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%.

Integrated air-ground communication (IAGC) has emerged as a promising solution to deliver seamless wireless coverage and high-data-rate services. However, potential malicious eavesdroppers pose a serious threat to the confidential transmission in IAGC due to their non-cooperative behaviors and the inherent openness of communication channels. To tackle this problem, a dynamic spectrum control (DSC)-based transmission scheme is proposed to enhance covert performance and communication reliability in IAGC. With the proposed scheme, we apply the principles of block cryptography, perform adaptive iterative and orthogonal transformations to generate sequence sets that drive transmission decisions. Guided by these sequences, multiple legitimate users can dynamically occupy different frequency slots and transmit data simultaneously. In addition, we analyze the probability of frequency slot multiplexing when several data groups occupy the same frequency slot in a time slot, resulting in the closed-form expression for the detection error probability. We then derive the maximum reliable transmission probability and ergodic rate subject to the covert communication constraints. Simulation results demonstrate that the proposed scheme can achieve superior covert performance compared with benchmark schemes. Furthermore, we evaluate and discuss the effects of key parameters in the proposed DSC-based transmission scheme on communication security and reliability.

The proliferation of unauthorized Unmanned Aerial Vehicles (UAVs) poses significant security risks, necessitating robust detection systems. However, in practical long-range surveillance scenarios, UAV signals often deteriorate due to severe attenuation and complex environmental interference, rendering traditional detection methods ineffective. To address this, this letter proposes a novel signal enhancement framework comprising an improved adaptive Wiener filter. We introduce a dynamic noise spectrum estimation strategy coupled with a decision-directed phase compensation mechanism. This approach effectively suppresses non-stationary background noise while reconstructing high-fidelity time-frequency features by rectifying phase distortions. Experimental results on a proprietary real-world dataset demonstrate that the proposed method significantly improves signal quality, enabling high-accuracy detection using YOLO models even in ultra-low Signal-to-Noise Ratio (SNR) regimes compared to raw data-based baselines.

In this paper, we utilize frequency hopping spread spectrum (FHSS) to help enhance covert performance in frequency diverse array (FDA) covert system. Compared to conventional FDA covert communication, FHSS signals can hide in different channels by rapidly switching the transmission frequency to avoid being detected. Therefore, FHSS-aided FDA covert system can effectively counter the warden with strong detection ability and enhance covert performance. We focus on maximizing the covert capacity during the entire transmission by jointly optimizing the beamfoming vector and antenna frequency vector at the transmitter. Based on the system equipment constraints, two different optimization schemes, i.e., instantaneous optimization scheme (IOS) and robust optimization scheme (ROS), are proposed to solve the problem. Moreover, to evaluate the covert performance provided by FHSS-aided FDA covert communication, we introduce three benchmark schemes, i.e., phased array (PA) and logarithmic distribution FDA (Log-FDA) and non-FHSS. Simulation results demonstrate that FHSS combined with IOS can significantly improve covert performance compared to other benchmark schemes. In contrast, FHSS combined with ROS can effectively enhance covert performance when transmitting signals over a wide bandwidth.

The beam focusing characteristic of frequency diverse array (FDA) in direction-distance dimension has been widely applied in covert communication. To reduce the covert performance loss caused by time-varying FDA channels, the instantaneous optimization of antenna beamforming vector (ABV) and antenna frequency vector (AFV) is required for FDA. However, due to the difficulty in acquiring instantaneous channel state information (CSI) and the limitations of transmit device, instantaneous optimization scheme (IOS) may not be feasible in practical communication scenarios. Moreover, the existing robust schemes only adopt some simple and fixed AFVs without optimizing AFV. This paper investigates robust optimization scheme (ROS) in FDA-aided covert system, where the optimization frequency of the ABV and AFV at the transmitter can be adaptively adjusted based on the acquired CSIs. We maximize the average covert rate during the transmission by jointly optimizing ABV and AFV, and modify the conventional block successive upper-bound minimization (BSUM) method to solve the optimization problem. Furthermore, both perfect CSI and partial CSI at the warden are considered. Numerical results demonstrate that our proposed ROS outperforms the existing robust schemes, especially in the cases of highly correlated channels and partial CSI. Although ROS is indeed inferior to IOS, enhancing the channel decoupling capability of FDA and deteriorating the detection capability of the warden can effectively narrow the covert performance gap between ROS and IOS. Moreover, the beampattern generated by the optimal ABV and AFV at each time slot ensures that the beamfoming gain is maximized at the legitimate receiver and minimized at the warden simultaneously.

Movable antenna (MA) is an emerging technology which can reconfigure wireless channels via adaptive antenna position adjustments at transceivers, thereby bringing additional spatial degrees of freedom for improving system performance. In this paper, from a security perspective, we exploit the MA-enabled legitimate jammer (MAJ) to subvert suspicious multiuser downlink communications consisting of one suspicious transmitter (ST) and multiple suspicious receivers (SRs). Specifically, our objective is to minimize the benefit (the sum rate of all SRs or the minimum rate among all SRs) of such suspicious communications, by jointly optimizing antenna positions and the jamming beamforming at the MAJ. However, the key challenge lies in that given the MAJ’s actions, the ST can reactively adjust its power allocations to instead maximize its benefit for mitigating the unfavorable interference. Such flexible behavior of the ST confuses the optimization design of the MAJ to a certain extent. Facing this difficulty, corresponding to the above two different benefits: i) we respectively determine the optimal behavior of the ST given the MAJ’s actions; ii) armed with these, we arrive at two simplified problems and then develop effective alternating optimization based algorithms to iteratively solve them. In addition to these, we also focus on the special case of two SRs, and reveal insightful conclusions about the deployment rule of antenna positions at the MAJ. Furthermore, we analyze the ideal antenna deployment scheme at the MAJ for achieving the globally performance lower bound. Numerical results demonstrate the effectiveness of our proposed schemes compared to conventional fixed-position antenna (FPA) and other competitive benchmarks.

The challenge of mitigating interference in Space-Air-Ground Integrated Networks (SAGINs) is exacerbated by the inherent channel uncertainty, which arises due to dynamic weather conditions, heterogeneous user deployment, and different altitude of transmitters. To tackle this problem, Rate-Splitting Multiple Access (RSMA) has been seen as a promising solution due to its robustness. However, conventional beamforming designs for RSMA often suffer from two major limitations: high processing delays and overfitting to specific channel conditions. When the channel conditions change, the performance of these predictors degrades significantly, limiting their effectiveness in dynamic environments. To address these challenges, we propose a novel Fast-Adaptive Predictive Beamforming (FA-PB) framework for RSMA in SAGINs. Unlike traditional predictive beamforming approaches that rely on fixed predictive models, FA-PB integrates a transfer-learning-based online learning mechanism. This innovative approach allows the predictor to dynamically adapt to new channel conditions with minimal computational overhead. FA-PB achieves this by leveraging few-shot Channel State Information at the Transmitter (CSIT) samples, enabling real-time updates and adjustments to the predictor. Consequently, FA-PB ensures that the beamforming process can rapidly adapt to fluctuating channel conditions, maintaining high levels of performance even in highly dynamic SAGIN environments. Extensive simulation results validate the superiority of the FA-PB framework, demonstrating its enhanced adaptability and improved beamforming performance in SAGINs.

