Benefiting from the flexible deployment capability and favorable propagation condition, the unmanned aerial vehicle (UAV) relay networks are crucial for rapid coverage extension and resilient connectivity in adversarial communication environments. However, the transmission effectiveness is constrained by scarce energy resource and severe hostile jamming attacks. Motivated by this need, this paper investigates the semantic communication (SemCom) empowered UAV relay networks that integrates SemCom with simultaneous wireless information and power transfer (SWIPT). For the first time, the semantic encoding/ decoding is embedded into the two-hop communication while the power-splitting (PS) jointly coordinates information transmission and energy harvesting. With the imperfect channel state information (CSI), we formulate the worst-case sum SemCom rate maximization problem subject to the constraints of semantic decoding feasibility, energy harvesting and transmit power. To address the intractable problem, we develop a robust three-step monotonic-optimization (MO) framework with low-complexity feasibility checking algorithm (LFCA). Firstly, a robust generalized discretization approach is used to convert the imperfect CSI. Then, the MO framework with LFCA is presented to obtain optimal solution of transmit beamformer, receive decoder and PS ratio with dimension reduction method. Simulations results show that proposed MO-LFCA scheme outperforms conventional bit-level SWIPT scheme and the other benchmark schemes versus different jamming power levels and antenna configurations, demonstrating that the integration of SemCom and SWIPT mechanism can enhance anti-jamming and efficiency performance in UAV relay networks.

This paper proposes a joint secure sensing and communication framework for full-link covert transmissions, which integrates an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system and compliant distributed cooperative jammers to enhance the communication quality of legitimate users while promoting the efficient utilization of limited resources. In the proposed scheme, upon sensing potential eavesdropper embodied by unmanned aerial vehicle (UAV), the dual-functional base station (BS) covertly transmits the acquired UAV state information to friendly jammers within relevant coverage area and issues activation commands promptly. Simultaneously, with IRS assistance, reconfigurable parameters such as signal phase in NOMA transmissions are adjusted to satisfy public user’s service requirements while facilitating covert communications for legitimate user. To ensure dynamic adaptability and link sustainability, the BS leverages historical sensing data to predict the UAV’s flight trajectory in real time and infer its movement intent. If the UAV exhibits a tendency to deviate from the currently effective jamming zone, the BS proactively activates friendly jammers in adjacent regions to maintain covert transmission rates and ensure robust system operation. To address the non-convex optimization challenge arising from jointly optimizing sensing beamforming, communication beamforming, and the IRS reflection matrix with highly coupled variables, we disassemble the problem into three subproblems. Correspondingly, an alternating optimization framework is designed by employing the semidefinite relaxation (SDR), Gaussian randomization, penalty-based methods, and Dinkelbach transformation to jointly maximize covert transmission rates while guaranteeing both sensing accuracy and communication quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves superior covert transmission rates compared with benchmark schemes. Moreover, the dual-covertness mechanisms for sensing and communication further enhance the system security, validating the framework’s robustness in dynamic resource-constrained environments.

Security and reliability have always evolved alongside advancements in communication technologies. Facing imminent the sixth generation of mobile communication (6G) era, this work explores a jamming exploitation-enabled covert transmission framework for satellite-aerial-terrestrial integrated networks (SATINs), aiming to meet user privacy requirements in future complex adversarial scenarios characterized by stereoscopic coverage and multi-domain collaboration. The research scenario involves a three-dimensional space comprising four core elements: a satellite, an unmanned aerial vehicle (UAV) equipped with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (active STAR-RIS), an eavesdropper possessing dual functionalities of jamming and detection, and a ground terminal. Upon sensing malicious jamming from the eavesdropper, the UAV aerial platform serving as a relay node utilizes its on-board active STAR-RIS to achieve the targeted reflection and manipulation of the malicious jamming signals while concurrently facilitating the effective forwarding of the legitimate signals. Namely, breaking through the conventional mindset of “jamming suppression”, it equivalently constructs a “self-interference loop” centered on the eavesdropper, thereby degrading the adversary’s detection sensitivity. For this process, we construct a covert analysis framework featuring the joint design of the static/dynamic scenarios and the active STAR-RIS reflection-transmission matrices dominated by the UAV’s limited power, derive the analytical expression of the Kullback-Leibler (KL) divergence, and establish rigorous covertness constraints for the system. To address the highly coupled non-convex problem in the joint optimization,we propose a solution combining semidefinite relaxation (SDR), Dinkelbach transformation, Gaussian randomization, and the proximal policy optimization (PPO) framework, maximizing the system covert transmission rate while satisfying various constraints. Numerical results demonstrate that, compared with benchmark schemes, the proposed scheme exhibits superior flexibility and covert transmission advantages in adversarial environments.

