適用於毫米波多輸入多輸出通訊系統下以壓縮感知輔助之分層波束搜索與低複雜度預編碼器演算法設計

Compressive Sensing (CS)-Assisted Hierarchical Beam Search and Low-Complexity Hybrid Precoding for Millimeter-Wave MIMO Communication Systems

劉佑新(Yu-Hsin Liu)

指導教授 ： 吳安宇

國立臺灣大學/電機資訊學院/電子工程學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201701069

摘要

下世代通訊系統之技術標準有三大應用方向：增強型移動寬頻（eMBB, Enhance Mobile Broadband）， 超高可靠性與超低延遲通訊（uRLLC, Ultra Reliable Low Latency Communication）以及巨量物聯網通訊（mMTC, Massive Machine Type Communication）。對於增強型移動寬頻此方向，毫米波頻帶被認為是高速率通訊所需之關鍵技術，而透過大量數量的天線陣列以及預編碼進行波束成型，能克服毫米波通訊環境下的高路徑損耗。然而，常見之預編碼需要通道資訊來進行奇異值分解，而通道資訊之維度正比於天線數量，因此在大量數量的天線陣列架構下，通道資訊變得難以取得，因此分層搜索（Hierarchical Search)為目前廣泛被討論之低複雜度估測通道技術，但分層搜索需要基地台（BS, base station）與使用者端設備(UE, user equipment）之間的訊息回授，以反覆縮小搜索範圍，在高維度的天線陣列下，此訊息回授的次數將會大幅增加，成為通道估測的嚴重成本。因此，本論文將著重於如何降低分層搜索中的訊息回授次數，同時維持估測的通道品質。
本論文中，我們首先採用廣度優先分層搜尋（BFHS, breadth first hierarchical search），相較於傳統的分層搜索，此方法能降低K倍回授次數，同時達到更好的估測通道品質。接著，透過壓縮感知（Compressive Sensing）技術的輔助，我們能基於廣度優先搜尋方法，更進一步減少回授次數，同時維持一定的估測通道品質。基於上述方法得到的通道資訊，我們接著提出低複雜度之混合預編碼器設計方法，相較於目前現有文獻中的方法，此方法可降低 98.44%的複雜度，同時達到99.27%的預編碼器效能。最後，我們將此低複雜度混合預編碼器演算法延伸到子陣列天線架構，相較於目前現有文獻，此方法能夠同時提高預編碼器效能，並降低大量的運算複雜度。

關鍵字

毫米波 ； 通道估測 ； 壓縮感知 ； 混合預編碼器

並列摘要

The next generation communication systems (5G) have three directions: enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable and low latency communications (URLLC). For eMBB, transmitting signals at millimeter-wave (mmWave) frequency bands is an essential technology, and precoding with large-scale antenna array can overcome the huge path-loss in mmWave frequency bands. However, precoding needs singular value decomposition (SVD) on the high-dimensional channel matrix, which is hard to acquire under large antenna array. Therefore, hierarchical search is proposed to estimate the mmWave channel with low-complexity. However, hierarchical search needs the feedback between BS and UE to refine the search range. Under large antenna array, the number of feedbacks will become unbearable. Therefore, in this thesis, we focus on how to reduce the number of feedbacks of hierarchical search, while estimating the mmWave channel with satisfying quality.
In this thesis, we first introduce BFHS to reduce the number of feedback by K times, while estimating channel with better quality. Next, assisted by compressive sensing (CS), we can further reduce the number of feedbacks. Based on the estimated channel state information (CSI), we further propose a low-complexity hybrid precoding algorithm. It can reduce 98.44% complexity, while achieving 99.27% performance of state-of-the-art hybrid precoding algorithms. Finally, we extend the proposed low-complexity hybrid precoding algorithm to sub-array structure, which can outperform other related works with much less computing time.

並列關鍵字

Millimeter-wave ； Channel estimation ； Compressive sensing ； Hybrid precoding

參考文獻

[1] Q. Li, H. Niu, A. Papathanassiou, and G. Wu, "5G Network Capacity: Key Elements and Technologies," IEEE Vehicular Technology Magazine, vol.9, no.1, pp.71-78, Mar. 2014.

[2] D.J. Love, R.W. Heath, Jr., "Limited feedback unitary precoding for spatial multiplexing systems," IEEE Trans. Inf. Theory, vol.51, no.8, pp.2967-2976, Aug. 2005.

[3] T.S. Rappaport, S. Sun. R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. Wong, J. Schulz, M. Samimi, F. Gutierrez, "Millimeter wave mobile communications for 5G cellular: It will work!," IEEE Access, vol.1, pp.335-349, May 2013.

[4] F. Rusek, D. Persson, B. K. Lau, E.G. Larsson, T.L. Marzetta, O. Edfors, and F. Tufvesson, "Scaling up MIMO: opportunities and challenges with very large arrays," IEEE Signal Process. Mag., vol.30, no.1, pp.40-60, Jan. 2013.

[5] ETRI, “Paving the way for 5G”, Nov, 2016, https://5g-ppp.eu/wp-content/uploads/2016/11/06_10-Nov_Session-3_Lee-JunHwan.pdf