臺灣博碩士論文加值系統

在本論文中，主要提出了干擾抑制的方法，而此方法能使兩層式異質性細胞網路與動態分時雙工小細胞網路的整體系統效能能夠有所提升。
在此兩種情境下，細胞間的干擾是影響系統效能的主要因素，也是本論文致力解決的問題。
而考量到用戶裝置的複雜度限制，我們認為處理基地台間的干擾在現實中較為可行。
因此在兩層式異質性細胞網路中，我們使用相反的雙工傳輸來產生基地台間的干擾，也就是說，大型細胞與所有小型細胞使用相反的傳輸模式。
而這便會產生等效的一對多干擾網路與多對一干擾網路。
在一對多干擾網路中，我們主要延伸了 Jovicic 和 Viswanath [1] 的結果並求得近似的通道容量，其中主要的技術是來自 Han 和 Kobayashi [2]。
而在多對一干擾網路中，我們主要延伸了 Bresler 和 Tse [3] 的結果並求得近似的通道容量，並且透過 Erez 和 Zamir [4] 的結果改善了傳輸速率。
除此之外，在上述兩種網路中，我們也得出了在大型基地台與小型基地台間如果存在有限回程鍊路的情況下的近似通道容量。
我們也能將上述的方法應用在動態分時雙工小細胞網路中，因為在此種網路中很自然地會產生基地台間的干擾。
一般來說，這些基地台間的干擾會產生等效的多對多干擾網路，而我們能將其拆解為一對多與多對一干擾網路來處理。
最後我們將上述兩種情境的干擾抑制結果與傳統的傳輸機制做比較。
在干擾或整體系統傳輸能量較強的情況下，我們所提出的干擾緩解方案能有效地提升整體系統效能。

In this thesis, we propose interference mitigation schemes to improve the overall system throughput of two-tier heterogeneous cellular networks (HCN) and dynamic time-division duplex (TDD) small cell networks (SCN).
In the above two scenarios, inter-cell interference is the major barrier against improving system throughput and the problem that we aim to solve.
Considering the complexity of user equipment (UE), it is more feasible in practice to deal with the interference across base stations (BSs).
Therefore, we use reverse duplex transmission to create the BS-BS interferences in two-tier HCN, i.e., macro cell and all of small cell use opposite duplex modes.
Our focus is then on the capacity characterization of the induced equivalent one-to-many interference networks (IN) and many-to-one interference networks.
In one-to-many IN, we mainly extend the results by Jovicic and Viswanath [1] and obtain approximate capacity region.
The main technique is inspired by Han and Kobayashi [2].
In many-to-one IN, we mainly extend the results by Bresler and Tse [3] and obtain the approximate capacity region.
Then, we improve data rates via the results by Erez and Zamir [4].
In addition, if there exists limited backhaul between macro base station (MBS) and small base station (SBS), we also obtain the approximate capacity region of above two INs.
We can also apply the above results in dynamic TDD SCN since the interference across BSs can be generated naturally.
In general, interferences across BSs induce equivalent many-to-many IN which can be decomposed into one-to-many and many-to-one IN.
Finally, we compare our proposed scheme with the conventional scheme.
When interference or overall system transmitting power are stronger, proposed scheme can increase the overall system throughput significantly.

摘要i
Abstract iii
1 Introduction 1
1.1 Relative works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Background 5
2.1 Information Theoretical Results . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Multiple Access Channel . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Broadcast Channel . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 Interference Channel . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Deterministic Channel Model . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 Point-to-Point Channel . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 Multiple Access Channel . . . . . . . . . . . . . . . . . . . . . . 10
2.2.3 Broadcast Channel . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.4 Interference Channel . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Lattice Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.1 Lattice Code Achieving AWGN Channel Capacity . . . . . . . . 13
2.3.2 Lattice Code in Multiple Access Channel . . . . . . . . . . . . . 15
3 Approximate Capacity Region for One-to-Many Gaussian Interference Network
with Limited Backhaul 17
3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 Approximate Capacity Region . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.1 Coding and Decoding Strategies . . . . . . . . . . . . . . . . . . 21
3.3.2 Limited Backhaul Cooperation . . . . . . . . . . . . . . . . . . . 23
3.4 Achievable Rate Region for the Sum Rate . . . . . . . . . . . . . . . . . 24
3.4.1 Single Small cell with Multiple Users . . . . . . . . . . . . . . . 25
3.4.2 Multiple Small cells with Single User . . . . . . . . . . . . . . . 29
3.4.3 General Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.5 Outer Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4 Approximate Capacity Region for Many-to-One Gaussian Interference Network
with Limited Backhaul 37
4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2 Approximate Capacity Region . . . . . . . . . . . . . . . . . . . . . . . 41
4.3 Capacity Region of Deterministic Channel Model . . . . . . . . . . . . . 42
4.3.1 Achievable Strategy . . . . . . . . . . . . . . . . . . . . . . . . 44
4.3.2 Outer Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3.3 Capacity Region . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4 Gaussian Channel Strategies . . . . . . . . . . . . . . . . . . . . . . . . 48
4.4.1 Coding and Decoding Strategies . . . . . . . . . . . . . . . . . . 48
4.4.2 Limited Backhaul Cooperation . . . . . . . . . . . . . . . . . . . 52
4.5 Achievable Rate Region for Sum Rate . . . . . . . . . . . . . . . . . . . 53
4.6 Gaussian Channel Outer Bounds . . . . . . . . . . . . . . . . . . . . . . 57
5 One-to-Many Interference Network Combine with Many-to-One Interference
Network 61
5.1 Capacity of Deterministic Channel Model . . . . . . . . . . . . . . . . . 62
5.1.1 achievable Strategy . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.1.2 Outer Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6 Numerical Evaluation 69
6.1 Two-tier Heterogeneous Cellular Network . . . . . . . . . . . . . . . . . 69
6.1.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . 69
6.1.2 Comparison with Conventional Scheme . . . . . . . . . . . . . . 70
6.2 Dynamic TDD Small Cell Network . . . . . . . . . . . . . . . . . . . . 74
6.2.1 Algorithm of Selecting Interference Mitigation Scheme . . . . . . 75
6.2.2 Comparison with Conventional scheme . . . . . . . . . . . . . . 77
7 Conclusion 81
8 Discussion: MIMO channel 83
8.1 Numerical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
A Proof of Proposition 13 89
B Proof of Proposition 15 93
C Proof of Proposition 4.3.2 99
D Proof of Corollary 28 103
Bibliography 107

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