Mega-constellation networks face great challenges in providing broadband reliable communication service. The main obstacle is the highly dynamic network topology. This article focuses on this issue and makes an effort to examine the topology dynamic of mega-constellation from a new perspective. Some metrics derived from complex network theory are presented to describe topology characteristics. Considering the impact of the topology dynamic on routing, the topology dynamic characteristics are then expressed by a newly defined topology correlation of the two adjacent snapshots. Three well-known mega-constellations are used as examples and compared by using these metrics. Simulation results show that fewer nodes need to update routes with the same snapshot length when there is a greater correlation between snapshots. In addition, an isochronic snapshot topology control and routing update scheme (ISTC) is proposed to mitigate the problem of the surging number of snapshots due to the complex dynamics of mega-constellations. The upper and lower bound of the snapshot length are presented. So that, local convergence of routes can be achieved and the number of routing updates are as small as possible within each snapshot. Moreover, a decreasing distance shortest distance (DDSD) access satellite selection algorithm is presented to work with the topology control scheme and reduce the number of satellite handovers. Simulation results show that these methods have advantages in coping with the dynamics of constellation networks.

Multiple-input multiple-output (MIMO) technology can effectively improve transmission throughput and reliability by utilizing spatial wireless resources, which has aroused widespread research attentions. Comparing with the stationary communication environment considered in most studies, the nonstationary channel may cause severe performance degradation of MIMO technology. This promotes the research of adaptive MIMO transmission strategy, which can intelligently adapt to dynamic environment and provide reliable and efficient communication. In this paper, our purpose is to design an intelligent MIMO system that can adjust the MIMO transmission mode and modulation order according to nonstationary environment. The dynamic decision problem is formulated as a markov decision process (MDP) and the state, action and reward function are designed. Then, an adaptive MIMO transmission strategy via leveraging proximal policy optimization (PPO) learning framework is proposed. The trained PPO agent can learn the proper joint transmission strategy so as to maximize the spectral efficiency subject to the constraint of target bit error rate (BER) performance. Simulation results demonstrate that the proposed scheme can achieve significant performance improvement over benchmark schemes.

Hostile jamming and eavesdropping threats bring severe challenges to reliable and secure communication demands of future networks. In light of the potentials of high-altitude platform (HAP) providing wide communication coverage with low cost and Ad-hoc network facilitating flexible access without support by hardware infrastructure, this paper proposes a multi-HAPs assisted air-terrestrial integrated Ad-hoc networks (HAIN) framework to defend against jamming and eavesdropping simultaneously. Specifically, the HAPs align the beamformer to the terrestrial users while nullifying the reception of eavesdropper. In addition, the Ad-hoc network enables cooperative anti-jamming transmission, where the cooperative users (CUs) provide communication assistance by forming anti-jamming coalition for blocked users (BUs). Building upon this framework, we aim to maximize the sum rate of BUs by jointly optimizing the beamforming and cooperative coalition formation with the imperfect channel state information (CSI). To handle the intractable problem, we first convert the imperfect CSI into the worst-case one, and then a sequential convex approximation combined with first order Taylor series expansion is proposed to optimize the beamforming. Furthermore, for the optimization of anti-jamming coalition formation, we reformulate it as the coalition formation game (CFG) and a partial best coalition preference order is put forward to enhance the sum rate of BUs. With the help of exact potential game (EPG), it’s proved that the CFG can converge to stable coalition formation by exploiting the proposed distributed anti-jamming coalition formation algorithm. Simulation results demonstrate that the proposed scheme has the superior secure transmission performance to benchmark schemes.

This work investigates the problem of finite blocklength covert communications with relay assistance in Internet of Things (IoT) to extend the communications range. We reconstruct the framework for analyzing covert communications under decoded and forwarded protocols based on Willie’s optimal detection method. The analytic expression of Kullback-Leibler (KL) divergence is derived, the upper bound of KL divergence is solved by using the convexity of KL divergence, and the strict covertness constraint of the system is obtained. Meanwhile, to maximize the effective throughput, a covert communication parameter configuration scheme is proposed. Theoretical analysis and simulation results indicate that the compromised relationship of transmit power between Alice and relay nodes, and a reasonable power allocation scheme can enhance the effective throughput of the system.

Massive devices have become a bottleneck restricting the improvement of computing networks. In this paper, we investigate massive multiple access (MA) schemes in low earth orbit satellite-unmanned aerial vehicle (UAV) integrated multi-tier computing networks to achieve higher connectivity. A game-theoretic MA approach is proposed for sum data minimization at UAVs. This approach is divided into two parts: data node (DN)-UAV assignment and UAV-satellite matching. In the first part, the DNs’ orthogonal MA process is formulated as a coalition formation game (CFG) with the derived optimal bandwidth allocation. The CFG with the proposed Max-Satisfaction order is proofed as an exact potential game, which can maximize the satisfaction between the collected data and UAVs’ real computing capacities. Besides, the overlapping CFG (OCFG) is utilized for higher satisfaction. In UAV-satellite matching, non-orthogonal MA-based sum rate maximization with imperfect successive interference cancellation is obtained by transmit power optimization firstly. The UAV-satellite matching process is formulated as a many-to-one matching game (MTOMG). A swap-based MTOMG algorithm is proposed for UAVs grouping. Finally, the simulation results show the proposed schemes have better performance than other schemes, and the proposed approach remains the lowest data at UAVs compared to other approaches.

We discuss how resilient satellite constellation networks are against attacks. Two types of robustness are focused in this paper. One is the topology-related network robustness, which mainly assesses the effect of attacks or faults on satellites and links. The other is the network function robustness related to routing mechanisms, which mainly assesses how resource allocation mechanisms affect network robustness. To this purpose, two satellite constellation network models based on physical network topology and traffic are proposed, along with a new satellite importance index and the robustness metrics. Based on the newly proposed satellite importance index, three different types of attacking strategies are implemented in this paper, i.e., random attacks, selective attacks based on the initial state of the network, and selective attacks based on the current state of the network. The routing methods involved in function robustness mainly include different local state routing algorithms, which we summarize into a brand-new routing model with tunable parameters. Simulation results show that the removals by the selective attacks based on the current state of the network are often more harmful than the other attack strategies. Constellation networks almost collapse for the above attack strategies as the attack ratio are nearly 0.6, 0.4, and 0.2 respectively. Meanwhile, the larger constellation is more robust than the smaller one against the attacks before collapses. However, the larger constellation collapses earlier for the selective attack strategies. In addition, local-state routing algorithms with a certain level of state awareness capability can be used to improve the network function robustness.

Satellite-enabled Internet of Things (IoT) (SatIoT) has drawn increasing attentions due to the ubiquitous coverage, high capacity and massive connectivity. The inherent openness and broadcast nature of the SatIoT are vulnerable to security threats, particularly the jamming attacks for interrupting transmissions. Nonorthogonal multiple access (NOMA) scheme has the potential to be applied in anti-jamming communication for SatIoT due to the characteristic of resource sharing. The severely jammed users can get more allocated power by forming NOMA groups with other users, and both parties can improve the spectrum efficiency by frequency sharing. In this article, we aim to improve the performance of sum rate for SatIoT under the jamming environments. An anti-jamming transmission scheme is developed by jointly considering the NOMA-based user grouping and the power allocation (PA) for each NOMA group. Specifically, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as an anti-jamming coalition formation game, and the equilibrium solution is proved by the exact potential game theory. Moreover, in order to further improve NOMA performance, we derive the PA solution for multiuser NOMA by considering the imperfect successive interference cancellation. Finally, simulation results briefly highlight some details of the proposed approaches.

As an emerging technology, mobile edge computing (MEC) network paradigm provides great computing potential for edge services, which has been widely applied in friendly city environment. However, there are still many challenges to deploy MEC technology in harsh tactical communication environment due to poor communication conditions, limited computational resources, and hostile malicious interference. Thus, this article investigates the computational resource pricing and task offloading strategy in tactical MEC ad-hoc network, which consists of multiple tactical edge nodes, ground MEC servers, unmanned aerial vehicle-MEC (UAV-MEC) servers and a low-Earth orbit-MEC (LEO-MEC) satellite server. Each edge node can offload its partial computation-intensive task to the MEC servers to reduce computational delay and energy consumption. First, a multileader and multifollower Stackelberg game (MLMF-SG) which includes leader subgame for MEC servers and follower subgame for edge nodes, is proposed to formulate the interaction between servers and edge nodes. It has been proved that there exists a Stackelberg equilibrium (SE) in the proposed MLMF-SG. In order to decrease the delay, energy consumption, and resource overhead, the follower subgame is further formulated as a multimode computation task offloading game. With the help of the exact potential game (EPG), we prove that the follower subgame can converge to the Nash equilibrium (NE). To achieve the SE, a hierarchical distributed iterative algorithm is designed to maximize the utilities of the leaders and followers. Finally, the simulation results demonstrate that the proposed scheme can achieve better performance compared with the existing schemes.

We investigate a computing task scheduling problem in space–air–ground integrated network (SAGIN) for Internet of Remote Things (IoRT). In the considered scenario, the unmanned aerial vehicles (UAVs) collect computing tasks from IoRT devices and make offloading decisions, in which the tasks can be computed at the UAVs or offloaded to the low Earth orbital (LEO) satellite. The optimization objective is to design offloading strategies for each UAV that maximum the number of tasks satisfies delay constraint and reduces the energy consumption of UAVs. We formulate this problem as a nonlinear integer optimization problem, and remodel it as a stochastic game to solve it. To copy with the dynamic and complexity environment, we propose a learning-based orbital edge offloading (LOEF) approach which can coordinate all UAVs to learn the optimal offloading strategies. This method is based on the actor–critic framework of multiagent reinforcement learning, the Actor observes the environment and output the current offloading decision, the Critic evaluates the action of the Actor and coordinate the actions of all UAV to increase the reward of system. The simulation results show that the proposed offloading strategy converges fast, increases the number of satisfying the delay constraints tasks and the energy utilization of UAVs compared with the baseline strategies.

Low-Earth orbit (LEO) satellites are recognized as one of the most promising infrastructures for realizing global Internet of Things (IoT) services. With the explosive growth of user terminals (UTs) and data traffic, the integration of massive multiple-input multiple-output (mMIMO) techniques and LEO satellite communication systems has been regarded as a novel idea to enhance system capacity and realize global seamless high-speed interconnection. However, obtaining effective downlink channel state information (CSI) and establishing a simple and efficient hybrid beamforming mechanism are challenging tasks due to the limitations of objective factors, such as high dynamic, long delay, and low payload in LEO satellite scenarios. It is embodied in three aspects: 1) the untenable channel reciprocity in time division duplex (TDD) systems; 2) the training feedback costs and feedback delay in frequency division duplex (FDD) systems; and 3) the complex nonconvex optimization process faced by hybrid beamforming design. Driven by the performance advantages of the deep learning (DL) technology to deal with various problems in the field of physical layer communications, this article proposes to use a deep neural network (DNN) to solve the above challenges, and constructs SatCP and SatHB schemes for realizing downlink CSI acquirement and hybrid beamforming design, respectively. By deeply mining the potential correlation of the uplink-downlink channels between LEO satellites and UTs and exploring the mapping relationship between CSI and beamformers, the SatCP can assist LEO satellites to directly predict the future downlink CSI based on the observed uplink CSI with no need for downlink channel estimation, while the SatHB can easily generate the corresponding beamformers based on the downlink CSI predicted by the SatCP without requiring complex optimization. Numerical results demonstrate that the proposed SatCP and SatHB can play an effective auxiliary role in LEO satellite mMIMO communication systems.

