IEEE Global Conference on Signal and Information Processing
Call for Papers for
Symposium on Machine Learning for Characterization of Cognitive
Communications and Radar
Scope and Motivation:
Over the next 3-5 years wireless communication is projected to grow exponentially, while
resources in terms of power and bandwidth will remain limited. The widening gap between
demand and available resources is emerging as one of the major challenges for all entities
sharing the electromagnetic spectrum. Cognitive Radio (CR), with its capability to sense its
environment and flexibly adjust its transceiver parameters, has established itself as an enabling
methodology for dynamic time-frequency-space resource allocation and management, offering
significant improvement of spectral utilization. However, existing cognitive radio models would
no longer be adequate, given the massive demands for capacity, connectivity, high reliability and
low latency, so novel models and algorithms are needed to help improve spectrum utilization.
A natural approach to handling these challenges is the development of a broad range of efficient
machine learning algorithms, as well as new frameworks for cooperative learning and sharing,
based on complex signal patterns in space, frequency and time. Proliferation of software defined
radio technology, as well as applications in Self-organized Networks, Machine-to-Machine
Communications, Internet of Things etc, will necessarily create even more complex
environments in which CR networks of secondary users will compete for spectrum access not
only with primary users, but also with other CR networks. Many of these dense multi-user
cognitive radio systems would be difficult to capture using conventional machine learning
models.
We recognize that characterization of cognitive communication and radar is emerging as a topic
area with rich potential, high relevance and broad applicability for machine learning research and
development. The goal of this Symposium is to bring together researchers from the cognitive
communication and machine learning communities, to showcase state-of-the-art machine
learning approaches to CR network problems, and to provide a forum for discussing ideas to
address present and future challenges using novel synergistic approaches.
Main Topics of interest
We are soliciting original and unpublished papers on topics including, but not limited to:
• Learning in partially observable RF environments
• Multi-agent learning in distributed cognitive radio networks
• Machine learning for cooperative spectrum sensing
• Autonomous learning in unknown RF environments
• Distributed learning techniques for cognitive radio networks
• Characterization of multi-dimensional activity dynamics of CRNs
• Machine learning of the topology and structural properties of CRNs
• Quality of learning with corrupted, censored and missing spectrum sensing samples
• Joint optimization and learning of spectrum usage dynamics and spectrum access control
• Challenges in machine learning for cognitive radars
• Privacy-preserving machine learning for cognitive radio
• Privacy-preserving machine learning for cognitive radar
• Machine learning for cognitive technologies in 5G cellular networks
• Cloud-based machine learning for cognitive communications and radar
• Non-parametric machine learning for cognitive radio and rada
• Generative Models for machine learning in cognitive communications and radar
• Machine learning for geolocation in cognitive communications and radar
• Adaptive information-centric cognitive networks
• Network estimation for cognitive networks
Paper Submission:
Submit papers, at most 6 pages long in two-column IEEE format, through the GlobalSIP website
at http://www.ieeeglobalsip.org/Papers.asp.
Important Dates
Paper Submission Deadline: June 05, 2016
Final Acceptance Announced: August 05, 2016
Symposium Organizers:
• Dr. Silvija Kokalj-Filipovic, Naval Research Laboratory, Washington DC
• Dr. George Stantchev, Naval Research Laboratory, Washington DC
Symposium Technical Chair:
• Prof. H. Vincent Poor, Princeton University