In overflying Low Earth Orbit (LEO) satellite beam hopping (BH), the rapid variation in topology, intermittent visibility, dynamic traffic evolution, and the large joint beam and power action space collectively lead to significant challenges in beam scheduling. Traditional deep reinforcement learning (DRL)-based methods require exhaustive exploration of beam positions and power combinations, resulting in excessive training time and high computational complexity, which limits their practical deployment during LEO satellite overflight. Future LEO satellite networks demand high throughput to meet the needs of various applications, but the intelligent beam scheduling schemes for overflying satellites face high training complexity. These challenges become more critical for satellite-assisted IoT services, where massive low-rate terminals require wide-area and efficient access. To address this issue, this paper investigates the BH problem for overflying LEO satellite clusters and proposes a complexity-efficient DRL framework. To mitigate the excessive action space and the difficulty of acquiring channel state information during satellite overflight, a statistical pre-association mechanism is first introduced to eliminate infeasible satellite-cell pairs based on long-term traffic and visibility information. On this structured decision space, a constrained scheduler based on Multi-Agent Proximal Policy Optimization performs coordinated beam selection. Furthermore, fractional programming (FP) based power control is embedded into the learning loop to generate high-reward guidance signals, improving policy learning efficiency. Simulation results demonstrate that the proposed framework outperforms conventional DRL-based methods in terms of throughput and delay. Compared to traditional DRL-based methods, the average throughput is increased by 5-30%, and the training time is reduced by 50-60%.

In order to overcome the vicious competition between different high-volume services, we study the wireless resource sharing problem in the transmission process of the MEC-caching-coexist (MCCe) system with the capability of mmWave communications. The multiobjective Markov decision process (MOMDP) is introduced to model the task scheduling and resource allocation problem for the mmWave links, which aims to minimize the transmission delay and energy consumption simultaneously. Note that, for practical consideration, the exact channel information of all links are not known. We propose a novel multiobjective deep reinforcement learning with discrete-continuous hybrid action space (MODRL/HA) algorithm. In particular, the envelope updated design (EUD) is designed to realize the multiobjective optimization from the perspective of the Bellman operator. On the other hand, the parameterized network design (PND) is developed to deal with the hybrid action space of discrete task scheduling and continuous beamwidth and power variables. Our simulations show that, the MODRL/HA algorithm can improve 22% performance in terms of the tradeoff between delay and energy consumption compared with the benchmark schemes, which are original deep deterministic policy gradient (DDPG) and multiobjective DDPG (MODDPG) algorithms.

Abstract This paper studies unmanned aerial vehicle (UAV)‐assisted cognitive network, where the UAV can improve the communication quality of edge users. Short packet communication (SPC) is widely used due to its low delay transmission characteristic. Unlike long packet communication in conventional wireless networks, SPC has a non‐negligible packet error rate and its data transmission rate is less than Shannon capacity. Considering the fact that the UAV is usually powered by battery, the energy efficiency (EE) maximisation problem is investigated based on short packet transmission in the UAV‐assisted cognitive network. Firstly, the closed‐form expression of EE is analysed, and then the optimisation problem is formulated by jointly optimising the spectrum sensing time, packet error rate, the flight speed, and the coverage range of UAV. Secondly, the optimisation problem is solved by dividing it into four subproblems. Then, an efficient iterative algorithm is proposed to tackle this problem. Simulation results show that the proposed optimisation scheme can evidently improve the EE performance compared with other benchmark schemes. In addition, the proposed joint optimisation algorithm not only has better convergence than exhaustive method, but also has higher stability than PSO algorithm.

Abstract Owing to the advantages of low cost, flexibility, and the transmission characteristics of air–ground (AG) channels, using the unmanned aerial vehicle (UAV) as mobile relay to assist wireless communication has received significant interest recently. A green non‐orthogonal multiple access (NOMA)‐based multi‐UAV‐enabled relaying system with different rate requirements is proposed here. To improve energy efficiency (EE) of the UAV relays, an optimization problem for user grouping, UAV trajectory design and resource allocation under the constraints of information causality constraint, UAVs' maximum service capacity, maximum transmit power constraint, and different communication rate requirements of the mobile users (MUs) is formulated. According to the grouping results, the UAVs' trajectory and power allocation by Dinkelbach method, successive convex approximation, and condensation algorithm are alternately optimized. To solve this highly coupled non‐linear mixed integer programming problem, a graph‐based grouping algorithm is proposed to reduce the relative distance between the UAV relay and the MU. Simulation results show that the proposed optimization algorithm can effectively improve the EE performance of this NOMA‐based multi‐UAV‐enabled relaying system.

