臺灣博碩士論文加值系統

在多細胞多輸入多輸出通道下，基地台有多根天線傳送訊號給其細胞中服務的使用者們，且每位使用者有多根天線以接收其多個資料流；在此系統中，干擾的傳送端以及傳給同細胞中其他使用者的資料留都會產生干擾而降低系統效能。藉由修正適用於單一細胞的二重性定理使其可應用於多細胞系統，我們提出一個有效的迭代方法去共同設計傳送端、接收端的波束成形濾波器庫以及設計分配功率給每位服務的使用者。
我們採用了群體最大訊號干擾雜訊比濾波做為波束成形濾波器庫，並且利用平
均訊號干擾雜訊比當作服務質量的依據。此外，利用此多波束濾波器庫，我們可以
找到一個平衡的訊號干擾雜訊比結構因而可以找到一個最佳的功率分配矩陣以保證
每位使用者的公平性。我們同時也做出簡單的修正，提出一個功率分配的方法使得
總傳輸速率最大化。我們提出的演算法有效協調處理不同細胞、同一細胞的不同使
用間、同一使用者的不同資料流而可以達到更好的表現。模擬結果同時證明了這些
提出的演算法可以有效的處理干擾並且優於其他現存的方法。

In multi-cell multi-input multi-output (MIMO) channel where multiple base stations with multiple antennas transmit signals to a group of users with multiple antennas in their own cell, both interfering transmitters and data streams to different users in the same cell will cause interference to one user and thus decrease system’s throughput. By judiciously modifying the duality principle which is developed for single cell scenario to our multi-cell case, we propose an efficient approach to the joint transmit-receive beamforming and power allocation for each cell based on iterative method.
We adopt group maximum signal-to-interference-plus-noise-ratio (SINR) filter banks (GSINR-FB) as our beamformers and the average SINR is served as a metric to measure the quality of service (QoS). Moreover, we find a balancing SINR structure for optimal power allocation form to guarantee fairness of each user. We also propose a heuristic power allocation for sum rate maximization. The proposed algorithm can coordinate signal across cells, users in one cell and even data streams in one user to achieve better performance. Simulation results verify these proposed algorithms can align interference effectively and outperform other existing methods.

1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Previous Work . . . . . . . . . . . . . . . . . . . . 4
1.3 Notations . . . . . . . . . . . . . . . . . . . . . . 6
2 System Model and Problem Formulation 7
2.1 System Model . . . . . . . . . . . . . . . . . . . . . 7
2.2 Problem Formulation . . . . . . . . . . . . . . . . . 10
2.2.1 QoS-Oriented Problem . . . . . . . . . . . . . . . 11
2.2.2 Sum-Rate-Oriented Problem . . . . . . . . . . . . . 12
2.3 Multi-cell Uplink-downlink Duality . . . . . . .. . . 13
2.3.1 Iterative methods based on uplink-downlink duality . . 15
3 Joint Beamforming for the Average SINR Constraint Based
on Interference Alignment 18
3.1 Group Maximum SINR Filter Bank . . .. . . . . . . . . 19
3.2 Average SINR criterion . . . . . . . . . . . . . . . 21
3.3 Downlink Interference Alignment . . . . . . . . . . . 22
4 Power Allocation 25
4.1 SINR balancing structure for power allocation based on GSINRFB
beamforming . . . . . . . . . . . . . . . . . . . . . . 26
4.2 Power Allocation with QoS constraints . . . . . . . . 27
4.2.1 Group Power Allocation . . . . . . . . . . . . . . 28
4.2.2 Per Stream Power Allocation . . . . . . . . . . . . 32
4.3 Power Allocation for Sum-Rate maximization .. . . . . 35
4.3.1 Group Power Allocation . . . . . . . . . . . . . . 35
4.3.2 Per-Stream Power Allocation . . . . . . . . . . . . 36
5 Simulation Results and Comparison 44
5.1 QoS-Oriented Problems . . . . . . . . . . . . . . . . 45
5.2 Sum-Rate Oriented Problems . . . . . . . . . . . . . 50
6 Convergence Behavior, Feedback Overhead and Computational
Complexity 53
6.1 Convergence Behavior . . . . . . . . . . . . . . . . 53
6.2 Feedback Overhead . . . . . . . . . . . . . . . . . . 58
6.3 Complexity . . . . . . . . . . . . . . .. . . . . . . 61
7 Conclusions 63
Bibliography 65

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