Orthogonal Frequency Division Multiplexing

1. Adaptive Resource Allocation for OFDMA SystemsMr. Zukang ShenMr. Ian WongProf. Brian EvansProf. Jeff AndrewsApril 28, 2005

2. Orthogonal Frequency Division Multiplexing channel carrier magnitude subchannel Subchannels are 312 kHz wide in 802.11a and HyperLAN II frequency • 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

3. Orthogonal Frequency Division Multiple Access (OFDMA) User 1 User 2 magnitude frequency . . . Base Station - has knowledge of each user’s channel state information thru ideal feedback from the users User K • 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

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 γk=2m for k=1,…, 4 and γk=1 for k=5,…,16. Normalized ergodic sum capacity distribution among users, γ1= γ2=…= γ4=8 and γ5= γ6=…= γ16=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. Computational Complexity 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

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