Affine frequency division multiplexing (AFDM) has emerged as a robust multi-carrier modulation candidate for high-mobility communications. This paper investigates an AFDM based integrated sensing and communications (ISAC) framework for unmanned aerial vehicle (UAV) links within space-air-ground integrated networks (SAGINs). A key contribution of this work is the novel design of the cyclic prefix and postfix (CPP) for AFDM, which is specifically tailored to accommodate wireless power transfer (WPT) requirements, thereby supporting simultaneous information and energy transmission. Specifically, the base station exploits the reflected echoes of AFDM signals to estimate sensing parameters, including the position, velocity, and angle of mobile users. To optimize the communication link, we propose an intelligent adaptive modulation and coding (AMC) decision-making process. A specialized dataset is established, integrating physically interpretable metrics—such as distance, velocity, and angle—with historical AFDM channel state information characterized by its unique chirp-domain representation. Subsequently, a hybrid deep learning architecture, designated as CNN-LSTM, is developed to establish a unified evaluation framework. This framework leverages the feature extraction capabilities of convolutional neural networks (CNNs) to process the spatial-temporal correlations of the AFDM channel, while utilizing Long Short-Term Memory (LSTM) networks to capture the long-term temporal dependencies of UAV trajectories. Simulation results demonstrate that the proposed modeling approach achieves superior separability and robustness, aligning closely with the ideal adaptive envelope while exhibiting enhanced cross-trajectory generalization capabilities compared to conventional methodologies.

The proliferation of unmanned aerial vehicles (UAVs) in low-altitude airspace necessitates sophisticated resource management supporting both cooperative communications and unauthorized intrusion detection. This paper investigates joint optimization of cell association and power allocation in integrated sensing and communication (ISAC)-enabled low-altitude networks. We propose a novel dual-function framework where ground base stations simultaneously provide communication services to authorized UAVs and localize non-cooperative UAVs for collision avoidance. We establish a channel model capturing the relationship between communication rate and sensing accuracy, formulating an optimization problem that maximizes the weighted sum of system average sum rate and localization quality of service (QoS). The problem jointly optimizes cell association, communication power allocation, and sensing power allocation under UAV localization QoS and cooperative sum rate constraints. To solve the resulting mixed-integer non-convex problem, we propose a joint optimization algorithm based on optimal transport theory (J2OT) that directly handles discrete variables without relaxation, avoiding accuracy losses of conventional approximation methods. J2OT decomposes the problem using optimal transport-based cell association optimization (OTC) and power allocation optimization (OTP). Simulation results demonstrate J2OT’s superiority, achieving 1.5 bits/s/Hz improvement in system objective and 7.5% reduction in localization Cramér-Rao bound compared to Weighted Voronoi and Iterative Water-filling baseline methods.

Emerging 6G wireless systems suffer severe performance degradation in challenging environments like high-speed trains traversing dense urban corridors and Unmanned Aerial Vehicles (UAVs) links over mountainous terrain. These scenarios exhibit non-Gaussian, non-stationary channels with heavy-tailed fading and abrupt signal fluctuations. To address these challenges, this paper proposes a novel wireless channel model based on symmetric α-stable Lévy processes, thereby enabling continuous-time state-space characterization of both long-term and short-term fading. Building on this model, a generalized optimal control framework is developed via a fractional Hamilton-Jacobi-Bellman (HJB) equation that incorporates the Riesz fractional operator to capture non-local spatial effects and memory-dependent dynamics. The existence and uniqueness of viscosity solutions to the fractional HJB equation are rigorously established, thus ensuring the theoretical validity of the proposed control formulation. Numerical simulations conducted in a multi-cell, multi-user downlink setting demonstrate the effectiveness of the fractional HJB-based strategy in optimizing transmission power under heavy-tailed co-channel and multi-user interference.

In this paper, we consider transmit beamforming and reflection patterns design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems, where the dual-function base station (DFBS) lacks channel state information (CSI). To address the high overhead of cascaded channel estimation, we propose an improved artificial fish swarm algorithm (AFSA) combined with a feedback-based joint active and passive beam training scheme. In this approach, we consider the interference caused by multipath user echo signals on target detection and propose a beamforming design method that balances both communication and sensing performance. Numerical simulations show that the proposed AFSA outperforms other optimization algorithms, particularly in its robustness against echo interference under different communication signal-to-noise ratio (SNR) constraints.

