臺灣博碩士論文加值系統

由於我們必須在有限的頻寬內追求更高的傳輸速度(throughput)以及更穩定的傳輸品質(QoS)，大規模多輸入多輸出(Massive-MIMO)目前被認為是能夠在未來的5G無線通訊系統中達成此目標的關鍵技術。大規模多輸入多輸出是指系統中的基地台擁有數百根天線提供更大的空間多樣性，能夠同時在同頻段服務數十個使用者。但要獲得這些好處，基地台需要取得所有使用者的即時通道資訊(CSI)才能避免使用者間的干擾。
在頻分雙工模式中（FDD)，量測通道資訊(CSI)時，基地台的每根天線都需要先傳送一段已知訊號（pilots)，使用者再將量測結果回報給基地台，這種方法在大規模多輸入多輸出並不適用的，所以目前傾向於在大規模多輸入多輸出系統利用時分雙工模式（TDD)中頻道互惠 (Channel Reciprocity) 的方式來達成通道資訊的取得，如此一來，基地台並不需要傳送已知訊號，基地台只要量測每位使用者所傳送的已知訊號來估計使用者到每根天線的通道資訊(CSI)。
但頻道互惠的成立與否目前還受到相當的質疑，此研究是藉由隨機波束成形（Random beamforming)的通道量測方法，讓大規模多輸入多輸出系統能夠在頻分雙工模式中傳輸。我們也把基地台在挑選使用者的過程（scheduling stage）加入考慮，並且提出一套方法進一步減少使用者的回報量。

The concept of Massive-MIMO is a system where a base station equipped with a large number of antennas, say 100 antennas or more, simultaneously serves several users in the same frequency band. However, to harvest the potential bene ts of Massive-MIMO, the base station must acquire the instantaneous channel state information (CSI) in order to suppress inter-user interference. With frequency division duplexing (FDD), channel estimation becomes much more challenging since radio resources required by common
pilots and feedback both grow linearly with the number of BS antennas. By exploiting channel reciprocity, CSI acquisition at base station is more feasible under time division duplexing (TDD) operation. So, time division duplexing
is more preferable in Massive-MIMO systems. Nonetheless, the assumption of channel reciprocity in TDD might not be practical.
In our work, we utilize the random beamforming CSI estimation method in order to make Massive-MIMO system work for FDD operation. Then, we take the scheduling mechanism into consideration and propose a novel scheme to reduce the feedback load at scheduling stage.

1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . 1
1.2 Previous Work and Motivation . .. . . . . 5
1.3 Thesis Organization . . . . . . . . . . . 8
1.4 Notations . . . . . . . . . . . . . . . . 9
2 System Model and Problem Formulation 10
2.1 System Model . . . . . . . . . . . . . . .10
2.1.1 Random Beamforming CSI Estimation . 11
2.1.2 Finite Rate Feedback for CDI Quantization . 14
2.1.3 CSI Estimation Weight Design . . .. 16
2.1.4 Data Transmission Weight Design . . 18
2.2 Problem Formulation . . . . . . . . . . . 22
2.2.1 User Selection Algorithm . . . . . 23
2.2.2 The Properties of the SUS Alogorithm 25
3 Opportunistic Feedback Reduction at Scheduling Stage 30
3.1 CQI Distribution . . . . . . . . . . . . . 31
3.2 Derivation of Multiple Thresholds . . . . 35
3.3 Sum Rate Loss Analysis and Minimum Number of Regions40
4 Numerical Results 43
5 Conclusion 46

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