Neural networks are shown to be a viable implementation for joint channel equalisation and symbol detection in a vehicular network. Experimental results using a hardware-in-the-loop approach at 5.9GHz validate the efficacy of the proposed implementation following the 802.11p parameters using orthogonal frequency division multiplexing (OFDM). Further results are obtained using a more spectrally efficient waveform, namely spectrally efficient frequency division multiplexing (SEFDM), to show a trade-off between loss of orthogonality, and therefore bit-error rate (BER) performance, versus increased spectral efficiency to enable higher data rates or the ability to service more users. SEFDM is tested with compression factors ranging from 20% up to 60% bandwidth compression. The results show the neural network is able to achieve an acceptable BER performance in a highway non-line-of-sight (NLOS) channel which is a well established harsh and dynamic vehicular channel. This is further validated via measurements of the error vector magnitude.
Neural Network Equalisation and Symbol Detection for 802.11p V2V Communication at 5.9GHz
2020-05-01
346461 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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