Design of Adaptive 4D-8PSK-TCM based on CNN for LEO Satellite Communication
저궤도 위성 통신을 위한 합성곱 신경망 기반의 4D-8PSK-TCM 설계
- 주제(키워드) 4D-8PSK-TCM , LEO Satellite Communication , CNN
- 발행기관 아주대학교
- 지도교수 김재현
- 발행년도 2021
- 학위수여년월 2021. 2
- 학위명 석사
- 학과 및 전공 일반대학원 AI융합네트워크학과
- 실제URI http://www.dcollection.net/handler/ajou/000000030521
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
The consultative committee for space data systems (CCSDS) recommends four dimensional eight-ary phase shift keying trellis coded modulation (4D-8PSK-TCM) for a high-speed transmission technology in the X-band. The 4D-8PSK-TCM can reduce computational complexity with T-algorithm that gains benefits by removing elements below the threshold.This thesis designs the digital communication system using 4D-8PSK-TCM and proposes adaptive decoder to maintain the bit error rate (BER) performance and minimize computational complexity for all intervals of energy per bit to noise spectral density ratio (Eb/N0). The designed system is applied with zero termination to prevent degradation of BER performance. The proposed adaptive decoder is applied with Eb/N0 classification model that optimize T-algorithm value by estimation of Eb/N0 of received signal using image generator and convolutional neural network (CNN). Moreover, this thesis compares the conventional 4D-8PSK-TCM and the 4D-8PSK-TCM using proposed adaptive decoder in terms of computational complexity and BER performance. The performance evaluation results show that proposed adaptive decoder can minimize to computational complexity while maintaining BER performance for all intervals of Eb/N0. It is expected that the proposed adaptive decoder can be used in low Earth orbit (LEO) satellite for low latency decoding.
more목차
1. Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Contributions 3
1.4 Overview 4
2, Related Works 6
2.1 TCM 6
2.2 Low latency decoder 8
3. Design of 4D-8PSK-TCM 10
3.1 Transmitter 10
3.1.1 Differential encoder 10
3.1.2 Convolutional coder 13
3.1.3 Constellation mapper 14
3.2 Receiver 19
3.2.1 Transition metric unit 19
3.2.2 Viterbi decoder and symbol decision unit 20
3.2.3 De-mapper unit and differential decoder 23
3.3 Performance evaluation 23
4.Proposed Adaptive Decoder 28
4.1 T-algorithm 28
4.2 Convolutional neural network 32
4.3 Eb/N0 classification model 33
4.4 Performance evaluation 39
5,Conclusion 43
Reference 44
