Dongxu Zhang, Yiding Sun, Cheng Tan, Wenbiao Yan, Ning Yang, Jihua Zhu, Haijun Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The rapid expansion of low earth orbit (LEO) satellite networks exposes critical limitations in conventional resource allocation schemes, which are unable to simultaneously optimize spectral utilization and adapt to heterogeneous traffic patterns under extreme mobility, necessitating a joint beam-frequency dynamic coordination framework. To address the challenges of multi-beam LEO systems, this paper introduces a hybrid framework that synergizes adaptive beam activation patterns and spectrum reuse optimization, enhanced by a deep reinforcement learning (DRL)-driven coordination mechanism for resource allocation in time. By dynamically adjusting beam activation patterns and frequency allocation, the framework optimizes spatial-temporal resource utilization while mitigating co-channel interference. Angular-constrained multi-criteria clustering achieves dynamic beam-user mapping with low-complexity adaptation for LEO mobility. The DRL-based component further coordinates multi-dimensional parameters, including transmit power and sub-channel assignment, to balance throughput and latency under time-varying channel conditions. Extensive simulations validate the framework’s capability to maintain high spectral efficiency and coverage performance across diverse scenarios, outperforming conventional static allocation methods. The results highlight its adaptability to dynamic traffic patterns and scalability for large-scale deployments, providing a robust foundation for next-generation LEO systems.

Wireless jamming identification, which detects and classifies electromagnetic jamming from non-cooperative devices, is crucial for emerging low-altitude wireless networks consisting of many drone terminals that are highly susceptible to electromagnetic jamming. However, jamming identification schemes adopting deep learning (DL) are vulnerable to attacks involving carefully crafted adversarial samples, resulting in inevitable robustness degradation. To address this issue, we propose a differential transformer framework for wireless jamming identification. Firstly, we introduce a differential transformer network in order to distinguish jamming signals, which overcomes the attention noise when compared with its traditional counterpart by performing self-attention operations in a differential manner. Secondly, we propose a randomized masking training strategy to improve network robustness, which leverages the patch partitioning mechanism inherent to transformer architectures in order to create parallel feature extraction branches. Each branch operates on a distinct, randomly masked subset of patches, which fundamentally constrains the propagation of adversarial perturbations across the network. Additionally, the ensemble effect generated by fusing predictions from these diverse branches demonstrates superior resilience against adversarial attacks. Finally, we introduce a novel consistent training framework that significantly enhances adversarial robustness through dual-branch regularization. Simulation results demonstrate that our proposed methodology is superior to existing methods in boosting robustness to adversarial samples.

Integrated Sensing and Communication (ISAC) is becoming increasingly important in next-generation wireless networks. This paper focuses on an ISAC system supported by a reconfigurable intelligent surface (RIS), where a full-duplex base station (BS) simultaneously performs uplink multi-user communication, downlink multi-user communication, and radar sensing tasks with the assistance of the RIS. To maximize the sum rate of all downlink and uplink users, an optimization problem is formulated, subject to multiple constraints, including target detection signal-to-interference-plus-noise ratio, self-interference, BS transmission power, user transmission power, and unit-modulus constraints of RIS reflection coefficients. To address the complex non-convex optimization problems, efficient solving algorithms are proposed, and their performance is validated through simulations. The results demonstrate that the RIS-assisted full-duplex ISAC (RAFD-ISAC) system significantly enhances both communication and sensing performance. The proposed joint beamforming and reflection design offers a novel solution for the deep integration of sensing and communication in next-generation networks.

Integrated sensing and communication (ISAC), as a rapidly advancing technique, introduces a fresh approach for achieving secure communication and intelligent sensing for future wireless networks. An ISAC framework empowered by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is explored in this paper, where a base station equipped with multiple antennas establishes wireless links to users each with a single antenna during the detection of a point target. The point target, regarded as an eavesdropper, trying to intercept users’ information. Cramér-Rao bound (CRB) serves as evaluation criterion to assess sensing accuracy of point eavesdropper, whereas the secrecy rate is employed to quantify the security level of the communication link. To optimize sensing-communication tradeoff, a joint optimization problem is constructed. To approach the formulated problem, a hybrid Block Coordinate Descent (BCD)-based algorithm is developed, which alternately updates the transmission beamforming and STAR-RIS phase shifts, using successive convex approximation (SCA) technique, penalty dual decomposition (PDD) framework and projected gradient method (PGM).

This paper presents a novel beamforming design that seamlessly integrates sensing, communication, and over-the-air computation (AirComp), enabling a critical multi-purpose functionality for next-generation wireless networks. Firstly, we formulate an optimization problem with the objective of minimizing the mean squared error of AirComp, subject to constraints that ensure the performance of both sensing and communication. The optimization problem is then parameterized and solved using unsupervised learning, employing real-valued and complex-valued deep neural networks (DNNs), respectively. For the complex-valued DNNs, we introduce its mechanism and then apply it for an intelligent beamforming design. Numerical results validate the convergence, ergodic rate, and ergodic mean square error of the proposed algorithms for the integrated sensing, communication, and computation. Also, our findings show that complex-valued DNNs outperform real-valued DNNs.

By focusing on the intrinsic meaning of information, semantic communication (SemCom) marks a fundamental paradigm shift from physical bit transmission to personalized semantic service. Considering the importance of personalized features related to the speaker in speech for source recovery and understanding, we propose a semantic-driven framework for personalized speech transmission, named PerSemCom, which combines speaker acoustic features with semantic information. Specifically, we first introduce an efficient semantic extraction mechanism to achieve the conversion from speech to text transcriptions, and design a semantic corrector coupled with multi-domain knowledge to mitigate the effects of wireless channel distortion. Building upon the reliable transcriptions at receiver, we further establish a speaker embedding vector knowledge base and achieve high-fidelity speech reconstruction through quantitative modeling of speaker-specific acoustic features. Extensive experimental results demonstrate that our proposed framework outperforms existing schemes in terms of subjective perception at harsh channel conditions. Complexity analysis and latency measurements also show competitive advantages in computational efficiency and real-time capabilities. Reconstructed personalized speech samples have been publicly available at https://kwtankw.github.io/PerSemCom/.

As sixth-generation (6 G) networks evolve towards AI-native architectures, Federated Learning (FL) is becoming a cornerstone for enabling intelligent services by leveraging distributed data from diverse sources such as Integrated Sensing and Communication (ISAC) devices and edge clients. However, a critical challenge lies in efficiently handling data removal requests, mandated by regulations like the “right to be forgotten”. This problem is significantly exacerbated by the extreme data heterogeneity ( non-IID) inherent across diverse 6 G devices and the communication constraints of wireless networks. To address these challenges, this paper proposes SeFUL, a novel two-stage federated unlearning framework tailored for the security and privacy demands of 6 G systems. SeFUL first proactively mitigates data heterogeneity by partitioning clients into clusters based on their data distribution similarity. Subsequently, a lightweight, information-theoretic unlearning strategy is deployed. This method surgically erases information by optimizing a composite loss function which, in the latent space, pushes the feature representations of forgotten data away from their original class cluster and towards samples from other classes, while reinforcing knowledge from the retain set. Comprehensive experiments on benchmark datasets demonstrate that SeFUL achieves unlearning performance on par with the gold standard of complete model retraining. It successfully reduces forget-set accuracy to nearly random guess levels while preserving high retain-set accuracy, significantly outperforming existing state-of-the-art methods. Furthermore, Membership Inference Attacks (MIAs) confirm that SeFUL effectively reduces privacy risks to a level statistically indistinguishable from a fully retrained model, validating its efficacy as a robust privacy-preserving mechanism.

User devices (UDs) with non-independent and identically distributed (non-IID) data will worsen accuracy performance of the global model in federated learning (FL). Therefore, the implementation of diffusion strategies in machine learning (ML) models can enhance the effectiveness of federated learning with non-IID data. However, in a device-to-device (D2D) wireless federated learning (WFL) system, limited wireless resources and severe wireless channel interference become the important bottleneck to restrict the diffusion performance and model aggregation so as the global model of WFL with non-IID suffers from the weight divergence challenge. Thus, we propose a novel joint over-the-air computation (OAC) aggregation and diffusion framework by using a graph neural network (GNN) for WFL, termed an OAC-GNN-Dif framework. By integrating the OAC with message passing neural network (MPNN) of GNN, we further develop the OAC-MPNN-Dif algorithm based on the OAC-GNN-Dif framework. To further reduce communication costs, we designed an OAC message recurrent neural network (OAC-MPRNN-Dif) algorithm, where each UD propagates local models via D2D communications to refresh the graph embedding in the current frame based on the graph feature extraction and localization state of the previous frame to reduce communication costs. Additionally, we introduce dynamic time-varying MPNN for federated diffusion within evolving D2D network topologies. The experimental results indicate that our approach significantly performs well in communication overhead, with a 30%-60% decreasing in wireless resources overhead and 1.2-3.5 times decreasing in the number of model transfers compared to the FedDif methods. Moreover, our approach also improves the global model test accuracy, which is about 2.7% higher than the existing communication diffusion FL with non-IID characteristics.

This paper addresses the challenges of unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) in high-density user mobility scenarios, a field that has not been extensively explored in current research. We introduce a novel deep reinforcement learning (DRL) framework, named “j-PPO+EN-ConvNTM”, specifically designed to optimize MEC performance in urban environments with user mobility. The framework integrates spatiotemporal data modeling and spatial transformer network (STN) through an enhance Convolution Neural Turing Machine (EN-ConvNTM) module, which includes a three-dimensional external memory. It also features a joint continuous and discrete action decision-making module, termed joint proximal policy optimization (j-PPO). This design enables effective handling of the dynamic and complex nature of urban mobility patterns. The proposed approach extends the PPO technique to accommodate joint continuous and discrete action decisions, thereby enhancing UAV adaptability and efficiency in providing MEC services. Extensive simulations demonstrate significant improvements over all baseline models, particularly in terms of equilibrium efficiency and service continuity in high-density scenarios. Our research addresses a critical gap in existing UAV-assisted MEC studies, which primarily focus on static or low-mobility user scenarios, and supports the development of more robust and efficient smart city applications, meeting the real-world demands of modern urban infrastructures.

Mobile Edge Computing (MEC) leverages computational heterogeneity between mobile devices and edge nodes to enable real-time applications requiring high information fresh ness. The Age-of-Information (AoI) metric serves as a crucial evaluator of information timeliness in such systems. Addressing AoI minimization in multi-user MEC environments presents significant challenges due to stochastic computing times. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in an MEC system, focusing on preemptive and non-preemptive task scheduling mechanisms. The problem is first reformulated as a Restless Multi-Arm Bandit (RMAB) problem, with a multi-layer Markov Decision Process (MDP) framework established to characterize AoI dynamics in the MEC system. Based on the multi-layer MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. This establishes a theoretical framework adaptable to various scheduling mechanisms, achieving efficient optimization through state stratification and index design in both preemptive and non-preemptive modes. Finally, the closed-form of the nested index is derived, facilitating performance trade-offs between computational complexity and accuracy while ensuring the universal applicability of the nested index policy across both scheduling modes. The experimental results show that in non preemptive scheduling, compared with the benchmark method, the optimality gap is reduced by 25.43%, while in preemptive scheduling, the gap has reduced by 21.21%. As the system scale increases, it asymptotically converges in two scheduling modes and especially provides near-optimal performance in a non preemptive structure.

Satellite network is envisioned as a key enabler for wide-area coverage and seamless connectivity. Nonetheless, satellite-terrestrial communications are confronted with critical challenges, including significant signal attenuation and complex inter-satellite interference, particularly in ensuring uplink performance for power-constrained ground users. Reconfigurable intelligent surface (RIS) is considered as a promising solution to compensate for the severe path loss, owing to their high-gain and dynamic beamforming capabilities. In addition, with the deployment of ultra-dense satellite constellations, multi-satellite cooperation is expected to effectively mitigate inter-satellite interference and enhance channel gains. In this paper, a novel RIS-aided multi-satellite cooperative reception scheme is proposed for multi-user uplink transmission. We first analyze five types of multi-satellite cooperative reception framework within cell-free paradigm and formulate a multi-user total energy efficiency (EE) maximization problem. Based on block coordinate descent method, this nonconvex optimization problem is decomposed into three subproblems: receiver detection vector design, RIS phase shift optimization, and multi-user transmit power control. Maximum ratio combining, zero forcing, and minimum mean square error are adopted for linear detection vector designs, respectively. Then the RIS phase shift is optimized based on Riemannian conjugate gradient algorithm. Furthermore, multi-user transmit power control is designed using Dinkelbach’s algorithm. Finally, the total EE optimization problem is solved in an iterative manner. Extensive simulation results validate the effectiveness of the proposed reception scheme and algorithm.

Multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) is a promising solution to achieve higher performances of dual functionalities. However, the existing cell-free/bistatic MIMO ISAC networks struggle to meet strict requirements for data-intensive communication and accuracy-sensitive radar positioning. To further achieve joint enhancement, we propose a novel network where two ISAC transmitters cooperatively perform communication and target positioning, fully leveraging the advantages of cell-free/bistatic principles in communication/radar systems, referred to as bistatic-enhancement MIMO ISAC. An optimization for joint beamforming design is established to maximize the sum data rate for communication users and minimize a novel positioning-enhanced Cramér-Rao lower bound (CRB) that evaluates positioning accuracy under their corresponding requirements. The established problem is solved under two schemes: cooperative block-level and symbol-level beamforming. The solution under the former scheme is derived by an iterative behavior. Under the latter one, inter-user interference is eliminated and co-channel interference is exploited for useful signal enhancement. The problem can be converted into a convex semi-definite problem (SDP) based on semi-definite relaxation (SDR). Experimental results substantiate the effectiveness of the proposed algorithms. More importantly, the proposed bistatic-enhancement network improves positioning accuracy by 32.5% ∼ 47.5% over the conventional bistatic-site one under different schemes.

The integration of intelligent reflective surfaces (IRS) on unmanned aerial vehicles (UAVs), termed UAV-IRS, to bolster wireless communications has emerged as a hotspot of academic research and industrial application. In this paper, we investigate the problem of secure communication in the harsh communication environment assisted by multiple UAV-IRSs, where the UAV-IRSs act as relays to assist the downlink secure communication between the base station and the users. To maximize the security sum rate between the base station and the users, the trajectory planning and phase shift design of multiple UAV-IRS needs to be jointly optimized. To solve this complex non-convex optimization problem, we introduce a distributed collaborative optimization scheme for multiple UAV-IRSs called credit-aware cooperative multi-agent reinforcement learning (MARL), which takes MARL as the base algorithm, and then solves the credit allocation problem among multiple UAV-IRSs by using cooperative game theory to facilitate exploration, and finally constrains non-cooperative behaviors among UAV-IRSs by using the primal-dual optimization algorithm to promote cooperation. Finally, the effectiveness and superiority of the proposed scheme is verified by comprehensive simulation experiments.

The emerging low-altitude economy leverages airspace below 1000 meters for intensive commercial and social aerial activities, where integrated sensing and communication (ISAC) service in 6G network for aircraft is critical to ensuring safe and efficient operations. However, aircraft often operate under constrained wireless resources, particularly in areas with limited or no network coverage. In such settings, exhaustive sensing and data communication among neighboring nodes lead to uncoordinated competition and prohibitive overhead. This paper investigates the ISAC service modeling and scheduling in aerial-vehicle-to-everything (Aerial-V2X) networks, which provides continuous high-accuracy sensing without compromising communication throughput under constrained resource budgets. First, we design a reconfigurable ISAC waveform tailored for aircraft, enabling flexible resource partitioning for both cellular communication (aerial vehicle-to-infrastructure, A-V2I) and cooperative sensing (aerial vehicle-to-vehicle, A-V2V). Second, we formulate a joint sensing-communication service model under this waveform and cast the optimization problem in a partially observable Markov decision process (POMDP). Third, a multi-agent deep reinforcement learning (MADRL) approach is developed to perform decentralized scheduling of sensing actions for each aircraft, minimizing time-frequency resource consumption. Experiments and trace-driven evaluations demonstrate that the proposed method can reduce sensing overhead by up to 70% compared to benchmark policies, while maintaining satisfactory communication and sensing performance.

Unmanned aerial vehicles (UAVs) acting as aerial base stations are regarded as an effective solution for emergency communication, due to their high mobility and low cost. In this paper, we consider the differentiated communication requirements in disaster relief scenarios, where the uplink demands high throughput and the downlink requires low latency and high reliability. To simultaneously capture the timeliness and reliability requirements of downlink transmissions, we propose a novel QoS evaluation metric based on finite blocklength theory. Building on this, we formulate a system utility maximization problem and solve it by jointly optimizing UAV trajectory and ground node access control. To address this problem, we propose a novel algorithm named PW-QMIX, which integrates a priority-based heuristic access control policy with a QMIX-based trajectory optimization scheme, effectively tackling the challenges posed by the high-dimensional joint action space. Furthermore, to tackle the challenge of dynamic observation caused by UAV movement and sensing limitations, we design a weight generation network (WGN) and incorporate it into the input layer of QMIX. The WGN dynamically generates weight matrices based on observed nodes, enhancing UAV’s adaptability to local observation variations. Simulation results validate the significance of jointly optimizing uplink and downlink communications. Moreover, compared with state-of-the-art algorithms, the proposed PW-QMIX demonstrates significant advantages in convergence and scalability.

As a typical scenario for the 6th Generation mobile communication systems (6G), Hyper Reliable Low Latency Communication (HRLLC) is expected to ensure extremely low delay and high reliability, while supporting wireless transmission of large-scale massive data. However, existing communication networks face the dual challenges of inadequate performance metrics and limited network resources. Therefore, this paper proposes the Flexible Bit and Semantic on-demand Transmission (FBST) framework, including three key technologies: adaptive transmission mode decision, flexible transmission time interval scheduling, adjustable semantic compression ratio. The FBST framework could satisfy the strict QoS requirements of users and provide on-demand services for users. Based on the Stochastic Network Calculus (SNC) modeling method, we conduct precise delay analysis and provided a general expression for the delay violation probability of the α - κ - μ channel, which could be extended to various complex channels. In addition, the Knowledge-base Parameterized Deep Q-Network (KP-DQN) algorithm is proposed to solve the resource allocation issue, which is a mixed action space problem with complex calculations caused by SNC. Finally, the simulation results show that FBST framework could satisfy extremely strict delay and reliability requirements of users, and the KP-DQN algorithm improving operational efficiency by over 76.8%.

As the essential complements to terrestrial networks, satellite networks have attracted widespread attention due to their potential to enable seamless coverage. However, satellite-terrestrial communications face several critical challenges, including severe path loss and inter-satellite interference, which pose significant obstacles, especially for energy-constrained uplink transmissions. To overcome these problems, a reconfigurable intelligent surfaces (RIS)-aided multi-satellite cooperative reception scheme is proposed in this paper. Specifically, an energy efficiency (EE) optimization problem for multi-user uplink transmission is first formulated. Based on block coordinate descent method, the total EE maximization problem is decomposed into three subproblems: receive beamforming design, RIS phase shift optimization, and multi-user transmit power optimization. Furthermore, the aforementioned subproblems are respectively tackled by applying linear receiver design, the Riemannian conjugate gradient algorithm, and the Dinkelbach’s algorithm. The superiority of the proposed scheme is validated by numerical results, which reveal significant gains in sum rate and total EE.

The integration of pinching antenna systems with non-orthogonal multiple access (NOMA) has emerged as a promising technique for future 6G applications. This paper is the first to investigate power minimization for NOMA-assisted pinching antenna systems utilizing multiple dielectric waveguides. We formulate a total power minimization problem constrained by each user’s minimum data requirements, addressing a classical challenge. To efficiently solve the non-convex optimization problem, we propose an iterative algorithm. Furthermore, we demonstrate that the interference function of this algorithm is standard, ensuring convergence to a unique fixed point. Numerical simulations validate that our developed algorithm converges within a few steps and significantly outperforms benchmark strategies across various data rate requirements. The results also indicate that the minimum transmit power, as a function of the interval between the waveguides, exhibits an approximately oscillatory decay with a negative trend.

Aiming to circumvent the low energy efficiency (EE) dilemma of multiple-input multiple-output (MIMO) systems induced by employing hundreds of antennas, this paper investigates the potentials of stacked metasurface (SM) and semantic communications (SemCom) for achieving energy-efficient holographic communications in MIMO systems. Specifically, SM enables hybrid beamforming with increased degrees of freedom (DoFs) and reduced energy consumption, while SemCom transmits dramatically compressed key informantion that comes with low power consumption and high EE. To this end, we formulate a worstcase semantic EE (Sem-EE) maximization problem in terms of the transmit beamformer and SM's phase shifts. By proposing a semantic majorization-minimization to handle the fractional and quasi-convex Sem-EE form, quadratically constrained quadratic programs and cyclic coordinate descent can be exploited to solve the optimization variables with low computational complexity. Numerical simulations demonstrate the enhanced EE performance of SMaided semantic beamforming scheme compared to the conventional MIMO systems.

The growing number of services and users leads to an increasing scarcity of spectrum resource and brings challenges to terrestrial-satellite networks (TSNs). For the limited spectrum resource and long-distance transmission characteristics, the improvement of spectrum efficiency is particularly important in TSNs. In this paper, we discuss the optimization problem of spectrum resource utilization in TSNs. By establishing a rate-split multiple-access and multiple-input-single-output (MISO) based terrestrial-satellite network architecture, a joint optimization scheme of user association, power and rate allocation is proposed. The objective of maximizing the system spectrum efficiency can be obtained, so as to realize the efficient utilization of spectrum resource while guaranteeing the communication requirements of users.

This paper proposes a movable array-enabled localization (MAL) framework for high-resolution and cost-efficient direction-of-arrival (DoA) estimation. A base station equipped with a movable uniform linear array (ULA) transmits sensing signals and receives echoes along a linear slide. By modeling the round-trip Doppler shifts caused by motion, we construct a spatio-temporal signal model and reinterpret the temporal phase variations as spatial shifts. This enables the synthesis of a virtual array with an aperture up to twice the physical displacement. A sparse recovery algorithm based on simultaneous orthogonal matching pursuit (SOMP) is employed for efficient DoA estimation. Cramér-Rao bound (CRB) analysis shows that the CRB scaling improves from first-order to third-order with respect to observation time, demonstrating the efficiency of motion-induced aperture synthesis. Simulations validate the analysis and confirm that MAL achieves accurate localization with minimal physical antennas, including the single-antenna case.

With the rapid advancement of the internet of vehicles (IoVs), new types of vehicle applications are emerging continuously. These applications impose increasingly stringent requirements on delay and quality of service standards, which are difficult to meet for vehicle terminals with limited resources. Meanwhile, in the complex computing task system of vehicles, there exists a close correlation between task priorities and computing tasks. As a core technology of space-ground integrated networks, low earth orbit (LEO) satellite communication integrated with vehicle networking can ensure high-efficiency and reliable real-time data transmission. However, additional delay and energy consumption are incurred during the communication between vehicle terminals and satellites. Introducing edge computing into satellite-assisted vehicular networking can satisfy the computing demands of vehicle terminals and reduce the processing delay of vehicle applications, with computing offloading being the key technology. We focus on the LEO satellite assisted vehicular edge computing network. Considering the varying sensitivities of different vehicle tasks to delay and energy consumption, it precisely sets task priorities and proposes a task-priority scheduling scheme. With the objective of minimizing the average delay under constraints of energy consumption, the problem is modeled as a markov decision process (MDP) and addressed by employing the proximal policy optimization (PPO) algorithm within the framework of deep reinforcement learning (DRL). Simulation results demonstrate that the proposed computational offloading algorithm can effectively decrease the system’s average delay, outperforming other benchmark testing methods significantly.

