Low Earth orbit (LEO) satellite is one of the most promising infrastructures for realizing next-generation global wireless networks with enhanced data rates. Applying massive multiple-input multiple-output (mMIMO) to LEO satellite communication systems is a novel idea to enhance communication capacity and realize the global high-speed interconnection. However, obtaining effective instantaneous channel state information (iCSI) is challenging due to the time-varying propagation environment and long transmission delay. In this letter, a deep learning (DL)-based CSI prediction scheme is proposed to address channel aging problem by exploiting the correlation of changing channels. Specifically, we design a satellite channel predictor (SCP) that is composed by long short term with memory (LSTM) units. The predictor is first trained by offline learning and then feeds back the corresponding output results online based on the input data to realize channel feature extraction and future CSI prediction in LEO satellite scenarios. Numerical results demonstrate that the proposed DL-based predictor can mitigate the channel aging problem in LEO satellite mMIMO system effectively.

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.

Cyclic redundancy check (CRC) assisted list successive cancellation decoding (SCLD) makes polar codes competitive with the state-of-art codes. In this letter, we try to find the optimal CRC for polar codes to further improve its performance. We first analyze the undetected error probability of CRC-aided SCLD as well as the characteristics of Hamming weight distribution of polar codes. Based on these characteristics, a multilevel SCLD-based searching strategy with moderate list size is proposed to compute the minimum Hamming weight distribution (MHWD) of different CRC-concatenated polar codes. Using the searched MHWD, the optimal CRC for polar codes are presented in this letter. Simulation results show that the performance of optimal CRC-aided SCLD significantly outperforms the standard one, especially at high code rate.

Non-orthogonal multiple access (NOMA) has the potential to provide higher throughput than conventional orthogonal multiple access (OMA), which has been considered as a key technology for 5G. NOMA in satellite communication system can provide anytime, anywhere access with improved spectral efficiency and system capacity because of its ubiquitous coverage. However, the characteristics of the satellite channels are different from that of the terrestrial network, i.e., huge time delay and Doppler shift. In this paper, different from the existing works, which mainly focus on the performance of NOMA in static terrestrial base stations and Geostationary orbit (GEO) scenarios, we investigate the performance analysis of downlink NOMA in dynamic low earth orbit (LEO) satellite communication system with Doppler shift considered. We combine NOMA and orthogonal frequency division multiplexing (OFDM) for better spectral efficiency. Our channel model includes both small scale model and large scale model. For simplify, we only consider two users in one spot beam. Besides, we express the performance analysis of downlink NOMA in LEO satellite communication system, like ergodic capacity, outage probability (OP) and mutual information. However, in traditional NOMA scheme, the proceeding error decision of the high power user will cause the deterioration of the subsequent detection performance. Therefore, a symmetrical coding (SC) scheme for different modulation mode is proposed for low power user to get better performance. Finally, simulation results validate the performance of the NOMA scheme is better than that of the OMA scheme. The proposed SC scheme can achieve a prominent increase performance contrasted to the traditional NOMA scheme.

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 the SaIoT performance. Thus, this paper investigates distributed dynamic anti-jamming scheme for SaIoT to decrease the energy consumption in the jamming environment. Firstly, a hierarchical anti-jamming Stackelberg game (HASG), which consists of the leader sub-game for jammers and the follower sub-game 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 sub-game 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 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 a 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 letter, we consider non-orthogonal multiple access (NOMA) applying into massive multiple input multiple output (mMIMO) low earth orbit (LEO) satellite communication system (SCS) to improve spectral efficiency. Specifically, we investigate sum rate maximization problem with transmit power, quality of service (QoS) constraints, imperfect successive interference cancelation (SIC) and imperfect channel state information (CSI) considered. We decouple the original problem into two parts: precoding vectors design and transmit power optimization. Precoding vectors are derived for maximizing the average signal power in each beam and eliminating inter-beam interference (IBI). Then, transmit power optimization problem is transformed into a convex optimization problem by utilizing the first order Taylor expansion, an iterative algorithm is proposed to get the optimal sum rate. Finally, simulation results verify the convergence of the proposed algorithm and our proposed mMIMO NOMA approach is better than other approaches.