CNN Equalizer for OFDM with Subcarrier Power Modulation – PDF Manuscript

28 £

You get the full PDF  (7 double column pages) of the article titled “Convolutional Neural Network Based Equalizer for Improving the Reliability Performance of OFDM with Sub-carrier Power Modulation.

  Contact us
Category: Tags: , , , ,


Convolutional Neural Network Based Equalizer for Improving the Reliability Performance of OFDM with Sub-carrier Power Modulation.

Summary: As the demand for higher data rates has rapidly been increasing day after day, researchers around the world have given serious attention and made significant efforts towards exploring new techniques that can improve the spectral efficiency of future wireless systems. Among these methods, the modulation technique named as orthogonal frequency division multiplexing with subcarrier power modulation (OFDM-SPM) is considered as a key potential candidate transmission method, which has the potential to effectively improve the per-user spectral efficiency of wireless networks. However, the reliability performance efficiency of OFDM-SPM is not that high, where it was found that the additional data stream conveyed by sub-carriers’ power has higher bit error rate (BER) performance compared to the data stream conveyed by conventional modulation schemes. To improve the reliability performance of  OFDM-SPM furthermore, in this paper, we propose the use of Convolutional Neural Networks (CNN) based equalizer for OFDM-SPM. Simulation results show that Convolutional Neural Networks (CNN) based equalizer can improve the reliability performance of OFDM-SPM by 5-10 dB compared to conventional OFDM-SPM scheme that does not use CNN.


ofdm spm cnn1

ofdm spm cnn2 

Refund Policy

We firmly believe in and stand behind our product 100%, but we understand that it might not meet the needs of everyone all of the time. If you are unhappy with your purchase, or you have an issue that we are unable to resolve that makes the system unusable, we will be happy to consider offering a refund.
  • Refunds will be offered at our sole discretion and must meet all of the following conditions fully:
  • You are within the first 24 hours of the purchase of the product.
  • Your issue(s) comes from not being able to install the product properly or get the product to perform its basic functions.
  • You have attempted to resolve your issue(s) with our support team.
  • No refunds will be granted after the first 24 hours of the original purchase whatsoever.
  • Issues caused by 3rd party products, or other software will not provide grounds for a refund.
  • Refund requests for renewal subscription orders will not be entertained.

*PS: RS Products’s purchased with discount or sale offer are non-refundable

General Inquiries

There are no inquiries yet.