大規(guī)模MIMO信道估計(jì)與綠色能效研究
發(fā)布時(shí)間：2018-11-23 07:32
【摘要】：在移動(dòng)互聯(lián)網(wǎng)和物聯(lián)網(wǎng)兩大趨勢(shì)的推動(dòng)下,移動(dòng)數(shù)據(jù)業(yè)務(wù)急劇增加、頻譜資源日趨緊缺、能源消耗快速增長(zhǎng)對(duì)下一代移動(dòng)通信系統(tǒng),即5G系統(tǒng),提出了更高的要求。作為5G的一種關(guān)鍵技術(shù),大規(guī)模MIMO系統(tǒng)通過(guò)在基站側(cè)配置大規(guī)模天線陣列可以顯著地提升系統(tǒng)的頻譜效率和功率效率。本論文針對(duì)大規(guī)模MIMO系統(tǒng)傳輸技術(shù)中存在的一些問(wèn)題進(jìn)行了研究,具體研究?jī)?nèi)容如下:首先,大規(guī)模MIMO基站側(cè)龐大的天線數(shù)導(dǎo)致信道瞬時(shí)狀態(tài)信息提取復(fù)雜度較高。本文利用無(wú)線信道狀態(tài)信息的稀疏性,在傳統(tǒng)DFT信道估計(jì)算法的基礎(chǔ)上加以改進(jìn),提出了一種適用于大規(guī)模MIMO系統(tǒng)的低復(fù)雜度稀疏信道估計(jì)算法,仿真結(jié)果表明該算法可獲得接近MMSE信道估計(jì)的性能。其次,在大規(guī)模MIMO系統(tǒng)中,隨著基站側(cè)天線數(shù)的增加,系統(tǒng)容量幾乎完全受限于相鄰小區(qū)的導(dǎo)頻復(fù)用,這是系統(tǒng)設(shè)計(jì)最嚴(yán)峻的挑戰(zhàn)。本文通過(guò)研究多小區(qū)多用戶系統(tǒng)的導(dǎo)頻污染特性,得出最嚴(yán)重的導(dǎo)頻污染來(lái)源于采用相同導(dǎo)頻的小區(qū)邊緣處用戶,利用不同用戶統(tǒng)計(jì)狀態(tài)信息非正交的特點(diǎn),提出了一種基于用戶與基站之間距離的智能導(dǎo)頻分配方案。理論分析表明該方案可以使系統(tǒng)下行鏈路的信號(hào)噪聲干擾比收斂于最優(yōu),仿真結(jié)果也表明該方案可以有效地提升大規(guī)模MIMO系統(tǒng)下行鏈路的性能。最后,大規(guī)模MIMO基站側(cè)天線使用大量的A/D轉(zhuǎn)換器對(duì)信號(hào)進(jìn)行量化,其量化精度嚴(yán)重地影響了系統(tǒng)的能量效率。本文根據(jù)上行鏈路接收端A/D轉(zhuǎn)換器的量化精度與系統(tǒng)能量損耗以及信息損失之間的關(guān)系,建立大規(guī)模MIMO系統(tǒng)量化模型,推導(dǎo)了A/D轉(zhuǎn)換器的量化比特?cái)?shù)和基站側(cè)天線數(shù)與系統(tǒng)頻譜效率以及能量效率之間的關(guān)系表達(dá)式。在保證能量效率最大的基礎(chǔ)上,提出了一種基于PSO算法的最佳天線選擇分組量化方案。仿真結(jié)果表明,低精度量的A/D轉(zhuǎn)換器可以使系統(tǒng)能量效率達(dá)到最大,并且當(dāng)系統(tǒng)采用PSO算法優(yōu)化出來(lái)的最佳組合值時(shí),無(wú)論相鄰小區(qū)的大尺度衰落系數(shù)如何變化,系統(tǒng)的魯棒性最好。
[Abstract]:Driven by the trends of mobile Internet and Internet of things, mobile data services increase rapidly, spectrum resources become increasingly scarce, and the rapid growth of energy consumption puts forward higher requirements for the next generation mobile communication system, that is, 5G system. As a key technology of 5G large scale MIMO systems can significantly improve the spectral efficiency and power efficiency of the system by configuring large scale antenna arrays on the base station side. In this paper, some problems in large-scale MIMO transmission technology are studied. The main contents are as follows: first, the large number of antennas in the large scale MIMO base station results in high complexity of extracting the instantaneous state information of the channel. In this paper, a low complexity sparse channel estimation algorithm for large scale MIMO systems is proposed, which is improved on the basis of the traditional DFT channel estimation algorithm by using the sparsity of wireless channel state information. Simulation results show that the proposed algorithm can achieve performance close to MMSE channel estimation. Secondly, in large-scale MIMO systems, with the increase of the number of antennas on the base station side, the system capacity is almost completely limited by the pilot multiplexing of adjacent cells, which is the most serious challenge in system design. By studying the pilot pollution characteristics of multi-cell multi-user system, it is concluded that the most serious pilot pollution comes from the users at the edge of the cell using the same pilot frequency, and makes use of the non-orthogonal characteristics of different users' statistical state information. An intelligent pilot allocation scheme based on the distance between the user and the base station is proposed. Theoretical analysis shows that the proposed scheme can converge to the optimal signal-to-noise ratio of the downlink, and the simulation results show that the scheme can effectively improve the downlink performance of large-scale MIMO systems. Finally, a large number of A / D converters are used to quantify the signal in the MIMO base station side antenna. The quantization accuracy seriously affects the energy efficiency of the system. Based on the relationship between the quantization accuracy of the uplink receiver and the energy loss and information loss of the system, a large scale quantization model of MIMO system is established in this paper. The relationship between the quantization bit number of A / D converter and the number of antennas on the base station and the spectral efficiency and energy efficiency of the system is derived. Based on the maximum energy efficiency, an optimal antenna selection grouping quantization scheme based on PSO algorithm is proposed. The simulation results show that the low precision A / D converter can maximize the energy efficiency of the system, and when the optimal combination value of PSO algorithm is adopted, no matter how the large-scale fading coefficient of adjacent cells changes, The system has the best robustness.