Abstract This paper introduces the short packet transmission in non‐orthogonal multiple access (NOMA)‐based unmanned aerial vehicle (UAV) assisted relay communication system. Short packet transmission has considerable potential to decrease the transmission latency of UAV communication, and NOMA can effectively enhance the spectrum efficiency and fairness. To improve the reliability of the system, an effective packet error rate (PER) minimization problem within short packet transmission is proposed by jointly optimizing the packet length, UAV placement, and power allocation under the reliability requirement and total power constraints. To address the intricate PER minimization problem, the optimization problem is firstly decomposed into three sub‐problems, and the corresponding monotonicity and convexity are analyzed, respectively. Then, an overall iterative optimization algorithm for PER minimization based on alternating direction method of multipliers algorithm and optimal solution algorithm is formulated by solving the three sub‐problems in an iterative manner. Simulation results validate the effectiveness and convergence of the proposed NOMA scheme and overall iterative optimization algorithm, respectively.

Abstract The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) is considered as a promising approach to tackle soaring computing requirements. The broadcast nature of air‐to‐ground (A2G) links makes UAV communications vulnerable to eavesdroppers, so secure UAV communications remain an open question. This paper proposes a secure communication scheme for reconfigurable intelligent surface (RIS)‐assisted UAV‐MEC systems, in which the RIS assists the user in offloading data to the legitimate UAV, and the legitimate UAV provides computing services to the user. To fully expand the security computing capacity of the system, the communication link is improved by introducing RIS, and the jammer interferes with the eavesdropper. The secure computing capability of the system is maximized by optimizing communication resources and trajectories. Since the proposed problem is non‐convex, successive convex approximation (SCA) technique and block coordinate descent (BCD) technique is combined to solve the problem. The simulation results show that the proposed scheme in this paper can effectively improve the system secure computing bits compared with the benchmark scheme.

This paper studies information surveillance over wireless-powered suspicious multiuser communications, where multiple suspicious transmitters (STs) first harvest wireless energy from the suspicious power beacon (PB) in phase I and then communicate with the suspicious destination (SD) in phase II over mutually orthogonal channels, and there is a legitimate monitor (M) aiming to overhear the suspicious signals of the STs based on wireless-powered cooperative jamming. Specifically, the jammers first harvest energy from M in phase I and then interfere with the SD in phase II. Considering the fairness issue, M aims to maximize the minimum eavesdropping success probability of these suspicious signals, by jointly optimizing its transmit power in phase I and the jammers’ power allocations in phase II. To solve the problem, first we strictly prove that M should exhaust its maximum power for the energy transfer, even the additional energy can be harvested by the STs to enhance their transmit power and rate. Then, the general successive convex approximation (SCA) technique and one low-complexity solution are respectively proposed to optimize the jammers’ power allocations. Further, the closed-form jamming power allocations are derived in the high signal-to-noise ratio range to reveal some interesting insights. The joint deployments of M and the jammers are also investigated to enhance the eavesdropping performance. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

Covert transmission is investigated for a cooperative deception strategy, where a cooperative jammer (Jammer) tries to attract a multi-antenna adversary (Willie) and degrade the adversary’s reception ability for the signal from a transmitter (Alice). For this strategy, we formulate an optimization problem to maximize the covert rate when three different types of channel state information (CSI) are available. The total power is optimally allocated between Alice and Jammer subject to the Kullback-Leibler (KL) divergence constraint, which can be expressed analytically and be widely used as a covertness measurement. Different from the existing literature, in our proposed strategy, we also determine the optimal transmission power at the jammer when Alice is silent, while existing works always assume that the jammer’s power is fixed. Specifically, we apply the S-procedure to convert infinite constraints into linear-matrix-inequalities (LMI) constraints. When statistical CSI at Willie is available, we convert double integration to single integration using asymptotic approximation and substitution method. Finally, our simulation results show that for the proposed strategy, the covert rate is increased with the number of antennas at Willie. Moreover, compared to the benchmark, our proposed strategy is more robust in the presence of imperfect CSI.

An excellent routing protocol is important for wireless sensor network (WSN) construction and efficient data transmission. With the continuous expansion of the application scenarios and scopes of the Internet of Things, the existing WSN routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this article proposes an energy-saving clustering protocol based on adaptive Voronoi dividing, named energy-saving clustering by Voronoi adaptive dividing (ESCVAD) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as low energy adaptive clustering hierarchy (LEACH) protocol and stable energy protocol (SEP), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle‐ (UAV‐) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy‐efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV‐assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

Mobile edge computing (MEC) has been envisaged as a promising technique in fifth generation (5G) and beyond wireless networks. In order to alleviate the explosive growth of computation and spectrum demand, cognitive radio (CR) and unmanned aerial vehicles (UAV) are studied in MEC‐aware networks. In this study, considering a local computation and partial offloading scheme, a UAV‐enabled CR‐MEC framework is proposed and the authors' aim is to maximise the energy efficiency (EE) of the wireless devices (WDs). The formulated optimisation problem is not convex and challenging to be solved. To deal with it, an equivalent reformulation of this EE maximisation problem is introduced, and the authors decompose the original problem into two sub‐problems, wherein the sub‐problems become tractable and can be solved by jointly optimising sensing time, offloading power and WD‐UAV scheduling. Numerical results highlight the EE enhancement with various system parameters and reveal the superiority of the proposed algorithm than other schemes with low computational complexity.