The integration of beam hopping (BH) technology into Low Earth Orbit (LEO) satellite communication systems has emerged as a critical strategy to enhance spectral efficiency and flexibility. However, conventional BH methods often exhibit insufficient robustness when confronted with uneven terrestrial traffic demands and dynamic jamming levels. This paper addresses this challenge by proposing a novel hybrid framework that synergizes statistical planning with multi-agent reinforcement learning (MARL) to achieve anti-jamming beam hopping for LEO satellites. The proposed framework decomposes the BH scheduling process into two phases: statistical planning and real-time adjustment. A portion of the beams are managed using a low-complexity potential game algorithm for statistical planning, ensuring stability and efficient resource allocation based on statistical traffic patterns and jamming conditions. The remaining beams are dynamically adjusted using a low information exchange Mean-Field Multi-Agent Proximal Policy Optimization (MFMAPPO) algorithm, enabling rapid adaptation to instantaneous jamming events and fluctuating user demands. This framework reduces the number of beams considered during training, thereby simplifying the action space. As a result, the system achieves quicker convergence and offers enhanced robustness, especially in environments where some information may be missing. Simulation results demonstrate that the proposed method significantly enhances throughput, improves robustness against jamming, and reduces training time compared to other baselines. The integration of statistical planning and mean-field MARL effectively balances long-term efficiency with real-time adaptability, achieving high-quality communication in low Earth orbit satellite coverage areas under high dynamic environments.

Frequency diverse array (FDA) is a promising antenna technology to achieve physical layer security by varying the frequency of each antenna at the transmitter. However, when the channels of the legitimate user and eavesdropper are highly correlated, FDA is limited by the frequency constraint and cannot provide satisfactory security performance. In this paper, we propose a novel movable FDA (MFDA) antenna technology where the positions of antennas can be dynamically adjusted in a given finite region. Specifically, we aim to maximize the secrecy capacity by jointly optimizing the antenna beamforming vector, antenna frequency vector and antenna position vector. To solve this non-convex optimization problem with coupled variables, we develop a two-stage alternating optimization (AO) algorithm based on block successive upper-bound minimization (BSUM) method. Moreover, to evaluate the security performance provided by MFDA, we introduce two benchmark schemes, i.e., phased array (PA) and FDA. Simulation results demonstrate that MFDA can significantly enhance security performance compared to PA and FDA. In particular, when the frequency constraint is strict, MFDA can further increase the secrecy capacity by adjusting the positions of antennas instead of the frequencies.

The frequency diverse array (FDA) is highly promising for improving covert communication performance by adjusting the frequency of each antenna at the transmitter. However, when faced with the cases of multiple wardens and highly correlated channels, FDA is limited by the frequency constraint and cannot provide satisfactory covert performance. In this paper, we propose a novel movable FDA (MFDA) antenna technology where positions of the antennas can be dynamically adjusted in a given finite region. Specifically, we aim to maximize the covert rate by jointly optimizing the antenna beamforming vector, antenna frequency vector and antenna position vector. To solve this non-convex optimization problem with coupled variables, we develop a two-stage alternating optimization (AO) algorithm based on the block successive upper-bound minimization (BSUM) method. Moreover, considering the challenge of obtaining perfect channel state information (CSI) at multiple wardens, we study the case of imperfect CSI. Simulation results demonstrate that MFDA can significantly enhance covert performance compared to the conventional FDA. In particular, when the frequency constraint is strict, MFDA can further increase the covert rate by adjusting the positions of antennas instead of the frequencies.

We propose a joint covert beamforming design and jamming strategy to protect the communication process between Alice and Bob from being discovered by Willie with the help of another pair of neutral nodes. Specifically, with the help of irrelevant communication parties that commonly exist in practical communication scenarios, Jammer increases his transmission power by interfering with the neutral receiver, thus indirectly increasing the interference to Willie, which can be viewed as leveraging the force to make a big impact with a small effect. In our designed beamformer, we jointly optimize the beam power allocation factor, Alice’s transmission power, and Jammer’s transmission power when Alice transmits, to maximize the covert rate, which also maximizes Alice’s transmission power. In the perfect channel state information (CSI) scenario, the transformed optimization problem is solved via a one-dimensional search method and CVX solver. Due to the solution’s high complexity, we further propose a method to determine the optimal power allocation factor. For the imperfect Willie’s CSI scenario, three cases are investigated: Alice to Willie imperfect CSI, Transmitter to Willie imperfect CSI, and Jammer to Willie imperfect CSI. We utilize the S-procedure to tackle the optimization problem. Simulation results demonstrate the effectiveness of our proposed strategy.

In-orbit computation offloading plays a crucial role in enhancing the performance of resource-constrained mobile devices by conserving energy and reducing application latency. However, the inherent channel uncertainty in uplink communications poses a significant challenge, often degrading the Quality of Service (QoS) provided by Satellite Edge Networks (SENs). This uncertainty cannot be effectively captured by static parametric modeling, limiting their applicability in dynamic environments. To address this limitation, we propose an environment-aware computational offloading strategy for SENs. Unlike previous studies that neglect the impact of uplink channel uncertainty, we focus on this key issue by formulating a stochastic optimization problem aimed at minimizing offloading latency. Our approach integrates channel state variability into the decision-making process, ensuring a more realistic and robust model for SEN applications. In particular, we design a novel Spatio-Temporal Mixing (STM) methodology to extract relevant features from both environmental data and historical Channel State Information (CSI). These features are then used to jointly optimize the task scheduling, satellite selection, and beamforming vector design. Extensive simulations demonstrate that the proposed STM approach significantly reduces latency compared to traditional methods. The results highlight the effectiveness of our strategy in addressing the challenges posed by uplink channel uncertainty, ultimately leading to more efficient and reliable SEN operations.

Jamming recognition plays a pivotal role in anti-jamming communication system, ensuring an effective anti-jamming strategy can be given quickly. Because the characteristics of jamming are manifested in multiple domains, fully extracting the characteristics of jamming can improve the accuracy of jamming recognition. Therefore, a multi-modal feature extraction network for jamming recognition is constructed in this paper. The network leverages both time-frequency diagram containing time-frequency domain information and cyclic spectrum diagram containing frequency domain information of jamming as inputs. By designing a multi-scale convolutional neural network, the long-range dependencies of the time-frequency diagram and the fine features of the cyclic spectrum are captured. To ensure the full fusion of features, the feature map matrix of each channel after network processing is subjected to Hadamard product, and then the sum of the results is averaged as the final feature vector. Simulation results demonstrate that the proposed method achieves an average recognition rate of 99.5% when the Jamming to Signal Ratio (JSR) exceeds 0dB.

This work investigates a cognitive satellite-terrestrial covert communication network, where a satellite serves as the primary transmitter to communicate with a primary user (PU) covertly under the surveillance of an unauthorized warden and a terrestrial base station (BS) serves as the secondary transmitter to communicate with a secondary user. Considering imperfect channel state information (CSI) of warden, we jointly design both satellite's transmit power and BS's transmit beamforming to maximize the PU's covert rate while satisfying the terrestrial network's information rate requirement, satellite network's covertness and both satellite's and BS's power constraints. The successive convex approximation method is employed to obtain the optimal BS beamforming. In addition, the imperfect CSI is tackled by the$S$-procedure technique. Simulation results reveal that compared to other benchmark schemes the proposed optimization algorithm performs better in terms of the covert rate.

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.

This paper investigates a dual-unmanned aerial vehicle (UAV) assisted short packet covert communication system in the presence of a warden. Specifically, one flying UAV serves as the base station to transmit covert information to a legitimate ground user, and the other flying UAV is deployed as a cooperative jammer to transmit artificial noise against detection by a warden. Considering a more practical scenario, where only imperfect location information of the warden is known at both UAVs, we jointly optimize both UAVs’ transmit powers and three-dimensional (3D) trajectories to maximize the average covert transmission rate under the constraints of both UAVs’ mobility, transmit powers and warden’s detection error probability (DEP). On the one hand, the incomplete Gamma function involved in warden’s DEP makes the covertness constraint intractable for further analysis. To facilitate the design, warden’s DEP is lower bounded by Pinsker’s inequality. On the other hand, the formulated optimization problem is intractable to solve directly owing to the multiple highly coupled variables and the uncertainty of the warden’s location. The alternating optimization algorithm combined with the successive convex approximation and S-procedure techniques is leveraged to solve three optimization subproblems iteratively. Numerical results reveal that superior performance can be achieved by employing the proposed joint optimization of both UAVs’ transmit powers and 3D trajectories algorithm compared with the traditional two-dimensional trajectory optimization algorithm and the scheme without the assistance of the cooperative UAV.