In recent years, with the rapid development of the Internet of Vehicles (IoV) and the widespread application of integrated sensing and communication (ISAC) in the IoV, the integrated sensing and computation offloading in vehicular edge networks has attracted widespread attention from academia and industry. Due to the generation of a large number of latency-sensitive tasks from vehicles’ real-time sensing of road conditions, coupled with the rise of other computing-intensive vehicle applications, the current computing capabilities of the onboard devices cannot meet the diverse demands of vehicular users. Therefore, it is necessary to combine the computing resources around the vehicle to complete computing tasks. With the goal of maximizing the difference between gain and consumption, this article studies the integrated sensing and computation offloading involving roadside units (RSUs) and vehicle platoon in vehicular edge networks. Specifically, we first consider that mobile vehicles can simultaneously offload computing tasks to the RSU and the vehicle platoon via nonorthogonal multiple access (NOMA) technology, and construct a multiobjective optimization problem with the goal of maximizing the utility of the three parties. Then, we construct a game model among the ISAC vehicle, the RSU, and the vehicle platoon based on the Stackelberg game. By seeking the equilibrium of the game, the optimal offloading and pricing strategy are derived, while the utility of the three parties is maximized. Finally, the simulation results show that the proposed scheme is superior to other traditional schemes, and each party in the game obtains its optimal strategy.

With the sixth-generation mobile communication technology (6G) experiencing a shift of spatial expansion, the integration of terrestrial architecture and satellites has become one core feature of 6G. While the scarcity of spectrum resources has become a significant challenge hindering the advancement of integrated satellite-terrestrial networks, it is crucial to push the development of effective strategies to optimize spectrum utilization. This letter introduces a flexible spectrum sensing technique in the nonorthogonal multiple access (NOMA) uplink system for low-earth orbit (LEO) satellite-terrestrial communications, striking an optimal balance between under-sensing and over-sensing. Confronted with diverse complicated scenarios of multisatellite coverage, we conceive the operational principles and derive the sensing thresholds, exploiting spectrum holes and mitigating the fluctuation of false alarms. Notably, numerical outcomes showcase that the proposed technique outperforms the benchmarks, achieving a maximum throughput gain of 61.5% in dynamic communication environments.

Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.

As in-vehicle services grow, the increasing size of cached content prolongs wait times for users. For electric vehicles, balancing efficient communication with reduced energy consumption remains a challenge. In this paper, a vehicle clustering cooperative caching model for urban Internet of Vehicles (IoV) systems is proposed. This model decreases system energy consumption and task delay by leveraging buses as regular mobile Roadside Units (RSUs) and pre-caching nodes. It includes a kinetic energy recovery scheme for vehicles and an Energy Harvesting (EH) mechanism for RSUs, both intended to further reduce energy consumption. Terahertz (THz) technology is harnessed for Vehicle-to-Vehicle (V2V) communication to accelerate caching tasks and reduce tasks delay. To address these challenges, we propose the Deep Deterministic Policy Gradient (DDPG)-based Power Splitting (DPS) algorithm to address the needs of information transmission and energy recharging of buses while in motion. The proposed ($1+1$)-Evolutionary Strategy (ES)-based joint Task Decomposition and Bandwidth Allocation (TDBA) algorithm, which transmits the decomposed task file segments in parallel on different paths, reduces the additional delay and energy consumption caused by frequent task switching. Furthermore, the proposed Time-Location Preference User Rated Recommendation (TLPURR) algorithm recommends appropriate content based on the vehicle user’s time and location preferences, reducing the delay and energy consumption of obtaining content from remote cloud resources. Simulation results demonstrate that our proposed algorithms have a significant improvement in the delay and energy consumption metrics compared to other algorithms.

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task; ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder.

During the check node (CN) update, the elements of input message vectors are redundant for the output message vectors. Hence, in this paper, we exactly select from the input message vectors the elements, which really have contributions to the error-correction performance and constitute the minimum set for the CN update. With adoption of the forward and backward (FB) scheme, an adaptive minimum-set min-sum algorithm (AMSA) is proposed to reduce the computation complexity of the FB process. In order to concurrently update the output vectors belonging to the same CN, we present a parallel minimum-set min-sum algorithm (PMSA) with lower memory complexity than the AMSA. Compared with the min-sum algorithms, the two proposed minimum-set based algorithms introduce no error performance loss.

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services.

Device-free gesture recognition using mobile communication signals is a convenient and efficient technology with broad application prospects in smart homes and human-computer interaction. It utilizes the effect of gestures on surrounding signals to achieve gesture recognition. The cell-specific reference signals (CRS) information can be used to achieve the task in close-range training scenarios. However, when gestures are performed at long-range or in non-training scenarios, the recognition performance will significantly degrade. To enhance device-free gesture recognition capability in arbitrary scenarios, we propose the signal quality enhancement algorithm and the gesture spectrogram construction method to solve this problem. Specifically, we superimpose the CRS information from multiple carriers to improve the gesture signal-to-noise ratio and increase the gesture sensing range. Then, we extract the gesture dynamic components from the CRS information and construct gesture spectrograms to represent scenario-independent gesture motion patterns. Using the gesture spectrogram features, we design a deep network to accomplish the gesture recognition task. We built a prototype system on a software-defined radio platform. Experimental results show that our proposed method can effectively increase the gesture sensing range from 30m² to 228m² and achieve an average recognition accuracy of 82.5% for five types of gestures in arbitrary scenarios.

To overcome the impact of information leakage, obstacle blocking, channel uncertainties, and hardware impairments (HWIs) in wireless communication systems, we design a robust secure transmission strategy for a multi-reconfigurable intelligent surface (RIS)-aided communication system with HWIs and channel uncertainties, where a multi-antenna base station (BS) serves multiple wireless users aided by multiple RISs and overcomes information leakage caused by multiple eavesdroppers. Based on bounded channel uncertainties, a total transmit power minimization problem is investigated subject to the secrecy rates of users, the maximum transmit power of the BS, and the phase shifts of RISs. To deal with the formulated non-convex problem with parameter perturbations, it is transformed into a deterministic problem by using the worst-case approach, S-procedure, and successive convex approximation. Then, the problem is decomposed into an active beamforming and artificial noise subproblem and a passive beamforming subproblem. The subproblems are converted into convex ones via the semi-definite relaxation method, singular value decomposition, penalty function, and eigenvalue decomposition approaches. Finally, an iteration-based robust resource allocation algorithm is proposed. Simulation results verify that by deploying more RISs or increasing the number of reflection elements, the impacts of eavesdroppers and HWIs can be effectively decreased even with channel estimation errors.

When communication signals are weak, the advantages of on-board edge intelligence cannot be fully utilized. To tackle this challenge, the introduction of a key technology in the sixth-generation mobile network (6G)—reconfigurable intelligent surface that can simultaneously transmit and reflect signals (star-RIS)—is proposed. In star-RIS-enhanced intelligent transportation system (ITS), intelligent vehicles use star-RIS to upload local training models and perform global model training on the roadside unit (RSU) side. In this paper, the goal is to minimize system delay and loss function of the learning model, and comprehensively considering constraints such as system bandwidth, star-RIS phase shift, vehicle transmission power and beamforming, and vehicle selection. Firstly, for the optimization of phase shift, transmission power and beamforming caused by the introduction of star-RIS, the block coordinate descent method and Lagrange dual algorithm are used to simplify the solution. Secondly, for system delay and global model training, federated learning (FL) algorithm based on double deep Q-network (DDQN) is utilized. This approach leverages policy optimization and data privacy protection to provide an intelligent resource optimization scheme for ITS. In addition, the effectiveness of the proposed algorithm is validated through extensive simulations and numerical analyses. The results show that the algorithm enhances the adaptability and service quality of the system significantly.

Large Language Models (LLMs), as advanced AI technologies for processing and generating natural language text, bring substantial benefits to the Industrial Internet of Things (IIoT) by enhancing efficiency, decision-making, and automation. Nevertheless, their deployment faces significant obstacles due to high computational and energy demands, which often exceed the capabilities of many industrial devices. To overcome these challenges, edge-cloud collaboration has become increasingly essential, assisting in offloading LLMs tasks to reduce the computational load. However, traditional reinforcement learning (RL)-based strategies for LLMs task offloading encounter difficulties with generalization ability and defining explicit, appropriate reward functions. Therefore, in this paper, we propose a novel framework for offloading LLMs inference tasks in IIoT, utilizing a Decentralized Identifier (DID)-based identity management system for trusted task offloading. Furthermore, we introduce an intelligence-based RL (IRL) approach, which sidesteps the need for defining specific reward functions. Instead, it uses “intelligence” as a metric to evaluate cognitive improvements and adapt to varying environmental preferences, significantly improving generalizability. In our experiments, we employ the GPT-J-6B model and utilize the Human Eval dataset to assess its ability to tackle programming challenges, demonstrating the superior performance of our proposed solution compared to existing methods.

Using the wireless waveform superposition property, over-the-air computation (OAC) enables federated learning (FL) to achieve fast model aggregation. However, this computing paradigm is vulnerable to poisoning attacks due to the openness of a wireless channel over time, where malicious mobile devices can introduce cumulative errors for the global FL model in a time-varying wireless environment for each communication round. This article presents a trust online OAC (TO-OAC) scheme to minimize impacts on the global model introduced by malicious devices adjusting to dynamic attack and wireless channel fluctuations over time. TO-OAC achieves this by utilizing trustworthy security quantification of OAC for each FL training round. To optimize the cumulative training loss at the aggregation node with the long-term power and trust constraints of mobile devices, we propose a joint trust, power, and channel-aware algorithm to flexibly update local and global models in response to the dynamic changes in the wireless and secure environment. We analyze the performance limits for the aggregation of trust models, considering metrics for computation and communication through time. We then propose another trust online regularization over-the-air computation (TOR-OAC) as an improved version of the TO-OAC scheme to decrease convergence time while ensuring long-term trust and power limitation. Experimental results performed on real-life datasets show that the two proposed schemes (TO-OAC and TOR-OAC) outperform prior works, especially in noisy, time-varying wireless channels and malicious attacks.

Low Earth Orbit (LEO) constellations and Unmanned Aerial Vehicle (UAV) networks enable wide coverage for the sixth generation (6 G) mobile communication. However, it is a challenge to achieve high scheduling success rate, ultra-low latency, and efficient load balance in the Space-Air-Ground Integrated 6 G Network (SAGGIN). This paper addresses the following issue:How to effectively and orderly transmit time-sensitive traffic in SAGGIN under strict deadlines, limited computational ability, and restrained link capacity?Specifically, this paper uses Software-Defined Networking (SDN) and designs a joint optimization method to enhance the traffic transmission ability of SAGGIN. Considering response time, computing cost, and link capacity in SAGGIN, the scheduling, computing, and load balance issues are modeled as a multi-objective optimization problem that minimizes the worst-case response time and computing cost of data frames while maximizing the network flow. Then, this paper leverages a Federated Reinforcement Learning (FRL) scheme to solve the problem. Results show that the FRL could achieve great scheduling, computing, and load balance performance. Specifically, our method can successfully schedule 80% of the traffic at most when the current network load is around 90%. Furthermore, the computational delay could reduce around 50%.

Integrated sensing and communications (ISAC) is emerging as one of the six application scenarios for future wireless networks. Characterizing the Pareto boundary is an urgent issue in multiple-input multiple-output (MIMO) ISAC systems. The lack of unified sensing metrics and the neglect of the instantaneous worst-case sensing requirement in the existing works present challenges to this issue. In this paper, we propose a more universal and operable theoretical limit analysis framework where the high-signal-to-noise ratio (SNR) channel capacity is characterized under instantaneous covariance mismatch constraint. We use the covariance mismatch that implies the distance to optimal covariance as the sensing metric. The optimal covariance can be computed by optimizing any key sensing metric. An MIMO ISAC Pareto boundary can be obtained by computing channel capacity under fine-grained sensing thresholds, below which the mismatch must be constrained. In the experiments, three radar modes are considered, and the results show that different radar modes affect capacity performance and a trade-off exists between communication and sensing. In addition, pure communication capacity is the upper bound of the communication capacity in ISAC. Moreover, capacity under instantaneous constraint approaches that under average one in pure MIMO communications when signal length approaches infinity.

Efficient and stable resource management in ultra-dense networks is essential for interference reduction and quality of service guarantee for large-scale users. Although reinforcement learning is currently the most talked-about method for achieving efficient resource allocation, it still faces significant challenges when dealing with the key adaptive issues, such as strong interference, large-scale users dynamic demands, and differentiated user priority requirements. In the case, this paper proposes a pervasive power allocation method in the framework of deep reinforcement learning. Concretely, we develop a noisy deep Q-network for guaranteeing stable exploration of average rate and accelerating the model convergence, where the parameterized noise is incorporated into the connection weights of neural networks. On the other hand, considering the varying priority levels of different users, we devise a prioritized experience replay mechanism aimed at enhancing the service quality for users with high importance. Simulation results demonstrate that our proposal can surpass the state-of-the-art methods in terms of average rate, convergence, stability, and model complexity, achieving more adaptive power allocation.

With the explosive growth of smart devices and applications, the demand for mobile service with higher data rate and better quality of service is growing rapidly. Ultra-dense networks, capable of providing higher network throughput, remain one of the key technologies for next-generation mobile communications. However, the densification of network further reduces the coverage of base stations and the distance between each other, which in turn leads to unnecessary and frequent handovers (HOs), affects the stability and reliability of communication links. HO failures can even occur due to the improper HO control parameter (HCP) values. To this end, a HO type prediction and parameter optimization method based on machine learning is proposed. First, the HO is divided into four categories: successful handover (SHO), ping-pong handover (PPHO), too-late handover (TLHO), and too-early HO (TEHO). Second, we combine reinforcement learning with supervised learning and propose a novel adaptive HCP adjusting scheme. Specifically, deep Q-network dynamically selects HCP values through environmental information and supervised learning-based HO prediction results. Simulation results demonstrate that our proposed scheme achieves a prediction accuracy of 94.83%, while reducing the PPHO rate by 15%, the TEHO rate by 2%, and the TLHO rate by 3%.

Next-generation Internet of Things (IoT) technology is vital for sixth-Generation (6G) communication systems, driving exponential growth in spectrum resource demand. Spectrum sensing, essential for identifying unused spectrum, and Non-Orthogonal Multiple Access (NOMA), which allows efficient frequency band sharing among users, can significantly improve spectrum efficiency. However, current sensing methods do not fully support NOMA, resulting in suboptimal performance. Motivated by such a circumstance, we propose a feature-based spectrum sensing method for uplink communication in power-domain NOMA IoT scenarios with multi-user interference, aiming to maximize both static and dynamic spectrum efficiency. Firstly, we introduce the notion of orbits to represent non-fully occupied spectral holes. Then we elaborate on the sensing criterion as well as workflow and design the two-stage spectrum sensing framework, which proposes the orbit estimation sensing algorithm in the stage 1 and accurate sensing threshold in the stage 2, to mitigate false alarm fluctuations. The closed-form solution for the estimation threshold and accurate threshold configuration are derived thereafter. Simulation results show that our proposed multi-orbit sensing method has stable performance and achieves average 30% system throughput gains compared to the latest NOMA techniques at 5 dB.

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and cannot well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from −10 dB to −5 dB.

Simultaneously transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) can assist in achieving full-space signal coverage enhancement. Considering eavesdropping channels, a downlink system model with full coverage of STAR-IRS enabled unmanned aerial vehicle (UAV) secure communication is proposed. The aim is to attain the maximal value of the energy efficiency (EE) by exploring the joint resource allocation of the system. To solve this coupling problem, the lower and upper bounds of the sum-rate for legitimate and eavesdropping users are derived respectively, and Lagrange duality theory is employed to deal with the power control problem. Then the reflection/transmission amplitude splitting coefficient optimization of STAR-RIS using the Hybrid whale-bat (HWB) method in energy splitting (ES) mode are considered to fully exploit the performance gains brought by STAR-IRS deployment. Finally, the simulation verifies that the proposed joint design scheme can enormously promote the EE and safety performance of the system.

The upcoming sixth-generation (6G) of ubiquitous connectivity communications systems is driven by the next-generation of Internet of Things (IoT) technology. Accordingly, the demand for spectrum resources is growing exponentially. Spectrum sensing, which dynamically explores spectrum holes, is expected to be crucial in the 6G era. In the meantime, Non-Orthogonal Multi-Access (NOMA), an efficient means of reusing resources, can enable multiple users to share the same frequency band stably. The combination of both technologies holds promise for more effective use of spectrum resources in future commu-nications. In this paper, we present a spectrum sensing method for the uplink communication scenario in NOMA with multi-user interference, which aims to make effective use of both static and dynamic gain on spectrum efficiency. Firstly, the sensing criterion and workflow are outlined. The closed-form solution for the sensing threshold configuration is derived thereafter. The simulation results demonstrate the feasibility of the proposed approach, which can increase the system throughput up to 19.7%, when compared to NOMA without spectrum sensing.

In recent years, unmanned aerial vehicles (UAVs) are considered to be integrated into wireless communication systems because of their tremendous advantages in mobility, cost, maneuverability, etc. In some real UAV-assisted communication scenarios, the dynamics of the environment, such as the roaming of served users, make it hard to obtain an optimal trajectory before the UAV is dispatched. Implanting an intelligent control policy into UAVs for distributed task execution is necessary to complete the task. In this paper, a UAV trajectory design problem is investigated for an orthorgonal-frequency-division-multiplexing (OFDM) wireless sensor network, which is dynamic because mobile sensors may randomly roam within a certain range. The UAV is expected to balance task efficiency with the safety constraint with a pre-trained onboard control policy. Compared to prior works, this work requires the policy to adapt to randomly generated obstacle maps, and also assumes that the UAV has no prior knowledge of the obstacles before it is dispatched, which brings about challenges to the problem. The motivation comes from adversarial environments without the specific obstacle distribution beforehand, such as a disaster area. The problem is formulated as a constrained Markov decision process (CMDP) model, which incorporates the safety constraint compared to basic MDP. Due to the assumption of randomized obstacle distribution and lack of prior knowledge, existing algorithms for CMDP can not be applied directly. To tackle this issue, we enhance reinforcement learning (RL) algorithm with a safety control mechanism to derive our novel safe reinforcement learning (Safe RL) algorithm, which is based on the framework of Lagrangian method. Compared to former algorithms about CMDP, our algorithm eliminates the premise that the safety model is known, the agent is able to learn safety judgement from scratch through its interactions with the environment. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithm under the problem’s setup.

Cognitive backscatter communication (BackCom) is a promising technology for improving the spectrum- and energy-efficiency of Internet of Things by enabling spectrum sharing and energy saving. However, the performance of cognitive BackCom networks is adversely affected by the mutual interference between the primary and secondary systems and the blocked links caused by obstacles. Additionally, assuming perfect channel state information (CSI) is unrealistic in practical cognitive BackCom networks due to the limited signal processing capabilities of cognitive backscatter nodes (CBNs) and channel delays. To address these challenges, we investigate a robust radio resource allocation and passive beamforming problem for a downlink reconfigurable intelligent surface (RIS)-enhanced cognitive BackCom network under the nonlinear energy-harvesting (EH) model and imperfect CSI. In particular, a primary base station serves multiple primary users (PUs), while multiple pairs of CBNs share the spectrum of PUs to communicate with each other in a harvest-then-transmit way. Our goal is to maximize the total energy efficiency (EE) of CBNs subject to the constraints of maximum interference power, minimum EH, time allocation, and the phase shift of the RIS. To solve the nonconvex optimization problem, we propose an iteration-based EE optimization algorithm that leverages methods of quadratic transform, variable substitution, and semidefinite relaxation. Simulation results verify that the proposed algorithm has improved its EE by 11.39% and reduced outage probabilities by 15% compared to the existing algorithms.

The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE’s location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes.

Human-centric Metaverse services requires novel communication and networking technologies to achieve seamless connectivity for Metaverse users. Reconfigurable intelligent surface (RIS) in 5G and beyond networks can provide highly reliable communication connections, superior user quality of service (QoS), seamless user connections, and extensive signal coverage for Metaverse. Deploying RIS in unmanned aerial vehicle (UAV) networks for Metaverse can enormously improve the signal propagation environment and human-centric communication experiences. Considering the channel uncertainty of the air-ground cascade communication link in Metaverse, an RIS-aided multi-UAV cross-layer network system is proposed. Under the cross-tier interference limitation and the rate outage probability constraint, the system EE improved by maximizing the minimal energy efficiency (EE) of UAV units. Different from the existing RIS schemes, which suffer from the significant channel acquisition cost or power consumption, this paper first proposes a topology design scheme of irregular RIS, which Metaverse user only connects a few RIS elements to obtain high EE. Secondly, with the imperfect cascade channel state information (CSI) error model, the rate outage probability constraint is approximated by Bernstein type inequality to enhance the seamless human-centric connectivity service. Hence a low complexity scheme is invoked to co-design the power control parameter at the UAV transmitter and RIS reflecting phase. Finally, affluent simulation curves verify that the irregular RIS controller deployment combined with low power loss topology design and low-complexity phase shift design contributes to improve human-centric QoS for Metaverse service.

User-centric clustering is a valid solution to enhance the coverage and throughput for future mobile communication networks. However, the size of clusters, the location of nodes, and the number of cooperating nodes within the cluster can all have an impact on the data rate of the typical user. In this paper, the user-centric clustering with limited cooperation (LC) in downlink cell-free (CF) architecture is considered, and the effect of composite channels and intra-cluster cooperation on the data rate of the typical user is analyzed from a theoretical derivation level. Specifically, the user classification, the distributions of distances between the serving nodes, and the average data rates of each type of user are given, respectively. The approximate expressions of the Laplace transform (LT) of interfering power for different types of users are obtained with the Gauss-Hermitian integral approximation, and the long-term average data rate of the typical user is derived. Finally, Monte Carlo simulations are executed to verify the accuracy of the theory. The results show that shadowing fading should not be ignored for accurately evaluating user performance, and it is particularly important to reasonably select the radius of the cluster and the cooperation threshold that controls whether the access points cooperate or not.

The benefits of mobile edge computing (MEC) systems cannot be fully exploited when the communication link is blocked or the communication signal is weak. Intelligent reflective surface (IRS) technology is introduced to build an IRS-assisted MEC system and to solve this issue. In this paper, devices offload part of their computing tasks to MEC through the multi-antenna access point with the help of the IRS, thereby reducing the completion time of computing tasks. We consider the weighted sum-latency minimization for single-device and multi-device scenarios in the uplink, which are constrained by computing offload allocation, edge node computational capability, IRS practical phase shift, beamforming, and device transmitting power. Firstly, block coordinate descent technology is used to decouple latency minimization problem into two subproblems of computation and communication. Secondly, in single-device scenario, the original problem is simplified and solved by the continuous refinement scheme. In multi-device scenario, an algorithm that combines alternating optimization and the Jaya algorithm is proposed for the first time to solve the weighted sum-latency minimization problem. In addition, compared with the conventional MEC systems without IRS, the effectiveness and high-performance gain of the proposed algorithm are proved through simulations.

