1. 考慮服務品質限制之具最大比率合成能力 中繼站無線網路成本最小化建置與路由策略 指導教授：林永松 博士 祝國忠 博士 研究生：劉翊恆 指導教授：林永松 博士 祝國忠 博士 研究生：劉翊恆 Minimum-Cost QoS-Constrained Deployment and Routing Policies for Wireless Relay Networks of Maximal Ratio Combining Capacities 國立台灣大學資訊管理研究所 碩士論文口試審查

2. 2 Outline Introduction Problem Description and Formulation Solution Approach Computational Experiments Conclusion and Future Work

3. Introduction Background - Relay - IEEE 802.16j - Diversity Techniques - Maximum Ratio Combining (MRC) Motivation Background - Relay - IEEE 802.16j - Diversity Techniques - Maximum Ratio Combining (MRC) Motivation

4. 4 Background – Relay Introduction Left: tree topology in relay network; Right: mesh topology in mesh network Relay technologies has been used widely in wireless communications, such as IEEE 802.16j, IEEE 802.11s, and seed concept in 3GPP Advantages of relay: - radio range extension - overcome shadow fading - reduce infrastructure deployment costs - enhance capacity - reduce outage probability

5. 5 City scenario of relays deployment with one BS Background – Relay (Cont ’ d) Relays are designed to improve the coverage of a BS and overcome the shadows caused by obstacles. Three types of relay protocols: - Amplify-and-Forward : Relays act as analog amplifier. - Decode-and-Forward: Relays act as a digital repeater with the same codewords. - Decode-and-Reencode: Relays act as a digital repeater with different codewords. Introduction

6. Background – IEEE 802.16j IEEE 802.16j is now a developing specification (renamed from 802.16 MMR, MMR stands for Mobile Multihop Relay) established by IEEE 802.16j task group The enhancement of original 802.16-2004/802.16e-2005 Compatible to the legacy standard A relay station (RS) will be recognized as a base station (BS) by the mobile station (MSs) for the transparency reasons 6 Introduction

7. Background – IEEE 802.16j (Cont’d) 7 Introduction

8. Background – Diversity Techniques Frequency diversity: Transmitting or receiving the signal at different frequencies Time diversity: Transmitting or receiving the signal at different times Space diversity: Transmitting or receiving the signal at different locations Polarization diversity: Transmitting or receiving the signal with different polarizations 8 Cooperative diversity is a relatively new class of spatial diversity techniques that is enabled by relaying To improve the reliability of communications in terms of, for example, outage probability, or symbol-or bit-error probability, for a given transmission rate Introduction

9. Background – MRC Three major diversity signal- processing techniques: - selection diversity (SD) - equal gain combining (EGC) - maximal ratio combining (MRC) 9 Soft handoff Introduction

10. Motivation Base Station (BS) Relay Station (RS) Mobile Cluster (MC) 10 Introduction BS coverage cell coverage Inner zone: MC connects to the BS directly Outer zone: MC connects to the BS through RSs

11. Motivation (Cont’d) Allow multiple source nodes jointly transmit one single information if the signal strength is not robust enough in the links between one source node to the destination. To develop a wireless network topology based on 802.16j relay environment: - Where to build a RS and its configuration ? - Which RSs should a MC rout to ? - What is the routing policy between a BS and a MC ? The routing policy is no longer a single path but with more complex multicast-tree algorithms. 11 Introduction

12. Problem Description and Formulation Problem Description Problem Notation Problem Formulation Problem Description Problem Notation Problem Formulation

13. Problem Description 13 Problem Description and Formulation Base Station (BS) Relay Station (RS) Mobile Cluster (MC) Empty Location

14. Problem Description (Cont’d) Assumption: The relaying protocol in this model is Decode-and-Forward. Each MC must home to either a BS or relay(s). The relays selected by one MC must associate with the same BS. The routing path of each OD pair in DL (UL) is a multicast tree. The spatial diversity gains are represented by the aggregate SNRs with MRC techniques. The BER of a transmission is measured by the receiving SNR value. The aggregate BER of the destination are the summation of BER of each node on the routing tree. The numbers of links of each path adopted by each MC are assumed to be equal to ensure the MRC is achievable within limited delay. Error corrections and retransmissions are not considered in this problem. 14 Problem Description and Formulation