Movable antenna (MA) array is a novel technology recently developed where positions of transmit/receive antennas can be flexibly adjusted in the specified region to reconfigure the wireless channel and achieve a higher capacity. In this letter, we, for the first time, investigate the MA array-assisted physical-layer security where the confidential information is transmitted from a MA array-enabled Alice to a single-antenna Bob, in the presence of multiple single-antenna and colluding eavesdroppers. We aim to maximize the achievable secrecy rate by jointly designing the transmit beamforming and positions of all antennas at Alice subject to the transmit power budget and specified regions for positions of all transmit antennas. The resulting problem is highly non-convex, for which the projected gradient ascent (PGA) and the alternating optimization methods are utilized to obtain a high-quality suboptimal solution. Simulation results demonstrate that since the additional spatial degree of freedom (DoF) can be fully exploited, the MA array significantly enhances the secrecy rate compared to the conventional fixed-position antenna (FPA) array.

Wireless information surveillance has received widespread attention due to the urgency of monitoring growing suspicious communications. This paper considers a challenging surveillance scenario, where the monitor (E) intends to eavesdrop the suspicious multihop communications from a long distance to ensure concealment, leading to the eavesdropping condition undesirable. To tackle this challenging, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted surveillance strategy, where the STAR-RIS, acts as a “bridge”, is deliberately deployed between the suspicious system and E, to adaptively transmit and reflect the suspicious signal and E's jamming signal, and then facilitate E's eavesdropping. Specifically, we consider the adaptive rate transmission and the delay-limited transmission for the suspicious system, and accordingly maximize E's instantaneous and average eavesdropping rate, by jointly optimizing the passive transmission- and reflection-coefficient matrices at the STAR-RIS, the jamming set and jamming power allocations of E (across all hops). The optimization problems in both transmission modes include numerous integer and continuous variables and thus are highly non-convex. Nevertheless, we show by detailed analysis that the original problem in each mode can be solved by only considering two possible cases, where E and the STAR-RIS intend to enhance and reduce the suspicious transmission rate, respectively. More importantly, in each case, many of necessary prerequisites for achieving the optimal solution are first determined analytically. Armed with these, the optimization problem then can be solved by leveraging the successive convex approximation technique and the simple search. As demonstrated by simulation results, since our proposed strategy is adaptive in term of varying the suspicious transmission rate, it will achieve significant eavesdropping performance gain as compared to other competitive benchmarks.

Movable antenna (MA) technology is highly promising for improving communication performance, due to its advantage of flexibly adjusting positions of antennas to reconfigure channel conditions. In this paper, we investigate MAs-assisted secure transmission under a legitimate transmitter Alice, a legitimate receiver Bob and multiple eavesdroppers. Specifically, we consider a practical scenario where Alice has no any knowledge about the instantaneous non-line-of-sight component of the wiretap channel. Under this setup, we evaluate the secrecy performance by adopting the secrecy outage probability metric, the tight approximation of which is first derived by interpreting the Rician fading as a special case of Nakagami fading and concurrently exploiting the Laguerre series approximation. Then, we minimize the secrecy outage probability by jointly optimizing the transmit beamforming and positions of antennas at Alice. However, the problem is highly non-convex because the objective includes the complex incomplete gamma function. To tackle this challenge, we, for the first time, effectively approximate the inverse of the incomplete gamma function as a simple linear model. Based on this approximation, we arrive at a simplified problem with a clear structure, which can be solved via the developed alternating projected gradient ascent (APGA) algorithm. Considering the high complexity of the APGA, we further design another scheme where the zero-forcing based beamforming is adopted by Alice, and then we transform the problem into minimizing a simple function which is only related to positions of antennas at Alice. Such problem is well-solved via another projected gradient descent algorithm developed with a lower complexity. As demonstrated by simulations, our proposed schemes achieve significant performance gains compared to conventional schemes based on fixed-position antennas.

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.

Unmanned aerial vehicle (UAV) plays an important role in wireless communication systems, due to the additional degree of freedom realized from its flexible deployment. Driven by this advantage and considering the security issue, this paper aims to investigate UAV-enabled proactive eavesdropping over distributed transmit beamforming-based suspicious communications. Specifically, for the suspicious system, there are multiple suspicious clusters aiming to communicate with the suspicious destination (D) using mutually orthogonal frequency bands, and distributed transmit beamforming is exploited by each cluster to strengthen the signal receiving quality at D. For the legitimate party, the full-duplex UAV exploits one antenna to jam D and uses the other antenna to overhear the signals of the suspicious clusters concurrently. By resorting to the Laguerre series approximation and the central limit theorem, we first characterize, in closed form, the approximated distributions of the receiving signal-to-interference-noise ratio (SINR) at D and the UAV, which are shown to be very tight. Based on this analysis and considering that the suspicious system works in the delay-limited transmission mode or the delay-sensitive transmission mode, we aim to maximize the minimum eavesdropping success probability of the UAV for those suspicious communications links, by jointly adjusting the UAV’s deployment and jamming power allocations over different frequency bands. The problem is highly non-convex. To tackle this, we develop an alternative optimization framework and further a novel and low-complexity solution in the high SNR regime to the optimization problem. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

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.

This paper investigates a cognitive information surveillance scenario, where a half-duplex legitimate monitor (E) aims to eavesdrop the frequency division multiple access based suspicious downlink broadcasting communication, which shares the same spectrum with a primary downlink broadcasting system. Specifically, the suspicious transmitter (ST) employs water-filling power allocation over all orthogonal frequency bands (FBs) to maximize the sum suspicious communication rate of all suspicious receivers (SRs), while the primary transmitter (PT) purposely maximizes the minimum communication rate among the primary receivers (PRs). Under this setup, E picks up certain FBs to jam and eavesdrops over the remaining FBs, and intends to control its transmit jamming beamforming over the jammed FBs to deliberately interfere with the corresponding PRs and SRs, such that the PT and the ST will reactively reallocate their power to facilitate E's eavesdropping. Our objective is to maximize the sum instantaneous (ergodic) eavesdropping rate at E, by jointly optimizing its jamming set and the corresponding jamming beamformers. The optimization problem is non-convex and its approximate and sub-optimal solutions are provided by some convex optimization procedures, along with the optimal and greedy jamming set selections. Results show the effectiveness of our proposed jamming-assisted eavesdropping compared to competitive benchmarks.

With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.

Toward 6G networks, such as virtual reality (VR) applications, Industry 4.0, and automated driving, demand mobile-edge computing (MEC) techniques to offload computing tasks to nearby servers, which, however, causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, enabling low latency and high throughput. For this sake, this article investigates the resource management for the offload transmission of the mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation, and power allocation is formulated as a multiobjective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using the$\epsilon $-constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that the gap between Pareto front generated by the three-stage iterative resource allocation algorithm and the real Pareto front is less than 0.16%. Furthermore, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.

We investigate proactive eavesdropping with one half-duplex legitimate monitor (E) in unmanned aerial vehicle (UAV)-enabled suspicious relaying systems, which consist of a suspicious transmitter (ST), a suspicious UAV-based relay (SU) and a suspicious destination (SD). Under this setup, we propose two jamming-assisted eavesdropping strategies, namely, “Eavesdrop-Then-Jam” (ETJ) and “Jam-Then-Eavesdrop” (JTE), to maximize the eavesdropping throughput of E. Specifically, for ETJ, E first eavesdrops the suspicious signal of the ST in the ST-SU phase and then jams the SD in the SU-SD phase; for JTE, E first jams the SU in the ST-SU phase and then eavesdrops the suspicious signal of the SU in the SU-SD phase.However, in both strategies, as the jamming power of E (independent variable) changes, the SU can also adaptively adjust its deployment (dependent variable) to maximize the benefit of the suspicious system. Facing this challenge, we can only employ the undesirable exhaustive search over both the independent and dependent variables to achieve the optimal solution. To decrease the complexity, we further derive a tight approximation of the Lambert function and then develop easy-to-implement algorithms to find the sub-optimal solutions. Numerical results demonstrate the effectiveness of our proposed strategies compared to conventional passive eavesdropping.

