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Title Page
Contents
Abstract 16
Chapter 1. Introduction 20
1.1. Background 20
1.2. Outline and Contributions 23
1.3. Common Notations 26
Chapter 2. A Vector Perturbation with User Selection for Multiuser MIMO Downlink 27
2.1. Introduction 27
2.2. System Model and Previous Works 28
2.3. Vector Perturbation with Virtual Users 31
2.3.1. Motivation 31
2.3.2. Virtual User Selection 32
2.3.3. Modified Sphere Encoding for Searching Optimal γ 35
2.3.4. Lower Bound of the Sum Rate Loss 40
2.3.5. Comments on Complexity 41
2.3.6. Scheduling for Fairness 43
2.4. Extension to Multi-user MIMO Downlink Scenario 44
2.5. Simulation Results and Discussions 49
2.6. Chapter Summary and Conclusions 55
Chapter 3. A MMSE Vector Precoding with Block Diagonalization for Multiuser MIMO Downlink 56
3.1. Introduction 56
3.2. Multi-user MIMO Downlink 57
3.2.1. System Model 57
3.2.2. BD Algorithm 58
3.2.3. BD-VP Algorithm 60
3.3. BD-MVP 61
3.3.1. The BD-MVP 61
3.3.2. BD-MVP with Geometric Mean Decomposition 64
3.3.3. The Sum Rate of the BD-MVP 65
3.3.4. MSE Performance between the BD-VP and BD-MVP 67
3.3.5. Comments on Complexity 68
3.3.6. Proof of Joint Optimization Problem 69
3.3.7. Proof of (3.16) 71
3.4. Simulation Results and Discussions 72
3.5. Chapter Summary and Conclusions 76
Chapter 4. Antenna Grouping based Feedback Reduction Technique 77
4.1. Introduction 77
4.2. MIMO Beamforming 79
4.2.1. System Model and Conventional Beamforming 79
4.2.2. Limited Feedback 82
4.3. Antenna Grouping based Feedback Reduction 83
4.3.1. AGB Algorithm 83
4.3.2. Antenna Group Pattern Generation 87
4.3.3. Quantization Distortion Analysis 89
4.3.4. Further Application of AGB Algorithm 97
4.4. Simulations 99
4.4.1. Simulation Setup 99
4.4.2. Simulation Results 100
4.5. Chapter Summary and Conclusions 104
Chapter 5. Antenna Group Selection based User Scheduling for Massive MIMO Systems 112
5.1. Introduction 112
5.2. System Model 113
5.3. Antenna Group Selection based User Scheduling 115
5.3.1. AGS Algorithm 116
5.3.2. Codebook Generation at User Terminal 117
5.3.3. Basestation Precoding 118
5.4. Sum Rate Analysis of Antenna Group Selection 118
5.4.1. Ergodic Sum Rate Analysis under Limited Feedback Systems 121
5.4.2. Comments on Scheduling for Fairness 123
5.5. Simulations 125
5.6. Chapter Summary and Conclusions 129
Chapter 6. Conclusions 130
Acronyms 132
Bibliography 134
Figure 2.1: Illustration of γ reduction using the vector perturbation. Lattice points with gray dots represent s + τℓ' in (a) and P(s + τℓ') in (b). We can observe from (b) that ℓ ＝ -1 provides smaller γ than ℓ ＝ 0. 30
Figure 2.2: a) E[ץ ]of the proposed method using (2.10) and (2.11) and Na,min ＝ 3 and Nc ＝ 10) as well as standard vector perturbation for 4 x 4 multi-user MISO downlink system. (b) E[γ] of the proposed method as a function of the number of virtual candidates Nc (SNR＝...(이미지참조) 34
Figure 2.3: Performance of 4 x 4 multi-user downlink system (є ＝ αR rg)(이미지참조) 50
Figure 2.4: Performance of 4 x 4 multi-user downlink system with Nv variation (є ＝ 0.15Rorg)(이미지참조) 51
Figure 2.5: Performance of 4 x 4 multi-user downlink system with various σe2,h.(이미지참조) 52
Figure 2.6: Performance of {4, 4} x 8 multi-user downlink system (є₁ ＝ є₂ ＝ 0.15R1,org)(이미지참조) 54
Figure 3.1: The transceiver structure of the proposed BD-MVP technique. 62
Figure 3.2: Achievable sum rates of DPC, conventional BD algorithms (BD, BD-WF and BD-VP), THP based schemes (BD-GMD and BD-UCD), and the proposed BD-MVP. 73
Figure 3.3: BER performance for {4, 4} x 8 multi-user MIMO systems. 74
Figure 4.1: CSI feedback in the multi-user downlink system. 80
Figure 4.2: Illustration of the AGB algorithm for Nt ＝ 8, Ng＝4. The reduced dimension channel vector hr is obtained by expressing subgroup elements of h as a representative value. Note that ^hr is the quantized version of ~hr(i) and h r is expanded from ^hr in order to measure(이미지참조) 84
Figure 4.3: Feedback packet structure 85
Figure 4.4: Example of antenna group patterns (Nt ＝ 16, Ng ＝ 8, Np ＝ 3). Antenna elements belonging to the same pattern are mapped to one representative value.(이미지참조) 86
Figure 4.5: A block diagram of the AGB algorithm in the multi-user downlink system. 87
Figure 4.7: Sum rate as a function of the correlation coefficient α. 106
Figure 4.8: Sum rate as a function of the number of feedback bits B (Nt ＝ 32, Ng＝24).(이미지참조) 107
Figure 4.9: Sum rate as a function of the number of transmit antennas Nt (B ＝ Nt, Ng ＝ Nt/2).(이미지참조) 108
Figure 4.10: Sum rate comparison between the NTCQ and analytical approximation of RVQ. 109
Figure 4.11: Average beamforming gain as a function of number of feedback bits (a) ULA channel (b) UPA channel. 110
Figure 4.12: Sum rate as a function of the number of pattern bits (Nt ＝ 16, Ng＝8).(이미지참조) 111
Figure 5.1: Example of antenna groups (Nt ＝ 12, Ng ＝ 3, J ＝ 4).(이미지참조) 115
Figure 5.2: CSI feedback in the antenna group selection based user scheduling in the multi-user downlink systems. 116
Figure 5.3: Sum rate as a function of the number of feedback bits B when Nt ＝ 32, Ng ＝ 8, p ＝ 10 dB.(이미지참조) 124
Figure 5.4: Sum rate as a function of the number of antenna elements per group Ng (Nt ＝ 32, K ＝ 4).(이미지참조) 127
Figure 5.5: Sum rate as a function of the number of feedback bits B (Nt ＝ 64, Ng ＝ 16, K＝4).(이미지참조) 128
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