本文選題：最小均方差 + 格約減�。� 參考：《南京信息工程大學(xué)》2016年碩士論文

【摘要】：多輸入多輸出(MIMO)技術(shù)是無線通信領(lǐng)域的一個(gè)重要突破,空間復(fù)用和空間分集技術(shù)使得空間資源得到充分利用,控制了信道衰減。MIMO技術(shù)在保證系統(tǒng)帶寬和發(fā)射功率的前提下,大大提高了頻譜使用效率和信道容量。信號檢測技術(shù)卻對MIMO技術(shù)在無線通信中的運(yùn)用產(chǎn)生了影響,所以本文在一些傳統(tǒng)檢測算法的基礎(chǔ)上,對改進(jìn)的檢測算法進(jìn)行了深入分析,并在性能和復(fù)雜度之間取得了良好的折衷點(diǎn)。主要工作如下：第一,對MIMO模型的概念進(jìn)行分析,詳細(xì)概述了MIMO信道的原理和MIMO系統(tǒng)的傳統(tǒng)檢測算法,對MIMO檢測的改進(jìn)算法進(jìn)行了深入分析。由于傳統(tǒng)檢測算法在性能和復(fù)雜度之間這種間存在局限,進(jìn)一步介紹了格約減(LR)技術(shù),主要介紹了對偶格(DLR)算法。在此基礎(chǔ)上結(jié)合格約減技術(shù)與傳統(tǒng)檢測算法,使得這些次優(yōu)、低復(fù)雜度的傳統(tǒng)檢測算法性能有明顯地改善。第二,串行排序干擾消除(OSIC)算法在信號迭代檢測過程中需重復(fù)求偽逆,使得計(jì)算復(fù)雜度會比較高。為了改善這一問題,本文提出了一種改進(jìn)的算法一噪聲投影按序逐次消除((OSNPC)算法,而且DLR輔助的OSNPC算法省去了對偶格約減基的求逆運(yùn)算,使得復(fù)雜度大大降低。由于OSNPC算法每次迭代優(yōu)先選擇信噪比最大的符號來檢測,而信號正確性其實(shí)應(yīng)取決于當(dāng)前的噪聲樣本,所以又提出了一種改進(jìn)的Improved-OSNPC算法,與DLR結(jié)合時(shí),檢測性能有了明顯提高,復(fù)雜度基本沒變化。第三,仿真各種檢測算法。首先仿真了MIMO傳統(tǒng)檢測算法誤碼率性能曲線,并分析復(fù)雜度情況。然后仿真了格約減輔助的檢測算法曲線,并與傳統(tǒng)檢測算法進(jìn)行比較。最后仿真了兩種改進(jìn)算法的性能曲線,利用仿真實(shí)驗(yàn)數(shù)據(jù)給上節(jié)理論推導(dǎo)提供數(shù)據(jù)支持,并且驗(yàn)證了兩種改進(jìn)方案的合理性和可行性。
[Abstract]:Multi-input-multiple-output (MIMO) technology is an important breakthrough in the field of wireless communication. Spatial multiplexing and spatial diversity technology make full use of space resources and control the channel attenuation. MIMO technology can guarantee the system bandwidth and transmit power. The spectrum efficiency and channel capacity are greatly improved. Signal detection technology has an impact on the application of MIMO technology in wireless communication, so this paper analyzes the improved detection algorithm on the basis of some traditional detection algorithms. A good compromise point between performance and complexity is obtained. The main work is as follows: first, the concept of MIMO model is analyzed, the principle of MIMO channel and the traditional detection algorithm of MIMO system are summarized in detail, and the improved algorithm of MIMO detection is deeply analyzed. Due to the limitation between the performance and complexity of the traditional detection algorithm, the lattice reduction (LR) technique is further introduced, and the dual lattice reduction (DLR) algorithm is mainly introduced. On the basis of this, the performance of these sub-optimal and low-complexity traditional detection algorithms is improved obviously by combining the lattice reduction technique with the traditional detection algorithm. Second, the serial sorting interference cancellation (SSI) algorithm requires repeated pseudo-inversion in the signal iterative detection process, which makes the computational complexity higher. In order to improve this problem, this paper proposes an improved algorithm, noise projection successive elimination (OSNPC) algorithm, and the DLR assisted OSNPC algorithm eliminates the inverse operation of the reduced basis of dual lattice, which greatly reduces the complexity. Because the OSNPC algorithm preferentially selects the symbol with the largest signal-to-noise ratio (SNR) for each iteration, and the accuracy of the signal should depend on the current noise samples, an improved Improved-OSNPC algorithm is proposed. When combined with DLR, the detection performance is obviously improved. The complexity is basically the same. Third, simulation of various detection algorithms. First, the BER performance curve of the traditional MIMO detection algorithm is simulated, and the complexity is analyzed. Then the algorithm curve is simulated and compared with the traditional detection algorithm. Finally, the performance curves of the two improved algorithms are simulated, and the rationality and feasibility of the two improved algorithms are verified by using the simulation experimental data to provide data support for the theoretical derivation of the above section.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN919.3
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本文編號：1866529