Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads.

In the Web 3.0 ecosystem, blockchain and Artificial Intelligence of Things (AIoT) construct the infrastructure, where blockchain transaction semantic detection (BTSD) aims to enhance blockchain security by identifying illegal transactions through distributed miners executing AI algorithms. However, the computational cost of performing semantic detection discourages miners from participating without adequate incentives. Existing studies focus on algorithmic aspects of BTSD, which generally ignore the critical issue of incentive mechanism. To fill this gap, we propose the first incentive-based BTSD framework in the transaction pool phase, emphasizing how incentives affect the behavior of miners and users. We use evolutionary game theory to model miner-user interactions and define three key scenarios to simulate the impact of reward decay and penalty factors on system dynamics. Our results demonstrate that adjusting these parameters significantly influences the number of miners engaging in semantic detection and users initiating legitimate transactions. Under certain conditions, a well-designed incentive mechanism can lead to an Evolutionary Stable Strategy (ESS), thereby achieving systemic stability. This study introduces a novel incentive mechanism for BTSD during the transaction pool phase and validates its effectiveness through both theoretical insights and numerical solutions to enhance blockchain security.

With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-end communication system with a multiuser Gaussian interference channel. In this context, the standard constellation is not optimal under high interference conditions. To address this issue, we propose an adaptive learning algorithm for learning and predicting dynamic interference. Note that existing DL-based autoencoders are unable to train end-to-end learning systems by deep learning without a known channel. Thus, we propose a generative adversarial network (GAN)-based training scheme to imitate the real channel. Simulation results show that compared with traditional PSK and QAM modulation schemes, our proposed adaptive learning-based auto encoder can achieve significantly lower block error rate (BLER) in presence of interference. Besides, the BLER performance of our proposed GAN-based training scheme is close to that of the optimal training scheme with known channel on different channel models.

In unmanned aerial vehicles (UAVs) assisted cellular networks, user association plays an important role in interference control and spectrum efficiency. In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in a multi-UAV assisted network in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicasting can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we propose a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. To be specific, the transmitted signal is split into a common part and a private part, and the interference is partially decoded and partially treated as noise. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL under the constraints of UAV backhaul capacity and power budget. Since the formulated problem is non-convex with intricate states and an individual UAV may not know the rewards of other UAVs, we convert it into a robust partially observable Markov decision process (POMDP). Then we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. To achieve an optimal policy, we further propose an improved clip and count-based proximal policy optimization (PPO) algorithm to train actor and critic networks. Simulation results demonstrate the superiority of the proposed decoupled association strategy with RSMA and the MADRL learning algorithm.

In this article, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in unmanned aerial vehicles (UAVs)-assisted cellular networks in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicast can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we develop a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation at the receiver. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL. Since the resultant problem is non-convex with complex states, we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. Simulation results show the superiority of the proposed decoupled association policy with RSMA and the MADRL learning algorithm.

In this article, we investigate the content transmission in a heavy-crowded multiple access cellular network, whose data traffic is offloaded through the combination of edge caching and unmanned aerial vehicle (UAV) communication. In this context, we formulate a novel optimization problem, which minimizes the sum content acquisition delay of users by optimizing the multiuser association and cache placement jointly with UAV trajectory and transmission power over a given flight duration. However, due to the uncertainty of the environment (e.g., random content requests and dynamic UAV positions), it is often difficult and impractical to solve the formulated problem using conventional optimization methods. To this end, we model our problem as a partially observable stochastic game where the macro base station (MBS) and UAVs act as agents to collectively interact with the environment to receive distinctive observations. Moreover, we take advantage of the Proximal Policy Optimization (PPO) learning strategy and propose a novel Dual-Clip PPO-based algorithm to solve the converted problem. To guide agent exploration, a new exploration criterion is proposed in which each UAV agent can obtain an intrinsic reward when it explores beyond the boundary of explored regions (BeBold). Note that the MBS agent has the extrinsic reward given by the environment only. Numerical results reveal that the proposed algorithm outperforms the standard PPO-based deep reinforcement learning algorithm. Moreover, the proposed joint design scheme can achieve a dramatic reduction of content acquisition delay compared with the benchmark schemes.

