techniques. This article provides a comprehensive survey on the interference management techniques in ISAC systems, involving network architecture, system design, signal processing, and resource allocation. We first review the channel modeling and performance metrics of the ISAC system. Then, the methods for managing self-interference (SI), mutual interference (MI), and clutter in a single base station (BS) system are summarized, including interference suppression, interference avoidance, and interference exploitation methods. Furthermore, cooperative interference management methods are studied to address the cross-link interference (CLI) in a coordinated multipoint ISAC (CoMP-ISAC) system. Finally, future trends are revealed. This article may provide a reference for the study of interference management in ISAC systems.

Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The performance of radar sensing in ISAC systems is closely related to the characteristics of radar sensing and communication channels. Therefore, ISAC channel modeling serves as a fundamental cornerstone for evaluating and optimizing ISAC systems. This article provides a comprehensive survey on the ISAC channel modeling methods. Furthermore, the methods of target radar cross section (RCS) modeling and clutter RCS modeling are summarized. Finally, we discuss the future research trends related to ISAC channel modeling in various scenarios.

Exploring the mutual benefit and reciprocity of sensing and communication (S&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems. This paper investigates a novel communication-assisted sensing (CAS) system within 6G perceptive networks, where the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the estimated information to end-users. In such a CAS system, we first establish an optimal waveform design framework based on the rate-distortion (RD) and source-channel separation (SCT) theorems. After analyzing the relationships between the sensing distortion, coding rate, and communication channel capacity, we propose two distinct waveform design strategies in the scenario of target impulse response estimation. In the separated S&C waveforms scheme, we equivalently transform the original problem into a power allocation problem and develop a low-complexity one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S&C waveform. In the dual-functional waveform scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff in this scheme. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations.

The rapid advancement of the low-altitude economy (LAE) necessitates a fundamental shift from fragmented systems toward deeply integrated communication, sensing, navigation, and control capabilities. To this end, this paper proposes a low-altitude digital-intelligent network (LADIN) as an overarching architecture, with integrated sensing and communication (ISAC) serving as the core enabling technology that pervasively unifies its three layers. At the heterogeneous infrastructure layer, we detail an ISAC waveform design based on orthogonal frequency division multiplexing, enabling dual-purpose hardware to simultaneously achieve high-speed data transmission and high-precision environmental sensing. Within the intelligent data fusion layer, ISAC’s role expands into a multimodal fusion paradigm, providing the crucial electromagnetic sensing modality. This layer constructs a unified spatiotemporal feature space by introducing pluggable back-projection adapters and spatiotemporal modeling. These adapters systematically integrate heterogeneous data from ISAC, optical cameras, and light detection and ranging (LiDAR) by inverting their respective observation models, thereby overcoming representational disparities and association ambiguities. At the service and management layer, this coherent representation directly drives algorithmic processes and control policies. ISAC resources are virtualized into dynamically allocable assets, enabling closed-loop control that responds to the real-time state of the feature space, such as reconfiguring base station operational modes based on live situational awareness. Validation through multi-frequency collaborative sensing and multimodal fusion use cases demonstrates significant performance gains in tracking robustness, detection of near-zero radar cross-section targets such as balloons, and seamless urban airspace governance, conclusively establishing the transformative potential of a deeply integrated, ISAC-centric approach for future LAE systems.

Physical layer security (PLS) technology based on the fixed-position antenna (FPA) has attracted widespread attention. Due to the fixed feature of the antennas, current FPA-based PLS schemes cannot fully utilize the spatial degree of freedom, and thus a weaken secure gain in the desired/undesired direction may exist. Different from the concept of FPA, movable antenna (MA) is a novel technology that reconfigures the wireless channels and enhances the corresponding capacity through the flexible movement of antennas on a minor scale. MA-empowered PLS enjoys huge potential and deserves further investigation. In this paper, for the first time, we investigate the secrecy performance of MA-enabled PLS system where a MA-based base station (BS) transmits the confidential information to multiple single-antenna Bobs, in the presence of the single-antenna eavesdropper (Eve) with no location information, by jointly optimizing the beamforming and antenna positions at the BS. Furthermore, the non-convex optimization problem can be solved via the methods of projected gradient ascent and alternating optimization. Also, and simulated annealing is adopted to find a high-quality feasible solution. Simulation results demonstrate the effectiveness and correctness of the proposed method. In particular, MA-enabled PLS scheme can significantly enhance the secrecy rate compared to the conventional FPA-based ones for different settings of key system parameters.

Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, different from current physical layer security methods, which depends on the additional communication interference costing extra power resources, in this paper, we investigate a novel physical layer security solution, under which the inherent radar sensing interference of the vehicles is utilized to secure wireless communications. To measure the performance of physical layer security methods in ISAC-based IoV systems, we first define an improved security performance metric called by transmission reliability and sensing accuracy based secrecy rate (TRSA_SR), and derive closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), success ranging probability (SRP) for evaluating transmission reliability, security and sensing accuracy, respectively. Furthermore, we formulate an optimization problem to maximize the TRSA_SR by utilizing radar sensing interference and joint design of the communication duration, transmission power and straight trajectory of the legitimate transmitter. Finally, the non-convex feature of formulated problem is solved through the problem decomposition and alternating optimization. Simulations indicate that the sensing interference utilization, combined with joint design of transmission power and straight trajectory of the transmitter, achieves a secrecy rate of 3.92bps/Hz for different noise powers for the case of perfect channel state information (CSI). The proposed method maintains robustness, achieving a 60.17% improvement of TRSA_SR under unavailable CSI and location information of the Eve.

A novel modular extremely large-scale multiple-input-multiple-output integrated sensing and communication system is investigated in this paper. The piecewise-far-field channel model is employed to characterize both communication and sensing channels, capturing the far-field propagation within each subarray and the near-field effects among subarrays due to the small subarray aperture and large inter-subarray spacing. Then, a joint transmit-receive beamforming problem is formulated to optimize communication spectral efficiency while satisfying the sensing signal-to-clutter-plus-noise ratio requirement. To solve this problem, an alternating optimization framework is proposed to iteratively update the transmit beamformer and receive beamformer until convergence. For a fixed receive beamformer, a closed-form optimal analog beamformer is firstly derived by exploiting the near-field propagation characteristics among subarrays, transforming the transmit hybrid beamforming problem into a low-dimensional digital beamforming optimization and substantially reducing the computational complexity. Then, two efficient algorithms are proposed to solve the rank-constrained digital beamforming problem. First, the semi-closed form of the optimal digital beamformer is derived and shown to form a complex Stiefel manifold. Based on this structure, a joint Riemannian-Euclidean gradient descent algorithm is developed for iterative optimization. Second, an semidefinite relaxation-based approach is proposed, where a near-optimal solution is obtained through rank constraint relaxation and randomization. Extensive simulations validate the superiority of the proposed algorithms, revealing that the optimal subarray scale balances spatial multiplexing and beamforming gains based on user distance, while increasing subarray numbers significantly enhances range resolution due to more pronounced spherical wavefronts.

Task offloading technology plays a crucial role in the Internet of Vehicles (IoV) by minimizing processing delays through the joint optimization of heterogeneous computing resources supported by vehicles, roadside units (RSUs), and macro base stations (MBSs). Previous works have often ignored the wireless interference during the exchange and sharing of task data. Additionally, the potential for vehicles with similar driving behaviors to form vehicle platooning (VEH-PLA) and effectively integrate individual vehicle resources has not been adequately addressed. Furthermore, as a novel resource management paradigm, VEH-PLA should consider task categorization since vehicles within a VEH-PLA may have identical task offloading requestsan aspect that has also received insufficient attention. In this paper, considering wireless interference, vehicle mobility, VEH-PLA, and task categorization, we propose four task offloading models aimed at minimizing processing delays. By utilizing centralized training and decentralized execution (CTDE) based on multi-agent deep reinforcement learning (MADRL), we present a task offloading decision-making method to find the global optimal offloading decision. This results in significant enhancements in resource load balancing and reductions in processing delays. Finally, simulations validate that the proposed method significantly outperforms traditional task offloading approaches in terms of minimizing processing delays while maintaining balanced resource utilization.

Federated Learning (FL) is a communication-efficient distributed machine learning method that allows multiple devices to collaboratively train models without sharing raw data. FL can be categorized into centralized and decentralized paradigms. The centralized paradigm relies on a central server to aggregate local models, potentially resulting in single points of failure, communication bottlenecks, and exposure of model parameters. In contrast, the decentralized paradigm, which does not require a central server, provides improved robustness and privacy. The essence of federated learning lies in leveraging multiple local updates for efficient communication. However, this approach may result in slower convergence or even convergence to suboptimal models in the presence of heterogeneous and imbalanced data. To address this challenge, we study decentralized federated averaging via random walk (DFedRW), which replaces multiple local update steps on a single device with random walk updates. Traditional Federated Averaging (FedAvg) and its decentralized versions commonly ignore stragglers, which reduces the amount of training data and introduces sampling bias. Therefore, we allow DFedRW to aggregate partial random walk updates, ensuring that each computation contributes to the model update. To further improve communication efficiency, we also propose a quantized version of DFedRW. We demonstrate that (quantized) DFedRW achieves convergence upper bound of order$\mathcal {O}(\frac{1}{k^{1-q}})$under convex conditions. Furthermore, we propose a sufficient condition that reveals when quantization balances communication and convergence. Numerical analysis indicates that our proposed algorithms outperform (decentralized) FedAvg in both convergence rate and accuracy, achieving a 38.3% and 37.5% increase in test accuracy under high levels of heterogeneities, without increasing communication costs for the busiest device.

Integrated sensing and communication (ISAC) is a potential technology of the sixth-generation (6G) mobile communication system, which enables communication base station (BS) with sensing capability. However, the performance of single-BS sensing is limited, which can be overcome by multi-BS cooperative sensing. There are three types of multi-BS cooperative sensing, including cooperative active sensing, cooperative passive sensing, and cooperative active and passive sensing, where the multi-BS cooperative passive sensing has the advantages of low hardware modification cost and large sensing coverage. However, multi-BS cooperative passive sensing faces the challenges of synchronization offset mitigation and sensing information fusion. To address these challenges, a non-line of sight (NLoS) and line of sight (LoS) signal cross-correlation (NLCC) method is proposed to mitigate carrier frequency offset (CFO) and time offset (TO). Besides, a symbol-level fusion method of multi-BS sensing information is proposed. The discrete samplings of echo signals from multiple BSs are matched independently and coherently accumulated to improve sensing accuracy. Moreover, a low-complexity joint angle-of-arrival (AoA) and angle-of-departure (AoD) estimation method is proposed to reduce the computational complexity. Simulation results show that symbol-level multi-BS cooperative passive sensing scheme has an order of magnitude higher sensing accuracy than single-BS passive sensing. This work provides a reference for the research on multi-BS cooperative passive sensing.

Future mobile wireless networks will catalyze substantial demand for precise distributed cooperative positioning (DCP), especially when the global navigation satellite systems are unavailable. However, conventional message passing based DCP methods may suffer considerable performance degradation due to message approximation and sparsity/mobility of nodes. In this paper, we first present a high-accuracy parametric message approximation method, which achieves closed-form representations of all types of messages involved and reduces the computational complexity of message passing procedures. Using these representations, we propose a model- and data-driven hybrid inference approach, dubbed graph neural network enhanced spatio-temporal message passing (GNN-STMP), which fine-tunes parametric messages passed on factor graph and obtains more accuratea posterioridistribution of nodes’ positions by exploiting GNN-generated messages. Furthermore, we develop a universal framework for the parametric message passing based DCP problem, by integrating GNN-STMP with the extend Kalman filter based node’s state prediction and refinement. This framework significantly reduces the positioning ambiguity caused by insufficient spatial ranging measurements from neighbor nodes. Simulation results and analyses demonstrate that, compared with state-of-the-art methods, our proposed approaches achieve the best and near-best positioning accuracy when insufficient and sufficient spatial ranging measurements are available, respectively, while incurring modest computational complexity.

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)-enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and low-frequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, spatial filtering and cyclic cross-correlation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cramér-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.

The rapid development of low altitude Unmanned Aerial Vehicles (UAVs) as a new mode of transportation has injected a new driving force into the market development, but at the same time, unreported “black flight” UAVs have also created new risks in civil aviation safety, citizen privacy protection and other social security areas. In this regard, the Integrated Sensing And Communication (ISAC) capability of Base Station (BS) can provide an effective means of communication and supervision of low-altitude UAVs. For example, by demarcating the electronic fence area, the ISAC BS can realize automatic detection of illegal invasion of UAVs, effectively guaranteeing low-altitude safety in the context of low-altitude economy. By leveraging the high mobility of UAVs and their strong air-ground Line-of-Sight (LoS) channels, UAV-enabled ISAC is anticipated to provide superior sensing and communication coverage, and enhanced sensing and communication performance compared to terrestrial ISAC. However, existing work mainly focus on single BS sensing with the assistance of communication, which may not fully activate ISAC’s potential and achieve high-precision long-range sensing. Given the above considerations, this paper provides a cellular-connected UAV system, where the BS and connected UAV are employed to perform cooperative detection tasks for precise detection. To unleash the potential of ISAC in cellular-connected UAV systems, on the one hand, we propose an Extended Kalman Filtering (EKF) based data fusion algorithm to provide precise environment information and achieve beyond LoS sensing. On the other hand, according to the fusion results, we optimize the communication rate performance by jointly designing the transmit beamforming and trajectory subject to the power and practical fight constraints to combat the effect of mobility, while ensuring the sensing requirements, which can achieve a positive feedback loop. Extensive simulation results demonstrate that the proposed data fusion algorithm improves the estimation accuracy by 67% and the joint design of beamforming and trajectory algorithm improves the communication data rate by more than 31%.

With the rapid application of unmanned aerial vehicles (UAVs) in urban areas, the identification and tracking of hovering UAVs have become critical challenges, significantly impacting the safety of aircraft take-off and landing operations. As a promising technology for 6G mobile systems, integrated sensing and communication (ISAC) can be used to detect high-mobility UAVs with a low deployment cost. The micro-Doppler signals from UAV rotors can be leveraged to address the detection of low-mobility and hovering UAVs using ISAC signals. However, determining whether the frame structure of the ISAC system can be used to identify UAVs, and how to accurately capture the weak rotor micro-Doppler signals of UAVs in complex environments, remain two challenging problems. This paper first proposes a novel frame structure for UAV micro-Doppler extraction and the representation of UAV micro-Doppler signals within the channel state information (CSI). Furthermore, to address complex environments and the interference caused by UAV body vibrations, the rotor micro-Doppler null space pursuit (rmD-NSP) algorithm and the feature extraction algorithm synchroextracting transform (SET) are designed to effectively separate UAV’s rotor micro-Doppler signals and enhance their features in the spectrogram. Finally, both simulation and hardware testbed demonstrate that the proposed rmD-NSP algorithm enables the ISAC base station (BS) to accurately and completely extract UAV’s rotor micro-Doppler signals. Within the observation period of 0.1 s, ISAC BS successfully captures eight rotations of the DJI M300 RTK UAV’s rotor in urban environments. Compared to the existing AM-FM NSP, NSP, MTD, EMD and VMD signal decomposition algorithms, the integrity of the rotor micro-Doppler features is improved by 60%.

This paper studies a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networked integrated sensing and communication (ISAC) system, in which multiple base stations (BSs) perform beam tracking to communicate with a mobile device. In particular, we focus on the beam tracking over a number of tracking time slots (TTSs) and suppose that these BSs operate at non-overlapping frequency bands to avoid the severe inter-cell interference. Under this setup, we propose a new cooperative sensing-assisted predictive beam tracking design. In each TTS, the BSs use echo signals to cooperatively track the mobile device as a sensing target, and continuously adjust the beam directions to follow the device for enhancing the performance for both communication and sensing. First, we propose a cooperative sensing design to track the device, in which the BSs first employ the two-dimensional discrete Fourier transform (2D-DFT) technique to perform local target estimation, and then use the extended Kalman filter (EKF) method to fuse their individual measurement results for predicting the target parameters. Next, based on the predicted results, we obtain the achievable rate for communication and the predicted conditional Cramér-Rao lower bound (PC-CRLB) for target parameters estimation in the next TTS, as a function of the beamforming vectors. Accordingly, we formulate the predictive beamforming design problem, with the objective of maximizing the achievable communication rate in the following TTS, while satisfying the PC-CRLB requirement for sensing. To address the resulting non-convex problem, we first propose a semi-definite relaxation (SDR)-based algorithm to obtain the optimal solution, and then develop an alternative penalty-based algorithm to get a high-quality low-complexity solution. Simulation results indicate that the proposed cooperative sensing design achieves higher target tracking accuracy than other benchmark schemes. The results also validate the benefits of multi-BS cooperative sensing in improving tracking performance compared with the conventional single-BS sensing.

Parametric message passing (MP) is a promising technique that provides reliable marginal probability distributions for distributed cooperative positioning (DCP) based on factor graphs (FG), while maintaining minimal computational complexity. However, conventional parametric MP-based DCP methods may fail to converge in dense wireless networks due to numerous short loops on FG. Additionally, the use of inappropriate message approximation techniques can lead to increased sensitivity to initial values and significantly slower convergence rates. To address the challenging DCP problem modeled by a loopy FG, we propose an effective graph neural network enhanced fast convergent parametric MP (GNN-FCPMP) method. We first employ Chebyshev polynomials to approximate the nonlinear terms present in the FG-based spatio-temporal messages. This technique facilitates the derivation of globally precise, closed-form representations for each message transmitted across the FG, and reduces MP's sensitivity to initial positional values. Then, the parametric representations of spatial messages are meticulously refined through data-driven GNNs. Conclusively, by performing inference on the FG, we derive more accurate closed-form expressions for the a posteriori distributions of node positions. Numerical results substantiate the capability of GNN-FCPMP to significantly enhance positioning accuracy within wireless networks characterized by high-density loops and ensure rapid convergence.

Addressing the communication and sensing demands of sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) has garnered traction in academia and industry. With the sensing limitation of single base station (BS), multi-BS cooperative sensing is regarded as a promising solution. The coexistence and overlapped coverage of macro BS (MBS) and micro BS (MiBS) are common in the development of 6G, making the cooperative sensing between MBS and MiBS feasible. Since MBS and MiBS work in low and high frequency bands, respectively, the challenges of MBS and MiBS cooperative sensing lie in the fusion method of the sensing information in high and low-frequency bands. To this end, this paper introduces a symbol-level fusion method and a grid-based three-dimensional discrete Fourier transform (3D-GDFT) algorithm to achieve precise localization of multiple targets with limited resources. Simulation results demonstrate that the proposed MBS and MiBS cooperative sensing scheme outperforms traditional single BS (MBS/MiBS) sensing scheme, showcasing superior sensing performance.

The application of inverse synthetic aperture radar (ISAR) is investigated in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. In contrast to velocity sensing of a point target of most ISAC works, ISAR enables rotational velocity sensing to obtain the cross-range values of different scatterers on an extended target. To utilize this characteristic, we initially derive the ISAR OFDM received signal reconstruction in the frequency domain, which demonstrates that the ISAR OFDM echo signal can be equivalent to the signal received by an array, including the decoupled radial range and cross-range parameters. According to the derived signal model, a supporting parameter estimation algorithm based on the equivalent array form is proposed to estimate the range and cross-range parameters for resolvable scatterers on the extended target. Finally, numerical results confirm the effectiveness of utilizing ISAR sensing in wideband ISAC systems.

The integrated sensing and communication (ISAC) has been identified as a crucial enabling technology for the development of the sixth-generation (6G). This paper specifically focuses on the uplink orthogonal frequency division multiplexing (OFDM) ISAC system. By utilizing uplink ISAC signals, the base station (BS) can obtain a comprehensive understanding of its surroundings. However, due to the rapid growth of the sensing and communication services, spectrum resources are becoming increasingly scarce, which consequently leads to the tradeoff between sensing and communication. To address this challenge, we employ subcarrier and power allocation techniques that minimize the Cramer-Rao lower bound (CRLB) while satisfying the requirements of peak-to-sidelobe level ratio (PSLR) and communication data rate (CDR). Through this approach, subcarriers exhibit exceptional sensing performance are screened out to create a wider virtual sensing bandwidth. The resulting optimization problem is formulated as a nonconvex one. By utilizing the convex relaxation and cyclic minimization algorithm (CMA), the resource allocation problems are solved iteratively. Numerical results prove that our proposed strategy outperforms the conventional methods in terms of sensing and communication tradeoffs.

In the Integrated Sensing and Communication (ISAC) system, the pilot signal has high sensing performance due to its good autocorrelation and high transmit power. However, the equally spaced pilot signal reduces the maximum unambiguous range and velocity compared with the OFDM signal with continuous resources, limiting the sensing performance of base station (BS). Additionally, BS fails to estimate the distances and velocities of multiple targets in coherent signals. To address these problems, we propose a pilot design scheme with coprime and periodic stepping values for pilot indices. Theoretical analysis and simulation results demonstrate that the proposed pilot signal does not reduce the maximum unambiguous range and velocity, which can be processed by the smoothing algorithm and the multiple signal classification (MUSIC) algorithm to accurately estimate the distances and velocities of multiple targets in coherent signals.

Integrated sensing and communication (ISAC) base stations can provide communication and wide range sensing for vehicles via downlink (DL) transmission, thus enhancing the driving safety. One major challenge for achieving the high performance of communication and sensing is how to deal with the DL mutual interference among adjacent ISAC base stations, which includes not only communication-related interference but also sensing-related interference. In this article, we establish a DL mutual interference model of adjacent ISAC base stations, and analyze the relationship between the communication and sensing mutual interference channels. To mitigate the mutual interference, we propose a collaborative precoding design for adjacent base stations under the transmit power constraint and constant modulus constraint. To solve the nonconvex collaborative precoding design problem, we first relax the problem into a convex programming by omitting the rank constraint, and propose a joint optimization algorithm to solve the problem. To reduce computational complexity, We further propose a sequential optimization algorithm, which divides the collaborative precoding design problem into four subproblems and finds the optimum via a gradient descent algorithm. Finally, we evaluate the collaborative precoding design algorithms by considering sensing and communication performance via numerical results.

Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for integrated sensing and communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning-based multinode ISAC 4D environment reconstruction method with the uplink-downlink (UL-DL) cooperation, which employs virtual aperture technology, constant false alarm rate (CFAR) detection, and mutiple signal classification (music) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving the multinode cooperation and UL-DL cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models attention gate gridding residual neural network (AGGRNN) and multiview sensing fusion network (MVSFNet) to enhance the density of the sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multilevel fusion strategy incorporating both the data-level and feature-level fusion to fully leverage the advantages of the multinode cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms the other comparative method, enabling high-precision environmental reconstruction using the ISAC system.

Integrated sensing and communication integrated sensing and communications (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this article, we investigate transmit beamforming design for the multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multitarget sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among the reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multiobjective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between the communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, the simulation results validate that the proposed method achieves a better tradeoff between the multiuser communication and multitarget sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multitarget resolution capability and angle estimation accuracy.

This paper investigates the energy efficiency of a multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) system for Internet of things (IoT), in which one multi-antenna IoT transceiver transmits unified ISAC signals to a multi-antenna communication user (CU) and at the same time use the echo signals to estimate an extended target. We focus on one particular ISAC transmission block and take into account the practical on-off non-transmission power at the IoT transceiver. Under this setup, we minimize the energy consumption at the transceiver while ensuring a minimum average data rate requirement for communication and a maximum Cramér-Rao bound (CRB) requirement for target estimation, by jointly optimizing the transmit covariance matrix and the “on” duration for active transmission. We obtain the optimal solution to the rate-and-CRB-constrained energy minimization problem in a semi-closed form. Interestingly, the obtained optimal solution is shown to unify the spectrum-efficient and energy-efficient communications and sensing designs. In particular, for the special MIMO sensing case with rate constraint inactive, the optimal solution follows the isotropic transmission with shortest “on” duration, in which the IoT transceiver radiates the required sensing energy by using sufficiently high power over the shortest duration. For the general ISAC case, the optimal transmit covariance solution is of full rank and follows the eigenmode transmission based on the communication channel, while the optimal “on” duration is determined based on both the rate and CRB constraints. Numerical results show that the proposed ISAC design achieves significantly reduced energy consumption as compared to the benchmark schemes based on isotropic transmission, always-on transmission, and sensing or communications only designs, especially when the rate and CRB constraints become stringent.

In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization root mean square error (RMSE) by about 6 dB compared with the maximum likelihood esimation (MLE)-based benchmark method.

In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset (CFO), leading to major challenges in uplink sensing. Unlike existing technologies, our scheme does not require knowing the location of the UE in advance, and retains the linearity of the sensing parameter estimation problem. We first estimate the angle-of-arrivals (AoAs) of multipaths and use them to spatially filter the CSI. Then, we propose a KF-based CSI enhancer that exploits the estimation of Doppler with CFO as the prior information to significantly suppress the time-varying noise-like TO terms in spatially filtered CSIs. Subsequently, we can estimate the accurate ranges of UE and the scatterers based on the KF-enhanced CSI. Finally, we identify the UE’s AoA and range estimation and locate UE, then locate the dumb scatterers using the bi-static system. Simulation results validate the proposed scheme. The localization root mean square error of the proposed method is about 20 dB lower than the benchmarking scheme.

