İdeal Olmayan Donanıma Sahip Yeniden Yapılandırılabilir Akıllı Yüzeyler için Geniş Bant Kanal Kestirimi

Öz

Kanal kestirimi, aktif ve pasif huzme oluşturma amaçlı kullanılan yeniden yapılandırılabilir akıllı yüzeyler (RIS) destekli iletişimin merkezi bir parçasıdır. Kanal kestiriminin arkasındaki temel zorluk, çok fazla RIS öğelerinden ve aynı zamanda baz istasyonundaki birçok antenden kaynaklanan büyük boyutluluktur. Büyük boyutluluk, OFDM sinyalleri için pilot tonların aşırı kullanımına yol açar, bu da fazla ek yük ve azalan veri hızı anlamına gelir. Kanal kestiriminde pilotların fazla kullanımını azaltmak için bu çalışmada, RIS'de düşük karmaşıklığa sahip bir vericiye sahip olmak için agresif bir kırpma politikası ve kırpmaya karşı dayanıklı bir kanal kestirimi öneriyoruz. Dayanıklı kanal tahmini için, kırpmadan kaynaklanan bilgi kaybını telafi etmek üzere önceki bilgileri kullanan üretken bir makine öğrenimi modeli uyarlanmaktadır. Simülasyon sonuçları, önerilen kanal kestiriminin, doğrusal kanal kestirimleri ile karşılaştırıldığında kırpılmış iletilen sinyaller için oldukça büyük bir esnekliğe sahip olduğunu açıkça göstermektedir.

Anahtar Kelimeler

• Kanal kestirimi
• Yeniden yapılandırılabilir akıllı yüzeyler
• Makine öğrenimi
• Kırpma

Wideband Channel Estimation with Imperfect Hardware for Reconfigurable Intelligent Surfaces

Öz

Channel estimation is central part of reconfigurable intelligent surface (RIS) aided communication for active and passive beamforming. The primary challenge behind channel estimate is large dimensionality stemming from not only massive number of RIS elements but also many antennas in base station. Large dimensionality leads to excessive usage of pilot tones for OFDM signals implying high overhead and decreased throughput. To alleviate the high usage of pilots for channel estimation, in this study we propose to have a low complexity transmitter at the RIS simplifed with an aggressive clipping policy and a robust channel estimator against clipping. For robust channel estimation, a generative machine learning model is adapted to exploit prior information to compensate the information loss due to clipping. The simulation results clearly indicate that the proposed estimator has quite a large resiliency for clipped transmitted signals as compared to linear channel estimators.

Anahtar Kelimeler

• Channel estimation
• Reconfigurable intelligent surface
• Machine learning
• Clipping

Kaynakça

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