| 摘要: | 6G是下一世代的行動通訊技術，它提供比當前5G更快的速度、更低的延遲和更高的頻譜效率，並支持更多設備和更廣泛的應用場景，其中低軌衛星通訊由於運行在地球軌道上，不像地面基礎設施會受到地理位置和地形的限制，因此可以實現全球性的通訊覆蓋，這使得低軌衛星通訊成為實現全球性物聯網的關鍵技術之一。
與高軌衛星通訊相比，低軌衛星通訊還具有低延遲及高可靠性等優點，這使得其成為實現高速數據傳輸、遠程操作和即時通訊等應用的理想選擇。然而，低軌衛星的移動速度非常快，為了應對高都卜勒效應所產生的通道變化，使用多輸入多輸出的波束成形技術進行的波束追蹤被視為有效抵抗快速衰減通道效應且具有高度波束靈活性的替代方案，但隨著波束數量以及用戶數量的增加，地面下鏈多用戶間的訊號干擾問題將更為嚴重。
本論文針對低軌衛星與地面用戶間的通訊服務進行設計，藉由波束成形技術設計多組波束，並結合數位預編碼方法進行波束權重係數設計，以抑制同區域地面用戶間的波束內干擾問題。而通過強化學習方法，吾人可根據低軌衛星軌跡資訊動態調整多波束角度追蹤策略，抑制不同區域地面用戶間造成的波束間干擾問題，提昇衛星通訊品質以達到最大總資料傳輸速率。數值模擬顯示，基於深度強化學習之低軌衛星下鏈通訊的傳輸吞吐量明顯優於本論文進行比較的其他三個演算法。
;6G is a next-generation mobile communication technology with faster speed, lower latency and higher spectral efficiency, supporting more devices and a wider range of application scenarios than the current 5G. Among them, LEO satellite communication can achieve global communication coverage due to its operation in earth orbit, unlike terrestrial infrastructure which is limited by geographical location and terrain, which makes LEO satellite communication one of the key technologies to realize global IoT.
Compared with high-orbit satellite communication, LEO satellite communication also has the advantages of low latency and high reliability, which makes it well suited for applications such as high-speed data transmission, remote operation, and real-time communication. However, LEO satellites move very fast, and in order to cope with the channel variations caused by high doppler effects, beam tracking with MIMO beamforming techniques is considered an option for high beam flexibility that effectively resists fast fading channel effects, but as the number of beams and users increases, the problem of signal interference among multiple users in the terrestrial downlink will become more severe.
In this paper, we design the communication service between LEO satellites and ground users by beamforming technique to design multiple beams and combine with digital pre-coding method to design beam weight coefficients to suppress the intra-beam interference problem between ground users in the same area. Through the reinforcement learning, we can dynamically adjust the multi-beam angle tracking strategy based on the LEO satellite trajectory information to minimize the interference between beams in different areas, suppress the inter-beam interference between ground users in different areas, and improve the quality of satellite communication to achieve the maximum total data transmission rate. Numerical simulations show that the transmission throughput of LEO satellite downlink communication based on deep reinforcement learning is significantly better than the other three algorithms compared in this paper. |
