Abstract

Abstract Using the impulse responses of these multipath channels, the bit error rate (BER) performance for binary pulse amplitude modulation impulse radio ultra-wideband communication system is calculated. The optimization location of receiving antenna is investigated by dynamic differential evolution (DDE) and genetic algorithm (GA) to minimize the outage probability. Numerical results show that the performance for reducing BER and outage probability by DDE algorithm is better than that by GA.

Highlights

  • Ultra-wideband (UWB) transmissions can legally operate in the range from 3.1 up to 10.6 GHz at a limited transmit power of −41 dBm/MHz [1]

  • Bit error rate (BER) degradation is caused by the inter-symbol interference due to a multipath propagation made up of radio wave reflections by walls, ceilings, floors, and office fixtures

  • Based on the BER formula, the outage probability is chosen as the cost function in our optimization procedure

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Summary

Introduction

Ultra-wideband (UWB) transmissions can legally operate in the range from 3.1 up to 10.6 GHz at a limited transmit power of −41 dBm/MHz [1]. All wireless systems must be able to deal with the challenges of operating over a multipath propagation channel, where the object in the environment can cause multiple reflections. Bit error rate (BER) degradation is caused by the inter-symbol interference due to a multipath propagation made up of radio wave reflections by walls, ceilings, floors, and office fixtures. Effective antenna selection and deployment strategies are important for reducing BER in indoor wireless systems [2,3]. To the best of our knowledge, there is still no investigation using the dynamic differential evolution (DDE) and genetic algorithm (GA) to optimize the location of receiving antennas for minimizing the BER in indoor wireless communication channel. The optimization of location of receiving antenna in this paper is using DDE and GA to overcome the above situation and to minimize the outage probability. The remainder of this paper is organized as follows: In Section 2, channel modeling, system description, dynamic

Channel modeling and system description
Dynamic differential evolution
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Conclusions
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