This letter studies jamming-assisted proactive eavesdropping over multiple orthogonal suspicious links with a wireless-powered monitor. Considering the shortage of energy, the monitor adopts the power splitting technique to divide each of the received suspicious signals into two parts for information decoding and energy harvesting, and then exploits the accumulated energy to jam over those suspicious links, to reduce the corresponding suspicious communication rate and facilitate the eavesdropping of the monitor itself. Our objective is to maximize the minimum eavesdropping success probability of the monitor for all received suspicious signals, by jointly optimizing its power splitting ratios and jamming power allocations. The formulated problem involves the complex Lambert function and is highly non-convex. To tackle this challenge, first we derive a very tight approximation of the Lambert function based on its key property. Then, we employ the general successive convex approximation (SCA)-based technique to iteratively find a locally optimal solution. Moreover, we propose a parallel coordinate descent (PCD)-based low-complexity algorithm to obtain a sub-optimal solution. Numerical results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This letter studies proactive eavesdropping with one legitimate monitor (E) in the classical single-hop suspicious communication. Specifically, unlike all previous works that ignored the suspicious channel training phase and just considered the jamming power optimization of E in the suspicious data transmission phase to facilitate eavesdropping, this letter advances the research by comprehensively investigating the jamming power allocation of E in both phases, with the purpose of maximizing its eavesdropping success probability. Under this setup, we first derive an exact expression of the objective and reveal the existence of a fundamental trade-off in deciding the jamming power allocation, for which a simple one-dimensional search is employed to find the optimal solution. To simplify the analysis, we further derive a tight approximation of the objective and then develop a very fast alternating optimization algorithm to find the sub-optimal jamming power allocation. Moreover, we extend our analysis to the case where the channel state information is available at the suspicious transmitter via channel feedback. Simulation results demonstrate the effectiveness of our proposed scheme compared to competitive benchmarks.

Covert communication, which can hide the communication behavior, has great potential in guaranteeing the security of information and transmission terminal to the greatest extent. In this paper, we propose a covert communication strategy based on improper Gaussian signaling (IGS) to increase the covert rate in the Internet of Things (IoT) system, in which the existing signals emitted by other transmitters were used as “Spectrum Shelter” to cover the IoT's communication. Specifically, we analyze the system's achievable transmission rate when the IoT node adopts IGS. Then, the optimal detection threshold and the minimum error detection probability of the warden are analyzed. Next, by jointly optimizing the transmission power and circularity coefficient of IGS, we maximize the covert rate under the constraints of the covertness. Finally, the simulation results verify that the proposed scheme can not only guarantee the covertness, but also improve the achievable covert rate.

Surveillance performance is studied for a wireless eavesdropping system, where a full-duplex legitimate monitor eavesdrops a suspicious user's link with artificial noise (AN) assistance. Different from the existing works, the suspicious receiver is assumed to be capable of detecting the presence of AN. Once such receiver detects the AN, the suspicious user will stop transmission, which can therefore degrade the surveillance performance. Hence, to improve the surveillance performance, AN should be transmitted covertly with a low detection probability. Under these assumptions, an optimization problem is formulated to maximize the surveillance performance under a covert constraint. Then, based on the detection ability at the suspicious receiver, a novel scheme is proposed to solve the optimization problem using an iterative search. Moreover, we investigate the impact of both the suspicious-transmitter-to-suspicious-receiver and the monitor-to-suspicious-receiver links uncertainties on the covert surveillance performance. Simulations are performed to verify the analyses. We show that the uncertainty in the suspicious user's link can enhance the surveillance performance, while the uncertainty in the monitor-to-suspicious-receiver link can degrade the surveillance performance.

Covert transmission is studied for an intelligent reflecting surface (IRS) aided communication system, where Alice aims to transmit messages to Bob without being detected by the warden Willie. Specifically, an IRS is used to increase the data rate at Bob under a covert constraint. For the considered model, when Alice is equipped with a single antenna, the transmission power at Alice and phase shift at the IRS are jointly optimized to maximize the covert transmission rate with either instantaneous or partial channel state information (CSI) of Willie's link. In addition, when multiple antennas are deployed at Alice, we formulate a joint transmit beamforming and IRS phase shift optimization problem to maximize the covert transmission rate. One local optimal algorithm and two low-complexity suboptimal algorithms are proposed to solve the problem. Furthermore, for the case of imperfect CSI of Willie's link, the optimization problem is reformulated by using the triangle and Cauchy-Schwarz inequalities. The reformulated optimization problems are solved using an alternative algorithm, semidefinite relaxation (SDR) and Gaussian randomization techniques. Finally, simulations are performed to verify our analysis. The numerical results show that an IRS can degrade the covert transmission rate when Willie is closer to the IRS than Bob.

Covert wireless communications aim to hide the existence of transmission behavior from watchful adversaries to enhance security. In this paper, we propose a spectrum mask based covert communication strategy in Internet of Things (IoT) networks, where the overt channels are leveraged to enhance the covertness. Specifically, the legitimate IoT transmitter superimposes its own message on the overt channel to avoid being detected by the warden. We assume that proper Gaussian signaling (PGS) is adopted at the overt channel, and improper Gaussian signaling (IGS) is adopted at the legitimate transmitter to improve the covert transmission performance. To maximize the covert rate of the legitimate IoT system, a joint transmit power and IGS factor optimization problem is formulated under the constraints of covertness requirement. The metric of minimum error detection probability, that represents the worst-case for the legitimate transmitter, is utilized to measure the covertness. By exploiting the piece-wise monotonic properties of the objective function and the constraints, we derive the optimal transmit power and IGS factor pairs in both the IGS and PGS schemes. Finally, extensive numerical results are presented to demonstrate that the IGS scheme can improve the covert rate compared to the PGS scheme under a given covertness constraint.

Secrecy transmission is investigated for a cooperative jamming scheme, where a multi-antenna jammer generates artificial noise (AN) to confuse eavesdroppers. Two kinds of eavesdroppers are considered: passive eavesdroppers who only overhear the legitimate information, and active eavesdroppers who not only overhear the legitimate information but also jam the legitimate signal. Existing works only treat the passive and active eavesdroppers separately. Different from the existing works, we investigate the achievable secrecy rate in presence of both active and passive eavesdroppers. For the considered system model, we assume that the instantaneous channel state information (CSI) of the active eavesdroppers is available at the jammer, while only partial CSI of the passive eavesdroppers is available at the jammer. A new zero-forcing beamforming scheme is proposed in the presence of both active and passive eavesdroppers. For both the perfect and imperfect CSI cases, the total transmission power allocation between the information and AN signals is optimized to maximize the achievable secrecy rate. Numerical results show that imperfect CSI between the jammer and the legitimate receiver will do more harm to the achievable secrecy rate than imperfect CSI between the jammer and the active eavesdropper.

A cooperative jamming and beamforming scheme is proposed for secrecy transmission. Different from the existing works, an internal eavesdropper (Eve) and multiple external Eves coexist in the system, where a multi-antenna jammer is deployed to confuse Eves by using artificial noise. Specifically, the jammer can obtain the instantaneous channel state information (CSI) of the internal Eve, but only has the partial CSI of the external Eves. Upon setting up, the transmission outage requirement at Bob and secrecy outage constraint at the Eves, the transmit power is optimized to derive the maximum secrecy rate for the transmission. Moreover, we investigate the optimal power allocation scheme by characterizing the beamforming vectors under considering both perfect and imperfect CSI cases for the internal Eve. Numerical results show that the proposed scheme can improve the secrecy rate significantly by making full use of instantaneous CSI between the jammer and the internal Eve.

