Performance and complexity analysis using a sparse deep learning method for indoor terahertz transmission.

In this Letter, we propose and experimentally validate a sparse deep learning method (SDLM) for terahertz indoor wireless-over-fiber by transmitting a 16-quadrature amplitude modulation (QAM) orthogonal frequency-division multiplexing (OFDM) signal over a 15-km single-mode fiber (SMF) and a wireless link distance of 60 cm at 135 GHz through a cost-effective intensity-modulated direct detection (IM-DD) communications system. The proposed SDLM imposes the L1-regularized mechanism on the cost function, which not only improves performance but also reduces complexity when compared with traditional Volterra nonlinear equalizer (VNLE), sparse VNLE, and conventional DLM. Our experimental findings show that the proposed SDLM provides viable options for successfully mitigating nonlinear distortions and outperforms conventional VNLE, conventional DLM, and SVNLE with a 76%, 72%, and 61% complexity reduction, respectively, for 8-QAM without losing signal integrity.

Computation efficient sparse DNN nonlinear equalization for IM/DD 112 Gbps PAM4 inter-data center optical interconnects.

In this Letter, we propose and experimentally demonstrate a novel, to the best of our knowledge, sparse deep neural network-based nonlinear equalizer (SDNN-NLE). By identifying only the significant weight coefficients, our approach remarkably reduces the computational complexity, while still upholding the desired transmission accuracy. The insignificant weights are pruned in two phases: identifying the significance of each weight by pre-training the fully connected DNN-NLE with an adaptive L2-regularization and then pruning those insignificant ones away with a pre-defined sparsity. An experimental demonstration is conducted on a 112 Gbps PAM4 link over 40 km standard single-mode fiber with a 25 GHz externally modulated laser in O-band. Our experimental results illustrate that, for the 112 Gbps PAM4 signal at a received optical power of -5dBm over 40 km, the proposed SDNN-NLE exhibits promising solutions to effectively mitigate nonlinear distortions and outperforms a conventional fully connected Volterra equalizer (VE), conventional fully connected DNN-NLE, and sparse VE by providing 71%, 63%, and 41% complexity reduction, respectively, without degrading the system performance.

Reducing computation complexity by using elastic net regularization based pruned Volterra equalization in a 80 Gbps PAM-4 signal for inter-data center interconnects.

Volterra equalization (VE) presents substantial performance enhancement for high-speed optical signals but suffers from high computation complexity which limits its physical implementations. To address these limitations, we propose and experimentally demonstrate an elastic net regularization-based pruned Volterra equalization (ENPVE) to reduce the computation complexity while still maintain system performance. Our proposed scheme prunes redundant weight coefficients with a three-phase configuration. Firstly, we pre-train the VE with an adaptive EN-regularizer to identify significant weights. Next, we prune the insignificant weights away. Finally, we retrain the equalizer by fine-tuning the remaining weight coefficients. Our proposed ENPVE achieves superior performance with reduced computation complexity. Compared with conventional VE and L1 regularization-based Volterra equalizer (L1VE), our approach show a complexity reduction of 97.4% and 20.2%, respectively, for an O-band 80-Gbps PAM4 signal at a received optical power of -4 dBm after 40 km SMF transmission.