本文選題：Massive　切入點：MIMO　出處：《電子科技大學(xué)》2016年碩士論文

【摘要】：隨著無線通信技術(shù)的不斷發(fā)展,傳統(tǒng)的MIMO技術(shù)已經(jīng)無法滿足日益增加的數(shù)據(jù)需求。Massive MIMO作為5G標(biāo)準(zhǔn)的備選方案之一,通過增加發(fā)射和接收天線數(shù)目,可以極大的提高信道容量,并且可以相對方便的從現(xiàn)有的MIMO系統(tǒng)下進(jìn)行平滑過渡。同時,大規(guī)模的天線陣列增加了信道的維度,對于信道矩陣相關(guān)的算法,比如預(yù)編碼、信道檢測和信道估計等,實現(xiàn)復(fù)雜度上也會迅速上升。SVD作為矩陣分解的重要手段,在這些算法上均有著廣泛的應(yīng)用,如何在大規(guī)模矩陣下實現(xiàn)低復(fù)雜度的SVD算法,成為亟待解決的問題。本文首先介紹了Massive MIMO的特點和存在的一些技術(shù)挑戰(zhàn),以及SVD在MIMO下的應(yīng)用。接著對常見的SVD分解算法做了介紹,對于Golub-Kahan算法,主要研究了塊對角化和QR迭代過程。對于Jacobi旋轉(zhuǎn)算法,分析了實數(shù)域雙邊Jacobi變換和對應(yīng)的脈動執(zhí)行過程。對于Hestenes-Jacobi算法,主要介紹了兩種重要的數(shù)據(jù)計算順序。這些算法都求解了矩陣的完整SVD,在大規(guī)模矩陣中擁有較高復(fù)雜度。在MIMO預(yù)編碼系統(tǒng)中只需要較大奇異值對應(yīng)的奇異向量。本文提出了一種基于Hestenes-Jacobi的局部SVD分解方法。該算法收斂后只得到矩陣的部分奇異值和對應(yīng)的奇異向量,不需要求解整個SVD,在一定程度上減少了運算量,但是同時會影響收斂性能。通過結(jié)合局部SVD和完整SVD的各自優(yōu)點,本文對該算法作了進(jìn)一步改進(jìn),使得收斂性能得到了極大的改善。另外,本文研究了一種基于格拉斯曼流形的梯度跟蹤算法,并且對其性能做了仿真驗證。該算法將最優(yōu)化問題引入到流形中,同時利用了常見場景中時變信道緩慢連續(xù)變化的特點,實現(xiàn)了奇異向量實時跟蹤信道變化,降低了SVD復(fù)雜度。最后對于提出的局部SVD分解方法,本文設(shè)計了VLSI硬件架構(gòu)和FPGA實現(xiàn),同時通過比特量化分析,提高了資源利用率。本文設(shè)計的架構(gòu)主要包括控制器、數(shù)據(jù)緩沖區(qū)、數(shù)據(jù)總線、存儲器、處理器和連接器,利用CORDIC核進(jìn)行角度計算和向量旋轉(zhuǎn)。該架構(gòu)可以滿足任意m×n(max(m,n)≤32)矩陣、所需奇異值個數(shù)為2的的局部SVD分解,并且具有優(yōu)秀的擴展性,可以很容易地增加矩陣維度以及奇異值個數(shù)。
[Abstract]:With the continuous development of wireless communication technology, the traditional MIMO technology can no longer meet the increasing data demand. Massive MIMO as one of the 5G standard options, by increasing the number of transmitting and receiving antennas, can greatly improve the channel capacity.And it is relatively convenient to smooth the transition from the existing MIMO system.At the same time, the large-scale antenna array increases the channel dimension. For the algorithms related to channel matrix, such as precoding, channel detection and channel estimation, the implementation complexity of SVD will rise rapidly as an important means of matrix decomposition.It is widely used in these algorithms. How to implement the low complexity SVD algorithm under the large-scale matrix has become an urgent problem to be solved.This paper first introduces the characteristics and some technical challenges of Massive MIMO, and the application of SVD in MIMO.Then the common SVD decomposition algorithm is introduced. For the Golub-Kahan algorithm, block diagonalization and QR iteration are mainly studied.For the Jacobi rotation algorithm, the two-sided Jacobi transform in real number domain and the corresponding pulsation execution process are analyzed.For Hestenes-Jacobi algorithm, two kinds of important data order are introduced.These algorithms solve the complete SVD of the matrix, and have high complexity in the large-scale matrix.In MIMO precoding systems, only singular vectors corresponding to large singular values are required.In this paper, a local SVD decomposition method based on Hestenes-Jacobi is proposed.After the algorithm converges, only the partial singular values of the matrix and the corresponding singular vectors are obtained. It does not need to solve the entire SVD, which reduces the computational complexity to a certain extent, but it will affect the convergence performance at the same time.By combining the respective advantages of local SVD and complete SVD, the algorithm is further improved in this paper, and the convergence performance is greatly improved.In addition, a gradient tracking algorithm based on Glassmann manifold is studied, and its performance is verified by simulation.The algorithm introduces the optimization problem into the manifold, and makes use of the slow and continuous variation of time-varying channels in common scenarios. It realizes the real-time tracking of channel changes by singular vectors and reduces the complexity of SVD.Finally, for the proposed local SVD decomposition method, this paper designs the VLSI hardware architecture and FPGA implementation, and improves the resource utilization rate by bit quantization analysis.The architecture of this paper mainly includes controller, data buffer, data bus, memory, processor and connector. Angle calculation and vector rotation are carried out by using CORDIC core.This scheme can satisfy the local SVD decomposition of arbitrary m 脳 nn maxm n) 鈮，

本文編號：1702238