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Journals / Gazi University Journal of Science / 2026 / Volume: 18 Issue: 3

ADAPTIVE LINEAR PREDICTION BASED DOWNLINK BEAMFORMING FOR WIRELESS COMMUNICATION SYSTEMS

KABLOSUZ HABERLEŞME SİSTEMLERİ İÇİN UYARLAMALI DOĞRUSAL TAHMİN TABANLI AŞAĞI BAĞLANTI HÜZME ŞEKİLLENDİRME

Halil Yiğit
Adnan Kavak
Pages
—
Publication Date
2010-08-12
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is observed that in the event that Doppler shifts in the multipaths are integer multiple of Doppler shift in the fist path (fixed Doppler shift), prediction based beamforming achieves better SNR improvements in the received signal at the mobile terminal with increasing V , p , and L .

📌 3 Key Takeaways

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Objective & Hypothesis:ABSTRACT In Time Division Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to the variation of spatial signatures in the fast fading environment.
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Key Findings & Data:In this study, the effectiveness of employing predicted spatial signatures as downlink weight vectors is demonstrated under varying mobile speed ( V ), prediction filter order ( p ) and the number of multipath ( L ) conditions.
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Conclusion & Impact:It is observed that in the event that Doppler shifts in the multipaths are integer multiple of Doppler shift in the fist path (fixed Doppler shift), prediction based beamforming achieves better SNR improvements in the received signal at the mobile terminal with increasing V , p , and L .
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Key Words: Smart antenna system · AR modeling · linear prediction

Abstract

ABSTRACT In Time Division Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to the variation of spatial signatures in the fast fading environment. To mitigate this, prediction based downlink beamforming can be applied, which relies on using updated weight vectors via adaptive linear prediction of spatial signatures in the downlink interval based on their autoregressive (AR) modeling in the uplink interval. In this study, the effectiveness of employing predicted spatial signatures as downlink weight vectors is demonstrated under varying mobile speed ( V ), prediction filter order ( p ) and the number of multipath ( L ) conditions. It is observed that in the event that Doppler shifts in the multipaths are integer multiple of Doppler shift in the fist path (fixed Doppler shift), prediction based beamforming achieves better SNR improvements in the received signal at the mobile terminal with increasing V , p , and L .

Özet

Zaman bölmeli çift yönlü (TDD) kablosuz haberleşmede, bir akıllı anten sisteminin aşağı bağlantı ışın şekillendirme performansı hızlı sönümlenen bir ortamda uzaysal imzaların değişiminden dolayı bozulabilir. Bu bozulmayı azaltmak için yukarı bağlantı aralığındaki uzaysal imza vektörlerinin özbağlanımlı modellenmesine bağlı olarak, aşağı bağlantı aralığında uyarlamalı doğrusal tahmini ile güncellenen ağırlık vektörlerinin kullanılmasını içeren hüzme şekillendirme uygulanabilir. Bu çalışmada, değişen mobil hızı (V), tahmin filtresi derecesi (p), ve çoklu yol sayısı (L) koşulları altında aşağı bağlantı ağırlık vektörü olarak tahmin edilen uzaysal imzaların kullanılmasının verimliliği gösterilmiştir. Çoklu yollardaki Doppler kaymalarının birinci yoldaki Doppler kaymasının tamsayı katı olduğu durumda (Sabit Doppler kayması) tahmine dayalı hüzme şekillendirmenin artan V, p, ve L ile birlikte mobil terminal tarafından alınan sinyalde daha iyi SNR iyileştirmesi başardığı gözlemlenmiştir

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Cite This Article

Yiğit, H., Kavak, A. (2010). ADAPTIVE LINEAR PREDICTION BASED DOWNLINK BEAMFORMING FOR WIRELESS COMMUNICATION SYSTEMS. Gazi University Journal of Science, Volume: 18 Issue: 3. https://ajindex.com/articles/1270146

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References (11)

  1. Godara, L.C.,“Applications of antenna arrays to mobile communications, part I: performance improvement, feasibility, and system considerations”, IEEE Proceedings, 85(7): 1031-1060 (1997).
  2. Kavak, A.,“Adaptive antenna arrays for downlink capacity increase in third generation wireless CDMA”, in Proc. IEEE Radio and Wireless Conf., 77-80 (2001).
  3. Winters, J.H.,“Smart antennas for wireless systems”, IEEE Personal Comm. Mag., 5(1): 23-27 (1998).
  4. Krim, H. and Viberg, M.,“Two decades of array signal processing research”, IEEE Signal Proc. Mag., 3(4): 67-94 (1996).
  5. Kavak, A., Yang, W., Xu, G., and Vogel, W.J.,“Characteristics of vector propagation channels in dynamic mobile scenarios”, IEEE Trans. on Ant. Prop., 39(12): 1695-1702 (2001).
  6. Hwang, J.K., and Winters, J.H.,“Sinusoidal modeling and prediction of fast fading processes”, in Proc. IEEE GLOBECOM, Sydney/Australia, 1: 892-897 (1998).
  7. Duel-Hallen, A., Hu, S., and Hallen, H.,“Long-range prediction of fading signals”, IEEE Signal Proc. Mag., 17(3): 62-75 (2000).
  8. Dong, L., Xu, G., and Ling, H.,“Prediction of fast fading mobile radio channels in wideband communication systems”, in Proc. IEEE GLOBECOM, 3287-3291 (2001)
  9. Haykin, S., “Adaptive filter theory”, Prentice Hall, NJ (1996).
  10. Haardt, M., et al, “The TD-CDMA based UTRA TDD mode”, IEEE J. Sel. Areas in Commun., 18(8):1375-1385 (2000).
  11. Duel-Hallen, A., Hu, S., and Hallen, H.,“Long-range prediction of fading signals”, IEEE Signal Proc. Mag., 17(3): 62-75 (2000).