Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Các thuật toán thích hợp để ước lượng kênh suy fading Rician MIMO chọn lọc theo tần số và hệ số Rice của kênh: Lợi ích đáng kể của mô hình Rician và các thỏa hiệp của ước lượng
Tóm tắt
Nghiên cứu về phương pháp ước lượng kênh dựa trên đào tạo (TBCE) trong các kênh suy fading Rician chọn lọc theo tần số với nhiều đầu vào và nhiều đầu ra (MIMO). Chúng tôi đề xuất kỹ thuật mới là bình phương tối thiểu có sẵn tỉ lệ dịch (SSLS) và ước lượng sai số bình phương tối thiểu (MMSE) phù hợp để ước lượng mô hình kênh đã đề cập ở trên. Các kết quả phân tích cho thấy rằng các ước lượng được đề xuất đạt được giới hạn dưới Cramér-Rao Bayes khả thi tối thiểu (CRLB) tốt hơn đáng kể trong các kênh Rician MIMO chọn lọc theo tần số so với kênh Rayleigh. Có thể thấy rằng ước lượng kênh SSLS yêu cầu ít thông tin hơn về kênh và/hoặc có hiệu suất tốt hơn so với các ước lượng bình phương tối thiểu truyền thống (LS) và MMSE. Kết quả mô phỏng xác nhận ưu điểm của các ước lượng kênh được đề xuất. Cuối cùng, để ước lượng hệ số Rice của kênh, một thuật toán được đề xuất và hiệu quả của nó được xác minh bằng cách sử dụng kết quả từ các ước lượng kênh SSLS và MMSE.
Từ khóa
#kênh MIMO #suy fading Rician #ước lượng kênh #bình phương tối thiểu #sai số bình phương tối thiểu #giới hạn Cramér-RaoTài liệu tham khảo
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