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN919.3
本文編號(hào)：2350782
[Abstract]:Driven by the trends of mobile Internet and Internet of things, mobile data services increase rapidly, spectrum resources become increasingly scarce, and the rapid growth of energy consumption puts forward higher requirements for the next generation mobile communication system, that is, 5G system. As a key technology of 5G large scale MIMO systems can significantly improve the spectral efficiency and power efficiency of the system by configuring large scale antenna arrays on the base station side. In this paper, some problems in large-scale MIMO transmission technology are studied. The main contents are as follows: first, the large number of antennas in the large scale MIMO base station results in high complexity of extracting the instantaneous state information of the channel. In this paper, a low complexity sparse channel estimation algorithm for large scale MIMO systems is proposed, which is improved on the basis of the traditional DFT channel estimation algorithm by using the sparsity of wireless channel state information. Simulation results show that the proposed algorithm can achieve performance close to MMSE channel estimation. Secondly, in large-scale MIMO systems, with the increase of the number of antennas on the base station side, the system capacity is almost completely limited by the pilot multiplexing of adjacent cells, which is the most serious challenge in system design. By studying the pilot pollution characteristics of multi-cell multi-user system, it is concluded that the most serious pilot pollution comes from the users at the edge of the cell using the same pilot frequency, and makes use of the non-orthogonal characteristics of different users' statistical state information. An intelligent pilot allocation scheme based on the distance between the user and the base station is proposed. Theoretical analysis shows that the proposed scheme can converge to the optimal signal-to-noise ratio of the downlink, and the simulation results show that the scheme can effectively improve the downlink performance of large-scale MIMO systems. Finally, a large number of A / D converters are used to quantify the signal in the MIMO base station side antenna. The quantization accuracy seriously affects the energy efficiency of the system. Based on the relationship between the quantization accuracy of the uplink receiver and the energy loss and information loss of the system, a large scale quantization model of MIMO system is established in this paper. The relationship between the quantization bit number of A / D converter and the number of antennas on the base station and the spectral efficiency and energy efficiency of the system is derived. Based on the maximum energy efficiency, an optimal antenna selection grouping quantization scheme based on PSO algorithm is proposed. The simulation results show that the low precision A / D converter can maximize the energy efficiency of the system, and when the optimal combination value of PSO algorithm is adopted, no matter how the large-scale fading coefficient of adjacent cells changes, The system has the best robustness.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN919.3
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 吳昌友;王福林;馬力;;一種新的改進(jìn)粒子群優(yōu)化算法[J];控制工程;2010年03期
，本文編號(hào)：2350782
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