PAGE TITLE
Overview
PAGE SUMMARY
This technology is a learning protocol for distributed antenna state selection in directional cognitive small-cell networks. The protocol addresses interference and performance improvement in wireless data transmission systems, specifically focusing on small-cell deployments with directional antennas. It formulates the antenna state selection as a nonstationary multi-armed bandit problem and proposes a solution using the adaptive pursuit method from reinforcement learning. The system leverages a practical implementation, referred to as WARP-TDMAC, which integrates electronically reconfigurable antennas into small-cell networks, enabling synchronized directional transmission.
ADVANTAGES
TITLE:Key Advantages
Improved Link Performance: The protocol uses machine learning-based distributed antenna state selection to enhance individual transmission links' performance by avoiding interference and adapting to changing environments
Total System Throughput: The patent introduces synchronous directional transmission across the network, improving total system throughput by enabling spatial reuse and efficient channel access
Adaptive Pursuit Algorithm: The use of the adaptive pursuit algorithm in antenna state selection allows for dynamic adaptation to non-stationary environments, making it suitable for real-world scenarios
Problem Solved
TITLE:Problems Solved
Interference Mitigation: The patent addresses the challenge of interference in dense small-cell deployments by using directional antennas and machine learning for optimal antenna state selection
Integration of Directional Antennas: It provides a solution for integrating directional antennas into small-cell networks, overcoming protocol overhead and adaptation difficulties
Efficient Network Synchronization: The patent proposes a hybrid synchronization mechanism and real-time scheduling for achieving synchronization in a multi-link wireless network
APPLICATIONS
TITLE: Market Applications
Directional Antenna Technology: The patent contributes to the advancement of directional antenna technology, which is vital for future wireless communication systems such as smart cities, smart homes, smart factories, smart transportation, and smart healthcare.
Small-Cell Deployments: The protocol is applicable to dense small-cell networks, including 5G deployments, to improve network capacity and reduce interference
Wireless Communication Systems: It can be applied to various wireless communication systems seeking to enhance link performance and network throughput
Machine Learning in Networking: The use of machine learning and adaptive algorithms for antenna state selection has broader applications in network optimization
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IP STATUS
Intellectual Property
United States Issued Patent- Adaptive pursuit learning method to mitigate small-cell interference through directionality
PUBLICATIONS
References
Pubinfo should be the citation for your publication. Publink is the full url linking to the publication online or a pdf.
A. Paatelma, D. H. Nguyen, H. Saarnisaari, N. Kandasamy, and K. R. Dandekar, “Reinforcement Learning System to Mitigate Small-Cell Interference Through Directionality,” in Proc. IEEE Intl. Symp. on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017.
D. H. Nguyen, A. Paatelma, H. Saarnisaari, N. Kandasamy, and K. R. Dandekar, “Demo: Enhancing Indoor Spatial Reuse through Adaptive Antenna Beamsteering,” in Proc. ACM Intl. Workshop on Wireless Network Testbeds, Experimental Eval., and Characterization (WiNTECH), 2016.
https://wireless.ece.drexel.edu/pdfs/tech_report_3_indoor_adaptive_beamsteering_using_pursuit_methods.pdf
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Inventor information
Kapil R. Dandekar, Ph.D.
Director, Drexel Wireless Systems Laboratory
E. Warren Colehower Chair Professor
Associate Dean for Enrollment Management and Graduate Education
Electrical and Computer Engineering
Office of the Dean
3101 Market St 232A; CAT 170
Philadelphia, PA 19104, USA
Phone: 1-215-895-2004
Email: dandekar@drexel.edu
Inventor Webpage
Drexel Wireless Systems Laboratory