Development of sub-THz phase noise compensation algorithm

다국어 초록 (Multilingual Abstract)

To support ultra-high data rate services such as extended reality and holographic communication, communication systems are expected to operate in sub-THz frequency bands where abundant spectrum is available. However, phase noise becomes increasingly severe at such high frequencies and degrades the performance of multicarrier waveforms. Moreover, phase noise not only distorts the received signal but also deteriorates channel estimation accuracy, which in turn further worsens phase noise estimation performance.
In this paper, we propose a joint channel and phase noise estimation framework for DFT-s-OFDM system which is a promising candidate for future communication due to its high PAPR property. By exploiting both time-domain and frequency-domain reference signals within a single DFT-s-OFDM symbol, the proposed method iteratively alternates between channel estimation and phase noise tracking. Simulation results confirm that the proposed joint estimation framework significantly improves channel NMSE, phase noise NMSE, and bit error rate (BER) performance compared to the conventional method, demonstrating the effectiveness of joint estimation in sub-THz communication systems.

국문 초록 (Abstract)

본 논문에서는 Sub-THz 대역에서 위상 잡음에 취약한 DFT-s-OFDM 시스템을 대상으로 채널과 위상 잡음을 동시에 추정하는 Joint Estimation 기법을 제안한다. 우선, 하나의 DFT-s-OFDM 심볼 내에서 시간영역과 주파수영역 파일럿 신호를 동시에 활용할 수 있는 구조를 제시했다. 이러한 파일럿 구조를 활용하여, 제안된 반복 추정 접근법은 채널 추정 과정에서 발생하는 위상 잡음의 영향을 보정하며, 이어 추정되는 위상 잡음 추정의 정확도 또한 함께 향상시킨다. 시뮬레이션 결과, 제안 기법은 기존 Wiener-filter 방식 대비 채널 NMSE, 위상 잡음 NMSE, 그리고 BER 측면에서 모두 개선된 성능을 보이며, Sub-THz와 같은 초고주파 주파수 대역을 활용하기 위한 Joint Estimation의 필요성과 이의 효과성을 확인하였다.

목차 (Table of Contents)

• Chapter 1. Introduction 1
• 1. Related works 2
• A. DFT-s-OFDM 2
• B. DMRS and PTRS 3
• C. Existing works 5
• D. Proposed reference signal architecture 7
• E. Iterative signal processing technique 8
• 2. Contributions 9
• 3. Paper Organization 10
• Chapter 2. System Model 11
• 1. Mathematical Notation 11
• 2. Transmitter Model 12
• 3. Phase noise and Channel Model 15
• 4. Receiver Model 18
• Chapter 3. Proposed Estimator 22
• 1. LS phase noise estimation algorithm 22
• 2. LMMSE phase noise estimation algorithm 25
• 3. Channel estimation algorithm 28
• Chapter 4. Experimental Results 30
• 1. Wiener-filter phase noise algorithm 30
• 2. Simulation parameters 32
• 3. Numerical Result 34
• 4. Parameter Optimization 39
• Chapter 5. Conclusion 41
• References 42
• Korean Abstract 45