Communication and Information
Sciences Laboratory
Communication and Information Sciences Laboratory

The Communication and Information Sciences Laboratory (CISL) at the Electrical and Computer Engineering department in the University of New Mexico (UNM) is dedicated for research in all aspects of information storage, processing and transmission.

News and Events

New!

February 2014

One of the current research focus areas at CISL is in machine-learning and decision-making techniques for interactive smart-grid technology. Some of our on-going research is reported in the following journal paper that was accepted for publication in IEEE Transactions on Parallel and Distributed Systems (TPDS) recently.

D. Li and S. K. Jayaweera, "Distributed Smart-home Decision-making in a Hierarchical Interactive Smart Grid Architecture", IEEE Transactions on Parallel and Distributed Systems, Feb. 2014, Accepted for publication.

Abstract: A hierarchical smart grid architecture is proposed for the Utility-customer interaction consisting of sub-components of customer load prediction, renewable generation integration, power-load balancing and demand response (DR). A scalable solution to the real-time scheduling problem is proposed by combining solutions to two sub-problems: (1) centralized sequential decision making at the controller to maximize an accumulated reward for the whole micro-grid and (2) distributed auctioning among all customers based on the optimal load profile obtained by solving the first problem to coordinate their interactions. For the centralized decision making problem, a hidden mode Markov decision process (HM-MDP) model is proposed. For the distributed decision making problem, a Vikrey auctioning game is designed to coordinate the actions of the individual smart-homes to actually achieve the optimal solution derived by the controller under realistic gird interaction assumptions.

July 2013

One of the current research focus areas at CISL is in machine-learning and decision-making techniques for Cognitive Radios. Some of our on-going research is reported in the following journal paper that was accepted for publication in IEEE Communications Surveys and Tutorials recently.

M. Bkassiny, Y. Li and S. K. Jayaweera, "A survey on machine-learning techniques in cognitive radios", IEEE Communications Surveys and Tutorials, vol. 15, no. 3, pp. 1136-1159, Third Quarter 2013.

Abstract: In this survey paper, we characterize the learning problem in cognitive radios and state the importance of artificial intelligence in achieving real cognitive systems. We review various learning approaches that have been proposed for cognitive radios classifying them under supervised and unsupervised learning paradigms.Unsupervised learning is presented as an autonomous learning procedure that is suitable for unknown RF environments, whereas supervised learning methods can be used to exploit prior information available to cognitive radios during the learning process. We describe some challenging learning problems that arise in cognitive radio networks, in particular in non-Markovian environments, and present their possible solution methods. Finally, we present some generic cognitive radio problems and show suitable machine learning approaches for learning in these contexts.

September 2011

Prof. Sudharman Jayaweera and his research group won an award for a paper presented at the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing in Victoria, Canada in Aug. 2011. The paper was awarded the "IEEE PACRIM 2011 Gold Award for Best Communications Paper".

IEEE Pacific Rim Conference on Communications, Computers and Signal Processing
S. Chen, C. Ghosh, A. M. Wyglinski and S. K. Jayaweera, "Impact of Group Cooperation over Competitive Secondary Subnetworks" IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, Canada, Aug. 2011.

August 2011

Prof. Jayaweera delivered the invited plenary talk at the 6th International Conference on Industrial and Information systems in Kandy, Sri Lanka, Aug 16-19th 2011 (ICIIS2011).

ICIIS 2011
The title of the talk was "Radiobots: Towards Self-learning Autonomous Cognitive Radios". [more]

January 2011

A plenary talk has been presented by Prof. Sudharman Jayaweera in the Radio Wireless Week (RWW2011) Conference.

Abstract: Although cognitive radios has seen an enormous interest from various research communities, arguably even the definition of what it is still not clear. In particular, depending on the person’s background the adapted definition varies: For example, RF antenna/reconfigurable hardware community treats it as an upgrade of an software-defined radios (SDR’s), PHY/MAC and communication theory researchers consider it to be all about dynamic spectrum sharing (DSS), and computer scientists seem to believe it is primarily a radio with machine learning. However, a more suitable notion of a true cognitive radio would need to encompass all these, plus perhaps some more. With this broad view of developing a radio with true cognitive abilities, we define a cognitive radio as “an intelligent wireless communications device that has the ability to reason and learn from the observed RF environment to self-decide optimal communications mode and can optimally self-configure its hardware to support the selected mode”. [more]

September 2010

Prof. Sudharman K. Jayaweera participated as a panelist in a workshop organized by EU FP7 project C2POWER. The workshop focused on power optimization techniques, which took place in Lisbon, Portugal on the 8th of September.

"EU FP7 project C2POWER (Cognitive Radio and Cooperative Strategies for Power Saving) main objective is to research, develop and demonstrate energy saving technologies for multi-standard wireless mobile devices, exploiting the combination of cognitive radio and cooperative strategies while still enabling the required performance in terms of data rate and QoS to support active applications." [more]

August 2010

Distributed tracking with consensus of satellites and other space-born objects (green) with a hybrid space surveillance network (SSN) made of ground-based sensor nodes and satellites. Consensus-tracked locations of multiple targets are shown in white. [more details]

Distributed tracking with centralized data fusion of satellites and other space-born objects (green) with a hybrid space surveillance network (SSN) made of ground-based sensor nodes and satellites. [more details]

Distributed Tracking Comparison between Consensus and Centralized Data Fusion of Satellites and Other Space-Born Objects.[more details]