The development of intelligent transportation systems (ITS) has attracted significant attention to connected and autonomous vehicles (CAVs). It is urgent to investigate multi-CAV intelligent cruise control solutions in mixed traffic environments. In addition, the impact of platoon dynamics and time delays, induced by shared wireless communications, data processing, and actuation cannot be ignored. This article investigates the development of a multi-agent deep reinforcement learning (MADRL) controller tailored for CAVs operating within mixed and dynamic traffic scenarios that involve time delays. Firstly, the error dynamics in the discrete-time domain for each subplatoon is derived by considering the time-varying delays and leading vehicle states, and then the optimal CAV cruise control problem is formulated. Subsequently, the partially observable Markov game (POMG) is used to construct the multi-agent environment, and then a centralized training decentralized execution (CTDE) algorithm framework is proposed based on the multi-agent deep deterministic policy gradient (MADDPG) method. Finally, the computational complexity and the influence of delay are analyzed. The simulation results illustrate the effectiveness of the proposed intelligent algorithm.

The integration of semantic communication with Internet of Things (IoT) technologies has advanced the development of Semantic IoT (SIoT), with edge mobile networks playing an increasingly vital role. This paper presents a framework for SIoT-based image retrieval services, focusing on the application in automotive market analysis. Here, semantic information in the form of textual representations is transmitted to users, such as automotive companies, and stored as knowledge graphs, instead of raw imagery. This approach reduces the amount of data transmitted, thereby lowering communication resource usage, and ensures user privacy. We explore potential adversarial attacks that could disrupt image transmission in SIoT and propose a defense mechanism utilizing Generative Artificial Intelligence (GAI), specifically the Generative Diffusion Models (GDMs). Unlike methods that necessitate adversarial training with specifically crafted adversarial example samples, GDMs adopt a strategy of adding and removing noise to negate adversarial perturbations embedded in images, offering a more universally applicable defense strategy. The GDM-based defense aims to protect image transmission in SIoT. Furthermore, considering mobile devices' resource constraints, we employ GDM to devise resource allocation strategies, optimizing energy use and balancing between image transmission and defense-related energy consumption. Our numerical analysis reveals the efficacy of GDM in reducing energy consumption during adversarial attacks. For instance, in a scenario, GDM-based defense lowers energy consumption by 5.64%, decreasing the number of image retransmissions from 18 to 6, thus underscoring GDM's role in bolstering network security.

Using satellite communications as backhaul links facilitates extending network coverage to unconnected areas. However, providing high-quality video streaming service via satellite backhaul is not economical. This paper presents SatSR, a mobile edge computing (MEC)-based super-resolution (SR)-enhanced adaptive on-demand video streaming system for mobile networks with satellite backhauls. Particularly, SR-based video quality enhancement is integrated into the video streaming process, so that low-quality videos with small sizes can be transmitted by satellite links and then enhanced to be high-quality. Meanwhile, SatSR offloads computation-intensive SR processing from user equipment (UE) to the MEC server to relieve UEs’ computation burden and speed up the SR processing. Specifically, the framework and the operation process of SatSR are designed first. Then, to mitigate the impact of SR processing delay, a pipelined mechanism is proposed, which can coordinate the video transmission and SR-based enhancement efficiently. Furthermore, an SR scale factor adaptation algorithm based on deep reinforcement learning is proposed to cope with the fluctuation of communication links. Finally, a system prototype and a chunk-level simulator of SatSR are built, respectively. The experiments results validate that SatSR outperforms baselines significantly, including both the UE-based SR-enhancement video streaming scheme and the traditional bitrate adaptation based video streaming scheme.

A wireless federated learning system is investigated by allowing a server and multiple workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via band-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients to relieve the communication bottleneck. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov’s momentum. For the non-convex empirical risk, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks.

In this work, a novel self-powered visible light communication and positioning (SPVLCP) system is constructed, which can realize communication, positioning, and energy harvesting simultaneously. The resource allocation scheme applying orthogonal frequency division multiple (OFDM) modulation and the power splitting (PS) method is proposed for the SPVLCP system. Based on this scheme, a corresponding optimization allocation strategy is provided, aiming to maximize the data rate by optimizing the PS factor and the power allocated to subcarriers while ensuring positioning accuracy, harvested energy, and the LED transmit power. To solve the optimization issue, the successive convex approximation (SCA) algorithm and a series of equivalent substitutions are applied to convert the optimization issue from non-convex to convex. The findings demonstrated that the optimization allocation strategy can solve the trade-off problem among communication, positioning, and energy harvesting better than the uniform allocation strategy. Furthermore, the SCA-based algorithm has an obvious superiority over the conventional heuristic algorithm in solving the optimization issue for the proposed resource allocation scheme of the integrated SPVLCP system.

The future integrated sensing and communication (ISAC) networks is expected to equip with sufficient computation resources. However, current research focuses on single-domain resource allocation in ISAC and computing force networks, leaving the joint optimization of sensing, communication, and computation resource allocation unexplored. In this paper, we propose a novel approach to this problem by deep incorporating computation resources, combined with a federated learning framework, while considering sensing precision and power consumption. Firstly, a multi-objective optimization is designed, involving Cramer-Rao Bound, sum rate of ISAC networks, and power consumption of computing force networks. Subsequently, the multi-objective optimization is transformed into a multi-task learning model. We aim to obtain joint optimization of sensing, communication, and computation resource allocation via deep learning techniques. Towards the multi-task learning model, the multiple-gradient descent algorithm is utilized to obtain the multi-objective optimization. Furthermore, a practical low-complexity the multiple-gradient descent algorithm is developed to reduce the computational cost. Finally, the effectiveness of the proposed deep learning algorithms is verified by simulations results.

High energy costs and carbon-neutral targets emphasize the economics and sustainability of mobile communications. This paper studies a long-term average energy transaction expenditure minimization problem for smart-grid powered heterogeneous cellular networks (SG-HCNs) where renewable energy is introduced. Renewable energy and wireless channel dynamics evolve over different timescales. Thus, we seek a two-timescale dynamic resource management solution in SG-HCNs, where the real-time joint issue of flow control, power allocation, and energy sharing of renewable energy, and the ahead-of-time two-way energy trading are considered. Based on a two-layer Lyapunov framework, a two-timescale dynamic optimization (TTDO) algorithm is developed for the proposed problem. Specifically, the real-time joint issue is decoupled into two subproblems, addressed by linear programming and successive convex approximation methods. An approximate solution for ahead-of-time two-way energy trading is achieved via the stochastic subgradient approach, where past data of related random events is referred to as prior knowledge that is required but difficult to acquire. Theoretically, the proposed TTDO algorithm can attain an asymptotic optimum and ensure queue stability. Simulation results verify the theoretical analysis and reveal that the proposed TTDO algorithm can obtain lower energy transaction expenditure than benchmarks. Besides, the proposed TTDO algorithm presents an energy-saving property.

Non-orthogonal multiple access (NOMA) with mixed numerology is a promising technology that blends flexibility and high spectral efficiency. However, since NOMA enables multiplexing of the same time-frequency resources for different users and mixed-numerology allows superimposing sub-signals with different numerologies, high peak-to-average power ratio (PAPR) as well as power fluctuation problem in NOMA detection becomes cumbersome especially when applying PAPR reduction techniques. This study considers minimizing PAPR in mixed numerology NOMA systems using selected-mapping (SLM) method. The Riemann sequence is one of the simplest phase sequence that can be used to generate a set of signal copies for PAPR reduction. Our analysis reveals that as the amplitude variation caused by the Riemann sequence grows, the upper bound of PAPR for signal copies decreases correspondingly. Leveraging this insight, we introduce a new maximum-range Riemann (MRR)-based phase sequences, in which the amplitude factor can be adjusted to control the power fluctuations. Compared to previous works, our study delves deeper into the influence of phase sequence design of SLM on PAPR reduction performance. Simulations show that the proposed method offers significant performance advantages of PAPR reduction and bit error rate (BER) improvement even with consideration of power-amplifier.

If the future traffic of small base stations (SBSs) can be foreseen, we can systematically adjust the system resources to meet the quality of service (QoS) of users and realize the effective assignment of network resources. To this end, a dynamic channel allocation schemes based on traffic prediction is proposed. First, the machine learning is adopted to extract temporal and spatial features of the service traffic or load in a certain region, and then the prediction results and graph theory are both used to realize the division of a given frequency band and bandwidth allocation, in order to achieve the purpose of coordinating the interference between SBSs and improve the system throughput; Secondly, oriented to the fluctuation of service traffic in the region, a dynamic channel allocation method based on user satisfaction is proposed to fulfill the dynamic adjustment of the total system bandwidth and channel allocation, so as to utilize the frequency band resources more effectively. Simulations show that our proposed method improves the number of bandwidths allocated per SBS by a factor of 4.932 and 1.225 on average compared to OCA and CA-CM, and it realizes the data rate requirement of most users with less bandwidth.

Non-orthogonal multiple access (NOMA) has drawn much attention due to its capability in massive connections. It enables various joint designs with advanced technologies, e.g., cooperative transmission and aerial base station (ABS)-assisted networks. In this paper, we consider an efficient NOMA enabled cross-tier joint transmission design. This design jointly applies zero-forcing beamforming and NOMA to realize joint transmission and regular transmission for the users in different tiers. We evaluate the performance of the design under a large-scale clustered ABS-assisted network scenario. Theoretical expressions for the outage probability and area outage spectral efficiency are derived. Numerical results show that, although splitting power may lead to slight degradation for the macro-cell users, the system can still expect a decreased overall outage probability because joint transmission improves the received signal quality for the ABS-tier users. The macro-cell users may not always suffer outage performance degradation since they can keep receiving signals instead of staying idle until the cross-tier joint transmission is suspended as in the orthogonal scheme. Besides, with properly selected NOMA power allocation coefficient, a more reliable communication link can be guaranteed for the macro-cell user, if compared with the conventional orthogonal scheme.

Unmanned aerial vehicle (UAV) and sidelink technology are becoming more and more important in emergency communication. To optimize overall energy efficiency, joint subchannel, transmit power, and multi-UAV trajectory optimization algorithms are examined in a multi-cell heterogeneous network of UAV and sidelink with quality of service (QoS) sensitivity restrictions. To allocate subchannel appropriately in each period, a grouping and matching approach is first developed that can handle the subchannel assignment of multi-cell. Then, successive convex approximation method is used to approximate the non-deterministic polynomial hard problem of power allocation. Taylor expansion approximation method is finally introduced to deal with multi-UAV trajectory optimization. In addition, the complexity analysis is provided and numerical results confirm the optimization methods’ reasonableness.

Since the unmanned aerial vehicle (UAV) network and intelligent reflecting surface (IRS) technology can flexibly change wireless network links signal, the UAV-IRS network system is a potential solution to increase the communication performance gain. Motivated by the practicality of UAV-IRS networks, a non-orthogonal multiple access (NOMA) heterogeneous UAV communication system with simultaneous wireless information and power transfer (SWIPT) is considered, which consists of multiple UAV base stations (UBSs), a macro base station (MBS), and multiple IRSs for auxiliary communications. This paper pursues a goal to receive the system energy efficiency (EE) maximization by resource allocation and reflecting design of IRSs. Due to the strong coupling among multiple parameters in the original problem, this complex non-convex problem is decomposed into three stages. In the first stage, this paper decouples the problem into two subproblems of NOMA subchannel assignment and SIC decoding order to find the optimal solution separately. For the second stage, under the constraints of UAV’s maximum transmit power, users’ quality of service (QoS) requirements, user energy harvesting threshold and cross-layer interference constraints, a beamforming design based on Lagrangian duality is exploited. For the third stage, the power splitting (PS) factors and the reflecting phases of the IRS are jointly optimized using the penalty-SDR algorithm to approximate the suboptimal solution. Finally, the simulation curves exhibit the validity and excellent performance of the co-design scheme in improving the system EE.

One of the main challenges of popularizing the integration of sensing and communication (ISAC) network is mutual interference between the two functions. A viable solution is the time division scheme where communication and sensing are separated in time domain. This paper considers a multi-cluster ISAC network model, where the time-domain radio resources are allocated to sensing and communication. At the same time, the time resources can be reused in space-domain. Particularly, the terminals in different work phases can access radio resources of different or the same clusters simultaneously, depending on interference. In this way, the interference is isolated while the resources utilization is improved. Two different resource allocation approaches are proposed according to how interference is considered. The aim is to maximize the sensing detection probability under the constraint of network throughput. The performance improvement in terms of target detection probability brought by the proposed schemes is shown by numerical results compared with benchmark methods.

Computation offloading tends to be an effective way for mitigating computing pressure of user equipments (UEs). By computation offloading, the task can be handled in network edge and/or cloud center to compensate insufficient resources and capabilities of UEs. In this study, we construct a three-tier cloud-edge integration network, where user tasks are offloaded to satellite based edge server and further to the remote ground cloud server via backhaul links. The optimization problem is modeled for minimizing system energy consumption and considers user association, power allocation, task scheduling, and bandwidth assignment jointly. By the proposed schemes based on relaxation transformation and fractional programming, four subproblems are transformed into corresponding convex optimization problems and solved respectively. In order to find the global optimal solutions, a joint iterative algorithm for three-tier computation offloading problem is designed. In numerical simulations, we compare different communication schemes and computation offloading schemes to present the rationality and superiority of the designed algorithm for reducing system energy consumption.

With the rapid development of intelligent vehicles in recent years, in-vehicle heterogeneous networks (HetNets) incorporating base stations (BSs) and vehicle access points (VAPs) have been widely deployed to support the ever-emerging diverse vehicular applications. However, most of the existing works focused on the HetNets covering multiple vehicles and considered the needs of all in-vehicle users as a holistic entity to maximize the overall performance of networks, which inevitably deviates from the local in-vehicle HetNet quality that most users are concerned about. Additionally, improving the energy efficiency (EE) of mobile devices in vehicles to extend their battery life is another significant issue, which has not been well addressed. To address the above issues, an intelligent in-vehicle power control and spectrum allocation mechanism is proposed in this work to maximize the EE of devices in the cabin while satisfying their dynamic traffic demands. Since this optimization problem has a non-convex mixed integer programming form, which is difficult to solve with traditional optimization methods, we further transform the optimization problem into a Markov Decision Process (MDP) and utilize a Proximal Policy Optimization (PPO) algorithm combined with the vehicle's location and historical channel state information (CSI) to achieve optimization objectives. Simulation results validate that the proposed algorithm can satisfy the dynamic traffic requirements of devices with high EE. Further comparison with baselines highlights the robustness of the proposed scheme under different device quantities and ratios.

With the rapid development of mobile communication and Internet of Things, the scarcity of spectrum resources is becoming increasingly severe. As a supplement to terrestrial networks, satellite communication can alleviate the pressure of the terrestrial spectrum. Meanwhile, dynamic spectrum utilization plays an important role in alleviating the scarcity of spectrum resources. To integrate the advantages, we propose the multi-level sliced sensing architecture and design the workflow combining satellites and spectrum sensing. The sensing threshold settings are deduced thereafter. According to the theoretical derivation above, the challenge of false alarm floating caused by high mobility of satellites is addressed by studying the relationship among false alarm probability velocity, Doppler frequency and slice number. Numerical results show that under highly dynamic channel conditions, the proposed method can improve detection probability by up to 54% over existing technique while stabilizing the equivalent limitation of Pf.

Integrated sensing and communication (ISAC) aims to use the basic functions of the wireless communication system to achieve sensing (by sharing the same frequency, signalling, hardware, etc.). At the same time, the results of wireless sensing are used in turn to optimize wireless communication. In this way, it is possible to realize the dual promotion of wireless communication system and wireless sensing, improve and fully differentiate the spectrum utilization, and reach a high level of integration and simplification of the equipment, and achieve accurate sensing. This paper reports an ongoing study on ISAC conducted by the Service & System Aspects Work Group 1 (SA WG1) of the third generation partnership project (3GPP), which is defining to study use cases and potential requirements for the enhancement of the 5G systems (5GS) to provide ISAC services addressing various target verticals/applications. We summarize the objectives of the ISAC Study Item (SI), and discuss some of the most interesting proposed use cases discussed thus far. We also introduce the potential new requirement for 5GS as well as a summary of potential research objective pertaining to ISAC in standardization evolution.

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide additional computation capability and wide coverage for mobile users (MUs), especially when MUs are not within the communication range of the infrastructure. In this article, a UAV-aided mobile-edge computing (MEC) network, including one UAV-MEC server, one BS-MEC server, and several MUs, is investigated for computation offloading, in which the edge service provider (ESP) manages two kinds of servers. It is considered that MUs have a large number of computation tasks to conduct, while the ESP has idle computational resources. MUs can choose to offload their tasks to the ESP to reduce their pressure and cost, and the ESP can make a profit by selling computational resources. The interaction among the ESP and MUs is modeled as a Stackelberg game, and both the ESP and MUs want to maximize their utility. The proposed game is analyzed by using the backward induction method, and it is proved that a unique Nash equilibrium can be achieved in the game. Then, a gradient-based dynamic iterative search algorithm (GDISA) is proposed to get the approximate optimal solution. Finally, the effectiveness of GDISA is verified by extensive simulations, and the results show that GDISA performs better than other benchmark methods under different scenarios.

Many real-time application scenarios are developed in 6G communications. Driven by the low-latency data processing requirements, multi-tier computing has become an important technology to improve user experience and reduce network overhead. In this paper, we consider a multi-tier computation offloading network structure for 6G applications, in which the cloud computing center and the nearby vehicle edge server (VES) are able to partially calculate the tasks offloaded from the user equipment (UE), and the remaining task is processed locally in the UE. By jointly optimizing user scheduling, cloud offloading ratio, VES offloading ratio, and VES mobility, the objective function is to minimize the delay of the system transmission and computation under the constraints of discrete variables and energy consumption. To solve the problem, a primal-dual deep deterministic policy gradient (PD-DDPG) algorithm based on multi-tier computation offloading is proposed. Simultaneously, compared with baseline algorithms, PD-DDPG algorithm has an obvious advantage in both the speed of convergence and the system delay.

The digital twin (DT) and terahertz (THz) wireless communication technologies have promoted the innovative development and application of 6G networks. Combining DT can obtain efficient, collaborative, and intelligent management for THz wireless networks. However, the conflicts between large amounts of twin data and limited network resources make it difficult to improve the performance of DT networks. In this paper, a DT architecture for THz wireless networks is proposed, which maps a physical network in the THz band into a virtual DT network and represents the DT network as a graph structure. Furthermore, the THz channel model is provided, and the resource management problem with weighted mean rate as the optimization objective is proposed, which is transformed into a graph optimization problem. Based on this, a distributed message propagation algorithm is proposed, which uses the graph neural network to provide a solution. Simulation results show that the proposed scheme improves the weighted mean rate of the DT network for the THz band and outperforms the benchmark methods. It is also proved that the proposed distributed message propagation algorithm is scalable and can maintain good performance under different conditions.

The breakthrough of artificial intelligence (AI) techniques has accelerated their applications in a wide range of industries, such as security protection, transportation, agriculture, and medical care. With the support of edge computing environments, providing latency guaranteed AI as a Service (AIaaS) can accelerate the deployment of data-intensive and computation-intensive AI applications and reduce the investment cost of the customers. However, the deployment architecture and working mechanism design, and performance optimization problems specific for AIaaS with configurable data quality and model complexity have not been studied in existing works. To address the problem, we propose a configurable model deployment architecture (CMDA) for edge AIaaS and present a flexible working mechanism by enabling the joint configuration of data quality ratios (DQRs) and model complexity ratios (MCRs) for the AI tasks. Along with commonly used resource allocation operations, the manager can improve the energy and delay performance of AI services with the desired quality of results (QoRs). We develop an energy-delay minimization problem under the framework of CMDA and propose a polynomial regression based relaxing method to solve the task configuration subproblem. We conduct experiments and simulations on the ImageNet classification and the common objects in context (COCO) object detection tasks using state-of-the-art deep learning models. We present the corresponding result quality tables (RQTs) and QoR regression models to illustrate the proposed method. The results of single task configuration and multi-task configuration and resource allocation on ImageNet classification and COCO object detection tasks demonstrate that the proposed method can achieve over$5\times$HDEC improvement compared with non-optimization schemes, and also show that joint configuration of DQR and MCR can achieve over$1.2\times$HDEC improvement compared with the methods that only configure DQR or MCR.

A set of low-cost sensors is deployed to infer the network topology of a self-organizing wireless network. The sensors operate in a non-invasive fashion, extracting only the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data also reports the source node of each packet, but not the destination nodes or the contents of the packets. A central processor collects the meta-data from the sensors, and the goal is for the processor to infer the network topology based solely on such information. Prior work leveraged causality metrics to identify which links are active. If the data timings and ACK timings of two nodes– say node 1 and node 2, respectively– are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1). This paper starts with the observation that packet losses can weaken the causality relationship between data and ACK timing streams. To obviate this problem, a new Expectation Maximization (EM)-based algorithm is introduced– EM-causality discovery algorithm (EM-CDA)– which treats packet losses as latent variables. EM-CDA iterates between the estimation of packet losses and the evaluation of causality metrics. The method is validated through extensive experiments in wireless sensor networks on the NS-3 simulation platform.

Semantic communication has been regarded as a promising technology to serve upcoming intelligent applications. However, few studies have addressed the problem of resource allocation in semantic communication networks. Most resource allocation mechanisms act fairly to all original data, ignoring the meaning behind the transmitted bits. In this paper, a dynamic resource allocation scheme for the task-oriented semantic communication network (TOSCN) based on deep reinforcement learning (DRL) is proposed, which allows data with richer semantic information to preferentially occupy limited communication resources. This paper aims to design a deep deterministic policy gradient (DDPG) agent at the micro base station to maximize the long-term transmission efficiency of tasks. Firstly, the relationship between semantic information and task performance is investigated. Subsequently, a novel wireless resource allocation model for TOSCN is proposed by taking the image classification task as an example. Then, a joint optimization problem of the semantic compression ratio, transmit power, and bandwidth of each user is formulated. The agent is trained in an interactive learning environment to obtain a decent trade-off between the amount of data delivered to the receiver and the accuracy of intelligent tasks. Simulation results demonstrate that the proposed scheme achieves significant advantages in relieving communication pressure and improving task performance in resource-constrained wireless networks.

Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource demand of the offloaded tasks of a UE is naturally a random variable, it is possible that the real-time computation capacity demand of a resource-limited hosting virtual machine (VM) or edge computing server (ECS) is larger than its computation capacity, causing unexpected delay or delay-jitter to the services, which should be avoided if possible, for delay-sensitive applications. We consider an edge computing scenario wherein the transmission links are unmanageable and computation resource demands of VM servers are stochastic. We propose a novel Logistic function-based service reliability probability (SRP) estimation model without specifying the distributions of the resource demands. We study the average SRP maximization problem (ASRPMP) in a VM-based edge computing server (ECS) by jointly optimizing the service quality ratios (SQRs) and the computation resource allocations, and we propose an alternative optimization algorithm (AOA) by decomposing the problem into a resource allocation problem (RAP) and a service quality control problem (SQCP). Based on the derived analytical solutions of the two subproblems, we propose an effective and low-complexity heuristic AOA (HAOA) to solve the ASRPMP. The simulation results obtained from both synthetic Gaussian workload data and PlanetLab trace data demonstrate that, given the same target SQR or computation resource, the proposed method can achieve similar performance compared with the convex AOA (CAOA) method with much higher complexity, and can improve the reliability of the services compared with the baseline weighted allocation method (WAM) in both high and low SRP regimes.

