D
Daniel C. Lee
Researcher at Simon Fraser University
Publications - 126
Citations - 1180
Daniel C. Lee is an academic researcher from Simon Fraser University. The author has contributed to research in topics: Cognitive radio & Evolutionary algorithm. The author has an hindex of 16, co-authored 125 publications receiving 1112 citations. Previous affiliations of Daniel C. Lee include University of Southern California & Ryerson University.
Papers
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Journal ArticleDOI
Resource Allocation Techniques in Cooperative Cognitive Radio Networks
TL;DR: This paper discusses the taxonomy of objectives and protocols used in the literature for resource allocation in cooperative CRN, and highlights the use of power control, cooperation types, network configurations and decision types used in cooperativeCRN.
Journal ArticleDOI
A network pump
TL;DR: In this article, the authors show how to extend the NRL data pump to a certain multi-level secure (MLS) network architecture in order to balance the requirements of congestion control, fairness, good performance, and reliability against those of minimal threats from covert channels and denial of service attacks.
Journal ArticleDOI
Expected file-delivery time of deferred NAK ARQ in CCSDS file-delivery protocol
Daniel C. Lee,W. Baek +1 more
TL;DR: An automatic repeat-request (ARQ) scheme of the Consultative Committee for Space Data Systems file-delivery protocol for the single-hop file-transfer operation is analyzed and the expected file-Delivery time is derived.
Journal ArticleDOI
Real-Time Adaptive VVO/CVR Topology Using Multi-Agent System and IEC 61850-Based Communication Protocol
Moein Manbachi,Maryam Nasri,Babak Shahabi,Hassan Farhangi,Ali Palizban,Siamak Arzanpour,Mehrdad Moallem,Daniel C. Lee +7 more
TL;DR: This paper proposes a new approach for real-time and adaptive Volt/VAr optimization (VVO)/conservation voltage reduction (CVR) system using Intelligent Agents, communicating through IEC 61850 Goose Messaging Protocol.
Proceedings ArticleDOI
A network version of the Pump
TL;DR: This work shows how to extend the NRL data Pump to a certain MLS network architecture in order to balance the requirements of congestion control, fairness, good performance, and reliability against those of minimal threats from covert channels and denial of service attacks.