On the use of particle swarm optimization for adaptive resource allocation in orthogonal frequency division multiple access systems with proportional rate constraints

Sharma, Nitin; Tarcar, Anand Kamat; Thomas, Varghese; Anupama, K. R.

doi:10.1016/j.ins.2011.01.021

Cited by 20 publications

(51 citation statements)

References 14 publications

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“…The learning factors c 1 and c 2 in (19) goes here represent the weighting of the stochastic acceleration terms that pull each particle toward pbest and gbest positions. From a psychological standpoint, the cognitive term which is the second term in (19) represents the tendency of individuals to duplicate past behaviors that have proven successful, whereas the social term which is the third term represents the tendency to follow the successes of others.…”

Section: ) Change Particle Positions If the Mss States Change Ormentioning

confidence: 99%

“…Particle swarm optimization (PSO), as a population based stochastic optimization technique, has been successfully used to solve highly non-linear mixed integer optimization problems in various domains of engineering [15]- [18]. In [19], PSO is used to solve the RA problem in OFDMA systems. In our previous work, multi-values discrete particle swarm optimization(MDPSO) is proposed for RA in Cooperative OFDMA Systems [12].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Optimization for Resource Allocation in Relay-Aided OFDMA Systems Under Multiservice

Lei

Wang

et al. 2016

IEEE Trans. Veh. Technol.

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This paper investigates the dynamic resource allocation (RA) problem in cooperative OFDMA systems, to maximize the average utility of all mobile stations (MSs) under different services. We propose a dynamic optimization framework for RA by considering three dynamic situations: time-varying fading channel, MSs states change, and relay stations (RSs) states change. Moreover, a dynamic RA algorithm based on discrete particle swarm optimization (DPSO) is proposed. The correlation between the adjacent frames is exploited to improve the performance of the dynamic RA algorithm. Simulation results show that the proposed dynamic algorithm achieves the better performance at linear complexity compared to the existing algorithms under different dynamic environments, while guaranteeing the fairness to a proper level.Index Terms-Cooperative communication, dynamic optimization, discrete particle swarm optimization, orthogonal frequency division multiple access (OFDMA), relay, resource allocation, utility function. 0018-9545 (c)

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Section: ) Change Particle Positions If the Mss States Change Ormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization for Resource Allocation in Relay-Aided OFDMA Systems Under Multiservice

Lei

Wang

et al. 2016

IEEE Trans. Veh. Technol.

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“…Algorithm in [29] O(n 2 m) Algorithm in [27] O(nm) Algorithm in [30] O(nm) Our proposed algorithm O(gcmnlog(4c)) antibodies evolve in different directions in the clone and mutation operations, making different subcarrier allocation proposals involved in the population and leading the diversity of the population. The convergence of the proposed algorithm is illustrated in Figure 2.…”

Section: Different Algorithms Complexitymentioning

confidence: 99%

“…We also take the algorithms in [27] and [30] as contrastive algorithms to testify to the effectiveness of the proposed algorithm. We also take the algorithms in [27] and [30] as contrastive algorithms to testify to the effectiveness of the proposed algorithm.…”

Section: The Effect Comparison With Other Intelligent Algorithmsmentioning

confidence: 99%

Discrete polynary coding immune clonal selection‐based joint subcarrier and power allocation in uplink cognitive OFDM network

Shang

Zhang

Jiao

et al. 2014

Int J Communication

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Cognitive radio has been considered to be one of the main technologies to solve the problem of low spectrum utilization, while the adaptive allocation of network resource is one of the key technologies. A discrete polynary coding immune clonal selection (DPICS)-based joint subcarrier and power allocation algorithm is proposed to solve the resource allocation problem in uplink cognitive OFDM networks. The novelties of DPICS include the following: A unique coding method is adopted to deal with multi-value discrete variables. Compared with the traditional methods, the proposed method can acquire the shortest code. Meanwhile, the constraints of the subcarrier allocation are avoided. A heuristic mutation scheme is used to direct the mutation. Subcarriers are reallocated randomly to the secondary users with larger homotactic noise, which has a large probability to produce the optimal solution and improves the searching process. Subcarriers and power are allocated simultaneously, which is different with the traditional biphasic resource allocation algorithms. The biphasic resource allocation algorithms cannot acquire the subcarrier allocation result and power allocation result simultaneously, which makes the final result imprecise. The proposed algorithm avoids this situation and improves the accuracy of the final result. Compared with state-of-the-art algorithms, the proposed algorithm is shown as effective by simulation results. Copyright

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“…This often allows PSO to find comparable solutions to naive GAs in fewer function evaluations [11]. The efficiency of PSO, combined with the relative simplicity of a GA, makes it a good general purpose search heuristic, particularly in engineering contexts when solution speed is an important concern [30].…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

The effect of load on agent-based algorithms for distributed task allocation

Goldingay

Mourik

2013

Information Sciences

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Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

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On the use of particle swarm optimization for adaptive resource allocation in orthogonal frequency division multiple access systems with proportional rate constraints

Cited by 20 publications

References 14 publications

Dynamic Optimization for Resource Allocation in Relay-Aided OFDMA Systems Under Multiservice

Dynamic Optimization for Resource Allocation in Relay-Aided OFDMA Systems Under Multiservice

Discrete polynary coding immune clonal selection‐based joint subcarrier and power allocation in uplink cognitive OFDM network

The effect of load on agent-based algorithms for distributed task allocation

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