Optimization of Wireless Station Time Slot Allocation with Consideration of Throughput and Delay Constraints 指導教授：林永松 博士 研究生：林岦毅

Outline Introduction Problem Description Model Description 2018/11/22 OPLab, IM, NTU

Introduction

Background The growth of the Internet has given rise to demands for higher data rate and more multimedia service. New wireless networks, such as 3G and WiMAX, were designed to provide higher capacity for data transmission QoS is more important for multimedia service 2018/11/22 OPLab, IM, NTU

Motivation Try to maximize the total system revenue by optimizing the time slot allocation at the wireless station with considering the throughput and delay constraints. 2018/11/22 OPLab, IM, NTU

Problem Description

Problem Packets arrive System Queue … … 2018/11/22 OPLab, IM, NTU

Problem Assumptions (1/2) There are four service classes, (1,2,3,4), in the system and service class 1 has the highest occupancy priority The data is divided into fixed size packets Each packet can be completely transmitted in a slot time, and each slot can transmit only one packet 2018/11/22 OPLab, IM, NTU

Problem Assumptions (2/2) The channel quality will remain the same during the transmission Transmission error will not happen during transmission Each service class will have its own traffic arrival process 2018/11/22 OPLab, IM, NTU

Problem Description (1/4) Given parameters: The size of system queue is B packets, which will be shared between the four service classes The maximum number of packets that can be transmitted in a frame is N We defined revenue matrix for the four service classes 2018/11/22 OPLab, IM, NTU

Problem Description (2/4) Given parameters: Each service class has its own throughput and delay constraints State transition matrix 2018/11/22 OPLab, IM, NTU

Problem Description (3/4) Objective: To maximize the total system revenue Subject to: The throughput and delay requirement of each service classes The number of packets that can be transmitted in a frame 2018/11/22 OPLab, IM, NTU

Problem Description (4/4) To determine: The best time slot allocation policy for the system 2018/11/22 OPLab, IM, NTU

Model Description

Given parameters (1/2) 2018/11/22 OPLab, IM, NTU

Given parameters (2/2) 2018/11/22 OPLab, IM, NTU

State transition probability (1/4) The arrival process of each service class is Poisson arrival with different arrival rate. Consider a discrete-time Markov decision processes 2018/11/22 OPLab, IM, NTU

State transition probability (2/4) 2018/11/22 OPLab, IM, NTU

State transition probability (3/4) (9,1,2,0) (1,1,1,0) B=12 N=6 (5,1,0,0) 2018/11/22 OPLab, IM, NTU

State transition probability (4/4) 2018/11/22 OPLab, IM, NTU

Decision Variable (1/2) 2018/11/22 OPLab, IM, NTU

Decision Variable (2/2) 2018/11/22 OPLab, IM, NTU

Problem Formulation (1/3) Objective: Subject to: (LP1) (LP1.1) (LP1.2) (LP1.3) 2018/11/22 OPLab, IM, NTU

Problem Formulation (2/3) (LP1.4) (LP1.5) (LP1.6) (LP1.7) 2018/11/22 OPLab, IM, NTU

Problem Formulation (3/3) (LP1.8) (LP1.9) (LP1.10) (LP1.11) 2018/11/22 OPLab, IM, NTU

Problem Reformulation (1/3) Objective: Subject to: (LP2) (LP1.1) (LP1.2) (LP1.3) 2018/11/22 OPLab, IM, NTU

Problem Reformulation (2/3) (LP1.4) (LP1.5) (LP1.6) (LP1.7) 2018/11/22 OPLab, IM, NTU

Problem Reformulation (3/3) (LP1.8) (LP1.9) (LP1.10) (LP1.11) 2018/11/22 OPLab, IM, NTU

Lagrangean Relaxation (1/3) Objective: (LP3) 2018/11/22 OPLab, IM, NTU

Lagrangean Relaxation (2/3) Subject to: (LP3.1) (LP1.2) (LP1.3) (LP1.4) 2018/11/22 OPLab, IM, NTU

Lagrangean Relaxation (3/3) (LP1.5) (LP1.6) (LP1.7) (LP1.8) (LP1.9) 2018/11/22 OPLab, IM, NTU

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