Research Interests

Teaching

Basic course:
Introduction to information and coding theory (361-2-6381)  Information measures, compression, typical sets, channel coding, MIMO.
Machine Learning:  Deep Learning  supervised/unsupervised/reinforcement learning, MAP, ML, KNN, Neural Networks, Back-propagation, regulaziation, dropout, aouto encoder, RBM, CNN, RNN
Reinforcement Learning: Markov Decision Processes, Dynamic Programming, Reinforcement learning setting,  
Advanced Wireless Communication : Advanced topics in wireless communications and related techniques.

Advanced course:
Multi-User Information Theory 1 : Entropy of Markov processes, Gambling,  Feedback, MAC,  States (compound, causal, non causal)
Multi-User Information Theory 2 : Methods of types, Rate distorstion, Coordination, Broadcast, Relay
Multi-User Information Theory 3 : Methods of types, Relay, Convex Optimization
Multi-User Information Theory 4 : Methods of types, Broadcast, degraded message set, Slepien-Wolf, Intereference channel


Students who are intersted in Machine learning or Information theory or in the combintion of math and engineering are welcome to contact me for research position. 

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Machine Learning
H. Permuter, J.M. Francos and I. Jermyn “A study of Gaussian mixture models of color and texture features for image classification and segmentation,” Pattern Recognition vol. 39, pp. 695-706, February 2006. (Conf. version)

Z. Aharoni, G. Rattner, H. Permuter  “Gradual Learning of Deep Recurrent Neural Networks,” Arxiv 2017,  CSCML (Cyber Security Cryptography and Machine Learning) 2018.

R. Shoham, H. Permuter  “Highway State Gating for Recurrent Highway Networks: improving information flow through time,” Arxiv 2018, CSCML (Cyber Security Cryptography and Machine Learning) 2018.

R. Shoham, H. Permuter  “Amended Cross Entropy Cost: Framework For Explicit Diversity Encouragement,” Arxiv 2020,  short version appeared in CSCML (Cyber Security Cryptography and Machine Learning) 2019.

D. Tsur, Z. Aharoni, Z. Goldfeld and H. Permuter,  “Neural Estimation and Optimization of Directed Information over Continuous Spaces,”   IEEE Trans. Info. Theory, 2023

D. Tsur, Z. Aharoni, Z. Goldfeld and H. Permuter,  “Data-Driven Optimization of Directed Information over Discrete Alphabets”  submitted to IEEE Trans. Info. Theory, 2023

Machine Learning for Communication
Z. Aharoni, O. Sabag, and H. Permuter,  “Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning.,”  ISIT 2019, Paris, France. Best student paper finalist.  [Slides]

O. Sholev, H. H. Permuter, E. Ben-Dror and W. Liang, "Neural Network MIMO Detection for Coded Wireless Communication with Impairments," 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), 2020,

Z. Aharoni, D. Tsur, Z. Goldfeld and H. Permuter,  “Capacity of Continuous Channels with Memory via Directed Information Neural Estimator.,”  ISIT 2020, LA, California.  [Slides]

Z. Aharoni, O. Sabag, and H. Permuter,  “Reinforcement Learning Evaluation and Solution for the Feedback Capacity of the Ising Channel with Large Alphabet,”   IEEE Trans. Info. Vol. 68, Theory, 2022.

B. Marinberg, H. H. Permuter, E. Ben-Dror, "A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks," 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, 2020.

Y. Huleihel, E. Ben-Dror,  H. H. Permuter, "Low PAPR Waveform Design for OFDM Systems Based on Convolutional Autoencoder," 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, 2020, Best Paper Award in Women in Engineering.

Y. Huleihel,  H. H. Permuter, "Low PAPR MIMO-OFDM Design Based on Convolutional Autoencode," submitted to IEEE tran on communication 2023.

