Abstract：Autoencoder (AE) based on deep learning is a new method to replace traditional communication transmitter and receiver. This paper proposes an autoencoder based on Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU), which integrates constellation mapping and channel coding. Specifically, this paper designs a parallel CNN structure and segment the input bitstream for one-hot encoding, which has two advantages:(1) Compared with the original one-hot encoding, the dimension of the input data is reduced; (2) The features of the data are not too sparse, which allows the network to converge faster and better. In addition, the GRU is introduced for channel coding. The proposed model can be applied to high-order modulation such as 4096QAM signal, and has better performance than traditional methods under both added white Gaussian noise (AWGN) channels and Rayleigh channels.

Abstract： NTN (non-terrestrial network) is an important application scenario for satellite communications and low-altitude communications, marking the transition of 5G technology applications from land communications to space communications. It is foreseeable that satellite networks will be an important component of future 6G communications networks. In order to meet the quality requirements of satellite communication and maximize the system capacity, it is necessary to apply adaptive coding and modulation technology to dynamically adjust the modulation order and coding rate according to the channel state information in the changing communication environment. AI has clear potential to solve problems arising from rapidly changing channel conditions in satellite high-dynamic scenarios. This paper adopts the low-orbit satellite adaptive coding and modulation strategy based on reinforcement learning to solve the problem of the mismatch between the threshold table and the actual channel caused by the change of the satellite communication environment, which is improved by above 20% compared with the traditional ARIMA (autoregressive integrated moving average) algorithm.

Abstract： As an important feature and key enabling technology of 6G, integrated sensing and communication (ISAC) achieves information sharing and collaborative gain for communication and awareness. This paper firstly introduces the current research status of ISAC technology, and then combs it from two aspects: academic research and standardization progress. Then, this paper deeply discusses and summarizes the integration design issues of communication technology and sensing technology in three aspects: network architecture, networking and waveform design. Finally, the development trend of ISAC technology in 6G is forecasted and the possible challenges are analyzed.

Abstract：Facing wide-area and non-uniformly distributed service requirements, this paper proposes a beam resource management scheme based on improved particle swarm optimization, which considers the joint optimization of beam hopping pattern design, beam frequency allocation and power allocation, in the scenario of multiple LEO satellites with moving beam cells. By system-level simulation, its performance is verified. Specifically, compared with beam resource management schemes based on spatial isolation angle and priority, results show that this proposal can significantly improve user SINR and system capacity under full-buffer service model, and lower frequency reuse factor results in higher system capacity.