Noisy chaotic neural network for resource allocation in high-speed train OFDMA system

Abstract

High-speed train communication system is a typical high-mobility wireless communication network. Resource allocation problem has a great impact on the system performance. However, conventional resource allocation approaches in cellular network cannot be directly applied to this kind of special communication environment. A multi-domain resource allocation strategy was proposed in the orthogonal frequency-division multiple access (OFDMA) of high-speed. By analyzing the effect of Doppler shift, sub-channels, antennas, time slots and power were jointly considered to maximize the energy efficiency under the constraint of total transmission power. For the purpose of reducing the computational complexity, noisy chaotic neural network algorithm was used to solve the above optimization problem. Simulation results showed that the proposed resource allocation method had a better performance than the traditional strategy.

Keywords

resource allocation / high-speed train / orthogonal frequency-division multiple access (OFDMA) / noisy chaotic neural network

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