Machine Learning for Polar Codes
Z. Aharoni, B Huleihel, H. Pfister, and H. Permuter,  “Data-Driven Neural Polar Codes for Unknown Channels With and Without Memorys,”  submitted to IEEE Trans. Info. Theory, 2023

Machine Learning for Routing
 Shahaf Yamin, Haim H. Permuter:  “Multi-agent reinforcement learning for network routing in integrated access backhaul networks,” Ad Hoc Networks 153, 2024

Machine Learning for Speaker Recognition
 A. Cohen, I. Rimon, E. Aflalo, H. H. Permuter:  “A Study On Data Augmentation In Voice Anti-Spoofing,” Speech Communication 56-67, June 2022

Machine Learning for Radars
Y. D. Dadon, S. Yamin, S. Feintuch, H. H. Permuter, I. Biliky, J. Taberkian,  “Moving Target Classification Based on micro-Doppler Signatures Via Deep Learning.,”  IEEE Radar Conference 2021.

S. Feintuch, H. H. Permuter, I. Bilik, J. Taberkian,  “Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter,”   IEEE Trans Aerospace and Electronic Systems. 2022.

S. Feintuch, H. H. Permuter, I. Bilik, J. Taberkian,  “Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference,”  IEEE Trans Aerospace and Electronic Systems. 2023.

Cooperative communication
H. Permuter,  S. Shamai, and A. Somekh-Baruch,   “Message and state cooperation in multiple access channels,” IEEE Trans. Info. Theory Vol. 57, pp 6379-6396,  2011.[Slides]

H. Permuter and H. Asnani  “Multiple Access Channel with Partial and Controlled Cribbing Encoders,” IEEE Trans. Info. Theory Vol. 59, pp  2252-2266 , 2013.[Slides]

T. Kopetz , H. Permuter and S. Shamai,   “Multiple Access Channels with Combined Cooperation and Partial Cribbing,” IEEE Trans. Info. Theory Vol. 62, pp  825-848 , 2016

L. Dikstein , H. Permuter and Y. Steinberg,   “On State Dependent Broadcast Channels with Cooperation,”  IEEE Trans. Info. Theory Vol. 62, pp  2308-2323, 2016 

Z. Goldfeld , H. Permuter and G. Kramer,   “Semi-Deterministic Broadcast Channels with Cooperation and a Dual Source Coding Problem,” IEEE Trans. Info. Theory Vol. 62, pp  2285-2307, 2016 [Slides] 

R. Kolte, A. Özgür and H. Permuter,   “Cooperative Binning for Semideterministic Channels,”  IEEE Trans. Info. Theory Vol. 62, pp  1231-1249 , 2016. [Slides] 

Network coding
M. Lvov and H. Permuter,  “Initialization Algorithms For Convolutional Network Coding,”  IEEE Trans. Info. Theory Vol. 64, pp  5277-5295 , 2018 [Slides]

N. Voskoboynik, H. Permuter, A. Cohen, "Network Coding Schemes for Data Exchange
Networks With Arbitrary Transmission Delays,"  IEEE/ACM Transactions on Networking 25(3): 1293-1309 (2017) 

State information
U. Basher, A. Shirazi and H. Permuter, “Capacity Region of Finite State Multiple-Access Channel with Delayed State Information at the Transmitters”   IEEE Trans. Info. Theory Vol. 58, pp 189-206, 2012. [Slides]

O. Simeone and H. Permuter  “Source Coding When the Side Information May Be Delayed” IEEE Trans. Info. Theory Vol. 59, pp  3607-3618 , Feb 2013.

Z. Goldfeld,  H. Permuter and B. Zaidel, “The Finite State MAC with Cooperative
Encoders and Delayed CSI” IEEE Trans. Info. Theory Vol. 60, pp  6181-6203 , Oct 2014.. [Slides]

A. Shirazi, U. Basher  and H. Permuter, “Channel Coding and Source Coding With Increased Partial Side Information”  Entropy 19, pp 467 2017.