Over-the-air computation (OAC) is a promising technology that can achieve rapid model aggregation by utilizing the wireless waveform superposition feature to harness the interference of multiple-access channel for wireless federated learning (FL). However, OAC-based aggregation for OAC faces critical security challenges due to unfavorable and wireless broadcast properties, such as privacy leaks and eavesdropping attacks. In this paper, we propose to utilize an intelligent reflecting surface (IRS) to support covert OAC-based FL. We first derive the optimal condition for covertness in OAC with IRS and formulate a joint optimization problem to select the maximum covert devices participating in the model aggregation while satisfying the mean squared error (MSE) requirement. We then design a covert difference-of-convex-functions program (CDC) to efficiently determine the transmission power of the device, aggregation beamforming of base station (BS), phase shifts, and reflection amplitudes at the IRS. Simulation results demonstrate that our proposed approach can achieve significant performance gain compared to the baseline algorithms by deploying IRS into covert OAC-based FL.

In this paper, a spatial-index modulation (SIM) based orthogonal time frequency space (OTFS) system, named SIM-OTFS, is proposed to enhance the effectiveness and the reliability of high mobility vehicular networks in intelligent transportation systems. The SIM-OTFS system adopts a three dimensional index modulation (IM) technique which utilizes the transmit antenna, delay, and Doppler indexes in the space and delay-Doppler domains, respectively, to achieve a higher transmission rate. Considering the characteristics of vehicular networks, we first present the SIM-OTFS system design and the corresponding signal processing. Then, we derive the average bit error rate (ABER) upper bound of the SIM-OTFS system by the union bound theory. Moreover, the diversity, the coding gain, and the complexity of the SIM-OTFS system are further investigated. Numerical results verify the theoretical analysis of the ABER and the diversity of the SIM-OTFS system, which shows the superiority of the SIM-OTFS system in the ABER performance over the multiple-input and multiple-output (MIMO) based OTFS (MIMO-OTFS) system. Meanwhile, the SIM-OTFS system realizes better performance than the spatial modulation (SM) and IM based orthogonal frequency division multiplexing (SM-OFDM-IM) system in high mobility vehicular communication with reasonable complexity sacrifice. Furthermore, the influence of the resolvable multipaths of the channel in vehicular networks on the ABER performance of the SIM-OTFS system is also illustrated.

Traffic control is regarded as a key issue to alleviate congestion in internet of vehicles (IoV) machine-type communications (MTC). Recently, many traffic control schemes have been studied, such as access class barring (ACB) scheme and back-off (BO) scheme. However, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid scheme, combining the priority dynamic ACB (PDACB) scheme and BO scheme. The IoV devices are classified depending on different delay characteristics, where the delay-sensitive devices are classified as high priority. The target is to maximum the successful transmission of packets with the success rate constraint by adjusting the various ACB factors. Proximal policy optimization (PPO) algorithm as a unique deep reinforcement learning (DRL) method is utilized in this paper, which can obtain continuous action space and solve for the optimal ACB factors without estimating backlog of nodes. A quick convergence is achieved by designing sensible state space, action space and reward. The access capability of the PDACB traffic control scheme is verified by simulations.

With the popularity of mobile devices with global positioning system (GPS), transportation network company (TNC) service has become an indispensable option of people's daily commute. However, it also provides opportunities for malicious parties to compromise TNC users’ location privacy. There are great challenges to preserve TNC users’ location privacy while improving the revenue of TNC and its quality of service (QoS). To address this issue, we propose a novel scheme to schedule the TNC vehicles while preserving the TNC users’ location differential privacy. Briefly, we add high dimensional Laplace noises to guarantee the TNC users’ geo-indistinguishability. Due to the differential private obfuscation, the demand for TNC vehicles in an area becomes uncertain. Thus, we employ the data-driven approach to characterize users’ demand uncertainty, formulate the TNC's revenue maximization problem into risk-averse stochastic programming, and provide corresponding feasible solutions. Using the released public data of Didi Chuxing, we conduct extensive simulations to evaluate the performance of the proposed scheduling scheme and compare the results under different$\zeta$-structure metrics. The results show that the proposed scheme can efficiently schedule the TNC vehicles, maximize the TNC's revenue and provide a better service for TNC users while protecting the TNC users’ location privacy.

In this paper, the issue of user-centric clustering in cloud radio access networks is investigated. Specially, the outage probability of the typical user is derived by taking void cell and composite fading into account. The locations of remote radio heads and users working on the same resource block are modeled as the Poisson point process (PPP) and Mat$\acute {e}$rn hard-core point process of type II (MHCPP), respectively. Due to intractability of MHCPP, the PPP-based approximation is adopted. Then the closed expression of Laplace transform of the probability density function of the interfering power under composite fading channels is derived by Gauss-Hermite quadrature. Based on the approximated PPP, we obtain the outage probability of the typical user with user-centric clustering in the presence of void cell. Finally, the outage probability with or without considering void cell is verified and analyzed under different system parameters (i.g., the density of nodes, the radius of cluster, and the threshold of signal quality) and channel fading conditions (i.g., the pathloss exponent, shadowing standard deviation) through extensive simulations. Simulation results show that the effect of void cell on the system performance should be considered, especially when the density of nodes or the size of cluster is limited.

In the past few years, satellite communications have greatly affected our daily lives, and the integrated terrestrial-satellite network can combine the advantages of satellite and base stations (BSs) to provide wider coverage and lower cost. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods has become a major challenge. This paper proposes a framework for resource allocation of terrestrial-satellite network based on non-orthogonal multiple access (NOMA). Then, a deployment method of local cache pools is given to achieve lower time delay and maximize energy efficiency in terrestrial-satellite network. In the proposed framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association, power control, and cache design. The MADDPG algorithm is divided into two stages, users and BSs are set as agents to complete the optimization problem in the framework. Finally, the simulation results show that the proposed method has better optimized performance compared with the traditional single-agent deep reinforcement learning algorithm and can efficiently solve the problems of resource allocation and cache design in the integrated terrestrial-satellite network.

Nowadays, the multi-access interference problem in the ISAC systems can not be ignored. The study on interference management in ISAC has been envisioned as one of key technologies to support ubiquitous sensing functions. Different from the current work, a communications-sensing-intelligence converged network architecture is proposed to coordinate interference in this paper. Each base station equips with the individual deep neural networks to allocate power and beamforming. On this basis, the interference management is transformed into a functional optimization with stochastic constraints. An unsupervised learning algorithm is proposed to allocate power for interference management. Furthermore, a transfer learning method is presented to obtain the interference management in terms of transmit beamforming. Finally, the distributed management is obtained from the local channel state information in the multi-cell scenario. Simulation results verify the effectiveness of the proposed unsupervised learning interference management method in the ISAC systems.

Intelligent reflecting surface (IRS) is a promising technology for the 6G networks and attracts much attention. However, existing research seldom considers its energy demand. In this paper, we study an IRS assisted multiple-input single-output downlink broadcasting system where there exist one access point (AP), multiple users and a wireless powered IRS. We focus on the broadcasting data transmission which includes two phases. In the first phase, the AP transmits broadcasting data to users and energy signals for IRS energy harvesting (EH). In the second phase, the AP broadcasts messages with the assistance of the IRS. We aim at maximizing the transmission throughput by designing the phase duration scheduling, the transmit beamforming at the AP in each phase, the energy signal covariance matrix and the IRS reflect beamforming with discrete phase shifts. We first propose a semidefinite relaxation (SDR) based transmission design by also employing one-dimensional line search and further proposing a randomization process. Then, a low complexity transmission design has been further developed. Simulation results demonstrate that the SDR based design can almost achieve the optimal and the low complexity design can perform close to the SDR based design with much lower complexity.

In this paper, multi-UAVs serve as mobile aerial ISAC platforms to sense and communicate with on-ground target users. To optimize the communication and sensing performance, we formulate a joint user association, UAV trajectory planning and power allocation problem to maximize the minimum weighted spectral efficiency among UAVs. This paper exploits the centralized and the decentralized deep reinforcement learning (DRL) solutions to solve the sequential decision-making problem. On one hand, we first introduce the centralized soft actor-critic (SAC) algorithm. Then, we explore the equivalent transformation of the optimization objective based on symmetric group, propose the random and the adaptive data augmentation schemes to design the replay memory buffer of SAC, and accordingly propose SAC algorithms assisted by data augmentation to tackle the transformed problem. On the other hand, the multi-agent soft actor-critic (MASAC), a decentralized solution, is also introduced to solve this sequential decision-making problem. The experiment results reveal the effectiveness of the centralized and the decentralized solutions in considered scenarios. Specifically, the SAC assisted by the adaptive scheme significantly outperforms other centralized solutions in the training speed and the weighted spectral efficiency. Meanwhile, the decentralized MASAC algorithm behaves best in the early training speed.

Signal detectors are quite important to attain the diversity of doubly-dispersive wireless channels. Detectors based on message-passing (MP) of factor graphs have been regarded as the way to achieve the near-optimal performance for OTFS. In this paper, by deriving the pattern of the multipath vectorized channel matrix of the orthogonal time frequency space (OTFS) system, it is shown that short girth (i.e. girth-4) may exist in the Tanner graphs, which will degrade the performance of MP detectors, especially with high modulation orders. By introducing interleavers at the transmitter and receiver, the vectorized channel matrix turns out to be a sparse upper block Heisenberg matrix, whose structure is beneficial for the computation of matrix QR decomposition (QRD). Successive interference canceling (SIC) detectors based on QRD and sorted QRD are constructed to eliminate the cross-symbol interference and improve the reliability of the symbol-level channel. Simulation results show that for 4QAM, the QRD-based SIC detectors can achieve about 4dB gain at 10−2 over the non-SIC detectors, while the sorted QRD-based SIC detectors can bring an additional 2dB at 10−3, which is only 1dB gap from the MP. For 16QAM, the sorted SIC detectors show superior BER performance than the MP method, and for 64QAM, the MP detector reaches the error floor while SIC detectors show their excellent performance in all configurations.

With the development of internet of things, latency-sensitive applications such as telemedicine are constantly emerging. Unfortunately, due to the limited computation capacity of wireless user devices, the real-time demands can not be met. Multi-access edge computing (MEC), which enables the deployment of edge access points (E-APs) to support computation-intensive applications, has become an effective way to meet the real-time demands. However, the number of WUDs that E-APs can serve are limited. To increase system capacity, the unmanned aerial vehicle (UAV) assisted computation offloading architecture in the terahertz (THz) band is proposed. In this paper, the problem of UAV placement optimization, resource allocation, and computation offloading is investigated considering the quality of service and resource constraints. The joint optimization problem is non-convex and hard to be solved in time by using traditional algorithms, such as successive convex approximation. Therefore, deep reinforcement learning (DRL) based approach is a promising way to solve the formulated non-convex problem of minimizing latency. Double deep Q-learning (DDQN) and deep deterministic policy gradient (DDPG) algorithms are provided to search for near-optimal solutions in highly dynamic environments. The effectiveness of the proposed algorithms is proved by simulation results in different scenarios.

Reconfigurable Intelligent Surface (RIS) has grown rapidly due to its performance improvement for wireless networks, and the integration of unmanned aerial vehicle (UAV) and RIS has obtained widespread attention. In this paper, the downlink of non-orthogonal multiple access (NOMA) UAV networks equipped with RIS is considered. The objective is to optimize the UAV trajectory with RIS phase shift to maximize the system capacity under the UAV energy consumption constraint. By deep reinforcement learning, a capacity maximization scheme under energy consumption constraints based on double deep Q-Network (DDQN) is proposed. The joint optimization of UAV trajectory with RIS phase shift design is achieved by DDQN algorithm. From the numerical results, the proposed optimization scheme can increase the system capacity of the RIS-UAV-assisted NOMA networks.

With the rapid development of mobile services and applications, the transmitting of massive data makes low-cost communication a challenge. Edge-based wireless communication technology is developed to be a promising approach to satisfy the communication requirements. Edge caching technology is one of effective methods to reduce the overhead of communication system and the pressure of backhauls. In this paper, the joint optimization problem of caching placement and power allocation in virtualized low earth orbit (LEO) satellite-terrestrial networks is proposed, which is based on cooperative caching, by considering cache size limits and power constraints. The optimization problem is solved using an algorithm inspired by the courtship movements and random flights of mayflies. Simulation results show the effectiveness of the proposed scheme in improving system performance and reducing power consumption.

Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compression and noise-resiliency performances for wireless image transmission tasks, especially in environments with low channel signal-to-noises (SNRs). However, existing DeepJSCC-based semantic communication frameworks still cannot achieve adaptive code rates for different channel SNRs and image contents, which reduces its flexibility and bandwidth efficiency. In this paper, we propose a predictive and adaptive deep coding (PADC) framework for realizing flexible code rate optimization with a given target transmission quality requirement. PADC is realized by a variable code length enabled DeepJSCC (DeepJSCC-V) model for realizing flexible code length adjustment, an Oracle Network (OraNet) model for predicting peak-signal-to-noise (PSNR) value for an image transmission task according to its contents, channel signal to noise ratio (SNR) and the compression ratio (CR) value, and a CR optimizer aims at finding the minimal data-level or instance-level CR with a PSNR quality constraint. By using the above three modules, PADC can transmit the image data with minimal CR, which greatly increases bandwidth efficiency. Simulation results demonstrate that the proposed DeepJSCC-V model can achieve similar PSNR performances compared with the state-of-the-art Attention-based DeepJSCC (ADJSCC) model, and the proposed OraNet model is able to predict high-quality PSNR values with an average error lower than 0.5dB. Results also demonstrate that the proposed PADC can use nearly minimal bandwidth consumption for wireless image transmission tasks with different channel SNR and image contents, at the same time guaranteeing the PSNR constraint for each image data.

This paper proposes a two-timescale online resource management solution for smart-grid supplied heterogeneous cel-lular networks, where grid-energy pre-ordering in advance and bidirectional energy trading in real time are performed. We for-mulate a long-term average energy transaction cost minimization problem considering grid-energy pre-ordering, power allocation, and energy sharing. Leveraging the Lyapunov technique, succes-sive convex approximation method, and stochastic subgradient approach, we develop a two-timescale dynamic optimization (TTDO) algorithm to make online decisions on two time scales. It is theoretically proved that the proposed TTDO algorithm can asymptotically achieve optimality via tuning a control parameter. Numerical tests verify the theoretical results.

Wireless systems are upgraded to use green energy (e.g., solar, wind, and tide energy) such that the greenhouse gas emission can be neutralized. This work incorporates the on-grid energy into a green coordinated multi-point (CoMP) system to handle the volatile arrival of green energy. In the green CoMP, the long-term weighted throughput maximization problem is investigated by expecting a non-positive consumption of the long-term on-grid energy. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning. A tradeoff relation is theoretically established to show that the long-term weighted throughput approaches the $\mathcal{O}(V)$ -neighbor of optimal value while the long-term consumed on-grid energy increases at a rate of $\mathcal{O}\left( {{{\log }^2}(V)/\sqrt V } \right)$, where V is an introduced control parameter. Numerical results are used to verify the performance of the online zero-forcing dirty paper precoder.

Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations.

Random access schemes in satellite Internet-of-Things (IoT) networks are being considered a key technology of new-type machine-to-machine (M2M) communications. However, the complicated situations and long-distance transmission can make the current random access schemes not suitable for the satellite IoT networks. The random access problem in the satellite IoT networks is studied in this article. A novel random access scheme for machine-type-communication devices (MTCDs) is proposed, to maximize the efficiency of random access for contention-based and contention-free random access. Under the set of random access opportunities (RAOs) and limited delay, the random access control model is designed via maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) algorithm is proposed to tackle the problem based on the random access model. Subsequently, the deep Dyna-$Q$learning algorithm is introduced to deal with the proposed random access control model. In this proposed scheme, the random access model-free DRL algorithm is developed using simulated experience. The proposed algorithms’ performances are discussed, and simulation results show the desirable performance of the proposed DRL methods on different system parameters.

The use of cell-free massive multiple-input–multiple-output (MIMO) is regarded as a novel technique in the Industrial Internet of Things (IIoT) networks, and many studies have been reported on its cross-layer optimization, including random access and power allocation. Nevertheless, the cooperation of deep reinforcement learning (DRL) and cell-free massive lacks of deep study. In this article, a primal–dual deep deterministic policy gradient (DDPG) algorithm is designed to obtain cross-layer radio resource management, including power allocation in the physical layer and random access in the medium access layer. Different from the current studies, the random access and power allocation is formulated in cell-free massive MIMO IIoT networks, utilized by the stochastic ergodic optimization. In contrast to the stochastic policy gradient algorithm, a primal–dual DDPG algorithm is designed for the cross-layer optimization. Moreover, a multiagent primal–dual DDPG algorithm is proposed to different scenarios in the cell-free massive MIMO IIoT networks. Simulations are presented to verify the effectiveness of the primal–dual DDPG algorithm for random access and power allocation in the cell-free massive MIMO IIoT networks.

In this paper, an IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints. Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario. Specifically, the actor part generates continuous transmit beamforming and the critic part takes charge of discrete phase shift design. Based on the MISO scenario, we investigate a distributed PPO (DPPO) framework with the concept of multi-threading learning in the multi-user multiple-input multiple-output (MIMO) scenario. Simulation results demonstrate the effectiveness of the primal-dual PPO algorithm and its multi-threading version in terms of transmit beamforming and phase-shift design.

In this paper, an uplink non-orthogonal multiple access (NOMA) terrestrial-satellite network is investigated, where the terrestrial base stations (BSs) communicate with satellite by backhaul link, and user equipments (UEs) share spectrum resource of access link. Firstly, a utility function which consists of the achieved terrestrial user rate and cross-tier interference caused by terrestrial BSs to satellite is design. Thus, the optimization problem can be modeled by maximizing the system utility function while satisfying the varying backhaul rate and UEs’ quality of service (QoS) constraints. The optimization problem is highly non-convex and can not be solved directly. Thus, we decouple the original problem into user association sub-problem, bandwidth assignment sub-problem, and power allocation sub-problem. In user association sub-problem, an enhanced-caching, preference relation, and swapping based algorithm is proposed, where the satellite UEs are selected by the channel coefficient ratio. The terrestrial UEs association considers the both caching state and backhaul link. Then we derive the closed-form expression of the bandwidth assignment. In power allocation sub-problem, we convert the non-convex term of the target function into the convex one by the Taylor expansion, and solve the transformed convex problem by an iterative power allocation algorithm. Finally, a three-stages iterative resource allocation algorithm by joint considering the three sub-problems is proposed. Simulation results are discussed to show the effectiveness of the proposed algorithms.

This paper studies the unmanned aerial vehicle (UAV) based multiple intelligent reflecting surface (IRS) network, where the hovering UAV acts as a base station, and the IRS enhances signal transmission to across obstacle between users and UAV. To achieve the maximum sum rate of proposed communication scenario, a non-convex problem considering IRS association results, hovering altitude of UAV, and the phase shift design of multi-IRS is formulated. From the IRS association problem, we can find that the IRS association results are coupled to the decoding order of non-orthogonal multiple access (NOMA). To tackle this, a mathematical interference expansion scheme is developed to decouple it and transform it to convex by binary relaxation method. The non-convexity of hovering altitude optimization problem is solved by logarithm operation, approximation, and auxiliary matrices. For the phase shift optimization problem of multi-IRS, we propose a gradient approximation based initial scheme and develop a univariate optimization based approach on the basis to achieve the users sum rate improvement in multi-IRS. In the end, we compare the proposed scheme with baseline scheme to present the superiority of this work under various network settings. The internal reasons for the variation of simulation results are also analyzed.

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

The cell-free massive multiple-input multiple-output (CFmMIMO) aims to provide uniform quality of service (QoS) for all users, and can be used in small-area scenarios such as indoor industrial networks. This paper studies the CFmMIMO system for indoor industrial scenarios. Firstly, an access point (AP) grouping based hierarchical network topology is proposed. Based on this, we propose an effective AP selection method. To reduce pilot contamination, a pilot assignment scheme based on inspection robot (IR) location is proposed. Considering the high reliability requirement of industrial data transmission, the power control and backhaul combining are jointly optimized to maximize the minimum signal to interference plus noise ratio (SINR). The scalability of the proposed CFmMIMO system is analyzed, and a scalable power control method is proposed. The simulations demonstrate the effectiveness of the AP selection method, pilot assignment scheme, and the joint optimization algorithm for power control and backhaul combining. Moreover, the impact of network scale and network load on system performance is evaluated and analyzed in the simulations.

Affective services will become a research hotspot in artificial intelligence (AI) in the next decade. In this paper, a novel service paradigm combined with wireless communication in integrated satellite-terrestrial network (ISTN) is proposed. On this basis, an affective services computing offloading and transmission network (ASCTN) with a three-tier computation architecture is proposed, which is able to assist users to obtain affective computing services and regulate emotions. The optimization problem is investigated in the ASCTN, which is a discrete, non-linear, and non-convex problem with the limitation of computation ability of satellite and transmit power. Specifically, with the objective to minimize the cost utility related to latency and energy consumption, a joint affective services tasks computing offloading strategy, sub-channel, and power allocation algorithm based on dueling deep Q-network (Dueling-DQN) is proposed, which is in possession of better stability. The simulation results reveal the effectiveness of the optimization algorithm in terms of the cost utility in the ASCTN system.

Following the roadmap of carbon neutrality, wireless communication systems are upgrading to use green energy that comes from renewable sources, e.g., sun, tide, and wind. Due to the volatile arrival of green energy, the on-grid energy is used as a backup for a green coordinated multiple point system. In this work, a weighted sum-rate maximization problem in thegreencoordinated multiple point system is investigated by expecting non-positive consumption of the on-grid energy in the long term. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning technique. A tradeoff relation is theoretically established to show that the long-term weighted sum rate approaches the${\mathcal{ O}}(V)$-neighbor of optimal value while the long-term on-grid energy increases at a rate of${\mathcal{ O}}({\scriptstyle {}^{\scriptstyle \log ^{2}(V)}}\hspace {-0.224em}/\hspace {-0.112em}{\scriptstyle \sqrt {V}})$, where$V$is an introduced control parameter. Numerical results are used to verify the performance of the proposed online adaptive precoder.

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses.

Supporting non-orthogonal multiple access (NOMA) in non-coherent joint transmission (NCJT) systems is beneficial for improving spectral efficiency (SE), but the interference coordination, user scheduling, and resource allocation problems in this new scenario have not been well studied. In this paper, a NOMA-enabled NCJT system is considered in which the connected users are jointly served by two multi-antenna transmitting-receiving points (TRPs) with non-ideal backhauls. Each user has two independent receiving (RX) chains that can work with orthogonal multiple access (OMA) or NOMA mode. A joint resource allocation and hybrid OMA/NOMA mode selection is proposed to maximize the throughput. The primal non-convex and NP-hard problem is decomposed into the following three subproblems, i.e., power allocation (PA) of a single TRP, hybrid mode selection (HMS) of a single TRP, and cross-TRP interference optimization (CIO). Firstly, a successive convex approximation (SCA) method is proposed to solve the non-convex PA subproblem, which achieves a local maximum solution. Secondly, the combinatorial HMS subproblem is transformed into finding the maximum matching of bipartite graphs. By constructing two weighted bipartite graphs for the OMA/near UEs and far UEs, a suboptimal solution is found. Thirdly, an alternating optimization is proposed to solve the CIO subproblem by iteratively performing PA and HMS of the two TRPs. Finally, simulation results demonstrate the superiority of throughput improvement of the proposed method, and the sum rate of the NOMA-enabled NCJT system can approach the sum rate of individual TRPs without interference.

