SIMULATION OF ADAPTIVE CHANNEL EQUALIZATION FOR BPSK,QPSK AND 8-PSK SCHEMES

The distortion and inter symbol interference caused by multipath effects of channel degrades the quality of signal transmission in transmission system of digital baseband. Adaptive channel equalization is used commonly to compensate these effects so as to increase the reliability of propagation. Recursive Least Squares (RLS) algorithm is most commonly used adaptive algorithm because of its simplicity and fast convergence. In this work, simulation model of finite impulse response adaptive equalizer based on RLS is developed to reduce distortion caused by channel. The constellation diagram before and after equalization is obtained. It is observed that bit error rate is decreased by fifty percent after equalization. Hence this shows that the algorithm appears to reduce channel effects effectively and achieves channel equalization

A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects

Equalization and channel decoding are “traditionally” two cascade processes at the receiver side of a digital transmission. They aim to achieve a reliable and efficient transmission. For high data rates, the energy consumption of their corresponding algorithms is expected to become a limiting factor. For mobile devices with limited battery’s size, the energy consumption, mirrored in the lifetime of the battery, becomes even more crucial. Therefore, an energy-efficient implementation of equalization and decoding algorithms is desirable. The prevailing way is by increasing the energy efficiency of the underlying digital circuits. However, we address here promising alternatives offered by mixed (analog/digital) circuits. We are concerned with modeling joint equalization and decoding as a whole in a continuous-time framework. In doing so, continuous-time recurrent neural networks play an essential role because of their nonlinear characteristic and special suitability for analog very-large-scale integration (VLSI). Based on the proposed model, we show that the superiority of joint equalization and decoding (a well-known fact from the discrete-time case) preserves in analog. Additionally, analog circuit design related aspects such as adaptivity, connectivity and accuracy are discussed and linked to theoretical aspects of recurrent neural networks such as Lyapunov stability and simulated annealing

Integrated Transversal Equalizers in High-Speed Fiber-Optic Systems

Intersymbol interference (ISI) caused by intermodal dispersion in multimode fibers is the major limiting factor in the achievable data rate or transmission distance in high-speed multimode fiber-optic links for local area networks applications. Compared with optical-domain and other electrical-domain dispersion compensation methods, equalization with transversal filters based on distributed circuit techniques presents a cost-effective and low-power solution. The design of integrated distributed transversal equalizers is described in detail with focus on delay lines and gain stages. This seven-tap distributed transversal equalizer prototype has been implemented in a commercial 0.18-µm SiGe BiCMOS process for 10-Gb/s multimode fiber-optic links. A seven-tap distributed transversal equalizer reduces the ISI of a 10-Gb/s signal after 800 m of 50-µm multimode fiber from 5 to 1.38 dB, and improves the bit-error rate from about 10^-5 to less than 10^-12

56+ Gb/s serial transmission using duo-binary signaling

In this paper we present duobinary signaling as an alternative for signaling schemes like PAM4 and Ensemble NRZ that are currently being considered as ways to achieve data rates of 56 Gb/s over copper. At the system level, the design includes a custom transceiver ASIC. The transmitter is capable of equalizing 56 Gb/s non-return to zero (NRZ) signals into a duobinary response at the output of the channel. The receiver includes dedicated hardware to decode the duobinary signal. This transceiver is used to demonstrate error-free transmission for different PCB channel lengths including a state-of-the-art Megtron 6 backplane demonstrator

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe