臺灣博碩士論文加值系統

近年來隨著行動通訊的普及，需要連網的設備數量持續的增長，為了維持通訊品質，目前普遍使用大型多輸入多輸出(massive mutiple-input multiple-output, massive MIMO)系統，來增加通訊容量。由於傳統的MIMO中天線以一對一的方式和射頻鏈路(radio frequency chain, RF chain)連接，而射頻鏈路是由許多元件所組成，因此天線數量的增加會造成硬體成本增加，為了降低硬體成本可以選擇使用混合式預編碼器的架構來減少射頻鏈路的數量，或是在射頻鏈路中使用低解析度的數位轉類比轉換器(digital-to-analog converters, DACs)，都可降低成本。因此有論文針對在PSK調變系統，提出在massive MIMO系統下結合1位元DACs的數位預編碼器設計方法，主要將數位預編碼器的輸出訊號以1位元量化的形式設計，以減少量化過程中的量化誤差，提出一演算法分別使用兩種方式進行設計，再從中挑選較好的結果作為輸出訊號。但這需要完整的計算兩種方法，其中的計算複雜度不少，因此本篇論文提出新的演算法，設計適當的條件將文獻中兩種計算方法合併進行設計，這樣可以大幅的減少演算法的計算複雜度，同時在模擬中證實此演算法在性能方面也有所提升。另外提出改良的精煉算法來進一步改善系統效能，在模擬中發現對於部分演算法的系統效能的提升有顯著的幫助。

In recent years, with the popularity of mobile communications, the number of devices that need to be connected has continued to grow.In order to maintain communication quality, massive multiple-input multiple-output (massive MIMO) system is currently widely used to increase the capacity. However, in the traditional MIMO system, antenna and radio frequency chain (RF chain) connect one by one. Increasing the number of antennas will increase the hardware cost, because RF chain is combined with many components. There are some methods to decrease the hardware cost. The method is using hybrid precoder structures to reduce the number of RF chains or using low resolution digital-to-analog converters (DACs) to reduce the cost. Some paper focusing on PSK modulation proposes the digital percoder design method on the massive MIMO system with 1-bit DACs. The main idea is to design a digital precoder output as the 1-bit quantized form to reduce the quantization error. One paper suggests an algorithm that contains two methods calculating respectively, then finds the best one as algorithm answer. In this case, the complexity will be a lot because it need to compute two methods complete. Therefore, we suggest a new algorithm designing an appropriate condition to combine with method, and we suppose it can reduce the complexity in the algorithm. It also proves the performance enhance in simulation results. Furthermore, we propose a refinement method to make better performance.

摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
符號說明 vii
第1章 緒論 1
第2章 多用戶massive MIMO 系統架構 6
2.1 多用戶massive MIMO 系統架構 6
2.2 預編碼器設計 7
2.3 非線性預編碼器設計問題 8
2.3.1 動態星座擴展圖(Active Constellation Extension, ACE)的應用 8
2.3.2 非線性轉換的預編碼器設計 10
2.4 分配方法(Allocation method) 13
2.4.1 初始化階段Initialization Stage 13
2.4.2 分配階段Allocation Stage Sum-Max 14
2.4.3 分配階段Allocation Stage Max-Min 15
2.4.4 精煉階段Refinement Stage 16
2.4.5 Symbol Scaling 17
第3章 1位元DACs非線性轉換的預編碼器設計 18
3.1 非線性預編碼器設計 18
3.2 分配法的改進 20
3.2.1 公平結合分配法(Fairness combine allocation method, FC) 20
3.2.2 條件式公平結合分配法(Fairness combine with constrain allocation method, FCC) 23
3.3 精煉演算法的改良方法 26
3.3.1 Sorting refinement method 26
第4章 電腦模擬 27
4.1 計算複雜度分析 28
4.1.1 Symbol Scaling的計算複雜度分析 28
4.1.2 FC與FCC的計算複雜度分析 29
4.2 電腦模擬分析 34
第5章 結論與未來展望 46
參考文獻 47

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