Link Performance Enhancements

2004 ◽  
pp. 397-423
Author(s):  
Riku Pirhonen ◽  
Matti Salmenkaita ◽  
Timo Kähkönen ◽  
Mikko Säily
Keyword(s):  
Link Performance

Analysis of graded-index polymer optical fiber link performance under fiber bending

2005 ◽  
Vol 23 (6) ◽  
pp. 2062-2072 ◽  
Author(s):  
K. Makino ◽  
T. Nakamura ◽  
T. Ishigure ◽  
Y. Koike
Keyword(s):  
Optical Fiber ◽  
Polymer Optical Fiber ◽  
Graded Index ◽  
Link Performance ◽  
Fiber Link

scholarly journals Variable Link Performance Due to Weather Effects in a Long-Range, Low-Power LoRa Sensor Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3128
Author(s):  
Thomas Ameloot ◽  
Patrick Van Torre ◽  
Hendrik Rogier
Keyword(s):  
Sensor Networks ◽  
Low Power ◽  
Sensor Network ◽  
Link Quality ◽  
Long Distance ◽  
Received Power ◽  
Link Performance ◽  
Weather Changes ◽  
Minimal Power Consumption ◽  
Seasonal Weather

When aiming for the wider deployment of low-power sensor networks, the use of sub-GHz frequency bands shows a lot of promise in terms of robustness and minimal power consumption. Yet, when deploying such sensor networks over larger areas, the link quality can be impacted by a host of factors. Therefore, this contribution demonstrates the performance of several links in a real-world, research-oriented sensor network deployed in a (sub)urban environment. Several link characteristics are presented and analysed, exposing frequent signal deterioration and, more rarely, signal strength enhancement along certain long-distance wireless links. A connection is made between received power levels and seasonal weather changes and events. The irregular link performance presented in this paper is found to be genuinely disruptive when pushing sensor-networks to their limits in terms of range and power use. This work aims to give an indication of the severity of these effects in order to enable the design of truly reliable sensor networks.

scholarly journals Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance

Photonics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 19
Author(s):  
Muhammad Hadi ◽  
Muhammad Awais ◽  
Mohsin Raza ◽  
Kiran Khurshid ◽  
Hyun Jung
Keyword(s):  
Neural Network ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Radio Over Fiber ◽  
Error Vector Magnitude ◽  
Computational Overhead ◽  
Link Performance ◽  
Modulation Signal ◽  
Experimental Findings ◽  
Memory Polynomial

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance.

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