【摘要】：通過(guò)使用大規(guī)模天線(xiàn)陣完成數(shù)據(jù)傳輸,大規(guī)模多輸入多輸出(Multiple Input Multiple Output, MIMO)系統(tǒng)可以將波束能量集中到很小的空間區(qū)域,從而節(jié)省發(fā)射功率并降低對(duì)非目標(biāo)用戶(hù)的干擾,因此可以實(shí)現(xiàn)比傳統(tǒng)MIMO系統(tǒng)更高的能量效率,是重要的5G (5 Generation,第五代移動(dòng)通信系統(tǒng))候選技術(shù)。隨著全球變暖的不斷加劇,能效優(yōu)化成為科研和工程實(shí)現(xiàn)的一個(gè)重要的關(guān)注點(diǎn),在大規(guī)模MIMO系統(tǒng)中,由于大量射頻鏈路消耗的功率較大,使用所有天線(xiàn)完成數(shù)據(jù)傳輸會(huì)造成能效損失,另外,系統(tǒng)復(fù)雜化為能效進(jìn)一步優(yōu)化提供了空間,因此大規(guī)模MIMO的能效優(yōu)化問(wèn)題近年來(lái)得到了廣泛的研究。本文重點(diǎn)關(guān)注考慮射頻鏈路功耗的天線(xiàn)選擇問(wèn)題以及天線(xiàn)數(shù)、用戶(hù)數(shù)和發(fā)射功率聯(lián)合優(yōu)化問(wèn)題,本文的主要工作有：1.對(duì)低復(fù)雜度天線(xiàn)選擇算法進(jìn)行了研究。針對(duì)大規(guī)模MIMO系統(tǒng)天線(xiàn)數(shù)目較多,天線(xiàn)選擇復(fù)雜度較高的問(wèn)題,首先介紹了一種以最小平均誤差作為目標(biāo)函數(shù)的上行天線(xiàn)選擇算法,該算法可以轉(zhuǎn)化為一個(gè)稀疏近似問(wèn)題,并通過(guò)現(xiàn)有成熟的稀疏近似方法進(jìn)行求解�；谙∈杞频姆椒�,提出了一種適用于大規(guī)模MIMO多用戶(hù)系統(tǒng)的天線(xiàn)選擇算法,該方法利用大規(guī)模天線(xiàn)陣所能獲得的信號(hào)分集,以信道矩陣作為測(cè)量字典,通過(guò)稀疏近似方法求解低相關(guān)性的天線(xiàn)集。該方法考慮天線(xiàn)相關(guān)性,在大規(guī)模MIMO系統(tǒng)中性能優(yōu)于不考慮天線(xiàn)選擇的低復(fù)雜度天線(xiàn)選擇算法,仿真結(jié)果表明,天線(xiàn)相關(guān)性越強(qiáng),該算法的性能優(yōu)勢(shì)越明顯。2.對(duì)迫零(Zero Forcing, ZF)預(yù)編碼方式下的天線(xiàn)數(shù)、用戶(hù)數(shù)和發(fā)射功率聯(lián)合能效優(yōu)化進(jìn)行了研究。通過(guò)對(duì)ZF預(yù)編碼方式下遍歷能效的分析,結(jié)合分式優(yōu)化算法,給出了多用戶(hù)場(chǎng)景下的天線(xiàn)數(shù)、發(fā)射功率和用戶(hù)數(shù)的聯(lián)合優(yōu)化方法及其退化形式。在優(yōu)化過(guò)程中,發(fā)射功率和天線(xiàn)數(shù)優(yōu)化問(wèn)題均可轉(zhuǎn)化為一種可以快速求解的形式,且滿(mǎn)足相互迭代的條件；用戶(hù)數(shù)優(yōu)化問(wèn)題也可以通過(guò)二分法來(lái)求解；三者的聯(lián)合優(yōu)化也可根據(jù)由表達(dá)式得出的性質(zhì)進(jìn)行簡(jiǎn)化。仿真結(jié)果表明,該算法在相關(guān)和非相關(guān)信道下均可實(shí)現(xiàn)近似最優(yōu)的能效。3.對(duì)最大比發(fā)射(Maximum Ratio Transmission, MRT)預(yù)編碼方式下的天線(xiàn)數(shù)、發(fā)射功率優(yōu)化算法進(jìn)行研究。根據(jù)ZF預(yù)編碼方式下的聯(lián)合優(yōu)化方法的思路,對(duì)MRT預(yù)編碼方式下的遍歷能效進(jìn)行推導(dǎo),給出了MRT預(yù)編碼方式下應(yīng)用該方法的相關(guān)證明及方法的具體形式。本文對(duì)多用戶(hù)系統(tǒng)和單用戶(hù)系統(tǒng)兩種系統(tǒng)模型均進(jìn)行了研究,對(duì)于單用戶(hù)系統(tǒng),考慮了信道狀態(tài)信息(Channel State Information, CSI)已知和未知兩種場(chǎng)景。
[Abstract]:By using large scale antenna array to complete data transmission, large scale multi-input and multi-output (Multiple Input Multiple Output, MIMO) system can concentrate beam energy into a small space area, thus saving transmission power and reducing interference to non-target users. Therefore, it is possible to achieve higher energy efficiency than traditional MIMO systems and is an important 5G (5 Generation, fifth generation mobile communication system) candidate technology. With the increasing global warming, energy efficiency optimization has become an important concern in scientific research and engineering implementation. In large-scale MIMO systems, a large number of RF links consume a large amount of power. Energy efficiency loss can be caused by using all antennas to complete data transmission. In addition, system complexity provides space for further optimization of energy efficiency. Therefore, energy efficiency optimization of