Low peak-to-average power ratio (PAPR) transmission is an important and favorable requirement prevalent in radar and communication systems, especially in transmission links integrated with high power amplifiers. Meanwhile, motivated by the advantages of reconfigurable intelligent surface (RIS) in mitigating multi-user interference (MUI) to enhance the communication rate, this paper investigates the design problem of joint waveform and passive beamforming with PAPR constraint for integrated sensing and communication (ISAC) systems, where RIS is deployed for downlink communication. We first construct a trade-off optimization problem for the MUI and beampattern similarity under PAPR constraint. Then, in order to solve this multivariate problem, an iterative optimization algorithm based on alternating direction method of multipliers (ADMM) and manifold optimization is proposed. Finally, the simulation results show that the designed waveforms can well satisfy the PAPR requirement of the ISAC systems and achieve a trade-off between radar and communication performance. Under high signal-to-noise ratio (SNR) conditions, compared to systems without RIS, RIS-aided ISAC systems have a performance improvement of about 50 % in communication rate and at least 1 dB in beampatterning error.

Orthogonal time frequency space (OTFS) modulation is widely acknowledged as a prospective waveform for future wireless communication networks. To provide insights for the practical system design, this paper analyzes the outage probability of OTFS modulation with finite blocklength. To begin with, we present the system model and formulate the analysis of outage probability for OTFS with finite blocklength as an equivalent problem of calculating the outage probability with finite blocklength over parallel additive white Gaussian noise (AWGN) channels. Subsequently, we apply the equivalent noise approach to derive a lower bound on the outage probability of OTFS with finite blocklength under both average power allocation and water-filling power allocation strategies, respectively. Finally, the lower bounds of the outage probability are determined using the Monte-Carlo method for the two power allocation strategies. The impact of the number of resolvable paths and coding rates on the outage probability is analyzed, and the simulation results are compared with the theoretical lower bounds.

Due to their flexibility and dynamic coverage capabilities, Unmanned Aerial Vehicles (UAVs) have emerged as vital platforms for emergency communication in disaster-stricken areas. However, the complex channel conditions in high-speed mobile scenarios significantly impact the reliability and efficiency of traditional communication systems. This paper presents an intelligent emergency communication framework that integrates Orthogonal Time Frequency Space (OTFS) modulation, semantic communication, and a diffusion-based denoising module to address these challenges. OTFS ensures robust communication under dynamic channel conditions due to its superior anti-fading characteristics and adaptability to rapidly changing environments. Semantic communication further enhances transmission efficiency by focusing on key information extraction and reducing data redundancy. Moreover, a diffusion-based channel denoising module is proposed to leverage the gradual noise reduction process and statistical noise modeling, optimizing the accuracy of semantic information recovery. Experimental results demonstrate that the proposed solution significantly improves link stability and transmission performance in high-mobility UAV scenarios, achieving at least a 3dB SNR gain over existing methods.

Existing deep learning-based semantic communication (DeepSC) systems are typically trained for specific single-channel condition, which restricts the overall adaptability and resilience to interference. To address this limitation, we propose an innovative semantic adaptive feature extraction (SAFE) network that dynamically generates and fuses multiple sub-semantics, each characterized by unique features that can be tailored to different channel conditions. This paper also introduces three advanced learning algorithms to refine and enhance the generated sub-semantics, optimizing the semantic successive refinement performance of the SAFE network. Furthermore, we integrate a novel interference-aware semantic transmission method based on non-orthogonal multiple access (NOMA) into this framework. This approach enables users to adaptively select appropriate subsets for efficient transmission and image reconstruction, tailored to the prevailing channel interference conditions. Through extensive simulation experiments, we demonstrate the framework’s capability to generate and transmit semantics under diverse channel interference scenarios adaptively, and verify the effectiveness through both objective and subjective quality evaluations.

This paper focuses on an intelligent reflecting surface (IRS)-assisted lossy communication system with correlated Rayleigh fading. We analyze the correlated channel model and derive the outage probability of the system. Then, we design a deep reinforce learning (DRL) method to optimize the phase shift of IRS, in order to maximize the received signal power. Moreover, this paper presents results of the simulations conducted to evaluate the performance of the DRL-based method. The simulation results indicate that the outage probability of the considered system increases significantly with more correlated channel coefficients. Moreover, the performance gap between DRL and theoretical limit increases with higher transmit power and/or larger distortion requirement.

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients’ data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm.

