In this paper, we address the problem of multiple frequency-hopping (FH) signal parameters estimation in the presence of random missing observations. A space-time matrix with random missing observations is acquired by a uniform linear array (ULA). We exploit the inherent incomplete data processing capability of atomic norm soft thresholding (AST) to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals. The hopping time is obtained by the sudden changes of the spatial information, which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components. Then, the frequency of multiple signal components can be estimated precisely by AST within each segment. After obtaining the above two parameters of the hopping time and the frequency of signals, the direction of arrival (DOA) can be directly calculated by them, and the network sorting can be realized. Results of simulation show that the proposed method is superior to the existing technology. Even when a large portion of data observations is missing, as the number of array elements increases, the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.

In the satellite-to-ground high-speed data transmission link, there are signal self-interference problems of symbols in the co-channel, as well as between orthogonal and polarized channels. A multichannel adaptive filter is designed by constructing a multichannel Wiener-Hopf equation, and the influence of five channel nonideal factors is suppressed to improve the BER performance. Experiments show that this method is effective to suppress the signal self-interference, and the BER floor is optimized from 1E-3 to 1E-7.

In LEO (Low Earth Orbit) satellite communication systems, the satellite network is made up of a large number of satellites, the dynamically changing network environment affects the results of distributed computing. In order to improve the fault tolerance rate, a novel public blockchain consensus mechanism that applies a distributed computing architecture in a public network is proposed. Redundant calculation of blockchain ensures the credibility of the results; and the transactions with calculation results of a task are stored distributed in sequence in Directed Acyclic Graphs (DAG). The transactions issued by nodes are connected to form a net. The net can quickly provide node reputation evaluation that does not rely on third parties. Simulations show that our proposed blockchain has the following advantages: 1. The task processing speed of the blockchain can be close to that of the fastest node in the entire blockchain; 2. When the tasks' arrival time intervals and demanded working nodes(WNs) meet certain conditions, the network can tolerate more than 50% of malicious devices; 3. No matter the number of nodes in the blockchain is increased or reduced, the network can keep robustness by adjusting the task's arrival time interval and demanded WNs.

Rate splitting multiple access (RSMA) has shown great potentials for the next generation communication systems. In this work, we consider a two-user system in hybrid satellite terrestrial network (HSTN) where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality. The non-convex weighted sum rate (WSR) problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error (WMMSE) algorithm. We propose to apply deep unfolding to solve the optimization problem, which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations. We also incorporate momentum accelerated projection gradient descent (PGD) algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping. The momentum and step size in deep unfolding network are selected as trainable parameters for training. As shown in the simulation results, deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.

In LEO (Low Earth Orbit) satellite communication system, the orbit height of the satellite is low, the transmission delay is short, the path loss is small, and the frequency multiplexing is more effective. However, it is an unavoidable technical problem of the significant Doppler effect caused by the interference between satellite networks and the high-speed movement of the satellite relative to the ground. In order to improve the target detection efficiency and system security of LEO satellite communication system, blind separation technology is an effective method to process the collision signals received by satellites. Because of the signal has good sparsity in Delay-Doppler domain, in order to improve the blind separation performance of LEO satellite communication system, orthogonal Time-Frequency space (OTFS) modulation is used to convert the sampled signal to Delay-Doppler domain. DBSCAN clustering algorithm is used to classify the sparse signal, so as to separate the original mixed signal. Finally, the simulation results show that the method has a good separation effect, and can significantly improve the detection efficiency of system targets and the security of LEO satellite communication system network.

In application to time convolutive mixing model or frequency domain blind separation model for wireless receiving applications, frequency domain independent component analysis (FDICA) has been a very popular method but with adverse random permutation ambiguity influence. In order to solve this inherent problem in FDICA assisted multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) based the Internet of Thing (IoT) systems, this paper proposes an new detection mechanism, named independent vector analysis (IVA), for realizing blind adaptive signal recovery in MIMO IoT green communication to reduce inter-carrier interference (ICI) and multiple access interference (MAI). IVA jointly implements separation work for different frequency bin data while the FDICA deals with it separately. In IVA, the dependencies of frequency bins can be exploited in mitigating the random permutation problem. In addition, multivariate prior distributions are employed to preserve the inter-frequency dependencies for individual sources, which can result in separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed method.

The space-air-ground integrated network (SAGIN) has gained widespread attention from academia and industry in recent years. It is widely applied in many practical fields such as global observation and mapping, intelligent transportation systems, and military missions.As an information carrier of air platforms, the deployment strategy of unmanned aerial vehicles (UAVs) is essential for communication systems' performance.In this paper, we discuss a UAV broadcast coverage strategy that can maximize energy efficiency (EE) under terrestrial users' requirements. Due to the non-convexity of this issue, conventional approaches often solve with heuristics algorithms or alternate optimization.To this end, we propose an iterative algorithm by optimizing trajectory and power allocation jointly.Firstly, we discrete the UAV trajectory into several stop points and propose a user grouping strategy based on the traveling salesman problem (TSP) to acquire the number of stop points and the optimization range.Then, we use the Dinkelbach method to dispose of the fractional form and transform the original problem into an iteratively solvable convex optimization problem by variable substitution and Taylor approximation. Numerical results validate our proposed solution and outperform the benchmark schemes in EE and mission completion time.

Aiming at the physical layer security (PLS) secure transmission existing in the information backhaul link of the satellite-UAV integrated (SUI) network, a two-layer Stackelberg game model (TSGM) that can resist full-duplex (FD) eavesdropping and jamming attacks is proposed. The confrontation relationship between the UAV network and the attacker is established as the first layer Stackelberg game. The source UAV adjusts its own transmission power strategy according to the attacker's jamming strategy to resist malicious jamming attacks. The internal competition and cooperation relationship in UAV network is modeled as the second layer Stackelberg game, and the optimal cooperative UAV transmits jamming signal to the attacker to resist malicious eavesdropping attacks. Aiming at the "selfishness" of UAV nodes, a price incentive mechanism is established to encourage UAV to actively participate in cooperation, so as to maximize the advantages of cooperative communication. For the proposed TSGM, we construct the utility function and analyze the closed equilibrium solution of the game model, and design a three-stage optimal response iterative (TORI) algorithm to solve the game equilibrium. The simulation results show that the proposed TSGM can effectively increase the utility of the source UAV and improve the enthusiasm of cooperation compared with other power control models.

This paper investigates the anti-jamming trajectory design to safeguard the effective data collection, where a unmanned aerial vehicle (UAV) is dispatched to collect data over multiple sensor nodes(SNs) in jamming environment. Under the limited power and transmission range of SNs, we aim to minimize the UAV's flight energy consumption in a finite task period, by jointly optimizing SNs collection sequence and UAV flight trajectory. Firstly, we propose a general optimization framework which consists of path planning and trajectory optimization for the formulated non-convex problem. In the path planning phase, a dynamic programming (DP) algorithm is used to provide the initial path of the UAV, which is the shortest path to visit each SN. In the trajectory optimization phase, we introduce the concept of Communication Flight Corridor (CFC) to meet the non-convex constraints and apply a piecewise Bézier curve, based on Bernoulli polynomial, to represent the flight trajectory of the UAV, which can transform the optimization variables from infinite time variables to polynomial coefficients of finite order. Finally, we simulate the flight trajectory of UAV in hovering mode and continuous flight mode under different parameters, and the simulation results demonstrate the effectiveness of the proposed method.

In this paper, we investigate a geosynchronous earth orbit (GEO) and low earth orbit (LEO) coexisting satellite communication system. To decrease the interference imposed on the GEO user caused by LEO satellites, we propose a joint beam-management and power-allocation (JBMPA) scheme to maximize signal-to-interference plus noise ratio (SINR) at the GEO user, whilst maintaining the ongoing wireless links spanning from LEO satellites to their corresponding users. Specifically, we first analyze the overlapping coverage among GEO and LEO satellites, to obtain the LEO-satellite set in which their beams impose interference on the GEO user. Then, considering the traffic of LEO satellites in the obtained set, we design a beam-management method to turn off and switch interference beams of LEO satellites. Finally, we further propose a deep Q-network (DQN) aided power allocation algorithm to allocate the transmit power for the ongoing LEO satellites in the obtained set, whose beams are unable to be managed. Numerical results show that comparing with the traditional fixed beam with power allocation (FBPA) scheme, the proposed JBMPA can achieve a higher SINR and a lower outage probability, whilst guaranteeing the ongoing wireless transmissions of LEO satellites.

The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper. The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process. Furthermore, a spectrum access scheme based on the prior knowledge enabled RL (PKRL) is designed, which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment. Compared with the existing methods, the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy. Simulation results show that, the convergence speed of the learning is improved by about 66% with the invariant average throughput.

In this paper, we investigate the spectrum sensing performance of a distributed satellite clusters (DSC) under perturbation, aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak signals. Specifically, we propose a cooperative beamforming (BF) algorithm though random antenna array theory to fit the location characteristic of DSC and derive the average far-field beam pattern under perturbation. Then, a constrained optimization problem with maximizing the signal to interference plus noise ratio (SINR) is modeled to obtain the BF weight vectors, and an approximate expression of SINR is presented in the presence of the mismatch of signal steering vector. Finally, we derive the closed-form expression of the detection probability for the considered DSC over Shadowed-Rician fading channels. Simulation results are provided to validate our theoretical analysis and to characterize the impact of various parameters on the system performance.