本文關(guān)鍵詞： MIMO Massive MIMO 信號(hào)檢測(cè) 矩陣求逆 FPGA　出處：《電子科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：多天線通信技術(shù)可以充分利用空間資源,提高空間自由度,在有效提高系統(tǒng)的信道容量和傳輸速率的同時(shí),而不引起在系統(tǒng)帶寬和信號(hào)發(fā)送功率上的額外開銷。作為典型多天線系統(tǒng)的,傳統(tǒng)MIMO的吞吐率已接近其理論上限,大規(guī)模MIMO的出現(xiàn),將進(jìn)一步挖掘頻譜使用效率,提升業(yè)務(wù)容量,已成為下一代無線通信系統(tǒng),即5G中極具潛力的一項(xiàng)關(guān)鍵技術(shù),因此,研究多天線通信情況下的信號(hào)檢測(cè)技術(shù)具有重要意義。本文主要針對(duì)MIMO及Massive MIMO系統(tǒng)中的信號(hào)檢測(cè)技術(shù)進(jìn)行了相關(guān)研究。首先對(duì)現(xiàn)代無線通信系統(tǒng)的模型以及下一代無線通信中的關(guān)鍵候選技術(shù)進(jìn)行分析與介紹,并對(duì)常用的多天線系統(tǒng)的信道模型及其信道容量進(jìn)行分析,從理論上證明,信道容量會(huì)隨著天線數(shù)的增加而提高。其次介紹了幾種常用的檢測(cè)算法。傳統(tǒng)的線性檢測(cè)算法主要介紹了匹配濾波檢測(cè)(MF)、迫零檢測(cè)(ZF)和最小均方誤差檢測(cè)(MMSE)等,以及一種基于紐曼級(jí)數(shù)展開的近似檢測(cè)算法,具有較低復(fù)雜度的,適用于基站天線數(shù)遠(yuǎn)遠(yuǎn)大于用戶數(shù)的情況。非線性檢測(cè)算法,通常以線性檢測(cè)結(jié)果為基礎(chǔ),主要介紹了串行干擾消除算法(SIC),以及兩種基于貪婪思想的局部搜索算法:似然上升搜索(LAS)和動(dòng)態(tài)禁忌搜索(RTS)等,并給出了各個(gè)算法的實(shí)現(xiàn)步驟。之后,對(duì)上述幾種算法進(jìn)行了復(fù)雜度分析,并在不同環(huán)境下,分別對(duì)各種算法進(jìn)行系統(tǒng)性能仿真,得到其誤碼率曲線,對(duì)其適用性進(jìn)行討論分析。仿真結(jié)果表明,隨著基站天線數(shù)與用戶天線數(shù)之比的增加,線性檢測(cè)算法與非線性檢測(cè)算法性能趨于一致,因此在Massive MIMO系統(tǒng)中,接收機(jī)采用簡單的線性檢測(cè)即可滿足系統(tǒng)要求。線性檢測(cè)的復(fù)雜度與天線數(shù)目成指數(shù)關(guān)系,其中矩陣求逆是最為復(fù)雜的部分,已成為制約線性檢測(cè)技術(shù)性能的主要瓶頸。首先就算法的復(fù)雜度和適用性,對(duì)幾種常用的矩陣求逆算法進(jìn)行了詳細(xì)分析,最后選取了適用于該系統(tǒng)的,基于改進(jìn)的cholesky分解的矩陣求逆算法,用于FPGA實(shí)現(xiàn)�？紤]資源消耗、最大時(shí)鐘頻率、流水、并行和延時(shí)等之間的關(guān)系,進(jìn)行矩陣求逆在FPGA上的設(shè)計(jì)實(shí)現(xiàn),并進(jìn)行了RTL級(jí)仿真及在Xilinx Virtex-7 FPGA上的測(cè)試。其中4*4規(guī)模矩陣采用全流水結(jié)構(gòu),延時(shí)為66cycle,最大時(shí)鐘頻率可達(dá)418MHz,吞吐率104Minv/s;16*16和8*8采用復(fù)用結(jié)構(gòu),最大延時(shí)分別為174cycle和490cycle,最大時(shí)鐘頻率均可達(dá)500MHz。
[Abstract]:Multi-antenna communication technology can make full use of space resources, improve the degree of freedom of space, and effectively improve the channel capacity and transmission rate of the system. As a typical multi-antenna system, the throughput of traditional MIMO is close to its theoretical upper limit. The emergence of large-scale MIMO will further exploit the spectral efficiency. Enhancing service capacity has become a key technology with great potential in the next generation of wireless communication systems, or 5G, so, It is of great significance to study the signal detection technology in multi-antenna communication. This paper mainly focuses on the signal detection technology in MIMO and Massive MIMO systems. Firstly, the model of modern wireless communication system and the next one are discussed. The key candidate technologies in wireless communication are analyzed and introduced. The channel model and channel capacity of the commonly used multi-antenna system are analyzed, and it is proved theoretically that, The channel capacity will increase with the increase of the number of antennas. Secondly, several commonly used detection algorithms are introduced. The traditional linear detection algorithms mainly introduce matching filter detection, zero forcing detection (ZF) and minimum mean square error detection (MMSE), etc. And an approximate detection algorithm based on Newman series expansion, which has low complexity and is suitable for the case where the number of antennas of base station is far larger than the number of users. The nonlinear detection algorithm is usually based on linear detection results. This paper mainly introduces the serial interference cancellation algorithm (SICI), and two local search algorithms based on greedy thought: likelihood ascending search (LAS) and dynamic Tabu search (RTS), and gives the implementation steps of each algorithm. The complexity of these algorithms is analyzed, and the system performance of these algorithms is simulated in different environments. The BER curve is obtained, and its applicability is analyzed. The simulation results show that, With the increase of the ratio of the number of base station antennas to the number of user antennas, the performance of linear detection algorithm and nonlinear detection algorithm tends to be consistent, so in Massive MIMO system, The receiver adopts simple linear detection to satisfy the system requirements. The complexity of linear detection is exponentially related to the number of antennas, and matrix inversion is the most complex part. It has become the main bottleneck that restricts the performance of linear detection technology. Firstly, several commonly used matrix inverse algorithms are analyzed in detail on the complexity and applicability of the algorithm. The matrix inverse algorithm based on improved cholesky factorization is used in FPGA implementation. Considering the relationship between resource consumption, maximum clock frequency, income, parallelism and delay, the inverse matrix is designed and implemented on FPGA. The RTL level simulation and the test on Xilinx Virtex-7 FPGA are carried out. Among them, the 4Q4 scale matrix adopts the full income structure, the delay is 66cycle. the maximum clock frequency can reach 418MHz, the throughput of 104Minvsr / s 16 / 16 and 8Y8 are multiplexed. The maximum delay is 174cycle and 490cycle.The maximum clock frequency can reach 500MHz.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN919.3;TN911.23

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本文編號(hào)：1532627