The power amplifier (PA) is an indispensable component in wireless communication systems, while the nonlinearity induced by PA can lead to significant performance degradation. The conventional nonlinearity equalization (NLE) method can effectively mitigate the nonlinear effects and provide superior BER performance but requires intensive computational complexity. To this end, we propose a novel NLE method in the time domain, which can significantly reduce the computational complexity without sacrificing the BER performance. Specifically, we first propose a novel PA representation of the sum of products (SPs), which is a unified time-domain representation for several typical memory and memoryless PA models. On this basis, the SPs-iterative least square equalizer (SPs-ILSE) method is proposed to mitigate the impact of both the memory and memoryless PA’s nonlinear distortions at the receiver side. The computational complexity of complex multiplication (CCCM) in the proposed method isO(NlogN) for the memoryless PA models andO(KN2) for the memory PA models. Moreover, considering the commonly utilized PA models, we also derive the closed-form expression for the achievable SINR of the SPs-ILSE method in the ideal conditions. Numerical results show that (i) the closed-form SINR expression is valid for both the memory and memoryless scenarios (ii) the proposed method exhibits the superior bit error rate (BER) performance in comparison to several relevant nonlinear signal processing methods such as digital pre-distortion (DPD), and power amplifier nonlinearity cancellation (PANC) (iii) the proposed NLE method achieves the same BER performance as the previous NLE method, i.e., reconstruction of distorted signals (RODS), while the CCCM of the proposed method is much lower.

Wireless communication with the enormous demands of sensing ability have given rise to the integrated passive sensing and communication (IPSAC) technology. The main challenge of IPSAC is how to achieve high sensing and communication performance by integrating the passive sensing and communication demodulation. In this paper, we propose an integrated sensing and communication (ISAC) signal processing optimization scheme by jointly processing the pilot and data signals. To solve the optimization problem, we propose an ISAC signal processing algorithm based on iterative optimization, which alternates the passive sensing and channel reconstruction to realize target sensing. However, the hyper-parameter configuration of the iterative optimization algorithm influences the performance of target detection and communication demodulation. Recognizing this fact, we propose a model-driven ISAC network (ISAC-NET) that adopts the block-by-block signal processing method to improve the communication and sensing performance. The proposed ISAC-NET obtains suitable hyper-parameters by deep learning to guarantee the performance and convergence of communication and sensing signal processing. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for the IPSAC systems.

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

Link scheduling plays a pivotal role in accommodating stringent reliability and latency requirements. In this paper, we focus on the availability and effectiveness of applying protocol interference model (PIM) under Rayleigh fading model to solve the problem. The motivation is that PIM caters to distributed link scheduling algorithm design, but usually lead to irrationality due to its localization behavior. While Rayleigh fading model can accurately describe the inherent characteristic of wireless signal propagation, but the features of global interference and channel fading make algorithm design more challenging. To be specific, we first remove the effect of channel fading on algorithmic design by establishing the relationship between Rayleigh fading model and non-fading model. We then propose a centralized once link elimination (OLE) algorithm by utilizing local nature of PIM, and achieve its distributed implementation based on the message delivery with time complexity of$O(\Delta _{\max }\ln \Delta _{\max })$, where$\Delta _{\max }$is the maximum number of nodes around a given node inside some range. Furthermore, based on random contention resolution, we design another distributed algorithm to schedule all the links within$O(\Delta ^{3}_{\max }\ln \Delta _{\max })$rounds. Simulations show that the PIM is of great confidence as same as Rayleigh fading model, and the proposed algorithms outperform three popular link scheduling algorithms.

Radio frequency fingerprint identification (RFFI) is a physical layer security methodology to recognize individual devices by leveraging hardware imperfections inevitably induced in the manufacturing process. However, the performance degradation caused by the time-varying channel impacts and interferences has severely restricted the development of RFFI. To this end, we present a channel-agnostic RFFI system, which consists of three modules, i.e., signal preprocessing module, feature extraction module, and classification module. In the signal preprocessing module, we first propose a novel approach, referred to as limiter-based spectral circular shift bidirectional division (LB-SCSBD), to generate two parallel spectral quotient (SQ) sequences. Then, we define the spectral quotient constellation (SQC) symbols according to different modulation formats, and thereby transform the SQ sequences into four magnitude-based sequences in terms of two channel-robust signal representations, i.e., the SQ magnitude (SQM) and SQC error vector magnitude (SQC-EVM). In the feature extraction module, we present a moment-based statistical feature extractor (MB-SFE) to extract the device-specific information from the above four sequences. In the classification module, the extracted statistics are fed into the multi-class support vector machine (SVM) for training and testing. We take WiFi as a case study and evaluate the performance of the proposed RFFI system by classifying eight simulated device models and six universal software radio peripheral (USRP) transmitter radios. Experimental results show that (i) the proposed method achieves the accuracies of 99.84% and 98.26% with eight devices in QPSK and 16QAM cases, as well as the accuracy of 92.42% with six USRP devices (ii) the proposed method exhibits superior classification performance in comparison to some existing RFFI methods, leading to a significant accuracy improvement of at least 38.33%.

Automatic modulation classification (AMC) is a crucial technique for the design of intelligent transceivers and has received considerable research attention. Conventional feature-based (FB) methods have the advantage of low computational complexity. However, these methods are highly sensitive to the distribution shifts of the received signal caused by the variation of channel effects and have rarely been studied in orthogonal frequency division multiplexing (OFDM) systems under unseen synthetic channels with multipath fading effects, carrier frequency offset (CFO), phase offset (PO) and additive noise. To solve this problem, this paper proposes a novel FB method using the error vector magnitude (EVM) features for AMC tasks (termed as EVM-AMC), which can achieve reliable classification performance for the communication scenarios considering unseen synthetic channels in OFDM systems. Specifically, we first propose the axisymmetric mapping-based self-circulant differential division (AM-SCDD) algorithm to convert the received signal into the non-negative spectral quotient (NNSQ) sequence, deeply suppressing the synthetic channel effects. Subsequently, we derive the EVM features by analyzing the matched error vectors between the generated NNSQ sequence and the predefined NNSQ constellation symbol (NNSQCS) masks. During this process, a percentile-based filter is utilized to remove the outliers in each matched error vector. Finally, the feature samples collected from various channel conditions are sent to the multi-class support vector machine (SVM) classifiers for training and testing. Two candidate modulation type sets are employed to evaluate the performance of the proposed EVM-AMC method under both the constant and changing channel conditions. Our numerical results demonstrate that 1) the proposed method exhibits impressive robustness and generalization when dealing with unseen synthetic channels, 2) the proposed method yields the best classification performance when compared to the conventional FB AMC methods in the presence of channel effects.

Perceptive mobile network (PMN) is an emerging concept for next-generation wireless networks capable of conducting integrated sensing and communication (ISAC). A major challenge for realizing high performance sensing in PMNs is how to deal with spatially separated asynchronous transceivers. Asynchronicity results in timing offsets (TOs) and carrier frequency offsets (CFOs), which further cause ambiguity in ranging and velocity sensing. Most existing algorithms mitigate TOs and CFOs based on the line-of-sight (LOS) propagation path between sensing transceivers. However, LOS paths may not exist in realistic scenarios. In this paper, we propose a cooperation based joint active and passive sensing scheme for the non-LOS (NLOS) scenarios having asynchronous transceivers. This scheme relies on the cross-correlation cooperative sensing (CCCS) algorithm, which regards active sensing as a reference and mitigates TOs and CFOs by correlating active and passive sensing information. Another major challenge for realizing high performance sensing in PMNs is how to realize high accuracy angle-of-arrival (AoA) estimation with low complexity. Correspondingly, we propose a low complexity AoA algorithm based on cooperative sensing, which comprises coarse AoA estimation and fine AoA estimation. Analytical and numerical simulation results verify the performance advantages of the proposed CCCS algorithm and the low complexity AoA estimation algorithm.

Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes.

To realize an intelligent cooperative vehicle infrastructure system and high-level autonomous driving, the introduction of the joint communication and sensing (JCS) technique in vehicular networks is indispensable. With directional beamforming, the vehicles equipped with JCS systems could utilize unified radio-frequency transceivers and frequency band resources to achieve vehicle-to-infrastructure (V2I) communication and sensing functions in different directions, respectively. In this concept, we study the computation offloading problem for JCS-based vehicular networks. Specifically, we formulate a long-term multi-objective problem that jointly optimizes the task execution latency and the sensing performance of multiple vehicles. Owing to the time-varying V2I channel gain, the time-varying impulse response of sensed target, and the stochastic traffic, we reformulate it as a Markov decision process and propose a double-stage deep reinforcement learning-based offloading and power allocation (DDOPA) strategy to determine the task offloading and power allocation for each vehicle. Simulation results demonstrate the efficacy of the proposed strategy compared with different strategies, and show that the proposed DDOPA strategy can achieve a trade-off between execution latency and sensing performance.

To realize higher-level autonomous driving and advanced transportation applications, the introduction of the integrated sensing and communication (ISAC) technique in vehicular networks is indispensable. Different from the existing works, this paper investigates the power allocation problem for onboard ISAC systems of vehicles, during the vehicle-to-infrastructure communication, vehicle-to-vehicle communication and sensing progress, in case of the time-varying communication channel gains, the time-varying impulse responses of sensed targets, and the stochastic traffic. Note that both the inter-beam interference of a single vehicle and the inter-vehicle interference are important considerations. Specifically, we formulate a stochastic programming problem, which optimizes the sensing performance, subject to constraints on the network stability, power limits and quality-of-service requirements. Leveraging the Lyapunov optimization technique, this stochastic programming problem is transformed into a single-time slot non-convex problem. Taking advantages of genetic algorithm and particle swarm optimization (PSO), a hybrid meta-heuristic algorithm is designed to solve the non-convex problem. Typically, we improve the traditional PSO to balance the global search ability and local search ability of particles. Finally, a dynamic power allocation strategy is proposed. The theoretical analysis and simulation results show that this strategy achieves a communication performance-sensing performance tradeoff of [$ {\mathrm {O(}}1/V{\mathrm {)}} $,$ {\mathrm {O(}}V{\mathrm {)}} $] with$ V $being a control parameter.

In developing the sixth-generation (6G) system, integrated sensing and communication technology is becoming increasingly essential, especially for applications like autonomous driving. This paper develops an architecture for integrated sensing, communication, and computation (ISCC) in the vehicular network, where vehicles perform environment sensing, sensing data computation, and transmission. To support low-latency cooperation between vehicles and extend vehicles’ sensing range, over-air-computation federated learning is employed. The optimization problem of joint beamforming design and power resource allocation in the ISCC scenario is formulated to maximize the achievable data rate while ensuring sensing and computing performance. However, solving this joint optimization problem is a great challenge due to the high coupling resource and time-varying channel environment. Therefore, a hybrid reinforcement learning scheme is proposed in this work. First, the semidefinite relaxation and Gaussian randomization techniques are leveraged to obtain the approximate solution of the aggregation beamformer. Then, the deep deterministic policy gradient algorithm is proposed to tackle the transmit beamforming design and resource allocation problem in continuous action space. Extensive simulation results validated the admirable performance of the proposed scheme in convergence and achievable sum rate compared with the benchmark schemes. In addition, the impact of variables on the optimization performance is demonstrated via numerical results.

This paper investigates a reconfigurable intelligent surface (RIS) assisted cooperative multicell integrated sensing and communication (ISAC) system with multiple users and targets. In particular, the RIS is leveraged to assist the joint transmission of the multiple base stations (BSs) to multiple users, while assisting cooperative sensing by multiple BSs to perform multiple targets sensing. We formulate a problem for the purpose of minimizing the transmit power via jointly designing the transmit beamforming of the BSs and phase shifts of the RIS, while guaranteeing the achievable communication rate requirements and the sensing mutual information requirements. To address this non-convex problem, a high-quality alternating optimization algorithm is developed to split the intractable problem into two sub-problems. Specifically, with the given phase shifts of the RIS, the transmit beamforming sub-problem is addressed by semidefinite relaxation-based algorithm. A successive convex approximation (SCA) method-based and penalty function-based convex-concave procedure algorithm is proposed to tackle the RIS phase-shift optimization sub-problem. To reduce the computational complexity, an efficient low-complexity alternating optimization algorithm is developed. For the transmit beamforming design, an SCA method-based second-order cone programming algorithm is proposed, while for the RIS phase-shift design, a circle manifold optimization-based algorithm is introduced by utilizing penalty function. Simulation results validate the advancement of deploying RIS in enhancing the performance of cooperative multicell ISAC systems in terms of transmit power. Furthermore, our results illustrate the significant superiority of the proposed algorithms over the benchmark schemes.

This paper studies a networked integrated sensing and communication (ISAC) system, where distributed base stations (BSs) implement coordinated transmit beamforming to communicate with their respective user and cooperatively perform multi-static target sensing. To fully reap the performance gains provided by the networked ISAC system, accurate channel state information (CSI) and time synchronization (TS) among distributed BSs are crucial. However, CSI errors and TS errors are inevitable in practice due to the imperfect channel training and the inaccurate synchronization. To reveal the effect of CSI errors on communication, a Gaussian distributed CSI error model is formulated based on the channel estimation process, and accordingly, the users’ achievable rates with CSI errors are derived. To characterize the effect of TS errors on multi-static sensing, the Cramér-Rao lower bound (CRLB) for estimating target position in the presence of TS errors is derived. It is shown that due to the existence of CSI errors and TS errors, additional terms are introduced in the achievable rate and CRLB formulas, degrading the communication and sensing performance, respectively. Based on the above derivations, we aim at maximizing the sum-rate of users by designing the coordinated transmit beamforming at the BSs, while guaranteeing the CRLB requirements for target sensing. In particular, we consider two cases with and without TS errors, for which the corresponding non-convex optimization problems are solved via a penalty-based algorithm and an alternating optimization algorithm, respectively. Simulation results show that the proposed algorithms significantly outperform benchmark schemes for both cases with and without CSI/TS errors, thus validating the robustness in ISAC performance optimization.

Airborne tactical networks (ATN) play a pivotal role in enabling information sharing between manned and unmanned military aircrafts. The design of effective ATNs faces two significant challenges: the network ossification problem and the complexity associated with managing heterogeneous resources. Wireless network virtualization provides a practical solution for the first challenge by abstracting, isolating, and sharing wireless resources among different entities. Flexible and scalable virtual request embedding (VRE) algorithms have the potential ability to address the other challenge. However, existing VRE algorithms are not suitable for the virtualization of an ATN because they do not adequately consider key factors such as global interference, reliability and delay-sensitive information sharing in the air-battlefield context. In this paper, we propose an analytical framework of joint wireless network virtualization and resource allocation in the context of an ATN. This framework ensures coordination between physical node and link resources for the VRE. Based on the proposed framework, we design a centralized embedding mechanism that maps available physical resources to served users by constructing a directed resource topology and designing wireless link scheduling algorithms. Furthermore, we design two VRE algorithms that account for two types of delay sensitivity: transmission time and waiting time, depending on whether virtual requests are split or not. Through simulations, we validate the effectiveness of our algorithms and analyze the impact of key system parameters on the delay performance.

5th Generation (5G) mobile communication systems operating at around 28 GHz have the potential to be applied to simultaneous localization and mapping (SLAM). Most existing 5G SLAM studies estimate environment as many point targets, instead of extended targets. In this paper, we focus on the performance analysis of 5G SLAM for multiple extended targets. To evaluate the mapping performance of multiple extended targets, a new mapping error metric, named extended targets generalized optimal sub-pattern assignment (ET-GOPSA), is proposed in this paper. Compared with the existing metrics, ET-GOPSA not only considers the accuracy error of target estimation, the cost of missing detection, the cost of false detection, but also the cost of matching the estimated point with the extended target. To evaluate the performance of 5G signal in SLAM, we analyze and simulate the mapping error of 5G signal sensing by ET-GOPSA. Simulation results show that, under the condition of SNR = 10 dB, 5G signal sensing can barely meet to meet the requirements of SLAM for multiple extended targets with the carrier frequency of 28 GHz, the bandwidth of 1.23 GHz, and the antenna size of 32.

With the advent of 5G era, factories are transitioning towards wireless networks to break free from the limitations of wired networks. In 5G-enabled factories, unmanned automatic devices such as automated guided vehicles and robotic arms complete production tasks cooperatively through the periodic control loops. In such loops, the sensing data is generated by sensors, and transmitted to the control center through uplink wireless communications. The corresponding control commands are generated and sent back to the devices through downlink wireless communications. Since wireless communications, sensing and control are tightly coupled, there are big challenges on the modeling and design of such closed-loop systems. In particular, existing theoretical tools of these functionalities have different modelings and underlying assumptions, which make it difficult for them to collaborate with each other. Therefore, in this paper, an analytical closed-loop model is proposed, where the performances and resources of communication, sensing and control are deeply related. To achieve the optimal control performance, a co-design of communication resource allocation and control method is proposed, inspired by the model predictive control algorithm. Numerical results are provided to demonstrate the relationships between the resources and control performances.

As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance.

Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated sensing and communication (ISAC) technology has been envisioned as a promising solution to enable efficient beam alignment in the dynamic UAV network. However, since the digital identity (DID) is not contained in the reflected echoes, the conventional ISAC solution has to either periodically feed back the D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by the separation of D-ID and physical identity (P-ID). This paper presents a novel dual identity association (DIA)-based ISAC approach, the first solution that enables specific, fast, and accurate beamforming towards multiple UAVs. In particular, the P-IDs extracted from echo signals are distinguished dynamically by calculating the feature similarity according to their prevalence, and thus the DIA is accurately achieved. We also present the extended Kalman filtering scheme to track and predict P-IDs, and the specific beam is thereby effectively aligned toward the intended UAVs in dynamic networks. Numerical results show that the proposed DIA-based ISAC solution significantly outperforms the conventional methods in association accuracy and communication performance.

The flying ad hoc network (FANET) will play a crucial role in the B5G/6G era since it provides wide coverage and on-demand deployment services in a distributed manner. The detection of Sybil attacks is essential to ensure trusted communication in FANET. Nevertheless, the conventional methods only utilize the untrusted information that UAV nodes passively “heard” from the “auditory” domain (AD), resulting in severe communication disruptions and even collision accidents. In this paper, we present a novel VA-matching solution that matches the neighbors observed from both the AD and the “visual” domain (VD), which is the first solution that enables UAVs to accurately correlate what they “see” from VD and “hear” from AD to detect the Sybil attacks. Relative entropy is utilized to describe the similarity of observed characteristics from dual domains. The dynamic weight algorithm is proposed to distinguish neighbors according to the characteristics' popularity. The matching model of neighbors observed from AD and VD is established and solved by the vampire bat optimizer. Experiment results show that the proposed VA-matching solution removes the unreliability of individual characteristics and single domains. It significantly outperforms the conventional RSSI-based method in detecting Sybil attacks. Furthermore, it has strong robustness and achieves high precision and recall rates.

This paper studies the wireless control system at network edge, in which one base station (BS) coordinates the closed-loop wireless control of multiple subsystems each consisting of a plant, sensor, and actuator. In this system, the BS first collects the state information from the sensors of plants, then processes the information via edge computing, and finally sends the obtained command signals back to the actuators for controlling the plants. In particular, we consider the ultra-reliable low-latency communication (URLLC) for the state and command signal transmission, by using the rate formulas based on short-packet communication. Under this setup, we first present a time-division-multiple-access (TDMA) protocol for coordinating the sensing, communication, and computation among the multiple plants. Then, we jointly optimize the communication and computation resource allocations to minimize the closed-loop control latency while ensuring the stability of the controlled plants. Though the considered problem is difficult to solve, we transform it into a non-convex problem with semi-definite constraints, and then present an efficient solution via the techniques of alternating optimization and convex approximation. Numerical results show that the proposed solution efficiently reduces the closed-loop control latency as compared to other benchmark schemes with heuristic resource allocations.

This paper designs a low peak-to-average power ratio (PAPR) Integrated Sensing and Communication (ISAC) waveform based on OFDM. Firstly, we propose an ISAC waveform structure, in which radar subcarriers within the OFDM symbols are randomly located anywhere within non-contiguous Physical Resource Blocks (PRBs). Using this OFDM-based ISAC waveform structure, the sensing mutual information (MI) between the radar channel and the received waveform is derived and maximized under the constraints of communication data information rate (DIR), PAPR, and transmit power. Then, an optimization algorithm is proposed to obtain the optimal power allocation of subcarriers. Finally, simulation results verify the effectiveness and flexibility of our designed waveform.

Integrated sensing and communication (ISAC) will greatly enhance the efficiency of physical resource utilization. The design of ISAC signal based on the orthogonal frequency division multiplex (OFDM) signal is the mainstream. However, when detecting the long-range target, the delay of echo signal exceeds CP duration, which will result in inter-symbol interference (ISI) and inter-carrier interference (ICI), limiting the sensing range. Facing the above problem, we propose to increase useful signal power through coherent compensation and improve the signal to interference plus noise power ratio (SINR) of each OFDM block. Compared with the traditional 2D-FFT algorithm, the improvement of SINR of range-doppler map (RDM) is verified by simulation, which will expand the sensing range.

Deep learning enhanced Internet of Things (IoT) is advancing the transformation toward smart manufacturing. Intelligent robot guidance is one of the most potential deep learning + IoT applications in the manufacturing industry. However, low costs, efficient computing, and extremely high localization accuracy are mandatory requirements for vision robot guidance, particularly in operational factories. Therefore, in this work, a low-cost edge computing-based IoT system is developed based on an innovative fine-grained attention model (FGAM). FGAM integrates a deep-learning-based attention model to detect the region of interest (ROI) and an optimized conventional computer vision model to perform fine-grained localization concentrating on the ROI. Trained with only 100 images collected from real production line, the proposed FGAM has shown superior performance over multiple benchmark models when validated using operational data. Eventually, the FGAM-based edge computing system has been deployed on a welding robot in a real-world factory for mass production. After the assembly of about 6000 products, the deployed system has achieved averaged overall process and transmission time down to 200 ms and overall localization accuracy up to 99.998%.

Millimeter automotive radar has great potential in advanced driver assistance systems (ADASs) to enable safety features, such as adaptive cruise control and collision avoidance. However, with widely deployment of millimeter radars on vehicles, the risk of radar mutual interference becomes a major factor limiting the high performance of radar detection. In this article, we analyze the mutual interference among multiple frequency modulated continuous wave (FMCW) radars. On the one hand, we study the interference in detail by considering co-channel interference (CCI) and adjacent channel interference (ACI) simultaneously. Besides, the CCI is analyzed by employing stochastic geometry model while the ACI is assessed by the deterministic analysis method. On the other hand, we propose a time-frequency division multiple access (TFDMA) scheme to mitigate the interference in a coordinated manner and evaluate it in terms of mitigation delay, the probability of interference, effective detectable density, maximum number of interference-free radar, and control signaling overhead. Finally, we study the power allocation strategy to enable the effectiveness of the coordinated interference mitigation approach based on the interference analysis. Simulation results verify the proposed framework for interference analysis by employing Monte Carlo method, and the performance improvement of the coordinated interference mitigation approach is 3.5 dB.

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing, and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A, and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method, and super-resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication systems.

Due to the shortage of spectrum resources in the Internet of vehicles, the diversified time-sensitive services for beyond fifth generation/sixth generation connected automated vehicles cannot be effectively transmitted and the existing research lacks efficient and intelligent resource scheduling methods. Therefore, this article proposes a spectrum resource scheduling model for time-sensitive services and designs a two-tier joint resource scheduling method based on the Age of Information and bandwidth requirements under macro base station (MBS) and roadside unit (RSU). In the first layer, we propose a Vickrey–Clarke–Groves (VCG)-enabled auction model to solve the time-sensitive services resource scheduling problem, in order to guarantee the authenticity of vehicle users (VUEs) bidding. In addition, the Lagrange relaxation algorithm is utilized to obtain the optimal solution of spectrum resource allocation to ensure the VUEs service delay and achieve low complexity resource scheduling. In the second layer, we design a federated learning-based MBS auxiliary communication method to alleviate RSU communication pressure. The communication links of differentiated time-sensitive services are predicted to improve the quality of perception services. Simulation results verify that the proposed algorithms can enhance the VUEs experience effectively compared with the conventional methods, in terms of the delay, throughput, and packet loss ratio.

Achieving seamless global coverage is one of the ultimate goals of space-air-ground integrated network, as a part of which High Altitude Platform (HAP) network can provide wide-area coverage. However, deploying a large number of HAPs will lead to severe congestion of existing frequency bands. Spectrum sharing improves spectrum utilization. The coverage performance improvement and interference caused by spectrum sharing need to be investigated. To this end, this paper analyzes the performance of spectrum sharing between HAP network and terrestrial network. We firstly generalize the Poisson Point Process (PPP) to curves, surfaces and manifolds to model the distribution of terrestrial Base Stations (BSs) and HAPs. Then, the closed-form expressions for coverage probability of HAP network and terrestrial network are derived based on differential geometry and stochastic geometry. We verify the accuracy of closed-form expressions by Monte Carlo simulation. The results show that HAP network has less interference to terrestrial network. Low height and suitable deployment density can improve the coverage probability and transmission capacity of HAP network.

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.

Radio frequency fingerprint identification (RFFI) is a promising physical layer security technique that employs the hardware-introduced features extracted from the received signals for device identification. In this paper, we consider an RFFI problem in the presence of hybrid time-varying distortions (HTVDs) induced by multipath fading channel, carrier frequency offset (CFO), and phase offset. To solve this problem, an HTVDs-robust RFFI framework is proposed. Firstly, we derive that the residual HTVDs after CFO correction can be approximated as multiplicative interference in the frequency domain. Secondly, we define a novel signal analysis dimension named spectral quotient (SQ) representation and then present the spectral circular shift division (SCSD) method to generate the HTVDs-robust SQ signals, where the multiplicative interference can be suppressed. Thereafter, the statistics including root mean square (RMS), variance (VAR), skewness (SKE), and kurtosis (KUR) are extracted from the real and imaginary components of the SQ signals, respectively. Finally, the statistical features are used for the training and testing of the support vector machine (SVM) classifiers. To further enhance the performance of the proposed RFFI scheme, we also present the spectral circular multi-shift division (SCMSD) method, which increases the flexibility in the generation of the HTVDs-robust SQ signals. Given what we knew, this is the first time attempting to mitigate the HTVDs by leveraging the strong frequency correlation at the neighboring subcarriers in the multivariate hypothesis tasks. Compared to several handcraft feature-based RFFI methods, the proposed method exhibits superior identification accuracy and strong robustness. Experimental results show that the proposed RFFI scheme can achieve the accuracy of 91.3%with five devices and 86.4% with sixteen devices when the classifiers are trained with the additive white Gaussian noise but are tested with the Rayleigh channel.

