Abstract

In this article, a novel scheme to effectively mitigate the nonlinear impairments in a PAM-8 radio-over-fiber (ROF) delivery is proposed by a joint deep neuron network (J-DNN) equalizer, which has more superiority in terms of good training accuracy, satisfactory tracking speed, and over-fitting suppression compared with a typical deep neuron network (DNN) equalizer. Our proposed J-DNN equalization scheme is mainly based upon back-propagation (BP) algorithm and blind cascaded multi-modulus algorithm (CMMA), which can be trained via two steps including DNN initialization and DNN optimization. By using the proposed J-DNN equalizer, 60-Gbps PAM-8 signal generation and transmission over 10-km SMF and 3-m wireless link at 135-GHz can be achieved. For the digital signal processing (DSP) at receiver, comparisons between CMMA equalizer, DNN equalizer, and J-DNN equalizer are demonstrated. The results indicate that J-DNN equalizer has a much better BER performance in receiver sensitivity than the traditional CMMA, and an improvement of receiver sensitivity can be achieved as much as 1 dB compared with a DNN equalizer at the BER of 3.8 × 10−3. To the best of our knowledge, this is the first time to propose a novel joint DNN equalizer, which is promising for the development in integrated microwave photonics and microwave/millimeter-wave photonics for 5G applications and beyond.

PDF Article

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription