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.

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.

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.

Pseudolites can overcome the shortcomings of Global Navigation Satellite System(GNSS), and simulate similar navigation satellite signals to be used as a stable and reliable positioning signal source in the indoor environment, which has gradually become a research hotspot in the field of indoor positioning. Due to the influence of factors such as multipath propagation, it is impossible to directly replicate the existing outdoor positioning method. To solve the above problems, we designed a BDS/GPS array pseudolite indoor high-precision positioning system PIP's (Pseudolite Indoor Positioning system). Relying on this system, we proposed a pseudo-satellite carrier phase differential positioning algorithm based on particle filtering, which avoids the problem of solving the integer ambiguity by calculating the pseudorange similarity. Then, aiming at the problem of divergence of positioning results caused by different carrier moving speeds, a method of dynamically estimating particle velocity based on Doppler frequency shift is proposed, which improves the accuracy of particle weight distribution, thereby improving the positioning accuracy and continuity of the positioning system. Finally, in order to verify the positioning performance of the proposed system, we conducted a large number of experiments in the microwave anechoic chamber and the test field environment. The results show that in the microwave anechoic chamber, the average positioning error in the X direction is 0.09m, and the average positioning error in the Y direction is 0.12m. In the test field environment, we analyzed the influence of different transmitting powers and different types of antennas on the positioning performance. At the same time, the positioning accuracy heat map is drawn, and the maximum positioning error is 0.67m.

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.

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.

With the increasing demand for location‐based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless‐based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image‐based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective‐n‐point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image‐based indoor localization methods do not require infrastructure and have a wide range of applications that include self‐driving, robot navigation, and augmented reality.

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.

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.

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 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.