3D Channel Tracking in Space-Air-Ground Integrated Networks.

PhD ThesesThe space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for beyond the fth generation (B5G) and future communications. As a multidimensional network, SAGIN adopts di erent communication links across three segments: the space segment with satellite networks, the air segment with aerial networks, and the ground segment with territorial networks. Apart from Ka-band millimetre wave (mmWave) frequencies being utilized for low earth orbit (LEO) satellites and medium earth orbit (MEO) satellites communications, with emerging smart devices brought online and crowded under-6GHz spectrum, mmWave frequencies have also been widely considered to support both aerial networks and territorial networks. To ensure stable wireless communications and tackle the severer propagation loss of mmWave transmission, massive multiple input and multiple output (MIMO) and intelligent re ecting surfaces (IRSs), which can con gure directional beams and bring huge improvements of radiated energy e ciency, are two technologies to be employed in SAGIN. Conventionally, perfect channel state information (CSI) is the fundamental knowledge to enable building reliable communication connections. With massive antenna arrays installed on orbiting satellites, navigation unmanned aerial vehicles (UAVs), and base stations, it's very challenging to acquire real-time mmWave CSI in SAGIN due to the heavy overheads and the dynamic environment. Most existing mmWave channel estimation work proposed compressive sensing (CS) based algorithms to reduce the heavy overheads with the assumption that the environment is in two-dimensional (2D) space and static without any movement. However, in SAGIN, 2D and static assumptions are not practical. Hence, tracking the dynamic three-dimensional (3D) CSI using small training overheads becomes a crucial and demanding task. i In this thesis, 3D channel tracking algorithms are proposed based on unique characteristics of air-ground and space-air links. For IRS-assisted air-ground links, we propose a 3D geometry dynamic channel model with both UAV navigation and mobile user movement. We further develop a deep learning (DL)-based channel tracking algorithms with two modules: deep neural network (DNN) channel pre-estimation for denoising and stacked bi-directional long short term memory (Stacked Bi-LSTM) for channel tracking. For space-air links, we exploit the on-grid and o -grid single user (SU) and multi-user (MU) UAV-satellite communications. Two statistical spatial and temporal correlation sparsity of the dynamic channel models called 3D two-dimensional Markov model (3D- 2D-MM) and multi-dimensional Markov model (MD-MM) are developed by introducing the more realistic 3D movement in the system. Based on the message passing rule and the proposed Markov structures, 3D dynamic turbo approximate message passing algorithm (3D-DTAMP) and multi-dimensional dynamic turbo approximate message passing (MD-DTAMP) are derived for channel tracking. Our proposed algorithms can achieve better channel estimation accuracy with comparable complexity and smaller training overheads