Sử dụng mô hình tensor để mở rộng bài toán ước lượng kênh LS cho các hệ thống MIMO có hỗ trợ bề mặt phản xạ thông minh

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Tác giả: Minh Hưng Đào, Đỗ Dũng Nguyễn

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Tạp chí Khoa học Trường Đại học Quy Nhơn, 2023

Mô tả vật lý: tr.14

Bộ sưu tập: Metadata

ID: 372237

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Bài báo đề xuất mở rộng hai thuật toán ước lượng kênh LS dựa trên mô hình tín hiệu tensor cho các hệ thống MIMO được hỗ trợ bởi bề mặt phản xạ thông minh (IRS). Hai thuật toán này khai thác cấu trúc tensor của tín hiệu hoa tiêu để thiết lập bài toán ước lượng kênh ghép tầng. Thuật toán thứ nhất mở rộng ước lượng LS dựa trên việc khai thác cấu trúc Khatri-Rao Factorization (KRF) của kênh MIMO ghép tầng, bằng cách giải các bài toán con xấp xỉ ma trận hạng 1. Bài toán ước lượng thứ hai dựa trên thuật toán BALS (Bilinear Alternating Least Squares), đây là phiên bản đơn giản hóa của thuật toán TALS (Trilinear Alternating Least Squares). Ngoài ra, bài báo này cũng trình bày mối quan hệ giữa các tham số kênh MIMO để các thuật toán ước lượng trên có tính khả thi. Kết quả mô phỏng cho thấy các phương pháp ước lượng LS mở rộng dựa trên mô hình tín hiệu tensor đã cải thiện hiệu suất so với ước lượng LS truyền thống.This paper proposes to extend two Least Squares (LS) channel estimation algorithms based on tensor signal model to MIMO systems supported by Intelligent Reflective Surfaces (IRS). These two algorithms exploit the tensor structure of the pilot signal to establish the cascaded channel estimation problem. The first algorithm extends the LS estimation based on exploiting the Khatri-Rao Factorization (KRF) structure of the cascaded MIMO channel, by solving subproblems approximating the 1-rank matrix. The second estimator is based on the Bilinear AlternatingLeast Squares (BALS) algorithm, which is a simplified version of the Trilinear Alternating Least Squares (TALS) algorithm. In addition, this paper also presents the relationship between the MIMO channel parameters for the above estimation algorithms to be feasible. The simulation results show that the extended LS estimation methods based on the tensor signal model have improved performance compared with the conventional LS estimation.

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