Optimization of Communication Performance for Non-Terrestrial Networks with Rate-Splitting Multiple Access
전송률 분할 다중접속기술을 통한 비-지상 네트워크의 통신 성능 최적화
- 주제(키워드) Non-terrestrial networks , Rate-splitting multiple access
- 주제(DDC) 004.6
- 발행기관 아주대학교 일반대학원
- 지도교수 Jae-Hyun Kim
- 발행년도 2024
- 학위수여년월 2024. 2
- 학위명 석사
- 학과 및 전공 일반대학원 AI융합네트워크학과
- 실제URI http://www.dcollection.net/handler/ajou/000000033786
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
With the intent of ubiquitous global and massive connectivity, non-terrestrial networks (NTN) has drawn extensive attention as one of the key technologies for the 6G mobile communication networks and beyond. In this thesis, the communication performance of NTN based on rate-splitting multiple access (RSMA) is optimized with the consideration of the imperfect channel state information (CSI) caused by the high altitude and rapid movement of aerial base stations (ABSs). On top of this, to capture more realistic scenarios in NTN, heterogeneous traffic demands of users due to the different geological conditions and user distributions, and the limited power of ABSs due to the unstable power supply and finite-sized battery are considered. To be specific, the RSMA-based rate-matching (RM) framework is proposed that minimizes the difference between traffic demands and actual offered rates in multibeam satellite communications (SATCOM). Channel phase perturbations arising from channel estimation and feedback errors are taken into account. To solve the non-convex formulated problem, it is converted into a tractable convex form via the successive convex approximation (SCA) approach. Simulation results show that RSMA flexibly arranges the powers of the common and private messages according to different traffic patterns between beams and users, efficiently satisfying non- uniform traffic demands. Second, to tackle the unstable supplied power and limited battery at ABSs, the joint power and beamforming framework in RSMA-based energy harvesting unmanned aerial vehicle (UAV) networks is proposed. A deep reinforcement learning (DRL) approach is utilized to allocate optimal trans- mission power at each time slot from harvested energy. The optimal power allocation strategy is determined according to the channel, harvested energy, and battery power status to maximize the sum rate from the long-term perspective. To design the RSMA precoder maximizing the sum-rate at each time slot with a given transmission power via DRL, sequential least squares programming (SLSQP) based on the Han–Powell quasi-Newton method is adopted.
more목차
1 Introduction 1
1.1 Background 1
1.1.1 Non-Terrestrial Networks 1
1.1.2 Fundamentals of RSMA 4
1.2 Motivation 7
1.3 Overview of Contributions 9
1.4 Notations 10
2 Rate-Matching Precoder Design for RSMA-Enabled Multibeam Satellite Communications 12
2.1 Introduction 13
2.1.1 Contributions 16
2.2 System Model and Problem Formulation 18
2.2.1 Channel Model 19
2.2.2 Signal Model 21
2.2.3 Problem Formulation 29
2.3 Proposed RSMA-Based Rate-Matching Scheme 31
2.4 Numerical Results 37
2.4.1 Performance Comparison for rtarget = [2, 2, 3, 3, 4] T 39
2.4.2 Performance Comparison for rtarget = [6, 4, 2, 2, 2] T 46
2.4.3 Performance Comparison Between L1-Based and L2-Based Objective Functions 51
2.5 Appendix A: Derivations of (2.6) and (2.18) 53
2.6 Appendix B: Proof of (2.14) and (2.15) 55
3 Joint Power and Beamforming Design for RSMA-based Energy Harvesting UAV Networks 58
3.1 Introduction 59
3.1.1 Contributions 60
3.2 System Model and Problem Formulation 61
3.3 Maximization of the Average Sum-Rate Over the Total Time Slot Based on the DRL Approach 65
3.3.1 Formulation to the Markov Decision Process 66
3.3.2 Optimization of Power Allocation Policy via SAC Algorithm 67
3.3.3 RSMA Precoder Design via MMSE and SLSQP Algorithm 69
3.4 Numerical Results 71
3.4.1 Performance Comparison with Greedy Power Allocation and Other Multiple Access Schemes 72
3.4.2 Performance Comparison with Other RSMA Precoders 75
4 Conclusion 81
References 83
