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  • 标题:Enhanced cognitive demodulation with artificial intelligence
  • 其他标题:Enhanced cognitive demodulation with artificial intelligence
  • 本地全文:下载
  • 作者:Hang Ren ; Sang-Hee Shin ; Stepan Lucyszyn
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2020
  • 卷号:10
  • 期号:1
  • 页码:1-16
  • DOI:10.1038/s41598-020-77262-0
  • 出版社:Springer Nature
  • 摘要:The low-cost ‘THz Torch’ wireless link technology is still in its infancy. Until very recently, inherent limitations with available hardware has resulted in a modest operational figure of merit performance (Range $$ imes$$ Bit Rate). However, a breakthrough was reported here by the authors, with the introduction of ‘Cognitive Demodulation’. This bypassed the thermal time constant constraints normally associated with both the thermal emitter and sensor; allowing step-change increases in both Range and Bit Rate with direct electronic modulation. This paper concentrates on advancements to the bit error rate (BER) performance. Here, separate techniques are introduced to the demodulation software that, when combined, result in enhanced Cognitive Demodulation. A factor of more than 100 improvement in BER was demonstrated within the laboratory and approximately a 60-fold improvement in a non-laboratory environment; both at the maximum Range and Bit Rate of 2 m and 125 bps, respectively, demonstrated recently. Moreover, demodulation speed is increased by almost a factor of 10,000; allowing for real-time demodulation while easing future computational hardware requirements. In addition to these software advancements, the paper demonstrates important improvements in hardware that has brought the technology out of the laboratory, with field trials being performed within an office corridor.
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