With the development of dual-function radar communication (DFRC) systems, waveform design has received increasing attention. At the same time, subcarrier superposition can lead to the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM). To solve the problem, in this paper, we propose an alternating direction method of multipliers (ADMM)-based low-PAPR OFDM waveform design algorithm for DFRC systems, which minimizes the signal PAPR with the constraint of the zero integrated sidelobe level (ISL). Moreover, we compare our algorithm with a recently proposed benchmark algorithm. Simulation results demonstrate that our algorithm has better performance compared to the$l$- norm cyclic algorithm.

Physical layer authentication (PLA) is a promising solution to address the security issue raised due to malicious jamming or spoofing. However, accurate and diversified channel state information is required to implement the PLA schemes. In this regard, reconfigurable intelligent surface (RIS) has the potential to quickly reshape the communication environment at a cheap cost, and thus has great potential to enhance the PLA. In this paper, we propose a RIS-assisted channel impulse response (CIR)-based dynamic PLA scheme. Specifically, the receiver exploits the geographic location information of the transmitters embedded in CIR to identify the message. In order to reduce the impact of the components representing environmental changes in CIR on the authentication, the method of regularly updating CIR database is adopted. In addition, with RIS enriched CIR information, we can achieve a high authentication rate by constructing a classification neural network. Experiments are conducted based on the communication system with DeepMIMO datasets, and the simulation results demonstrate that the proposed authentication scheme is effective for the identification of both first-attack and non-first-attack spoofers.

Unmanned aerial vehicle (UAV) communications have become a prominent technology for emergency communications to enhance network services. This article investigates computing offloading and resource allocation in nonorthogonal multiple access (NOMA)-based UAV emergency communication scenarios. To minimize the computational overhead of the terminal device, a joint task offloading and resource allocation problem is investigated, where the computation overhead of the marine Internet of Things (IoT) device is measured as a weighting of the task completion time and the energy consumption of the device. The optimization of the transmission of IoT devices, the allocation of computing resources to UAVs, task offloading, and carrier allocation are formulated in the considered problem, which is an NP-hard mixed integer nonlinear programming problem. To reduce the complexity, we decompose it into two parts from the property of the problem: 1) the resource optimization problem and 2) the task offloading problem. To solve the resource allocation problem, we first decouple the problem and then use the proposed quasi-convex and convex optimization methods. Meanwhile, a low-complexity task offloading algorithm is designed to achieve a Nash-stable solution by introducing a coalition game approach based on this. Numerical results verify the algorithm’s effectiveness and are compared with other schemes in the literature.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution-linked signal transformer (ClST) and a novel knowledge distillation method named signal knowledge distillation (SKD). The ClST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel attention mechanism named parallel spatial-channel attention (PSCA) mechanism and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The SKD is a knowledge distillation method to effectively reduce the parameters and complexity of neural networks. We train two lightweight neural networks using the SKD algorithm, KD-CNN and KD-MobileNet, to meet the demand that neural networks can be used on miniaturized devices. The simulation results demonstrate that the ClST outperforms advanced neural networks on all datasets. Moreover, both KD-CNN and KD-MobileNet obtain higher recognition accuracy with less network complexity, which is very beneficial for the deployment of AMR on miniaturized communication devices.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments is critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution signal transformer (CST). The CST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel signal-specific self-attention mechanism to replace the multi-headed self-attention mechanism in Transformer, and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The simulation results demonstrate that the CST outperforms advanced neural networks on all datasets, which is very beneficial for the deployment of AMR in complicated channel environments.

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this article, we consider the anti-eavesdropping communication established through an RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst case security provisioning as compared with the baselines.

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.

Mobile crowdsensing (MCS) is an effective perception paradigm for large-scale tasks, driven by the proliferation of mobile devices with more powerful sensing and computing capabilities. An effective incentive mechanism is critical to the operation of an MCS system in promoting public engagement. However, the great majority of works discuss fixed task pricing, while the inherent inequality of the supply–demand relationship of the tasks exists. Therefore, it is essential to study the dynamic task pricing problem in the peer-to-peer data sharing MCS system. In this article, we formulate the interactions between the requester and the sensors as a two-stage Stackelberg differential game model, while considering the average behavior of sensors to solve the dynamic task pricing problem. Specifically, in the game model, the requester is the leader who first announces the issued task rate and provides decisive state-changing task pricing dynamics to the sensors. Then, the sensors are the followers who decide the rate of tasks completed noncooperatively based on requesters’ observed strategy, using the level of effort as the state dynamics. The requester and the sensors interact through a mean-field term included in the dynamic state functions, which catches the average behavior of all users. By solving the model, the optimal strategies for the users and the optimal tasks pricing trends in the dynamic environment are obtained. Furthermore, the effectiveness and feasibility of the scheme are verified by a series of numerical simulation experiments.

