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

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

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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

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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

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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

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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), nnumber of
  iterations, typically 10 for the ZEROIN subroutine

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Simulations Shens Method
N64 K16 The average channel power of users
1-4 is 10 dB higher than the rest of 12 users
The rate constraints are ?k2m for k1,, 4 and
?k1 for k5,,16. Normalized ergodic sum
capacity distribution among users, ?1 ?2 ?48
and ?5 ?6 ?161.
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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)

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Simulations Wongs 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
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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
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Channel Prediction to Combat Delay
stationary
t0 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