large-scale MIMO has been widely studied in recent years. This paper focuses on the antenna selection problem considering RF link power consumption and the joint optimization of antenna number, number of users and transmit power. The main work of this paper is as follows: 1. A low complexity antenna selection algorithm is studied. Aiming at the large number of antennas in large scale MIMO systems and the high complexity of antenna selection, an uplink antenna selection algorithm with minimum average error as the objective function is introduced, which can be transformed into a sparse approximation problem. It is solved by the existing sparse approximation method. Based on the sparse approximation method, an antenna selection algorithm for large-scale MIMO multiuser systems is proposed. The channel matrix is used as the measurement dictionary, and the signal diversity can be obtained by large scale antenna array. The sparse approximation method is used to solve the low correlation antenna set. This method takes antenna correlation into account, and its performance is better than the low complexity antenna selection algorithm without antenna selection in large-scale MIMO systems. The simulation results show that the stronger the antenna correlation is, the more obvious the performance advantage of the algorithm is. 2. The optimization of antenna number, number of users and transmit power under zero forcing (Zero Forcing, ZF) precoding is studied. Based on the analysis of ergodic energy efficiency in ZF precoding and the fractional optimization algorithm, the joint optimization method of antenna number, transmit power and number of users in multi-user scenarios and its degenerate form are presented. In the process of optimization, the optimization problem of transmit power and antenna number can be transformed into a form that can be solved quickly, and the condition of iteration can be satisfied, and the optimization problem of number of users can also be solved by dichotomy. The joint optimization of the three can also be simplified according to the properties obtained from the expression. Simulation results show that the proposed algorithm can achieve approximately optimal energy efficiency in both correlated and uncorrelated channels. The optimization algorithm of antenna number and transmit power in (Maximum Ratio Transmission, MRT) precoding mode is studied. According to the idea of joint optimization method in ZF precoding mode, the ergodic energy efficiency under MRT precoding mode is deduced, and the relevant proof and the concrete form of the method applied in MRT precoding mode are given. In this paper, two kinds of system models, multiuser system and single user system, are studied. For single user system, the known and unknown scenarios of channel state information (Channel State Information, CSI) are considered.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN919.3

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本文編號(hào)：2394010