Adaptive resource allocation based on modified Genetic Algorithm and Particle Swarm Optimization for multiuser OFDM systems

Ahmed, Imran; Majumder, S. P.

doi:10.1109/icece.2008.4769202

Cited by 29 publications

(16 citation statements)

References 8 publications

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“…In [8], a Genetic Algorithm (GA) based scheme is proposed to minimize the overall transmit power with the QoS constraints of users. In [9], both GA and Particle Swarm Optimization (PSO) are applied for subcarrier allocation while the bit allocation part is based on water-filling algorithm. Ant colony optimization (ACO) is adopted to solve resource allocation problem in [10], where a multiple edges ACO graphic is developed to allocated subchannels and bit jointly.…”

Section: Introductionmentioning

confidence: 99%

Discrete Power Allocation via Ant Colony Optimization for Multi-Cell OFDM Systems

Chen

et al. 2012

2012 IEEE Vehicular Technology Conference (VTC Fall)

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Abstract-The majority studies on resource allocation are based on continuous power allocation. However, the transmit power can be assumed as a finite set of discrete power levels only, especially for practical digital cellular systems. By simply rounding up or down, the conventional continuous power allocation algorithm will not suffice. In this paper, via transmitting signal on discrete power levels, multi-cell power allocation is modeled as a combinatorial optimization problem, which proved to be NP-hard. Ant colony optimization (ACO) is applied to get nearoptimal solution of the problem. In particular, a multiple power level based searching graph is designed, and conventional ACO is improved that ant colonies in each cell cooperate to maximize system throughput. Simulation results indicate that with only 4 power levels, the proposed algorithm can achieve a significant rate gain. And the throughput improvement increases to roughly 12% and 33% compared with binary power allocation and equal power allocation

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Section: Introductionmentioning

confidence: 99%

Discrete Power Allocation via Ant Colony Optimization for Multi-Cell OFDM Systems

Chen

et al. 2012

2012 IEEE Vehicular Technology Conference (VTC Fall)

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“…In this paper, the proposed algorithm is compared with the algorithms in Ahmed and Majumder, (2012) and Seo and Lee (2004) based on the following three goals:…”

Section: Performance Evaluationmentioning

confidence: 99%

“…However, as the user number increases, the performance of the algorithm presented by this paper is better than the other two algorithms. Then we test the proposed algorithm's minimum transmit power and compare it with MPSO and PFPA from Ahmed and Majumder (2012) and Seo and Lee (2004) respectively. The result is shown in Figure 6.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In the simulations, parameter is set up according to Ahmed and Majumder (2012). Slow-varying Rayleigh fading channel has been used and it has been assumed to be known to the resource allocator at base-station.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Intelligent algorithms have attracted more and more scholars' attention to solve optimisation problems (Mo et al, 2011;Gu et al, 2013). In the studies of OFDM resource allocation, a lot of reports make use of intelligent algorithm to solve the OFDM resource allocation model (Perea-Vega et al, 2010;Odhah et al, 2012;Wang et al, 2012b;Ahmed and Majumder, 2012). Sadeque et al (2011) and Annauth and Rughooputh (2011) use intelligent algorithm to allocate power to each sub-carrier.…”

Section: Introductionmentioning

confidence: 99%

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A resource allocation evolutionary algorithm for OFDM system

Wang

Liu

Li³

et al. 2017

IJCSE

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Abstract:Resource allocation for orthogonal frequency division multiplexing Orthogonal Frequency Division Multiplexing (OFDM) system, as a core technology for the 4th generation mobile communication system, has shown significant importance in the improvement of system transmission rate. At present, the two-step algorithm is described as a main method to deal with resource allocation for OFDM system. As carrier allocation and power allocation are not independent of each other, the two-step algorithm may result in lower complexity but poor convergence. This paper proposes a mixed evolutionary algorithm to allocate sub-carrier and power at the same time. It combines the simulated annealing algorithm and evolutionary algorithm. This paper also improves the definition of fairness in OFDM system. It can achieves perfect fairness among users while maintaining the efficient capacity performance. Simulation results demonstrate that the proposed resource allocation algorithm can achieve perfect performance, and increase the convergence rate greatly meanwhile efficiently improving the system capacity.Keywords: OFDM; resource allocation; evolutionary algorithm; simulated annealing algorithm.Reference to this paper should be made as follows: Wang, Q., Liu, H-L., Li, J-c. and Li, Y. Q. Wang et al.This paper is a revised and expanded version of a paper entitled 'Resource allocation for OFDM system based on evolutionary algorithm' presented at International

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

Sharma

Anpalagan

2013

Trans Emerging Tel Tech

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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.

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Adaptive resource allocation based on modified Genetic Algorithm and Particle Swarm Optimization for multiuser OFDM systems

Cited by 29 publications

References 8 publications

Discrete Power Allocation via Ant Colony Optimization for Multi-Cell OFDM Systems

Discrete Power Allocation via Ant Colony Optimization for Multi-Cell OFDM Systems

A resource allocation evolutionary algorithm for OFDM system

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

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