This paper investigates the sensor selection problem for time difference of arrival (TDOA) localization in wireless sensor networks. Specifically, a multi-objective optimization problem is formulated in which a Boolean vector is involved to find the best tradeoff between the localization accuracy and the energy consumption. The ε- constraints method is introduced to convert the original multi- objective optimization problem to a tractable single-objective problem. To solve the converted sensor selection problem, we propose the polyblock outer approximation (POA) algorithm based on discrete monotonic optimization (DMO) in order to find the global optimal solution, which however can not be obtained by the traditional semidefinite relaxation (SDR) approach. Further, for the sake of practical implementation, we propose another two suboptimal algorithms, namely, POA-based accelerated cutting (POA-AC) algorithm and POA-based monotonic cutting (POA-MC) algorithm. Simulation results validate that the localization accuracy for sensors selected by the POA-AC algorithm and POA-MC algorithm is greater than the semidefinite relaxation (SDR) solution and achieves the same results as that by the exhaustive search method.

In this paper, when transmit antenna selection (TAS) and maximal ratio combining (MRC) schemes are deployed for secrecy enhancement, the impact of channel correlation on secrecy performance is investigated over Rayleigh fading channels. Specially, both the exact and asymptotic secrecy outage probability (SOP) for TAS/MRC are derived based on a novel correlation model, where the correlated legitimate channels and eavesdropper channels are modeled as a set of conditional independent channel gains. Moreover, the lower bound of SOP is obtained in high signal-to-noise ratio (SNR) region. Finally, simulation results verify our analysis, and show that the channel correlation between the legitimate receiver and the eavesdropper is beneficial to the SOP at high SNR.

In this paper, considering both the antenna correlation and the channel correlation between the legitimate receiver and the eavesdropper, we comprehensively investigate the secrecy performance for transmit antenna selection and maximal ratio combining (TAS/MRC) scheme in a multi-input multi-output multi-antenna eavesdropper (MIMOME) system. Different from the existing works, the correlated legitimate channels and correlated eavesdropper channels experience Nakagmai-${m}$fading with distinct fading parameters, and are modeled as a combination of conditionally independent channel gains and independent channel gains. By this novel correlation representation, the combined effect of antenna correlation and channel correlation between the legitimate receiver and the eavesdropper on secrecy performance is quantified via the secrecy outage probability (SOP) under a more general fading circumstance. In addition, two special cases of correlated channels are addressed in detail. Finally, simulations are performed to verify our analysis. The analysis and simulation results show that at high signal-to-noise ratio (SNR), antenna correlation at the legitimate receiver can improve the SOP performance for the correlated MIMOME wiretap channels, and this finding is contrary to the conventional wisdom that antenna correlation degrades the SOP performance.

This paper presents a detailed secrecy performance analysis of correlated multi-antenna wiretap channels with three popular diversity combining schemes, namely maximal ratio combining, selection combining, and equal gain combining at the legitimate receiver. For single-input multiple-output wiretap channels, both exact and asymptotic expressions are derived for the secrecy outage probability (SOP) of these systems. The findings suggest that, compared with the scenario where all the channels are independent, correlation of the main channels alone increases the SOP by a factor of 1/ det(U), where det(U) is the determinant of correlation matrix U. In contrast, when the average SNR at the legitimate receiver is much larger than that at the eavesdropper, correlation between the main channels and the eavesdropper channels has a positive effect on SOP. Moreover, in the case of multiple-input and multiple-output wiretap channels, two transmit antenna selection schemes with or without the eavesdropper's channel state information are evaluated by the SOP and the average secrecy capacity for the considered correlated channel model, respectively. Finally, numerical simulations are conducted to corroborate the analytical results.

Secrecy performance is investigated for relay networks with multiple antennas Nt at the transmitter. As K relays are available, we propose an incremental threshold relaying scheme with low complexity, where both transmit antenna selection (TAS) and relay selection are deployed without eavesdropper channel state information (CSI). Both the exact and approximate secrecy outage probability (SOP) of proposed scheme are derived with outdated CSI under the cases of maximal ratio combining (MRC) scheme at the legitimate receiver. In the high signalto-noise ratio (SNR) regime, due to outdated CSI, the secrecy diversity order of MRC decreases to Nt+1 for relay selection and K + 1 for antenna selection. Finally, simulation results validate our analysis, and show that the proposed scheme alleviates the effect of the transmitter and relay links on SOP.

Cooperative relaying communication is one of the green communication technologies since it shortens the communication distance and saves the transmit power. In this paper, the physical‐layer security (PLS) of a multisource multirelay cooperative relaying communication network is investigated by considering the influence of cochannel interference from a security‐reliability tradeoff (SRT) perspective. First, the SRT performance is characterized by the outage probability (OP) and the intercept probability (IP). In particular, the IP encountered at the eavesdropper is used to evaluate the security performance, while the reliability performance is analyzed in terms of the OP experienced at the destination. Then, under the impact of multiple cochannel interferers, the intercept probabilities and the outage probabilities of both the conventional direct transmission (DT) strategy and relay selection (RS) strategy are derived in closed‐form expressions over Rayleigh fading channels, respectively. Simulation results are provided to validate the theoretical analysis. It is shown that when the OP (reliability) requirement is relaxed, the IP (security) performance improves and vice versa. It confirms that there is an SRT existing between the OP and the IP. Meanwhile, a better SRT performance can be achieved by increasing the number of sources, relays, and cochannel interferers. In addition, it is also shown that the RS strategy generally outperforms the conventional DT strategy in terms of the product of the IP and the OP.

We focus on the 3-dimensional (3D) source localization passively by using TDOA and AOA in the presence of sensor errors. Determining the position from the TDOA and AOA measurements is not an easy task because the relationship between them is nonlinear. We present a new WLS solution that the TDOA equation is simpler than relevant literature (Yin and Wan, A Simple and Accurate TDOA-AOA Localization Method Using Two Stations). However, the bias of the WLS solution is larger. Hence, we propose a bias reduced method by imposing a quadratic constraint so that the expectation of cost function can attain the minimum value at the true position. The simulation illustrates the method is effective and the performance of this method can achieve the Cramer-Rao Lower Bound (CRLB) when the noises are Gaussian and in small region.

In this study, a cooperative wireless network is studied based on physical‐layer security, where multiple co‐channel interferers exist at relays, the destination and eavesdropper. Under the influence of multiple co‐channel interferers, the tradeoff between the security and reliability of wireless communications against eavesdropping attacks is analysed in terms of the intercept probability (IP) and the outage probability (OP). The exact closed‐form expressions for the IP and OP are derived for the direct transmission (DT) and the relay selection (RS) schemes over Rayleigh fading channels. The theoretical analysis is validated by numerical results. It is shown that the increase of the number of relays and (or) the number of co‐channel interferers can efficiently improve the security–reliability tradeoff (SRT). Moreover, the RS strategy can achieve a higher SRT performance compared with the DT strategy.

This study considers the effect of channel estimation error (CEE) on the soft‐output bit log‐likelihood ratio (LLR) in a multiple‐input–multiple‐output detection system which the source sends their quadrature amplitude modulation to the detector through Rayleigh fading channels. The new expressions of LLR with maximum‐likelihood CE for deterministic channel model and minimum mean square error (MMSE) CE for random channel model are obtained separately for linear MMSE receiver using known pilot‐symbol‐aided CE. Meanwhile, the optimal power allocation between training and data transmissions for these two different LLR expressions, which attends to get the maximum ratio of signal and interference plus noise ratio, are obtained under the total transmitting power constraints. Numerical results show that the power allocation ratio sets to be ∼0.5 is optimal for two new different LLRs at the same total power and system noise. Moreover, the derived LLR expressions match the simulation result and outperform the conventional system without the consideration of CEE and optimal power allocation. Meanwhile, the system performance is better than the existing research with the consideration of CE.