The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit-sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated the performance superiority of our proposal as compared with the baselines.

Federated learning (FL) has been proposed to coordinate multiple edge user equipments (UEs) for training a global model. However, the FL's performance is affected by the channel state of wireless network. Specifically, the performance of the random selecting UE is seriously limited by the millimeter-wave (mmWave) channel. In this paper, we propose to deploy intelligent reflecting surface (IRS) to reconstruct the non-line-of-sight (NLoS) mmWave channel. A novel UE scheduling strategy is then proposed to optimize the FL system performance. For selecting the particular UEs and achieving higher convergence, we formulate an optimization problem that jointly optimizes the aggregation vector of the base station (BS) and the IRS phase shift matrix. To figure out the formulated problem, we propose a two-step difference-of-convex (DC) algorithm. Simulation results demonstrate that the proposed algorithm can achieve higher convergence and a lower training loss than the benchmark algorithm.

In this paper, we investigate the resource allocation and vehicle to everything (V2X) offloading in the cognitive vehicular networks. The cognitive radio (CR), mobile edge computing (MEC), and non-orthogonal multiple access (NOMA) schemes are applied aim to solve the combinational problem of resource allocation and V2X offloading. The problem for jointly optimizing power and time allocation in the MEC based CR (CR-MEC) networks is conceived. We decompose the joint optimization problem into two subproblems, which are power allocation and time allocation problems. In order to solve this joint optimization problem, an advanced comprehensive resource allocation (ACRA) algorithm based on three-sided matching theory is employed. More specifically, the proposed algorithm is to realize the most reasonable matching among primary users (PUs), cognitive users (CUs) as well as a cognitive base station (BS), and put forward a V2X offloading strategy, by appropriately allocating power and time aim to minimize the system energy consumption. The simulation results show that, our proposed algorithm converges to stable. Furthermore, the proposed NOMA based CR-MEC networks can achieve lower energy consumption compared to the orthogonal multiple access (OMA) based CR-MEC networks.

Attracted by the advantages of multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA), this article studies the resource allocation problem of a NOMA-MEC system in an ultra-dense network (UDN), where each user may opt for offloading tasks to the MEC server when it is computationally intensive. Our optimization goal is to minimize the system computation cost, concerning the energy consumption and task delay of users. In order to tackle the non-convexity issue of the objective function, we decouple this problem into two sub-problems: user clustering as well as jointly power and computation resource allocation. Firstly, we propose a user clustering matching (UCM) algorithm exploiting the differences in channel gains of users. Then, relying on the mean-field game (MFG) framework, we solve the resource allocation problem for intensive user deployment, using the novel deep deterministic policy gradient (DDPG) method, which is termed by a mean-field-deep deterministic policy gradient (MF-DDPG) algorithm. Finally, a jointly iterative optimization algorithm (JIOA) of UCM and MF-DDPG is proposed to minimize the computation cost of users. The simulation results demonstrate that the proposed algorithm exhibits rapid convergence, and is capable of efficiently reducing both the energy consumption and task delay of users.

The number of users engaged in social media through social networks continues to grow as people become more passionate on current social issues and events. People using social networks tend to have different opinions or positions regarding these issues and events. However, social network users share similar characteristics such as political orientation, age, and gender. Since the users of social networks can be grouped according to their similarities, then we would like to observe how these users affect the belief and opinion of other users in the same or different groups. Inspired by this phenomenon, we propose a multi-population mean field game approach to capture the belief and opinion evolution of a social network with several populations. Through the proposed model, we can gain information on the behavior of social network users belonging to different groups. Moreover, we can utilize the proposed model to predict how social network users affect the belief and opinion of each other. The multi-population social network mean field game problem is solved analytically using an adjoint method. Then, simulations are provided to show the belief and opinion evolution of users in a multi-population social network.

To reduce content transmission power and network load pressure, content caching technology based on a large number of small base stations (SBSs) is considered to be an effective solution. However, due to the limited cache capacity and unknown content popularity, how to design an intelligent content caching policy has become a great challenge. In this paper, we propose a generative adversarial network (GAN) based on the distributional deep Q-Network (DDQN) algorithm, named QGAN, to learn the content caching policy. A content caching network that contains several cooperative SBSs is considered in the case of unknown content popularity, where each SBS fetches cached content from the adjacent SBS or cloud. Moreover, compared with three classical content caching policies and one reinforcement learning algorithm, the performance of the QGAN algorithm is verified. The simulation results show that the convergence rate is improved and the transmission cost is reduced with the proposed algorithm.