Abstract In this study, a coded cooperation scheme using polar codes is explored for high‐reliability and low‐user complexity applications such as Internet of Things. Firstly, the cooperation way among the users is carefully designed based on systematic polar codes and cooperative decoding, without decode‐and‐forward protocol at the cooperative user. Meanwhile repetition aided code construction and decoding algorithm of polar code is proposed for the non‐cooperative case in terms of imperfect inter‐user channel. Performance analysis and numerical simulation demonstrate the bit‐error‐rate advantages of proposed polar coded cooperation in fading channels, compared to the no‐cooperation case and normal coded cooperation schemes. Furthermore, overall rate of proposed polar‐coded cooperation is considered to optimize adaptively to increase throughput efficiency.

In this article, we investigate max completion time optimization for Internet of Things (IoT) in LEO satellite-terrestrial integrated networks (STINs), in which IoT devices use non-orthogonal multiple access (NOMA) scheme to transmit data to central earth stations (CESs), and orthogonal multiple access (OMA) scheme is used for data transmission from CESs to LEO satellite. We decouple this problem into two subproblems: 1) max completion time optimization in terrestrial networks and 2) max completion time optimization among satellite beams. Different from the existing works about NOMA data transmission in terrestrial networks, we propose a cooperative NOMA scheme, and derive the closed expressions of the optimal cooperative data and the optimal transmit power of IoT devices. Based on the closed-form expressions, a joint subcarrier assignment and cooperative NOMA pairing (JSACNP) approach is proposed to minimize the max completion time in terrestrial networks by utilizing matching theory. Then, to minimize the max completion time among satellite beams, the optimal linear receiver expression is derived with fixed transmit power. Convex optimization is utilized to solve transmit power optimization, we propose an algorithm to solve it by CVX tool. An iterative algorithm is proposed for improved performance. Finally, numerical results are provided to evaluate our proposed algorithms, compared with some other proposed approaches or algorithms.

Considering the high-density and high-dynamic feature of cooperative unmanned aerial vehicles (UAVs) swarm, also referred to as flocking of flying ad hoc networks (FANETs), reliable medium access control (MAC) protocol design for network connectivity maintaining and network information sharing is a challenging issue. In this article, we propose a self-adaptive carrier sense multiple access with collision avoidance (CSMA/CA)-based MAC protocol for flocking of FANET, namely, FMAC, to provide reliable broadcast information service under density-varying flocking scenarios. To represent the varying trend of UAV density during flocking, we define the collective neighboring potential (CNP) in the FMAC protocol. Specifically, at the beginning of each period, each UAV computes the current CNP based on available neighbors' motion states. Then, the value of CNP at the start of the next period regarding the same neighbors is predicted using UAV's kinetic equation. After that, each UAV can update the contention window (CW) size by comparing the current CNP and the predicted CNP, and CW will be decreased (increased) if the current CNP is larger (smaller) than the predicted one for enough period. The simulation results show that the proposed FMAC protocol can ensure high successful transmission probability under density-varying flocking scenarios and outperforms the typical MAC solutions.

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

The anti-jamming routing for the Internet of Satellites (IoS) has drawn increasing attentions due to the unknown interrupts, unexpected congestion and smart jamming. This paper investigates anti-jamming routing scheme for heterogeneous IoS, with the aim of minimizing anti-jamming routing cost. Firstly, to tackle the smart jamming which can automatically change jamming strategies according to the jamming effect, we formulate the routing anti-jamming problem as a hierarchical anti-jamming Stackelberg game. Secondly, we propose a deep reinforcement learning based routing algorithm (DRLR) to obtain an available routing path subset. Furthermore, based on this set, a fast response anti-jamming algorithm (FRA) is proposed to achieve fast and reliable antijamming routing. Finally, the simulations have shown that the proposed algorithm have lower routing cost and better antijamming performance than existing approaches.