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain by dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only-based and ISAC kinematic-only-based technique in tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.

Multi-access edge computing (MEC) enables the computation-intensive and latency-critical application to be processed at the network edge, which reduces the transmission latency and energy consumption. The quality of the wireless channel seriously affects the performance of the edge network. Consequently, the performance of the edge network can be significantly improved from the perspective of communication. The recently advocated intelligent reflecting surface (IRS) intelligently controls the radio propagation environment to improve the quality of wireless communication links. This paper proposes an edge heterogeneous network with the assistance of intelligent reflecting surface. Specifically, the macro base station and small base stations are equipped with MEC servers, and IRS is adopted to provide an additional computation offloading link. The user association, computation offloading and resource allocation, as well as IRS phase shift design are optimized with the aim of minimizing the long-term energy consumption subject to the constraints imposed on quality of service (QoS) and available resources. The challenge of the optimization problem is rooted from the fact that update timescale of user association is different from others. Hence, a two-timescale mechanism is invoked by marrying tools from matching theory and deep reinforcement learning. More specifically, the user association decision takes place in the long timescale. In the short timescale, the computation offloading, resource allocation and IRS phase shift design strategy is performed. The effectiveness of the proposed two-timescale mechanism is verified by the simulation results.

We propose a geographic and spatio-temporal in-formation based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and non- line-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.

The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in the B5G/6G network thanks to its appealing features, such as wide coverage and on-demand deployment. Emergency communication (EC) is essential to promptly inform UAVs of potential danger to avoid accidents, whereas the conventional communication-only feedback-based methods, which separate the digital and physical identities (DPI), bring intolerable latency and disturb the unintended receivers. In this paper, we present a novel DPI-Mapping solution to match the identities (IDs) of UAVs from dual domains for visual networking, which is the first solution that enables UAVs to communicate promptly with what they see without the tedious exchange of beacons. The IDs are distinguished dynamically by defining feature similarity, and the asymmetric IDs from different domains are matched via the proposed bio-inspired matching algorithm. We also consider Kalman filtering to combine the IDs and predict the states for accurate mapping. Experiment results show that the DPI-Mapping reduces individual inaccuracy of features and significantly outperforms the conventional broadcast-based and feedback-based methods in EC latency. Furthermore, it also reduces the disturbing messages without sacrificing the hit rate.

The collaborative sensing of multiple Integrated sensing and communication (ISAC) base stations is one of the important technologies to achieve intelligent transportation. Interference elimination between ISAC base stations is the prerequisite for realizing collaborative sensing. In this paper, we focus on the mutual interference elimination problem in collaborative sensing of multiple ISAC base stations that can communicate and radar sense simultaneously by transmitting ISAC signals. We establish a mutual interference model of multiple ISAC base stations, which consists of communication and radar sensing related interference. Moreover, we propose a joint optimization algorithm (JOA) to solve the collaborative precoding problem with total power constraint (TPC) and per-antenna power constraint (PPC). The optimal precoding design can be obtained by using JOA to set appropriate tradeoff coefficient between sensing and communication performance. The proposed collaborative precoding design algorithm is evaluated by considering sensing and communication performance via numerical results. The complexity of JOA for collaborative precoding under TPC and PPC is also compared and simulated in this paper.

Automatic modulation classification (AMC) is to identify the modulation type of a received signal, which plays a vital role to ensure the physical-layer security for Internet of Things (IoT) networks. Inspired by the great success of deep learning in pattern recognition, the convolutional neural network (CNN) and recurrent neural network (RNN) are introduced into the AMC. In general, there are two popular data formats used by AMC, which are the in-phase/quadrature (I/Q) representation and amplitude/phase (A/P) representation, respectively. However, most of AMC algorithms aim at structure innovations, while the differences and characteristics of I/Q and A/P are ignored to analyze. In this article, lots of popular AMC algorithms are reproduced and evaluated on the same data set, where the I/Q and A/P are used, respectively, for comparison. Based on the experimental results, it is found that: 1) CNN-RNN-like algorithms using A/P as input data are superior to those using I/Q at high signal-to-noise ratio (SNR), while it has an opposite result in low SNR and 2) the features extracted from I/Q and A/P are complementary to each other. Motivated by the aforementioned findings, a multitask learning-based deep neural network (MLDNN) is proposed, which effectively fuses I/Q and A/P. In addition, the MLDNN also has a novel backbone, which is made up of three blocks to extract discriminative features, and they are CNN block, bidirectional gated recurrent unit (BiGRU) block, and a step attention fusion network (SAFN) block. Different from most of CNN-RNN-like algorithms (i.e., they only use the last step outputs of RNN), all step outputs of BiGRU can be effectively utilized by MLDNN with the help of SAFN. Extensive simulations are conducted to verify that the proposed MLDNN achieves superior performance in the public benchmark.

Flying ad hoc networks (FANETs) play a crucial role in numerous military and civil applications since it shortens mission duration and enhances coverage significantly compared with a single unmanned aerial vehicle (UAV). Whereas, designing an energy-efficient FANETs routing protocol with a high packet delivery rate (PDR) and low delay is challenging owing to the dynamic topology changes. In this article, we propose a topology-aware resilient routing strategy based on adaptive$Q$-learning (TARRAQ) to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way. First, we analyze the dynamic behavior of UAVs nodes via the queuing theory, and then the closed-form solutions of neighbors’ change rate (NCR) and neighbors’ change interarrival time (NCIT) distribution are derived. Based on the real-time NCR and NCIT, a resilient sensing interval (SI) is determined by defining the expected sensing delay of network events. Besides, we also present an adaptive$Q$-learning approach that enables UAVs to make distributed, autonomous, and adaptive routing decisions, where the above SI ensures that the action space can be updated in time with low cost. The simulation results verify the accuracy of the topology dynamic analysis model, and also prove that our TARRAQ outperforms the$Q$-learning-based topology-aware routing (QTAR), mobility prediction-based virtual routing (MPVR), and greedy perimeter stateless routing based on energy-efficient hello (EE-Hello) in terms of 25.23%, 20.24%, and 13.73% lower overhead, 9.41%, 14.77%, and 16.70% higher PDR, and 5.12%, 15.65%, and 11.31% lower energy consumption, respectively.

Three-dimensional detection is essential for autonomous driving and intelligent transportation system, as it enables vehicles to detect and track surrounding objects. Usually, autonomous vehicles are equipped with multiple sensing modalities to achieve robust and precise detection. This work focuses on fusing millimeter-wave radar data with monocular images, as radar can make up for the lack of explicit depth information. We propose a novel approach that fuses radar data and images at the feature level for 3-D detection. Radar points are first merged into a raw feature map with data set statistics by a novel transformation method. With this transformation, radar features can be extracted by convolutional neural networks and fused with image features. Object properties, including location, dimension, and rotation are regressed from the fused features. In this article, the proposed fusion strategy is implemented with a keypoint-based 3-D detection framework and evaluated on the challenging NuScenes data set. Experimental results suggest that the fusion of radar data promotes 3-D detection capability in public benchmarking.

Neighbor discovery (ND) is a key initial step of network configuration and prerequisite of vehicularad hocnetwork (VANET). However, the convergence efficiency of ND is facing the requirements of multivehicle fast networking of VANET with frequent topology changes. This article proposes the gossip-based information dissemination and sensing information-assisted ND with multipacket reception (GSIM-ND) algorithm for VANET. The GSIM-ND algorithm leverages efficient gossip-based information dissemination in the case of multipacket reception (MPR). Besides, through the multitarget detection function of multiple sensors installed in roadside unit (RSU), RSU can sense the distribution of vehicles and help vehicles to obtain the distribution of their neighbors. Thus, the GSIM-ND algorithm leverages the dissemination of sensing information as well. The expected number of discovered neighbors within a given period is theoretically derived and used as the critical metric to evaluate the performance of the GSIM-ND algorithm. The expected bounds of the number of time slots when a given number of neighbors needs to be discovered are derived as well. The simulation results verify the correctness of theoretical derivation. It is discovered that GSIM-ND algorithm proposed in this article can always reach the short-term convergence quickly. Moreover, the GSIM-ND algorithm is more efficient and stable compared with the completely random algorithm (CRA), scan-based algorithm (SBA), and gossip-based algorithm. The convergence time of the GSIM-ND algorithm is 40%–90% lower than that of these existing algorithms for both low density and high density networks. Thus, GSIM-ND can improve the efficiency of ND algorithm.

Unmanned aerial vehicles (UAVs) are widely applied in civil applications, such as disaster relief, agriculture and cargo transportation, and so on. With the massive number of UAV flight activities, the anti-collision technologies aiming to avoid the collisions between UAVs and other objects have attracted much attention. The anti-collision technologies are of vital importance to guarantee the survivability and safety of UAVs. In this article, a comprehensive survey on UAV anti-collision technologies is presented. We firstly introduce laws and regulations on UAV safety which prevent a collision at the policy level. Then, the process of anti-collision technologies is reviewed from three aspects, i.e., obstacle sensing, collision prediction, and collision avoidance. We provide a detailed survey and comparison of the methods of each aspect and analyze their pros and cons. Besides, the future trends on UAV anti-collision technologies are presented from the perspective of fast obstacle sensing and fast wireless networking. Finally, we summarize this article.

Thanks to the advantages of flexible deployment and high mobility, unmanned aerial vehicles (UAVs) have been widely applied in the areas of disaster management, agricultural plant protection, environment monitoring, and so on. With the development of UAV and sensor technologies, UAV-assisted data collection for the Internet of Things (IoT) has attracted increasing attention. In this article, the scenarios and key technologies of UAV-assisted data collection are comprehensively reviewed. First, we present the system model, including the network model and the mathematical model of UAV-assisted data collection for IoT. Then, we review the key technologies, including clustering of sensors, UAV data collection mode as well as joint path planning and resource allocation. Finally, the open problems are discussed from the perspectives of efficient multiple access as well as joint sensing and data collection. This article hopefully provides some guidelines and insights for researchers in the area of UAV-assisted data collection for IoT.

The Internet of Things (IoT) permeates every aspect of our daily lives as billions of interconnected devices are deployed in the physical world. However, IoT networks operate in an untrusted environment and often suffer from many malicious active attacks. Automatic modulation classification (AMC), which can identify the modulation format of intercepted signals without prior knowledge, is a vital technology in countering physical-layer threats of IoT. However, most of the existing algorithms assume the channel is time invariant, and the AMC in time-varying channels is not been well studied. To deal with this dilemma, a novel AMC algorithm MCBLDN consisting of multiple convolutional neural networks (CNNs), a bidirectional long short-term memory network (BLSTM), and a deep neural network (DNN) is proposed. In MCBLDN, a multislot constellation diagram (CD) method is proposed to extract time-evolution characteristics for generating more discriminative features. Specifically, different grayscale subimages generated by slotted CDs are processed serially by their respective CNNs. Therefore, MCBLDN is overparameterized and time consuming. In addition, the frequency offset and phase offset caused by time-varying channels are neglected in MCBLDN, which is detrimental to the performance of AMC. To address the mentioned disadvantages, a lightweight MCBLDN with a spatial transformer network (SLCBDN) is proposed. First, the multiple CNNs in MCBLDN are pruned into a lightweight classification model, and the input data are rearranged to facilitate parallel processing by the lightweight CNN. Additionally, the spatial transformer network (STN) is utilized to reduce the influence of frequency offset and phase offset. Numerical results verify that the proposed method achieves superior performance and higher speed compared to the baseline algorithm MCBLDN.

Considering the low-latency and high data rate requirements for automated vehicles (AVs), the millimeter-wave (mmWave) technology can support tens of Gb/s raw sensor information sharing for connected AVs (CAVs). However, the challenging problem is how to achieve fast and robust mmWave beam tracking for CAVs. To solve this problem, we propose a novel vehicle behavior cognition-based particle-filter (VBC-PF)-enabled beam tracking algorithm. The beam-space subset is predicted based on the beam change rate and the position-yaw information from the vehicle behavior cognition in CAVs, which effectively reduces the beam search overhead. In the proposed VBC-PF algorithm, the particle weight updating schemes are designed based on the optimal vehicle behavior cognition to avoid the particle divergence and the error accumulation. Simulation and hardware testbed results verify that the accuracy and efficiency of the proposed VBC-PF algorithm outperform the conventional particle filter (PF) and the extended Kalman filter (EKF) algorithms.

To overcome the bottleneck of unreliable environment sensing caused by sensor failure and obstacle blockage, the cooperation among connected automated vehicles (CAVs) is crucial for the reliable and efficient raw sensing data sharing in order to guarantee the driving safety. Empowered by the narrow beamwidth and high data rate abilities, the millimeter wave (mmWave) communication technology can substantially improve the environment sensing ability among multiple CAVs. In this paper, a mmWave enabled CAVs cooperation algorithm is designed based on the proposed time-division integrated sensing and communication (TD-ISAC) system for raw sensing data sharing among CAVs. Considering various computing abilities at vehicle and infrastructure, a closed-form solution to the V2V or V2V/V2I cooperative communication mode selection is theoretically achieved based on response delay analysis to guarantee the timeliness of raw sensing data sharing. And the age of information based system status update algorithm is proposed for the V2V/V2I collaborative communication mode. The feasibility of the proposed TD-ISAC system is verified by simulation and hardware testbed results. Based on simulation results, the proposed communication mode selection algorithm can effectively minimize the response time delay in different conditions. The mmWave enabled TD-ISAC hardware testbed is developed and the position error of target detection can be reduced by 18.5 % using the sensing data fusion from two vehicles, while the communication throughput remains over 2.2 Gbps.

Unmanned Aerial Vehicles (UAVs) hold great potential to support a wide range of applications due to the high maneuverability and flexibility. Compared with single UAV, UAV swarm carries out tasks efficiently in harsh environment, where the network resilience is of vital importance to UAV swarm. The network topology has a fundamental impact on the resilience of UAV network. It is discovered that scale-free network topology, as a topology that exists widely in nature, has the ability to enhance the network resilience. Besides, increasing network throughput can enhance the efficiency of information interaction, improving the network resilience. Facing these facts, this paper studies the throughput of UAV network with scale-free topology. Introducing the hybrid network structure combining both ad hoc transmission mode and cellular transmission mode into UAV network, the throughput of UAV network is improved compared with that of pure ad hoc UAV network. Furthermore, this work also investigates the optimal setting of the hop threshold for the selection of ad hoc or cellular transmission mode. It is discovered that the optimal hop threshold is related with the number of UAVs and the parameters of scale-free topology. This paper may motivate the application of hybrid network structure into UAV network.

Automatic modulation classification (AMC) identifies a received signal’s modulation scheme without prior knowledge of the intercepted signal, which enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), lots of neural networks are introduced into AMC. To further improve classification performance, various complementary cues including in-phase/quadrature (I/Q), amplitude/phase (A/P), constellation, and other formats are used together to enhance the discrimination of the DL model, where only outputs of the last layer are used. In this paper, we find that different layers’ outputs in the DL model are also complementary to each other. As a result, a hierarchical classification head based convolutional gated deep neural network (HCGDNN) is proposed by utilizing different layers’ output, which only uses the I/Q cue. The proposed HCGDNN consists of three groups of convolutional neural networks (CNN) blocks, two groups of bidirectional gated recurrent units (BiGRU), and a hierarchical classification head. Compared to the long short-term memory (LSTM), the BiGRU has a smaller computational complexity and also releases the gradient dispersion and explosion in the training phase. With the help of the hierarchical classification head, three groups of modulation predictions are made for a received I/Q signal. After that, a novel nonlinear optimization fusion method is derived to generate fusion weights to fuse different groups, then a final classification decision is made. Compared to AMC methods using various cues, the proposed HCGDNN only uses I/Q cue and has low computational overhead. Numerical results suggest that the newly developed HCGDNN achieves superior performance on the public benchmark.To help other researchers, the source code will be uploaded to the github as long as the paper is published.

To enable user diversity while balancing tradeoffs between cost and flexibility, we exploit hybrid analog-digital beamforming for multi-user mobile systems. By combining array and spatial signal-processing techniques, highly directional beams can be formed with high beamforming gain to achieve sufficient link budget. In this way, fine selection of codewords for analog beamforming is essential to ensure the uplink rate, which may increase the latency of establishing a reliable communication link, especially for mobile millimeter-wave communication systems. In order to guarantee the reliability of communication, adaptive data-driven beam tracking is proposed to find the suitable beamformer/combiner pair to achieve the given signal-to-interference-plus-noise ratios constraint. Unlike the model-based approach, the proposed approach is dependent only on the real-time measurement data based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Numerical analyses show that the proposed beam tracking algorithm can achieve good tracking performance with lower training overhead compared with traditional schemes.

Connected Automated Vehicle (CAV) is a promising paradigm for achieving safe and intelligent transportation systems. In CAV scenario, massive raw sensor data needs to be shared among vehicles under strict latency and high data rate constraints, which poses critical challenges on existing low data rate vehicular communication on 5.9 GHz band. To address these challenges, we propose a millimeter wave (mmWave) enabled CAV network with high capacity to support the raw sensor data sharing among vehicles. A joint vehicle association and power allocation (JVAPA) algorithm is proposed to maximize date rate while minimize energy consumption for CAVs. The one-to-many vehicle association problem is formulated as a swap matching model, and the stable matching states for CAVs and BSs association are achieved. The non-convex power allocation problem is transformed into a convex problem using the first order Taylor expansion, and the optimal power allocation results are achieved. Numerical results verify that the proposed JVAPA algorithm can significantly improve the energy efficiency compared with the benchmark algorithms.

Driven by the vision of intelligent connection of everything and digital twin towards 6G, a myriad of new applications, such as immersive extended reality, autonomous driving, holographic communications, intelligent industrial internet, will emerge in the near future, holding the promise to revolutionize the way we live and work. These trends inspire a novel technical design principle that seamlessly integrates two originally decoupled functionalities, i.e., wireless communication and sensing, into one system in a symbiotic way, which is dubbed symbiotic sensing and communications (SSaC), to endow the wireless network with the capability to “see” and “talk” to the physical world simultaneously. Noting that the term SSaC is used instead of ISAC (integrated sensing and communications) because the word “symbiotic/symbiosis” is more inclusive and can better accommodate different integration levels and evolution stages of sensing and communications. Aligned with this understanding, this article makes the first attempts to clarify the concept of SSaC, illustrate its vision, envision the three-stage evolution roadmap, namely neutralism, commensalism, and mutualism of SaC. Then, three categories of applications of SSaC are introduced, followed by detailed description of typical use cases in each category. Finally, we summarize the major performance metrics and key enabling technologies for SSaC.

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar-sensing signals to improve the communication reliability and radar-sensing accuracy, we propose a novel code-division orthogonal frequency-division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancelation-based signal processing technique that obtains code-division multiplex gain, which is compatible with the prevalent orthogonal frequency-division multiplex (OFDM) communication system. To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system and the error propagation performance. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in a low signal-to-interference-and-noise ratio regime.

The Internet of Things (IoT) pervades every aspect of our daily lives and industrial productions since billions of interconnected devices are deployed everywhere of the globe. However, the seamless IoT unveils a number of physical-layer threats, such as jamming and spoofing that decrease the communication performance and the reliability of the IoT systems. As the process of identifying the modulation format of signals corrupted by noise and fading, automatic modulation classification (AMC) plays a vital role in physical-layer security as it can detect and identify the pilot jamming, deceptive jamming, and sybil attacks. In this article, we propose a novel cyclic correntropy vector (CCV)-based AMC method using long short-term memory densely connected network (LSMD). Specifically, cyclic correntropy model-driven feature CCV is first extracted using the received signals as it contains both the second-order and the higher order characteristics of cyclostationary. Then, the extracted CCV feature is put into the data-driven LSMD which mainly consists of long short-term memory (LSTM) network and dense network (DenseNet). Moreover, an additive cosine loss is utilized to train the LSMD for maximizing the interclass feature differences and minimizing the intraclass feature variations. Simulations demonstrate that the proposed CCV-LSMD method yields superior performance than other recent schemes.

Subspace methods, e.g., multiple signal classification algorithm (MUSIC), show great promise to high-resolution environment sensing in the 6G-enabled mobile Internet of Things (IoT), e.g., the emerging unmanned systems. Existing schemes, aiming to simplify the computational 1-D search of the MUSIC pseudospectrum, unfortunately have still an unaffordable complexity or the compromised accuracy, especially when the millimeter-wave massive multiple-input–multiple-output (MIMO) radar is considered. In this work, we address the fast and accurate estimation of the high-resolution pseudospectrum in massive MIMO radars. To enable real-time automotive sensing, we first formulate this computational procedure as one matrix product problem, which is then solved by leveraging randomized matrix sketching techniques. To be specific, we compute the large matrix productapproximatelyby the product of two small matrices abstracted via random sampling. To minimize the approximation error, we further design another sampling, pruning, and recomputing (SaPRe) algorithm, which refines the approximated results and thus attains the exact pseudospectrum. Finally, the theoretical analysis and numerical simulations are provided to validate the proposed methods. Our fast approaches dramatically reduce the time complexity and simultaneously attain the accurate Direction-of-Arrival (DoA) estimation, which have the great potential to real time and high-resolution automotive sensing with massive MIMO radars.

The advent of vehicle-to-everything communication facilitates the emergence of vehicular sensing networks, where vehicles equipped with advanced sensors continuously sample informative status updates of its surroundings and forward the sampled data to roadside infrastructure based on a certain routing strategy. The collected data is analyzed to obtain real-time situational awareness to impose certain behaviors on the vehicles. In such networked control systems, the timeliness of collected data is of critical importance to system performance, which can be quantified by the concept of Age of Information. Note that to obtain timely perception of its surroundings, each vehicle tends to sample status updates at the maximum frequency, which may congest the network due to limited communication resource. Moreover, the highly dynamic nature of vehicular network poses a great challenge in finding a reliable route for timely data forwarding. Therefore, the data collection scheme should be carefully designed to balance the timeliness of collected information and network stability. In this article, we study an age optimization problem by jointly considering the data sampling at source vehicles and the data forwarding process for multiple information flows across the network. We employ the Lyapunov optimization technique to develop a distributed age-aware data collection scheme consists of a threshold-based sampling strategy at source vehicles and a learning-based data forwarding strategy. Simulation results show that our proposed scheme outperforms existing strategies in collecting status updates in a timely manner.

To meet the increasing demands for wide-band communications and network densification, a new paradigm of millimeter-wave (mmWave)-enabled integrated access and backhaul (IAB) is urgently needed in the sixth-generation (6G) cellular Internet-of-Things (IoT) network. However, the mmWave-enabled IAB technology brings new challenges on network capacity in terms of the differentiated backhaul capacities of small-cell base stations and the various interferences among access links in the 6G cellular IoT network. Therefore, this article proposes a joint traffic load-balancing and interference mitigation framework to maximize the network capacity for 6G cellular IoT services. A novel two-step resource allocation scheme is designed by optimizing the user equipment association and the transmit power allocation (PA), iteratively. Moreover, to minimize both the backhaul burden and the interference, a novel backhaul capacity and interference-aware matching utility function using the many-to-many matching model is designed to measure the interference penalty and the backhaul capacity. By transforming the nonconvex PA subproblem into a convex problem using the successive convex approximation method, both upper and lower bounds of the optimal transmit power are theoretically achieved. Simulation results prove that the proposed algorithms can significantly improve the network sum rate by 71.9% compared to the conventional algorithms, and the successful transmission probability can be well guaranteed.

The efficient and reliable cooperation of unmanned aerial vehicles (UAVs) is crucial for the UAV-enabled Internet-of-Things (IoT) services. However, one utmost challenge is how to effectively solve the many-to-many bandwidth allocation problem between UAVs and users (UEs) in a highly dynamic network, where the uncertain Non-Line-of-Sight (NLoS) links and the UE's mobility can seriously impair the stability of network topology. In this article, a task-driven dynamic multiconnectivity matching game framework with multiple service requirements is proposed to maximize the system throughput while ensuring the UEs' delay requirement. A three-layers auction-based dynamic many-to-many full matching algorithm is proposed to achieve the global network bandwidth resource optimization and update all UEs' channel access strategies. A simplified matching algorithm is proposed to achieve the efficient local resource exchange and the dynamic matching quota adjustment between unstable single connectivity (SC) UEs and multiple connectivity (MC) UEs, which can achieve the suboptimal with a lower complexity solution in contrast to the full matching algorithm. Both the full matching and simplified matching algorithms are proved theoretically to achieve the stable solutions. Simulation results show that the system throughput of both proposed algorithms can improve 55% and 38% compared with that of the conventional many-to-one matching, respectively. Both proposed algorithms have the lower complexity than the conventional centralized optimization algorithm, and the complexity of simplified matching algorithm is only 40% of the full matching algorithm. Moreover, the UE's satisfaction is increased by 28% compared with the case without considering the delay factor into the utility function.

Spectrum trading benefits both secondary users (SUs) and primary users (PUs), while it poses great challenges to maximize PUs' revenue, since SUs' demands are uncertain and individual SU's traffic portfolio contains private information. In this paper, we propose a data-driven spectrum trading scheme which maximizes PUs' revenue and preserves SUs' demand differential privacy. Briefly, we introduce a novel network architecture consisting of the primary service provider (PSP), the secondary service provider (SSP) and the secondary traffic estimator and database (STED). Under the proposed architecture, PSP aggregates available spectrum from PUs, and sells the spectrum to SSP at fixed wholesale price, directly to SUs at spot price, or both. The PSP has to accurately estimate SUs' demands. To estimate SUs' demand, the STED exploits data-driven approach to choose sampled SUs to construct the reference distribution of SUs' demands, and utilizes reference distribution to estimate the demand distribution of all SUs. Moreover, the STED adds noises to preserve the demand differential privacy of sampled SUs before it answers the demand estimation queries from the PSP. With the estimated SUs' demand, we formulate the revenue maximization problem into a risk-averse optimization, develop feasible solutions, and verify its effectiveness through both theoretical proof and simulations.

