Low Complexity Resource Allocation Algorithm for IEEE 802.16 OFDMA System

1. Low Complexity Resource Allocation Algorithm for IEEE 802.16 OFDMA System SeyedMohamadAlavi, Chi Zhou, Yu Cheng Department of Electrical and Computer Engineering Illinois Institute of Technology, Chicago, IL, USA ICC 2009

2. Outline • Introduction • System model • Reduced Complexity Proposed Model • Performance Evaluation • Conclusions

3. Introduction • The orthogonal frequency division multiple access, also known as Multiuser-OFDM, is a class of multiple access schemes for the 4thgeneration wireless networks. • OFDMA is immune to intersymbol interference and frequency selective fading as it divides the frequency band into a group of orthogonal subcarriers

4. Introduction • The combination of OFDMA with adaptive modulation and coding (AMC) and dynamic power allocation is of great prominence in the design of future broadband radio systems 64-QAM 16-QAM 16-QAM QPSK 64-QAM QPSK

5. Introduction • Radio Resource Allocation problems are usually divided into two classes: • Margin Adaptive (MA) problem • minimizing total transmission power while satisfying QoS requirements of each user • Rate Adaptive (RA) problem • maximize throughput in a system subject to a constraint on maximum total transmission power, while satisfying each user’s QoS requirements

6. Introduction • To formulize the resource allocation problem with constraints on rate, BER, power and delay requirements • To propose a heuristic algorithm that is superior to the linearized algorithm in terms of complexity, but with a little lower capacity.

7. System model • Assume that the base station has perfect channel estimation which is made known to the transmitter via a dedicated feedback channel

8. System model Number of time slot Number of subcarrier Number of user Bit loading values number of bits per symbol that can be carried by modulation scheme, m

9. System model rate requirement transmission power

10. System model delay requirement

11. Reduced Complexity Proposed Model • Step 1 • Determine the number of subcarriers assigned to each user • Step 2 • Assign the subcarriers to each user based on rate requirement. • Step 3 • Allocate the time slots to different users based on delay requirement. • Step 4 • Solve the optimization problem with the only constraint on power

12. Reduced Complexity Proposed Model • A. Step 1-Number of subcarriers per user Rate requirement Delay requirement

13. Reduced Complexity Proposed Model • A. Step 1-Number of subcarriers per user total number of subcarriers Unallocated subcarriers

14. Reduced Complexity Proposed Model • B. Step 2-Subcarrier assignment • all subcarriers will be sorted in descending order for all users • If there is any unsatisfied user, subcarrier replacement is done with the most satisfied user. This process will be finished when all users required data rate is satisfied.

15. Reduced Complexity Proposed Model • C. Step 3- providing user delay requirement

16. Reduced Complexity Proposed Model • D. Step 4-power allocation • In this step the optimization problem with only a constraint on maximum power allocation assigns the power of each user on its specified subcarrier.

17. Performance Evaluation • Implemented using Matlab • Frequency selective multipath channel model • Eight independent Rayleigh multipaths • Maximum Doppler shift of 30 Hz is assumed • The channel information is sampled every 0.5 ms to update the subchannel and power allocation

18. Performance Evaluation • The possible modulation schemes that can be used, are BPSK, QPSK rectangular 16-QAM and 64-QAM, U = {0,1,2,4,6} • Maximum number of Users are chosen from the set of K = {4, 8, 12, 16} • total number of subcarriers are selected from the set of N = {8, 16, 24, 32} • K and N are chosen somehow that always K < N

19. Performance Evaluation • Computational complexity comparison

20. Performance Evaluation • Total capacity versus number of users

21. Conclusions • In this paper, we have proposed a linear optimization formulation that considers delay in addition to rate requirement. • It is shown through simulation that that the proposed heuristic method performs better than the previous models in terms of significantly decreasing the computational complexity, and yet achieving almost same total capacity.

22. Thank you