A carrier synchronisation technique for multiple symbol Trellis‐coded continuous phase frequency shift keying (MSTC‐CPFSK) system in burst‐mode transmission is proposed. For the synchronisation technique, a joint data‐aided (DA) acquisition and phase‐locked loop (PLL) tracking algorithm is presented. First, an all‐zero sequence is utilised as a pilot for DA acquisition, where the modulated pilot waveform is a direct current (DC) signal, eliminating the operation of modulation removal; and then, a second‐order PLL structure is introduced for tracking processing, and we make an analysis of the tracking performance by calculating the equivalent Cramer–Rao bound (ECRB) for the second‐order PLL structure; subsequently, we extend the obtained result to any order PLL structures; moreover, the ECRB for the MSTC‐CPFSK system is also derived through arithmetical calculation; finally, some numerical results are given to verify the performance of the authors presented synchronisation algorithm, and the simulations illustrate that their algorithm can improve the throughput compared with the classical DA‐only algorithm.

A multiple-input single-output cognitive radio downlink network is studied with simultaneous wireless information and power transfer. In this network, a secondary user coexists with multiple primary users and multiple energy harvesting receivers. In order to guarantee secure communication and energy harvesting, the problem of robust secure artificial noise-aided beamforming and power splitting design is investigated under imperfect channel state information (CSI). Specifically, the transmit power minimization problem and the max-min fairness energy harvesting problem are formulated for both the bounded CSI error model and the probabilistic CSI error model. These problems are non-convex and challenging to solve. A 1-D search algorithm is proposed to solve these problems based on S-Procedure under the bounded CSI error model and based on Bernstein-type inequalities under the probabilistic CSI error model. It is shown that the optimal robust secure beamforming can be achieved under the bounded CSI error model, whereas a suboptimal beamforming solution can be obtained under the probabilistic CSI error model. A tradeoff is elucidated between the secrecy rate of the secondary user receiver and the energy harvested by the energy harvesting receivers under a max-min fairness criterion.

Cooperative spectrum sensing, proposed to improve the performance of spectrum sensing in cognitive radio systems where there are multiple secondary users who can cooperatively detect the presence of one primary user, is receiving significant attention. However, few cooperative sensing algorithms take the correlation among the received primary user signals into account. A feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix of the received signals is proposed. The ratio of the maximum eigenvalue to the minimum eigenvalue of the matrix obtained by Cholesky decomposition is used to construct the test statistic. Analytical approximations for the false alarm probability and decision threshold are derived using a moment matching method. The new scheme is in the category of blind cooperative spectrum sensing schemes requiring neither information about the primary user signal nor the channel nor the noise power. The new scheme can work better than the existing eigenvalue‐based cooperative spectrum sensing methods in some conditions, and it has lower complexity.

Spectrum sensing is performed at the beginning of each time slot in traditional cognitive radio networks, which is unreasonable and needless since the presence or the absence of a primary user (PU) always lasts several time slots. A hidden Markov model is used to describe the imperfect spectrum sensing process over Rayleigh fading channels. On the basis of the sensing results, a hybrid interweave/underlay mode is exploited by the secondary user (SU) to achieve a higher throughput. To solve the tradeoff problem among the average energy consumption for spectrum sensing, the average throughput of SU and the average interference to the PU, an optimisation problem is proposed. The optimal sensing interval to determine when the next spectrum sensing should be performed is obtained by solving the optimisation problem. Numerical results are given to verify the authors’ analysis.

A definition of least cyclostationary order (LCO) is proposed based on the inner cyclic period of single carrier and orthogonal frequency division multiplexing (OFDM) signals. The LCOs of single carrier and OFDM signals are derived. Since the LCO has a relationship with the carrier frequency offset (CFO), a blind CFO estimation scheme for single carrier and OFDM signals can be proposed. The novel scheme is entirely blind without any priori information and is proved to be the same as cumulants estimation when the LCO is known. However, the new scheme is superior to the estimation based on cumulant when the LCO is unknown. The mean‐square error of M ‐phase shift keying signals approaches the Cramér–Rao bound in additive white Gaussian noise (AWGN) channel. Simulation results are given to verify the effectiveness of the proposed blind estimation scheme in Rician fading channel and in AWGN channel.

Performance analysis is presented for the joint estimation of symbol timing, frequency offset, and phase offset with continuous‐phase modulation. In this study, undesirable influences on parameters to be estimated, which arise from the inaccuracy of the already estimated parameters, are computed mathematically. Based on performance analysis, a conditional Cramer‐Rao bound (CRB) is put forward, and a hypothesise is made that phase offset variance with frequency error present should match with the conditional CRB for any digital phase modulation. Moreover, the hypothesis is manifested by analysing the log‐likelihood function in a more general mathematical manner. To alleviate the influences, a data‐aided acquisition and phase‐locked loop tracking algorithm is proposed. The results of Monte Carlo simulations are presented, showing good agreement with theoretical analysis. Simulation results also show that the proposed algorithm can improve performance in terms of phase error variance at the expense of a high signal‐to‐noise ratio threshold.

An efficient cooperative spectrum sensing (SS) scheme based on Cholesky decomposition of the covariance matrix is proposed. The maximum eigenvalue (ME) of the matrix obtained by Cholesky decomposition is used as a test statistic. Analytical expressions for the false alarm probability and the decision threshold are derived. The effects of noise uncertainty on the ME SS algorithm and on the Cholesky ME cooperative SS scheme are assessed. It is proved that the proposed SS scheme is more robust than the conventional ME SS scheme in terms of noise uncertainty. Simulation results show that the performance of the proposed scheme is better than that of existing ME SS scheme in some conditions.

Green communications is an inevitable trend for future communication network design, especially for a cognitive radio network. Power allocation strategies are of crucial importance for green cognitive radio networks. However, energy-efficient power allocation strategies in green cognitive radio networks have not been fully studied. Energy efficiency maximization problems are analyzed in delay-insensitive cognitive radio, delay-sensitive cognitive radio, and simultaneously delay-insensitive and delay-sensitive cognitive radio, where a secondary user coexists with a primary user and the channels are fading. Using fractional programming and convex optimization techniques, energy-efficient optimal power allocation strategies are proposed subject to constraints on the average interference power, along with the peak/average transmit power. It is shown that the secondary user can achieve energy efficiency gains under the average transmit power constraint, in contrast to the peak transmit power constraint. Simulation results show that the fading of the channel between the primary user transmitter and the secondary user receiver and the fading of the channel between the secondary user transmitter and the primary user receiver are favorable to the secondary user with respect to the energy efficiency maximization of the secondary user, whereas the fading of the channel between the secondary user transmitter and the secondary user receiver is unfavorable to the secondary user.

The performance of underlay cognitive relay networks with multiple primary users and multiple proactive decode-and-forward relays is investigated. Particularly, both interference power constraints at multiple primary receivers and multiple primary transmitters' interference at secondary transmission nodes are taken into account. Considering correlations among the received signal-to- interference-plus-noise ratios, exact closed-form expressions for the outage probabilities of secondary users are derived for the cases with or without a direct secondary link over Rayleigh fading channels. A novel method is proposed to analyze the outage probability for cognitive proactive decode- and-forward relay networks in the presence of a direct secondary link. Simulation results show that the diversity gain is zero in the presence of multiple primary transmitters' interference, but the outage floor in the high SNR can be significantly decreased by an increase of the number of relays or by exploiting a direct secondary link.