This study investigates the information security problem in amplify‐and‐forward (AF) satellite transmission with eavesdroppers at both the transmitter and the receiver. Two multi‐antenna earth stations aim to exchange messages through a multi‐antenna satellite in two hops. The satellite channels (both uplink and downlink) are assumed to be shadowed‐Rician fading. In the first hop, the multiple term weighted fractional Fourier transform (MWFRFT) is adopted to secure the messages from the eavesdropper at the transmitter. To overcome the simplicity of parameters in MWFRFT, the authors enhance the MWFRFT with frequency hopping and propose the frequency hopping multiple term weighted fractional Fourier transform (FH‐MWFRFT) to improve the secrecy. In the second hop, the satellite broadcasts the signal to the receiver after null space beamforming (NSBF). Since the energy at the satellite is confined, the NSBF is simplified by applying the singular value decompose to avoid the complicate optimisation calculations at the satellite. The one‐way and two‐way transmission scenarios with full channel state information (CSI) and statistic CSI are both considered in this study. Simulation results demonstrate the proposed method could achieve a better security at the transmitter while maintain a similar secrecy performance to the optimisation method at the legitimate receiver.

The energy-harvesting cognitive radio (CR) network is proposed to improve the spectrum efficiency and energy efficiency. We focus on the optimization of sensing time and power allocation to maximize the throughput of the energy-harvesting CR network subject to the energy causality constraint and collision constraint. Based on the classification of operating regions, the optimization problem is divided into two sub-problems. Then, the efficient iterative Algorithm 1 and Algorithm 2 are proposed to solve sub-problem (A) and sub-problem (B), respectively. Numerical results show that a significant improvement in the throughput is achieved via joint optimization of sensing time and power allocation.

A weighted fractional Fourier transform (WFRFT) based on channel state information (CSI), aiming to safeguard the physical (PHY) layer security of wireless communication system, is proposed. With the proposed scheme, WFRFT is first applied to satellite communications such that the transmitted signal is distorted and can only be neutralized by inverse‐WFRFT with the same parameter. Moreover, by exploiting the physical properties of wireless channels, the CSI matrix is transformed as a secret key. In addition, by adding phase rotation (PR) factors to each branch of the WFRFT system, a new unitary matrix with the encryption properties is constructed, and hence, the satellite communications secrecy is reliably guaranteed due to the variation in signal characteristics. Finally, the efficacy of the security enhancement is evaluated in terms of the average bit error rate (BER) and the secrecy capacity. Simulation results show that the proposed encryption method can make the detection and demodulation more difficult for the eavesdropper.

Increasing spectrum-efficiency (SE) as well as energy-efficiency (EE) has attracted much attention recently due to the fact that the future wireless networks need to address the issues of high throughput and low power consumption. However, the objective for optimizing SE sometimes conflicts with the one for optimizing EE, and the methods for improving EE may result in a decrease in SE. In this paper, we consider the SE-EE tradeoff for cognitive radio (CR) networks with co-operative spectrum sensing (CSS). First, we formulate the general problem, and analyze two special cases: the SE maximization problem and the EE maximization problem. The SE and EE are optimized separately via joint optimization of sensing duration and final decision threshold in CSS. Based on the solutions of the two special cases, the general problem for SE-EE tradeoff is solved. Then, we consider the tradeoff of SE and EE from two perspectives: (1) maximizing EE while satisfying SE requirement; and (2) maximizing SE while satisfying EE requirement. Efficient algorithms for sensing strategy design are proposed for each scenario. Finally, we demonstrate the effectiveness of the proposed sensing strategies and illustrate the tradeoff between SE and EE via simulations.

Spectrum sensing is one of the most important components of cognitive radio (CR) technology. In this paper, we investigate the design of the sensing time to minimize the secondary user (SU) transmission delay under the condition of sufficient protection to primary users (PUs). It has been proven that there exists one optimal sensing time which yields the minimum transmission delay. Then, a novel cooperative spectrum sensing (CSS) framework is proposed. In the novel CSS framework, one SU's reporting time is also used for other SUs' sensing. For time varying channels, the novel multi-slot CSS is derived. In order to minimize the transmission delay, some algorithms are derived to obtain the optimal fusion scheme. Computer simulations show that fundamental improvement of delay performance can be achieved by the optimal fusion scheme. In addition, the novel multi-slot CSS scheme shows a much lower transmission delay than CSS based on general frame structure.