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

【摘要】：多輸入多輸出(MIMO)技術是無線通信領域的一個重要突破,空間復用和空間分集技術使得空間資源得到充分利用,控制了信道衰減。MIMO技術在保證系統(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。信號檢測技術卻對MIMO技術在無線通信中的運用產生了影響,所以本文在一些傳統(tǒng)檢測算法的基礎上,對改進的檢測算法進行了深入分析,并在性能和復雜度之間取得了良好的折衷點。主要工作如下：第一,對MIMO模型的概念進行分析,詳細概述了MIMO信道的原理和MIMO系統(tǒng)的傳統(tǒng)檢測算法,對MIMO檢測的改進算法進行了深入分析。由于傳統(tǒng)檢測算法在性能和復雜度之間這種間存在局限,進一步介紹了格約減(LR)技術,主要介紹了對偶格(DLR)算法。在此基礎上結合格約減技術與傳統(tǒng)檢測算法,使得這些次優(yōu)、低復雜度的傳統(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求偽逆,使得計算復雜度會比較高。為了改善這一問題,本文提出了一種改進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算,使得復雜度大大降低。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,而信號正確性其實應取決于當前的噪聲樣本,所以又提出了一種改進的Improved-OSNPC算法,與DLR結合時,檢測性能有了明顯提高,復雜度基本沒變化。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)檢測算法進行比較。最后仿真了兩種改進算法的性能曲線,利用仿真實驗數(shù)據(jù)給上節(jié)理論推導提供數(shù)據(jù)支持,并且驗證了兩種改進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.
【學位授予單位】：南京信息工程大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN919.3
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本文編號：1866529