Internet of Things (IoT) technology suffers from the challenge that rare wireless network resources are difficult to meet the influx of a huge number of terminal devices. Cache-enabled device-to-device (D2D) communication technology is expected to relieve network pressure with the fact that the requesting contents can be easily obtained from nearby users. However, how to design an effective caching policy becomes very challenging due to the limited content storage capacity and the uncertainty of user mobility pattern. In this article, we study the jointly cache content placement and delivery policy for the cache-enabled D2D networks. Specifically, two potential recurrent neural network approaches [the echo state network (ESN) and the long short-term memory (LSTM) network] are employed to predict users' mobility and content popularity, so as to determine which content to cache and where to cache. When the local cache of the user cannot satisfy its own request, the user may consider establishing a D2D link with the neighboring user to implement the content delivery. In order to decide which user will be selected to establish the D2D link, we propose the novel schemes based on deep reinforcement learning to implement the dynamic decision making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system. The simulation results suggest that the cache hit ratio of the system can be well improved by the proposed content placement strategy, and the proposed content delivery approaches can effectively reduce the request content delivery delay and energy consumption.

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

In unmanned aerial vehicle (UAV)-assisted massive multi-input multi-output (MIMO) millimeter-wave (mmWave) networks, beam-steering guarantees reliable and steady connection between flying base stations and ground users with the challenge of strict angular deviation. In this paper, we investigate a joint optimization problem of beamforming and beam-steering in the multi-UAV mmWave networks, considering line-of-sight (LoS) communication for UAVs. For the hybrid beamforming optimization of massive MIMO mmWave, we propose a hybrid beamforming scheme based on the cross-entropy estimation with the robustness algorithm inspired by machine learning, which aims to optimize the hybrid precoding matrix. For the beam-steering optimization, we propose a novel mean field game (MFG)-based massive MIMO angle control scheme to model the optimal mmWave channel optimization problem between UAVs and ground users. In addition, when dealing with the problem of initial sensitivity and difficulty to solve the partial differential equations in the MFG, we utilize reinforcement learning to achieve the mean field equilibrium, which is described as the mean field learning game algorithm. Finally, a joint beamforming and beam-steering optimization algorithm is proposed to maximize the system sum-rate. Simulation results show the significant improvements in sum-rate, energy efficiency, and spectral efficiency, which verify the effectiveness of the proposed algorithm.

Caching is a promising technique that can effectively reduce peak traffic by pushing popular content items proactively to the users during off- peak hours. Two key performance metrics for proactive caching are the energy efficiency and hit ratio. In this paper, we are interested in balancing the hit ratio and energy efficiency in social-aware proactive caching. More specifically, we present a social behavior driven pushing and caching policy that is capable of maximizing the hit ratio while satisfying the power constraint. For two notable schemes, namely uncoded caching and coded caching, we formulate two optimization problems that give the optimal tradeoff between the energy efficiency and caching hit ratio. Furthermore, the optimal solutions present the energy efficient joint pushing and buffer update polices. Our simulation results show that the social-aware pushing and update policies may bring a significant hit ratio gain when the buffer size is far less than the number of items.

Nowadays content caching at base stations (BSs) has attracted more and more attention in 5G networks with the ability of saving resources and reducing data traffic. However, in practice, it's a challenge to design a caching policy intelligently due to the limited storage capacity as well as time and space varying users' requests. In this paper, we propose an algorithm based on asynchronous advantage actor-critic (A3C) to solve the content caching problem. Considering some cooperative BSs, with each BS having a cache, every BS can fetch contents from either neighboring BSs or the backbone network, with different degrees of expenditure. In order to learn the optimal caching and sharing policy, the online A3C-based algorithm is designed to minimize the total transmission cost without knowing content popularity distribution. To evaluate the proposed algorithm, we compare the performance with the classical caching policies, including Least Recently Used (LRU), Least Frequently Used (LFU), Adaptive Replacement Cache (ARC) and one distributed algorithm in the literature. The simulation results show that the proposed A3C-based algorithm can achieve a low transmission cost and improve the convergence rate in the dynamic environment.

