Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, Ting Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

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.

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.

The evolution of industrial intelligent manufacturing necessitates wireless communication systems capable of replacing conventional wired infrastructures, offering superior flexibility, scalability, and reduced maintenance overhead. While 5 G New Radio (NR) Ultra-Reliable Low-Latency Communication (uRLLC) standards (Release 15-17) have shown promise for mission-critical applications, current implementations remain constrained by their unidirectional optimization paradigm, unable to simultaneously satisfy the dual imperatives of sub-millisecond latency ($\lt 1$ms) and 99.9999% reliability demanded by industrial control systems. To address these challenges, we present a transformative subband full-duplex (SBFD) network architecture that ensures persistent time-domain spectral availability for concurrent uplink/downlink operations, thereby eliminating direction-switching latency. Our solution introduces three key innovations: (1) an interference-aware SBFD resource allocation framework that strategically isolates UL/DL subbands to minimize cross-link interference (CLI), (2) a dual-optimization algorithm that jointly maximizes spectral efficiency while guaranteeing channel-adaptive reliability thresholds, and (3) a practical implementation scheme compatible with existing 5G NR physical layer specifications. Extensive simulations under realistic factory channel models demonstrate 58.3% reduction in aggregate CLI and 41.2% improvement in control command decoding accuracy compared to legacy half-duplex systems. This research establishes a new paradigm for wireless industrial networks, effectively closing the performance gap between 5G URLLC specifications and the exacting demands of Industry 4.0 applications.

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.

With the rapid development of the low-altitude economy, the widespread deployment of unmanned aerial vehicles (UAVs) necessitates effective sensing technologies for airspace monitoring. Integrated sensing and communication (ISAC) enables mobile communication base stations (BSs) to function as sensing nodes, providing a promising solution for UAV detection. Accurate identification of UAVs often relies on the extraction of distinctive micro-Doppler signatures generated by their rotating blades. However, in urban environments, these signatures are frequently obscured due to low signal-to-noise ratio (SNR) and strong dynamic interference from vehicles, pedestrians, and, in particular, birds. To address this challenge, this paper proposes a robust micro-Doppler feature extraction method based on multicarrier integration and a rotor micro-Doppler null space pursuit (rmD-NSP) algorithm. In one real-world scenario, both a bird and a UAV appeared within the same range cell, with the UAV’s micro-Doppler signals heavily masked by the bird’s strong reflections. After applying the proposed algorithm, distinct micro-Doppler features are successfully extracted, revealing approximately eight rotor blade flashes of the UAV within a 0.1s interval and two wingbeat cycles of the bird within the 0.5s observation window.

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.

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.

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.

The increasing consumer application demands of unmanned aerial vehicles (UAVs) in low-altitude airspace lead to many challenges for traditional UAV monitoring in terms of small UAV target sensing probability, target imaging resolution, equipment cost, etc. To solve these problems cost-effectively, the integrated sensing and communication (ISAC) technology based on cellular networks shows potential communication and sensing capabilities by sharing the same hardware equipment. This paper proposes a novel UAV target imaging algorithm using the channel state information (CSI) based ISAC signal to improve the target sensing probability and achieve target imaging. An ISAC signal model is designed to reconstruct the target image from CSI based on the time-frequency transformation relationship between target motion and CSI. Considering the sparsity of target scattering points, a two-dimensional alternating direction method of multipliers joint autofocus (2D-ADMM-AF) algorithm is proposed using compressed sensing theory, which achieves super-resolution reconstruction of the target image while eliminating the high side lobe problem caused by the sparse aperture problem that may exist in the ISAC scene. Further, the image entropy is introduced in the reconstruction process to achieve target translation compensation, eliminating the image blur problem caused by the translational motion of the UAV target. The simulation results show that compared with the traditional algorithm, the image entropy reconstructed by the 2D-ADMM-AF is reduced by 27% and the operating efficiency is improved by 85.5%. In addition, the real data of the DJI M300 UAV is collected using the ISAC testbed to verify the ability to image targets with RCS=0.3m2, 0.7m×0.7m.

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%.

The emergence of connected autonomous vehicles (CAVs) has transformed the realms of transportation and communications. Meeting the demands of future CAVs networks requires the seamless integration of two essential functions: communications and sensing. However, in dense CAVs scenarios, meeting the demands for one-to-one beam-CAV services become challenging due to limited spatial freedom and severe beams interference. In this paper, a novel sensing-assisted CAV s clustering and beams alignment method is proposed. Based on the echo signal, the kinematic parameters are measured, and the extended Kalman filter (EKF) is designed for angle tracking. On this basis, a CAV s clustering method is also proposed. Simulation results verify superior tracking performance compared to other methods. The root mean square error (RMSE) is less than 0.03 and the sum rates can be improved by up to 4 times.

