QoS Aware Adaptive
Subcarrier Allocation
in OFDMA Systems
Mustafa Ergen & Sinem Coleri
{ergen,csinem}@eecs.berkeley.edu
University of California Berkeley
Introduction





Motivation
Orthogonal Frequency Division Multiple Access(OFDMA)
OFDMA System
Resource Allocation Problem
Algorithms




Optimal
Suboptimal
Simulation
Conclusion
Motivation

Broadband Wireless Access
 Ex:

IEEE 802.16, Wireless MAN
OFDM
 Eliminates

OFDMA
InterSymbol Interference
OFDM Diagram
OFDM-TDMA
Time
Multiuser OFDM
Subcarrier
OFDM-FDMA
Time



OFDM-TDMA
OFDM-FDMA
OFDMA
Subcarrier
OFDMA
Time
User 1
User 2
User 3
…
…
Subcarrier
Resource Allocation
Goals:
 Dynamic subcarrier selection
 Improve system performance with adaptive
modulation
 More

bits transmitted in large channel gain carriers
Provide QoS
 Rate
and BER
Resource Allocation
Assumptions:
 Base station knows
the channel
 Base station informs
the mobiles for
allocation
Base
Station
System
oCoS=Ptotal for downlink
oCoS=Pu
for uplink
Application
Network
rQoS=[rR,rBER]
oQoS=[oR,oBER,oCoS]
Resource Allocation
[User x Subcarrier]
Physical Layer
OFDMA
X(k)
User 1 (Rate R1, BER1)
User 2 (Rate R2, BER2)
.
.
.
.
User K (Rate RK, BERK)
Subcarrier
allocation
with
Different
Modulation
Adaptive Modulation
Adaptive Modulation
.
.
.
.
Adaptive Modulation
x(n)
xf(n)
Guard
Insertion
IDFT
P/S
h(n)
Channel Information
from
user k
Resouce Allocation Module
Maximum Total
Power
Y(k)
User k
y(n)
Channel
Path
Loss
yf(n)
Adaptive Demodulation
Adaptive Demodulation
.
.
.
Adaptive Demodulation
Subcarrier
Extraction
for
user
k
DFT
Guard
Removal
S/P
AWGN
w(n)
+
Resource Allocation RATE:
BER:
[12
6
6
8
]
[1e-2 1e-2 1e-4 1e-4]
Resource Allocation
Subcarrier
User
Channel
QoS
64-QAM
16-QAM
4-QAM
Notation
Transm it Power : Pkc,n 
user :
f k ( ck , n )
 k2,n
k  {1,...,K }
subcarrier: n  {1,..., N }
assigned bit : c
 {0,1,...,M }
k, n
channelgain:  2
k, n
No  1 BER
M  QAM : f (c) 
Q (
3 
4
2

) (2c  1)

Optimal
Subcarrier
Integer Programming
K
min
 k ,n ,c
N
M

f ( ck , n )
k 1 n 1 c 1

2
k ,n
 k ,n,c for  k ,n ,c {0,1}
Pc2
User

Pc1
User
Subcarrier
subject to Rk 
N
M
 c
k ,n
. k ,n ,c for all k ,
n 1 c 1
M
 
k , n ,c
 1, for all n.
Subcarrier
k 1 c 1
User
and 0 
K
Pc3
User
Subcarrier
Motivation for Sub-optimal
Algorithms
IP is complex
 Allocation should be done within the
coherence time
 Time increases exponentially with the
number of constraints

Current Suboptimal Algorithms
2-step:
 Subcarrier Allocation
 Assume
the data rate for all subcarriers
 Assume modulation rate is fixed
 Assign the subcarriers

Bit Loading
 Greedy
approach to assign the bits of user
Current Suboptimal Algorithms
Subcarrier
Subcarrier Allocation
 Hungarian
algorithm
Optimal, very complex

User

 LP
approximation to IP
problem
Bit Loading
Subcarrier
For each k , repeat the following Rk tim es:
n  arg min Pk ,n (ck ,n )
nS k
c
c
k ,n
1
k ,n
evaluateP (c ).
k ,n
k ,n
User

Close to optimal
User

Subcarrier
Problems in Current Suboptimal
Algorithms

Subcarrier assignment and bit loading are
separated
 Users
with bad channels may need higher
number of subcarriers

Not iterative subcarrier assignment
Iterative Algorithm
Iterative algorithm based on
 Assignment of bits according to highest
modulation
 Finding


the best places
Distributing the assigned bits to other
subcarriers or to non-assigned subcarriers
Exchanging the subcarriers among user
pairs for power reduction.
Iterative Algorithm
Fair Selection(FS)
 Greedy Release(GR)
 Horizontal Swaping(HS)
 Vertical Swaping(VS)

Iterative Algorithm
GREEDY
RELEASE
ASSIGNMENT
Modulation--
ITERATION
VERTICAL SWAP
Ptotal<Pmax
FAIR
SELECTION
HORIZONTAL SWAP
Start
Simulation Environment
Build the OFDMA system
 Modulations:4-QAM,16-QAM,64-QAM
 Independent Rayleigh fading channel
to each user
 Number of subcarriers =128
 Nodes are perfectly synchronized
CDF of total transmit power
without Pmax constraint
CDF of total transmit power with
Pmax constraint
Average bit SNR vs. RMS delay
spread
As RMS delay spread increases, the fading variation increases
hence higher gains are obtained by adaptive allocation
Average bit SNR vs. number of
users
As the number of users increases, the probability of obtaining
good channel at a subcarrier increases
Instantaneous Average bit SNR
vs Time
Iterative Algorithm improves its Average Bit SNR by the time.
Conclusion

OFDMA
 Broadband

Wireless Access
Resource Allocation
 Channel
Information
 QoS Requirement

Optimal Algorithms
 complex

Iterative Algorithms