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success ratio of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a contribution-based dual-clip proximal policy optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all subproblems are solved. Simulation results reveal that the proposed algorithm can outperform other algorithms in terms of the MOS and transmission success ratio.

In this paper, we propose a novel delay-aware selective admission and scheduling algorithm for time-critical applications to guarantee the delay requirement of each packet in a single-hop downlink network. We consider a series of priorities among packets. To avoid always starving low-priority packets, we define a delay-laxity concept and introduce a new output gain model as our network utility function. In this context, we formulate a multi-objective optimization problem that minimizes the average queue backlog and maximizes the average network utility under the constraints of guaranteeing per-packet delay and achieving fairness among users. To solve this problem, we model our problem as a Markov Decision Process and propose a Double Deep Q Network-based algorithm to learn the optimal policy. Simulation results show that the proposed algorithm can achieve significant improvements in average delay, delay-outage drop rate, and goodput compared with the existing stochastic schemes. Moreover, the proposed algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed.

This paper investigates the content distribution in a hotspot area in which multiple cache-enabled unmarried aerial vehicles (UAVs) are deployed to offload part of the data traffic in a heavy-crowded cellular network. We formulate an optimization problem which minimizes the sum content acquisition delay of all users by designing the multiuser association and cache placement jointly with UAV transmission power and trajectory over a given flight duration. The non-convexity of the formulated problem and the uncertainty of the dynamic environment make it difficult and impractical to solve using traditional optimization methods. Thus we model our problem as a partially observable stochastic game where the macro base station (MBS) and UAVs act as agents and interact with the environment to receive distinctive observations. To guide exploration, we propose a new exploration criterion that gives each UAV agent an intrinsic reward when it explores beyond the boundary of explored regions (BeBold). Then we propose a Dual-Clip Proximal Policy Optimization (DC-PPO) algorithm to solve our problem. Extensive numerical results demonstrate that the proposed algorithm is superior than the PPO-based algorithm and the DC-PPO-based algorithm without exploration criterion.

With the exponential growth of data traffic, the use of caching and device-to-device (D2D) communication has been recognized as an effective approach for mitigating the backhaul bottleneck in unmanned aerial vehicle (UAV)-assisted networks. In this article, we investigate the issue of secure transmission in a cache-enabled UAV-relaying network with D2D communications in the presence of an eavesdropper. Specifically, both UAVs and D2D users are equipped with cache memory, which can prestore some popular content to collaboratively serve users. Considering the fairness among users, we formulate an optimization problem to maximize the minimum secrecy rate among users, by jointly optimizing the user association and UAV scheduling, transmission power, and UAV trajectory over a finite period. The joint design problem is a nonconvex mixed-integer programming problem. To efficiently solve this problem, we propose an alternating iterative algorithm based on the block alternating descent and successive convex approximation methods. Specifically, the user association and UAV scheduling, UAV trajectory, and transmission power are optimized alternately in each iteration, and the convergence of the algorithm is proven. Extensive numerical results show that the proposed joint design scheme significantly outperforms other benchmark schemes in terms of the secrecy rate.

