Population-adaptive differential evolution-based power allocation algorithm for cognitive radio networks

Abstract

Cognitive radio (CR) networks have drawn great attention in wireless communication fields. Efficient and reliable communication is a must to provide good services and assure a high-quality life for human beings. Resource allocation is one of the key problems in information transmission of CR networks. This paper studies power allocation in cognitive multiple input and multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Power allocation is modeled as a minimization problem with three practical constraints. To deal with the problem, a population-adaptive differential evolution (PADE) algorithm is proposed. All algorithmic parameters are adaptively controlled in PADE. In numerical experiment, three test cases are simulated to study the performance of the proposed algorithm. Particle swarm optimization, differential evolution (DE), an adaptive DE, and artificial bee colony algorithms are taken as baseline. The results show that PADE presents the best performance among all test algorithms over all test cases. The proposed PADE algorithm can also be used to tackle other resource allocation problems.

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

“…In order to verify the performance of our proposed solution method using the PA-Jaya algorithm, the simulation model was run 10 times under the same parameter settings, and the average value was obtained. Moreover, the computational performance was also compared between our solution method and other popular algorithms, including SA [ 20 ], GA [ 21 ], PSO [ 22 , 23 ], DE [ 24 ], ICO [ 25 ], and traditional Jaya [ 33 ]. The detailed parameter settings for each algorithm can be found in Table 2 .…”

“…Generally speaking, the existing solutions mostly use the traditional mathematical optimization method or some greedy searching algorithms, which may suffer from a quite high computational complexity during the implementation process [ 18 , 19 ]. Some evolutionary algorithms, including simulated annealing (SA) [ 20 ], genetic algorithm (GA) [ 21 ], particle swarm optimization (PSO) [ 22 , 23 ], differential evolution (DE) [ 24 ], and immune clonal optimization (ICO) [ 25 ], are employed to deal with this issue, with the help of their effective computational features in the swarm intelligence paradigm. Through the use of those algorithms, a satisfactory solution effect was achieved during the resource allocation in CRNs [ 20 , 21 , 22 , 23 , 24 , 25 ].…”

“…Some evolutionary algorithms, including simulated annealing (SA) [ 20 ], genetic algorithm (GA) [ 21 ], particle swarm optimization (PSO) [ 22 , 23 ], differential evolution (DE) [ 24 ], and immune clonal optimization (ICO) [ 25 ], are employed to deal with this issue, with the help of their effective computational features in the swarm intelligence paradigm. Through the use of those algorithms, a satisfactory solution effect was achieved during the resource allocation in CRNs [ 20 , 21 , 22 , 23 , 24 , 25 ]. However, there is still room to further improve the optimization effect due to the inherent disadvantages of those algorithms in the practical engineering applications of IoT, such as the possible ease of falling into the local optimal solutions in some cases, the difficulty adjusting many key parameters during the implementation process of the algorithms, and many drawbacks [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”

Optimization of Spreading Code Using Modified Differential Evolution for Wireless Communication

“…It is seen by C. Chiu that in both of sight and non-line, the SADDE had better results. Not only are that other different methods of modification of DE and also application in various fields also reflected in [11][12][13][14][15][16][17][18].…”

Differential Evolution in Wireless Communications: A Review

“…Energy consumption optimisation is a predictor of overall network performance and remains the most important constraint. DE has been used to achieve efficient energy optimisation in WSN [110] and power allocation in orthogonal frequency division multiplexing (OFDM) systems [111] thereby decreasing the gross impact of the limited available energy [112].…”