Propagation impairments due to Raman effect on the coexistence of GPON, XG-PON, RF-video and TWDM-PON

We analyze propagation effects in the coexistence of GPON, XG-PON, RF-Video and TWDM-PON. We show that high power TWDM-PON channels excite Stimulated Raman Scattering inducing extra-loss on GPON due to power depletion. We address the problem through simulations and propose and validate a simple analytical model for the effec

Comparison of DSP-based TDMA and FDMA channel aggregation techniques in mobile fronthauling

Abstract Cloud Radio Access Network (C-RAN) is perceived as a future essential technology to satisfy the ever-increasing demand of mobile data traffic. Considerable research efforts are expending in the optimization of C-RAN architecture. In this paper, we perform a comparison of two DSP-based fronthauling techniques for aggregation of radio waveforms: time division multiple access (TDMA) and frequency division multiple access (FDMA), in terms of error vector magnitude (EVM), spectral bandwidth efficiency and digital signal processing (DSP) complexity as a performance metrics. The two techniques are compared by means of simulation and validated experimentally on an intensity modulation and direct detection (IM-DD) optical fronthaul link capable of aggregating 48 and 96 LTE-A (20 MHz) channels. Moreover, we made simulation comparison on 24 (100 MHz) new radio (NR) waveforms which will be used in the upcoming 5G applications. We reveal that there is ∼50% and ∼20% spectral efficiency gain by TDMA aggregation on LTE-A and NR waveforms respectively. Hence TDMA gives slightly better performance in the case of 96 LTE-A channels which is attributed to slightly better linearity over the optical channel frequency response for larger number of channel. In addition, we show that TDMA is more efficient in terms of complexity than FDMA system that requires an additional pre-emphasis technique to equalize the overall per channel performance

A Multi-Rate Approach for Nonlinear Pre-Distortion Using End-to-End Deep Learning in IM-DD Systems

Modern intra-data center (IDC) interconnects leverage robust and low-cost intensity modulation (IM) and direct detection (DD) optical links, based on multimode fibers (MMFs) and vertical-cavity surface-emitting lasers (VCSELs). Current solutions, based on on-off keying (OOK) modulations, reach up to 25-50 Gbps per lane over nearly 100 meters. The actual target for IDCs is to increase VCSEL-MMF links capacity up to 100 Gbps, using PAM-4 on the same devices. To counteract the consequent linear and nonlinear distortions affecting the transmitted signals, an effective solution is to exploit digital signal processing (DSP). In this manuscript, we propose a novel method to optimize a nonlinear artificial neural network (ANN) digital pre-distorter (DPD), based on End-to-end (E2E) learning, that, trained jointly with a Feed-Forward Equalizer (FFE), fulfills physical amplitude constraints and handles different ratio between the sampling rates incurring along with an optical IM-DD system. We indeed propose an E2E ANN system operating simultaneously at different sampling frequencies. We moreover propose in our training method a substitution to the time-domain injection of the receiver noise in the system with an additive regularization term in the FFE gradient loss. We experimentally show the advantages of our proposed DPD comparing the bit error rate (BER) performance against the same scenario without DPD. We assess the gain in terms of Gross Bit Rate and Optical Path Loss (OPL), at given BER targets, for different fiber lengths

Integrated assessment of the EU’s greening reform and feed self-sufficiency scenarios on dairy farms in Piemonte, Italy

Specialised dairy farms are challenged to be competitive and yet respect environmental constrains. A tighter integration of cropping and livestock systems, both in terms of feed and manure flows, can be beneficial for the farm economy and the environment. The greening of the direct payments, which was introduced in the European Union’s greening reform in 2013, is assumed tostimulate the transition towards more sustainable systems. The aim of this study was to quantitatively assess the impacts of greening policies on important economic and environmental indicators of sustainability, and explore potential further improvements in policies. The Farm System SIMulator (FSSIM) bioeconomic farm model was used to simulate the consequences of scenarios of policy change on three representative dairy farms in Piedmont, Italy, i.e., an ‘intensive’, an ‘extensive’, and an ‘organic’ dairy farm. Results showed that in general, there is a large potential to increase the current economic performance of all of the farms. The most profitable activity is milk production, resulting in the allocation of all of the available farm land to feed production. Imposing feed self-sufficiency targets results in a larger adaptation of current managerial practice than the adaptations that are required due to the greening policy scenario. It was shown that the cropping system is not always able to sustain theactual herd composition when 90% feed self-sufficiency is imposed. Regarding the greening policies, it is shown that extensive and organic farms already largely comply with the greening constrains, and the extra subsidy is therefore a bonus, while the intensive farm is likely to sacrifice the subsidy, as adapting the farm plan will substantially reduce profit. The introduction of nitrogen (N)-fixingcrops in ecological focus areas was the easiest greening strategy to adopt, and led to an increase in the protein feed self-sufficiency. In conclusion, it is important to note that the greening policy in its current form does not lead to reduced environmental impacts. This implies that in order to improveenvironmental performance, regulations are needed rather than voluntary economic incentives