Margin adaptive resource allocation for multiuser OFDM systems by modified Particle Swarm Optimization and Differential Evolution

Ahmed, Imran; Sadeque, Sonia; Pervin, Suraiya

doi:10.1109/conielecomp.2011.5749365

Cited by 5 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Solution to the adaptive resource allocation in OFDMA has been broadly divided into two categories: margin adaptive and rate adaptive (RA) . Some typical margin adaptive schemes have been developed to find the solution minimizing the overall transmit power, given constraint on the users' data rates, or bit error rate (BER) .In this paper, RA is considered. The aim of RA is to maximize the minimum data rate of users subject to power constraint .…”

Section: Introductionmentioning

confidence: 99%

Rate adaptive resource allocation in orthogonal frequency division multiple access system using multi‐objective immune algorithm

Chai

Zhu

Shen

2013

Int J Communication

View full text Add to dashboard Cite

SUMMARYRate adaptive downlink resource allocation in orthogonal frequency division multiple access system is a constraint optimization problem, which is to maximize the minimum data rate of the user subject to constraint that total power cannot exceed a given value. How to handle the constraint is a key issue for constrained optimization problem. Different with the available schemes on constraint handling, the proposed algorithm converts the constraint into an objective. Then, the resource allocation is combined into a multi-objective optimization problem. An improved multi-objective optimization algorithm based on artificial immune system is proposed to solve it. The simulation results show that, compared with previous schemes, the proposed algorithm performs remarkable improvement in sum capacity. Copyright

show abstract

Section: Introductionmentioning

confidence: 99%

Rate adaptive resource allocation in orthogonal frequency division multiple access system using multi‐objective immune algorithm

Chai

Zhu

Shen

2013

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In [11] PSO was applied for sub-channel allocation in downlink OFDMA and provided better sum-capacity values. Ahmed [12] has shown that the Differential Evolution techniques is better than PSO but takes more time to converge. The Bees algorithm has not yet applied for the problem of resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Rate Adaptive Resource Allocation in Ofdma Using Bees Algorithm

Archana¹

2014

IJRET

View full text Add to dashboard Cite

Orthogonal Frequency Division Multiple Access (OFDMA) is the promising access technique for future networks which allows multiple users to transmit simultaneously. The problem of allocating resources (subcarriers, bit and power) among the communicating users in multiuser orthogonal frequency division multiplexing (OFDM) system is a constraint optimization problem. Bio-inspired Networking represents an emerging area to obtain optimal solutions for handling various challenges in the networking scenario. Nature is our mother and an inspiration from nature always gives the best. Bio-inspired algorithms are known for their efficiency in solving NP hard problems. This paper focusses on realizing the rate adaptive resource allocation problem in OFDM systems using bio-inspired approach. The resource allocation problem using Genetic Algorithm, algorithms based on foraging behavior in ants and flocking behavior in birds have already been modelled. The paper suggests the application of Bees Algorithm for resource allocation in OFDMA with the goal of maximizing the data rate of each user.

show abstract

“…GAs were shown to outperform other conventional iterative methods in these results. There are a few reports in the literature where EAs other than GAs were used for resource allocation .…”

Section: Related Workmentioning

confidence: 99%

“…This approach allows efficient use of all the subcarriers. Resource allocation in OFDMA includes subcarrier allocation, power allocation and bit loading. Developing efficient resource management techniques for such a set‐up has drawn enormous attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Joint subcarrier and power allocation in downlink OFDMA systems: an multi‐objective approach

Sharma

Anpalagan

2013

Trans Emerging Tel Tech

View full text Add to dashboard Cite

In this paper, we present a new technique for resource allocation in multi‐user orthogonal frequency division multiple access systems. The goal is to maximise the minimum data rate available to any user while minimising the total transmitted power. In order to achieve an optimal solution and capacity bounds, the subcarrier and power should be allocated simultaneously. Multi‐objective genetic algorithm can be used for joint allocation of subcarriers and power in such a case, and in this paper, it is achieved using non‐dominated sorting genetic algorithm‐II. The simulation results indicate that the proposed algorithm achieves high data rates as compared with previous algorithms. The algorithm allocates both subcarriers and bits jointly without being computationally expensive. The faster convergence of the algorithm to near optimal value, as compared with previous algorithms, is indicative of its less complexity. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Margin adaptive resource allocation for multiuser OFDM systems by modified Particle Swarm Optimization and Differential Evolution

Cited by 5 publications

References 16 publications

Rate adaptive resource allocation in orthogonal frequency division multiple access system using multi‐objective immune algorithm

Rate adaptive resource allocation in orthogonal frequency division multiple access system using multi‐objective immune algorithm

Rate Adaptive Resource Allocation in Ofdma Using Bees Algorithm

Joint subcarrier and power allocation in downlink OFDMA systems: an multi‐objective approach

Contact Info

Product

Resources

About