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Realizing Green Symbol Detection via Reservoir Computing: An Energy-Efficiency Perspective | IEEE Conference Publication | IEEE Xplore

Realizing Green Symbol Detection via Reservoir Computing: An Energy-Efficiency Perspective


Abstract:

Reservoir Computing (RC) is a class of machine learning approaches that is suitable for prediction tasks with low computational complexity. In this paper, an RC-based sym...Show More

Abstract:

Reservoir Computing (RC) is a class of machine learning approaches that is suitable for prediction tasks with low computational complexity. In this paper, an RC-based symbol detection for MIMO- OFDM systems is presented where RC is realized through the echo state network (ESN). Detailed energy-efficiency analysis is conducted to characterize the energy-efficiency of the introduced symbol detector. To be specific, the transmit power, the circuit power, and the computational power at both transmitter and receiver are jointly considered for the energy-efficiency analysis. The overall system energy-efficiency as well as the receiver energy-efficiency of the introduced RC-based symbol detector are compared with those of the popular linear minimum mean squared error (LMMSE)-based approach. Simulation and numerical results show that the RC-based symbol detector is a ``green'' solution compared to the traditional LMMSE-based method with lower energy consumption per information bit.
Date of Conference: 20-24 May 2018
Date Added to IEEE Xplore: 30 July 2018
ISBN Information:
Electronic ISSN: 1938-1883
Conference Location: Kansas City, MO, USA

References

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