Covariance Estimation Of Millimeter Wave Channels Using Sparse Signal Recovery Algorithms In A Hybrid MIMO Architecture

Rıfat Volkan Şenyuva

Makale detayları

Covariance Estimation Of Millimeter Wave Channels Using Sparse Signal Recovery Algorithms In A Hybrid MIMO Architecture

Yazarlar:

Rıfat Volkan Şenyuva

Yayıncı:

Fırat University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.4 Issue 1, pp.30-43

Belge Tipi:

Makale

Yayın Tarihi:

2/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341160

pISSN:

2822-2881

Anahtar kelimeler:

Özet:

 In this paper, the channel covariance estimation of a single mobile station MS in a narrowband millimeter wave mmWave communication system was addressed. The communication system worked in time division duplex TDD mode and the channel covariance was estimated in the uplink communication. The base station BS had multiple antennas with a hybrid architecture of radio frequency RF chains made up of analog and digital combiners, while the MS had a single antenna. The investigated system model assumed the shared combining matrix scheme where the same combining matrix was used across multiple coherence blocks of the mmWave channel. The application of the sparse signal recovery algorithms including the simultaneous orthogonal matching pursuit SOMP, the multiple response sparse Bayesian learning MSBL, and the correlated sparse Bayesian learning CSBL to the system model were shown. The algorithms were evaluated numerically, and their normalized mean square error NMSE performances were compared against the benchmark oracle minimum mean square error MMSE estimator in multiple scenarios of varying number of RF chains at the BS and sparsity ratios for modeling the mmWave channel. The numerical results indicated that the CSBL algorithm provided the NMSE results closest to that of the oracle MMSE estimator in all the scenarios.

 In this paper, the channel covariance estimation of a single mobile station MS in a narrowband millimeter wave mmWave communication system was addressed. The communication system worked in time division duplex TDD mode and the channel covariance was estimated in the uplink communication. The base station BS had multiple antennas with a hybrid architecture of radio frequency RF chains made up of analog and digital combiners, while the MS had a single antenna. The investigated system model assumed the shared combining matrix scheme where the same combining matrix was used across multiple coherence blocks of the mmWave channel. The application of the sparse signal recovery algorithms including the simultaneous orthogonal matching pursuit SOMP, the multiple response sparse Bayesian learning MSBL, and the correlated sparse Bayesian learning CSBL to the system model were shown. The algorithms were evaluated numerically, and their normalized mean square error NMSE performances were compared against the benchmark oracle minimum mean square error MMSE estimator in multiple scenarios of varying number of RF chains at the BS and sparsity ratios for modeling the mmWave channel. The numerical results indicated that the CSBL algorithm provided the NMSE results closest to that of the oracle MMSE estimator in all the scenarios.

 In this paper, the channel covariance estimation of a single mobile station MS in a narrowband millimeter wave mmWave communication system was addressed. The communication system worked in time division duplex TDD mode and the channel covariance was estimated in the uplink communication. The base station BS had multiple antennas with a hybrid architecture of radio frequency RF chains made up of analog and digital combiners, while the MS had a single antenna. The investigated system model assumed the shared combining matrix scheme where the same combining matrix was used across multiple coherence blocks of the mmWave channel. The application of the sparse signal recovery algorithms including the simultaneous orthogonal matching pursuit SOMP, the multiple response sparse Bayesian learning MSBL, and the correlated sparse Bayesian learning CSBL to the system model were shown. The algorithms were evaluated numerically, and their normalized mean square error NMSE performances were compared against the benchmark oracle minimum mean square error MMSE estimator in multiple scenarios of varying number of RF chains at the BS and sparsity ratios for modeling the mmWave channel. The numerical results indicated that the CSBL algorithm provided the NMSE results closest to that of the oracle MMSE estimator in all the scenarios.

 Bu çalışmada milimetrik dalga bandını kullanan darbant haberleşme sisteminde tek bir kullanıcıya ait kanalın kovaryans kestirimi incelenmektedir. Haberleşme sistemi zaman bölüşümlü çalışmaktadır ve kullanıcıdan baz istasyonu yönündeki kanal için kovaryans kestirimi yapılmaktadır. Baz istasyonu çok antenli ve radyo frekans RF zinciri hem analog hem sayısal birleştiricilerden oluşan çok-girişli çok-çıkışlı karma mimariye sahipken, kullanıcının tek anteni vardır. Ele alınan sistem modelinde ortak birleştirici matris yönteminin, milimetrik dalga kanalı uyum süresi boyunca gönderilen pilot bloklar için aynı birleştirici matrisinin uygulanması, kullanıldığı varsayılmaktadır. Seyrek sinyal geriçatım yöntemlerinden eşzamanlı normal uyum kovalama, çok ölçümlü seyrek Bayes öğrenme ve korelasyonlu seyrek Bayes öğrenmenin sistem modeline uygulanışı gösterilmektedir. İncelenen yöntemlerin sayısal sonuçları hesaplanarak normalleştirilmiş en küçük ortalama karesel hata başarımları değişen RF zincir sayısı ve milimetrik kanal seyreklik oranları için referans en küçük ortalama kare EKOK kestiricisiyle karşılaştırılmaktadır. Sayısal sonuçlar tüm deneylerde referans EKOK kestiricisine en yakın başarımların, korelasyonlu seyrek Bayes öğrenmeye ait olduğunu göstermektedir.

Makale detayları

Covariance Estimation Of Millimeter Wave Channels Using Sparse Signal Recovery Algorithms In A Hybrid MIMO Architecture

Yazarlar:

Rıfat Volkan Şenyuva

Yayıncı:

Fırat University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.4 Issue 1, pp.30-43

Belge Tipi:

Makale

Yayın Tarihi:

2/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341160

pISSN:

2822-2881

Millimeter wave

Spatial channel covariance

Sparse signal recovery

Hybrid precodingMilimetrik dalga

Uzamsal kanal kovaryansı

Seyrek sinyal geriçatımı

Karma önkodlama

Sayı

Makale detayları

Yazarlar: Rıfat Volkan Şenyuva

Yayıncı: Fırat University

Ana Başlıklar: Elektrik Mühendisliği (Diğer)

Dergi: Firat University Journal of Experimental and Computational Engineering Vol.4 Issue 1, pp.30-43

Belge Tipi: Makale

Yayın Tarihi: 2/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001341160

pISSN: 2822-2881

Sayı

Yazarlar:

Rıfat Volkan Şenyuva

Yayıncı:

Fırat University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.4 Issue 1, pp.30-43

Belge Tipi:

Makale

Yayın Tarihi:

2/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341160

pISSN:

2822-2881