The outage behavior of a dual-hop multi-source multi-relay cooperative underlay cognitive radio network is investigated in the presence of multiple primary transmitters and multiple primary receivers. Firstly, with primary users' (PUs') outage constraints, an adaptive power control scheme is adopted to obtain maximum allowed secondary transmission power. Then, an SINR-based scheme is proposed for the combined use of cooperative diversity and multiuser diversity, where the best secondary source-relay pair is selected to communicate with the secondary destination. In this context, exact closed-form expressions for the outage probability of reactive decode-and-forward (DF) relaying are derived for maximum ratio combining (MRC) and selection combining (SC) over Rayleigh fading channels, respectively. In particular, we consider the dependence among the received signal-to-interference-plus-noise ratios (SINRs) at the destination caused by multiple PUs. It is shown that although there exists outage floor in the high signal-to-noise ratio (SNR) regime, increasing the number of secondary sources or available relays are efficient ways to decrease the outage floor.

In this study, the authors investigate the impact of the frequency‐hopping (FH) sequence on the packet transmission performance of the asynchronous frequency‐hopping multiple‐access (FHMA) network. According to the model of the asynchronous FHMA network given in this study, the authors analyse the impact of the two important FH sequence factors – uniformity and frequency slot number – on the correct transmission probability and the packet transmission frequency efficiency. According to the non‐collision probability analysed in this study, the uniformity of the FH sequence is an important issue to determine the correct packet transmission probability. Moreover, based on the definition of the packet transmission frequency efficiency given in this study, the optimal frequency slot number employed to obtain the maximum frequency efficiency is obtained. By employing three typical FH sequences, the simulation results are also included, which validates the theoretical analysis about the packet transmission performance of the asynchronous FHMA network.

In this study, the authors investigate the effect of imperfect channel state information (CSI) on underlay cognitive radio networks in the presence of multiple primary users (PUs). Particularly, with multiple proactive partial decode‐and‐forward (DF) relays available, both the relay selection channels and interference channels are assumed to be imperfect. In this context, firstly, based on the instantaneous CSI (ICSI) of interference channels, exact closed form expressions for the outage probability (OP) and the interference probability (IP) are derived, where IP is used as a performance metric to quantify the impact of harmful interference caused by secondary user (SU) on PUs. It is shown that, when the ICSI of interference channels are imperfect, the secondary networks always interfere with PUs' communications. Then, to reduce the harmful interference caused by secondary nodes, we propose an adaptive power control scheme relying on the partial CSI (PCSI) of interference channels. Finally, simulation results verify the accuracy and effectiveness of the author's analytical study, and indicate the proposed scheme strikes a good tradeoff between the interference introduced by secondary nodes on PUs and SU's performance.

Spectrum sensing has been well studied because of its significance in cognitive radio. Different from the existing works which a primary user (PU) is assumed to have only one constant transmit power, a more practical scenario that the PU transmitting with multiple power levels is considered. A continuous hidden Markov model (CHMM)‐based blind algorithm for not only detecting the presence of PU but also recognising the transmit power level of the PU is proposed. The training problem of CHMM is solved by combining the wavelet singularity detection with k ‐means clustering algorithm. An effective method for estimation of the number of power levels is proposed. Two different strategies are designed to perform spectrum sensing. Simulation results show the efficiency of the proposed algorithm.

The opportunistic spectrum access technology is one of the most promising methods for alleviating the spectrum scarcity problem, which enables a secondary user (SU) to utilise a primary user spectrum band that is detected idle. However, the throughput achieved by cognitive radios is limited by the interference constraint imposing on the SU. Multiple input single output antenna techniques and antenna selection (AS) techniques are exploited to combat the interference constraint and improve the achievable average throughput of the SU. The optimal power allocation strategy is proposed to maximise the achievable average throughput. Performance analyses for the achievable average throughputs are performed under the maximum channel gain AS strategy, the minimum interference channel gain AS strategy and the ratio AS strategy. It is proved that the optimal transmitted AS strategy is the ratio AS strategy when the optimal power allocation strategy is used. The optimal sensing parameters are designed to further improve the maximum average throughput. Extensive simulation results are conducted to verify this analysis.

Spectrum sensing is the precondition of implementation of cognitive radio. Motivated by the fact that eigenvalue detection algorithms are based on eigenvalue decomposition over the covariance matrix, we propose an efficient spectrum sensing algorithm based on Cholesky decomposition over that matrix. Using eigenvalues of the matrix which is obtained by Cholesky decomposition over finite covariance matrix, the efficient spectrum sensing algorithm is proposed. Attractive advantages of our proposed technique are: a) no assumptions on the sampling size and the dimension of the random matrix are required; b) exact and simple closed-form analytical expressions for the false alarm probability and decision threshold are derived under practical scenarios of finite size of the covariance matrix and samples; c) numerical simulations show that the presented algorithm achieves performance improvement compared with previous algorithms based on eigenvalue.

Traditional symbol rate estimators based on wavelet transform (WT) suffer from difficulties in choosing wavelet scale. Additionally, the most estimators are only adapted to limited signal types. Furthermore, those estimators are susceptible to noise, which is impractical to implement. In this paper, to overcome shortages of those traditional estimators, a self- adapting symbol rate estimator is proposed. The proposed estimator has advantages in reliably working under low Eb-to-N0. More importantly, it can be widely used to estimate various types of signal. The numerical simulations show that the proposed estimator has superiority in performance compared with the improved estimators based on wavelet transform.

The accuracy of sources locations and velocities estimate is very sensitive to the accurate knowledge of sensor locations and velocities. In the presence of sensor position and velocity errors, this study considers the problem of simultaneously locating multiple disjoint sources and refining erroneous sensor positions and velocities using time differences of arrival and frequency differences of arrival. The previous work by Sun and Ho to solve this problem provided an efficient estimator for multiple disjoint sources, but it cannot provide optimum accuracy for the sensor positions and sensor velocities. In many practical applications, it is necessary and helpful to refine sensor locations and velocities while localising multiple sources. The proposed method improves the previous method so that both the source and the sensor position and velocity estimates can achieve the Cramér–Rao lower bound accuracy very well over small noise region. The theoretical derivation is corroborated by simulations.

In practice, sensing results often affect the data transmission in cognitive radio systems. Therefore, a hybrid interweave/underlay cognitive radio system is presented by jointly considering spectrum sensing and data transmission. In particular, multiple relays are deployed for both the spectrum sensing and data transmission. The secondary user (SU) source and the relays cooperatively sense the spectrum with energy detection and decision fusion. According to the cooperative spectrum sensing results, the SU adaptively switches between interweave and underlay spectrum access mode. Moreover, to improve the spectral efficiency, an adaptive modulation technique is integrated into SU's transmission. In this context, undertaking the interference from the primary user (PU), the upper bound for average spectral efficiency of the SU with continuous-rate adaptive modulation is derived over Rayleigh fading channels. Further, we study the performance of SU, where adaptive L-ary quadrature amplitude modulation (L-QAM) with fixed switching threshold is adopted. Simulation results verify our analysis and show that both hybrid interweave/underlay scheme and adaptive modulation can improve the SU's performance significantly.

As spectrum sensing detects the presence of PU signal, an efficient and reliable spectrum sensing scheme plays a critical role in CR. For this purpose, sequential sensing technique is introduced to reduce the sensing time to the minimum while desirable detection performance is maintained. However the sensing time could still be unacceptably long due to the weak PU signal, especially in non-Gaussian noise. To improve spectrum sensing efficiency, we propose a novel sequential sensing scheme based on suprathreshold stochastic resonance (SSR). We address the theoretical bound to achieve potential performance improvement and give the applicable algorithm of SSR-based sequential sensing scheme. In the scheme, the average sample number (ASN) is reduced in a single sensing node using nonlinear stochastic resonance method. The simulation results show that the proposed scheme significantly outperforms the conventional scheme, especially in low signal-to-noise ratio (SNR) scenario.

