This document proposes an iterative subcarrier allocation algorithm for OFDMA systems that aims to improve system performance and quality of service. The algorithm begins with an initial assignment based on highest modulation rates and then iteratively distributes bits to other subcarriers and exchanges subcarriers between users to reduce total transmit power. Simulation results show the iterative algorithm achieves higher average bit SNR than existing algorithms and improves its performance over time.

1. QoS Aware Adaptive
Subcarrier Allocation
in OFDMA Systems
Mustafa Ergen & Sinem Coleri
{ergen,csinem}@eecs.berkeley.edu
University of California Berkeley

2. Introduction





Motivation
Orthogonal Frequency Division Multiple Access(OFDMA)
OFDMA System
Resource Allocation Problem
Algorithms




Optimal
Suboptimal
Simulation
Conclusion

3. Motivation

Broadband Wireless Access
 Ex:

IEEE 802.16, Wireless MAN
OFDM
 Eliminates

OFDMA
InterSymbol Interference

4. OFDM Diagram

5. OFDM-TDMA
Multiuser OFDM
Time
Subcarrier
OFDM-FDMA
Time



OFDM-TDMA
OFDM-FDMA
OFDMA
Subcarrier
OFDMA
Time
User 1
User 2
User 3
…
…
Subcarrier

6. 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

7. Resource Allocation
ri
ar
bc
su
Assumptions:
 Base station knows
the channel
 Base station informs
the mobiles for
allocation
er
Base
Station
user

8. 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

9. OFDMA

10. Resource Allocation RATE:
BER:
[12
6
6
8
]
[1e-2 1e-2 1e-4 1e-4]
Resource Allocation
Subcarrier
r es U
Channel
QoS
64-QAM
16-QAM
4-QAM

11. Notation
Transmit Power : Pkc,n =
f k (ck , n )
α k2,n
user :
k ∈ {1,..., K }
subcarrier : n ∈ {1,..., N }
assigned bit : c
∈ {0,1,..., M }
k, n
channel gain : α 2
k, n
No  −1 BER
M − QAM : f (c) =
Q (
3 
4
2

) (2c − 1)


12. K
min
γ k ,n ,c
N
M
∑∑∑
f ( ck , n )
k =1 n =1 c =1
subject to Rk =
α
N
2
k ,n
γ k ,n ,c for γ k ,n ,c ∈{0,1}
M
∑∑ c
k ,n
.γ k ,n ,c for all k ,
n =1 c =1
and 0 ≤
K
M
∑∑ γ
k , n ,c
Pc2
Pc3
r es U
Integer Programming
≤ 1, for all n.
Subcarrier
Subcarrier
Subcarrier
k =1 c =1
es U

Pc1
r es U
Optimal
r es U
Subcarrier

13. Motivation for Sub-optimal
Algorithms
IP is complex
 Allocation should be done within the
coherence time
 Time increases exponentially with the
number of constraints


14. 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

15. Current Suboptimal Algorithms
Subcarrier
 Hungarian
algorithm
Optimal, very complex

 LP
approximation to IP
problem

Bit Loading
For each k , repeat the following Rk times :
n = arg min ∆Pk ,n (ck ,n )
n∈S k
c
k ,n
=c
Subcarrier
Close to optimal
r es U

r es U
Subcarrier Allocation
k ,n
+1
evaluate ∆P (c ).
k ,n
k ,n
r es U

Subcarrier

16. 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

17. 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.

18. Iterative Algorithm
Fair Selection(FS)
 Greedy Release(GR)
 Horizontal Swaping(HS)
 Vertical Swaping(VS)


19. Iterative Algorithm
Start
ASSIGNMENT
Modulation--
ITERATION
PA W L A C T REV
S
I
GREEDY
RELEASE
PA W L AT N OZ R OH
S
I
Ptotal<Pmax
FAIR
SELECTION

20. 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

21. CDF of total transmit power
without Pmax constraint

22. CDF of total transmit power with
Pmax constraint

23. Average bit SNR vs. RMS delay
spread
As RMS delay spread increases, the fading variation increases
hence higher gains are obtained by adaptive allocation

24. Average bit SNR vs. number of
users
As the number of users increases, the probability of obtaining
good channel at a subcarrier increases

25. Instantaneous Average bit SNR
vs Time
Iterative Algorithm improves its Average Bit SNR by the time.

26. Conclusion

OFDMA
 Broadband

Wireless Access
Resource Allocation
 Channel
Information
 QoS Requirement

Optimal Algorithms
 complex

Iterative Algorithms