Robust channel estimation in time-varying channels is used to guarantee the quality of communication services, especially for Vehicle-to-Everything (V2X) scenarios. To improve the channel estimation accuracy and reduce the pilot overhead, multi-input multi-output (MIMO) radar is deployed to assist millimeter wave (mmWave) channel estimation. In this paper, we propose a MIMO radar aided channel estimation scheme using deep learning (DL) for the uplink mmWave multiuser (MU)-MIMO communications. To allocate pilot resources reasonably, we design a transmission frame structure of joint radar module and communication module, which divides the estimation scheme into two stages, i.e., the arrival/departure (AoA/AoDs) estimation stage and the gain estimation stage. In view of the imperfections of array elements in practice, we propose an AoA/AoDs estimation algorithm based on subspace reconstruction in the AoA/AoDs estimation stage named two-step angle estimation (TSAE) algorithm. In the gain estimation stage, a DL based channel gain estimator is designed. An autoencoder combined with residual structure named residual denoising autoencoder (RDAE) is proposed to eliminate the noise on wireless signals, which is passed into the least square (LS) estimation module to obtain gains. Simulation results demonstrate that the MIMO radar aided and DL-based channel estimator provides the efficient estimation performance of the high-mobility mmWave channel with fewer training resources.

Unmanned aerial vehicles (UAVs) have extensive civilian and military applications, but establishing a UAV network providing high data rate communications with low delay is a challenge. Millimeter wave (mmWave), with its high bandwidth nature, can be adopted in the UAV network to achieve high speed data transfer. However, it is difficult to establish and maintain the mmWave communication links due to the mobility of UAVs. In this paper, a beam management scheme utilizing angular domain information (ADI) is proposed to rapidly establish and reliably maintain the communication links for the mmWave UAV network. Firstly, Gaussian process machine learning (GPML)-enabled position prediction is proposed to facilitate coarse-ADI acquisition through the proposed UAV clustering algorithm. Then, with the proposed confined-ADI acquisition which removes the redundancy in the coarse-ADI acquisition, fast beam tracking with respectively the single-beam pattern and the multi-beam pattern is achieved. Finally, a data-driven beam pattern selection scheme is proposed for improving the spectrum efficiency. Simulation results verify the outstanding performance of the proposed beam management for mmWave UAV networks.

Adaptive compressed spectrum sensing (ACSS) can effectively save sampling resources in wideband spectrum sensing. Almost all of the existing ACSS algorithms are based on the discrete multitone signal model. However, real-world spectra are always multiband signals. In this paper, we derive mathematical models and algorithms enabling the ACSS suitable for multiband signals, which can save sampling resources and has lower computational complexity. Firstly, we introduce the multicoset sampling system into ACSS to sample multiband signals. Besides, we propose a leave-one-out cross-validation (LOOCV) based ACSS scheme with low sampling costs. To save sampling resources, we choose only one sampling channel as a testing subset to validate reconstructed signal and repeat this several times with different sampling channels. Then, we use the mean of the multiple validation results to determine the accuracy of the reconstructed signal. To reduce computational complexity, we propose a LOOCV-ACSS algorithm, in which we only perform the least square method several times in the LOOCV procedure, rather than the complicated compressed sensing reconstruction algorithms. Numerical simulations and real-world signal test results demonstrate that our derivation and algorithms are effective to reduce the sampling cost while keeping the same performance as conventional algorithms.

The integrated radar and communications system (IRCS) is promising for Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a tremendous challenge for the fusion center to collect detection information within the delay threshold. Based on only path loss of the channel, this paper performs power allocation to minimize the total transmit power while meeting the detection performance for radar and guaranteeing the latency violation probability (LVP) for communication. Using effective capacity theory, the latency constraint is expressed with introduced latency exponents. The resource allocation problem is non-convex and formulated to a convex one, which can be solved with a global optimum. Simulation results demonstrate the effectiveness of the proposed algorithm from the perspectives of the total transmit power and the latency of the communication links.

To guarantee the safety requirements of autonomous driving vehicles, the efficient and robust sensing information sharing among vehicles is crucial to overcome the environment sensing limitations beyond a single sensor or vehicle. The millimeter wave (mmWave) technology is considered as one of the potential mobile broadband communication solutions to solve high data rate sensing information sharing problem among vehicles with the low latency constraint. However, the Doppler shift effect is much more severe, which will deteriorate the performance of vehicular communication links, especially in a high speed vehicle communication scenario in the mmWave frequency band. Therefore, we propose a training sequence based estimator (TSBE) by designing a hybrid structure combining the fixed section and the repeated sequences, aiming to achieve a fast and accurate Doppler shift estimation (DSE) in the mmWave vehicular scenario. The fixed section provides a fast estimation, and the accuracy of DSE can be effectively improved by the random correlation selection of repeated sequences. Furthermore, the key performances of our proposed DSE algorithm are evaluated by both the link-level simulation and the hardware testbed results. Results prove that the mean-squared error of our proposed TSBE algorithm outperforms other conventional algorithms under various signal to noise ratio values. The hardware testbed shows a remarkable performance improvement on the error vector magnitude and the bit error rate using our proposed TSBE and compensation algorithm.

The development of the Internet-of-Things (IoT) security is comparatively slower than the pace of the IoT innovations. The seamless IoT network operates in an untrusted environment and is exposed to many malicious active attacks. As the process of identifying the modulation format of signals is corrupted by noise and fading, automatic modulation classification (AMC) can be viewed as an effective approach to counter physical-layer threats for IoT as it can detect and identify the pilot jamming, deceptive jamming, and Sybil attacks. Nowadays, data-driven deep learning (DL) techniques, which are capable of extracting discriminative features and perform better robustness to channel and noise conditions, have drawn widespread attention. The deep residual network (ResNet) has a strong representative ability, which can learn latent information repeatedly from the received signals and improve the classification accuracy. Meanwhile, the gated recurrent unit (GRU), which is capable of exploiting temporal information of the received signal can expand the dimension of the signal features for satisfactory classification performance. Considering the advantages of the above networks, this article proposes a novel gated recurrent residual neural network (GrrNet) for feature-based AMC, where the amplitude and phase of the received signal are utilized as the inputs of GrrNet. In GrrNet, a ResNet extractor module is first designed to extract the highly representative features and then temporal information is obtained by the subsequent GRU module which is capable of processing the representative features with the arbitrary length for modulation classification. Moreover, extensive simulations are conducted to verify the classification performance and robustness of the proposed GrrNet and it is shown that GrrNet outperforms other recent DL-based AMC methods. Moreover, the influence of the network parameters, symbol length, and frequency offset on performance is also explored.

With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for the mobile broadband service with gigabyte per second data rate in the UAV-aided fifth generation (5G) IoT system. However, Doppler frequency shift (DFS) deteriorates the link performance of UAV-aided 5G system in the highly dynamic and mobile scenarios. Therefore, a data-aided DFS estimation and compensation approach is proposed to optimize the DFS estimation process using historical estimation results, aiming to achieve a fast and accurate DFS compensation. The performance of the proposed DFS estimation algorithm is evaluated by both cost function of accuracy based on frame structure and Cramer-Rao lower bound in terms of the mean-squared error and signal-to-noise ratio. Furthermore, an adaptive frequency-domain DFS compensation algorithm is designed by leveraging DFS estimation results to enhance the quality of communication link for UAV-aided 5G system, achieving an optimal tradeoff between accuracy and complexity. Finally, both link-level simulation platform and hardware testbed are designed and developed to evaluate the performance of our proposed data-aided approach over other conventional algorithms.

In this paper, we study a joint placement, resource allocation, and user association problem for UAV-assisted wireless networks with constrained backhaul links, where multiple UAV-mounted base stations (UBSs) are deployed to provide wireless services for ground users. We propose a novel framework to maximize the user throughput within the flight-time of UBSs and provides fairness among the users. We first obtain the optimal resource allocation schemes based on different fronthaul and backhaul conditions, and an efficient iterative algorithm is then developed to jointly optimize user association and UBS placement. The optimal UBS placement can be achieved by solving an unconstrained optimization problem which is a simplification of the initial constrained optimization problem based on the optimal resource allocation. We develop a dual-domain coordinated descent and bipartite graph matching based sub-process to identify an optimal user association that prefers the nearby UBSs, as the user association under constrained backhaul links have non-unique optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm, and results show that our proposed method under constrained backhaul can improve both the average throughput by 49% and the fairness among the users by 47% in comparison with the method under ideal backhaul.

Motivated to understand the increasingly severe threat of unmanned aerial vehicles (UAVs) to the confidentiality of terrestrial radio links, this paper analyzes the ergodic and E-outage secrecy capacities of the links in the presence of multiple cooperative aerial eavesdroppers flying autonomously in three-dimensional (3D) spaces and exploiting selection combining (SC) or maximal ratio combining (MRC). The “cut-off” density of the eavesdroppers under which the secrecy capacities vanish is identified. By decoupling the analysis of the random trajectories from the random channel fading, closed-form approximations with almost sure convergence to the secrecy capacities are devised. The analysis is extended to study the impact of the oscillator phase noises and finite memories of the aerial eavesdroppers on the secrecy performance of the ground link. Validated by simulations, the cut-off density only depends on the range of the link in the case of SC eavesdropping, while it depends on the flight region of the eavesdroppers in the case of MRC eavesdropping.

Signal classification plays an important role in cognitive communication networks to identify and avoid interference. Contrary to traditional cooperative spectrum sensing based on binary hypothesis testing, we study a network of cognitive radios that jointly perform linear cooperation based signal classification via M-ary hypothesis testing. To maximize the probability of successful classification subject to constraints on individual probabilities of misclassification, we divide the problem into M independent binary hypothesis testing subproblems in parallel before selecting the hypothesis that is most likely true. Furthermore, we consider a problem that maximizes the probability of successful classification subject to a constraint on the total probability of misclassification. We reformulate such an optimization problem into two different subproblems, where the optimal solution is obtained by alternating the two optimization sub-problems iteratively. Numerical simulations demonstrate the near-optimality of the proposed methods with low computational complexity for the cooperative signal classification problems.

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning (C2ML) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one- dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the C2ML- enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA- TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline C2ML-enabled multicast grouping and the online CA-TSP inspired UAV- trajectory optimization, is shown to achieve excellent energy-saving performance.

Due to the three dimensional mobility advantage of unmanned aerial vehicles (UAVs), a swarm of UAVs can be conveniently deployed and work cooperatively to enlarge the coverage and relay data for environment monitoring and emergency communication scenarios. However, the high mobility of UAV brings new challenges to guarantee the robust and efficient routing in a dynamic changing network topology. Therefore, a mobility prediction based virtual routing (MPVR) algorithm is proposed to enhance the performances of connectivity and routing lifetime among cooperative UAVs. And the probability density function of UAV movements is theoretically achieved using the Gaussian distribution model. Moreover, an optimal virtual routing model is designed to select the optimal relay node with the minimum distance between UAVs. Finally, our proposed MPVR algorithm is evaluated by simulation results, which can improve the average routing lifetime, the average link delay, and the packet delivery ratio compared to other conventional algorithms.

Unmanned aerial vehicle (UAV) millimeter-wave (mmWave) communication is emerging as a promising technique for future networks with flexible network topology and ultra-high data transmission rate. Within such full-dimensionally dynamic mmWave network, beam-tracking is challenging and critical, especially when all the UAVs are in motion for some collaborative tasks that require high-quality communications. In this paper, we propose a fast beam tracking scheme, which is built on an efficient position prediction of multiple moving UAVs. In particular, a Gaussian process based machine learning scheme is proposed to achieve fast and accurate UAV position prediction with quantifiable positional uncertainty. Based on the prediction results, the beam-tracking can be confined within some specific spatial regions centered on the predicted UAV positions. In contrast to the full-space searching based scheme, our proposed position prediction based beam tracking requires little system overhead and thus achieves high net spectrum efficiency. Moreover, we also propose a practical communication protocol embedding our beam-tracking scheme, which monitors the channel evolution and triggers the UAV position prediction for beam-tracking, transmit-receive beam pair selection and data transmission. Simulation results validate the advantages of our scheme over the existing works.

The widespread application of unmanned aerial vehicles (UAVs) urgently requires an effective tracking algorithm as technical support. Particle filter has been widely applied in maneuvering target tracking, however, there has been no suitable solution to the trade-off between weight degeneracy and particle diversity during the process of resampling. In this paper, we propose an improved particle filter algorithm based on systematic resampling with additional random perturbation. This method ensures that particle filter maintains particle diversity and reduces weight degeneracy under environments with different noise types, simultaneously. The simulation results demonstrate that the proposed algorithm generates more accurate filtered trajectory than generic particle filter, especially under the environment with low noise.

Millimeter wave offers large bandwidth for high data-rate unmanned aerial vehicle (UAV)-to-UAV communications. Because of high mobility and attitude variations, it is challenging to maintain the communication link among the navigating UAVs with narrow beam in the mmWave band. To the best of our knowledge, this is the first paper to establish a transmission-oriented UAV attitude prediction model for the UAV-to-UAV mmWave communication link. In particular, a position-attitude prediction based beam tracking algorithm is proposed. First, a Guassian Process (GP) based learning algorithm is presented for the transmitting UAV to predict the position and attitude of the receiving UAV by using the previous position-attitude data and exploiting the relationship between the position and attitude. Then, the analog beamforming vectors are derived by using the predicted spatial angles. Simulation results demonstrate that the proposed learning algorithm can achieve high accurate position-attitude prediction, and the beam tracking algorithm considering UAV attitude variations significantly outperforms the existing algorithms with only position information.

In the coming fifth generation (5G) or beyond 5G next generation networks (NGNs), the small cell deployment is a promising solution to meet the ever increasing demands of mobile devices, and the proliferation of wireless services. The low power base station (BS), such as femtocell BS, is a cost-effective and environmental friendly substitution for the power-hungry macrocell BS. One potentially effective way to deploy those small cells is to use two-tier NGN architecture, where the first-tier carrier can authorize the second-tier carrier's access to users' transmission information database (e.g., uplink/downlink service demands), and thereafter the second-tier carrier can decide how to place small cell BSs according to the mobile users' requirements locally. However, the second-tier carriers/operators for small cell placement may not be trustworthy, and the NGN users' data privacy might be compromised. To address this issue, we integrate differential privacy (DP) preserving techniques into data-driven optimization, and propose a novel scheme that not only preserves the privacy of NGN users' transmission information, but also maximizes the revenue of small cell deployment. Briefly, differential private noises are intentionally added into the users' transmission information database. Based on queries, the second-tier carrier can aggregate a given set of users' differentially private historical data, estimate the users' demands, and formulate the data-driven revenue maximization problem. Given the stochastic programming optimization formulation, we develop feasible solutions and conduct extensive simulations with real-world transmission datasets (i.e., transmission data collected hourly from 3072 4G eNBs deployed in several southern cities of China in 2015) to verify the effectiveness of the proposed scheme.

To address the issue of multi-scale detection, current detectors usually generate hierarchical feature pyramid by a naive combination of top-down features with lateral features. Due to the limited effective receptive fields in the top-down pathway, the generated regions are only associated with the neighbor regions of the coarse feature maps, which is harmful for pyramidal feature generation. And considering the weak representation of simply merging of top-down and lateral pathways, the pyramidal feature maps with strong semantics are difficult to obtain. In this paper, we present the Residual Dilation based Feature Pyramid Network (RDFPN) to exploit the inherent correlation of regions in feature pyramid. The goal of RDFPN is to produce more appropriate hierarchical feature maps for multi-scale detection. With Residual-50 network in Faster R-CNN framework, RDFPN outperforms the original FPN on the challenging COCO dataset without bells and whistles.

Automatic modulation classification (AMC) plays a crucial role in the cognitive radio networks, to which feature-based (FB) methods are the dominating solutions. However, the original features in FB methods are redundant, leading to the ambiguity of classification. To tackle this problem, this paper proposes a novel multi-objective modulation classification (MOMC) method. To reduce the redundant features, the original multi-features are recombined into a single feature by multiobjective genetic programming (MOGP) algorithm. Two quantitative objectives, the classification error rate and the variance for robustness, are then presented to jointly optimize the algorithm as two fitness functions. Furthermore, the single feature generated by MOGP is classified by logistic regression (LR) with low computational complexity. Simulation results verify the enhanced robustness and classification accuracy performance yielded by our proposed MOMC method compared to the existing classification methods.

With the unique agility and flexibility, unmanned aerial vehicles (UAVs) are widely applied in various scenarios. Especially in the areas with disasters, UAVs can act as base stations (BSs) to provide wireless communication services for ground users. In order to reduce the costs, we prefer to use as few UAVs as possible. However, due to the coverage constraint, each UAV can only provide services for a certain number of ground users. Moreover, considering the acceptable receiving power, the coverage radius of UAV is limited. Combined with the above considerations, we present an efficient 3D placement algorithm of UAV-Mounted BSs to cover all of the ground users. In the horizontal direction, the Edge-Prior Placement Algorithm is proposed, which gives the preferential coverage to the outermost users. The complexity of the algorithm is O(n2log n). Then, the optimal height of each UAV is assigned. As a result, these UAVs fly at different heights and cover the ground users with different radii. Simulation results are provided to evaluate the performance of the proposed algorithm. We demonstrate the impacts of the upper bound of coverage radius and capacity constraint on the number of UAVs that are used to cover the ground users, which could provide a guideline for the deployment of UAVs.

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with n UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence, the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is θ(n/logn) times higher than that of the multihop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.

Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.

This paper studies the threat that an aerial eavesdropper can pose to terrestrial wireless communications, from an information-theoretic point of view. The achievable ergodic and the average ε-outage secrecy rates with no channel state information at the transmitter (i.e., with no CSIT) are analyzed for a transmitter-receiver pair on the ground, in the presence of an aerial eavesdropper which flies a random trajectory following a smooth turn (ST) mobility model in a three-dimensional (3D) space. The ST mobility model induces a uniform distribution (of the eavesdropper's waypoints) within the considered 3D volume. Closed-form asymptotic approximations of the achievable secrecy rates are derived based on the almost sure convergence and non-trivial mathematical manipulations. Validated by simulations, our analysis is tight and reveals that the ground transmission is particularly vulnerable to aerial eavesdropping which can be carried out in a distance without being noticed. 3D spherical regions are identified, within which the secrecy rates vanish. This sheds useful insights to protect terrestrial wireless networks from aerial eavesdropping.

In existing communication systems, the channel state information of each user equipment (UE) should be repeatedly estimated when it moves to a new position or when another UE takes its place. The underlying ambient information, including the specific layout of potential reflectors, which provides more detailed information about all UEs' channel structures, has not been fully explored and exploited. In this paper, we rethink the mm-wave channel estimation problem in a new and indirect way, i.e., instead of estimating the resultant composite channel response at each time, and for any specific location, we first conduct the ambient perception exploiting the fascinating radar capability of a mm-wave antenna array and then accomplish the location-based sparse channel reconstruction. In this way, the sparse channel for a quasi-static UE arriving at a specific location can be rapidly synthesized based on the perceived ambient information, thus greatly reducing the signaling overhead and online computational complexity. Based on the reconstructed mm-wave channel, single-beam mm-wave communication is designed and evaluated which shows an excellent performance. Such an approach, in fact, integrates the radar with communication, which may possibly open a new paradigm for future communication system design.

This paper investigates the joint power allocation with spectrum sharing for vehicle-to-everything (V2X) communications with Long Term Evolution Unlicensed (LTE-U) technology in a heterogeneous network. The vehicle users (VUEs) are classified into safety VUEs and non-safety VUEs based on the corresponding services. With the purpose to maximize the total throughput of CUEs, safety VUEs, and non-safety VUEs in contend free period (CFP) based LTE-U mode, a matching based resource allocation scheme is proposed under the constraints of fairness coexistence. The ergodic sum rate is considered with respect to statistical channel state information (CSI), and a lower bound evaluation of the objective function is presented with reduced computation complexity. Numerical results demonstrate the analysis and performance of the proposed strategy.

This paper studies an energy beamforming and time duration joint optimization problem for maximizing the sum-rate of a battery-free-multi-antenna-relay-assisted wireless powered communication network (WPCN). The considered problem is non-convex due to the strongly coupled optimization variables. By introducing new optimization variables and reformulating the problem into a convex problem, the optimal solution is obtained based on convex optimization methods. Furthermore, an alternate iteration algorithm is proposed by decoupling the problem into two subproblems, and a semi-closed form solution of the optimal energy beamforming matrix is derived with given time allocation. Simulation results indicate that the proposed battery-free multi- antenna relay architecture can deliver a significant sum-rate gain for the WPCN. In addition, the proposed suboptimal algorithm only has 5% sum-rate loss compared to the optimal solution.

Millimeter wave band (mmWave) integrates with a wide variety of signals under manifold communication standards due to its high-capacity feature, which enables mmWave beam sensing to serve a valuable function in discriminating different signals. In this paper, we propose a novel frame structure consisting of variant beam sensing process and data transmission process. In the beam sensing process, multi-power-level beam sensing method is conducted in every direction to discriminate multi-users under multiple standards. The sensing duration varies with the number of directions. Several performance metrics are correspondingly proposed to quantify the beam sensing for multiple mmWave users, such as the probability of correct detection and the false alarm probability. In the second process, the signal with the biggest received signal-to-noise ratio (SNR) is given priority to communicate. On this base, sensing-throughput tradeoff is analyzed to balance the time division between two processes for throughput maximization. Finally, numerical evaluations and simulations are conducted to verify the correctness of the proposed methods.

As a promising candidate for future 5G and beyond, millimeter-wave (mmWave) has received considerable attention. However, mmWave channel estimation remains to be a challenging problem. To tackle this issue, this paper proposes a mmWave joint radar and communication system for indoor scenarios. By cooperating with mmWave radar, the propagation environment is perceived. Then, high-accuracy channel estimates are obtained, which helps in designing more accurate precoders and combiners. Compared with the conventional channel estimation methods, the proposed scheme significantly reduces the spectrum overhead and computational complexity, especially when massive antennas and multiple users are involved. Our results may shed some lights on the system design for future mmWave communications.

Channel tracking has been a promising technology to sustain the directional link in the millimeter wave (mmWave) communication. However, the complex dynamic environment alters the movement of the mobile station (MS) and increases the inaccuracy of tracking. To tackle this issue, this paper proposes a novel variational mobility oriented channel tracking (VMCT) algorithm for three-dimensional (3D) mmWave system, where the MS moves with variational direction and velocity. The corresponding angles of arrival (AoA) variation is modeled as a multiple linear regression (MLR) process, which contributes to acquire the temporal correlation of sequential AoA states. To further train the weight coefficients of the MLR model with small-scale dataset, an integration of maximum likelihood function and Bayesian conjugate prior distribution is exploited. Simulation results verify the enhanced mean square error (MSE) performance yielded by our proposed tracking algorithm compared to the existing tracking methods.

Neighbor discovery is a crucial step to establish links among the nodes in wireless ad hoc network, such as Unmanned Aerial Vehicle (UAV) networks. Most existing studies on neighbor discovery are based on 1-way or 2-way handshake mechanism, where nodes send 1-way or 2-way handshake packets without getting acknowledgement from their neighbors. The nodes will not stop transmitting handshake packets until the end of the neighbor discovery. However, when the node scale becomes huge, this procedure may increase the collisions and deteriorate the discovery efficiency. In this paper, considering the multi-carrier system, we analyze a Hybrid 3-way handshake Synchronous Algorithm (HAS-3-way) to reduce the discovery time. Hybrid 3-way handshake is a combination of 2-way and 3-way handshake. When the neighbor node confirms that it has been found by the discovery node, it will stop transmitting unnecessary handshake packets. In this situation, collision probability is decreased and the discovery efficiency is enhanced. Besides, the effect of sleep probability is also taken into account in the hybrid 3-way handshake. Through extensive simulations, we demonstrate that HAS-3-way can significantly decrease the expected time to discovery all the neighbors.

In the Internet of Things (IoT) services, the data of wireless sensor network needs to be collected. However, in the scenarios that have no infrastructure support, the data collection of sensors has great difficulty. Since unmanned aerial vehicle (UAV) has the characteristics of flexibility, it can be applied in the data collection for wireless sensor network (WSN). In this paper, we study UAVs supported data collection for WSN. Firstly, the entire region is divided into multiple cells. Secondly, the flight paths for single UAV and multiple UAVs are designed to cover all cells. The per-node capacity of sensor is derived, which is a function of the number of cells, the height of UAV, the number of sensors and the energy capacity of UAV. It is found that the per- node capacity with multiple UAVs is much larger than that with single UAVs. Then the optimal number of cells is derived to maximize the per-node capacity of WSN. Finally, we provide simulation results to verify our analysis. The discoveries in this paper may provide guideline for the UAVs assisted data collection in WSN.

Due to the similarities between neural networks and ad hoc networks, ad hoc networks can be applied to construct distributed neural network to perform complex computation. In this paper, the broadcasting nature of wireless signal and communication interactions among the nodes in ad hoc network are applied to construct neural network, which reveals the interplay between communication and computing. In the constructed neural network, the neuron is the wireless node. The communication interactions are applied to increase the number of hidden layers. The constructed neural network is further applied in target positioning and its accuracy is verified by simulation results. This paper shows that the wireless networks can be applied in computation, which may motivate the construction of large-scale neural networks via the wireless networks.

Feature selection (FS) is a key factor for the performance of machine learning algorithms, as not all data and hence features are related to the various tasks. In this paper, we propose a novel scheme for convolutional FS for machine learning algorithms in computer vision. As not all the convolutional features are related to visual tracking, removing the unrelated ones will dramatically reduce the complexity and improve the algorithm performance. However, how to identify and select features related to the visual tracking task is still a challenge for machine learning algorithms. In the proposed scheme, a novel adaptive weights-objective function approach is established to evaluate and select the features. Furthermore, a quadratic programming method is introduced which improves the optimization efficiency. The experimental results demonstrate that our proposed scheme achieves superior performance compared to the state-of-art trackers on the challenging benchmarks in computer vision.