In the presence of primary user (PU), most of researches have often neglected the effect of the interference from the primary transmitter to secondary receiver in cognitive radio (CR) systems. In this paper, with the PU's interference, we investigate the problem of optimal joint relay selection (RS) and power allocation (PA) to achieve maximum throughput in spectrum sharing cognitive two-way relaying networks. A closed-form solution is proposed for optimal allocation of transmit power among the secondary user (SU) transceivers and the SU relay. Simulation results show the PU's interference degrades the effectiveness of the system. Moreover, our proposed power allocation and relay selection methods can achieve maximum throughput in the presence of PU's interference.

Passive source localization has been the focus of considerable research efforts due to its usefulness in various applications. This paper performs a fundamental investigation of whether the gain ratios of arrival (GROAs) can be utilized in conjunction with the time differences of arrival (TDOAs) to improve the multiple sources localization accuracy with erroneous sensor positions. It's an urgent need to find a closed-form solution with good localization accuracy for this challenging problem. This paper takes the advantage that the TDOAs and GROAs from different sources have the same sensor position displacements and proposes an estimator that jointly estimates the unknown sources and sensor positions. In order to establish an closed-form solution for multiple sources location estimates using GROAs and TDOAs, we use the idea of hypothesized source locations in the algorithm development to enable the formulation of pseudo-linear equations. The proposed algebraic solution does not require initialization and does not have divergence problem. Our conclusion is that the improvement from GROAs is obvious for multiple sources localization compared with methods only using TDOAs. Numerical simulations are included to support and corroborate the theoretical developments.

In this paper, with multiple relays available for cooperative sensing and data transmission, we investigate the performance of primary user (PU) and secondary user (SU) in the hybrid interweave and underlay cognitive relay networks. For the cooperative sensing phase, all the relays independently sense the spectrum with energy detection, and hard fusion is deployed to achieve the final sensing result by a distributed manner. Subsequently, for the data transmission phase, when cooperative sensing results assume there exists a spectrum hole, the SU transmitter and the selected relay transmits the message with maximum transmission power. Otherwise, to guarantee the PU's transmission quality, the SU transmits the message under a peak power constraint at the PU receiver. It is noted that the selected relay can decode the SU's message correctly and has the largest signal to noise ratio (SNR) or signal to interference and noise ratio (SINR) at the SU receiver. In this case, considering the interference caused by the PU, we derive the outage probability of SU under Rayleigh fading channels. In addition, the impact of cooperative sensing on the PU are discussed. Finally, simulation results verify our analysis.

In this paper, we investigate the performance of adaptive modulation in spectrum sharing cognitive relay networks, where the secondary user (SU) transmits the message by one best relay with amplify and forward (AF) relaying scheme. Moreover, to guarantee the primary user (PU)'s transmission quality, the interferences caused by the SU transmitter and the selected relay are less than a predetermined interference threshold. The capacity of adaptive modulation with opportunistic AF relaying is firstly derived under independent Rayleigh fading channels. Then, with adaptive L-QAM modulation and fixed switching threshold, the outage probability, average spectral efficiency, and the average error bit rate (BER) of the SU are derived. Finally, simulation results validate our analysis and show that adaptive modulation can improve the SU's performance significantly.

High complexity of frequency-hopping (FH)/spread- spectrum(SS) sequence is of great importance to high-security multiple-access communication systems, for it makes FH/SS sequence difficult to be analyzed. In this paper, a new complexity metric to evaluate the unpredictability of FH/SS sequence based on the Fuzzy Entropy(FuzzyEn) is presented. Simulation and analytical results show that, the proposed FuzzyEn works can effectively discern the changing complexities of the FH/SS sequences, and are compared with complexity metric based on the Approximate Entropy(ApEn). The FuzzyEn scheme has obvious advantages in the robustness to resolution parameter, the dependence to observation length and the sensitivity to vector dimension.

In cooperative wireless networks, relay selection plays a significant role in the system performance. In this paper, with the presence of interferences, we firstly propose two relay selection protocols according to the forms of the interferences. Then considering that the interference caused by the selected relay has an effect on other transmissions, a threshold based relay selection protocol is proposed to select one best relay forwarding the message, where the interference caused by the selected relay is below than a predetermined threshold. Furthermore, in the proposed protocols, all the relays and the destination are subject to the interferences from other transmissions. The exact outage probabilities for the protocols are derived over the Rayleigh fading channels. Finally, simulation results validate our analysis,and show that our proposed relay selection protocols are better than the available relay selection protocols in terms of outage probability.

In this letter, as multiple interferers have an effect on both the relays and the destination, the exact outage probabilities for the decode-and-forward (DF) opportunistic relaying are derived in three different cases: a) with selection combining (SC) deployed at the destination, b) with maximal ratio combining (MRC) deployed at the destination, and c) with hybrid selection and maximal ratio combining (HSMC) deployed at the destination. Simulation results validate our analysis, and show that in terms of outage probability, opportunistic relaying with HSMC is much better than that with SC or MRC.

High complexity of frequency-hopping (FH)/spread-spectrum (SS) sequence is of great importance to high-security multiple-access communication systems, for it makes FH/SS sequence difficult to be analyzed. With the growing development in the design of FH/SS sequence in much wider fields, the well-known complexity measures - the linear complexity (LC), the linear complexity profile (LCP) and the k-error linear complexity (k-error LC) - are widely used but not sufficient to evaluate the complexities of the sequences available, such as the cryptographical sequence and the chaotic sequence families. In this paper, a new complexity metric to evaluate the unpredictability of FH/SS sequence based on the approximate entropy (ApEn) is proposed in the view of the maximal randomness of the sequences with arbitrary length. And the theoretical bounds of the ApEn are derived from a probabilistic point of view. Simulations and analysis results show that, the proposed ApEn works effectively to discern the changing complexities of the FH/SS sequences with small number of samples, which provide superior performance over its candidates.

Minimizing the total transmission power is one of the main objectives of efficient broadcast algorithms in wireless networks, where all nodes are powered by battery with limited energy supply. Cooperative transmission is an important way to make full use of the space diversity and can save the transmission power significantly, especially in the wireless broadcast transmission, where the nodes can accumulate the overheard information. In this paper, firstly a centralized cooperative broadcast algorithm, which permits multiple nodes to cooperate in transmitting the broadcast message, is proposed to save the total transmission power. Considering that the centralized algorithm requiring a global knowledge of the networks is impractical in large wireless networks, the distributed version of the centralized algorithm requiring only 1-hop neighborhood information is proposed under the assumption that limited frequency band is available. The distributed algorithm combines the physical (PHY) and medium-access-control (MAC) layer mechanisms to reach all the nodes in a cooperative way. Though the proposed distributed algorithm is inferior to the centralized algorithm in terms of total transmission power, it takes less resource to broadcast than the centralized algorithms. Simulation results show that both the centralized algorithm and distributed algorithm can get a better performance than the existing broadcast algorithms. Furthermore, the distributed algorithm can achieve the total transmission power close to the centralized algorithm.

According to the characteristics of meteor burst channels, variable rate data transmission is often employed to improve the average system throughput, and the time-varying meteor channel in the presence of multi-path rays brings difficulties in both data detection and channel estimation at the receiver. As we know, per-survivor processing (PSP) based on the joint data and channel estimation of maximum likelihood sequence detection (MLSD) is often considered to obtain the optimal detection performance for time-varying channels. However, the great computational complexity of PSP is the major problem in practice. In this paper we propose an adaptive state reduction of PSP (ASRP) algorithm with few states in the trellis diagram for exponential decay of meteor channels. Based on the estimation of the meteor channel parameters, the adaptive threshold is derived to select the states close to the right one in the trellis to reduce the complexity of PSP. Theoretical analysis and computer simulations results show that the ASRP we proposed provides suboptimal performance and makes good tradeoff between the performance and the computational complexity, so that the reliable data transmission can be realized for MBC systems with adaptive modulation and coding (AMC).