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.

In the past few years, satellite communications have greatly affected our daily lives. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods have become a major challenge. We propose a framework for energy efficiency optimization of terrestrial-satellite network based on Non-orthogonal multiple access (NOMA). In our framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association and power control. Finally, the simulation results show that the proposed method has better optimization performance compared with the traditional singleagent deep reinforcement learning algorithm and can efficiently solve the problems of user association and power control in the integrated terrestrial-satellite network.

Joint caching and transmission optimization problem is challenging due to the deep coupling between decisions. This paper proposes an iterative distributed multi-agent learning approach to jointly optimize caching and transmission. The goal of this approach is to minimize the total transmission delay of all users. In this iterative approach, each iteration includes caching optimization and transmission optimization. A multi-agent reinforcement learning (MARL)-based caching network is developed to cache popular tasks, such as answering which files to evict from the cache and which files to storage. Based on the cached files of the caching network, the transmission network transmits cached files for users by single transmission (ST) or joint transmission (JT) with multi-agent Bayesian learning automaton (MABLA) method. And then users access the edge servers with the minimum transmission delay. The experimental results demonstrate the performance of the proposed multi-agent learning approach.

As the preferred frequency band for future high frequency communication, the terahertz (THz) band has at-tracted wide attention. In this paper, an energy efficient resource optimization problem in THz band is studied. The massive Multiple-Input Multiple-Output (MIMO) technology and intelligent reflecting surface (IRS) are adopted to improve the capacity and energy efficiency (EE) of proposed network. An IRS assisted THz-MIMO downlink wireless network system is established. The original EE problem is decomposed into phase-shift matrix optimization and power allocation. On this basis, a distributed EE optimization algorithm is designed, which transforms the original nonlinear problem into a convex optimization problem. The simulation results reveal that the proposed distributed optimization method converges rapidly and abtains the maximum EE. This also proves that it is feasible and effective to apply both the IRS and the massive MIMO technology into THz communication network.

The problem of power control in the uplink network of cellular users communicating with Device-to-Device (D2D) is mainly studied. Since cellular users and D2D users share spectrum resources, several serious interference will be caused by them. Reasonable measures are taken to control the interference caused by the sharing spectrum resources, otherwise that influences the quality of service (QoS) of cellular users. The reduction of entire system interference can be achieved by power control, so a method of power control based deep reinforcement learning (DRL), namely Asynchronous Advantage Actor Critic (A3C) Algorithm, is proposed. At the same time, the spectrum resources utilization of the system is improved and QoS of cellular users is guaranteed. The simulation results prove rationality of the proposed algorithm, and have better convergence performance than the traditional DRL algorithm.

In this paper, an uplink non-orthogonal multiple access (NOMA) satellite-terrestrial network is investigated, where the terrestrial base stations (BSs) can simultaneously communicate with the satellite by backhaul, and user equipments (UEs) share fronthaul spectrum resource to communicate. The communication of satellite UEs is influenced by crosstier interference caused by terrestrial cellular UEs. Thus, a utility function which consists of system achieved rate and crosstier interference is build. And we aim to maximize the utility function while satisfying the constraints of the varying backhaul rate and quality of service (QoS) of UEs. The optimization problem is decomposed into AP-UE association, bandwidth assignment, and power allocation sub-problems, and solved by proposed matching algorithm and successive convex approximation (SCA) method, respectively. The simulation results show the effectiveness of the proposed algorithm.

Terahertz communications is regarded as a promising technology due to its higher bandwidth and narrower beamwidths, which can improve capacity and coverage for indoor wireless users. In this paper, the intelligent reflecting surface (IRS) technique and non-orthogonal multiple access (NOMA) are utilized to compensate drawbacks of indoor transmission mismatch in the terahertz band. Then wireless resource allocation optimization in indoor terahertz IRS-aided systems is transformed into a universal optimization problem with ergodic constraints. With the aid of parametrization features of deep neural networks (DNNs), proximal policy optimization (PPO) is adopted to train the policy and corresponding actions to allocate power and bandwidths. The actor part generates continuous power allocation, and the critic part takes charge of discrete bandwidths allocation. In the design of a deep reinforcement learning (DRL) framework, primal dual ascent is proposed to realize model-free training. Simulation results demonstrate the effectiveness of the primal dual PPO learning algorithm in different settings.

With the rapid increase of demand on mobile data traffic of user equipment (UE), network operators have begun to deploy abundant heterogeneous base stations (BSs) to ensure the quality of service (QoS) of UEs, which will cause new problems such as network congestion and load imbalance. If the pattern of user association (UA) can be adjusted in accordance with the results of traffic prediction, the performance of system will be greatly improved. Therefore, a new neural network approach based on spatial and temporal characteristics of traffic data is proposed for traffic prediction. The fluctuations of traffic in the future week are predicted by the proposed method. Then, UA is represented as a problem of maximizing the utility function of load balancing index, and a dynamic user association based on load prediction algorithm (DUALP) which aims to achieve a proactive load balancing is proposed. The QoS of UEs is ensured and the long-term stability of the system is achieved by DUALP. Experimental results show that compared to the classic UA strategies, the most optimal load distribution is realized by DUALP.

Terahertz (THz) technology and non-orthogonal multiple access (NOMA) technology have shown a high potential to enhance the spectrum efficiency of wireless communications. This paper aims to study the resource scheduling problem by maximizing energy efficiency (EE) of NOMA two-tier heterogeneous network in THz frequency band, taking a full account of the influence of subchannel and power allocation on the downlink of THZ-NOMA network. In order to achieve the research goal better, it's the first time that a subchannel allocation method and power allocation scheme based on improved Whale Optimization Algorithm (WOA) is proposed in this system. The simulation results indicate that, compared with the existing methods, the proposed scheme has faster convergence speed and has certain advantages in performance.

In current research, reinforcement learning (RL) is widely applied to resource management of wireless communication networks. However, many optimization problems have high computational complexity, and traditional RL fails to solve continuous high-dimensional problems. This paper investigates the sum rate problem in single-cell multiuser multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network. In our scenario, users are separated into two groups, while ensuring the lowest target rate among one group of users, compute the maximum sum rate for the other group of users. For the sake of tackling with the non-convex optimization problem and acquiring the maximum sum rate, we design the joint beamforming and power control algorithm based on deep reinforcement learning (DRL) for deep Q-network (DQN) and double DQN. The final simulation section verifies the convergence and feasibility of the proposed algorithm which can achieve significant sum-rate gains.

This article focuses on the joint optimization of beamforming and baseband unit (BBU) computing resource allocation, with the goal of minimizing the network power consumption for the downlink cloud radio access network (C-RAN). To reduce the computational complexity, we split the joint optimization problem into two subproblems for transmission and computation respectively. The subproblem of minimizing power consumption for transmission is a network-wide beamforming design problem. By using positive semi-definite relaxation (SDR) technology, we transform it into a convex positive semi-definite programming (SDP) problem, which can be solved with effect. For the second subproblem, which intends to minimize the power consumption for computation, a computing resource allocation scheme based on the Simulated Annealing algorithm is proposed, which minimizes the active servers in the BBU pool to save power while meeting each user’s computing resource requirement. The simulation results demonstrate that the algorithm proposed in this paper has a better performance compared with the existing algorithms.

During the COVID-19 pandemic, Internet of Medical Things (IoMT) has been playing an important role in controlling the development of the epidemic, including enabling doctors in different grade hospitals to make a diagnosis and treatment, isolating and care for confirmed and suspected cases promptly, and preventing infection of patients with the novel coronavirus. In this article, we investigate the minimization optimization problem for healthcare monitoring in fog computing-based IoMT (FogC-IoMT), which is nonlinear and nonconvex problem, by considering Quality-of-Service requirement, power limit, and wireless fronthaul constraint. In order to solve the problem effectively, three independent subproblems are decoupled, and the suboptimal low-complexity computation offloading and resource management scheme is proposed in FogC-IoMT. The simulation results reveal the effectiveness of the proposed optimization algorithm in terms of cost utility.

Driven by the practicality of unmanned aerial vehicle (UAV), we consider a dual-UAV based non-orthogonal multiple access (NOMA) scenario, which consists of one communication UAV for services and one jamming UAV against eavesdropping. The goal is to maximize the secrecy energy efficiency through the successive convex approximation based communication resource, UAV trajectory, and artificial noise optimization. Considering the probabilistic constraint of outage probability from imperfect channel state information, we transform it to a non-probabilistic problem by Markov inequality and Marcum$Q$-function, then the problem is decomposed into three subproblems. We apply matching-swapping method to assign subchannel in non-orthogonal multiple access (NOMA) UAV networks before the joint process, then convert the power optimization problem to a standard convex optimization form by upper bound of the concave function. The communication UAV trajectory is studied under the constraints of flying energy consumption, maximum speed, and flying altitude. To track this NP-hard problem, Taylor expansion and various slack variables sets are introduced to transform the non-convex problem to convex one. For the artificial noise optimization problem, we use the lower bound to replace the convex term turning it into an easy-to-solve convex optimization problem. In the end, simulations results reveal that: 1) The reasonable jamming scheme can improve the secrecy energy efficiency of the NOMA UAV networks, even if it can cause interference for legitimate users; 2) UAV will fly to a place where the performance gain from users is high when flying energy consumption permits.

Offloading cellular traffic via Device-to-Device communication (or D2D offloading) has been proved to be an effective way to ease the traffic burden of cellular networks. However, mobile nodes may not be willing to take part in D2D offloading without proper financial incentives since the data offloading process will incur a lot of resource consumption. Therefore, it is imminent to exploit effective incentive mechanisms to motivate nodes to participate in D2D offloading. Furthermore, the design of the content caching strategy is also crucial to the performance of D2D offloading. In this paper, considering these issues, a novel Incentive-driven and Deep Q Network (DQN) based Method, named IDQNM is proposed, in which the reverse auction is employed as the incentive mechanism. Then, the incentive-driven D2D offloading and content caching process is modeled as Integer Non-Linear Programming (INLP), aiming to maximize the saving cost of the Content Service Provider (CSP). To solve the optimization problem, the content caching method based on a Deep Reinforcement Learning (DRL) algorithm, named DQN is proposed to get the approximate optimal solution, and a standard Vickrey-Clarke-Groves (VCG)-based payment rule is proposed to compensate for mobile nodes' cost. Extensive real trace-driven simulation results demonstrate that the proposed IDQNM greatly outperforms other baseline methods in terms of the CSP's saving cost and the offloading rate in different scenarios.

With the growing interest in providing internet access and cellular connectivity in the commercial aircraft, the compatibility between Air-to-Air (A2A) communications and current Air-to-Ground (A2G) macro-cellular communications is necessary to form an Aeronautical Ad hoc Network (AANet). Due to the typical features of airborne communications, such as high mobility, long transmission range and three-dimensional macro-cells, AANet is affected by intermittent links, which results in variant delayed data transmissions. To this end, we exploit a dynamic graph approach to model such an AANet with rapidly changing topology under a realistic airborne scenario. Targeting the problem of intermittent transmissions, by employing A2A, an AANet can bear three transmission modes: direct transmission, connected relay transmission and opportunistic transmission. To investigate the potential gain of A2A transmissions in terms of the end-to-end data flow transmission delay and the data traffic amount, we formulate an integer non-linear programming problem that minimizes the average end-to-end delay from Internet Gateways (IGWs) on shore to the aircraft receivers through multiple options of the transmission modes. Through solving this minimization problem based on the realistic dynamic graph model, the tradeoffs between the end-to-end transmission delay and other key network factors related to AANet formation and resource allocation can be obtained without much compromise on the transmission delay. The results show that the average end-to-end delay of the AANet is comparable to the direct transmission delay when having a tradeoff with the data traffic amount, buffer size and the spectrum sharing.

Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in the sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell networks, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for the THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes.

Replacing base stations with unmanned aerial vehicles (UAVs) to serve the communication of ground users has attracted a lot of attention recently. In this paper, we study the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirements. To handle the user scheduling problem, a heuristic algorithm based on matching and swapping theory is proposed first to allocate users that access UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem using logarithmic approximation. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem and a joint optimization algorithm is presented with the algorithm’s convergence and computational complexity. Finally, numerical results are provided to support the rationality of the proposed algorithm.

Heterogeneous cellular networks with hybrid energy supplies can relieve traffic pressure and reduce grid energy consumption. In heterogeneous cellular networks, rational resource management can help improve system performances. In general, more than one performance is expected to do well, but there can exist a trade-off among different performance metrics, thus making resource management a multi-objective problem. The existing solution usually transforms a multi-objective problem into another single-objective problem by assigning weights for various objectives. However, it is difficult to know the exact weights in advance, and different systems call for different requirements for objectives. Hence, a multi-objective optimization approach based on the gravitational search algorithm (GSA) is proposed to find a series of Pareto optimal solutions. The decision-makers can select an appropriate solution according to the system requirement. In this work, three different multi-objective GSA-based algorithms are proposed to determine user association and power control, with the goal to optimize the traffic load balancing among small base stations and grid energy consumption per unit throughput simultaneously. The complexity of the proposed algorithms is analyzed, and simulations compare the performances of the proposed algorithms and the benchmark algorithm. Experimental results reveal the feasibility and effectiveness of this approach.

To satisfy future wireless network's requirements of huge capacity, ultra low delay and supermassive connectivity, it is urgent to study novel wireless communication network architecture and technology. Fog-computing radio access network (F-RAN) is a newly developed network paradigm, in which the edge devices perform the storage, communication, control, configuration and management. Meanwhile, non-orthogonal multiple access (NOMA) has been considered to be a hopeful multiple access mechanism for future radio access networks (RANs). The distinctive feature of NOMA is to assign the same channel for various users simultaneously by utilizing the power domain. With the deployment of NOMA, F-RAN can further promote system throughput, time latency, access capability and spectrum efficiency. The coordination between NOMA and F-RAN provides powerful support to extensive application of augmented/virtual reality (AR/VR), vehicular networking, intelligent medical and other emerging applications. In this paper, we focus on a noncooperative game resource optimization to decrease the complexity while the dynamic optimization problem is decoupled to subchannel assignment and power allocation. Matching theory is applied to propose a subchannel assignment scheme. Then, we convert and decouple the power allocation problem into three subproblems and solve them separately at each slot. Simulation results illustrate the potency of the proposed noncooperative resource allocation scheme.

Non-orthogonal multiple access (NOMA) has been extensively studied to improve the performance of space-terrestrial satellite networks on account of the shortage of frequency band resources. In this paper, terrestrial network and satellite network synergistically provide complete coverage for ground users. A user association scheme on account of the channel gain and distance between the ground users and the BSs is proposed to identify the users to be associated by the BSs, and there is an upper limit for the number of users associated with each BS. Then calculate the channel condition ratio to select the users served by the satellite. The all BSs provide service for those unselected users, and the NOMA technology is applied to terrestrial network. Then, a user pairing scheme which maximize the minimum the ground user channel correlation coefficient is formulated to match the terrestrial users in a NOMA group. On account of multiple antennas equipped by the BSs and satellite, beamforming is performed among groups of BSs and among satellite users so as to reduce multi-user interference. In the power allocation scheme, we introduce the alternative direction method of multipliers (ADMM) algotithm so as to optimize system energy efficiency. In addition, the objective function is a non-convex function, so the Dinkelbach-style scheme is presented to convert non-convex function into the convex-form function. Eventually, the performance of the presented algorithm is simulated and compared with the existing NOMA-FTPA algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to this network.

Interference management and power transfer can provide a significant improvement over the 5th generation mobile networks (5G) dense Internet of Things (IoT) heterogeneous networks (HetNets). In this paper, we present a novel approach to simultaneously manage inferences at the downlink (DL) and uplink (UL), and to identify opportunities for power transfer and additional UL transmissions integrated with existing protocols and infrastructures for enhanced inter-cell interference coordination (eICIC) protocol in dense IoT HetNets, while considering practical non-linear energy harvesting (EH) model. The design is formulated as the joint optimization of interference aware UL/DL decoupling, airtime resource allocation and energy transfer. The key insight of our algorithm is to translate the original, intractable joint-optimization problem into a problem space where a good approximate solution can be quickly found. We evaluate our scheme through theoretical analysis and simulation. The evaluation shows that our approach improves the system utility by over 20% compared to start-of-the-art in dense IoT HetNets. Compared to alternative schemes, our approach maintains the best user fairness and rate experience and can solve the problem in a fast and scalable way.

Network slicing is capable of satisfying differentiated service demands of vertical industries by tailoring a common infrastructure to multiple logical networks which are isolated. Considering that the dynamic of service demands makes it difficult to maintain high quality of user experience and high revenue of tenants, slice reconfiguration is necessary to avoid performance degradation. Hence, this paper proposes an optimal and fast slice reconfiguration (OFSR) solution by leveraging advanced deep reinforcement Learning. To deal with the uncertain changes in resources requirement, a demand prediction model based on Markov renewal process is introduced in decision-making. Taking into account the operation costs of reconfiguring diversified slices and the constraints of available resources, the proposed OFSR scheme aims at obtaining high long-term revenue with low operation cost. Given that the convergence of the conventional reinforcement learning approach is slow to learn the optimal reconfiguration policy for different classes of slices, deep dueling neural network combined with Q-learning is applied to improve the speed of convergence. Simulation results validate that the proposed framework is effective in achieving long-term revenue for tenants and the dueling deep Q-learning approach performs better than other current approaches.

In this paper, we mainly studied the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirement. To track the joint optimization problem, a heuristic algorithm based on matching theory in cellular networks is proposed firstly to allocate users which connect the UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem by logarithmic approximation, and solved to get an optimal solution. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem for its near-optimal solution. Finally, numerical results are provided to support the rationality of the proposed algorithm.

In this paper, the problem of dynamic resource management in a cognitive radio network (CRN) with multiple primary users (PUs), multiple secondary users (SUs), and multiple channels is investigated. An optimization problem is formulated as a multi-agent partially observable Markov decision process (POMDP) problem in a dynamic and not fully observable environment. We consider using deep reinforcement learning (DRL) to address this problem. Based on the channel occupancy of PUs, a multi-agent deep Q-network (DQN)-based dynamic joint spectrum access and mode selection (SAMS) scheme is proposed for the SUs in the partially observable environment. The current observation of each SU is mapped to a suitable action. Each secondary user (SU) takes its own decision without exchanging information with other SUs. It seeks to maximize the total sum rate. Simulation results verify the effectiveness of our proposed schemes.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the energy efficiency of the UAVs cells using non-orthogonal multiple access (NOMA) with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we maximize energy efficiency in the downlink NOMA UAV network considering imperfect CSI between the UAV and users. Resource allocation schemes including user scheduling as well as power allocation are designed for system energy efficiency optimization. Because of the non-convexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a nonprobability problem and then decoupled into two convex subproblems by successive convex approximation method. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm. The simulation results prove that our proposed algorithm is more effective compared with existing resource allocation schemes.

This paper intends to study the energy efficiency in caching based UAV networks, where fog radio access network (FRAN) and non-orthogonal multiple access (NOMA) are considered meanwhile. Taking full account of the impact of caching, subchannel assignment, and power allocation in UAV enabled wireless networks, we formulate the problem of maximizing energy efficiency. In order to better solve the proposed non-convex problem, we propose a subchannel assignment algorithm and a power allocation algorithm applying alternating direction method of multipliers (ADMM). The final simulation section verifies the fast convergence of the algorithm and compares the advantages with existing algorithms.

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multiantenna also enable the network to serve more users. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering and hybrid precoding. Based on channel correlation characteristics, a fast convergence scheme for user clustering using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. The simulation results show that the proposed scheme can achieve faster convergence and higher EE.

Fog-computing radio access network (F-RAN) and non-orthogonal multiple access (NOMA) have been recognized as the promising technologies with high mobility and low delay support. In this paper, we propose a new network architecture of the NOMA based F-RAN, and investigate the noncooperative radio resource optimization for time-varying wireless network environment. The subchannel assignment is modeled as the two-side matching issue, and deal with by the matching theory. Then, the dynamic power allocation is deal with the Lyapunov theory, which is decoupled into there subproblems. Simulations results illustrates that the dynamic resource management scheme can obtain the high utility performance gain of communication systems.

Software defined network (SDN) is considered as one of the most promising network architectures in the next generation mobile networks. SDN-enabled ultra dense network (UDN) has a simpler and more flexible network architecture, but its mobility management is still a challenging task. The major problem is the occurrence of frequent handover (FHO). Therefore, a SDN-enabled UDN architecture is firstly proposed to make the network more agile. Then, a deep Q-learning (DQN) method is used to control the handover (HO) procedure of the user equipments (UEs) by well capturing the characteristics of wireless signals/interferences and network load. In details, we use the SINR and the access rate per node to characterize the state of the UE. Thanks to the generalization ability of deep neural network (DNN), newly arrived UEs can use the trained neural network to avoid possible bad initial points. Experimental results show that the proposed scheme can reduce HO rate and guarantee the system throughput, which is better than the traditional HO scheme.

Open wireless channels make a wireless ad hoc network vulnerable to various security attacks, so it is crucial to design a routing protocol that can defend against the attacks of malicious nodes. In this paper, we first measure the trust value calculated by the node behavior in a period to judge whether the node is trusted, and then combine other QoS requirements as the routing metrics to design a secure routing approach. Moreover, we propose a deep learning-based model to learn the routing environment repeatedly from the data sets of packet flow and corresponding optimal paths. Then, when a new packet flow is input, the model can output a link set that satisfies the node's QoS and trust requirements directly, and therefore the optimal path of the packet flow can be obtained. The extensive simulation results show that compared with the traditional optimization-based method, our proposed deep learning-based approach cannot only guarantee more than 90% accuracy, but also significantly improves the computation time.

In this article, we propose an energy-efficiency-aware integrated architecture of cooperative computing (CC) to support the demands of computing amount in the blockchain-based Internet of Things (IoT). Specifically, we assume that multiple computing servers are placed at each data access point (DAP). The computing servers across multiple DAPs can be virtualized to constitute a CC pool to flexibly allocate the computing resource. When the amount of received data from a DAP is accumulated to a certain length of one data block, blockchain computing will be implemented to generate a correct Nonce value meeting the threshold of hash value. After the correct Nonce has been generated, the data block will be transmitted and stored in cloud caches, where the hash value is written into blockchain to guarantee the security of data block. We maximize system energy efficiency defined by the overall power consumption per unit of throughput transmitted from DAPs to cloud caches. We formulate the system optimization model by considering the constraints of data delay to avoid data overflow in the system. In order to solve the optimization model for maximizing system energy efficiency, we employ a geometric programming method to obtain the optimal power and resource allocation in blockchain-based IoT. By extensive simulations, we verify the effectiveness of our proposed energy-efficiency-aware optimization mechanism in the blockchain-based IoT.