The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. In this study, we propose a novel trust model for UAVNs that is based on the behavior and mobility pattern of UAV nodes and the characteristics of inter-UAV channels. The proposed trust model consists of four parts: direct trust section, indirect trust section, integrated trust section, and trust update section. Based on the trust model, the concept of a secure link in UAVNs is formulated that exists only when there is both a physical link and a trust link between two UAVs. Moreover, the metrics of both the physical connectivity probability and the secure connectivity probability between two UAVs are adopted to analyze the connectivity of UAVNs. We derive accurate and analytical expressions of both the physical connectivity probability and the secure connectivity probability using stochastic geometry with or without Doppler shift. Extensive simulations show that compared with the physical connection probability with or without malicious attacks, the proposed trust model can guarantee secure communication and reliable connectivity between UAVs and enhance network performance when UAVNs face malicious attacks and other security risks.

In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.

Considering the fast deployment and flexible networking abilities, unmanned aerial vehicles (UAVs) are widely used as relay nodes for the emergency communication in disaster scenario. But the dynamic changing three-dimension (3D) topology of UAV network brings new challenges for the robust and efficient routing algorithm selection. Therefore, a cube based space region partition (CSRP) routing algorithm is proposed to improve the quality of service (QoS) of UAV network. Furthermore, the 3D space is divided into several cubes which are selected as the optimal routing path for UAV network based on the connectivity among different UAVs. Finally, numerous results verify the performance of proposed routing algorithm, in terms of end-to-end average delay, delay jitter, and delivery ratio.

With field measurement data, it indicates that the data traffic of cellular networks is highly nonuniform along the city streets. The nonuniformity of the data traffic leads to overload in hot zones and inefficient use of wireless resources in light-loaded zones. However, the intuitive solution is costly for such scenario of streets. To solve this problem, we propose a vehicle-assisted offloading (VAO) scheme, which utilizes the vehicle queue stopping at the red light to offload data from the busy intersection cell to its adjacent cells that are largely idle. The performance of VAO is theoretically analyzed by considering both the road traffic conditions and the vehicle-pedestrian communications. An architecture is also proposed to enable the offloading scheme. Simulations with practical parameters have confirmed our analysis and showed that the VAO is capable of achieving significant performance gain.

Since unmanned aerial vehicles (UAVs) can be flexibly deployed in the scenarios of environmental monitoring, remote sensing, search-and-rescue, etc., the network protocols of UAV networks supporting high data rate transmission have attracted considerable attentions. In this paper, the three-dimensional aerial sensor network is studied, where UAVs act as aerial sensors. The delay tolerant multiple access control protocol which is called UD-MAC1, is designed for UAV networks. The UD-MAC exploits the returning UAVs to store and carry the data of the UAVs along the returning paths to the destination. The returning UAVs are discovered by ground via the Control and Non-Payload Communication (CNPC) links, which contain the sense and avoid information among UAVs. And they are confirmed by the other UAVs via data links. Simulation results show that UD-MAC protocol increases the enhances of accessing channel by 31.8% compared with VeMAC protocol.

In face of the exponentially growing demands for various mobile data services, the long term evolution-unlicensed (LTE-U) technology has been proposed to offload over-loaded traffics on the unlicensed spectrum band by leveraging carrier aggregation (CA) technology. However, the quality of service (QoS) requirements of different or mixed services have not been fully considered in existing traffic allocation strategies in terms of various spectrum qualities on both licensed and unlicensed spectrum bands. Therefore, a novel two-step traffic allocation approach is proposed for mixed services to maximize the overall utility of LTE and WiFi systems. And two data rate allocation algorithms for licensed band (DRA-L) and unlicensed band (DRA-UL) are designed based on theoretical analysis. Simulation results verify the enhanced network utility performance of the proposed algorithms compared to the conventional stand-alone DRA-L algorithm.

In practice, signals may be interfered by hostile jamming or illegal transmission and it is a very challenging task to determine the modulation formats of mixed signals. To tackle this problem, we propose a three-step algorithm called PFS algorithm. In the first step, principal component analysis (PCA) is conducted to suppress the noise. In the second step, the mixed signals are separated via fast independent component analysis (FICA), which transforms the received signals into the components that are maximally independent of each other. In the third step, high-order cumulants (HOCs) and support vector machines (SVMs) are adopted to determine the modulation format of the signal. The numerical experiments show that the PFS algorithm has a superior performance compared to other existing methods.

In this paper, the problem of joint dynamic spectrum access and multi-relay selection is investigated in relayenabled cooperative communication systems to maximize the system sum-capacity. Since the considered problem is a mixed integer nonlinear program, which is generally intractable to find the optimal solution, two matching theory-based suboptimal algorithms are proposed to reduce the computational complexity for two different cases. For the case that each source node can only be assisted by one relay, a cyclic three-sided matching algorithm is firstly proposed to attain the stable matching results for the selection of the source node and the relay with the spectrum band used. Then, for the case that each source node can be assisted by more than one relay, a two-step matching algorithm is proposed to perform joint dynamic spectrum access and multi-relay selection. Simulation results show that the proposed algorithms, with much lower complexity compared to the optimal exhaustive search, can achieve the near-optimal performance with a gap to the optimum being less than 5%.

The 5G wireless network which is regarded as the future critical infrastructure, is faced with many formidable challenges. To deal with them, resource sharing is the trend, including sharing the wireless resources and the infrastructure. Thus, the wireless network virtualization has been raised to integrate and abstract the resources and make network management more flexible. In this paper, a wireless network virtualization architecture is designed, which consists of the cognitive plane, the control plane and the data plane. These three planes collaborate to optimize the resource allocation according to the network condition. Then, we introduce the cell clustering method in the proposed architecture, which can largely reduce the complexity of resource slicing and make it possible in wireless network. Finally, we have implemented a trial environment to test the architecture. The test data indicate the network performance is obviously improved. Spectrum efficiency is doubled and the packet loss rate reduces to 1/20.

In this paper, we study the throughput performance of LTE-licensed-assisted access (LAA) networks coexisting with wireless local area networks (WLAN) in the presence of imperfect spectrum sensing. By considering the false-alarm probability and miss-detection probability of widely-used energy detection, we analyze the potential impact of imperfect spectrum sensing on the access performance of unsaturated LTE-LAA networks along with binary slotted exponential backoff. The access probabilities of LTE-LAA networks and WLAN systems are derived based on the discrete-time Markov chain (DTMC) model. Furthermore, the throughput of LTE-LAA networks is maximized by jointly optimizing the sensing duration and threshold. Numerical results confirm the great impact of imperfect spectrum sensing on the LTE-LAA system throughput, and indicate the optimized sensing duration and threshold can achieve a significant performance gain compared to the fixed ones.

In this paper, the non-orthogonal multiple access technology is integrated into cognitive orthogonal frequency division multiplexing (OFDM) systems, referred to as NOMA-OFDM, to boost the system capacity as well as the number of accessible users. The considered problem is formulated as jointly optimizing the sensing duration, user selection, and power allocation under the constraints of maximum transmitted power and maximum allowable interference. In order to overcome the non- convexity, we decompose the formulated problem into three subproblems, i.e., the sensing duration optimization, user selection optimization and power allocation optimization. By exploiting the individual characteristic of each subproblem, three efficient algorithms, i.e., bisection search method, matching-theory-based user selection and difference of convex (DC) programming, are proposed to solve the corresponding subproblems, respectively. Moreover, an alternate iteration algorithm is also provided to perform joint optimization of three subproblems. Simulation results validate the fast convergence and considerable performance gain of the proposed algorithms.

The automatic recognition of modulation type for a detected signal is a significant task in wireless communication, which is the intermediate step between signal detection and demodulation. There are two general methods adopted in modulation recognition, i.e., likelihood-based (LB) method and feature-based (FB) method. Both LB and FB approach do not perform well when the signals are incomplete and received from a very limited number of observations. Therefore, we adopt a method based on dictionary learning to identify the modulation type of incomplete signals. The orthogonal matching pursuit (OMP) method is used to obtain the sparse representation and the sequential generalization of K-means (SGK) method is used to update the dictionary set. The experimental result shows that the recognition accuracy of our method is much higher, compared with the method based on higher-order cumulant.

Automatic modulation classification (AMC) technology, used to identify the modulation type of the received signal, plays an important role in the radio detection and electronic warfare applications. In this paper, we propose a novel featurebased AMC method in MIMO system. Firstly, the independent component analysis (ICA) is applied to separate the mixed signals at the receiver side. Then, the multiple features based on higher-order cumulants are extracted, and are used to identify the modulation type of signals. In classification process, the identification operation is modeled as an optimization problem, and we adopt a Quasi-Newton method to solve it. Simulation results show that the proposed method can classify various modulation types, and implying the effectiveness of the proposed scheme. Moreover, the analysis based on measured data shows that our proposed scheme is practical and efficient. In excellent signal-to-noise (SNR) range, it proves that the proposed method outperforms the other classical feature based methods in terms of probability of correct identification.

This paper studies the spectrum efficiency (SE) maximization problem for cooperative multi-antenna power beacon (PB)-enabled wireless powered communication networks (WPCN), where each transmitter harvests energy from surrounding PBs and then transmits data to the corresponding receiver within its allocated duration. The considered problem is formulated as jointly optimizing the energy beamforming vectors of PBs, the transmission duration, and the transmit power of users to maximize the total SE. In order to derive an efficient algorithm, the SE maximization problem is decomposed into two subproblems: the SE maximization problem for data transmission and the energy consumption minimization problem for energy transfer.We prove that the optimal SE is concave with the harvested energy of each user, and based on this concavity, an efficient algorithm is proposed to achieve the optimal solution. Finally, simulation results validate the optimality of the proposed algorithm, and verify the superiority of the proposed scheme-more than 150% performance gain is obtained, compared with the scheme of single PB with single antenna.

In this paper, we investigate the angle-domain spectrum opportunities of secondary users with directional transmission, and analyze the detection probability of spectrum holes. We also analyze the detection probability of interference allowed scenarios. By calculating the angle mean of spectrum holes, we get the mathematical statistical properties. Through the results of the numerical analysis in different scenarios, we demonstrate that when N is large enough (for example, N amp;#62; 10), the size of the angle delta has a dominant impact on the spectrum opportunity. Meanwhile, we prove that utilizing directional antennas and allowing interference can obtain more angle-domain spectrum opportunities and improve the spectrum utilization.

Device‐to‐device (D2D) communications underlaying a cellular network is an efficient way to enhance spectral efficiency via resource sharing between D2D and cellular users (CUs). In this study, a discrete location‐aware (DLA) interference model for D2D users is presented to allocate cellular resources. The vicinity of a CU is discretised into multiple regions, and the number of active D2D users in each region is constrained to satisfy the CU QoS requirements. Considering the locations of D2D users affecting the interference to CUs and thus the achievable rate, the formulated non‐linear 0–1 knapsack resource allocation (RA) problem is divided into two subproblems: (i) the optimal amount of shared resources between the two types of users; (ii) the optimum subset of D2D users which transmit . The conditions of D2D users spatial deployment and resources reuse portion to achieve the solutions of the two subproblems are theoretically derived and proven. Then a DLA‐RA algorithm is proposed to solve the corresponding subproblems in both single CU and multiple CUs cases. Extensive simulations results are presented which verify the effectiveness of the proposed DLA interference model and the RA scheme.

Traditional cellular networks require the downlink (DL) and uplink (UL) of mobile users (MUs) to be associated with a single base station (BS). However, the power gap between BSs and MUs in different transmission environments results in the BS with the strongest downlink differing from the BS with the strongest uplink. In addition, the significant increase in the number of wireless machine type communication (MTC) devices accessing cellular networks has created a DL/UL traffic imbalance with higher traffic volume on the uplink. In this paper, a joint user association and resource partition framework for downlink-uplink decoupling (DUDe) is developed for a tiered heterogeneous cellular network (HCN). Different from the traditional association rules such as maximal received power and range extension, a coalition game based scheme is proposed for the optimal user association with DUDe. The stability and convergence of this scheme are proven and shown to converge to a Nash equilibrium at a geometric rate. Moreover, the DL and UL optimal bandwidth partition for BSs is derived based on user association considering fairness. Extensive simulation results demonstrate the effectiveness of the proposed scheme, which enhances the sum rate compared with other user association strategies.

Cellular technology has the potential to support large numbers of machine-type communications (MTC) devices for a variety of applications in fifth generation wireless systems. As MTC devices are a recent addition to cellular networks, a major concern is how to effectively manage and limit cellular resources for MTC data transmission without degrading traditional human-type communications (HTC) performance. To tackle this problem, a two-stage resource management framework is proposed with the goal of maintaining traffic equilibrium. In the first stage, an ecological prey-predator model is introduced to model the resource partition for the two types of devices. The steady-state properties of the traffic are analyzed, and the value regions of the allocated MTC resources for stable equilibrium points are derived. In the second stage, given an appropriate resource partition for MTC traffic, these devices are grouped based on their buffer conditions. The optimal resource allocation solution is derived so that the MTC traffic is stable. It is shown that to maintain MTC traffic stability, the resources can be allocated to only two groups. Furthermore, results are presented which show that HTC and MTC traffic can maintain a stable equilibrium using the two-stage resource management framework.

In face of the explosive service demands, solving the problem of spectrum scarcity is becoming more important than ever. To utilize the spectrum resources more thoroughly and efficiently, virtualization technologies have been proposed, which can be a means to mitigating resource granularity and increasing efficiency in heterogeneous network environments. In this paper, a traffic-oriented resource virtualization with demand-supply dynamic analysis is proposed for optimized resource allocation of heterogeneous networks with multiple types of services. On the supply side, i.e., the network side, a low-complexity matching game approach is introduced with the novel “Match-Degree” conception, which could be defined with the Grey relational analysis. The complexity of generating preference list can be reduced by unifying various dimensions of network parameters. On the demand side, i.e., the user side, bandwidth allocation algorithm is designed to consider the comprehensive network traffic characteristics, energy consumption, and network price factors, to maximize the overall utility. Except from theoretic analysis, simulation has also been employed to compare the proposed scheme with prior and traditional ones. To further verify the practicability, tractability, and effectiveness of the proposed demand-supply scheme, a test bed is designed and developed in this paper.

In this paper, an energy-incentive cooperative transmission (EICT) scheme is proposed for wireless ad hoc networks, where a node uses energy as reward to seek for cooperative transmission from neighboring nodes and then the cooperative node adopts a decode-and-forward (DF) protocol to relay data. An optimal time slot and power allocation algorithm is proposed to maximize the sum-rate under the constraints of peak power, energy consumption, and individual data rate when the channel state information (CSI) is perfectly known at transmitters. Furthermore, the scenario that only the statistical CSI is available at transmitters is investigated, and an alternative algorithm is presented to optimize time slot and power allocation. Simulation results confirm the superiority of the proposed scheme over existing ones, demonstrating a more effective mechanism to stimulate cooperation in wireless ad hoc networks.

To reduce the huge consumption of traditional sensing, a multi-centers estimation based sensing scheme is proposed in this paper. Firstly, all potential channels are clustered into highly related groups with some channels selected as detecting channels (DCs) using an unsupervised algorithm. In each group, the states of other channels (estimated channels, ECs) are estimated according to their correlations with the DCs and the dependence on history to save sensing time. Specifically, number of groups (Ng) and number of DCs in each group (NDC) can be adjusted jointly to improve sensing performance. Moreover, two Hidden Markov Model (HMM) based estimation methods, namely joint estimation (JE) and cooperative estimation (CE), are formulated. In JE, the DCs are modeled as the observed vectors and utilized jointly to estimate ECs' states. While in CE, each DC estimates ECs' states separately and a weight-based cooperative algorithm is designed to merge their results. Tested with real-world measurement data, results show the reduced sensing consumption is considerable at the expense of slight sensing accuracy loss. On these bases, it is significant to note that NdC should be adjusted according to sensing consumption to optimize performance.

The exploitation of spectrum opportunities in the dimension of height will bring another transmission degree of freedom for wireless networks. Besides, the modern wireless networks are deployed in the three dimensional (3D) space, which need cognitive radio technologies to enhance their performances. With these motivations, the capacity of 3D cognitive radio networks (CRNs) is addressed in this paper. Since there is one additional dimension of interference in 3D CRNs, the network protocols need to be designed to coordinate the interference and guarantee the connectivity of CRNs. Then the link capacity and routing density of 3D CRNs are investigated. Finally, we have derived the per-node capacity of primary network and secondary network respectively. We have verified that the path loss factor α has an impact on the capacity of 3D CRNs, namely, α = 3 is a watershed of capacity scaling laws. Besides, when α > 2.5, the capacity of 3D CRNs is higher than 2D CRNs with the same amount of nodes asymptotically. Therefore our results may provide an insight into the design of 3D cognitive radio networks.

As traditional spectrum sensing approaches unable to deal with the contradiction between detection accuracy and complexity in cognitive radio network, a novel q-weighed sequential cooperative energy detection method for spectrum sensing in time varying channel is proposed in this paper to achieve better performance with lower complexity. By adding the q- weighted log likelihood ratio (LLR) of the past local observations from previous sensing slots to the current LLR sequentially, cognitive radio nodes can aggregate the current and previous received energy values to yield the improvement of sensing performance. Moreover, we pose a q-weighted K-out of-N voting rule at the fusion center to minimize the total error probability. For different probability of primary signal for turning its state from active to idle, we employ corresponding different weighted value q to make the sensing scheme more flexible and efficient.

Pedestrian detection systems are receiving increasing attention in both industry and academia with the rapid development of autonomous automobiles which employ artificial intelligence. These systems must detect specific classes of objects such as pedestrians rather than generic objects. In this paper, we present a faster RCNN based pedestrian detection system which improves upon previous solutions. The proposed model takes arbitrary size images as inputs and generates bounding boxes and confidence scores for pedestrians. The system achieves good performance and is faster than the well known and frequently used methods in the literature.

Device-to-device (D2D) communication is a promising method to reduce power consumption and improve the throughput of cellular networks. However, densely deployed D2D pairs could result in severe interference to cellular users without proper power control. Therefore, the discrete location-aware power control (DLPC) scheme is proposed in uplink D2D underlaid cellular networks. The entire cell area is divided into several regions, and a total power budget for each region is conducted with weighted allocation, to meet the constraint on the outage probability of cellular user. Then DLPC scheme requires only the locations of active D2D pairs rather than channel information or massive calculations, which is of low complexity in implementation. Simulation indicates that DLPC can improve the outage probability of D2D pairs and the network throughput compared with the traditional greedy power control scheme.

Automatic modulation classification (AMC) conducted by a single receiver plays a crucial role in spectrum monitoring and signal interception. To improve the accuracy of feature based AMC, a novel multi-cumulant based modulation classification scheme using uniform linear array is proposed in this paper. Moreover, two methods are formulated to combine the signal from different antenna branches, i.e., DOAC (Direction of arrival estimation based Combination) and CC (Cooperative Combination). With an estimate of the incident angle of the signal, DOAC combines signals from different branches using maximum ratio combining. CC calculates the feature value of each branch independently and utilizes the average feature value of all branches for classification. Simulation results prove that using multiple cumulants yields performance gain over traditional methods using a single cumulant. Moreover, the influence of antenna number and sample length on performance is also explored.

In military and civilian communications, signals are often interfered by hostile jamming or illegal transmission. In these situations, determining the modulation format of mixed signals is a challenging task, which is tackled using a three step algorithm named PFCC(PCA, FICA, Cumulants Based Classification Algorithm) in this paper. In the first step, centering and whitening is conducted using principal component analysis (PCA) to suppress noise. In the second step, mixed signals are separated using fast independent component analysis (FICA), which can transform received signal into components that are maximally independent from each other. In the third step, high-order cumulants (HOC) are calculated to determine the modulation format of each signal. Through extensive simulation, the convergence speed and performance of PFCC are validated. We also notice that the relative power of mixed signals has a big influence on performance.

The fifth-generation (5G) mobile network is facing severe challenges of the traffic demand surge and spectrum scarcity. To improve the network capacity and data rate, the deployment of Long Term Evolution (LTE) cellular system on unlicensed band is considered as one of the promising solutions. But the interference problem among LTE and WiFi system on the same unlicensed band hinders the deployment process, which is a critical issue unsolved for network operators. Therefore, a novel channel access scheme integrating the energy detection scheme and back-off mechanism in LTE system has been proposed in this paper, to minimize the interference to WiFi system. The throughput of both LTE and WiFi systems and the optimal sensing time and back-off time are theoretically analyzed with proofs. Furthermore, an efficient adaptive reconfiguration strategy of access parameters is proposed based on the channel occupancy cognition results. Simulation results verify that the proposed channel access scheme with optimal back-off time can double the throughput of WiFi system when coexisting with LTE system on unlicensed band.

Recent studies suggest nodes in practical networks are more likely to communicate with nearby nodes than far away nodes, which is referred to as proximity preference. In this paper, we model proximity preference by assuming the probability of communication follows a power law distribution with respect to distance and analyze its influence on the throughput of a hybrid network. Moreover, L-maximum-hop routing strategy is adopted to enforce delay constraints. Throughput is derived as a function of maximum hop L, proximity preference index α and the number of base stations (BSs) m. It is also found that per-node throughput changes with α. When 0 ≤ α ≤ 2, proximity has no influence on throughput. When 2 ≤ α ≤ 3, the throughput increases with α. Otherwise, the throughput reaches its maximum and remains constant. Our results demonstrate the interplay of various networks parameters with proximity preference and provide guidelines for the design of practical networks.

Neighbor discovery is a crucial initial step to establish connections among the nodes in Ad hoc network. Multi-carrier modulation possesses superiority on parallel processing, which is widely used in neighbor discovery researches with directional antennas. However, when the node scale becomes larger, the existing algorithms of 1-way and 2-way handshake may increase the collision probability, leading to a high discovery latency. Therefore, a novel multi-carrier asynchronous neighbor discovery algorithm named 3-way MC-NDA has been proposed in this paper based on the feedback acknowledgement mechanism to minimize discovery time. The 3-way MC-NDA improves the interaction rules of handshake process by adding ack-packets substate to avoid the data delivery failure caused by unnecessary collisions and shorten the discovery latency. Simulation results denote that the proposed 3-way MC-NDA can decrease the expected neighbor discovery time by 95.6% than 1-way's and 98.3% than 2-way's on average. Furthermore, it can overcome the sensitivity to the large node density in traditional 2-way algorithm.

To underpin the predicted growth of the Internet of Things (IoT), a highly scalable, reliable and available connectivity technology will be required. Whilst numerous technologies are available today, the industry trend suggests that cellular systems will play a central role in ensuring IoT connectivity globally. With spectrum generally a bottleneck for 3GPP technologies, TV white space (TVWS) approaches are a very promising means to handle the billions of connected devices in a highly flexible, reliable and scalable way. To this end, we propose a cognitive radio enabled TD-LET test-bed to realize the dynamic spectrum management over TVWS. In order to reduce the data acquisition and improve the detection performance, we propose a hybrid framework for the dynamic spectrum management of machine-to-machine networks. In the proposed framework, compressed sensing is implemented with the aim to reduce the sampling rates for wideband spectrum sensing. A noniterative reweighed compressive spectrum sensing algorithm is proposed with the weights being constructed by data from geolocation databases. Finally, the proposed hybrid framework is tested by means of simulated as well as real-world data.

In face of the explosive surge of mobile data services, spectrum aggregation or carrier aggregation technology has been proposed to improve system throughput and spectrum efficiency (SE) by aggregating licensed and unlicensed spectrum bands. However, the system performances would be severely deteriorated by the channel access collision if the channel access and resource scheduling approaches are not coordinated among different networks in the same spectrum band. Therefore, in order to improve the system throughput and the SE, a fairness-based license-assisted access and resource scheduling scheme are designed for the coexisting systems, incorporating long term evolution-advanced and WiFi systems in the unlicensed band. The optimal sizes of the contention window in the proposed fairness-based channel access approach are obtained in terms of various density ratios between these two systems. Furthermore, a novel resource scheduling approach employing linear programming is proposed to maximize the utility function with the goal of improving the service experience of users and the SE with various spectrum qualities. The theoretical proofs and simulation results verify the enhanced performances of the proposed approaches in terms of key metrics, such as throughput, SE, delay, and packet loss ratio.

This paper investigates an energy efficiency optimization problem in cognitive orthogonal frequency division multiplexing systems. The goal is to maximize the energy efficiency by adapting the sensing duration, detection threshold, and transmit power to the constraints of the energy consumption of the secondary network and the interference to the primary network in a statistical manner. First, the case of identical detection threshold for all subcarriers is considered. In order to circumvent the intractability of the resulting problem, an alternate iteration framework is proposed to iteratively solve the three decoupled subproblems: sensing duration optimization, detection threshold optimization, and power allocation optimization. By exploiting the characteristics of each subproblem, the proposed framework is proved to be convergent. Then, the case with individual detection threshold for each subcarrier is explored. By proving that the optimal detection threshold is the root of a quadratic equation with one unknown variable, the proposed framework can be applied with minor modification. Simulation results show that the proposed alternating optimization framework can approach rapidly to the optimal solution, with less than 1% gap. Compared with the existing schemes, both the cases with identical and individual detection thresholds can achieve a considerable energy efficiency gain, with the latter further outperforming the former.

Abstract For the problem of spectrum scarcity and wastage, cognitive radio (CR) technology provides a solution to utilizing the vacant spectrum more efficiently. As one of the most promising techniques to obtain the cognitive information in TV white spaces, geo‐location database approach has attracted a lot of recent attentions, with its goal of enhancing the efficiency of spectrum usage and avoiding the interference to TV receivers. However, existing works mainly focus on the construction and applications of geo‐location database, and seldom consider how to deliver the cognitive information from the database to TV band devices. In this paper, we investigate the tradeoff between increasing the accuracy of cognitive information delivery and reducing the overhead. We design two mesh fusion algorithms to reduce the redundancy of cognitive information and improve the efficiency of cognitive information delivery. Finally, we verify our analysis and evaluate the efficiency of the proposed mesh fusion algorithms through numerical studies. Copyright © 2016 John Wiley & Sons, Ltd.