Ultra-reliable and low latency (uRLLC) plays an important role in the context of automatic driving, in terms of the road safety, which mainly depends on fast data transmission and precise positioning. However, handling this data and positioning efficiently faces a significant challenge, because of high data volume and vehicles' mobility. To support the high rate of sensing data and positioning information sharing between vehicles and base stations, mmWave communication technology, featured by high frequency and bandwidth, is identified as an potential solution. In addition, constrained by the strong directionality of mmWave, to improve the performance of high reliability, low latency and precise sensing, realtime mmWave beam direction regulation is urgently needed. In this paper, based on the multi-source sensing information, we propose a joint design of fast and robust communication and sensing 3D mmWave beam tracking (MSI-ISACBT) algorithm for two kinds of dynamic motions. In detail, for the case of low dynamic motion, the beam direction can be updated by using historical state information and reflected echo signal strength, while it can be adjusted according to the image information acquired by the camera for the case of high dynamic motion. Simulations validate the effectiveness and correctness of the proposed MSI-ISACBT. Compared with other popular methods, MSI-ISACBT achieve a stable throughput of 2.8Gbps and more smooth beam angle control with 16ms delay.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

For future connected automated vehicles (CAVs) networks, the joint optimization of communication, sensing, and computing resources is crucial to guarantee the performance of cooperative automated driving’s safety, which is attracting more and more attention. However, the existing works have not considered the low-latency requirement for the raw perception data sharing with both wireless communication link capability and computing efficiency constraints, causing a serious threat to the cooperative automated driving’s safety in CAVs networks. In this article, a vehicle–road–base station cooperation architecture is designed, and a federated reinforcement learning (FRL)-based task offloading and resource allocation algorithm in the CAVs network is proposed to reduce the task execution delay with different communication and computing constraints. The problem of execution delay minimization is theoretically formulated and analyzed under three task practical offloading modes. To adapt to the dynamic topology of the CAVs network, we design a deep reinforcement learning algorithm to achieve the optimal task offloading and resource allocation. To further reduce the data transmission overhead of the centralized reinforcement learning algorithm, the FRL-enabled algorithm is proposed to minimize the execution delay of the optimal task offloading and resource allocation among multiple CAVs. Both the simulation and hardware testbed results verify that the proposed algorithms can not only reduce the execution delay and the communication overhead but also improve the system throughput.

The spectrum-sharing access technology using unlicensed spectrum bands is considered as a promising solution to solve the spectrum deficiency problem for various Internet of Things services. But, the efficient and fairness-guaranteed spectrum-sharing access among different systems is challenging by using the listen-before-talk (LBT) technology due to the uncertain channel quality and the channel access collision issues in unlicensed spectrum bands. To solve these problems, a novel three dimension-based Markov chain model is designed to formulate the collision probability of the spectrum-sharing access process using the contention window (CW) back-off algorithm based on the channel quality indicator feedback information. The key reasons for the packet transmission failure are comprehensively analyzed by considering both the channel collision and the channel quality deterioration conditions. A fairness-based spectrum-sharing access algorithm is proposed by assigning the optimal CW for the LBT system to minimize the collision probability and guarantee the fairness among LBT and wireless fidelity coexisted systems. Hardware platform-based evaluation results prove that our proposed algorithms can improve the system throughput and guarantee the fairness among different systems in the unlicensed spectrum band.

The millimeter-wave (mmWave) technology can support the wide-band communication requirements for the environment raw perception information sharing to guarantee the safety of connected automated vehicles (CAVs). But, how to achieve the fast and robust mmWave beam tracking is a challenging problem in a high mobility CAVs scenario. Therefore, this article proposes a camera-sensing-assisted joint offline and online beam tracking (CS-JBT) algorithm in the mmWave frequency band. First, the mobility model is designed to predict the next moment of CAV and reduce the beam searching space overhead. In the offline learning phase of the proposed CS-JBT algorithm, the optimal beam pair under the current mobility state is obtained to guarantee the timeliness of the beam tracking process. Furthermore, to solve the mobility state deviation problem caused by the camera sensing error, the online learning phase is designed to achieve the optimal beam tracking performance efficiently for the practical scenario. Simulation and hardware testbed results verify that the proposed CS-JBT algorithm can minimize the beam tracking latency from 500 to 20 ms in contrast to the existing VBC-PF algorithm, while achieving a stable throughput over 2.5 Gb/s in the 28-GHz mmWave frequency band.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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, 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.