Satellite-enabled army Internet of Things (SaIoT) has drawn increasing attention due to the wide-coverage and large-capacity transmission. However, the smart jamming based on artificial intelligence technologies has seriously degraded SaIoT performance. Thus, this article investigates the distributed dynamic anti-jamming scheme for SaIoT to decrease energy consumption in the jamming environment. First, a hierarchical anti-jamming Stackelberg game (HASG), which consists of the leader subgame for jammers and the follower subgame for SaIoT devices, is proposed to formulate the confrontation interaction between jammers and SaIoT devices. It has been proved that there exists a Stackelberg equilibrium in the proposed HASG. Then, an anti-jamming coalition formation game (CFG) is proposed for the follower subgame to decrease the energy consumption in the jamming environment, and the modified coalition preference order and coalition change principle are put forward to enhance the performance of the proposed anti-jamming CFG. Furthermore, with the help of the exact potential game, we have demonstrated that the proposed anti-jamming CFG could converge to the stable coalition formation and it is able to achieve similar performance to the centralized optimization via a distributed approach. Finally, reinforcement-learning-based algorithms are utilized to obtain the suboptimal anti-jamming policies according to the dynamic and unknown jamming environment, and simulation results validate that the proposed approach achieves better performance than the existing approaches.

In this study, a rateless transmission scheme with information unequal error protection (UEP) is proposed by using polar codes. The proposed scheme is suitable for transmissions in time‐varying channels, as well as pursues UEP goal of information with different reliability requirements. Firstly, the authors design rateless transmission for an unknown channel via extending polarisation matrix and importance‐based puncturing. In particular, an algorithm ensuring full coding gain for UEP performance is proposed on the basis of rateless transmission. Moreover, in consideration of combining flexible successive cancellation list (SCL) decoding with UEP, they further present a size‐adapted SCL decoding scheme with reduced complexity. Numerical simulation shows that their proposed rateless scheme based on polar codes could achieve good UEP performance with low complexity in unknown channels.

The link adaptive transmission system chooses the optimum combinations of modulation order and code rate according to the characteristics of wireless channels to make use of the channel resources. To improve the spectral efficiency further, the authors apply the faster‐than‐Nyquist (FTN) signalling to the adaptive coding and modulation (ACM) system. The ACM system based on FTN signalling can take advantage of the channel resources by adjusting the packing ratios, modulation orders and code rates. However, the variable packing ratios correspond to variable sampling rate, which challenges the receiver of the ACM system. They propose a new transceiver of the FTN‐based ACM system which can detect the FTN signal at a constant sampling rate. Moreover, they search for the optimum modulation and coding schemes of ACM based on FTN by analysing the extrinsic information transfer diagram. The simulation results show that the FTN‐based ACM system has additional gains compared with the traditional way and the proposed transceiver may be a better choice in the ACM system.

Multicarrier faster-than-Nyquist (MFTN) signaling is a spectral efficient modulation scheme for broadband and broadcasting applications. However, due to the intentionally introduced intersymbol interference (ISI) and intercarrier interference (ICI) by time-frequency packing, the detection of MFTN signals is long considered to be a challenging problem. In this paper, we propose two low complexity detection schemes for MFTN signals based on turbo frequency domain equalization (FDE). Firstly, we extend the one dimensional FDE to two dimensional model to deal with the ISI/ICI interference induced by MFTN signaling, and validate its practical bit-error-rate (BER) performance combined with turbo equalization. Secondly, a one dimensional FDE combined with soft successive interference cancellation (SIC) is further proposed to detect the MFTN signals, and it shows that the FDE combined with SIC could asymptotically approach the maximum a posterior (MAP) equalization coupled with SIC with negligible BER performance loss under moderate ISI and ICI. Computational complexity analysis and numerical results show that the FDE combined with SIC is better than 2-D FDE and may be a better choice than MAP equalization combined with SIC in the practical detection of MFTN signals.

