Orthogonal Frequency Division Multiplexing
11
Adaptive Resource Allocation for OFDMA Systems Mr. Zukang Shen Mr. Ian Wong Prof. Brian Evans Prof. Jeff Andrews April 28, 2005
description
Adaptive Resource Allocation for OFDMA Systems Mr. Zukang Shen Mr. Ian Wong Prof. Brian Evans Prof. Jeff Andrews April 28, 2005. Orthogonal Frequency Division Multiplexing. channel. carrier. magnitude. subchannel. Subchannels are 312 kHz wide in 802.11a and HyperLAN II. frequency. - PowerPoint PPT Presentation
Transcript of Orthogonal Frequency Division Multiplexing
Adaptive Resource Allocation for OFDMA Systems
Mr. Zukang ShenMr. Ian Wong
Prof. Brian EvansProf. Jeff Andrews
April 28, 2005
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
ma
gn
itude
carrierchannel
Subchannels are 312 kHz wide in 802.11a and HyperLAN II
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
mag
nitu
de
Base Station - has knowledge of each user’s channel state information thru ideal feedback from the users
User 2
User 1
. . .
User K
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
Rate Adaptive with Proportional Fairness
Objective functionSum capacity:
ConstraintsTotal power constraintNo two users share a subchannelCapacities of users are proportional to each other
AdvantagesSum capacity maximizedProportional fairness maintained
)/
1(log1
max0
2,,
21, , NBN
hp
Nnknk
K
k SnPSknkk
Two-Step Resource Allocation [Shen, Andrews, & Evans, 2003]
Subchannel allocationGreedy algorithm – allow the user with the
least allocated capacity/proportionality to choose the best subchannel O(KNlogN)
Power allocationGeneral 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
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.
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)
Simulations: Wong’s Method
4 6 8 10 12 14 164.45
4.5
4.55
4.6
4.65
4.7
4.75
number of users
ca
pac
ity
(b
it/s
/Hz)
Proposed Method
Shen's Method
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
User Number (k)N
orm
aliz
ed
Ra
te P
rop
ort
ion
s
Proportions
Proposed Method
Shen'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]
2 4 6 8 10 12 14 160
1
2
3
4
5
6
7
8
9
10x 10
5 DSP Imlementation Clock cycle count
Number of users
Clo
ck c
ycle
s
Channel Allocation-shenPower Allocation-shen
Channel Allocation-wong
Power Allocation-wong
Total-shenTotal-wong
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
Channel Prediction to Combat Delay
InternetBack haul
stationary
t=0: Mobile estimates channel and feeds this back to base stationt=ase station receives estimates, adapts transmission based on these
t=0t=
Channel Mismatch Higher BERLower bps/Hz
10 15 20 25 30 35-50
-45
-40
-35
-30
-25
-20
-15
-10
-5Prediction NMSE vs. SNR, 10 coherence times ahead
SNR in dB
NM
SE
in d
B
Previous Method
Proposed Method
AsymptoticCramer-RaoLower Bound
Solution:Efficient OFDM Channel PredictionAlgorithm
10 dB