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

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

Unmanned aerial vehicles (UAVs) can now be considered as aerial base stations (BSs) to support ultra-reliable and low-latency communications by establishing line-of-sight (LoS) connections to ground users. Moreover, combining UAVs with millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) will be a promissing solution. It can provide potentially high capacity wireless services due to their aerial positions and their ability to deploy on demand at specific locations. In this paper, we propose a low-cost and energy-efficient hybrid precoding architecture for UAVs, where the antenna part is realized by lens array. We investigate an efficient and energy-saving hybrid precoding scheme with robustness, which is inspired by the cross-entropy (CE) optimization in machine learning and the relative error estimation optimization. As for each selection of the hybrid precoders for obtaining the optimized precoder, we regarded it as a training process in machine learning, in which the training target is the CE-loss function between the predicted precoders and the target precoders. It aims to minimize the relative error between the predicted and actual values for optimizing the probability distributions of the elements in the analog hybrid precoder. Simulation results show that our proposed scheme can achieve higher sum rate and energy efficiency.

Three-dimensional (3D) convolutional networks have been proven to be able to explore spatial context and spectral information simultaneously for super-resolution (SR). However, such kind of network can't be practically designed very `deep' due to the long training time and GPU memory limitations involved in 3D convolution. Instead, in this paper, spatial context and spectral information in hyperspectral images (HSIs) are explored using Two-dimensional (2D) and One-dimenional (1D) convolution, separately. Therefore, a novel 2D-1D generative adversarial network architecture (2D-1D-HSRGAN) is proposed for SR of HSIs. Specifically, the generator network consists of a spatial network and a spectral network, in which spatial network is trained with the least absolute deviations loss function to explore spatial context by 2D convolution and spectral network is trained with the spectral angle mapper (SAM) loss function to extract spectral information by 1D convolution. Experimental results over two real HSIs demonstrate that the proposed 2D-1D-HSRGAN clearly outperforms several state-of-the-art algorithms.

Super-resolution (SR) of hyperspectral images (HSIs) aims to enhance the spatial/spectral resolution of hyperspectral imagery and the super-resolved results will benefit many remote sensing applications. A generative adversarial network for HSIs super-resolution (HSRGAN) is proposed in this paper. Specifically, HSRGAN constructs spectral and spatial blocks with residual network in generator to effectively learn spectral and spatial features from HSIs. Furthermore, a new loss function which combines the pixel-wise loss and adversarial loss together is designed to guide the generator to recover images approximating the original HSIs and with finer texture details. Quantitative and qualitative results demonstrate that the proposed HSRGAN is superior to the state of the art methods like SRCNN and SRGAN for HSIs spatial SR.

In this paper, we study the offloading problem in a mobile edge computing (MEC) network consisting of two-tier UAV. The high-altitude platform unmanned aerial vehicle (HAP-UAV) is equipped with a MEC server to complete the computing tasks of the low altitude platform unmanned aerial vehicle (LAP-UAV). We propose a multi-leader multi-follower Stackelberg game to formulate the two-tier UAV MEC offloading problem. As the leaders of the game, the HAP-UAVs optimize their pricing by considering the behavior of their competitors to maximize their revenue. Each LAP-UAV selects the best computing tasks offload strategy to minimize latency. From this perspective, the stochastic equilibrium problem of equilibrium program with equilibrium constraints (EPEC) model is proposed to develop the optimal supply strategies for HAP-UAVs to maximize their profits and minimize LAP-UAVs' cost. Simulation results show that the offloading delay of LAP-UAVs can be reduced by the proposed scheme.

In recent years, unmanned aerial vehicles (UAVs) are more widely applied due to low cost and high flexibility. Facing the suddenness of emergency events and the uncertainty of user service requests, the rational deployment of the UAV aerial base stations (ABSs) has become an effective solution. However, how to deploy multiple UAVs with the variety of properties (power, service radius, and so on) is a challenge problem. There is a mean-field-type game (MFTG) to obtian optimal startegies which has been applied in practical applications. The arbitrariness of the number, the distinguishability of agents and the non-negligible effect on system are considered in MFTG. Because the homogeneity of multiple UAVs cannot be guaranteed in the practical communication model, this paper formulates the deployment of multiple UAV ABSs with various properties as a MFTG. In this scenario, each UAV decides the flight strategy at the next moment to minimize its own cost function by analyzing the communication requests of the ground users. In addition, the existence of the Nash equilibrium of the MFTG problem is proved. And the direct square complement method is used to solve the problem to minimize the cost function of each UAV. The simulation results show the correctness of the proposed method and the rationality of the deployment.

