Integrated Downlink Resource Management for Multiservice WiMAX Networks

Integrated Downlink Resource Management for Multiservice WiMAX Networks

Bo Rong, Yi Qian, and Kejie Lu

University of Puerto Rico

IEEE Transaction on Mobile Computing

Outline Introduction WiMAX OFDMA TDD system Integrated APA-CAC Downlink Resource

Management Framework Downlink APA Optimization Downlink CAC Optimization Simulation Results Conclusions

Introduction To handle heterogeneous traffic load in a WiMA

X network Efficiently allocate resources to different subscribers

and applications

Radio power Determine the aggregated downlink data rate of ea

ch subscriber Adaptive power allocation (APA)

Access bandwidth assigned to different applications in a subscriber’s local network. Call admission control (CAC)

Introduction

APA Produce high revenue for service providers Keep most users satisfied.

CAC Requirement of WiMAX subscribers A policy

Good tradeoff between service providers and subscribers

WiMAX OFDMA TDD system

WiMAX OFDMA TDD system-Full Usage of SubChannel

OFDMA mode of 2048 subcarriers, NE is 32

Integrated APA-CAC Downlink Resource Management Framework

Downlink APA Optimization

Develop a fairness-constrained greedy revenue algorithm Maximize the revenue of service provider Provide fairness amongst all subscribers

ExampleBS

SS SS SS SS

1,2,3,…,M classes of traffic load

class i traffic 1. requests arrive from a random process with average rate λi

2. demands bi bandwidth resources3. average connection holding time is 1/μi seconds

ExampleBS

SS SS SS SS

1,2,3,… j

Example

Power Revenue

downlink datatransmission rate

Subscriber can not get the bandwidth it demands

Selected subcarrier

Algorithm Power constrain and Fairness threshold

K subscriber

subcarriers

Initialization

Required power to transmit b b/s/Hz on subcarrier J

downlink traffic rate

downlink bandwidth capacity

Total potential revenueof the given subscribe

Request arrive from random process

Avg. connection hold timedownlink data

transmission rate

Traffic rate

Transmission rate

Required power to transmit b b/s/Hz on subcarrier J

Downlink CAC optimization CAC is used to accept or reject connection req

uests State information QoS requirements of these connections.

Brute force searching Straightforward method to achieve the optimal solut

ion. Unbearable complexity:O(B2M)

off-line scenarios

Design Criteria

Optimal revenue criterion long-run average

Optimal utility criterion

Number of connections

steady state probability that the systemis in state

Bandwidth requirement

CP structured admission control policy

Complete partition policy allocates each class of traffic a certain amount of non-overlapping bandwidth a CP policy can be decomposed into MM independ

ent sub-policies A class i connection request will be accepted if a

nd only if there is enough free bandwidth in BiCP

Greedy Approximation Algorithm

load

Erlang B formula Erlang is a unit of traffic measurement. Erlang B formulation

Calculate the probability that a resource request from the customer will be denied due to lack of resources.

Greedy Approximation Algorithm

CP*:optimal CP policy with maximum revenue

Load carried in a M/M/N/N queuing system

Greedy Approximation Algo. for CP*

Blocking prob.

Utility-constrained Greedy Approximation Algorithm for CPU∗

CP+: CP policy of maximum utility CPU*:optimal CP policy with maximum

revenue under the utility constrains

With constrains

Without constrains

Simulation results Downlink APA optimization in OFDMA-FUSC mo

de of 32 subscribers 2 to 10 km

1024 subcarriers 10 kHz

x=80 revenue rate,

rerUGS = 5 rerrtPS =2 rernrtPS = 1 rerBE = 0.5

Fairness constraint Fth = 80%.

Simulation results

Traffic load PPBE: uniformly distributed in [10%, 30%] PPUGS : uniformly distributed in [10%(1-PPB

E), 30%(1-PPBE)] PPrtPS : uniformly distributed in [20%(1-PPB

E), 60%(1-PPBE)] PPnrtPS: (1- PPBE - PPUGS - PPrtPS )

APA revenue/ Potential revenue

Out of fairness %

Overall performance

Normalized revenue or utility-brute force

Greedy

Overall performance APA optimization:

APA1: equal power allocation criterion APA2: pure greedy revenue algorithm; APA3: fairness-constrained greedy

revenue algorithm CAC optimization:

CAC1: complete sharing (CS) policy; CAC2:greedy approximation algorithm

for CP∗ CAC3:utility-constrained greedy

approximation algorithm for CPU∗;

Conslusion Downlink resource allocation problem in

WiMAX networks APA and CAC optimization problems Demands of both WiMAX service providers a

nd subscribers are considered. Simulation study demonstrates

Requirements of service providers and subscribers can be satisfied