Unmanned aerial vehicles (UAVs) have been introduced into wireless communication systems to provide high-quality services and enhanced coverage due to their high mobility. In this article, we study a UAV-assisted mobile-edge computing (MEC) system in which a moving UAV equipped with computing resources is employed to help user devices (UDs) compute their tasks. The computing tasks of each UD can be divided into two parts: one portion is processed locally and the remaining portion is offloaded to the UAV for computing. Offloading is enabled by uplink and downlink communications between UDs and the UAV. On this basis, two types of access modes are considered, namely, nonorthogonal and orthogonal multiple access. For both access modes, we formulate new optimization problems to minimize the weighted-sum energy consumption of the UAV and UDs by jointly optimizing the UAV trajectory and computation resource allocation, under the constraint on the number of computation bits. These problems are nonconvex optimization problems that are difficult to solve directly. Accordingly, we develop alternating iterative algorithms to solve them based on the block alternating descent method. Specifically, the UAV trajectory and computation resource allocation are alteratively optimized in each iteration. Extensive simulation results demonstrate the significant energy savings of our proposed joint design over the benchmarks.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is an appealing concept, where a fixed-wing UAV equipped with computing resources is used to help local resource-limited user devices (UDs) compute their tasks. In this paper, each UD has separable computing tasks to complete, which can be divided into two parts: one portion is processed locally and the other part is offloaded to the UAV. The UAV moves around above UDs and provides computing service in an orthogonal frequency division multiple access (OFDMA) manner. This paper aims to minimize the weighted sum energy consumption of the UAV and UDs by jointly optimizing resource allocation and UAV trajectory. The resulted optimization problem is nonconvex and challenging to solve directly. With that in mind, we develop an iterative algorithm for solving this problem based on the block coordinate descent method, which iteratively optimizes resource allocation variables and UAV trajectory variables till convergence. Simulation results show significant energy saving of our proposed solution compared to the benchmarks.

With the exponential growth of data traffic, the use of caching and device-to-device (D2D) communications has been regarded as an efficient approach for alleviating the backhaul congestion in unmanned aerial vehicle (UAV) assisted networks. In this paper, we investigate the security issue of a cache-enabled UAV-relaying network with D2D communications in the presence of eavesdropper. Specifically, a UAV and multiple D2D users are equipped with cache memory, which can pre-store some popular contents to cooperatively provide content transfer services for users. To achieve secure and fair transmission, an optimization problem is formulated with the aim of maximizing the minimum secrecy rate among receivers, by jointly optimizing the cache placement and UAV flight trajectory in a finite flight period. The joint design problem is a non-convex mixed-integer programming problem. To facilitate solving this problem, we propose an alternating iterative algorithm based on the block alternating descend and successive convex approximation methods. Numerical results show that the joint design scheme significantly outperforms other benchmark schemes in terms of the secrecy rate.

With the exponential growth of data traffic, caching is regarded as a promising solution to combine with unmanned aerial vehicle (UAV)-assisted networks, which can offload cellular traffic and improve the system performance. Moreover, the cache capacity at user side can be leveraged, e.g., through local data storage or device-to-device (D2D) sharing. In this paper, we focus on the performance analysis and trajectory optimization of cache-enabled UAV-assisted networks with underlaid D2D communications. We consider both static and dynamic UAV deployments. For static UAV deployment, we first formulate an optimization problem to design the cache placement in order to maximize the cache hit probability. Then, the successful transfer probability (STP) and sum-rate are analyzed by using stochastic geometry, and their closed-form expressions are derived. For dynamic UAV deployment, the UAV moves over the cell and stops at several path points to serve users. To shorten the time required for the UAV to cover all users, a spiral algorithm is proposed to optimize the UAV trajectory, aiming at minimizing the number of UAV path points. Moreover, since at different locations, the UAV communication will incur different interference on D2D users, we derive the outage probability for the D2D users. Simulation results show the significant performance gain of our proposed probabilistic cache placement over existing strategies. For a given user density, we show that the optimal values for the UAV height which lead to the maximum UAV-STP and sum-rate exist.

It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE for caching, that is, improving the cache‐hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul‐aware cellular networks jointly considering these two approaches. Note that most existing works are based on the assumption that the content catalog and popularity are static. However, in practice, content popularity is dynamic. To timely estimate the dynamic content popularity, we propose a method based on shot noise model (SNM). Then we propose a distributed caching policy to improve the cache‐hit rate in such a dynamic environment. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed‐form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.