Considering the dynamics of vacant spectrum in cognitive radio networks, spectrum sensing is one of the most challenging technologies. However, traditional spectrum sensing technologies fail to resolve the contradiction between accuracy and complexity. To solve this paradox, this paper proposes a novel spectrum sensing scheme based on the distribution of phase difference (PD) between noise-perturbed signal and Gaussian noise. By using the variance of PD as the test statistics, the proposed PD variance detection (PDVD) is formulated for efficient spectrum sensing and its performance is analyzed under Rayleigh fading and Gaussian noise, which has a low complexity of O(K)and is immune to the noise uncertainty in contrast to the energy detection scheme. Both simulations and field measurement results show that the proposed PDVD can achieve a performance gain of 2-4dB for SNR requirement compared to the energy detection scheme when the sample length reaches 500.

In this paper, we analyze the outage probability of a three-node decode-and-forward (DF) relay network, where the relay adopts a power-splitting protocol to harvest energy from the received signal, and utilizes the harvested energy to forward information. First of all, the theoretical expression of outage probability is derived, and then the closed forms of the optimal amount of harvested energy and the best relay location are achieved analytically in order to minimize the outage probability. Furthermore, the condition that the outage performance with energy harvesting (EH) surpasses that without EH is also deduced to figure out when EH is indispensable to relay enhancements. Numerical results verify the correctness of our theoretical derivation, and validate that there exist the optimal amount of harvested energy and the best relay location to reach the minimum outage probability in the energy harvesting relay network. The work of this paper will provide valuable insights into the effect of harvested energy and relay location on outage probability, and be instrumental in how to deploy relays with RF energy harvesting functions.

With the development of Time Division Long Term Evolution (TD-LTE) system, the TD-LTE base station's density is increasing rapidly. The working spectrum of Compass Navigation Satellite System (COMPASS, also called BeiDou Navigation Satellite System) is adjacent to TD-LTE system. Because the signal received by COMPASS Equipment (CE) is very weak, large frequency isolation between TD-LTE downlink and COMPASS is necessary to avoid inter-system interference, which causes severe spectrum waste. There is still no effective solution to fix this problem. In this paper, we propose an algorithm to change the subbands power allocation of TD-LTE to reduce the interference in COMPASS system caused by TD-LTE downlink, meanwhile reduce the necessary frequency isolation between the two systems. In this algorithm, it is assumed that BSs receive the position information and interference information of CE. The algorithm decreases the interference received by CE to an acceptable level by adjusting the transmit power of nearby BSs' subbands within preset scope, thus, reduce the necessary frequency isolation. Finally, a system level simulation is conducted to investigate the performance of IEA-SPR.

In this paper, we study the relation between network capacity and the density of micro base stations in heterogeneous networks (HetNets) scenario consisting of macro base station (MaBS) and micro base station (MiBS) tiers. First, the distribution of the distance between a typical user and its serving base station (BS) is derived in a stochastic geometry model. Assuming users access the BS with the strongest received signal, we obtain the probability of users' association with the MiBS tier as a function of MiBS density. Then the impact of BS density on the interference inside the MiBS tier is also achieved in closed form. Finally, we derive the closed form solution of the network capacity as a function of BS density. We find that although there are more available channels with higher MiBS density, the rate of each channel is degraded because of stronger interference. Therefore the problem of maximizing network capacity with respect to MiBS density is formulated and the optimal MiBS density is obtained. Simulations are provided to verify the correctness of our analysis. One interesting finding is that network capacity doesn't increase monotonously with BS density. Thus deploying more MiBS may not always be a good choice1.

In this paper, a distributed two-level Stackelberg game for bandwidth allocation and pricing in heterogeneous wireless networks is proposed. The proposed Stackelberg game consists of two competition game levels, namely, a user level game and a network level game. Networks are the Stackelberg leaders which play the network level game and decide price to maximize the revenue of networks. The multi-mode users are the Stackelberg followers which play the user level game and decide bandwidth allocation to maximize the utility of users. In the user level game, the notion of Match-Degree is introduced to take into account the suitability of networks to various traffics. Then the existence of Stackelberg equilibrium (SE) is verified for this Stackelberg game. To obtain the SE, an iterative algorithm is constructed. Simulation results show that our proposed game not only can significantly increase the utility of users compared with traditional bandwidth allocation schemes, but also can set suitable network price by considering network competition and user behavior.

Compressed sensing (CS) is often utilized to lower the complexity of cyclostationary detector (CD) in wideband sensing. In this paper, a methodology is proposed to further simplify the complexity for the combination of CS and CD. Firstly, the relationship between spectral coherence function (SCF) and the compressed samples is deduced in matrix form, thus the reconstruction of original signal can be skipped. Secondly, we notice complexity is highly dependent on compression ratio, which is determined by signal sparsity. Through careful analysis, a strong dependence of sparsity on modulation mode and symbol rate is discovered. Therefore we propose to adjust compression ratio according to the modulation mode and symbol rate of the target signal. To facilitate the adjustment, a modulation classification algorithm based on correlation of SCF is formulated. Moreover, the relationship between compression ratio and performance loss is also explored. Simulation proves our method can reduce complexity significantly with marginal loss in accuracy.

To better utilize TV white space, the paper considers deploying secondary users (SUs) on vacant channels inside TV coverage. In this heterogeneous case, TV and SU receivers suffer not only mutual adjacent channel interference (ACI) from each other, but also co-channel interference (CCI) from cellular network (CN) outside TV coverage. Therefore SU access opportunity is formulated as the maximal capacity of available SU channels under both TV and SU receiver outage probability constraints. To facilitate the maximization, the analytical interference from both CN and SUs is deduced using Poisson Point Process model. Since the exact interference distribution is too complex to analyze, we approximate it as Gaussian and prove the accuracy of our approximation with Kolmogorov-Smirnov test. Main results include that SU access opportunity follows a volcano-shaped distribution geographically and optimal network-status-aware SU transmit power exists to maximize the access opportunity, which can contribute to the practical deployment of secondary system and the utilization improvement of white space.

Due to the severe traffic overload in wireless networks, offloading traffic to other networks is envisioned as a promising solution. However, since networks are dynamic, they can not accurately determine when and how much traffic to offload. To address the problem, this paper proposes a practical network state motivated traffic offloading scheme for two-tier heterogeneous networks, where users connect to the base station with the highest biased received signal strength. We derive the congestion probability of macro base station (MBS) in closed-form and introduce the Traffic Offloading Region (TOR). According to the location of network state in TOR, network can get the appropriate time to offload and the amount of offloaded traffic of MBS. To guide practical traffic offloading, our scheme obtains the association bias that can offload desired amount of traffic when base stations follow Poisson point process. Finally, simulation results are provided to validate our scheme.

This paper addresses resource management problem in a heterogeneous network consisting of cellular users and multiple D2D pairs. The D2D users share spectrum with cellular uplink under the QoS constraint of cellular system and pay for the interference they cause. We propose a two-step resource management scheme to optimize D2D user's transmitting power and the spectrum efficiency of the network. Firstly, the interference pricing and power allocation problem between a D2D pair and their allocated cellular uplink is formulated as a Stackelberg game. Then the Stackelberg equilibrium, i.e., the optimal interference price and transmitting power that maximize utilities of two players (the base station and the D2D transmitter), is obtained in close form. In the second step, with the close form expressions as important parameters, the situation is extended to multiple D2D pairs and a Hungarian algorithm based method is utilized to assign spectrum band to each D2D pair with the purpose of maximizing system spectrum efficiency. Simulation results demonstrate the advantageous performance of our scheme in network capacity and spectrum efficiency.

To understand the usage of TV spectrum, a comprehensive measurement is conducted in Beijing, China. The measurement consists of two parts, i.e., fixed measurement and radio environment mapping (REM). In the fixed measurement, spectrum utilization is calculated considering not only time domain utilization but also specific Chinese TV standards. Thus the spectrum utilization is 7% higher than reported previously. To study the geographical distribution of signal strength, REM is constructed for a small area in the downtown. Since traditional systematic sampling may place sample positions in inaccessible areas, a novel sampling algorithm named simulated annealing assisted electron repulsion (SAER) is proposed. Results show SAER results in smaller error in REM than systematic sampling and signal strength variation can reach 30 dB in the considered area, which means spatial spectrum access opportunities may exist for cognitive radio.

The heterogeneous smart grid (SG) poses two major challenges for wireless networks, namely, providing sufficient bandwidth for a wide variety of applications and high reliability for critical real-time applications. To address these challenges, the impact of communication outage on the demand response management as a typical SG application is analyzed in this paper. A dynamic spectrum management (DSM) technique is proposed to allocate resources, considering the QoS and application priorities. Vacant digital TV frequency bands are utilized to support SG applications. An algorithm to estimate the SG capacity is introduced, which can be applied to various user distributions and SG environments. This is used in conjunction with a low-complexity coloring theory algorithm to allocate the spectrum. The results presented show that DSM provides better performance than traditional fixed spectrum management, in terms of QoS and secondary spectrum utilization.

Traditional cellular network standards require users downlink(DL) and uplink(UL) associated to the same BS. However, due to the power gap between BSs and users and different transmission environment(e.g. the path gain) of DL and UL, a serving BS with the strongest downlink would yield to a non-serving BS in the uplink. In this paper we propose the DL/UL splitting user association framework under a two-tier heterogeneous network, in which femtocell base stations are randomly located over a macro base station in a specific area. Downlink and Uplink network ergodic throughput are analysed according to our DL/UL splitting association model. Simulation results show a great throughput promotion compared with the traditional DL/UL coupled association manner. We believe downlink and uplink splitting as a flexible connection mode between users and base stations would be an essential approach to realize the high capacity requirements, which is a evolution of the network connection art.

This paper proposes an efficient bandwidth allocation scheme to fully utilize the multi-access ability of multimode terminals in heterogeneous networks. To model the fact that the suitability of different networks to different traffics varies, which is seldom considered in previous literature, the term “Match-Degree” is introduced and calculated using grey relational analysis. Afterwards, two methods, namely weighted bandwidth factor and cumulative utility function are proposed to integrate the influence of “Match-Degree” into the conventional utility-functions. Moreover, energy consumption and network price are also considered to make our model more comprehensive. The bandwidth allocation problem is formulated as maximizing the overall utility by adjusting the bandwidth allocated to each user. Based on optimization theory, an iterative algorithm is constructed to solve this problem. And numerical results validate the performance enhancement of our scheme compared with the existing ones.

Unrelenting demand of mobile data can be met by intensive spectrum and spatial reuse which can be achieved by femtocells and multi-stream MIMO transmission. However, random deployment of femtocells may cause severe co-channel interference (CCI). In this paper we propose an algorithm, where femto base stations form a cluster and cooperatively generate precoding matrix for interference mitigation to nearby macro user (MUE) and other femto users (FUEs). We propose a modified version of conventional block diagonalization (cBD) linear precoding, where some antennas in femto cluster are de-activated for interference alignment in the null space of MUE. The cBD involves two singular-value- decomposition (SVD) operations which introduces high computational complexity. Therefore we propose a low complexity precoding algorithm which involves generalized zero forcing channel inversion (ZF-CI), QR decomposition and lattice reduction (LR) transformation. Simulation and analytical results show the superior performance of our proposed scheme in terms of sum-rate and computational complexity.

Understanding the characteristics of spectrum utilization is essential in providing guidelines for resource allocation. In this paper, a detailed measurement analysis of spectrum efficiency is performed, with data collected from tens of thousands of base stations during fifteen months. We examine the characteristics of radio resource margins (RM) extensively, including its temporal skewness, diurnal patterns, weekly periodicity and spatial skewness. Main findings include that radio resources are not utilized efficiently both temporally and spatially, and radio RM and traffic load show strong weekly periodicity which is predictable. Inspired by the inefficient utilization of radio resources, we then devise an optimization scheme for dynamic radio resources reconfiguration and experimental results prove that it improves radio resources utilization efficiency and traffic load balance significantly.

Under current popularity of full duplex radio, this paper explores the adaption of energy detector (ED) to full duplex scenario. To improve sensing performance,firstly a correlation based channel estimation algorithm is formulated to cancel out self-interference. We then notice traditional ED has a long delay in finding sensing error. To tackle this problem, sliding window ED is proposed, in which samples utilized for different sensing instances are allowed to overlap. Moreover, it is also observed that sensing performance degrades seriously when primary user sate changes in the sensing period, which happens frequently in full duplex sensing. Therefore, weighted ED is proposed, in which samples at the end of the sensing period are assigned bigger weights. Simulation results show that our strategies can better adapt energy detector to full duplex sensing with marginal loss in accuracy.

With recent breakthroughs in full-duplex radio, there is a growing trend to combine it with cognitive radio. Thus the topic of spectrum sensing in full-duplex scenario is addressed in this paper. Firstly, the concept of full-duplex spectrum sensing and its advantage over traditional half-duplex spectrum sensing are analyzed. Then a correlation based least square algorithm is formulated to cancel out self-interference, in which no preamble is required. We notice that the phase distribution of noise differs greatly from that of noise-perturbed signals. Therefore, a novel spectrum sensing scheme using phase difference as test statistics is introduced. Its theoretical performance is also analyzed by approximating the test statistics as Gaussian distribution. The proposed detector is simple and immune to noise uncertainty due to the independence of its threshold on noise power. Simulation results show that robust sensing performance can be achieved in full-duplex scenario using correlation based least square and the phase based sensing scheme.

The scaling laws for the throughputs of cognitive network with transmission power of secondary users being constrained are studied. The primary nodes and secondary nodes are randomly and uniformly distributed over the same unit square, with density of n and m (n > m). In this paper, we propose a new hybrid access scheme of multi-hop cognitive network, with primary users and secondary users using TDMA scheme and CDMA scheme respectively. It is shown that primary network achieves the same throughput scaling as that in the case when both primary and secondary network adopts TDMA scheme. Meanwhile, we demonstrate that the upper bound of secondary per- node throughput increases with node density m, in contrast to the decreasing pernode throughput in the existing research, which shows that our scheme not only guarantees primary network but also improves the performance of secondary network.

Powering wireless cellular system by green renewable energy cuts greenhouse gas emissions and electricity bill. However, green energy sources have the limitation of unsustainable availability and capacity. Therefore, the criteria of optimizing network planning for such system should involve both static and dynamic aspects. In this paper, the network planning scheme aims to jointly maximize cell coverage and energy sustainability, which indicate static and dynamic system performance respectively. Firstly, the energy buffer describing green energy evolution is modeled as a G/G/1 queue to investigate energy sustainability. Based on this model, the closed-form expression of service outage probability, which characterizes energy sustainability, is given. Secondly, effective coverage is introduced as a comprehensive parameter which balances cell coverage and energy sustainability. The network planning problem turns to be an effective coverage maximizing problem and solved by automatic cell management. Finally, simulation results are illustrated to validate the proposed automatic cell management.

Spectrum sensing is one of the most challenging tasks in cognitive radio. Unfortunately traditional schemes fail to balance between accuracy and complexity, which are the key indicators for the performance of spectrum sensing. In this paper, a new spectrum sensing scheme based on phase difference is proposed. Through analyzing the distributions of phase difference between adjacent samples of noise and noise-perturbed primary signal, we notice that the mean of phase difference varies from noise when primary signal is present. On this basis, a novel sensing scheme using the accumulation of phase difference as test statistics is formulated. Then the analytical performance of our scheme is derived and its complexity is analyzed. Our proposed scheme is simple, accurate and immune to noise uncertainty. Simulation results show that our scheme outperforms conventional energy detection and can achieve a detection probability of 99% at −7dB signal-to-noise ratio using 500 data samples.

Throughput scaling laws for two coexisting ad hoc networks with m primary users (PUs) and n secondary users (SUs) randomly distributed in an unit area has been widely studied. Early work showed that the secondary network performs as well as stand-alone networks, namely, the per-node throughput of the secondary networks is equation. In this paper, we show that by exploiting directional spectrum opportunities in secondary networks, the SU throughput can be improved. If the main lobe of the SU antenna pattern can be as narrow as possible, then the SUs can achieve a per-node throughput of equation which is Θ(log n) times higher than the the throughput without directional transmission. If we consider practical constraints and assume the minimum angle of the main lobe is δth, then the SU throughput gain is equation compared with the throughput without directional transmission. We also explore the statistics of directional spectrum holes in this paper.

This paper considers a heterogeneous communication network consisting of a Smart Grid Neighborhood Area Network (SG-NAN) and a Digital Video Broadcasting (DVB) system. A Dynamic Spectrum Management (DSM) technique is proposed for this network to allocate resources to the SG-NAN and DVB systems to satisfy SG-NAN QoS requirements while limiting interference to the DVB system. A novel algorithm for estimating SG-NAN capacity is introduced which can be applied to various user distributions and hybrid service situations. The DVB capacity is also calculated under interference constraints. Based on these capacities, spectrum is allocated using a coloring theory algorithm to reduce the complexity. Simulation results are presented which show that DSM provides better performance than traditional fixed spectrum management (FSM) in terms of QoS and secondary spectrum utilization.

There is a growing interest around the world in supplying the communication networks with green energy from natural resources, e.g., solar, wind, and hydro, to reduce carbon footprints. However, the green energy sources and the energy buffer have the limitation of unstable availability and capacity. It is challenging to ensure that the fluctuant energy supply meets the demands of dynamic traffic loads. In this paper, we study the problem of how to ensure the sustainability of green energy powered cellular networks (i.e., green cellular networks). We firstly construct a generalized model to describe the energy evolution process of green energy powered cells. Then, energy dynamics metrics, which are energy level transfer time and energy outage probability, are analyzed by adopting diffusion approximation. Based on the results obtained in the analysis, a liquid cell management scheme is proposed to ensure the sustainability of green energy powered cells by adjusting the cell radii. The scheme performs excellently in improving both the lifetime and green energy utilization of green HeNBs.

Detection of wideband communication signals is critical for cognitive radio (CR) as it enables secondary users to dynamically access the unoccupied bands. However, accurate and fast spectrum sensing is still a challenge in low signal to noise ratio (SNR) environment. To encounter this problem, a feature detector based on compressed sensing (CS) and wavelet transform (WT) (CS-WT feature detector) is proposed. Feature detector is chosen for its accuracy under low SNR, and CS is introduced to alleviate the sampling bottleneck of wideband sensing. Moreover, noise caused by the CS process is analyzed, and a traditional noise reduction method-two dimensional wavelet transform is utilized to cope with it by treating the spectral correlation function (SCF) as a grey image. It is verified by simulation that WT can effectively reduce the noise introduced by CS, and the proposed detector can achieve 90% detection probability under -10dB, making cyclostationary detection based on CS applicable.

White space and spectrum holes in the TV bands bring potential opportunities to relieve the apparent spectrum scarcity. Because cellular networks are now universally deployed and propagation characteristics of the TV band are superior to those of the existing cellular bands, it is essential to consider the behavior of cellular network in the TV bands. As interference is one of most important issues we need to study in TV white space (TVWS), we investigate the behavior of aggregate interference from cellular network generated by cognitive radio (CR) in TVWS. Three models of CR cellular network are proposed in this paper. We find that the behavior of aggregate interference is mainly determined by the keep-out distance which is used to protect TV receivers from cellular networks interference and the radius of the cellular network. The numerical results can help in obtaining a conceptually and computationally improved understanding of adjacent channel interference aggregation in cellular network and guiding the deployment of cellular network in the TV bands.

To utilize TV white space (TVWS), mutual interference should be restrained to guarantee both TV and secondary systems normal operation. Introducing guard bands is a feasible solution and it naturally constrains the available resources in TVWS. In this paper, we investigate the available spectrum resource distribution under aggregate interference constraints within the TV coverage. A statistical approach is brought up to analyze the mutual interference and based on that, available resource determination is formulated as an optimization problem. In terms of its non-polynomial time complexity, we put forward the near-optimal spectrum resource retreat algorithm (SRRA) to solve this problem. Simulation results verify the accuracy of SRRA and prove that our proposed statistical model matches the actual scenario closely. And we find that the spectrum resource distribution consists of a series of resource contours (RCs) within coverage of TV transmitter and available resources can reach the upper bound at certain RC, which can guide secondary systems for better usage of TVWS.

In this paper we study the joint price and power allocation for interference management in macro- femto spectrum sharing network based on the Stackelberg game. In our model, the macro base station (MBS) works as leader and overlaid femto base stations (FBSs) as followers. The MBS allocates its power and interference price to the FBSs to guarantee its user's minimum rate requirement and reap the revenue from the femto network. Based on this price, FBSs calculates their power in distributed way. The game is formulated for joint utility maximization of both types of players. We consider the two cases of fixed and dynamic MBS power and propose uniform price for all FBSs. In this game model, first of all the number of players (FBSs) is calculated that can participate in the game against different macro user rate requirement and then price and power are determined. We propose unique closed form solution to the both cases based on convex optimization which always guarantee the convergence. The numerical results validate the effectiveness of our proposed solutions.

Second order statistics (SOS) matrix has been utilized in near field source localization with an extremely low complexity. However, in practice there exits a vector matching defect seriously deteriorating the robustness of the algorithm. In this paper, we introduce the Minimum Description Length (MDL) as a data pre-processing mechanism and generate a merging algorithm with signal detection which can effectively solve this problem. The integral algorithm merely draws in an additional covariance matrix formulation and the eigenvalue decomposition of it, which can be effectively fulfilled by parallel computing. Above all it is the robustness the algorithm gains that potentiates the practical use of the approach. Simulation outcome indicates that even in a highly adverse circumstance with only 4 sensors, the localization error proves to be constrained in 0.001, demonstrating the excellent overall performance of the proposed algorithm. Before that with specific matrix manipulation imposed, a generalized form of algorithm of the type is derived in terms of two delay factors. A concrete approach referring to single delay is proposed and verified to be accurate but highly unstable in unfavorable circumstance, even unable to localize with insufficient sensors available as stated previously.

Spectrum access opportunity is an important research topic in cognitive radio networks, which is both related to the transmission constraints from primary users and cognitive users. This paper analyses the relationship between deployment of cognitive network and mutual interference constraints from primary network to cognitive network and cognitive network itself. By utilizing the primary exclusive region scenario, the closed-form expression of the upper bound transmission probability for a cognitive user is obtained considering the interference constraints from the primary users and other cognitive users. Based on these results, we then derive the expected interference value at a primary receiver and obtain the more exact transmission range for the cognitive network compared to the past works. We also proposed an iteration process to evaluate the transmission probability and guard region width. The simulation results show that compared to the conservative guard region analysis, our analysis is more reasonable in terms of considering the mutual interference constraints and it guarantees more spatial access opportunities for cognitive users.

To dynamically and efficiently utilize the vacant spectrum resources, cognitive radio is proposed as a potential solution, where spectrum sensing is one of the indispensable techniques. As one of the remaining issues in spectrum sensing, how to achieve the fast and energy efficient spectrum sensing in face of a wide band spectrum with an acceptable sensing accuracy is still a big challenge. Therefore, to reduce the spectrum sensing cost, a fast spectrum sensing scheme is proposed in this paper. Firstly, the channels of the same service are classified into highly correlated groups by a modified version of the greedy algorithm for set covering problem. In each group, only one representative channel (RC) is detected and the current busy-idle states of other channels, namely, estimated channels (EC), can be inferred according to their historical states and the current state of RC by the proposed joint Markov and channel correlation algorithm. Based on the real-time measurement results on GSM service and TV service in Beijing, the proposed scheme is proved and verified to be efficient on the premise of low estimated error.

The secondary usage of TV white spaces by cognitive devices is a widely concerned topic in recent years. Both the Federal Communications Commission (FCC) in USA and the Electronic Communications Committee (ECC) in Europe have proposed rules to enable the secondary spectrum access in TV white spaces by geolocation database method. Geolocation database in cognitive network maintains records of all authorized services in the TV frequency bands and has the capability of providing a list of available frequencies and power levels for each geographical pixel based on the interference protection requirements. Since the data in geolocation database are defined for each geographical pixel, the complexity and cost of the database is highly correlate with the size of the pixel. In this paper, a novel method to reduce the complexity and cost is proposed and explained, which compress some compressible geographic pixels and use non-uniform dimensions of pixels in populating the database. With simulation and performance evaluations, we concluded that this method can significantly reduce the computational complexity and implement cost of geolocation database.

In this paper, a novel cooperative sensing based on local spatial distance sensing and reliability clustering scheme is proposed. Based on the signal propagation model, the distance between primary user and secondary user follows the logarithmic normal distribution. Then we propose a distance threshold to detect the primary user and put forward a reliability-based clustering cooperative sensing scheme, taking into account the accuracy and throughput. In time domain, different locations may have different sensing periods. Through theoretical formulations, we can conclude that the cluster numbers will influence the system detection performance. Compared with the conventional energy detection, the performance of cooperative sensing has been improved. Based on the accurate fixed position of primary users, the system throughput has been increased.

The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Traditional spectrum sensing technique is neither efficient nor necessary, wasting the spectrum access opportunities on the vacant spectrum holes of primary users (PUs). Considering the sparse signal feature, a novel compressed sensing technique is proposed by using the minimal sampling rate to detect spectrum holes, which is more efficient than the Nyquist sampling rate and traditional compressed sampling rate that is required to reconstruct the original signal. The proposed compressed sensing process is divided into two stages called approaching stage and monitoring stage. The first stage is to gradually approach the minimal sampling rate required to achieve the spectrum detection performance by using the feedback mechanism. And the second stage is to monitor the status of PU according to the threshold using the sampling rate from the first stage. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm. Numerous results show that the proposed compressed sensing technique can reduce the sampling rate to 35%, with acceptable detection probability over 0.9.