The Internet of Things (IoT) technology has recently drawn more attention due to its ability to achieve the interconnections of massive physic devices. However, how to provide a reliable power supply to energy-constrained devices and improve the energy efficiency in the wireless powered IoT (WP-IoT) is a twofold challenge. In this paper, we develop a novel wireless power transmission (WPT) system, where an unmanned aerial vehicle (UAV) equipped with radio frequency energy transmitter charges the IoT devices. A machine learning framework of echo state networks together with an improved k -means clustering algorithm is used to predict the energy consumption and cluster all the sensor nodes at the next period, thus automatically determining the charging strategy. The energy obtained from the UAV by WPT supports the IoT devices to communicate with each other. In order to improve the energy efficiency of the WP-IoT system, the interference mitigation problem is modeled as a mean field game, where an optimal power control policy is presented to adapt and analyze the large number of sensor nodes randomly deployed in WP-IoT. The numerical results verify that our proposed dynamic charging policy effectively reduces the data packet loss rate, and that the optimal power control policy greatly mitigates the interference, and improve the energy efficiency of the whole network.

Pro-active pushing is a promising emerging communication technology to improve the resource efficiency and quality of service provisioning in mobile networks. This paper characterizes users' requests with request delay information (RDI), and proposes to use the cache-hit ratio (CHR) as the metric of the system performance in pushing strategy design. Different from conventional instant on-demand services, the pro-active pushing system can merge different users' requests for the same file at different time instants. The base station can therefore push files more efficiently by employing multicasting technologies. However, it is revealed that if one of the users' channel condition is significantly poor, it is a better choice to ignore the user in the pushing system from the overall CHR performance perspective. In the two-user one-file scenario, we derive analytical expressions of the CHR to help the BS determine the optimal pushing rate, which is shown to be a two-value-selection. In particular, for two users with uniformly distributed RDIs, the decision space is specifically characterized through analysis and calculation.

Pansharpening has been an important technique to increase the spatial resolution of the multispectral (MS) images provided by many earth observation satellites. Since the different spatial resolutions exist between the multispectral and panchromatic (PAN) images, pansharpening usually upsamples the MS images to the same size as the PAN image and then injects the spatial details into the upscaled MS ones. In this paper, we propose a novel pansharpening method focusing on the structure injection into the MS images through a joint Gaussian guided upsampling. The original spectral information is transferred to the joint upsampling outputs by using the hyperspherical color transformation (HCT). The experimental results show that our proposed method can obtain high-quality pansharpened results and outperforms some existing methods.

The remote sensing images provided by satellites usually contain complex earth objects with different scales. In the fusion of such images, most of the existing filtering-based pansharpening methods often suffer from spectral and/or spatial information distortions due to the inaccuracy of the detail extraction. Motivated by this, we propose an effective and straightforward multi-scale structure perception pansharpening method, which uses the structure-preserving filter with great structure-aware ability to progressively perceive the structures and accurately extract the details. The experiment is carried out on GeoEye-1 satellite images. Visual and objective analysis show that our method can produce high-quality pansharpened results and outperform some existing methods.

Massive multiple-input multiple-output (massive MIMO) and the millimeter wave (mmWave) communication are known to be among the key technologies for the fifth generation (5G) mobile networks. The implementation of massive MIMO with the mmWave architecture requires the utilization of hybrid precoding technique with a low dimensional digital precoder and a high dimensional analog precoder. In this paper, we investigate the hybrid precoding design problem for the case of cooperative multicell multiuser massive MIMO system. This problem is formulated with the consideration of the user and base station (BS) association problem, and we propose an iterative algorithm to solve the formulated problem. The precoding problem is solved using the Eigen precoding algorithm whereas the Lagrangian based approach is proposed for the solution of the association problem. Simulation results show that our proposed solution can achieve higher energy efficiency and convergence rate.

The application of drone small cells (DSCs) which are unmanned aerial vehicles (UAVs) carrying communication payload to complete the construction of the high-altitude base stations, is playing an increasingly important role for providing emergent wireless services in different scenarios. In order to coordinate interference and reduce huge backhaul overhead among static ultra-dense DSCs on the low-altitude platform, the paper studies a DSC cluster precoding network with massive multiple-input multiple-output (mMIMO). Considering the disadvantage of the energy-constrained unmanned aerial base station (UABS), we investigate the problem of designing precoding at cluster DSCs to minimize the transmission power of UABSs. A modified cluster scheme based on the Euclidean distance is adopted to cluster the DSCs. We eliminate the intra-cluster interference via performing the modified zero-forcing method and coordinate the inter-cluster interference to achieve our target of reducing transmit power. A non-cooperative game among the DSC clusters is formulated, and the existence and uniqueness of the Nash equilibrium of the proposed game are proved. The non-convex optimization problem is solved via the iterative methods and the numerical results show the effectiveness of our proposed scheme.