Physical layer security
Z. Goldfeld , P. Cuff and H. Permuter, “Arbitrarily Varying Wiretap Channels With Type Constrained States,” IEEE Trans. Info. Theory Vol. 62, pp 7216-7244, 2016 

Z. Goldfeld , H. Permuter and G. Kramer,   “Broadcast Channels with Privacy Leakage Constraints,” IEEE Trans. Info. Theory Vol. 63, pp 5138-5161, 2017

Z. Goldfeld , P. Cuff and H. Permuter, “Semantic-Security Capacity for Wiretap Channels of Type II,” IEEE Trans. Info. Theory Vol. 62, pp  3863-3879, 2016

Z. Goldfeld ,G. Kramer, P. Cuff and H. Permuter, “Strong Secrecy for Cooperative Broadcast Channels,” IEEE Trans. Info. Theory Vol. 63, pp  469-495, 2017 

Z. Goldfeld , H. Permuter,   “MIMO Gaussian Broadcast Channels With Common, Private, and Confidential Messages,” IEEE Trans. Info. Theory Vol. 65, pp 2524-2544, 2019

Z. Goldfeld , H. Permuter,   “Wiretap and Gelfand-Pinsker Channels Analogy and Its Applications,” IEEE Trans. Info. Theory Vol. 65, pp 4979-4996, 2019

Communication Through Finite State Channels with Feedback:
H. Permuter, P. Cuff, B. Van Roy, and T. Weissman, “Capacity of the Trapdoor Channel with Feedback,”   IEEE Trans. Info. Theory,  July 2008 [Slides]

H. Permuter, T. Weissman and A. Goldsmith, “Finite state channels with time-invariant deterministic feedback,”  IEEE Trans. Info. Theory, Feb 2009. [Slides]

H. Permuter, J. Chen and  T. Weissman, “Capacity Region of the Finite-State Multiple Access Channel with and without Feedback,” IEEE Trans. 
Info. Theory, June 2009

B. Shrader and H. Permuter “Feedback capacity of the compound channel,”  IEEE Trans. Info. Theory, Vol 55, pp 3629 - 3644,  August 2009 [Slides]

L. Zhao and H. Permuter  “Zero-error feedback capacity via dynamic programming,”  IEEE Trans. Info. Theory. Vol. 56, June 2010. [Slides]

J. Chen, H.  Permuter,  and  T. Weissman, “Tighter Bounds on the Capacity of Finite-State Channels via Markov Set-Chains,”  IEEE Trans. Info. Theory. Vol. 56, pp 3660 - 3691, Aug. 2010 

O. Elischo and H. Permuter “Capacity and coding for the Ising Channel with Feedback,”  IEEE Trans. Info. Theory Vol. 60, pp  5138-5149 , Sep 2014.  [Slides]

H. Permuter, H. Asnani and T. Weissman, “Capacity of a POST Channel with and without Feedback, ”IEEE Trans. Info. Theory Vol. 60, pp  6067 , Oct 2014. [Slides]

Oron Sabag,  H. Permuter and N. Kashyap,  “Capacity of the Binary Erasure Channel With a No-Consecutive-Ones Input Constraint,” IEEE Trans. Info. Theory Vol. 62, pp  8-22,  2016 [Slides]

O. Sabag,  H. Permuter and H. Pfister,  “A Single-Letter Upper Bound on the Feedback Capacity of Unifilar Finite-State Channels,” IEEE Trans. Info. Theory Vol. 63, pp  1392-1409,  2017 [Slides]

O. Sabag,  H. Permuter and N. Kashyap,  “Feedback capacity and coding for the BIBO channel with a no-repeated-ones input constraint,”  IEEE Trans. Info. Theory , 2018.

O. Peled,   O. Sabag, and  H. Permuter,  “Feedback Capacity and Coding for the (0, k)-RLL Input-Constrained BEC,”  IEEE Trans. Info. Theory , Vol. 65, pp  4097-4114,  2019.

B. Huleihel,   O. Sabag, and  H. Permuter, N. Kashya, S. Shama “Computable Upper Bounds on the Capacity of Finite-State Channels,”  IEEE Trans. Info. Theory , Vol. 67, pp  5674-5692,  2021.