This study addresses the training sequence design problem for multipath channel estimation in faster‐than‐Nyquist (FTN) system. The authors process the estimation in the frequency domain and give the search criterion which is only the function of the power spectrum of the training sequence. The optimal sequence according to this criterion is found by using search strategy described in detail in this study and the performance of it is tested by Monte Carlo simulation.

Faster‐than‐Nyquist (FTN) signalling, as a candidate technology of fifth generation, can transmit more data with the same bandwidth. In the adaptive transmission system based on FTN signalling (FNS), the transmitter changes the packing ratios to adapt to the wireless channels. The scheme that combines the adaptive coding and modulation with the FNS has higher spectral efficiency. However, the symbol rate changing with the packing ratio challenges the receiver of adaptive transmission system based on FNS. Moreover, the decoding complexity based on viterbi algorithm (VA) is increasing exponentially when the packing ratios of FNS decrease. In this study, the authors proposed an adaptive detection method to detect FTN signal at constant sampling rate. The proposed method adopted an linear minimum mean square error (LMMSE) equaliser to solve the mismatch of the symbol rate and the sampling rate. Their simulation results show that the adaptive detection method of FNS can approach the optimal bit error rate performance of the coded FTN system. At the same time, the proposed method can avoid the complexity induced by the variable sampling rate in the adaptive transmission system based on FNS.

In this study, the authors propose a secure transmission scheme using polar codes and artificial noise (AN) for the wiretap system without assuming the channel quality advantage of main channel over wiretap channel. They first derive lower and upper bounds on the symmetric capacity of the polarised bit‐channels , which depend on the signal‐to‐noise ratio of each use of physical channel. According to the bounds, they prove existence of the bit‐channels that are beneficial to the signal reception of the main channels but hostile to the wiretap channel, and a method based on injecting the AN at the transmitter is introduced to achieve those bit‐channels. Through theoretical analysis, the security of the proposed AN method is proven. Thereafter, they elaborate two AN power allocation schemes denoted by AN‐PA‐I and AN‐PA‐II for each use of physical channel. The former proves the existence of the minimum AN power to achieve the secrecy rate that is equal to the capacity of the main channel, referred to as the maximum secrecy rate, and the latter introduces how to obtain it with an optimisation method. Numerical results show that both the two schemes can achieve the maximum secrecy rate.

In this paper, a parallel concatenated structure is proposed to improve the performance of polar codes in finite length in which multiblocks systematic polar codes (SPC) and recursive systematic convolutional codes (RSC) are combined. We first design a weighted iterative decoding algorithm for the proposed structure such that better bit error rate performance can be obtained. Then, a deliberated interleaver is optimized for the new scheme. Thereafter, we introduce a strategy to estimate the minimum distance of the concatenation codeword, where an asymptotic performance and convergence behavior analysis are elaborated. Finally, the error performance of the proposed scheme is evaluated via simulations. The results show that our scheme almost outperforms all the existed concatenation ones using polar codes, making the codes more practical.

Continuous phase modulation (CPM) is a widely used modulation scheme in communication systems. However, difficulties arise with the design of CPM receivers, due to the nonlinear nature of CPM. One popular solution to this problem is to linearize CPM with pulse amplitude modulation (PAM) representation. In this paper, a reduced-complexity superbaud timing recovery method is proposed for PAM-based multi-h CPM receivers. The proposed method is non-data-aided, feedforward, and suitable for M-ary, multi-h CPM in general. The novelties of the proposed method are twofold. On one hand, the proposed method is reduced-complexity for PAM-based receivers, because it shares the same match filters with the PAM-based detectors. On the other hand, it is shown that the proposed method is able to outperform the existing method with some modulation schemes. Therefore, the proposed method provides an important synchronization component for PAM-based multi-h CPM receivers.