Integrated Downlink Resource Management for Multiservice WiMAX Networks

1. Integrated Downlink Resource Management for Multiservice WiMAX Networks Bo Rong, Yi Qian,and Kejie Lu University of Puerto Rico IEEE Transaction on Mobile Computing

2. Outline • Introduction • WiMAX OFDMA TDD system • Integrated APA-CAC Downlink Resource Management Framework • Downlink APA Optimization • Downlink CAC Optimization • Simulation Results • Conclusions

3. Introduction • To handle heterogeneous traffic load in a WiMAX network • Efficiently allocate resources to different subscribers and applications • Radio power • Determine the aggregated downlink data rate of each subscriber • Adaptive power allocation (APA) • Access bandwidth assigned to different applications in a subscriber’s local network. • Call admission control (CAC)

4. 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

5. WiMAX OFDMA TDD system

6. WiMAX OFDMA TDD system-Full Usage of SubChannel OFDMA mode of 2048 subcarriers, NEis 32

7. Integrated APA-CAC Downlink Resource Management Framework

8. Downlink APA Optimization • Develop a fairness-constrained greedy revenue algorithm • Maximize the revenue of service provider • Provide fairness amongst all subscribers

9. Example BS 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 bibandwidth resources 3. average connection holding time is 1/μiseconds

10. Example BS SS SS SS SS 1,2,3,… j

11. Example downlink datatransmission rate Selected subcarrier Subscriber can not get the bandwidth it demands Power Revenue

12. Algorithm Power constrain and Fairness threshold K subscriber Required power to transmit b b/s/Hz on subcarrier J subcarriers Initialization

13. Total potential revenue of the given subscribe downlink traffic rate downlink bandwidth capacity Request arrive from random process downlink datatransmission rate Avg. connection hold time

14. Transmission rate Traffic rate Required power to transmit b b/s/Hz on subcarrier J

15. Downlink CAC optimization • CAC is used to accept or reject connection requests • State information • QoS requirements of these connections. • Brute force searching • Straightforward method to achieve the optimal solution. • Unbearable complexity:O(B2M) • off-line scenarios

16. Design Criteria • Optimal revenue criterion • long-run average • Optimal utility criterion Number of connections steady state probability that the system is in state Bandwidth requirement

17. 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 Mindependent sub-policies • A class i connection request will be accepted if and only if there is enough free bandwidth in BiCP

18. Greedy Approximation Algorithm load

19. 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.

20. Greedy Approximation Algorithm • CP*:optimal CP policy with maximum revenue • Load carried in a M/M/N/N queuing system

21. Greedy Approximation Algo. for CP* Blocking prob.

23. Utility-constrained Greedy Approximation Algorithm for CPU∗ • CP+: CP policy of maximum utility • CPU*:optimal CP policy with maximum revenue under the utility constrains

25. With constrains Without constrains

26. Simulation results • Downlink APA optimization in OFDMA-FUSC mode 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%.

27. Simulation results • Traffic load • PPBE: uniformly distributed in [10%, 30%] • PPUGS : uniformly distributed in [10%(1-PPBE), 30%(1-PPBE)] • PPrtPS : uniformly distributed in [20%(1-PPBE), 60%(1-PPBE)] • PPnrtPS: (1- PPBE - PPUGS - PPrtPS )

28. APA revenue/ Potential revenue

29. Out of fairness %

30. Overall performance

31. Normalized revenue or utility-brute force

32. Greedy

33. 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∗;

36. Conslusion • Downlink resource allocation problem in WiMAX networks • APA and CAC optimization problems • Demands of both WiMAX service providers and subscribers are considered. • Simulation study demonstrates • Requirements of service providers and subscribers can be satisfied