E. Shmuel,   O. Sabag, and  H. Permuter “The Feedback Capacity of Noisy Output is the STate (NOST) Channels”  IEEE Trans. Info. Theory , Vol. 68, pp  5044-5059,  2022

E. Shmuel,   O. Sabag, and  H. Permuter “Finite-State Channels with Feedback and State Known at the Encoders”  accepted to IEEE Trans. Info. Theory ,  2023

B. Huleihel,   O. Sabag, and  H. Permuter, V. Kostina “Capacity of Finite-State Channels With Delayed Feedback,”  IEEE Trans. Info. Theory , Vol. 67, pp  5674-5692,  2024.

Coordination and Rate Distortion:
T. M. Cover and H. Permuter,  “Capacity of Coordinated Actions,”  ISIT 2007, Nice, France.[Slides]

P. Cuff, H. Permuter, T. M. Cover. “Coordination Capacity.” IEEE Trans. Info. Theory. Vol. 56, Sep. 2010.

H. Permuter, Y. Steinberg and  T. Weissman, “Two-way source coding with a helper,” IEEE Trans. Info. Theory, Vol. 56, pp 2905 - 2919, June 2010 [Slides]

J. Wang, J. Chen, L. Zhao, P. Cuff, H. Permuter, “On the Role of the Refinement Layer in Multiple Description Coding and Scalable Coding”  IEEE Trans. Info. Theory, Vol. 57, pp 1443 - 1456, March 2011 [Slides]

H. Permuter and T. Weissman, “Cascade and Triangular Source Coding with Side Information at the First Two Nodes ” IEEE Trans. Info. Theory. Vol. 58, pp 3339-3349, 2012..[Slides]

Y.K. Chia,  H. Permuter and T. Weissman, “Cascade, Triangular and Two Way Source Coding with 
degraded side information at the second user”  IEEE Trans. Info. Theory, Vol. 58, pp 189 -206, Jan 2012

Causal conditioning, Directed information, Estimation and Portfolio Theory:
H. Permuter, Y.-H Kim and T. Weissman, “Interpretations of Directed Information in Portfolio Theory, Data Compression, and Hypothesis Testing” IEEE Trans. Info. Theory, Vol. 57, pp 3248 -3259,  2011. [Slides]

I. Naiss and H. Permuter, “Extension of the Blahut-Arimoto Algorithm for Maximizing Directed Information”  IEEE Trans. Info. Theory, Vol. 59, pp 204 -222,  2013.[Slides]

I. Naiss and H. Permuter, “Computable Bounds for Rate Distortion with Feed-Forward for Stationary and Ergodic Sources” IEEE Trans. Info. Theory, Vol. 59, pp 760 -781,  2013.

Y. -H Kim,  H. Permuter and T. Weissman, “Directed Information, Causal Estimation, and Communication in Continuous Time,”  IEEE Trans. Info. Theory, Vol. 59, pp 1271-1287,  2013.[Slides]

J. Jiantao, H. H. Permuter, Z. Lei,  Y.-H Kim and T. Weissman, “Universal Estimation of Directed Information” IEEE Trans. Info. Theory, Vol. 59, pp 6220-6242,  2013. [code] [Slides]

Actions in communication 
T. Weissman and H. Permuter , “Source Coding with a Side Information “Vending Machine” ”  IEEE Trans. Info. Theory 

Vol. 57, pp 4530-4544,  2011

H. Asnani,  H. Permuter and T. Weissman, “Probing Capacity” IEEE Trans. Info. Theory Vol. 57, pp 7317-7332,  2011.

L. Dikstein,  H. Permuter and ,  S. Shamai,, “MAC with Action-Dependent State Information at One Encoder” 
IEEE Trans. Info. Theory Vol. 61, pp 173-188,  2015.. [Slides]

H. Asnani,  H. Permuter and T. Weissman, “To Feed or Not to Feed Back” IEEE Trans. Info. Theory Vol. 60, pp 5150-5172, 2014

B Ahmadi,  H. Asnani,  O. Simeone and H. Permuter , “Information Embedding on Actions” IEEE Trans. Info. Theory Vol. 60, pp 6902-6916, 2014.