In order to investigate the status of the radio spectrum usage with different services in Beijing, we have conducted a 24-hour spectrum measurement and spectrum usage pattern analysis in May 2012 in the frequency bands ranging from 440 MHz to 2700 MHz. Based on the measurement results, we try to identify the suitable bands for new spectrum access technologies such as cognitive radio which can improve the efficiency of spectrum usage. The results from the spectrum measurements taken over a two weekday period reveal that a significant amount of spectrum has a very low occupancy over the time. On average, the actual spectrum usage in all bands is about 15.2%. The observed low spectrum occupancy in the business center in Beijing indicates that there is a great potential for employing the dynamic spectrum access technology such as the cognitive radio to accommodate enormous demands for future wireless services and improve the vacant spectrum usage.

Stochastic Knapsack Queuing Model (SKQM) can be applied to estimate the network capacity of a telecommunication network where different services are transmitted under various QoS constraints. However, the serious scrutiny and refinement are required before the original model can be fitted into the real-world network deployment. In this paper, we start from the assumption, principle, proof and theoretical result of our improved stochastic knapsack queuing model and apply it to tackle real-world network capacity planning problem in TD-SCDMA system. We develop a general network planning procedure based on SKQM in TD-SCDMA and prove its effectiveness through the case study. Numerical results show that the proposed SKQM have a good performance in capacity planning in TD-SCDMA.

We consider the problem of channel allocation in cognitive radio network (CRN) with the restraints for adjacent-channel interference (ACI) caused by CRN, which is significant in the existing communication system and is easily ignored by researchers. In order to mitigate the effects of the adjacent-channel interference, it is a convenient way to use guard bands in CRN, when cognitive radio (CR) users dynamically exploit the idle spectrum owned by the primary user (PU). This paper focuses on a joint power control and channel assignment in a multi-channel CRN which realizes the use of spectrum effectively under the interference restrictions. Moreover, there are two important aspects of this guard bands method, which are the difference of primary networks and the operational mechanism of CRN, that we take into account. Since the optimization problem is, in general, NP-hard, we apply the coloring theory to reduce the complexity while providing the near-optimal performance. Finally, simulation results are provided, and the detailed numerical results are analyzed.

To efficiently utilize the vacant spectrum resources, different environment awareness techniques, such as the spectrum sensing, have been applied in cognitive radio networks (CRNs). However, the existing research works ignore the effects and differences between the long term and short term vacant spectrum quality of primary users. Therefore, the cognitive information sequence (CIS) concept has been proposed in this paper to represent the information sequence of the environment awareness results. Besides, the temporal entropy is proposed to reveal the uncertainty of CIS and the cognitive information is used to measure the uncertainty of the internal and external states of primary user (PU) that can be removed by the secondary user (SU). Moreover, the information theory techniques are applied to analyze the mathematical properties of temporal entropy and cognitive information in CIS. By utilizing the temporal entropy values which reveal the vacant spectrum quality, the optimal solutions for the efficient spectrum sensing techniques are proposed and verified by numerous results, such as the optimal period of spectrum sensing and the threshold of energy detector.

In this paper, we analyze the sensing performance of multi-cycle cyclostationary (MC) detection- based spectrum sensing in a secondary user (SU) possessing multiple correlated antennas when the channel from the primary user (PU) to the SU suffers from Nakagami fading. We first propose an improved MC detector, aiming at reducing the computational complexity of conventional MC detector by simplifying its test statistic. Compared with the conventional one, our proposed detector achieves low-computational complexity and high- accuracy on sensing performance. Based on the proposed detector, square-law combining (SLC) diversity of multiple antennas technique is introduced to improve the detection capability over the Nakagami fading channel. Subsequently, the effect of multiple correlated antennas is investigated. A special correlated antenna case of a linear array of 2 and 4 arbitrarily correlation is then treated. The corresponding closed-form average detection probability is derived by using the moment generation function (MGF) approach. Finally, results show the efficiency and reliability of the proposed detector and the degradation on sensing performance with correlated antennas over the Nakagami fading channel.

Efficient spectrum utilization is of great importance in cognitive networks, however current spectrum sharing techniques in the temporal or spatial domains all have deficiencies. In this paper, we propose a joint spatial and temporal spectrum sharing scheme based on the concept of regions. We define four regions, namely a primary exclusive region (PER), a temporal spectrum sharing region (T-SSR), a joint spectrum sharing region (J-SSR), and a spatial spectrum sharing region (S-SSR). TSSR and J-SSR are proposed to utilize temporal spectrum holes which have not previously been exploited. Cooperative spectrum sensing is employed to expand the J-SSR, which is beneficial for system design. Closed-form bounds for the four regions are obtained, and conditions on T-SSR existence and the presence of a transition zone between the JSSR and S-SSR are determined. Both analytical and simulation results are presented which show how the key factors, including primary user interference constraints, spectrum sensing factors, and secondary user coverage constraints, influence these bounds. This has great practical value in improving spectrum efficiency in cognitive networks.

In this work, we present an optimal power allocation scheme dedicated for variable-hop cooperative relays in cognitive networks, which consists of Secondary users (SUs) operating in Amplify-and-Forward (AF) mode, with a Primary user (PU) receiver nearby. In order to assure the quality of service (QoS) for PU, transmit power of cognitive radio (CR) nodes must be tightly controlled which leads to narrow communication range. The issue of minimal feasible hop count Nminand energy-saving with relays which can only cause limited interference to PU are considered to enable the communication between remote nodes under the constraint of minimal signal to noise ratio (SNR) γth. Closed-form solution for the optimal power allocation scheme is obtained by Lagrangian algorithm along with an O(N) complexity dichotomy method to achieve Nmin. We prove that the energy consumption in the system is reduced with the growth of N by both theoretical derivation and extensive simulation, and the general relation between Nminand γth.

In this paper, we investigate the connectivity of random cognitive radio networks with different routing schemes. Two coexisting ad hoc networks are considered with m primary users (PUs) and n secondary users (SUs) randomly distributed in a unit area. The relation between n and m is assumed to be n = mβ. We show that with the HDP-VDP routing scheme, which is widely employed in the analysis of throughput scaling laws of ad hoc networks, the connectivity of a single SU can be guaranteed when β > 1, and the connectivity of a single secondary path can be guaranteed when β > 2. While circumventing routing can improve the connectivity of cognitive radio ad hoc network (CRAHN), we verify that the connectivity of a single SU as well as a single secondary path can be guaranteed when β > 1. Thus to achieve the connectivity of secondary networks, the density of SUs should be larger (asymptotically) than that of the PUs. A smart routing scheme can also improve the connectivity of CRAHN. Our results serve as a guide to deployment and routing design for cognitive radio networks.

The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.

Besides energy-efficient technologies, increasing attention is paid on powering cellular networks with renewable energy sources, concerning climate change, fossil fuel prices and energy security. In this paper, we not only aim to reduce the absolute energy consumption of cellular networks, but also provide a guideline to utilize the renewable energy efficiently in cellular networks. The renewable energy sources have the limitation of unstable availability and capacity. It is thus challenging to improve renewable energy efficiency while maintaining the energy supply sustainability. The energy supply sustainability problem is modeled as an optimization problem aiming to maximize the network energy residue ratio (ERR), which is NP-hard. To solve the optimization problem in polynomial time, the network ERR maximization algorithm is proposed after analyzing the relation between energy efficiency and energy depleting rate (EDR). The algorithm maximizes link energy efficiency via power control at physical (PHY) layer and maximizes network ERR via access control at media access control (MAC) layer jointly in a cross-layer manner. The network ERR maximization algorithm performs excellently in improving both the lifetime and the number of users served by renewable energy.

Spectrum sensing, as a key technology of cognitive radio (CR), needs to reliably and efficiently detect spectrum holes in wireless environments, which challenges the traditional spectral estimation methods typically operating at or above Nyquist rates. This paper develops a novel compression ratio allocation (CRA) method for wideband spectrum sensing in CR networks. In our scheme, each CR terminal performs compressed sensing with sub-Nyquist rate samples to scan a wide spectrum range at practical signal-acquisition complexity. It can greatly reduce the sensing measurements through fewer sample numbers. Meanwhile, the cognitive base station optimizes the compression ratio at each CR terminal according to their local signal-to-noise ratio (SNR), so the total sample number can be further cut down. Simulation results show that the CRA algorithm provides an optimal performance while requiring a relatively low complexity of sensing process.

In cognitive radio networks (CRNs), Secondary Users (SUs) are provided opportunities to access Primary Users' (PUs') idle spectrums but the availability of spectrums is dynamic due to PUs' uncertain activities of channel reclamation. In this paper, we investigate such spectrum mobility by proposing Singleton Bayesian Spectrum Mobility Games based on the Singleton Congestion Games, where each SU distributively reselects one switch-to (and available) channel which can bring it the maximum SINR when the spectrum environment varies, accounting for other SUs' switching strategies at the same time. Unlike previous game-theoretic schemes for handling the spectrum mobility that assume SUs' complete knowledge of the CRN, we present our scheme in two information scenarios. We first demonstrate the proposed game in the complete-information scenario and prove the existence of pure Nash equilibriums. Then the game is extended to the incomplete-information scenario with the existence of Bayesian equilibriums. Besides, the other major contribution of this paper is that we provide a polynomial-time algorithm for finding the socially optimal equilibrium among all possible equilibriums, which can optimize the (expected) overall performance of the entire CRN in terms of SUs' average SINR.

In order to improve the spectrum utilization in cognitive radio networks, the spectrum holes in space-time-frequency multiple dimensions should be exploited accurately and efficiently. Therefore, a novel three region scheme, which includes the black region, grey region and white region, has been designed and proposed with one primary transmitter at the center, taking into account key interference factors from secondary users (SUs) and the miss detection and false alarm probabilities in spectrum sensing. Between the black region where only primary users (PUs) have exclusive right to use the spectrum and the white region where SUs can utilize the same spectrum without causing severe interference to PUs, the grey region has been designed, which has temporal spectrum access opportunities in time dimension once neglected by existing works. Moreover, the condition of the existence of a transition zone between grey region and white region is analyzed with theoretical results, where power control should be applied to SUs. The closed-form bounds of three regions are obtained, which can be used in the space-time spectrum sensing and access in cognitive radio networks.

In this paper, we consider a cognitive relay network which contains a source node, a destination node and a group of network clusters each consisting of several cognitive relay nodes. We choose one relay node with the optimal power value in each cluster to aid the data transmission from source to destination, with the goals of minimizing the total transmitting power and maximizing the network capacity under outage and interference constraints. The research is based on a dual-hop scenario and a multi-hop scenario. The proposed schemes achieve the optimal power allocation and efficient relay selection in both scenarios. The closed-form optimal power allocation and relay selection for a conventional relay network are also listed in this paper.

In this paper, we investigate the power saving issue in cognitive radio (CR) multi-hop relay network. Due to the dynamic property of the wireless channel, the quality of service (QoS) guarantee for multi-hop transmission is quite challenging. To deal with these problems, automatic repeat-request (ARQ) protocol in an end-to-end manner is incorporated. For multi-hop transmission evaluation purpose, the end-to-end packet delivery probability is put forward as a QoS indicator in this paper. Besides, by underlay spectrum sharing, each relay is possessed of a power budget (i.e., maximum transmit power) to protect primary user from suffering intolerable interference. This paper addresses the power saving problem under each relay's power budget constraint, which means the end-to-end QoS constraints can be satisfied with the minimum total power consumption for relays along the optimal path. Motivated by this, we propose a joint Lagrange dual method based power allocation and exhaustive search based relay selection algorithm to obtain the solution. Numerical simulations are presented to validate the theoretical analysis. The results show that the proposed algorithm achieves a good performance in power saving.

As one of the essential characteristics for CRN, the cognitive reconfiguration can automatically adjust the cross-layer parameters to meet the user requirements, realize interoperability between heterogeneous networks and adapt to the time-varying environment. However, the cross-layer parameters reconfiguration implementation is still challenging due to its need for complex environment cognition and multi-objects optimization. In this direction, ant colony optimization (ACO) technique, as an intelligent technology to solve the complex issues, is introduced to the reconfiguration process to achieve the adaption. The aim of this paper is to present a generic cross-layer parameters reconfiguration framework including indispensable function entities for autonomous reconfiguration decision making with regard to the multiple and complex objectives. Finally, numerous results prove the effective performance improvements of ACO based reconfiguration solution in CRN.

This paper studies a spectrum sharing cognitive radio (CR) network coexisting with a primary network. In particular, the channel state information (CSI) between the secondary transmitter (STx) and the primary receiver (PRx) is assumed to be outdated due to channel feedback latency. We assume that the secondary user (SU) shall satisfy a given delay quality-of-service (QoS) constraint as well as the average interference power constraint. Our aim is to obtain the maximum arrival rate of the SU under aforementioned constraints with the outdated CSI. In this respect, we derive the optimal power allocation scheme to achieve the maximum effective capacity, and further derive the effective capacity. The closed-form expressions for the lower and upper bounds on the effective capacity are also provided. Numerical and simulation results are presented to show the effects of the outdated CSI. It is shown that the effective capacity of the SU is insensitive to the channel correlation coefficient especially under low channel correlation coefficient.

In this paper, the subcarrier and power allocation problem for energy efficiency maximization is addressed, which is different from traditional throughout maximization. Lagrangian dual decomposition (LDD) is applied in this problem and a multilevel water-filling for power allocation is derived. But the water-filling in this paper is a transcendental equation, which is different from traditional water-filling form. To solve this equation, fixed point iteration is applied. Besides, a sufficient condition for the existence of the fixed point is derived and joint resource allocation algorithms are designed. Finally, numerical results verify our work and the energy efficiency is improved compared with the capacity maximization scheme.

In this paper, a mini-slot sensing with selective coordinator in cognitive radio is investigated. In traditional sensing, a whole sensing duration is only used to sense one channel. In proposed mini-slot sensing, a sensing duration is divided into many mini-slots and each mini-slot can be used to sense one channel independently, then data from all slots are been sent to a coordinator for final decisions. A proof is put up to demonstrate that mini-slot sensing has a better performance than the traditional sensing. In this research, the ability of different secondary users sensing different channels are not equal. Based on this assumption, we prove that in mini-slot sensing, not all slots do benefits to CR system. Slots can be divided to two groups: contributor (slots which improve system performance) and destroyer (slots which decrease the performance). A selective coordinator is proposed to assist mini-slot sensing which receive data from all slots and reject the destroyers in making final decisions. Based on the analysis above, an optimization problem is formulated to find the optimal assignment for slots to channels. A Greedy based two stage assignment method is proposed as the sub-optimal solution. Simulation results show that our proposed method has a better performance than traditional methods.

In this paper, we consider the power saving issue in the cluster based multi-hop cognitive radio (CR) network with one pair of primary user (PU) in presence. By underlay spectrum sharing, the transmit power of CR nodes are strictly restricted to protect PU from suffering severe interference. Moreover, the end-to-end outage probability is put forward as an essential QoS indicator for multi-hop transmission and has been carefully studied in the article. The objective of this paper is to minimize the total power consumption of CR transmitters along relay path. Both the end-to-end outage requirement and power budget of relays are incorporated as constraints. To solve the formulated problem and obtain the optimal solution, we propose a joint Lagrange dual method based power allocation and objective oriented optimal relay selection algorithm, and give thorough evidences and illustrations as well. Finally, numerical simulations are made and results demonstrate that the proposed algorithm has a good performance in power saving.

In this paper, a novel MAC-layer spectrum sensing scheme base on continuous-time semi-Markov process is investigated for the purpose of improving spectrum sensing efficiency of cognitive radio (CR) systems. The scheme focuses on identification of the optimal sensing sequence of channel by modeling a group of licensed channels' usage pattern as a continuous-time semi-Markov process. Experimental results show that the proposed algorithm has the potential to achieve noticeably improved performance in terms of reduction the sensing overhead and second user's average throughput when compared to the conventional non-prioritization spectrum sensing approach, thus is suitable for CR networks.

With the trend of technology innovations in recent years, network heterogeneity and inefficient spectrum usage are the great challenges in Cognitive Radio Networks (CRNs). As one of the solutions for efficient heterogeneous network information delivery in CRNs, a common broadcast signaling channel named Cognitive Pilot Channel (CPC) is proposed with its large coverage and easy implementation characteristics in contrast to usually inefficient and time-consuming spectrum sensing techniques. In order to improve the accuracy of network information delivery, the geographical regions are divided into small meshes and the network information in each mesh is broadcast one by one. This paper proposes an efficient coding scheme for broadcast CPC, called Differential Mesh Information Coding (DMIC), to reduce the redundancy of similar network information among different meshes. The strategies of choosing the basic mesh with popular commonality and quantizing the differential information among meshes are also proposed and proved by numerous results.

In spectrum sharing systems, a secondary user (SU) is permitted to share frequency bands with a primary user (PU) as long as its transmission does not interfere with the PU's communication. In this paper, the outage probability is investigated for the cognitive relay system over Nakagami-m fading channel. By applying the interference temperature constraints at the source nodes and relay nodes in secondary systems, we analyze the outage performance in two-hop underlay spectrum sharing with the best relay selection criterion. The probability density function (PDF) and cumulative distribution function (CDF) of the signal to noise ratio (SNR) at the SU's receiver are derived to obtain the closed-form upper bound of the outage probability of the secondary relay system. Simulations results demonstrate the validity and accuracy of the theoretical analysis.

Spectrum sensing is regarded as a key technology in cognitive radio (CR). Energy detector has been performed as an alternative spectrum sensing method because of its low computational complexity and not requiring a priori information of the primary signal. This study proposes an enhanced energy detector by making an arbitrary positive power operation of the received signal amplitude instead of the squaring operation in the traditional energy detector (TED). The detection probability of the proposed detector is theoretically derived under a constant false alarm probability in additive white Gaussian noise (AWGN) channels. Performance analysis and simulation results indicate that the enhanced energy detector with the optimum power operation outperforms the traditional energy detector, especially in low signal-to-noise ratio (SNR) regime.

In future B3G/4G communication systems, the special application of femtocell in the enterprise offices and public places has broad prospect. However, as the femtocell operation under such multi-femtocell environment is significantly different from that of usual residential femtocells, the femtocell configuration and optimization might be much more complex. One key issue is to find how will the femtocell network performance be influenced by the change of the femtocell access point's (FAP's) parameters (power, assigned channel, e.g.) in the multi-FAPs environment. This paper proposes a neural network (NN) modeling approach to approximate the relation between the femtocell network performance and the FAPs' operating parameters. Simulation results show that the proposed model works well and is very close to the actual situation.

In the future B3G/4G communication systems, it is of broad prospect to implement the femtocell in the enterprise offices or other public places. However, the femtocell operations under such environment are significantly different from the macrocell as well as the usual residential femtocell, and the configuration and optimization are more complex. Although there have been a few works studying the optimization of enterprise femtocell networks, much more issues need to be further investigated. In this paper, we proposed an approach for improving the enterprise femtocell network's performance by automated optimizing the femtocell base station's (FBS's) pilot power as well as antenna pattern, and the recently proposed multi-element antenna which is appropriate for femtocell is also introduced. The aim of the optimization is to maximize femtocell network's coverage while minimize interference between femtocells, and thus improve network's performance parameters such as call drop ratio, average throughput, etc. To reduce the complexity of this algorithm, the optimizing procedure is divided into two steps: first a pilot power optimization approach based on Newton's method is used to maximize the coverage while reduce overlap area of femtocells; then a simulated annealing (SA) algorithm based FBSs' antenna patterns joint selection scheme is considered to further optimize the network. The numerical results showed that the proposed approach can significantly improve the network performance.

In this paper, we consider a cognitive radio (CR) multicast network sharing spectrum with a primary network. To protect the primary transmission, interference power constraint is applied to restrict the transmit power of the cognitive base station (CBS). The objective is to minimize the weighted aggregate outage probability for given target rates for the CR multicast network. Specifically, two types of outage probability are concerned, that is, group outage probability and individual outage probability. For each type of outage probability, the optimal power/rate control scheme is derived. The simulation results are illustrated to validate the proposed power/rate control schemes.

Cognitive relay is considered as a remarkable technology that increases the network coverage and raises the system spectral utilization by exploiting those detected spectrum holes. We mainly investigate a multi-hop scenario that is more complex than a dual-hop network because of the high correlation between hops. In this article, we consider an energy-constrained network which includes a source node, a destination node and a group of network clusters each consisting of several cognitive relay nodes. To accomplish data transmission from source to destination, the best path including nodes with optimal power is selected to max imize the network capacity using proposed efficient algorithms, taking into account the sensing results of relay nodes, the channel gains and the connectivity of links. In addition, these algorithms can also be adopted in the situation of selecting two relay paths by making only a few slight modifications, which means that the scheme provides good expansibility. Simulation results illustrate the strategies of relay selection and power allocation constrained by different energy consumptions and compare the performance between one-path and two-paths selection.

For wireless sensor networks (WSNs), which have crucial limitation on energy as all the nodes rely on nonrenewable batteries and are often inconvenient to be replaced or recharged, the energy efficiency that has a close relationship with system longevity becomes a significant issue. In this paper, a cross-layer strategy in terms of physical layer and network layer is proposed for maximizing equilibrium lifetime of WSNs. In the proposed strategy, the residual energy ratio (RER) of individual node is taken into account and regarded as a key factor in power control and routing selection. Utilized to identify the relay capability of an intermediate node, the RER is quantized as a specific access probability, which is highly related to the contention window of each node in the end-to-end transmission link. By way of intelligent and efficient allocation of transmission power, and reasonable routing, the proposed cross-layer tactic can achieve preferable performance with energy efficiency. Simulation results prove that the cross-layer based equilibrium power control and routing (EPCR) strategy could greatly prolong the system lifetime of WSNs.

With the increasing demands for high data rate applications with high quality of service in the next generation networks, OFDMA based femtocell technology is a promising solution for indoor coverage extension and network capacity boosting with its automatic and easy deployment features in contrast to the high CAPital Expenditure (CAPEX) and OPerating EXpense (OPEX) investments for macrocell deployments and operations. Lots of researches have been done on interference mitigation and resource allocation in femtocell networks. However, little attention has been paid to the analysis of the upper bound of macro/femtocell hierarchical network capacity by using the joint femtocell scheduling and optimization strategies. Therefore, this paper has formulated the problem by optimizing the weighted sum capacity of the macro/femtocell hierarchical network with two levels of solutions: one is the optimal solution; the other is a suboptimal local solution, which is raised to approximate the optimal setting with reduced complexity. And a distributed Soft Frequency Reuse (SFR) based approach named Soft Control (SC) is proposed for the fully localized solution. Simulation results verify that the proposed suboptimal and distributed solutions can provide the good performance and approximate the optimal solution with low complexity, with only small loss of the weighted capacity.

One of the most important features of the evolving Fourth Generation (4G) wireless communication system is heterogeneous wireless access in which users could connect to several wireless access networks simultaneously. The new feature brings new challenges for the bandwidth allocation (BA) among heterogeneous networks. A non-cooperative bandwidth allocation game (NCBAG) algorithm for heterogeneous wireless networks is proposed in this paper. A BA problem is modeled as a non-cooperative game, and formulated to maximize the total utility of different networks. The existence of Nash equilibrium is verified for the proposed game model. The utility functions are developed for different applications to avoid assigning too much bandwidth to single user. Simulation results show that our proposed scheme can not only achieve high utility, but also reduce the blocking probability within a few steps of iteration.

To deal with the problem that cognitive information and queuing information are not both taken into account when performing Resource Allocation (RA), we have proposed a cross-layer RA scheme in a multiuser cognitive system with Orthogonal Frequency Division Multiple Access (OFDMA). In this scheme, we assume that several crucial factors influence the RA decision, including subchannel occupancy state, subchannel link state and packets queuing state. The RA strategy is formulated as a convex optimization problem to maximize the whole system throughput with users' heterogeneous Quality of Service (QoS) requirements, considering the dynamic variations of available resources caused by the activities of licensed users. In order to solve the problem with low complexity, we present Joint Resource Allocation (JRA) algorithm involving two sub-algorithms, Minimal-Cost-Maximal-Flow Subchannel Allocation (MCMFSA) and Suboptimal Power Allocation (SPA). The innovations of the entire paper are the integrity of system model and the novelty of these algorithms, especially combining subchannel allocation with graphical theory. Simulation results indicate that the proposed scheme can achieve an excellent performance, with all QoS requirements satisfied.

OFDMA femtocells are generally accepted as a very promising solution for indoor coverage with high data rate. However, the lack of systematic schemes to effectively mitigate macro/femtocell hierarchical interference, fully utilize radio resources, provide quality-of-service (QoS) and fairness guarantee among users suffocate the performance realization of femtocells. In this paper, an Adapted Soft Frequency Reuse (ASFR) approach is raised to combat traditional inter-cell interference (ICI)1by inheriting the conventional soft frequency reuse (SFR) functionality and to mitigate inter-tier interference (ITI) of macro/femtocells by applying an orthogonal spectrum reuse between macro/femtocells. Moreover, a powerful inter-femtocell interference (IFI) coordination mechanism is provided to complete the interference solution for the three types of interference in the macro/femtocell hierarchical networks. While the interference handling propositions are targeted at optimized spectrum partition for base stations (macro/femtocells included), to further increase spectrum efficiency, a Maximum Weighted Sum Capacity (MWSC) based scheduling design is adopted, to optimize spectrum allocation for users, with restraints to QoS and fairness guarantees. In this way, spectrum efficiency is enhanced at both the BS and UE sides. Simulation results show that the proposed solutions provide fairly good performance in comparison with conventional co-channel macro/femtocell utilizing Proportional Fair (PF) scheduling method.

Femtocell networks have great potential for mobile applications. However, interference due to the co-existence of macrocells and femtocells is a serious problem. In addition, dense femtocells introduce severe inter-system interference. In this paper, a graph theory based dynamic sub-band allocation technique is presented to avoid downlink interference. We model the cells and their mutual interference as graph elements, nodes and weighted edges, respectively. To maintain a tolerable interference level, the total bandwidth is divided into a number of sub-bands, and these are assigned to the femtocells using a graph coloring algorithm. The division is optimized to minimize the bandwidth used to meet user traffic requirements, and minimize the femto-to-macro interference. In addition, sub-bands are assigned to the femtocells to avoid inter-femto interference. An iterative spectrum management algorithm is also introduced.