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.

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.

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.

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.

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.

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.

In cognitive radio networks, wideband spectrum sensing is a key requirement for cognitive radio access. Different node allocation schemes will lead to the different sensing performance between the sub-bands that compose the wideband. If the detection accuracy of some sub-band is much low, the primary user (PU) will be interfered severely. Thus, the system fairness which is measured by the minimum sensing performance between the sub-bands is proposed. In order to guarantee wideband spectrum sensing fairness, in this paper, we investigate node allocation schemes to maximize the minimum sensing performance between the sub-bands. we propose iterative hierarchical hungarian allocation(IHHA), bow-shaped allocation(BSA), class division allocation(CDA) to realize the Max-Min objective. Furthermore, on the basis of PU priority level and anti-interference capability, the frequency band property parameter (BPP) is defined. By improving the minimum sensing performance with BPP, modified fairness that suits for the actual scene is obtained. Simulation results demonstrate that our proposed schemes perform excellently in improving the overall sensing fairness of the whole system.

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.

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.

This paper analyzes the connectivity of cognitive radio networks. The connectivity of cognitive network which is more consistent with reality is redefined in our article and the closed-form formula of relation between connectivity and density of PUs, density of SUs, and transmission radius of SU is given. Specifically, the impact of correlation of adjacent nodes for connectivity of cognitive radio ad hoc network is illustrated. On the basis of Random geometric graph and probability theory, a novel method that divides connectivity of cognitive radio ad hoc network into topological connectivity and physical connectivity is proposed to derive the close-form formula. We prove theoretically that once the density of secondary users is large enough and the transmission radius is appropriate, the probability of cognitive network connectivity tends to a stable non-zero value under the condition that density of primary users is small.

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.

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.

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, 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 the underlay spectrum sharing systems, secondary users (SUs) are allowed to transmit their data in the licensed spectrum band when primary users(PUs) are also transmitting, as long as the transmission of SUs do not interfere PUs' communications. In cognitive relay networks, the source and relay nodes both need to tune their transmit power to mitigate the interference to PU. In this paper, we investigate the outage performance of cognitive relay networks with PU's interference to signal ratio (ISR) constraint, where both average and peak ISR constraint are considered. Finally, We derive the exact outage probability in the scenario without cooperation and the upper bound of outage probability in the scenario with cooperation.

Cognitive radio is considered to be a key technology for future heterogeneous networks. Cognitive radio network is an evolution of the cognitive radio by extending the radio link scope to network scope, and is defined as a network that can observe its environment, make decisions based on the observations, and then reconfigure according to the decisions, all while taking into account the end-to-end goals. This paper proposes a high level abstraction of the cognitive radio network architecture. The operation of the proposed architecture is guided by the end-to-end goals. The proposed architecture consists of four components: end-to-end goals management, cognition management, self-organization management, and reconfiguration management. The proposed architecture provides the functionality to manage these components, enable communication between them, and facilitate the interfaces between cognitive radio network and its surrounding environment. In order to demonstrate the functionality of the proposed architecture, we present a use case of ubiquitous wireless access services and show that the proposed architecture enables ubiquitous connectivity with harmonized networks and integrated services.

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.

Considering the inevitable trends for heterogeneous network convergence and self-adaptation ability, Cognitive Radio Network (CRN) concept has been proposed with some essential characteristics to achieve global end-to-end goals. CRNs are composed of cognitive devices which have the capable of changing network configurations based on the dynamic environment. This capability opens up the possibility of designing flexible and dynamic topology control strategies with the purpose of opportunistically reusing idle licensed spectrum and effectively achieving data transmission. This work focuses on the problem of designing effective topology reconfiguration algorithm to offer optimal routing solutions. We analyze the topology control for CRNs finding the topology problem can be formularized as a multi-objective optimization problem. In this context, as an intelligent technology for complex multi-objective optimization, the ant colony optimization (ACO) techniques are applied in topology reconfiguration for optimal decision making in this paper. Finally, the reconfiguration algorithm is simulated, and the simulation results with detailed are analyzed.

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.

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.

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.

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.

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.

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.

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.

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.

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.