O. Sabag,  H. Permuter and  A. Cohen,  “Lossless Coding of Correlated Sources with Action,” IEEE Trans. Info. Theory Vol. 63, pp  469-495, 2017 

Interference channel 
R. Kolte, A. Özgür and H. Permuter, “Multicoding Schemes for Interference Channels,” IEEE Trans. Info. Theory Vol. 62, pp  4936-4952,  2016 [Slides] 

Energy Harvesting Wireless Communications 
D. Shaviv, A. Özgür and H. Permuter,   “Capacity of Remotely Powered Communicatio,” IEEE Trans. Info. Theory Vol. 63, pp  469-495, 2017

D. Shaviv, A. Özgür and H. Permuter,   “A Communication Channel with Random Battery Recharges,” IEEE Trans. Info. Theory Vol. 64, pp  38-56, 2018

Source coding with cooperation/cribbing
H. Asnani,  H. Permuter and T. Weissman, “Successive Refinement with Decoder Cooperation and its Channel Coding Duals”  IEEE Trans. Info. Theory Vol. 59, pp 5511-5533, 2013.

Relation between Statistical Physics and Information Theorey
D. A. Vinkler, H. Permuter and N. Merhav, “Analogy Between Gambling and Measurement-Based Work Extraction”  Journal of Statistical Mechanics: Theory and Experiment, 2016. [Slides]

Image and signal processing:
H. Permuter and J.M. Francos , “Estimating the orientation of planar Surfaces: Algorithms and Bounds.” IEEE Trans. Info. Theory, vol. 46 pp. 1908-1920, August 2000.

J.M. Francos and H. Permuter, “Parametric Estimation of Orientation of Textured Planar Surfaces,” IEEE Trans. Image Process., vol. 10, pp. 403-418, March 2001. [ Slides]

H. Permuter, J.M. Francos and I. Jermyn “A study of Gaussian mixture models of color and texture features for image classification and segmentation,” Pattern Recognition vol. 39, pp. 695-706, February 2006. (Conf. version)

 

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FME-IT package for Mat-Lab: Fourier-Motzkin elimination (FME) algorithm for information theoretic inequalities. This package combines the FME procedure together with an information theoretic inequality prover (ITIP). It was written primarily by Ido B. Gattegno.

Blahut-Arimoto Algorithm for optimizing the the directed information: we provide algorithm based on Blahut-Arimoto for maxmizing the dircted information for computing  channel capacity with feedback and and minimizng the directed information for computing the rate distorstion with feedforward. this code was written in c by Iddo Naiss.

Universal Estimation of Directed Information: The software is a MATLAB package that can calculate the directed information and mutual information between any two input sequences. It uses the universal sequential probability assignment induced by Context-Tree Weighting Method, and has desirable convergence properties.

DINE: Directed Information Estimation by Ziv Aharoni, Dor Tsur and Ziv Goldfeld.

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IEEE Information theory society, Information Theory student resources, ISIT 2020, COCO 2020, Machine Learning for Communication MLCOM 2020,  movie on Shannon, Information theory on wikipedia. Wiki Page of our group, Youtube videos of our lab 

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Haim Permuter received his B.Sc. (summa cum laude) from Ben-Gurion University (BGU) and Ph.D. from Stanford University, both in in Electrical Engineering, in 1997 and 2008, respectively.  Between 1997-2004, he served as a scientific research officer in an R&D unit in the Israeli Defense Forces. In summer 2002 he worked for IBM, Almaden research center. He is a recipient of several rewards including Eshkol Fellowship, Wolf Award,  Fulbright Fellowship,  Stanford Graduate Fellowship, U.S.-Israel Binational Science Foundation Bergmann Memorial Award, and Allon Fellowship.  Haim joined the faculty of Electrical Engineering Department at BGU in Oct 2008 as a tenure-track faculty, and is now a  Professor, Luck-Hille Chair in Electrical Engineering. Haim also serves as head of the communication, cyber and information track in his department. Haim served on the editorial boards of the IEEE Transactions on Information Theory in 2013-2016.