CALL FOR BOOK CHAPTERS
Book Title: Machine Learning for Wireless Communication with Simulation Illustrations
To be published in the series Signals and Communication Technology,
Springer publications
Outline of the book: The chapters in the book are broadly clustered into two parts. The first part consists of various machine learning algorithms that include Linear discriminant analysis, Linear regression, Probabilistic discriminant approach of classification, Probabilistic generative approach, Deep learning for classification and regression, Deep generative model. These algorithms are demonstrated with an aid of the simulation applied to the particular wireless communication applications. They are grouped under four chapters namely (a)Discriminant approach to wireless communication (b) Probabilistic approach to wireless communication (c) Deep learning approach to approach to wireless communication and finally Deep generative approach for wireless communication. The simulations are performed using either MATLAB or Python. The source code will also be included in the book, along with the pseudo code.
The second part consists of the survey on the usage of machine learning algorithms on various wireless communication applications. They are grouped under three chapters namely (a) Wireless networks (b) Modulation, Coding and Security (c) Broadband techniques. The survey includes the related mathematical model of the corresponding wireless model.
Topics
Part-1 Machine Learning algorithms for wireless communication
Section 1 Discriminant approach to wireless communication
Chapter 1-1 Dimensionality reduction techniques for wireless data representation
Chapter 1-2 Linear regression-based channel propagation models
Chapter 1-3 Probabilistic discriminative model (logistic regression, for wireless communication) for datafusion in wireless sensor networks
Chapter 1-4 Nearest Mean (NM), Nearest Neighbor (NN), Support Vector Machine (SVM) based classifiers for for signal classification.
Section 2: Probabilistic approach to wireless communication
Chapter 2-1 Probabilistic generative model for wireless communication
Chapter 2-2 Unsupervised clustering algorithm for wireless communication
Chapter 2-3 Gaussian Mixture Model for mixtures of noise model in cooperative localization of Wireless Sensor Networks
Chapter 2-4 Hidden Markov Model to predict error trace in wireless communication
Section 3: Deep learning approach for wireless communication
Chapter 3-1 Convolutional Neural Network for MIMO fingerprint-based positioning
Chapter 3-2 Graph Neural Network for wireless network parameter estimation
Chapter 3-3 Relation Network for wireless network
Chapter 3-4 Recursive Neural Network for MIMO-OFDM channel modelling
Chapter 3-5 Recurrent Neural Network for link quality prediction in wireless communication.
Chapter 3-6 Representation Learning for data compression
Chapter 3-7 Organizing Resource allocation using Reinforcement learning based Long Short-Term Memory Model
Section 4: Deep generative model for wireless communication
Chapter 4-1 Deep generative model for channel modelling
Chapter 4-2 Auto encoder for wireless data augmentation
Chapter 4-3 Monte-carlo methods for MIMO-OFDM detection (Gibbs sampling, Importance sampling)
Chapter 4-4 Approximate inference of the transmitted sequence for the frequency selective channel
Chapter 4-5 Generative Adversarial Network for wireless channel modelling
Part-II Wireless System model for machine learning applications
Chapter 5: Wireless Networks
Chapter 5-1 Backhaul and Fronthaul
Chapter 5-2 Cloud communications and Networking
Chapter 5-3 Communications in Wireless Networked control
Chapter 5-4 Network Localization and Navigation
Chapter 5-5 UAV Assisted Wireless Networks
Chapter 5-6 Nanoscale communication Networks
Chapter 6: Modulation, Coding and Security
Chapter 6-1 Massive MIMO
Chapter 6-2 Non-orthogonal Multiple Access
Chapter 6-3 Power line communication
Chapter 6-4 Reconfigurable Intelligent surfaces
Chapter 6-5 Optical wireless communication
Chapter 6-6 Polar coding
Chapter 6-7 Communication and Information Systems security
Chapter 7: Broad band and other related techniques
Chapter 7-1 Broadband access
Chapter 7-2 Cognitive radio
Chapter 7-3 Device-to-Device Communication
Chapter 7-4 Green communications
Chapter 7-5 Internet of Things
Chapter 7-6 Smart grid applications
Kindly submit the book chapter through Easychair https://easychair.org/my/conference?conf=mdcwc2022 for review.
For further details contact the editors of the book
Editors of the book:
Contact : esgopi@nitt.edu / nalin@tpu.ru for submitting the chapters
IEEE Senior member
Associate professor
Co-ordinator,
Pattern recognition and Computational intelligence Laboratory,
Department of Electronics and Communication Engineering,
National Institute of Technology Tiruchirappalli
E-Mail: esgopi@nitt.edu
Dr. Dush Nalin Jayakody Ph.D.(Dubin),
IEEE Senior member,
IET Fellow
Professor, School of Computer Science and Robotics
Director, Infocomm Lab,
National Research Tomsk Polytechnique University (TPU), Russia
E-Mail: nalin@tpu.ru