大量多輸入多輸出系統能夠藉由更有效率的利用空間資源來大幅提升通道容量，並且不需要提升傳送能量和通道帶寬，此項技術也被認為是未來5G通訊中的關鍵技術，然而有了這麼多天線後，實作上的複雜度就變成了在設計接收器時的主要問題，目前已有些針對這問題開發出來的低複雜度演算法，但大多數的都只有接近MMSE的效能，TASER有接近ML的效能，但是其複雜度大於大多數只有MMSE效能的演算法，而且TASER只能使用BPSK和QPSK調變技術，我們提出了基於虛擬反向的TASER演算法，能讓TASER使用16-QAM調變技術，並提出了資料略失真但又保有效能的架構，在常被使用的128×8天線設定下，能讓原來的TASER演算法在幾乎不損失效能的情況下，節省5/6的運算複雜度，如此一來運算複雜度就能比大多數有MMSE效能的演算法還要低，此架構也能套用到提出的基於虛擬反向的TASER演算法上，能有效降低其運算複雜度。

Massive multiple-input and multiple-output (MIMO) system have significant advantages in improving channel capacity by making higher use of spatial resources without increasing channel bandwidth and transmit power. It is believed to be a key techniques for 5G wireless systems; however, with so many antennas, implementation complexity becomes the major challenge in designing receiver. There are many low-complexity detectors proposed but most of them have only near-MMSE performance. TASER has near-ML performance, but its complexity is stll higher than those have near-MMSE performance. Also, it can use BPSK and QPSK modulation only. We propose a VA-SDR based TASER which can apply 16-QAM modulation. Also, we propose a lossy architecture for TASER. For a commonly used antenna setting, 128$ \times $8, the lossy architecture can reduce about $ \frac{5}{6} $ computational complexity of original TASER such that its computational complexity is less than most of near-MMSE detectors.

1 Introduction
1

1.1 Multiueser Uplink Massive MIMO Systems . . . . . . . . . . . . . . . . . 1

1.2 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Organization of This Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.4 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3


2 Convex Optimization for Massive MIMO Systems 5

2.1 Uplink Massive MU-MIMO System Model . . . . . . . . . . . . . . . . . 5

2.2 Gauss-Seidel(GS) Based Minimum Mean Square Error Algorithm . . . . 6

2.3 Optimized Coordinate Descent Method (OCD) . . . . . . . . . . . . . . . 7

2.4 Semidefinite Relaxation (SDR) . . . . . . . . . . . . . . . . . . . . . . . . 8

2.5 Triangular Approximate Semidefinite Relaxation (TASER) . . . . . . . . 12

2.5.1 Triangular SDP Fomulation via the Cholesky Decomposition . . . 13

2.5.2 Forward-Backward Splitting (FBS) . . . . . . . . . . . . . . . . . 13

2.5.3 The TASER Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5.4 Coveragence Method . . . . . . . . . . . . . . . . . . . . . . . . . 16


3 Proposed TASER Algorithm
19

3.1 Virtually Antipodal SDR (VA-SDR) . . . . . . . . . . . . . . . . . . . . 19

3.2 TASER Combined with VA-SDR . . . . . . . . . . . . . . . . . . . . . . 20

3.3 Simulation Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
ii
CONTENTS





3.4.1 Square Antenna Setting . . . . . . . . . . . . . . . . . . . . . . . 26

3.4.2 64-QAM Modulation . . . . . . . . . . . . . . . . . . . . . . . . . 26


4 Proposed Lossy Hardware Architecture 31

4.1 Lossy System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.3 Computation Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.4 Fixed Point Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42


5 Conclusion
43

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