Efficient Uplink Transmission in Ultra-Dense LEO Satellite Networks With Multiband Antennas

被引：6

作者：

Chen, Tianjiao ^{[1
,2
]}

Liu, Jiang ^{[1
,2
]}

Ye, Qiang ^{[3
]}

Tang, Qinqin ^{[1
,2
]}

Zhang, Weiting ^{[4
]}

Huang, Tao ^{[1
,2
]}

Liu, Yunjie ^{[1
,2
]}

机构：

[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China

[2] Purple Mt Labs, Nanjing 211111, Peoples R China

[3] Mem Univ Newfoundland, Dept Comp Sci, St John, NL A1B 3X5, Canada

[4] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2022年 / 26卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Satellites; Resource management; Switches; Low earth orbit satellites; Satellite antennas; Rain; Optimization; LEO satellite; uplink transmission; multiband antenna; resource allocation; deep reinforcement learning;

D O I：

10.1109/LCOMM.2022.3160839

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

In this letter, we propose an efficient uplink transmission scheme over ultra-dense low earth orbit (LEO) satellite networks with multiband antennas. A two-timescale transmission resource allocation and frequency band switching optimization framework is established with the objective of maximizing the overall data rate while reducing the band switching cost. The small-timescale transmission resource allocation problem is described as a Markov decision process (MDP) considering continuous LEO satellite movements and Markovian rainfall changes, while the large-timescale satellite band switching is formulated as an integer programming problem. A hierarchical algorithm based on the deep reinforcement learning (DRL) approach and the greedy method is proposed to make efficient decisions. Extensive simulations are conducted to validate the effectiveness and advantages of our proposed scheme.

引用

页码：1373 / 1377

页数：5

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