This paper considers a secondary user (SU) sharing the spectrum licensed to a primary user (PU) if limited interference caused to the latter can be guaranteed. In particular, besides the interference power constraint at the PU to protect the PU, the transmit power of the SU is also considered. Under such a setup, we consider the average bit error rate (BER) as the performance metric for the SU, and then derive the optimal power allocation strategies to achieve the minimum average BER of the SU. Simulation results are presented and discussed. It is shown that the optimal power allocation strategies can achieve substantial performance gain for the SU over the water-filling method.

This paper proposes a novel Dynamic Spectrum Management (DSM) scheme for Wideband Code Division Multiple Access (WCDMA) / Digital Video Broadcasting (DVB) heterogeneous systems. Capacity estimation algorithms for both WCDMA and DVB are developed which consider both the user distribution and characteristics of the hybrid services. Based on these algorithms, a new dynamic spectrum allocation scheme is presented which allows for optimum allocation of resources and maximum secondary spectrum usage. Coloring theory is used to significantly reduce DSM complexity while providing near-optimal performance. Numerical results are given which show that the proposed DSM scheme has better performance than Fixed Spectrum Management (FSM).

Dynamic spectrum access has become a promising technique to fully utilize the scarce spectrum resources. However, spectrum allocation schemes with high efficiency and Quality of Service (QoS) guarantee for the primary users have yet to be designed. In this paper, two novel dynamic spectrum access schemes are proposed. The proposed schemes are based on continuous-time Markov chains (CTMC), through which the interactions between primary and secondary users are explicitly modeled. The effects of sensing errors (i.e. miss-detection and false alarm) are taken into consideration. Since miss-detection may lead to collision between primary and secondary users and false alarm will leave spectrum opportunities unused, we derive the optimal access probabilities for each secondary user, so that the QoS of primary user in terms of collision probability constraint is guaranteed, and the missing spectrum opportunities caused by false alarm can be utilized by secondary users. Simulation results show that the proposed schemes can guarantee primary user''s QoS effectively. Moreover, the scheme with buffer can improve the channel occupancy remarkably.

With the irreversible trend of the convergence and cooperation among heterogeneous wireless networks, the need for network information awareness of user equipments (UEs) becomes increasingly imperative in the Cognitive Wireless Networks (CWN). As one of the candidate solutions for network information delivery for UEs, the Cognitive Pilot Channel (CPC) concept has been brought forward recently, providing UEs with the necessary network information for network selection by using the public signaling channel. Besides, both broadcast and on-demand CPC modes have been proposed for the network information delivery under the assumption that the geographical region is divided into meshes. In this paper, a novel homogeneous mesh grouping (HoMGP) scheme based broadcast CPC mode is designed to improve the efficiency of broadcast CPC mode in the CWN. The homogeneous meshes are selected and grouped based on the frequency occupancy graph, which is obtained by using the image processing techniques. By grouping the homogeneous meshes together, both the frame format and flow of the HoMGP broadcast CPC mode are designed to deliver heterogeneous network information to the UEs efficiently, which is verified by numerous simulation results.

With the increasing convergence and cooperation among wireless networks, network awareness of user equipment (UE) has become very important. A practical solution for network information delivery for UE, Cognitive Pilot Channel (CPC), has recently been proposed. It can provide UE with the necessary network information for network selection by using the public signaling channel. In this paper, a cognitive multicast CPC scheme is proposed. It can greatly improve the on-demand CPC delivery efficiency, and also reduce the time delay of information delivery. The cognitive characteristics of the proposed multicast CPC scheme can easily be adapted to different heterogeneous network architectures.

Cognitive Radio (CR) is proposed as a promising means to utilize the spectrum more efficiently. However, in cognitive radio system, how to achieve the users' QoS requirements is a challenge. To solve this problem, cooperative relay transmission has been proposed as a powerful approach. In the relay-assisted CR system, users can not only obtain required service directly from the Cognitive Base Station (CBS), but also select other nodes as its relay and receive data with the relay cooperative transmission. This paper mainly focuses on how to allocate resource appropriately to fulfill users' call-drop and datarate demands, and meanwhile increase the meaningful system throughput in the CR relay system. At first, a system model with Joint Relay selection, Spectrum allocation and Rate control (JRSR) is built and the problem is formulated. Then, a three-stage heuristic algorithm is proposed to address the JRSR problem and a suboptimal solution is found. In the three-stage JRSR scheme, the throughput directly from the CBS is maximized first, then some adjustments and supplements are made to achieve the media stream users' service demands with priority as much as possible. Simulation results illustrate that our proposed JRSR scheme is effective on fulfilling the users' call-drop and datarate demands, and it also performs well on increasing the meaningful system throughput.

To improve the performance of spectrum sensing, cooperation among Cognitive Radios (CRs) has been proposed recently as an effective solution. A great part of the existing spectrum sensing algorithms adopt synchronous spectrum sensing method, while the rest asynchronous spectrum sensing algorithms have a severe demand for terminals' sensing performance. This paper proposes a novel Triggered Asynchronous Spectrum Sensing (TASS) scheme , which loosens the demand for sensing terminals' performance (i.e. local sensing algorithm, detection time, observation time length and sensing period). Not only presents the advantage of great instantaneity in asynchronous sensing, the algorithm also reduces calculation in fusion center by deploying the altered-result triggered nature and the Period Hierarchy Management (PHM) system. Since the proposed scheme considers the occupancy of the licensed band in sensing data fusion, the advantage of sensing accuracy will be more distinct when the idle and the busy time length of licensed band is not equal. With the simulation comparison of several typical asynchronous sensing methods, the scheme in this paper shows an excellent performance.

Recently, femtocell has been proposed as a good solution to increase the coverage area and capacity of the wireless communication system as well as meet the increasing demand for high data rate. The femtocell is low-cost, low-power and can be realized by deploying a femto-base-station connected to the core network via a wired-backhaul. Since the femtocell reuses the macrocell spectrum, co-channel interference between femtocell and macrocell should be taken into consideration first of all. The leakage of femtocell's pilot power to the outside of a residence would cause passing users' handover events and result in increasing load to the network. In this paper, firstly we put forward a simple method for the femtocell to gain recognition of the residence and network environment, and then according to the obtained knowledge the femtocell carries out self-cofiguration and self-optimization for the purpose of achieving an optimized coverage to improve the in door Quality of Service (QoS) and minimum the interference in the existing network. To implement that we bring up a low-cost multi-element antenna solution and a series of corresponding algorithms, which is mainly based on low-complexity shaped beam forming and power adjusting. The simulation results show that the approaches proposed in the paper greatly reduce the femtocell's interference to the macrocell and optimize indoor coverage at the same time. These results can provide guidelines for the deployment of femtocell.

Spectrum sensing is the key functionality of cognitive radio. To combat with the effects of destructive channels, cooperative spectrum sensing technique among multiple secondary users has been proposed in cognitive radio networks. In this paper, we present an optimal cooperative spectrum sensing strategy to maximize the sensing efficiency, which not only concerns with the system overhead of spectrum sensing but also fulfills the interference restriction from the primary networks. The proposed scheme results in the optimal sensing parameters according to the channel-usage characteristic of single primary channel. Then we extend the work into the multichannel environment. Simulation results verified the performances of our strategies.

In the beyond 3G (B3G) radio environment, the cooperation of different wireless technologies has become an irreversible trend. The challenge for reconfigurable terminal is how to select an appropriate Radio Access Technology (RAT) in a multi-RATs environment. Successful selection establishes on full cognizing of surrounding radio environment, however, the cognitive process causes extra burden to terminals. To solve this problem, the Cognitive Pilot Channel (CPC) is proposed to send necessary network information to terminals. In this solution, to ensure reliability of CPC information and raise working efficiency, the coverage region of CPC is divided into several meshes. Network information is collected and saved in accordance with mesh, and terminals receive corresponding mesh information according to their location. Reasonable mesh division mechanism could highly raise CPC working efficiency. Under these considerations, this paper focuses on the information selecting approach as well as information accuracy based mesh division mechanism. The key factors, which affect information accuracy, are highlighted and discussed. Simulation results show that the proposed mechanism can dynamically adapt to changing environment and ensure high information accuracy.

As the spatial dimension is exploited to further improve the spectrum utilization for cognitive radio (CR), the position of the primary user transmitter has become very useful information for the CR system. Most existing localization algorithms require transmitting power of the primary user, making them less practical. In this paper, we propose a practical semi range-based (PSRB) localization algorithm in which the primary user transmitting power is another parameter to estimate. Meanwhile, highly reliable decisions of the primary user occupancy status are made by virtue of cooperative spectrum sensing, so the possibility of detection of the sensing nodes are more accurate, leading to an improved performance of the algorithm. Simulation results show that the performance of the PSRB exceeds the iterative semi range-based algorithm.

This paper considers the problem of subcarrier allocation in the downlink of multi-cell OFDMA networks. Firstly, a novel fractional frequency reuse architecture is proposed, where all available subcarriers are partitioned into two groups to which subcarriers are dynamically allocated. The first group is used in the center of each cell whereas the other which is partitioned into three sectors orthogonally is used for the edge of the cell. All subcarriers are owned by both of the groups but in every scheduling slot each subcarrier is allocated to only one of them. For each subcarrier is used in both of the groups, this architecture improves the spectrum utilization efficiency dramatically. Next, to mitigate the inter-cell interference in our proposed architecture, a new inter-cell interference coordination (ICIC) scheme based on adaptive sub-band avoidance on the inter-cell level is proposed, where for each cell a certain ratio of the subcarriers are avoided in the subcarriers group used for the center region. The avoiding ratio is dynamically generated on the basic of the reports of interference levels from the neighboring cells. Simulation results illustrate the effectiveness of our proposed architecture and scheme.

Dynamic spectrum access has become a promising approach to fully utilize the scarce spectrum resources. In this paper, two new dynamic spectrum access schemes for secondary users are proposed, which are based on continuous-time Markov chains (CTMC). Both the interactions between primary and secondary users and the effects of imperfect spectrum sensing(i.e. miss-detection and false alarm) are considered in the schemes. Due to the fact that miss-detection would lead to collision between primary and secondary users, we derive the optimal access probabilities for secondary users, so that the QoS of primary users in terms of collision probability is guaranteed. Simulation results show that the proposed dynamic spectrum access approach under proportional fairness criterion achieves much higher fairness over max-throughput criterion. Moreover, the scheme with buffering mechanism can raise the channel occupancy remarkably.

This paper concentrates on self-healing in reconfigurable WLANs. To cope with the accidental network failure, a Distributed Network Self-Healing (DNSH) mechanism, including automatic network failure detection and appropriate network failure recovery is designed for independent reconfigurable APs in high-density WLANs. By automatically applying the DNSH mechanism, not only immediate network failure detection but also effective network failure recovery which taking into account the network coverage and the system capacity both, are achieved. Benefiting from the automatic operation, the external involvement in network management could be minimized, and hence the network operating expenditures could be reduced. Simulation results show that both network coverage and system capacity are ensured by using this DNSH mechanism in reconfigurable WLANs.

In this paper, a new technique for dynamic spectrum management of a Wideband Code Division Multiple Access (WCDMA)/Digital Video Broadcasting (DVB) heterogeneous system is introduced. The solution is obtained using coloring theory, and significantly reduces the complexity of spectrum management.

In order to obtain a more accurate assessment of relay performance in real-world outdoor propagation environment and to provide guidelines for the relay-based system deployment in the IMT-Advanced frequency band, the performance of a variety of relay schemes is investigated based on wideband measurements. The measurements were conducted at 2.35 GHz with 50 MHz bandwidth, which is within the frequency bands allocated to the IMT-Advanced system. We pay attention to two aspects: 1) the performance evaluation of a variety of transmission schemes in real propagation environment, and 2) the impact of propagation environment on the relay performance. Based on the measured channel transfer matrix, the achieved signal-to-noise ratio (SNR), spatial diversity and capacity of different transmission schemes are analyzed and compared. The measurement results reveal that in the NLOS region of the base station (BS) or in the region far away from the BS, the decode-and-forward (DF) relaying can significantly enhance the system performance.When the quality of the link between the BS and the relay station (RS) is good, the DF can provide larger performance improvement than the amplify-and-forward relaying. It is also found that propagation condition in the link between the RS and MS has major impact on the SNR, but minor impact on the spatial diversity.

In cognitive radio systems, cooperative spectrum sensing is executed among multiple secondary users to detect spectrum holes without causing deleterious interference to primary users. The larger the number of cooperating secondary users, the greater the performance gain of cooperative sensing. However, the complexity of the cognitive radio system will rapidly increase in the meantime. This paper introduces a novel cooperative spectrum sensing mechanism, which jointly determines the crucial sensing parameters including observation duration, transmission duration and the number of cooperating secondary users. The proposed strategy is able to balance between the channel utilization efficiency and the system complexity as well as to reduce the harmful interference to primary users under a given level. The sensing parameters selection problem is formulated as an optimization problem in such a way as to maximize the target function subject to interference avoidance constraints. A numerical optimization algorithm is proposed to attain the optimal solution. Simulation results illustrate that the mechanism can achieve maximum sensing efficiency in resource-limited cognitive radio systems while meeting interference avoidance limitation.

This paper discusses the joint session scheduling (JOSCH) problem for multimedia services in heterogeneous wireless networks. The adaptive JOSCH based on layer-encoded streaming is designed for real-time multimedia service simultaneously transmitted by several heterogeneous radio access technologies (RATs). Considering the layered characteristics of the layer-encoded streaming, an adaptive JOSCH mechanism, along with the supporting network architecture, is designed to make efficient usage of the heterogeneous wireless resources and adapt to the dynamic network changes. Simulation results show that the adaptive JOSCH is effective in guaranteeing the QoS of multimedia and maintaining a high transmitting adaptability in multi-RATs environment.

True integration and common operation and management of different packet based cellular radio networks will be a main characteristic of next generation (4G) mobile radio systems. These features will provide enhanced flexibility for both operators and end users to adapt to changing environments and demands. To reach this goal, however, an efficient usage of resources such as frequency, power, and space has to be realised across the complete communication chain. Within the EU funded project E3network self-organization principles are investigated which support automation of radio resource management (RRM) across multiple radio cells, technologies, administrative domains, and services. Important design criterion for such a collaborative cognitive Joint RRM is the improved operational performance to grant technical and economic success of the new mobile platform. Aim of this paper is to describe the current status of conceptual and architectural analysis as well as first results and to outline the proposed elaboration and comparison of different approaches versus requirements mainly from an operator point of view.

Spectrum occupancy measurements are significant to obtain spectrum utilization, which will conduct the development and deployment of dynamic spectrum access technology. Threshold level setting is one of the most important parts in data processing. Most spectrum occupancy measurement methods adopt fixed threshold (such as experience value) to distinguish signal from noise. These methods can not be operated autonomously and sometimes has low accuracy. To solve the problems, we investigate the data processing technology in spectrum occupancy measurements. Particularly, an automated processing procedure is provided and an adaptive threshold method based on the analysis of measurement data's statistic characteristic is proposed to calculate the spectrum occupancy. Finally, experiments are performed to validate the adaptive threshold method with the data derived from the spectrum measurements of GSM UL/DL band in Beijing. The experiment results indicate that the proposed method realizes the selfadjusting of threshold level setting and decrease the workload of data processing effectively.

Joint session scheduling (JOSCH) enables traffic to be split and transmitted over multiple radio access networks (RANs) simultaneously, which improves the quality-of-service (QoS) in terms of transmission rate, reliability and stability. Transmission synchronization is one of the key issues which JOSCH should consider, as it has direct effect on the performance of buffer management and data reassembly at the traffic receiver. In this paper, a segment-based adaptive JOSCH mechanism was proposed to guarantee the transmission synchronization during multi-RANs simultaneous transmission. The proposed mechanism restrains the delay differences between different RANs, by adjusting the traffic allocation ratios to multiple transmission RANs adaptively, based on the feedback from traffic receiver. System architecture and functional modules are designed to support the proposed mechanism. Meanwhile, a generic transport protocol model supporting multi-RANs transmission is introduced as well. Simulation results reveal that the proposed JOSCH mechanism well adapts to the dynamic change of network conditions, and guarantees the synchronization and stability of transmission.

In the context of B3G heterogeneous environment, the convergence and cooperation of different wireless network technologies are inevitable. The concept of Cognitive Pilot Channel (CPC), which is one of the candidate transmission solutions for the dynamic spectrum sharing of network information in the Cognitive Radio (CR) environment, is proposed to provide the user equipments (UEs) with the necessary network information for both the switch-on and the on-going phases. Optimal mesh division problem appears based on the basic assumptions in CPC concept that the geographical region is organized in meshes and the coverage of different Radio Access Technologies (RATs) overlaps with each other. In this paper, a novel optimal mesh division scheme is designed, taking into account both the Global Position System (GPS) localization shift scenario and multi-RATs scenario. Both the error probability and the information loss ratio are investigated and their impacts to the optimal mesh division scheme are studied by using the Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) algorithms. Optimal mesh division scheme is verified by numerous simulation results.

With the wide deployment of wireless local area networks (WLANs), the problem of configuration and maintenance of access points (APs) arises. The traditional manual configuration method greatly increases the cost of configuration and maintenance of the WLANs system and hinders the scalability of WLANs. The plug-and-play (PnP) of APs, which means that APs auto-configure themselves based on the network environment, is a good solution to this problem. The PnP of APs can reduce both the complexity and cost of configuration and maintenance for WLANs and also improve the system performance. The selection of working mode and channel is one of the major challenges for the PnP of multi-mode APs. In this paper, we propose a mode and channel selection scheme that combines analytic hierarchy process (AHP) with grey relational analysis (GRA). The goal of our proposed scheme is to select the most suitable mode and channel for multi-mode AP to improve the performance of WLANs and offer better service to users. In the proposed scheme, AHP is responsible for deciding the weights of criteria factors according to their contributions to the final goal. GRA combines the weights of criteria factors with the values of criteria factors to rank all alternative modes and channels combination and make a decision. It is seen from the simulation results that our proposed scheme can select the most suitable working mode and channel for AP in different scenarios.

Peer-to-Peer SIP (P2PSIP) is proposed to provide fully distributed multimedia communication systems. This paper focuses on analysis of call setup delay "(CSD) of P2PSIP system to evaluate whether it could provide equivalent signaling performance to that of traditional SIP or telephone networks. Call setup delay is composed of lookup latency in DHT overlay and INVITE transaction latency, and mainly decided by Round Trip Time (RTT) experienced on hops and message exchange procedures of lookup and INVITE transactions. Simulation with active measurement RTT data showed that call setup delay ranges widely from under 100 ms to more than ten seconds, and lookup latency takes up more than 60% of call setup delay. It is concluded that P2PSIP system within limited geographic range could achieve acceptable signaling latency, and efficient latency optimization should be considered to ensure the signaling performance when P2PSIP system is used spread across the public Internet.

In the future wireless communication environment, many radio access technologies will coexist in the same or border areas to provide users with ubiquitous access. The heterogeneous infrastructure makes reconfigurability an important characteristic which is very necessary in the mobile terminals. Moreover, the reconfiguration technique is also introduced to the design of protocol stack to meet the ever-changing link conditions and service requirements. Hence, in order to facilitate the realization of the protocol reconfiguration capabilities, a component-based protocol architecture is introduced by end-to-end reconfigurability (E2R) II project. This paper focuses on the supply of a customized protocol stack for each application according to the communication environment. To achieve this goal, an evaluation mechanism for protocol components is proposed. Besides, we put forward an optimization scheme that finds better protocol components from the network-side library to replace ill-suited ones running in the current system. The major advantage of the proposed protocol management schemes is that users may enjoy a better quality of service due to the protocol reconfigurability.

The pouring of more and more attractive radio access technologies (RATs) with complementary characteristics has inspired the trends of interworking and convergence in future wireless systems. End-to-end reconfigurability (E2R) is an approach toward the convergence of diverse RATs. This paper aims at investigating an effective seamless vertical handover (VHO) scheme for E2R systems. The system model supporting the VHO scheme is brought forward. A network sort algorithm is developed enabling terminals to select the most appropriate network to handover. Particularly, we propose a novel handover trigger algorithm based on quality of service (QoS) evaluation allowing terminals to initiate a handover when the QoS is lower than their expected value. Simulation results reveal that the proposed VHO scheme enhances the user satisfaction as well as optimizes the overall network performance.

The scarcity of spectrum resource in future wireless networks has inspired the demand of dynamic spectrum management (DSM). This paper investigates a cell based dynamic spectrum management (CBDSM) scheme to enhance the spectrum utilization and maximize the profit of operators for cognitive networks. In this scheme, the economic factor of the spectrum is taken into account in order to guarantee the rationality for spectrum trading. Especially, we focus on the interference mitigation method, which is considered as a fundamental issue for applying DSM to wireless systems. As a potential tool for promoting the distributed autonomous radio resource optimization algorithms, game theory is applied in the DSM scheme to investigate a win-win solution for spectrum trading between RATs. The simulation results reveal that the proposed CBDSM scheme improves the spectrum utilization and the profit of operators while effectively mitigating mutual interference between wireless networks.

This article proposes an auction mechanism based scheme for joint radio resource management (JRRM) in a reconfigurable system in a multi-operator scenario. Through the periodical auction and transaction for the radio resource between different radio access technologies (RATs) of multiple operators, the spare radio resource can be fully utilized to meet the demand of the RATs being short of radio resource. In order to rationally handle the profit assignment between multiple operators, a specific pricing strategy is put forward. In addition, a novel architecture is also presented to support this JRRM scheme. Simulation results reveal that the proposed scheme not only effectively reduces the total session blocking probability, but also greatly improves the total radio resource utilization ratio and the profits of operators.

To overcome the disadvantage in traditional manual configuration method for WLANs, such as the high configuration and maintenance cost and low efficiency of system performance, we propose the plug-and-play (PnP) of multi-mode APs that APs auto-configure themselves based on the network environment. In order to implement the PnP, the Multi-mode AP must obtain its neighboring APs' configuration and environment information. Existing neighboring APs discovery method can not provide the precise neighboring APs information to satisfy the requirement of PnP of AP. In this paper, we propose three kinds of neighboring AP discovery and information exchange methods, which are passive discovery method, active discovery method and station assistant discovery method. Using these three neighboring AP discovery methods, AP can discover all neighboring APs and obtain needed information. We further propose two whole process flows, which combine three discovery methods in different manner, to achieve different goal. One process flow is to discover the neighboring AP as fast as possible, called fast discovery process flow. Another one is to discover the neighboring AP with minimal interference to neighboring APs, called the minimal interference process flow. The efficiency of the two process flows is shown in the simulation results.

Future wireless systems are characterized by the pouring of diversified services supported by heterogeneous radio access technologies (RATs). In parallel with this, the tremendous demand for spectrum has brought the requirement of dynamic spectrum allocation (DSA) into our sight. This article aims at designing a centralized DSA scheme to enhance the spectrum utilization and maximize the profit of operators for cooperative wireless networks. In this scheme, the economic factor of the spectrum of wireless systems is considered in order to guarantee the fairness for the spectrum allocation. RATs are considered as cooperative players participating in cooperative games for spectrum allocation. As an attractive solution for n-person cooperative game with transferable utility, the Shapley value is adopted to handle the profit allocation among RATs. Particularly, we focus on the interference mitigation method, which is a fundamental issue for applying DSA to real wireless systems. The simulation results reveal that the proposed DSA scheme not only improves the spectrum utilization and the profit of operators, but also effectively restrains the inter-system interference between wireless networks under an acceptable level.

In order to optimize usage of radio resource for heterogeneous radio access technologies (RATs) and jointly designed from the user perspective, the joint session scheduling (JOSCH) mechanism has been introduced to split traffic over tightly coupled radio network. This paper presents distributed reinforcement learning (RL) as an autonomic approach for the JOSCH. Through the "trial-and-error" interaction with its radio environment, the JOSCH agent learns to split the traffic in a best way and allocate sub-streams in the proper RATs. A backpropagation neural network is adopted to generalize the large input state space of the RL algorithm to reduce memory requirement. Extensive simulations show that the proposed algorithm not only realizes the autonomy of JOSCH through the online learning process, but also improves the service quality at user side and the spectrum utility at operator side base on the suitable strategies.

This driven by the need to promote a more efficient use of radio resources and improve the operators' profits, resource allocation has turned into a joint technical and economical problem. At the same time, as a possible enabling solution, game theory has been applied to either dynamic spectrum access (DSA) or joint radio resource management (JRRM) in wireless communication research recently. In this paper, we propose a novel DSA and JRRM combined approach to resource allocation in cooperative networks. With the scenario that distributed reconfigurable radio access networks (RAN) are controlled by different operators, the emerging concept of resource trading is introduced and new entities, such as trading agents (TA), are described. Meanwhile, Shapley value in cooperative game as well as its economic model is exploited to share the profits among the trading RANs. Numerical results show that comparing with existing DSA or JRRM methods, our scheme has better effect in maximizing the individual operator's profits and improving the efficiency of radio resources utilization.

Next-generation wireless networks will be a conglomeration of different networks technologies and will support multiple radio access technologies (Multi-RATs). This will put high demands on radio resource management support. In this paper, we describe an intelligent multiagent radio resource management system, which is self-organized and distributed to ensure the coexistence of Multi-RATs. This paper discusses how to implement macro control and management by using control factors instead of micro control concerning individual users, and also describes the mechanism of coexistence of Multi-RATs. Further, message flow and open questions are discussed.

Coexistence of heterogeneous networks such as cellular, wireless local area network (WLAN), ultra-wideband (UWB) etc. brings new challenges. It is perceived that current radio resource management mechanism cannot meet the requirements of multi-radio access technologies (multi-RATs). This paper proposes a novel intelligent multi-agent radio resource management system, which is self-organized and distributed to ensure the coexistence of multi-RATs. Radio resource is managed by a macro control and management system using control factors and validation mechanism, instead of micro control for individual users. The goal is to increase radio resource utilization efficiency, maximize system capacity and meet the QoS requirements of different services