Nariman Farsad

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Stanford University

Model-Based Machine Learning for Communications

We present an introduction to model-based machine learning for communica...
01/12/2021 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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A Survey on Modulation Techniques in Molecular Communication via Diffusion

This survey paper focuses on modulation aspects of molecular communicati...
11/25/2020 ∙ by Mehmet Şükrü Kuran, et al. ∙ 0 ∙ share

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Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes

Reed-Muller (RM) codes are one of the oldest families of codes. Recently...
11/25/2020 ∙ by Dorsa Fathollahi, et al. ∙ 0 ∙ share

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Inference from Stationary Time Sequences via Learned Factor Graphs

The design of methods for inference from time sequences has traditionall...
06/05/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Capacities and Optimal Input Distributions for Particle-Intensity Channels

This work introduces the particle-intensity channel (PIC) as a model for...
05/20/2020 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Data-Driven Symbol Detection via Model-Based Machine Learning

The design of symbol detectors in digital communication systems has trad...
02/14/2020 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Data-Driven Factor Graphs for Deep Symbol Detection

Many important schemes in signal processing and communications, ranging ...
01/31/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Deep Neural Network Symbol Detection for Millimeter Wave Communications

This paper proposes to use a deep neural network (DNN)-based symbol dete...
07/25/2019 ∙ by Yun Liao, et al. ∙ 0 ∙ share

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ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Symbol detection plays an important role in the implementation of digita...
05/26/2019 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Low Noise Non-Linear Equalization Using Neural Networks and Belief Propagation

Nonlinearities can be introduced into communication systems by the physi...
05/13/2019 ∙ by Etsushi Yamazaki, et al. ∙ 0 ∙ share

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Diffusive Molecular Communications with Reactive Molecules: Channel Modeling and Signal Design

This paper focuses on molecular communication (MC) systems using two typ...
02/27/2019 ∙ by Vahid Jamali, et al. ∙ 0 ∙ share

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Communication System Design and Analysis for Asynchronous Molecular Timing Channels

Two new asynchronous modulation techniques for molecular timing (MT) cha...
12/08/2018 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems

The design and analysis of communication systems typically rely on the d...
02/19/2018 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Deep Learning for Joint Source-Channel Coding of Text

We consider the problem of joint source and channel coding of structured...
02/19/2018 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Neural Network Detection of Data Sequences in Communication Systems

We consider detection based on deep learning, and show it is possible to...
01/31/2018 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Exploiting Diversity in Molecular Timing Channels via Order Statistics

We study diversity in one-shot communication over molecular timing chann...
01/17/2018 ∙ by Yonathan Murin, et al. ∙ 0 ∙ share

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Detection Algorithms for Communication Systems Using Deep Learning

The design and analysis of communication systems typically rely on the d...
05/22/2017 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications

This work presents a new multi-chemical experimental platform for molecu...
04/16/2017 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Nariman Farsad

Featured Co-authors

Yonina C. Eldar

Robert Schober

Muriel Medard

Nir Shlezinger

Vahid Jamali

Weisi Guo

Andrea J. Goldsmith

Andrea Goldsmith

Seyyed Ali Hashemi

Marco Mondelli

Chan-Byoung Chae

Model-Based Machine Learning for Communications

A Survey on Modulation Techniques in Molecular Communication via Diffusion

Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes

Inference from Stationary Time Sequences via Learned Factor Graphs

Capacities and Optimal Input Distributions for Particle-Intensity Channels

Data-Driven Symbol Detection via Model-Based Machine Learning

Data-Driven Factor Graphs for Deep Symbol Detection

Deep Neural Network Symbol Detection for Millimeter Wave Communications

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Low Noise Non-Linear Equalization Using Neural Networks and Belief Propagation

Diffusive Molecular Communications with Reactive Molecules: Channel Modeling and Signal Design

Communication System Design and Analysis for Asynchronous Molecular Timing Channels

Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems

Deep Learning for Joint Source-Channel Coding of Text

Neural Network Detection of Data Sequences in Communication Systems

Exploiting Diversity in Molecular Timing Channels via Order Statistics

Detection Algorithms for Communication Systems Using Deep Learning

A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications