DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 최준일 | - |
dc.contributor.author | Kang, Yoonseong | - |
dc.contributor.author | 강윤성 | - |
dc.date.accessioned | 2024-08-08T19:31:44Z | - |
dc.date.available | 2024-08-08T19:31:44Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1100096&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/322190 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2024.2,[iv, 57 p. :] | - |
dc.description.abstract | In this dissertation, we delve into Bayesian sparse recovery to enhance sparse recovery performance and reduce computational complexity in wireless communication systems. To address the significant computational complexity of existing sparse recovery algorithms, we explore a random likelihood decoding technique based on Markov chain Monte Carlo sampling. We utilize Metropolis-Hastings sampling for support identification and Gibbs sampling for sparse signal estimation. Specifically, we propose a two-stage algorithm that iteratively conducts both sampling processes and the entire process to improve the performance of sparse vector recovery. Additionally, with next-generation 6G communication systems incorporating more antennas and higher frequency bands, there is an anticipation of greater utilization of the near-field region compared to traditional communications that predominantly focus on the far-field region. To address this shift, we propose a pilot signal and channel estimator co-design technique aiming to optimize sparse recovery performance in hybrid-field extremely large-scale MIMO systems where both far-field and near-field regions coexist. Specifically, we propose an alternating direction method of multipliers (ADMM)-based pilot signal design algorithm that can maximize sparse vector recovery performance and a hybrid-field channel estimation algorithm using Bayesian approaches based on the designed pilot signal. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 희소 복원▼a압축 센싱▼a베이지안 방법▼a마르코프 체인 몬테카를로 샘플링▼a무작위 우도 복호화▼a하이브리드 필드 통신 | - |
dc.subject | Sparse recovery▼acompressed sensing▼aBayes methods▼aMarkov chain Monte Carlo sampling▼arandomized likelihood decoding▼ahybrid-field communications | - |
dc.title | (A) study on statistical sparse recovery for detection and channel estimation in communication systems | - |
dc.title.alternative | 통신 시스템에서 검출 및 채널 추정을 위한 통계적 희소 복원에 관한 연구 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Choi, Junil | - |
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