15. Problem Description (Cont’d) Given: The set of BSs, candidate RS locations, relay configurations, MCs Required data rate of a MC in DL and UL Fixed and configuration cost of a relay Distance between every two node Attenuation function Link SNR function The minimum SNR requirement for a MC in DL and UL to home to a BS or relay Link BER function The maximum BER threshold of a OD pair transmission in DL and UL Nodal and link capacity functions The maximum spatial diversity of a mobile cluster in DL and UL 15 Problem Description and Formulation

16. Problem Description (Cont’d) Objective: To minimize the total cost of wireless relay network deployment Subject to: Relay selection constraints Nodal capacity constraints Cooperative relaying constraints in DL and UL Routing constraints in DL and UL Link capacity constraints in DL and UL To determine: Whether or not a location should be selected to build a relay The cooperative RSs of each MC The DL and UL multicast tree of each MC 16 Problem Description and Formulation

17. 17 Problem Notation Problem Description and Formulation

18. Problem Notation (Cont’d) 18 Problem Description and Formulation

19. Problem Notation (Cont’d) 19 Problem Description and Formulation

20. Problem Notation (Cont’d) 20 Problem Description and Formulation

21. Problem Notation (Cont’d) 21 Problem Description and Formulation

22. Problem Notation (Cont’d) 22 Problem Description and Formulation

23. Problem Notation (Cont’d) 23 Problem Description and Formulation

24. Problem Formulation 24 (IP 1) Subject to: (General Constraint) Objective function: Problem Description and Formulation

25. Problem Formulation (Cont’d) 25 Problem Description and Formulation

26. Problem Formulation (Cont’d) 26 Problem Description and Formulation

27. Problem Formulation (Cont’d) 27 Problem Description and Formulation

28. Problem Formulation (Cont’d) 28 Problem Description and Formulation

29. Problem Formulation (Cont’d) 29 Problem Description and Formulation

30. Solution Approaches Lagrangean Relaxation Method Problem Decomposition Getting Primal Feasible Solutions Lagrangean Relaxation Method Problem Decomposition Getting Primal Feasible Solutions

31. Lagrangean Relaxation 31 Lagrangean Relaxation Problem (LR) Primal Problem (P) Subproblem 1Subproblem 7 Optimal Solution LB <= Optimal Objective Function Value <= UB LB UB Lagrangean Dual Problem Adjust Lagrangean Multiplier Solution Approaches

32. Problem Decomposition 32 Subproblem 1 can be further decomposed into |R| independent problem. Time complexity: Solution Approaches

33. Problem Decomposition (Cont’d) 33 Subproblem 2 can be further decomposed into |R| x |B| x |DIR| independent problem. Time complexity: Solution Approaches

34. Problem Decomposition (Cont’d) 34 Subproblem 3 can be further decomposed into |N| independent problem to choose whether BS or RSs should MC n route to and the correlative SNR value. Time complexity: Solution Approaches

35. Problem Decomposition (Cont’d) 35 Solution Approaches Subproblem 4 can be further decomposed into |N| x |R| x |B| x |DIR| independent shortest path problem which can be optimally solved by bellman ford’s minimum cost shortest path algorithm. Time complexity:

36. Problem Decomposition (Cont’d) 36 Solution Approaches Subproblem 5 can be further decomposed into |R| x |R| independent problem to determine whether link uv be selected by MC n in DL and UL and the correlative SNR value. Time complexity:

37. Problem Decomposition (Cont’d) 37 Subproblem 6 can be further decomposed into |N| independent problem to determine the SNR value received by MC n in DL. Time complexity: Solution Approaches

38. Problem Decomposition (Cont’d) 38 Solution Approaches Subproblem 7 can be further decomposed into |N| x |R| independent problem to determine the SNR value received by RS v in UL transmission of MC n. Time complexity:

39. Getting Primal Feasible Solutions 39 Solution Approaches BS radius for MC BS radius for RS BER is over! SNR is not enough! X BS capacity is full X RS s which MC n routes to link uv which MC n selects Step 1: All RSs and MCs home to proper BS and sorted by the distances to the BS Step 2: Determine whether the BS or which RS should MC n route to refer to the coefficient of, then build the RS. Step 3: Find a shortest path from the selected RS to the BS via all built RSs with cost=BER of each link. Step 4: If the SNR of one link uv is not strong enough, find a shortest BER path between u and v with references of Step 5: If the BER value of the path is over the predefined threshold, repeat step 2 to step 4 to find another RS and path until the BER value is small enough. CheckCapacityofNode(); CheckCapasityofLink(); CheckLinkAmount(); SetConfiguration(); CheckCapacityofNode(); CheckCapasityofLink(); CheckLinkAmount(); SetConfiguration();

40. Computational Experiments Experiment Environments Experiment Designs Experiment Results Experiment Environments Experiment Designs Experiment Results

41. Experiment Environments Environment Parameters 41 Computational Experiments ParametersValueParameters Operation Frequency2500MHzAttenuation Factor3.2 Channel Bandwidth10MHzThermal Noise figure-174 dB BS Antenna Gain15dBiMin. RS to RS SNR7.9515 dB RS basic Antenna Gain5dBiMin. SNR received by MC2.6505 dB MS Antenna Gain-1dBiBER threshold0.0001 BS noise figure4dBMax. Spatial Diversity3 RS noise figure5dBTraffic Required by MC (DL)1 Mbps MC noise figure7dBTraffic Required by MC (UL)0.5 Mbps BS Transmit Power43dBmBS Capacity100 M bps RS Basic Transmit Power33dBmRS Basic Capacity15 Mbps MC Transmit Power23dBmRS Fixed Cost1M dollars RS Config. Cost0.2M dollars From: “Mobile WiMAX”, WiMAX Forum, May 2006 Shadow Urban Area

42. Experiment Environments (Cont’d) Modulation and Code Rate 42 Computational Experiments ModulationCode RateSNRDL Rate (Mbps)UL Rate (Mbps) QPSK1/2 CTCSNR <= 9.46.344.70 3/4 CTC9.4 < SNR <= 11.29.507.06 16 QAM1/2 CTC11.2 < SNR <= 16.412.679.41 3/4 CTC16.4 < SNR <= 18.219.0114.11 64 QAM2/3 CTC18.2 < SNR <= 22.725.3418.82 3/4 CTC22.7 < SNR28.5121.17 From: “Mobile WiMAX”, WiMAX Forum, May 2006

43. Experiment Environments (Cont’d) SNR Formulation: Path Loss Function: Thermal Noise Function:, transfer into (dB): 43 2500 MHz 10 MHz Noise Figure n: Attenuation Factor Computational Experiments Distance Transmit Power Transmit Gain Receive Gain

44. Experiment Environments (Cont’d) BER Function: 44 Computational Experiments

45. We proposed two topologies, grid and random, to compose the RS candidate locations, and examine two sizes of network radius with matrix of different number of RSs and MCs within one BS coverage. Then we proposed random topology with three different network radiuses within two BSs coverage to examine multiple BSs network environment. We introduced two algorithms to compare with the LR result: - Minimum BER Algorithm (MBA) - Density Based Algorithm (DBA) Experiment Designs 45 Computational Experiments

46. Experiment Results 46

47. Conclusion and Future Work Conclusion Contribution Future Work Conclusion Contribution Future Work

48. Conclusion Fixed MC number RS number increased=> Reduce cost Fixed RS number MC number increased=> Induce cost For a given networkscale, the farthest locations from BS to receive signals under BER threshold should be included in the candidate RS locations to reach the minimum cost objective. 48 Conclusion and Future Work

49. Contribution Constructed the network architecture with multicast tree routing concepts based on IEEE 802.16j specifications and spatial diversity techniques. Mathematically modeled the network development problem of previous environment. Proposed the solution approaches for engineering guidelines of RS buildings to minimize the total development cost. 49 Conclusion and Future Work

50. Future Work Applying different diversity techniques ex. Time diversity, frequency diversity...etc. Applying different fading models ex. Flat fading (time dispersion), Fast fading (doppler spread)...etc. Considering different performance matrixes ex. delay, throughput...etc. 50 Conclusion and Future Work

51. Thanks for Your Listening

52. MRC Verification 52