Blast furnace gas (BFG) produced from steel industries is generally one of the most important energy supplies in enterprises. Due to a great deal of output, fluctuation, and heterogeneity in data, it is very difficult to provide profound insights into its internal dynamic. In this article, a novel analysis framework is developed for the BFG data processing, considering the recurrence plot (RP) and the recurrence quantification analysis (RQA). The specific aim is to investigate the relationship between BFG output and its potential influencing factors. This framework can be deemed as a uniform and consistent system with functional components of qualitative visualization and quantitative analysis. Concretely, the BFG outputs related to five factors are separately projected to high-dimensional spaces, followed by that their internal dynamics can be embodied through a 2-D recurrence representation of states. Finally, five RQA parameters are used to quantify the influence of these factors on the BFG output. This is the first attempt revealing the relations among the BFG data from the qualitative and quantitative perspectives. The experimental results show that RP can discover the BFG output patterns of laminar state, chaos, and instability over given three states of influencing factors, and the ranked influential degree can be given by a two-stage standard deviation of all considered RQA measures. Besides, we also demonstrate that the temperature of the hot-blast stove is most relevant to the BFG output, while the influence of considered other factors directly depends on the selected series length.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the channel errors of low-altitude communication links formed by mobile deployment of UAVs cannot be ignored. And the energy efficiency of the UAVs communication with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we focus on system performance optimization in non-orthogonal multiple access (NOMA) UAV network considering imperfect CSI between the UAV and users. A suboptimal resource allocation scheme including user scheduling and power allocation is designed for maximizing energy efficiency. Because of the nonconvexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a non-probability problem and then decoupled into two convex subproblems. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm by successive convex approximation method. The simulation results prove that the proposed algorithm is effective compared with existing resource allocation schemes.

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network.

This paper is concerned with the secrecy rate based optimization problems in a mixed radio frequency (RF) / free space optical (FSO) spectrum sharing network, wherein multiple single-antenna secondary users (SUs) are connected to a decode and forward (DF) single-antenna relay through RF links and the relay is connected with the secondary destination through an FSO link. The RF and FSO channels are assumed to follow Rayleigh and Gamma-Gamma distributions with the effect of pointing errors, respectively. The three schemes, i.e., direct transmission, single-user cooperative jamming and multi-user cooperative beamforming jamming, are proposed to improve the physical-layer information security in the presence of a single-antenna eavesdropper. The mutual interference between the primary and secondary networks is considered. The problems are to jointly optimize the SU transmit power and jamming power for the best secrecy performance under both the RF and FSO dominant cases. Such problems are nonconvex. The Dinkelbach approach is adopted to solve the optimization in the direct transmission and single-user cooperative jamming schemes and a two-level optimization approach with semi-definite relaxation (SDR) is applied to obtain the solution for the multi-user cooperative beamforming scheme. Moreover, the SDR optimality is proved by Karush-Kuhn-Tucker conditions analysis. Simulation results are provided, and the results show that the proposed multi-user cooperative beamforming jamming can attain the higher achievable secrecy rate than the zero-forcing beamforming scheme and other two suboptimal designs.

Device-to-Device (D2D) communication has become a promising candidate in future cellular networks to improve spectrum efficiency and energy efficiency, while reducing the latency. As the capacity of D2D user equipments (DUEs) increases, it makes DUEs caching possible, and it can offload traffic from macro base stations, perform computation-intensive and latency-critical tasks. In this paper, in-band communication is considered, and the Poisson cluster process is utilized to model and analyze the clustered D2D networks under cluster-centric caching strategy. Firstly, we use the Thomas cluster process to model cellular user equipments (CUEs) and DUEs, and give a deployment scheme of clustered D2D caching networks. Secondly, the aggregated interference of the typical D2D receiver is analyzed in the clustered D2D networks. Then the Laplace transform of the aggregated interference is analyzed, and the expressions of coverage probability, average achievable rate and cache hit probability of the typical D2D receiver are deduced. The simulation results show that we can adjust the path loss exponent, densities of DUEs and CUEs, transmitting power of CUEs, mean of simultaneously active transmitters in each cluster and Zipf exponent to improve the performance of clustered D2D caching networks.

Fuel-powered UAVs have long endurance of flight, heavy payload and adaptation to extreme environment. The mechanical operation and communication power are supported independently by fuel and batteries. In the paper, we study the energy efficiency of the communication system with a fuel-powered UAV relay. We consider a three-node communication network, consisting of a mobile relay, a source node, and a destination node. The UAV relay is able to change its 3-D trajectory to maintain high probability of LoS channels, receiving information from the fixed source node and transmitting it to the fixed destination node. The power allocation scheme and UAV's trajectory are designed to maximize the system energy efficiency, considering the constraints of speeds, UAV's altitudes, communication and mechanical energy consumption, the required data rates of the destination node and information-causality. We solve the power allocation sub-problem by splitting the domain of variables and transforming it into a convex optimization problem. And then a suboptimal scheme is provided to design the trajectory based on successive convex approximation method. Numerical results show the convergence of the proposed schemes and the performance of the proposed algorithms. The influences of time slots, constraints of fuel, communication power and required data rates are discussed.

Although current proposed compression schemes achieve better performance than traditional data compression schemes, they have not fully exploited the spatial and temporal correlations among the data, and the design of the projection (measurement) matrix cannot satisfy the requirement of real scenarios adaptively. Hence, well-designed clustering algorithm is needed to further explore strong spatial correlation, and an adaptive measurement matrix is also needed to ensure exact data recovery. In this paper, we propose a fog-based optimized Kronecker-supported compression scheme to address the above shortcomings and achieve better compression results in the industrial Internet of Things (IIoT). Our scheme first leverages a k-means-based clustering algorithm that explores the spatial correlation among sensory data, which can obtain better compression effects with less communication overhead. It then develops a novel Kronecker-supported two-dimensional data compression mechanism at the fog node, which can ensure the recovery of the original data from the compressed data with high precision; this mechanism can also reduce the communication overhead between fog and cloud nodes significantly. Next, a Kronecker concatenated measurement matrix optimization problem is formulated for meeting the requirement of real scenarios adaptively, and an efficient solution algorithm is developed to obtain the optimal value and ensure that the stringent precision requirements of industrial applications are satisfied. Finally, simulation results show that our proposed scheme is energy efficient and can achieve better clustering results and recovery performance for sensory data, for example, the energy consumption is reduced by 6.8 percent after clustering operation, and the relative reconstruction error of temperature data is improved by an average of 15.8 percent with the same energy saving effect.

The property of performing data processing near the source of data (i.e., at the edge of the network) enables fog computing that can effectively reduce computation latency, bandwidth and energy consumption, especially for big data network scenarios. For the sake of achieving efficient and secure big sensory data collection in fog-assisted Internet of Things (IoT), this paper proposes an efficient privacy preserving data collection and computation offloading scheme. In the proposed scheme, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and fog computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation, and it also facilitates the simultaneous execution of decrypting and decompressing operations on encrypted sampling data. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction at the end user. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65 percent at fog node with a low relative recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of compression computation task at fog node can be reduced by 14.6 percent compared with other computation offloading method.

In this paper, we propose the quality-of-service (QoS) driven robust power allocation policy with channel uncertainty in the secure multicarrier full-duplex (FD) relay communication system, where the statistical delay QoS is given by the secure effective capacity (SEC). To maximize the SEC under the constraints of both the total system power and the residual loopback interference (LI) power of the FD relay, an optimization problem is formulated and the corresponding power allocation strategy has been presented in the case of perfect channel state information (CSI) and imperfect CSI. Specially, considering the imperfect CSI, we propose a robust power allocation scheme, where the channel uncertainty is modeled by a bounded region. Furthermore, the worst-case method and the Bernstein approximations are utilized to deal with the uncertainty. After the Taylor approximation based simplification of the formulated optimization problem, the optimal solution is obtained by subgradient descent algorithm based on Lagrangian dual method and Karush-Kuhn-Tucker (KKT) conditions. With known error range of CSI, numerical results and analysis illustrate that the proposed robust power allocation strategy can satisfy the statistical delay QoS requirement very well. Furthermore, the trade-off between the SEC and the robustness can be achieved with channel uncertainty.

Heterogeneous networks provide flexible deployments for operators to improve spectrum efficiency and increase coverage. However, driven by new generation wireless devices, the exponential increase of data traffic has triggered new challenges of wireless networks to meet the green communications requirement. Therefore, energy-efficient design has emerged as a promising technique in heterogeneous networks. In this paper, we investigate the energy efficiency maximization problem for downlink transmissions by jointly considering user association and power allocation in a two-tier heterogeneous network with multiple small cells. We first consider a system model without the co-channel interference between small cells. The energy efficiency maximization problem is formulated under certain prescribed quality-of-service requirement and maximum power limit constraint. The original optimization problem is non-convex and NP-hard, and it involves integer programming. We first relax the formulate problem into a continuous one and decouple it into user association and power allocation subproblems. A gradient-based algorithm is used to solve the power allocation problem. Then, an iterative joint user association and power allocation algorithm is proposed to achieve the maximum energy efficiency. Moreover, we consider a more sophisticated system with the limited bandwidth resource, in which multiple small base stations require to share the same frequency band to serve users, and the co-channel interference is introduced. Inspired by the original Dinkelbach method, we use a lower bound approximation and the Lagrangian approach to derive a closed-form expression of power allocation, which reduces the computational complexity. Simulation results show that the proposed algorithms have improved energy efficiency when compared with other the existing schemes.

Mixed-numerology transmission is proposed to support a variety of communication scenarios with diverse requirements. However, as the orthogonal frequency division multiplexing (OFDM) remains as the basic waveform, the peak-to average power ratio (PAPR) problem is still cumbersome. In this paper, based on the iterative clipping and filtering (ICF) and optimization methods, we investigate the PAPR reduction in the mixed-numerology systems. We first illustrate that the direct extension of classical ICF brings about the accumulation of inter-numerology interference (INI) due to the repeated execution. By exploiting the clipping noise rather than the clipped signal, the noise-shaped ICF (NS-ICF) method is then proposed without increasing the INI. Next, we address the in-band distortion minimization problem subject to the PAPR constraint. By reformulation, the resulting model is separable in both the objective function and the constraints, and well suited for the alternating direction method of multipliers (ADMM) approach. The ADMM-based algorithms are then developed to split the original problem into several subproblems which can be easily solved with closed-form solutions. Furthermore, the applications of the proposed PAPR reduction methods combined with filtering and windowing techniques are also shown to be effective.

Cellular networks are bound to connect an ever-increasing number of subscribers. The issue of securing both sufficient capacity and reliable coverage remains to be resolved. This paper introduces the maximum coverage problem in wireless cellular networks and gets insight into the metric structure of the solution space for antenna orientation variables. We construct two metric spaces in mathematical views, in which both the deterministic search method (e.g., Nelder-Mead simplex algorithm) and the stochastic search method (e.g., Genetic Algorithm) have been fully discussed without using gradient information. Accordingly, we propose the improved deterministic and stochastic search methods to boost the coverage optimization procedure. Experiments show that the proposed algorithms not only obtain the close-to-optimal solution but greatly improve the convergence speed by reason that redundant exploration is avoided in the tailored solution spaces. Metric structure, as an essential topology in the antenna orientation solution space, therefore, provides a new perspective to settle other antenna orientation-related coverage and capacity optimization problems.

Non-orthogonal multiple access (NOMA) in heterogeneous network (HetNet) is a very promising scheme to meet the exponential growth of mobile data expected in the coming years. However, since wireless networks are becoming denser, the energy consumption of such networks is increasingly severe. Therefore, it is necessary to design novel energy efficiency (EE) maximization technologies under the constraint of limited energy supply. This paper investigates the resource optimization problem of NOMA heterogeneous small cell networks with simultaneous wireless information and power transfer (SWIPT). By decoupling subchannel allocation and power control, a low-complexity subchannel matching algorithm is designed. Furthermore, to maximize the energy efficiency, a power optimization algorithm is proposed using Langrangian duality. Aiming at the power allocation problem, the original non-convex and non-linear energy efficiency optimization problem is transformed into a more tractable one. Simulation results demonstrate the effectiveness and convergence of the proposed optimization scheme in terms of system energy efficiency.

In the current researches, artificial intelligence (AI) plays a crucial role in resource management for the next generation wireless communication network. However, traditional RL cannot solve the continuous and high dimensional problems. To handle these problems, the concept of deep neural network (DNN) is introduced into RL to solve high dimensional problems. In this paper, we first construct an information interaction model among primary user (PU), secondary user (SU) and wireless sensors in a cognitive radio system. In the model, the SU is unable to get the power allocation information of the PU, and needs to use the received signal strengths (RSSs) of the wireless sensors to adjust its own power. The PU allocates transmit power relying on its power control scheme. We propose an asynchronous advantage actor critic (A3C)-based power control of SU that is a parallel actor-learners framework with root mean square prop (RMSProp) optimization. Multiple SUs learn power control scheme simultaneously on different CPU threads, reducing neural network gradient update interdependence. To further improve the efficiency of spectrum sharing, the distributed proximal policy optimization (DPPO)-based power control is proposed which is an asynchronous variant of actor-critic with adaptive moment (Adam) optimization. It enables the network to converge quickly. After several power adjustments, the PU and the SU meet quality of service (QoS) requirements and achieve spectrum sharing.

NOMA is a key technology for future wireless communication, which can improve the spectral efficiency (SE) of mobile network. In this paper, a subchannel assignment algorithm is applied to maximize the energy efficiency of downlink heterogeneous NOMA network. Different from previous works, the subchannel assignment problems and power allocation problem are formulated as non-convex problem, then we transform the original problems to convex optimization problems and solve it using difference of convex functions (DC) programming. The simulation results confirm that the applied scheme not only enhance the sum rate of heterogeneous NOMA network but also energy efficiency (EE) of small cell base station (SBS).

Ultra dense heterogeneous network (UDHN) has become one of the main frameworks of 5G. Traditional user association methods are difficult to satisfy this new scenario for load balancing. On the other hand, the concept of green communication requires the network to increase energy efficiency. Therefore, it is necessary to study power allocation and user association in UDHN. This paper focuses on load balancing and energy efficiency of UDHN. The joint user association and power allocation is modelled as an appropriate optimization problem. Then we introduce reinforcement learning and propose a multiagent Q-learning based algorithm for solving the optimization problem. According to analysis of simulation result, the convergence of the proposed scheme is verified and the proposed approach is effective on achieving load balancing and enhancing energy efficiency in UDHN.

Millimeter wave (mmWave) communication technology has become an attractive solution to meet exponential growth demand for mobile data services. In this paper, we propose a deep neural networks (DNN) based algorithm for user association and power optimization problem in mmWave heterogeneous network on the basis of gradient iterative algorithm. We jointly design the user association and power optimization to maximize energy efficiency (EE) utilizing Lagrange dual decomposition and then approximate it by DNN models. In addition, an asynchronous distributed DNN based scheme is proposed, which divides the large network model into small distributed networks for distributed data processing on each small base station side to reduce computational time. Simulation results show that the proposed scheme can achieve a high EE with low computation time.

In this paper, we focus on the energy efficiency aware architecture of caching the necessary production messages and the transaction process to support a readable and tamper proof internet of things (IoT). To achieve this, blockchain can provide a reliable distributed storage of the messages, because any changes of the cached messages will break the structure of the blockchain. Specifically, we assume that the access points belonging to different telecom operators collect the messages in the IoT network, wherein multiple servers used for either caching or computing are placed at each access point. We define that the caching servers can be divided into the data loading caches for caching the received wireless IoT data and the data transmission caches for transmitting the IoT data into the blockchain based cloud caching servers. The blocks generated in the data loading caches at each access point will be written into the blockchain based on both the proof-of-work and the capacity of the data loading caches at each access point. Then, we formulate the optimization problem maximizing the system energy efficiency by optimizing the allocation of cache, computation and communication resources by a geometric programming model. By the CVX tool in matlab software, the geometric programming model can be solved effectively. We study the impact of different parameters involved in the blockchain on the system performance, and verify the effectiveness of our proposed energy efficiency aware optimization mechanism in the blockchain based IoT.

Spectrum sensing is an important tool to resolve coexistence issue and optimize spectrum efficiency for Internet of things (IoT) systems. Nonetheless, IoT applications in fifth generation (5G) communications involve complicated scenarios with unstable channel conditions, which impedes spectrum sensing performance and will further hinder the spectrum efficiency of entire IoT systems. Motivated by such a circumstance, this paper proposes an Automatic Repeat Sensing (ARS) mechanism. The proposed mechanism exploits the difference degree of sensing results among multiple sensing antennas, and executes repeat-sensing if such a degree satisfies the given requirement. Accordingly, the negative impact of unstable channel conditions can be alleviated. Throughout the paper, we provide the concept and working principle for ARS, and derive the false-alarm probability under ARS mechanism. Numerical results manifest that ARS mechanism can markedly improve the sensing accuracy over other sensing mechanisms. Moreover, ARS mechanism has excellent flexibility and extensibility: its rules and configurations can be freely designed according to practical requirements.

In the past few years, it has become increasingly popular to analyze the information obtained to develop services by conducting a decentralized survey of private data for specific populations. Privacy security requirements for data providers force operators to implement reasonable privacy protections. But increasing the investment in privacy protection will also lead to a decline in operator revenue. In this case, operators need to ensure the privacy and security requirements of users while ensuring the sustainability of customized services. To this end, We study the relationship between collecting data quality and operator strategy, quantifying the price of private data, and building a model to maximize operator profitability. Specifically, closed-form solutions for best privacy data prices and subscription fees are designed to maximize the gross profit of service providers. Also includes the collection of data quality factors to ensure that the user perceived quality of service can be guaranteed to a certain extent. Finally, we explored the relationship between spending, subscription fees, and maximum gross profit of carriers during the data collection phase, based on the distribution of different user groups' privacy attitudes. In particular, we also explored the relationship between adding additional noise and collecting data utility in a decentralized privacy protection scenario. The simulation results show that compared with the existing methods, the algorithm can maximize the collected data quality while ensuring the provider's privacy security requirements. In addition, we demonstrate the benefits of our dynamic pricing approach and its applicability to other private data pricing algorithms.

Recently, increasingly heterogeneous wireless networks are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing technology paradigms. By achieving an efficient spectrum sharing among heterogeneous networks (HetNets), traffic offloading is a promising solution for boosting the capacity of traditional macro-cell networks. In this paper, a cognitive spectrum sharing and traffic offloading mechanism is proposed to realize the cooperation and competition between the macrocell base station (MBS) and small-cell base stations (SBSs). Under the cooperation mode, the MBS stops occupying a corresponding channel, and a selected SBS helps offload the traffic from the MBS by exclusively using this channel. To facilitate the offloading negotiation between the MBS and SBSs, we design a secondprice auction mechanism, which presents positive allocative externalities, i.e., other uncooperative SBSs can benefit from the cooperation between the MBS and the SBS performing offloading. Meanwhile, the unique optimal biding strategies for different SBSs to achieve the symmetric Bayesian equilibrium are derived and obtained in this paper. The performance of the proposed cognitive traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MBS to achieve the maximum utility.

Recently, caching techniques have been regarded as efficient approaches to alleviate the data traffic loaded over backhaul channels, which can reduce the transmission delay and improve the quality and experience of video services. This work investigates a small-cell based caching system composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, different VSPs have their caching requirements, and the MNO, who manages and operates its small base stations (SBSs), will assign these SBSs' storage to VSPs for placing videos. Considering different video popularities and MUs' preferences of VSPs, the caching service brings different utilities to VSPs, as well as that providing caching service to different VSPs causes distinct costs to the MNO. However, such privacy information of utility and cost cannot be aware of among VSPs and the MNO. In addition, malicious VSPs may break the fairness of caching systems by requesting undeserved caching resource. Concerning these problems above, this paper designs a secure caching mechanism based on double auction, which can encourage both the MNO and VSPs to truthfully report their acceptances and requirements of caching resource, respectively. Moreover, the proposed caching mechanism ensures the efficient operation of market by maximizing the social welfare. The performance and economic properties of the designed caching mechanism are validated with simulation results.

Aerial base stations (ABS) provide promising solutions for wireless coverage in adverse scenarios. By jointly designing with cooperative transmission, the system performance can even be boosted. In this paper, we consider an ABS-assisted cooperative system, where multiple ABSs hover around the macro base station (MBS) and relay the downlink signals through non- coherent joint transmission (NC-JT) to the user equipment (UE). Interfering nodes are randomly distributed over the 2D plane. Non-uniform line-of- sight (LoS) channel model is assumed for the desired signal propagation, while the communication links between ground terminals follow a distance related probabilistic LoS and non-line-of-sight (NLoS) channel model. Based on stochastic geometry framework, we derived closed form success probability for the cooperative system. Numerical results verify the accuracy of our expressions and show that jointly transmitting signals from the sky can bring in significant enhancement for the coverage performance of the system.

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In this paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

Spectrum harvesting is needed for large-scale wireless networks to access underutilized spectrum and support multiple heterogeneous users. Cooperative spectrum harvesting (CSH) allows for improved co-channel existence and intra-/inter-cell interference mitigation, which dramatically improves spectral efficiency. However, good performance metrics to quantify CSH schemes are not available. For example, existing metrics such as data rate, error/outage probability, and multiplexing/diversity gains may not clearly distinguish large signal-to-interference-plus-noise ratio (SINR) scenarios and sum-rate performance for multiple links. To overcome these limitations, we propose two new metrics called spectrum access level (SAL) and user participation level (UPL). The advantages of these metrics are: 1) achieving distinct upper bounds for multiple links; 2) upper bounds being evaluated directly by basic CSH system model; and 3) determining the performance at any power level of CSH schemes even if they are not interference exempt. Moreover, a novel CSH system model is conceived to achieve satisfying spectrum access ability based on SAL and UPL, and an interference-exempt scheme is designed to achieve relevant upper bounds. Numerical results verify the efficiency of SAL and UPL, and the spectrum access ability of proposed system model with interference-exempt scheme.

A full-duplex ultra-dense network (FDUDN) has been envisioned as a promising network paradigm for spectrum efficiency enhancement. This paper presents a novel joint spectrum and power management scheme, which maximizes the total capacity of the FDUDN, under given quality-of-service and cross-tier interference constraints. The proposed scheme is decomposed into inter-cell and intra-cell allocation. A novel approach, which performs initial capacity-maximization allocation and successive adjustment based on the constraints, is proposed for the allocation of the inter-cell subchannels. The inter-cell power control is formulated as a non-convex optimization problem, and variable substitution is used to transform it into a convex one. Furthermore, we solve this problem through a low-complexity heuristic scheme, which utilizes the water-filling theorem in inter-cell power allocation. Finally, a time-sharing relaxation method is adopted to solve the intra-cell subchannel allocation problem and reduce the computation complexity. The Computer simulation demonstrates the enhancement effect of the proposed scheme in terms of the capacity, spectrum efficiency, and power efficiency.

With the development of online applications based on social networks, many different approaches have emerged to evaluate the service that these applications provide. Reports made by end users regarding the consumer's experience or opinion are commonly used to rate the quality of different online services. Therefore, ensuring the authenticity of the users' reports, and the detection of malicious users' dishonest reports, have both become important issues to achieve accuracy in the rating of such services. In this paper, we propose and evaluate a private-prior peer prediction-based trustworthy service rating system, which requires users to report their prior and posterior beliefs regarding whether their peers will report a high-quality opinion of the service. The reports are made to a data processing center which evaluates the users' trustworthiness by applying a strictly proper scoring rule, and removes reports received from users whose trustworthiness rating is low. This peer prediction method is compatible with incentives to motivate users to report honestly. In addition, an unreliability index is proposed to identify malicious users, and malfunctioning or unreliable users who have a high error rate in making judgments about quality. Thus, reports with high unreliability values will also be excluded from the service rating system. By combining trustworthiness and unreliability, malicious users face the dilemma that they cannot receive both a high trustworthiness and low unreliability rating simultaneously when their reports are false. Simulation results indicate that the proposed peer prediction-based trustworthy service rating can identify malicious and unreliable behaviors effectively and motivate users to report truthfully, and that a relatively high service rating accuracy is achieved by the proposed system.

