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

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 Minimum-Cost QoS-Constrained Deployment and Routing Policies for Wireless Relay Networks of Maximal Ratio Combining Capacities 指導教授：林永松博士祝國忠博士研究生：劉翊恆國立台灣大學資訊管理研究所碩士論文口試審查

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

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

Left: tree topology in relay network; Right: mesh topology in mesh network Background – Relay • 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 Introduction

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

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 Introduction

Background – IEEE 802.16j (Cont’d) Introduction

Background – Diversity Techniques • 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 • 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 Introduction

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

Motivation Inner zone: MC connects to the BS directly Outer zone: MC connects to the BS through RSs BS coverage cell coverage Introduction

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. Introduction

Problem Description and Formulation Problem Description Problem Notation Problem Formulation

Problem Description Problem Description and Formulation

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 equalto ensure the MRC is achievable within limited delay. • Error corrections and retransmissions are not considered in this problem. Problem Description and Formulation

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 Problem Description and Formulation

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 Problem Description and Formulation

Problem Notation Problem Description and Formulation

Problem Notation (Cont’d) Problem Description and Formulation

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

Problem Formulation (Cont’d) Problem Description and Formulation

Solution Approaches Lagrangean Relaxation Method Problem Decomposition Getting Primal Feasible Solutions

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

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

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

Problem Decomposition (Cont’d) 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

Problem Decomposition (Cont’d) 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: Solution Approaches

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

Problem Decomposition (Cont’d) 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

Problem Decomposition (Cont’d) 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: Solution Approaches

Getting Primal Feasible Solutions X BS capacity is full X SNR is not enough! BER is over! BS radius for MC BS radius for 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 1: All RSs and MCs home to proper BS and sorted by the distances to the BS Step 5: If the BER value of the path is over the predefined threshold, repeat step 2to step 4 to find another RS and path until the BER value is small enough. CheckCapacityofNode(); CheckCapasityofLink(); CheckLinkAmount(); SetConfiguration(); Step 2: Determine whether the BS or which RS should MC n route to refer to the coefficient of , then build the RS. Solution Approaches 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

Computational Experiments Experiment Environments Experiment Designs Experiment Results

Experiment Environments • Environment Parameters From: “Mobile WiMAX”, WiMAX Forum, May 2006 Shadow Urban Area Computational Experiments

Experiment Environments (Cont’d) • Modulation and Code Rate From: “Mobile WiMAX”, WiMAX Forum, May 2006 Computational Experiments

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

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

Experiment Designs • 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) Computational Experiments

Experiment Results Computational Experiments

Conclusion and Future Work Conclusion Contribution Future Work

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. Conclusion and Future Work

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. Conclusion and Future Work

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. Conclusion and Future Work

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