In emergency situations such as earthquakes, the cellular infrastructure cannot support communication services because of equipment damage. The use of the large number of unmanned aerial vehicles (UAVs) has been drawn significant attentions as an important solution for providing air-to-ground communication services in such situations. In this paper, we research the flight direction policy (velocity vector) of the UAVs where every UAV acts as the base station to serve the multi-users communications. As the trajectory of UAVs have a huge impact on the performance of communication, we investigate an adaptive coverage problem, that all the UAVs can adjust their velocities to increase the number of served users. However, such behavior may cause larger flight energy consumption. We propose a discrete-time mean-field game (MFG) framework that each UAV adjusts its velocity in order to minimize the flight energy consumption. In this framework, each UAV evolves according to the dynamic equation and seeks to minimize its flight energy consumption containing the average distribution of all UAVs. We investigate a deterministic function φ to approximate the average distribution of all UAVs as the number of UAVs tends to infinity. Furthermore, the optimal velocity vectors generate a certain asymptotic Nash equilibrium as time tends to infinity, which implies that the flight energy consumption of each UAV can reach its minimal value as the number of UAVs increases to infinity. The simulation results show the optimal trajectory and optimal flight tendency of the UAVs. Moreover, we show that as users move, the amount of the users served is maintained at a relatively stable range, which represents met the demand of user's adaptive coverage.

Polar codes can provably achieve the capacity of a symmetric binary discrete memoryless channel and have low encoding and decoding complexity. However, the error rate performance of polar codes decoding in short and moderate length is not very well, moreover, the encoding and decoding of polar codes with the conventional serial mode will lead to poor throughput. In this paper, we propose a hardware architecture of parallel encoding and decoding scheme for polar codes concatenation with LDPC, and take advantage of the parallelism of belief propagation (BP) decoding algorithm of the two codes to reduce the decoding delay. We compare the performance of concatenated scheme with polar codes and investigate the throughput implemented on graphic processing unit (GPU) for Gaussian channel. Experiment results show that the performance of the concatenated scheme outperform only polar codes, and the throughput of the proposed parallel architecture is obviously faster than that of the serial.

In this paper, we study a multi-pair bidirectional (or two-way) full-duplex (FD) massive MIMO relay (FDMMR) system, where the relay employs massive antennas and is operated in the amplify-and-forward (AF) mode. We analyze its spectral efficiency (SE) performance, when both imperfect channel estimation and channel aging effect are considered. We propose four power-scaling schemes based on the zero-forcing reception/zero-forcing transmission (ZFR/ZFT) relaying processing. Assuming that the number of relay antennas approaches infinity, the SE is analyzed in the context of the proposed power scaling schemes. Our analytical results show that the inter-pair interference caused by the other user pairs as well as the self-interference can be completely eliminated by the ZFR/ZFT processing at the relay. The self-loop interference and the inter- user interference can also be cancelled, if a power scaling scheme is carefully selected. Furthermore, our studies show that the channel aging may significantly degrade the SE of the system.

Pansharpening technology has been an important tool in remote sensing applications. It aims at increasing the spatial resolution of multispectral (MS) image with the aid of panchromatic (PAN) image. A key point of pansharpening is spatial detail extraction and injection. Since MS and PAN images contain objects in different sizes and structures of various scales, scale-sensitive detail extraction is desired. In this paper, we present a scale-aware pansharpening method which uses rolling guidance filter to separate structure from details and injects the details through Gram-Schmidt transformation. The experimental results show that our proposed method can obtain high-quality sharpened results and outperforms some existing methods.

Pansharpening methods generally inject the missing spatial details from a high spatial resolution panchromatic (PAN) image into the corresponding co-registered low spatial resolution multispectral (MS) images while preserving the spectral information. However, most of methods extract the details only from PAN image, which may lead to distortions in the sharpened results. Motivated by this, we present a novel two-stage guided filtering based pansharpening method. In the preliminary stage, an injection model based on multi-channel guidance filtering is designed to keep the spectral fidelity of MS imagery. Then a single channel guidance filtering based injection model is proposed to enhance the details in the second stage. The proposed method is tested and verified by GeoEye-1 satellite images. Qualitative and quantitative analyses demonstrate the superiority of the proposed method compared with some state-of-the-art guided filtering based pansharpening methods.