877 citations · 2k across the 39 of their papers we have counts for
52 papers
Multi-User Diversity Scaling in Heavy-Tailed Fading
Yonathan Murin, Ali Özer Ercan, Nariman Farsad
Classical multi-user diversity theory predicts that throughput over Rayleigh fading channels grows as . In this work, we demonstrate a fundamental shift in this s…
Multi-Task Reinforcement Learning Enables Parameter Scaling
Reginald McLean, Evangelos Chatzaroulas, Jordan Terry +3
Multi-task reinforcement learning (MTRL) aims to endow a single agent with the ability to perform well on multiple tasks. Recent works have focused on developing novel sophisticate…
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Minttu Alakuijala, Reginald McLean, Isaac Woungang +4
Natural language is often the easiest and most convenient modality for humans to specify tasks for robots. However, learning to ground language to behavior typically requires impra…
Deep Unfolding for Iterative Stripe Noise Removal
Zeshan Fayyaz, Daniel Platnick, Hannan Fayyaz +1
The non-uniform photoelectric response of infrared imaging systems results in fixed-pattern stripe noise being superimposed on infrared images, which severely reduces image quality…
MICAL: Mutual Information-Based CNN-Aided Learned Factor Graphs for Seizure Detection from EEG Signals
Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger +2
We develop a hybrid model-based data-driven seizure detection algorithm called Mutual Information-based CNNAided Learned factor graphs (MICAL) for detection of eclectic seizures fr…
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection
Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger +2
We propose a convolutional neural network (CNN) aided factor graphs assisted by mutual information features estimated by a neural network for seizure detection. Specifically, we us…
Neural Capacity Estimators: How Reliable Are They?
Farhad Mirkarimi, Stefano Rini, Nariman Farsad
Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks and without the knowing closed form distribution of t…
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
Bahareh Salafian, Eyal Fishel Ben, Nir Shlezinger +2
We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combinin…
Neural Computation of Capacity Region of Memoryless Multiple Access Channels
Farhad Mirkarimi, Nariman Farsad
This paper provides a numerical framework for computing the achievable rate region of memoryless multiple access channel (MAC) with a continuous alphabet from data. In particular,…
Model-Based Machine Learning for Communications
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
We present an introduction to model-based machine learning for communication systems. We begin by reviewing existing strategies for combining model-based algorithms and machine lea…
A Survey on Modulation Techniques in Molecular Communication via Diffusion
Mehmet Sukru Kuran, H. Birkan Yilmaz, Ilker Demirkol +2
This survey paper focuses on modulation aspects of molecular communication, an emerging field focused on building biologically-inspired systems that embed data within chemical sign…
Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes
Dorsa Fathollahi, Nariman Farsad, Seyyed Ali Hashemi +1
Reed-Muller (RM) codes are one of the oldest families of codes. Recently, a recursive projection aggregation (RPA) decoder has been proposed, which achieves a performance that is c…
Learned Factor Graphs for Inference from Stationary Time Sequences
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed…
Capacities and Optimal Input Distributions for Particle-Intensity Channels
Nariman Farsad, Will Chuang, Andrea Goldsmith +6
This work introduces the particle-intensity channel (PIC) as a model for molecular communication systems and characterizes the capacity limits as well as properties of the optimal…
Data-Driven Symbol Detection via Model-Based Machine Learning
Nariman Farsad, Nir Shlezinger, Andrea J. Goldsmith +1
The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols an…
Data-Driven Factor Graphs for Deep Symbol Detection
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
Many important schemes in signal processing and communications, ranging from the BCJR algorithm to the Kalman filter, are instances of factor graph methods. This family of algorith…
Deep Neural Network Symbol Detection for Millimeter Wave Communications
Yun Liao, Nariman Farsad, Nir Shlezinger +2
This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed. In particular, we consider a sliding bidi…
Low Noise Non-Linear Equalization Using Neural Networks and Belief Propagation
Etsushi Yamazaki, Nariman Farsad, Andrea Goldsmith
Nonlinearities can be introduced into communication systems by the physical components such as the power amplifier, or during signal propagation through a nonlinear channel. These…
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not requi…
Two-Way Molecular Communications
Jong Woo Kwak, H. Birkan Yilmaz, Nariman Farsad +2
For nano-scale communications, there must be cooperation and simultaneous communication between nano devices. To this end, in this paper we investigate two-way (a.k.a. bi-direction…
Diffusive Molecular Communications with Reactive Molecules: Channel Modeling and Signal Design
Vahid Jamali, Nariman Farsad, Robert Schober +1
This paper focuses on molecular communication (MC) systems using two types of signaling molecules which may participate in a reversible bimolecular reaction in the channel. The mot…
Communication System Design and Analysis for Asynchronous Molecular Timing Channels
Nariman Farsad, Yonathan Murin, Weisi Guo +3
Two new asynchronous modulation techniques for molecular timing (MT) channels are proposed. One based on modulating information on the time between two consecutive releases of indi…
Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems
Nariman Farsad, Andrea Goldsmith
The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel. However, in some syste…
Deep Learning for Joint Source-Channel Coding of Text
Nariman Farsad, Milind Rao, Andrea Goldsmith
We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and…