seyed mohamad alavi, chi zhou, yu cheng department of electrical and computer engineering illinois...
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Low Complexity Resource Allocation Algorithm for IEEE 802.16 OFDMA System
Seyed Mohamad Alavi, Chi Zhou, Yu Cheng Department of Electrical and Computer Engineering Illinois Institute of Technology, Chicago, IL, USA
ICC 2009
Outline
Introduction System model Reduced Complexity Proposed Model Performance Evaluation Conclusions
Introduction
The orthogonal frequency division multiple access, also known as Multiuser-OFDM, is a class of multiple access schemes for the 4th generation wireless networks.
OFDMA is immune to intersymbol interference and frequency selective fading as it divides the frequency band into a group of orthogonal subcarriers
Introduction
The combination of OFDMA with adaptive modulation and coding (AMC) and dynamic power allocation is of great prominence in the design of future broadband radio systems
64-QAM
16-QAM
16-QAM
QPSK
64-QAM
QPSK
Introduction
Radio Resource Allocation problems are usually divided into two classes: Margin Adaptive (MA) problem
minimizing total transmission power while satisfying QoS requirements of each user
Rate Adaptive (RA) problem maximize throughput in a system subject to a
constraint on maximum total transmission power, while satisfying each user’s QoS requirements
Introduction
To formulize the resource allocation problem with constraints on rate, BER, power and delay requirements
To propose a heuristic algorithm that is superior to the linearized algorithm in terms of complexity, but with a little lower capacity.
System model
Assume that the base station has perfect channel estimation which is made known to the transmitter via a dedicated feedback channel
System model
Bit loading values
number of bits per symbol that can be carried by modulation scheme, m
Number of time slot Number of subcarrier Number of user
System model
rate requirement
transmission power
System model
delay requirement
Reduced Complexity Proposed Model
Step 1 Determine the number of subcarriers assigned to each
user
Step 2 Assign the subcarriers to each user based on rate
requirement.
Step 3 Allocate the time slots to different users based on delay
requirement.
Step 4 Solve the optimization problem with the only constraint
on power
Reduced Complexity Proposed Model
A. Step 1-Number of subcarriers per user
Rate requirement
Delay requirement
Reduced Complexity Proposed Model
A. Step 1-Number of subcarriers per user
total number of subcarriers
Unallocated subcarriers
Reduced Complexity Proposed Model
B. Step 2-Subcarrier assignment all subcarriers will be sorted in descending order
for all users
If there is any unsatisfied user, subcarrier replacement is done with the most satisfied user. This process will be finished when all users required data rate is satisfied.
Reduced Complexity Proposed Model
C. Step 3- providing user delay requirement
Reduced Complexity Proposed Model
D. Step 4-power allocation In this step the optimization problem with only a
constraint on maximum power allocation assigns the power of each user on its specified subcarrier.
Performance Evaluation
Implemented using Matlab Frequency selective multipath channel model Eight independent Rayleigh multipaths Maximum Doppler shift of 30 Hz is assumed The channel information is sampled every 0.5
ms to update the subchannel and power allocation
Performance Evaluation
The possible modulation schemes that can be used, are BPSK, QPSK rectangular 16-QAM and 64-QAM, U = {0,1,2,4,6}
Maximum number of Users are chosen from the set of K = {4, 8, 12, 16}
total number of subcarriers are selected from the set of N = {8, 16, 24, 32}
K and N are chosen somehow that always K < N
Performance Evaluation
Computational complexity comparison
Performance Evaluation
Total capacity versus number of users
Conclusions
In this paper, we have proposed a linear optimization formulation that considers delay in addition to rate requirement.
It is shown through simulation that that the proposed heuristic method performs better than the previous models in terms of significantly decreasing the computational complexity, and yet achieving almost same total capacity.
Thank you