透過您的圖書館登入
IP:18.191.212.211
  • 學位論文

隨機線性分散時空碼的效能分析

Performance analysis of random linear dispersion space-time code

指導教授 : 陸曉峯

摘要


隨著行動通訊的普及與影像資料的傳送需求,傳送速率被要求增加,多輸入輸出天線系統(Multiple-input Multiple-output,MIMO)也因應而生。系統的傳輸效能因此大幅增加,而且頻譜效益(spectral efficiency)也有顯著的提升。在傳輸技術上,編碼與解碼的方式也有許多的討論。本論文針對時空區塊碼中的黃金碼與完美時空區塊碼的效能與高斯隨機線性分散碼進行比較。在接收端的解碼方式使用的是樹狀搜尋球形解碼演算法。 黃金碼在低訊雜比時的表現與高斯隨機線性分散碼幾乎相近,隨著訊雜比的提升,黃金碼的字碼錯誤率就比高斯隨機線性分散碼低,兩者之間的差異也隨著訊雜比的增加而增加,由於黃金碼是經由最佳化的結果,其編碼增益已經達到最大,因此其字碼錯誤率效能比高斯隨機線性分散碼好。4X4的完美時空區塊碼並沒有達到最佳化的效果,因此其在低訊雜比時,其字碼錯誤率比高斯隨機線性分散碼高,隨著訊雜比的增加,兩者字碼錯誤率交會於 之間,然後隨著訊雜比的增加,4X4完美時空區塊碼其字碼錯誤率的表現就遠比高斯隨機線性分散碼更好。

關鍵字

隨機分散碼

並列摘要


The transmission rate is increasing due to the video data information are transmitted through the mobile communication system. In order to satisfy the requirement of high transmission rate, the Multiple-input Multiple-ouput (MIMO) were invented. The spectral efficiency had great improvement in MIMO system. There are lots of studies on how to improve the performance of MIMO system. The space time block code is one of the studies. The performance of the golden code and perfect space time code versus Gaussian Random dispersion code are discussed in this thesis. The sphere decoder which is tree search algorithm is used to decode the code. The golden code and the Gaussian random code had almost the same performance during low SNR. As the SNR increasing, the code error rate of the golden code had better performance than the Gaussian code. This is because the golden code is optimal. Because the 4X4 perfect space-time block code was not optimal, the Gaussian random linear dispersion code had better performance than the perfect space-time block code in low SNR. There was an intersection between the codeword error rate from to . As the SNR increasing, the code error rate of the perfect space-time block code was much lower than the Gaussian random code.

並列關鍵字

random code

參考文獻


1.V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space-Time codes for high data rate wireless communications: performance criterion and code construction,” IEEE Trans.Information Theory, 44:744-765, March 1998.
2.B. Hassibi and H. Vikalo, “On the Sphere-Decoding Algorithm I. Expected Complexity,” IEEE Trans.on Signal Processing, vol. 53, pp.2806-2818, August 2005.
3. F.Oggier, G. Rekaya, J.-C. Belfiore, and E. Viterbo, “Perfect Space-Time Block Codes,” IEEE Trans. on Information Theory, vol.52, no.9, September 2006.
4. U.Fincke and M. Pohst, “Improved methods for calculation vectors of short length in a lattice, including a complexity analysis,” in Pro. Math Computation, vol. 44, no.170, pp.463-471, April 1985.
5. M.O.Damen, A.Tewfik, and J.-C.Belfiore, “A construction of a space-time code based on the theory of numbers,” IEEE Trans. Inf. Theory, vol.48, pp.753-760, March 2002.

延伸閱讀