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs' storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs' caching utilities and MNO's caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results.

Network coverage and capacity optimization is an important operational task in cellular networks. The network coverage maximization by adjusting azimuths and tilts of antennas is focused and the existing approaches are mainly gradient-free methods. A standard gradient descent algorithm and its improved version, namely a Stochastic Gradient Descent (SGD) algorithm are proposed on the basis of a novel coverage indicator, named as the soft coverage indicator, to approximate the hard version of the original coverage indicator. We prove that the gradient vector is sparse, which accelerates gradient calculation, due to the number limitation of base stations within a specific distance from a given sampling point even if there are many decision variables of azimuths and tilts. Also, the SGD algorithm only requires a small amount of computation based on cheap estimates of the gradients, and thus is applicable to large-scale networks in an efficient manner. The experiments show that the proposed approaches perform well both in their near-optimal solutions and in their computation efficiency compared with the meta-heuristic algorithms. The extensibility and practicality of the proposed algorithms are also discussed.

In this paper, we propose a novel successive two-way relaying (STWR) system that uses a pair of conventional half-duplex (HD) relays to mimic a full-duplex two-way relay (FD-TWR). Although classical FD-TWR is spectral efficient and expands cell coverage, the proposed STWR utilizes the existing HD infrastructure to boost the FD implementation and offers bi-directional data exchange and low-complexity residual self-interference (RSI) mitigation. To formulate STWR, we develop a unified signal model to facilitate the mitigation of the generalized self-interference (GSI). GSI consists of back-propagating interference due to two-way relaying, RSI of FD sources and inter-relay interference caused by the pairs of HD relays. Because the GSI channel matrix has a distinct row linearity, we propose an efficient digital approach to remove the GSI and design two low-complexity algorithms. These algorithms avoid RSI channel estimation, full-rank matrix, and complex matrix computation. Our analysis and simulations show that: 1) the proposed STWR achieves the multiplexing gain of the true FD-TWR; 2) the distance between the two HD relays should be optimized to achieve the highest spectral efficiency; and 3) the STWR system with two algorithms can achieve a diversity order of one or two, respectively. Therefore, the STWR concept achieves a flexible tradeoff between performance and complexity, potentially enabling large-scale relay deployments.

The use of aerial base stations (ABSs) is gaining attention due to its potentials to provide a flexible wireless coverage in adverse scenarios. Investigations on key performance metrics are desirable to ensure the feasibility of these ABS-assisted networks, especially when they are jointly designed with advanced transmission technologies, e.g., cooperative transmissions and non-orthogonal multiple access (NOMA). In this paper, we consider an ABS-assisted cooperative system with NOMA enabled to boost connectivity ability. It is assumed that multiple ABSs hover around a macro base station to relay downlink signals to user equipments, while interfering nodes are randomly distributed on the ground. We assume a more realistic channel model featured with a distance-related probabilistic line-of-sight and non-line-of-sight propagation, as well as non-identical small-scale fading. We derive the outage probability, and study the impacts of various parameters on the system performance. Numerical results unveil that: 1) The reliability of backhauls plays an important role in the system and determines the outage performance floor. 2) Joint transmissions from the sky can bring in a significant performance enhancement for the system. 3) The NOMA based transmission outperforms the traditional orthogonal multiple access with an improved outage performance, provided a properly selected NOMA power allocation coefficient.

The property of performing data processing near the source of the data (i.e., at the edge of the network) makes the fog computing more suitable for networking environment of big data. For the sake of achieving efficient big sensory data collection with privacy preservation, this paper proposes a fog computing assisted efficient privacy preserving data collection scheme for big sensory data. In the proposed scheme, the designed layer-aware fog computing architecture provides effective support for exploring the spatio-temporal correlations and avoids long-distance communication with cloud center for utilizing the computation capabilities of local devices. Meanwhile, the proposed sampling perturbation encryption method protects the data privacy against eavesdropper and active attackers without sacrificing the data correlation, and it facilitates the simultaneous executing of decrypting and decompressing operations for encrypted sampling data. Furthermore, the developed data processing at fog node reduces the amount of redundant data transmission significantly, and the formulated optimization model for measurement matrix ensures the high precision of data reconstruction. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection scheme with strong privacy preservation property.

Recently, it was shown that non-orthogonal multiple access (NOMA) became a hot topic due to its capacity for ameliorating spectral efficiency. In this paper, power allocation in heterogeneous NOMA networks with multiple users are formulated as a Stackelberg game. The competition between the leaders and followers is considered as the energy efficiency (EE) maximization between the small base stations (SBSs) and macro base stations (MBSs). We propose an algorithm to obtain optimal power allocation in MBSs layer and SBSs layer, respectively. Then, Stackelberg iteration is used among MBSs and SBSs to reach the equilibrium point during the game. Simulation results demonstrate the effectiveness of proposed algorithms.

Unmanned aerial vehicles (UAVs) cooperating with satellites and base stations (BSs) constitute a space-air-ground three-tier heterogeneous network, which is beneficial in terms of both providing the seamless coverage as well as of improving the capacity for the users. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks. In our paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem. Furthermore, Lagrange dual decomposition and concave-convex procedure (CCP) method are used to solve this problem. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those ``espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

Non-orthogonal multiple access (NOMA) and heterogeneous networks are promising candidate technologies to meet the exponential growth of mobile data. However, because the wireless network is becoming more and more dense, the energy consumption problem has become increasingly prominent and severe. This paper studies the resource allocation problem of NOMA heterogeneous small cell networks with energy harvesting. By decoupling subchannel allocation and power control, a low complexity subchannel matching algorithm is designed, and a power optimization algorithm is proposed based on Lagrange dual method. The simulation results demonstrated the convergence and effectiveness of the proposed algorithms in terms of the system energy efficiency.

User-centric ultra-dense network (UUDN) has been envisioned as a promising network paradigm due to its advantages over traditional cell-centric structure. This paper introduces full duplex (FD) technology into UUDN and presents a joint scheme of user access, subchannel allocation and power control for FD UUDN. The scheme firstly maximizes the total network capacity under given rate requirements and transmit power constraints. Then if there are no feasible solutions, the system finds the minimal total power of the network to satisfy the rate requirements. The capacity maximization problem is solved by MAPEL algorithm and the power minimization problem is solved by a proposed heuristic algorithm. Simulation demonstrates the spectrum efficiency enhancement and the consumed power reduction of UUDN compared with cell-centric UDN.

The combination of resource allocation and fog computing based radio access network (Fog-RAN) have great potential for future wireless networks. However, the cross-tier interference in the spectrum-sharing deployment of Fog BSs could affect the network performance seriously and most of the solutions focus on the spectral efficiency optimization. In this paper, the user association, caching strategy, and power allocation are investigated in Fog-RAN with consideration of energy efficiency and cross-tier interference mitigation. The user association, caching, and power allocation are formulated as a non-convex optimization problem and then transformed into a convex problem, which is solved by Alternating Direction Method of Multipliers (ADMM). Then ADMM-based resource allocation algorithms are proposed to improve the energy efficiency of Fog-RAN. Simulation results demonstrate the proposed algorithms's convergence and effectiveness by comparing with existing method.

In this paper, we investigate distributed subchannel allocation problem in small cell network (SCN) with non-orthogonal multiple access (NOMA) enhancement. Different from existing studies, we exploit NOMA for the purpose of mitigating aggregate inter-cell and intra-cell interference. The problem is analyzed through a locally cooperative game model, in which information is exchanged only among neighboring small cell base stations (SBSs) instead of all SBSs in the SCN. The existence of Nash equilibrium (NE) is confirmed by proving the formulated game as an exact potential game, which allows the application of best response algorithm to solve the problem locally or globally. It is shown that the aggregate interference can be more efficiently suppressed in the NOMA system, compared with traditional orthogonal multiple access (OMA) system. Furthermore, as the network grows denser, higher percentage of SBSs have the incentive to multiplex users via the NOMA technique, revealing the superiority of NOMA over OMA in future ultra dense network.

Full duplex ultra-dense network (FDUDN) has been envisioned as a promising network paradigm for spectrum efficiency enhancement. This paper presents a novel power management scheme, which maximizes the total capacity of FDUDN, under given Quality-of-Service (QoS) and cross-tier interference constraints. The inter-cell power control is formulated as a non-convex optimization problem and variable substitution is used to transform it into a convex one. Furthermore, we solve this problem through a low-complexity heuristic scheme, which utilizes the water-filling theorem in inter-cell power allocation. Simulation demonstrates the enhancement effect of the proposed scheme in terms of the capacity and the power efficiency.

The adaptive enhanced inter-cell interference coordination (eICIC) configuration is critical for interference management. This problem is challenging especially from energy efficiency perspective and taking individual user fairness into account. Therefore, we formulate a max-min energy efficiency eICIC configuration problem, i.e., determining the number of almost blank subframes (ABS) and user associates with macro or pico while considering fairness jointly. Since the mixed combinatorial and non-smooth features of the problem, an iterative- distributed algorithm is proposed with using fractional programming and Lagrangian dual theory. Numerical results demonstrate the effectiveness of the proposed algorithm and verify fairness achieved among users, and validate the tradeoff between energy efficiency and fairness for eICIC in HetNets comparing with the existing algorithms.

Fog Computing which extends the cloud computing paradigm to the edge of the network provides great opportunities for applications with stringent latency requirement. How to allocate the limited caching resources of Fog Nodes (FNs)influences the performance of the fog computing system. In contrast to previous works on caching resource allocation with users' utility as the only consideration, we propose OptCaching which jointly optimize the utility of all network participants including Content Provider (CP), Internet Service Provider (ISP)and users. With caching incentive introduced, utility functions of these three roles are defined. Our joint utility optimization caching scheme is conducted in two stages combining global and local decision making. Firstly, interaction between CP and ISP is modeled as a non-cooperative hierarchy Stackelberg game to make decision on incentive caching prices and global caching amount aiming at optimizing the utility of all network participants. Secondly, for the purpose of further optimizing the utility of users, a belief propagation based cache placement algorithm which utilizes global caching amount constraint and local information is conducted by FNs to reduce users' average download delay. Mathematical analysis and simulation results show that the utility of CP, ISP and users are jointly optimized at Stackelberg equilibrium. The utility of users is further optimized by belief propagation based cache placement algorithm with users' average download delay reduced by 33.7% compared with global popularity based caching strategy.

NDN is one of promising underlying network architectures that supporting 5G because of its characteristics such as decoupling senders and receivers, hop by hop transmission, in-network caching, etc. However, it still faces challenges in producer mobility management like the triangle routing problem (non-optimal routing path) and global centralization of the home agent, causing a poor scalability in large network scales and long handover delay. In this paper, we propose the ESN - PBA, a NDN producer mobility management scheme using the ESN in prediction to realize a lightweight machine learning based seamless handover algorithm. Better than the existing fixed and post-adjustment management schemes of producer mobility management, the ESN-PBA can perceive nodes movements heuristically and pre-configure the adjustment in advance to reduce overall processing overhead. In addition, with fine-grained home router status feedbacks and NDN content data oriented philosophy, the training process of normal machine learning method can be mutually enhanced. The experimental results in ndnSIM show that, in the case of successful prediction, the effect of seamless handover can be achieved straightly on the fly. In order to improve the hit rate of cache, we take advantage of NDN's multipath forwarding support, the utilization of ESN prediction of multiple candidates and synchronous forwarding. Compared with PIT-based approach and DNS-based approach, the handover delay of ESN-PBA reduces by 66.7% and 75% respectively. Besides, its handover overhead reduces by 38.4 %, compared with DNS-based approach.

In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access (NOMA) amplify-and- forward two-way relay wireless networks. We aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information (CSI) at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming (GP) problem, and then solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms.

Recently, hybrid satellite-terrestrial networks (H-STNs) are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing and interference control technology paradigms. By achieving an efficient spectrum sharing among H-STN, traffic offloading is a promising solution for boosting the capacity of traditional cellular networks. In this paper, a software-defined network-based spectrum sharing, and traffic offloading mechanism is proposed to realize the cooperation and competition between the ground base stations (BSs) of the cellular network and beam groups of the satellite-terrestrial communication (STCom) system. Assume that all BSs are operated by the same mobile network operator (MNO). Under the cooperation mode, all the BSs stop occupying a corresponding channel, and a selected beam group of the satellite helps offload the traffic from the BSs by exclusively using this channel. To facilitate the offloading negotiation between the MNO and satellite, we design a second-price auction mechanism which presents positive allocative externalities, i.e., other uncooperative beam groups of the satellite can benefit from the cooperation between BSs and the beam group performing offloading. Meanwhile, the unique optimal biding strategies for different beam groups of the satellite to achieve the symmetric Bayesian equilibrium as well as the expected utility of the MNO are derived and obtained in this paper. The performance of the proposed traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MNO to achieve the maximum expected utility.

In this paper, we consider a scenario where the satellite-terrestrial network is overlaid over the legacy cellular network. The established communication system is operated in the millimeter wave (mmWave) frequencies, which enables the massive antennas arrays to be equipped on the satellite and terrestrial base stations (BSs). The secure communication in this coexistence system of the satellite-terrestrial network and cellular network through the physical-layer security techniques is studied in this paper. To maximize the achievable secrecy rate of the eavesdropped fixed satellite service, we design a cooperative secure transmission beamforming scheme, which is realized through the satellite's adaptive beamforming, artificial noise, and BSs' cooperative beamforming implemented by terrestrial BSs. A non-cooperative beamforming scheme is also designed, according to which BSs implement the maximum ratio transmission beamforming strategy. Applying the designed secure beamforming schemes to the coexistence system established, we formulate the secrecy rate maximization problems subjected to the power and transmission quality constraints. To solve the nonconvex optimization problems, we design an approximation and iteration-based genetic algorithm, through which the original problems can be transformed into a series of convex quadratic problems. Simulation results show the impact of multiple antenna arrays at the mmWave on improving the secure communication. Our results also indicate that through the cooperative and adaptive beamforming, the secrecy rate can be greatly increased. In addition, the convergence and efficiency of the proposed iteration-based approximation algorithm are verified by the simulations.

Visible light communication (VLC) is considered as a promising candidate to improve the performance of indoor communication as the complement of wireless radio frequency (RF) communications due to the scarcity of RF resources. Combining the VLC with software-defined small-cell networks will substantially improve the user data rates in indoor heterogeneous networks. In this paper, we introduce the software-defined philosophy into orthogonal frequency-division multiple access-based heterogeneous software-defined and twinned VLC and RF small-cell networks. The pivotal issues of energy efficient (EE) subchannel and power allocation are investigated in the context of software-defined VLC and RF small-cell networks. We formulate the EE resource allocation problem as a non-convex optimization problem, and then, transform it into a convex one using Dinkelbach's method. In addition, distributed subchannel and power allocation algorithms for both VLC and RF are proposed for solving the problem based on the powerful alternative direction method of multipliers. Simulation results verify the effectiveness of resource allocation algorithms conceived for the heterogeneous software-defined twinned VLC and RF small-cell networks in terms of its good convergence and overall performance.

Secure communication is a promising technology for wireless networks because it ensures secure transmission of information. In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access amplify-and-forward two-way relay wireless networks, in the presence of eavesdroppers. By exploiting cooperative jamming (CJ) to enhance the security of the communication link, we aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming problem, and then, solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms under scenarios of using and not using CJ, respectively.

The widespread application of wireless services and dense devices access has triggered huge energy consumption. Because of the environmental and financial considerations, energy-efficient design in wireless networks has become an inevitable trend. To the best of our knowledge, energy-efficient orthogonal frequency division multiple access (OFDMA) heterogeneous small cell optimization comprehensively considering energy efficiency maximization, power allocation, wireless backhaul bandwidth allocation, and user quality of service is a novel approach and research direction, and it has not been investigated. In this paper, we study the energy-efficient power allocation and wireless backhaul bandwidth allocation in OFDMA heterogeneous small cell networks. Different from the existing resource allocation schemes that maximize the throughput, the studied scheme maximizes energy efficiency by allocating both transmit power of each small cell base station to users and bandwidth for backhauling, according to the channel state information and the circuit power consumption. The problem is first formulated as a non-convex nonlinear programming problem and then it is decomposed into two convex subproblems. A near optimal iterative resource allocation algorithm is designed to solve the resource allocation problem. A suboptimal low-complexity approach is also developed by exploring the inherent structure and property of the energy-efficient design. Simulation results demonstrate the effectiveness of the proposed algorithms by comparing with the existing schemes.

The challenging requirements of fifth-generation (5G) Internet-of-Things (IoT) applications have motivated a desired need for feasible network architecture, while Named Data Networking (NDN) is a suitable candidate to support the high density IoT applications. To effectively distribute increasingly large volumes of data in large-scale IoT applications, this paper applies network coding techniques into NDN to improve IoT network throughput and efficiency of content delivery for 5G. A probability-based multipath forwarding strategy is designed for network coding to make full use of its potential. To quantify performance benefits of applying network coding in 5G NDN, this paper integrates network coding into a NDN streaming media system implemented in the ndnSIM simulator. The experimental results clearly and fairly demonstrate that considering network coding in 5G NDN can significantly improve the performance, reliability, and QoS. Besides, this is a general solution as it is applicable for most cache approaches. More importantly, our approach has promising potentials in delivering growing IoT applications including high-quality streaming video services.

In this paper, we consider a single cell downlink non-orthogonal multiple access (NOMA) network and aim at maximizing the energy efficiency. The energy-efficient resource allocation problem is formulated as a non-convex and NP-hard problem. To decrease the computation complexity, we decouple the optimization problem as a subchannel matching scheme and power allocation subproblems. In the subchannel matching scheme, a non-cooperative game is applied to model this problem. To discuss the existence of Nash equilibrium (NE), we introduce a super-modular game and then design an algorithm to converge to the NE point. Moreover, a greed subchannel matching algorithm with low complexity is given through a two-way choice between users and subchannels. However, for given subchannel matching scheme, power allocation is still a non-convex problem, which is difficult to get the optimal solution. We then transform the non-convex problem to a convex problem by applying a successive convex approximation method. Afterward, we provide an algorithm to converge to suboptimal solution by solving a convex problem iteratively. Finally, simulation result demonstrates that the energy efficiency performance of the NOMA system is better than the orthogonal frequency division multiple access system.

Aerial base station (ABS) provides a flexible solution for hotspot scenarios in traditional cellular networks, where macro-cell base stations (MBSs) are challenged by overwhelming short-time traffic demands. However, due to stretched transmission distances from sky, the system performance of this ABS-scheme is sometimes questioned. In this paper, we study the system performance of ABS-assisted networks by tools from stochastic geometry. The two-tier network consisting of MBSs and ABSs is modeled as the superposition of a Poisson point process over the infinite plane and a binomial point process within an overlapped finite circular area. We consider a more general probabilistic line-of-sight and non-line-of-sight propagation model and derive coverage probability as well as area spectral efficiency. Based on the proposed analytical framework, we study the impacts of various parameters and compare the ABS-scheme with a benchmark scheme, in which network densification is realized through deploying additional ground base stations (GBSs). Simulation results unveil that: 1) the height and ABS number should be carefully designed to obtain the optimal performance and 2) when the number of assisting BSs is limited, the ABS-scheme can achieve even better performance than the GBS-scheme, which validates the feasibility of enhancing system performance through ABSs in hotspot scenarios.

Heterogeneous small cell network with energy harvesting is a promising technique in the next generation mobile communications. However, the cross tier and co-tier co-channel interference can be severe due to the spectrum sharing in inter tier and intra tier of a heterogeneous small cell network. This paper investigates the problem of power allocation and subchannel assignment with the consideration of cross tier/co-tier interference mitigation, energy harvesting, and incomplete channel state information. The power allocation problem in heterogeneous small cell network is modeled as a non-cooperative game by introducing a time-varying cross tier/co-tier interference pricing with simultaneous wireless information and power transfer. Subchannel allocation is modeled as a non-cooperative potential game by minimizing the total interferences experienced by users on each subchannel. Iterative algorithms of power optimization and subchannel allocation are proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in the heterogeneous small cell network.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service and the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems, two of which are linear and the rest can be solved by introducing a Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

We consider auction mechanism design and performance analysis for data transactions in mobile social networks. Existing mobile network plans can result in some users ending a monthly plan with excess data, while others may have to pay a costly fee to buy more data. Thus we suggest data auctions with a single seller, or a multiple-seller networked data auction, that operate in mobile social networks, to deal with the asymmetry between extra unused data resources and urgent data demands. Based on earlier work on the analysis of auctions, we design the data transaction mechanism, and summarise the analysis on state transmission, stationary probabilities of the system, and the expected income for data sellers. To improve the efficiency and performance of the system, socially- aware mobility models are also proposed. The proposed data auction mechanisms and friendship-based mobility model are then simulated as operating on Flickr, a real-world online social network database. Results show that the number of data bidders in different auctions can be balanced through the proposed mobility model, and also increase the income per unit time of sellers in the networked data auction.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service (QoS), the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

In this paper, we investigate the dynamic subchannel and power allocation in licensed/unlicensed spectrum sharing heterogeneous small cell networks with incomplete channel state information (CSI). We explore the formulated problem using the Lyapunov optimization method by considering co-tier interference and cross- tier interference in both licensed and unlicensed spectrums. The constraints of the minimum user quality of service (QoS), the maximum transmit power limit and the unique of subchannel allocation are also considered to achieve the optimal power and subchannel allocation. Based on the framework of Lyapunov optimization, the problem of energy efficient (EE) optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the EE-delay tradeoff.

Non-orthogonal multiple access (NOMA) is considered as a promising technology for the fifth generation mobile communications. Energy-efficient resource allocation scheme is studied for a downlink NOMA wireless network, where multiple users can be multiplexed on the same subchannel by applying successive interference cancellation technique at the receivers. Most previous works focus on resource allocation for sum rate maximization with perfect channel state information (CSI) in NOMA systems. We formulate the energy-efficient resource allocation as a probabilistic mixed non-convex optimization problem by considering imperfect CSI. To solve this problem, we decouple it into user scheduling and power allocation sub-problems. We propose a low-complexity suboptimal user scheduling algorithm and a power allocation scheme to maximize the system energy efficiency under the maximum transmitted power limit, imperfect CSI and the outage probability constraints. Simulation results are provided to show that the proposed algorithms yield much improved energy efficiency performance over the conventional orthogonal frequency division multiple access scheme.

Heterogeneous small cell network is a promising technique in the next generation mobile communications. Many works have been studied in small cells, including resource allocation and interference mitigation, but most studies didn't consider the quality-of-service (QoS) and power consumption. This paper focuses on the power allocation based on non-cooperative scheme to mitigate the interference and increase the energy efficiency in small cells. The delay constraint is introduced in small cells to guarantee the QoS. We reconsider the capacity according to Shannon' capacity formula and bring in the concept of effective capacity. We take the total power consumption of the small cells into account and employ energy efficiency metric to formulate the problem of power allocation. The power allocation problem is modeled as non-cooperative supermodular game, and it is shown to converge to Nash equilibrium, and then it is transformed into a convex optimization problem, which is solved by the multi-agent Q-learning algorithm based on conjecture. The effectiveness of the proposed supermodular game based power allocation is verified by the simulations.

Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We investigate maxmin fairness energy-harvesting optimization problem in such a system by taking into account of the individual link fairness. In particular, we maximize the energy harvested by the worstcase link when the problem is subject to the rate requirements, transmit power constraint, interference power constraint and subchannels assignment constraint. Due to the nonconvexity of the formulated problem, we relax the integer variable and introduce an auxillery variable. The Lagrangian and subgradient methods are adopted to obtain a suboptimal solution. Simulation results are presented to verify the fairness performance of the proposed algorithm, and to reveal a new tradeoff between the network harvested energy and link fairness.

According to the practical requirements of high recovery precision and low latency in wireless big sensory data networks, this paper proposes an accelerated distributed rate control method for minimizing the recovery error of big sensory data. This method can guarantee the error minimization of reconstructed data and converge to the optimal value fast with a lower latency. In order to achieve these effects, an accelerated distributed solving algorithm is constructed by designing accelerated subgradient method for dual decomposition. This solving algorithm achieves convergence rate O(1/t2) in practical implementation, which significantly improves the convergence rate of regular solving algorithms. Meanwhile, the convergence analysis testifies the convergence property of the proposed distributed solving algorithm, and this algorithm is applicable to other convex optimization problems. Finally, the performance evaluation shows that the proposed accelerated method can converge to the unique optimal value successfully and the convergence speed is faster than the regular optimization method, and this proposed method can be extended to networks of different sizes without sacrificing the accelerated effect.

In heterogeneous ultra-dense networks (HetUDNs), the software-defined wireless network (SDWN) separates resource management from geo-distributed resources belonging to different service providers. A centralized SDWN controller can manage the entire network globally. In this paper, we focus on mobile traffic offloading and resource allocation in SDWN-based HetUDNs, constituted of different macro base stations and small-cell base stations (SBSs). We explore a scenario where SBSs' capacities are available, but their offloading performance is unknown to the SDWN controller: this is the information asymmetric case. To address this asymmetry, incentivized traffic offloading contracts are designed to encourage each SBS to select the contract that achieves its own maximum utility. The characteristics of large numbers of SBSs in HetUDNs are aggregated in an analytical model, allowing us to select the SBS types that provide the off-loading, based on different contracts which offer rationality and incentive compatibility to different SBS types. This leads to a closed-form expression for selecting the SBS types involved, and we prove the monotonicity and incentive compatibility of the resulting contracts. The effectiveness and efficiency of the proposed contract-based traffic offloading mechanism, and its overall system performance, are validated using simulations.

Non-orthogonal multiple access (NOMA) exploits successive interference cancellation technique at the receivers to improve the spectral efficiency. By using this technique, multiple users can be multiplexed on the same subchannel to achieve high sum rate. Most previous research works on NOMA systems assume perfect channel state information (CSI). However, in this paper, we investigate energy efficiency improvement for a downlink NOMA single-cell network by considering imperfect CSI. The energy efficient resource scheduling problem is formulated as a non-convex optimization problem with the constraints of outage probability limit, the maximum power of the system, the minimum user data rate, and the maximum number of multiplexed users sharing the same subchannel. Different from previous works, the maximum number of multiplexed users can be greater than two, and the imperfect CSI is first studied for resource allocation in NOMA. To efficiently solve this problem, the probabilistic mixed problem is first transformed into a non-probabilistic problem. An iterative algorithm for user scheduling and power allocation is proposed to maximize the system energy efficiency. The optimal user scheduling based on exhaustive search serves as a system performance benchmark, but it has high computational complexity. To balance the system performance and the computational complexity, a new suboptimal user scheduling scheme is proposed to schedule users on different subchannels. Based on the user scheduling scheme, the optimal power allocation expression is derived by the Lagrange approach. By transforming the fractional-form problem into an equivalent subtractive-form optimization problem, an iterative power allocation algorithm is proposed to maximize the system energy efficiency. Simulation results demonstrate that the proposed user scheduling algorithm closely attains the optimal performance.

Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology are expected to increase both energy efficiency and spectral efficiency. In this paper, user association and power allocation in mmWave-based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. The joint user association and power optimization problem are modeled as a mixed-integer programming problem, which is then transformed into a convex optimization problem by relaxing the user association indicator and solved by Lagrangian dual decomposition. An iterative gradient user association and power allocation algorithm is proposed and shown to converge rapidly to an optimal point. The complexity of the proposed algorithm is analyzed and its effectiveness compared with existing methods is verified by simulations.

Cognitive radio enabled small cell network is an emerging technology to address the exponential increase of mobile traffic demand in next generation mobile communications. Recently, many technological issues, such as resource allocation and interference mitigation pertaining to cognitive small cell network have been studied, but most studies focus on maximizing spectral efficiency. Different from the existing works, we investigate the power control and sensing time optimization problem in a cognitive small cell network, where the cross-tier interference mitigation, imperfect hybrid spectrum sensing, and energy efficiency are considered. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. We solve the proposed problem in an asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed by considering imperfect hybrid spectrum sensing, cross-tier interference mitigation, minimum data rate requirement, and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in the cognitive small cell network.

Energy-efficient design has emerged as a promising technique in heterogeneous networks. We study the energy efficiency problem of joint user association and power allocation in a two-tier heterogeneous network with small cells. The energy efficiency is maximized under certain prescribed quality-of-service requirement and maximum power limit constraint. The original optimization problem is a nonconvex integer programming and is NP-hard. A continuous and convex relaxation method is employed to solve this problem. Then, an iterative joint user association and power allocation algorithm is proposed to maximize the energy efficiency. Simulation results show that the proposed algorithm has improved energy efficiency when compared with a reference scheme using fixed power allocation.

Heterogeneous cloud small cell network (HCSNet) with energy harvesting is a promising technique in the next generation mobile communications. However, the cross- tier and co-tier co-channel interference can be severe due to the spectrum sharing in inter-tier and intra- tier of HCSNet. This paper investigates the problem of power allocation and subchannel assignment with the consideration of cross-tier/co-tier interference mitigation, energy harvesting and incomplete channel state information. The power allocation problem in HCSNet is modeled as a non-cooperative game by introducing a time-varying cross-tier/co-tier interference pricing with simultaneous wireless information and power transfer. Subchannel allocation is modeled as a non-cooperative potential game by minimizing the the total interferences experienced by users on each subchannel. Iterative scheme of power optimization and subchannel allocation is proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in HCSNet.

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation mobile communication due to its high spectrum efficiency. By applying superposition coding and successive interference cancellation techniques, multiple users can be multiplexed on the same subchannel in NOMA systems. Previous works focus on subchannel and power allocation to maximize the sum rate; however, the energy-efficient resource allocation problem has not been studied for NOMA systems. In this paper, we aim to optimize subchannel assignment and power allocation to maximize the energy efficiency for the downlink NOMA network. Assuming perfect knowledge of the channel state information at base station, we propose low-complexity suboptimal algorithms which include subchannel assignment and power allocation for subchannel users. In the power allocation scheme, difference of convex functions programming approach is exploited to transform and approximate the original optimal problem into a convex optimization problem. Simulation results show that our proposed algorithms yield much better improvements than orthogonal frequency division multiple in terms of sum rate and energy efficiency.

The last decade has witnessed a booming era of wireless vehicular networks, supporting diverse road traffic services and applications. Information dissemination/sharing among vehicles is the fundamental goal of vehicular networks. Although diverse information dissemination/sharing mechanisms have been proposed in the existing literature, the physical layer outage performance of information sharing has not been analyzed. Against this background, in this paper, we study the outage probability of road traffic information sharing in underlay cognitive vehicular networks under both a general scenario and a specific highway scenario. The general scenario relies on the Nakagami-m channel, while the highway scenario is its special case associated with the Rayleigh fading channel. Moreover, we also invoke a real-world dataset containing the locations of Beijing taxis to conduct simulations, the results of which verify the accuracy of our theoretical analysis.

Energy-efficient power allocation and wireless backhaul bandwidth allocation are studied for orthogonal frequency division multiple access heterogeneous small cell networks. Different from the existing resource allocation schemes that maximize the throughput, the studied scheme maximizes energy efficiency by allocating both transmit power of each small cell to users and bandwidth for backhauling, according to the channel state information and the circuit power consumption. The problem is formulated as a non-convex nonlinear programming problem which is then decomposed into two convex subproblems. A near optimal iterative resource allocation algorithm is designed to solve the resource allocation problem. We also develop a suboptimal but low-complexity approach by fixing bandwidth allocation factor. Simulation results demonstrate the effectiveness of the proposed algorithm.

Traffic density estimation with vehicular ad hoc networks (VANETs) can facilitate many applications. Traditional density estimation is achieved by counting the number of vehicles occupied in a certain area with inductive loop detectors and cameras, which is applied over limited coverage and brings high cost. In this paper, we propose to fuse vehicle spacing information and and compute average spacing in a specific area during a short period of time for density estimation. The robustness of the proposed scheme against Byzantine attack is then analyzed. Finally, we carry our experiments with U.S. Highway 101 data and the results show that the average spacing estimation is consistent with the real value.

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation mobile communication due to its high spectral efficiency. By applying superposition coding and successive interference cancellation techniques at the receiver, multiple users can be multiplexed on the same subchannel in NOMA systems. Previous works focus on subchannel assignment and power allocation to achieve the maximization of sum rate; however, the energy-efficient resource allocation problem has not been well studied for NOMA systems. In this paper, we aim to optimize subchannel assignment and power allocation to maximize the energy efficiency for the downlink NOMA network. Assuming perfect knowledge of the channel state information at base station, we propose a low-complexity suboptimal algorithm, which includes energy-efficient subchannel assignment and power proportional factors determination for subchannel multiplexed users. We also propose a novel power allocation across subchannels to further maximize energy efficiency. Since both optimization problems are non-convex, difference of convex programming is used to transform and approximate the original non-convex problems to convex optimization problems. Solutions to the resulting optimization problems can be obtained by solving the convex sub-problems iteratively. Simulation results show that the NOMA system equipped with the proposed algorithms yields much better sum rate and energy efficiency performance than the conventional orthogonal frequency division multiple access scheme.

We consider secure resource allocations for orthogonal frequency division multiple access (OFDMA) two-way relay wireless sensor networks (WSNs). The joint problem of subcarrier (SC) assignment, SC pairing and power allocations, is formulated under scenarios of using and not using cooperative jamming (CJ) to maximize the secrecy sum rate subject to limited power budget at the relay station (RS) and orthogonal SC allocation policies. The optimization problems are shown to be mixed integer programming and nonconvex. For the scenario without CJ, we propose an asymptotically optimal algorithm based on the dual decomposition method and a suboptimal algorithm with lower complexity. For the scenario with CJ, the resulting optimization problem is nonconvex, and we propose a heuristic algorithm based on alternating optimization. Finally, the proposed schemes are evaluated by simulations and compared with the existing schemes.

In this paper, we consider an orthogonal frequency division multiple access based full duplex distributed antenna system (FDDAS) with energy recycling capability and develop an energy efficient resource allocation scheme for such system. In particular, in order to optimize the energy efficiency of FDDAS, we formulate the problem of joint subchannel allocation and power control as a mixed integer nonlinear programming problem. Since the formulated optimization problem is a NP-hard problem whose computation complexity will rise exponentially with larger network scale, we develop a low complexity subchannel allocation and power control algorithm. The convergence property of the proposed algorithm is established. Simulation results show that the proposed algorithm for FDDAS results in a higher system energy efficiency than the existing algorithm for distributed antenna systems without energy recycling capability.

Cognitive radio enabled small cell network is an emerging technology to address the exponentially increasing mobile traffic demand of next generation mobile communications. Recently, many technological issues pertaining to cognitive small cell network have been studied, such as resource allocation, but most studies focus on spectral efficiency maximization. Different from the existing works, we investigate the power control and sensing time optimization problem in cognitive small cell, where imperfect hybrid spectrum sensing and energy efficiency are considered. The energy efficient sensing time and power allocation optimization is modeled as a non-convex optimization problem. We solve the problem in asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed with the consideration of imperfect hybrid spectrum sensing and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in cognitive small cell network.

The issue of data pricing is becoming more important than before in order to build an internet ecosystem. In this paper, we consider a data redistribution market where users with extra data quota are able to sell data to users that have used up their data quota. We consider this market problem with multiple users on both sides, and analyze on two possible market environment to achieve market equilibrium: the exogenous pricing scenario and endogenous pricing scenario. The stable matching algorithm and message-passing algorithm are used respectively. Simulation results show that while exogenous pricing market achieves equilibrium at a certain market price, the endogenous pricing market achieves equilibrium for each pair with better overall performance although without stability.

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude-time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted.

For the emerging 5G millimeter-wave communications, the nonlinearity is inevitable due to RF power amplifiers of the enormous bandwidth operating in extremely high frequency, which, in collusion with frequency-selective propagations, may pose great challenges to signal detections. In contrast to classical schemes, which calibrate nonlinear distortions in transmitters, we suggest a nonlinear equalization algorithm, with which the multipath channel and unknown symbols contaminated by nonlinear distortions and multipath interferences are estimated in receiver-ends. Attributed to the nonlinearity and marginal integration, the involved posterior density is analytically intractable and, unfortunately, most existing linear equalization schemes may become invalid. To solve this problem, the Monte-Carlo sequential importance sampling based particle filtering is suggested, and the non-analytical distribution is approximated numerically by a group of random measures with the evolving probability-mass. By applying the Taylor's series expansion technique, a local-linearization observation model is further constructed to facilitate the practical design of a sequential detector. Thus, the unknown symbols are detected recursively as new observations arrive. Simulation results validate the proposed joint detection scheme. By excluding transmitting pre-distortion of high complexity, the presented algorithm is specially designed for the receiver-end, which provides a promising framework to nonlinear equalization and signal detection in millimeter-wave communications.

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

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

Recently, cognitive radio enabled femtocell is regarded as a promising technique in wireless communications, where the issues of resource allocation and interference management have been investigated intensively. However, spectrum sensing errors are neglected in most of the existing works. In this paper, we propose a resource allocation scheme for orthogonal frequency division multiple access (OFDMA) based cognitive femtocells. The target is to maximize the sum rate of all femtocell users (FUs) under QoS constraints and co-tier/cross-tier interference constraints under imperfect channel sensing. The subchannel and power allocation problem is first modeled as a mixed integer programming problem, and then transformed into a convex optimization problem by relaxing subchannel sharing and imposing co-tier interference constraints, which is finally solved using the dual decomposition method. Based on the obtained solution, an iterative subchannel and power allocation algorithm is proposed. The effectiveness in terms of instantaneous maximum achievable rate of the proposed algorithm as compared with perfect spectrum sensing schemes is verified by simulations.

The limited sensor node energy and the large number of nodes with dynamic network topology information have always been the important design concerns in Wireless Sensor Networks (WSN). Node clustering is an effective way to tackle with the two issues by grouping the nodes into hierarchies in order to reduce communication distance and the amount of message. This paper mainly focuses on the unification of the node energy consumption in WSN. The distributions of the energy consumption for various scenarios in the hierarchical network are analyzed for the first time and two main reasons are found leading to the asymmetry of the energy consumption among nodes. One is the energy consumption from the communications between nodes and base station, and the other is that from the cluster head for receiving data from other nodes. It is concluded that the probability of the node acting as cluster head should depend on the distribution of the head's energy consumption, and a variable sampling space oriented to the potential number of cluster heads is established thereafter. Furthermore, a new clustering algorithm, the Segment Equalization Clustering based on Cluster Head Energy Consumption (SECHEC) algorithm is proposed, which can effectively improve the network lifetime and ensure the availability of the system within its entire lifespan.

In this paper, we consider the problem of interference mitigation in the downlink of multicell networks via base station coordination. In this paper, a simple and efficient scheme for interference management based on potential game is proposed. The main emphasis of this paper is placed on the problem of users' quality of service (QoS) in order to maximize the efficient throughput of system. Meanwhile, a pricing factor is introduced which is proportion to the co-channel interference to other base stations. Furthermore, an improved gradient projection rule with variable step size and Jacobi iterative algorithm are utilized to solve the optimization problem. Pareto optimal is verified by using "price of anarchy" as an optimize performance indicators in potential game. Simulation results show that our proposed scheme can significantly improve the performance of multicell networks.

This paper addresses the energy-efficient power allocation problem of downlink transmission with delay quality of service (QoS) constraint in the femtocell networks. Particularly, in order to provide statistical delay guarantee, the effective capacity (EC) is employed as the network performance measure instead of the conventional Shannon capacity. As a result, the energy efficiency (EE) metric of the femtocell is defined to be the total-EC-to-the-overall-power-consumption ratio of the femtocell base station (FBS). The optimization problem is firstly modeled as a supermodular game. Then the existence and characteristics of the Nash Equilibrium (NE) are investigated. A distributed energy-efficient power allocation algorithm is also designed to implement the game. Simulation results demonstrate that, our proposed algorithm delivers substantial energy efficiency improvement while satisfying a wide range of delay requirements.

Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.

Femtocells have attracted growing attentions in academia, industry, and standardization forums in recent years. However, most of existing works on femtocell networks are focused on spectrum efficiency and interference mitigation, energy efficiency aspect is neglected. In this paper, we investigate the maximization of energy efficiency of downlink OFDMA dense femtocell networks by efficient resource allocation. To decrease the complexity, joint subchannel allocation and power control are decomposed into two steps. Power control has been modeled as a non-cooperative game, a closed-form best response of transmit power is obtained. Considering fairness and low complexity, a fair time-averaged subchannel allocation metric have been derived out. Based on that, we propose a distributed suboptimal subchannel allocation and optimal power control algorithm. Simulation results show that the proposed algorithm has a low complexity with slight loss of energy efficiency compared with Round-Robin Scheduling and a noncooperative energy-efficient power optimization algorithm.

In this paper, a novel joint resource allocation including sub-channel scheduling and power control is proposed for downlink multi-cell orthogonal frequency division multiple access (OFDMA) networks. We provide a utility function consisting of energy efficiency and interference pricing, in which power and sub-channel resources have been jointly considered. To reduce the complexity of the joint resource allocation, a novel iterative two-step algorithm is presented. First, we utilize a non-cooperative supermodular game in power control given the sub-channel scheduling. Then we schedule sub-channels to maximize the utility function given the power allocation. Because power control and sub-channel scheduling share the same utility function, the iterative algorithm can be proved to converge to the best response pair which achieves the higher throughput and better utility. The numerical results are provided to evaluate the performance of the proposed algorithm and show that both throughput and energy efficiency can be improved compared with traditional methods.

In this paper, we consider the problem of secure resource allocation in orthogonal frequency division multiple access (OFDMA) two-way relay networks. Multiple sources exchange information with the assistance of an amplify-and-forward (AF) relay node in the presence of an eavesdropper. The joint subcarrier allocation, subcarrier pairing and power allocation problem aims to maximize the secrecy capacity for legitimate sources subject to limited power budget and orthogonal subcarrier allocation constraints. The optimization problem is modeled as a mixed integer programming problem, and then solved in an asymptotically optimal manner based on the dual method. Moreover, a suboptimal algorithm is proposed to reduce the complexity. Simulations are conducted to evaluate the effectiveness of the proposed near optimal and suboptimal algorithms.

In this paper, we consider the joint subchannel and power allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing macrocells and femtocells. A joint subchannel and power allocation scheme for co-channel femtocells is proposed, aiming to maximize the capacity for delay-tolerant users subject to delay-sensitive users' quality of service and interference constraints imposed by macrocells. The joint subchannel and power allocation problem is modeled as an mixed integer programming problem, then transformed into a convex optimization problem by relaxing subchannel sharing, and finally solved by a dual decomposition approach. The effectiveness of the proposed approach is verified by simulations and compared with existing scheme.

Macrocells may suffer serious uplink interference introduced by the deployment of co-channel femtocells. In this paper, an interference-aware pricing-based resource allocation algorithm for co-channel femtocells is proposed to alleviate their interference to macrocells without degrading the femtocell's capacity. The subchannel and power allocation problem is modeled as a non-cooperative game. A suboptimal subchannel allocation algorithm and an optimal power allocation algorithm are proposed to implement the resource allocation game. Simulation results show that the proposed algorithm not only improves the capacity of the macrocell but also the total capacity of the two-tier network, as compared with the unpriced subchannel allocation and Modified Iterative Water Filling (MIWF) power allocation algorithm.

In order to suppress cross-tier interference (CTI) and inter-cell interference (ICI) between adjacent macrocells, this paper proposes an effective resource allocation scheme for the two-tier OFDMA femtocell networks, in which macro BSs adopt the soft frequency reuse (SFR) strategy. Consider that macrocell users (MUEs) are prior to femtocell users (FUEs), firstly the subbands are allocated to MUEs utilizing SFR for the ICI mitigation, based on which the sub-bands are efficiently allocated to femocells for CTI mitigation. The proposed scheme is optimized in terms of energy efficiency, while guarantees that both MUEs and FUEs attain at least a given data rate, especially for cell-edge MUEs. With a three dimensional two-tier femtocell network model and practical LTE parameters, simulation results show that the proposed scheme rivals other two prominent related schemes on the CTI and ICI suppression, while outperforms them on energy efficiency for wireless overlay networks.

A large-scale deployment of femto BSs (FBSs) will result in the substantial energy consumption. One fairly intuitive way to improve energy efficiency for femtocells is a sleep mode where the FBS periodically transmits pilot signal especially in low traffic scenario. Nevertheless, it is inefficient when not involved an active call in the FBS coverage area. To address this issue, this paper proposes a dynamic FBS-cooperative power management algorithm (FCPMA) for virtual cell based femto network, where the FBS without the active user can entirely switch off pilot transmissions and the related processing all the time. Based on the proposed scheme, the state transition model is established, and the analytic formulas of energy consumption and average cumulative delay are derived. With the practical LTE system parameters, the numerical simulation and the theoretical analysis match pretty well. Furthermore, the tradeoff relation between energy consumption and average cumulative delay is also showed. The results provide some guidelines for deploying energy efficient femtocell networks.

Femtocell is a small access point using the wire broadband connections or wireless technologies to access the mobile operator's network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for wireless communication system.Femtocell related handover cost reduction is one of the important targets in LTE-Advanced SON (Self-Organising Networks). In this paper, a handover optimization algorithm based on the UE's mobility state is proposed. An analytical model was presented for the handover signalling cost analysis. Numerical results are provided to compare the signalling cost of different handover management schemes. The comparison between the proposed algorithm and the traditional handover control algorithm shows that the algorithms proposed in this paper have a significant reduction in the signalling overhead.

Home eNodeB (HeNB) is a low-power access point using the local broadband connections or a separate RF backhaul to access the mobile operator's network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for users. Considering the potential frequent mobility between HeNB-HeNB and HeNBeNB, HeNB mobility enhancement is proposed as one of the most important work items in 3GPP LTE-Advanced. In this paper, four X2 interface based HeNB mobility enhanced architectures are explicitly discussed in terms of signalling overhead evaluation. The numerical results show that the direct-X2 based option 2 in HeNB-HeNB scenario and the X2-GW based option 3 in eNB-HeNB scenario have the best trade-off in signalling overhead and complexity respectively.