1. Adaptive Resource Allocation for OFDMA Systems Mr. Zukang Shen Mr. Ian Wong Prof. Brian Evans Prof. Jeff Andrews April 28, 2005

2. Orthogonal Frequency Division Multiplexing  Adapted by current wireless standards  IEEE 802.11a/g, Satellite radio, etc…  Broadband channel is divided into many narrowband subchannels  Multipath resistant  Equalization simpler than single-carrier systems  Uses time or frequency division multiple access subchannel frequency magnitude carrier channel Subchannels are 312 kHz wide in 802.11a and HyperLAN II

3. Orthogonal Frequency Division Multiple Access (OFDMA)  Adapted by IEEE 802.16a/d/e BWA standards  Allows multiple users to transmit simultaneously on different subchannels  Inherits advantages of OFDM  Exploits multi-user diversity frequency magnitude Base Station - has knowledge of each user’s channel state information thru ideal feedback from the users User 2 User 1... User K

4. Rate & Margin Adaptive Methods  Rate Adaptive I (RA-I) [Jang & Lee, 2003]  Maximize sum capacity within total transmit power constraint  Rate Adaptive II (RA-II) [Rhee & Cioffi, 2000]  Maximize minimum user's error-free capacity within total transmit power constraint  Margin Adaptive (MA) [Wong et al. 1999]  Achieve minimum over all transmit power with constraints on the users' quality of service

5. Rate Adaptive with Proportional Fairness  Objective function  Sum capacity:  Constraints  Total power constraint  No two users share a subchannel  Capacities of users are proportional to each other  Advantages  Sum capacity maximized  Proportional fairness maintained

6. Two-Step Resource Allocation [Shen, Andrews, & Evans, 2003]  Subchannel allocation  Greedy algorithm – allow the user with the least allocated capacity/proportionality to choose the best subchannel O(KNlogN)  Power allocation  General Case Solution to a set of K non-linear equations in K unknowns – Newton-Raphson methods O(nK)  High-channel to noise ratio case Function root-finding O(nK), n=number of iterations, typically 10 for the ZEROIN subroutine

7. Simulations: Shen’s Method N=64; K=16; The average channel power of users 1-4 is 10 dB higher than the rest of 12 users; The rate constraints are γ k =2 m for k=1,…, 4 and γ k =1 for k=5,…,16. Normalized ergodic sum capacity distribution among users, γ 1 = γ 2 =…= γ 4 =8 and γ 5 = γ 6 =…= γ 16 =1.

8. Low Complexity Resource Allocation [Wong, Shen, Andrews, & Evans, 2004]  Relax strict proportionality constraint  In practical scenarios, rough proportionality is acceptable  Require a predetermined number of subchannels to be assigned to simplify power allocation  Reduced power allocation to a solution of linear equations O(K)

9. Simulations: Wong’s Method N = 64; SNR = 38dB; SNR Gap = 3.3; Based on 10000 channel realizations; Proportions assigned randomly from {4,2,1} with probability [0.2, 0.3, 0.5]

10. Code developed in floating point C and run on the TI TMS320C6701 DSP EVM run at 133 MHz http://www.ece.utexas.edu/~bevans/projects/ofdm Computational Complexity

11. Channel Prediction to Combat Delay Internet Back haul stationary t=0: Mobile estimates channel and feeds this back to base station t=  ase station receives estimates, adapts transmission based on these t=0 t=   Channel Mismatch Higher BER Lower bps/Hz Solution: Efficient OFDM Channel Prediction Algorithm 10 dB