call for papers, IEEE Journal on SELECTED AREAS IN COMMUNICATIONS

[Jianchang Mao 927-1932]

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                                 CALL FOR PAPERS

                  IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS

            COMPUTATIONAL AND ARTIFICIAL INTELLIGENCE IN HIGH SPEED NETWORKS


Recent research in high speed networks has resulted in key architectural
trends which are likely to fundamentally influence all facets of the
communications infrastructure. A major opportunity is now the integration of
diverse services on these networks. Unlike traditional teletraffic, many of
these current and emerging services have poorly understood traffic parameters
and user behaviors. These networks must be self-managing and self-healing and
be able to maintain their quality of service, deal with congestion and
failures, and allow dynamic reconfiguration with minimal intervention.
This has led many researchers to investigate algorithms that have adaptive
and even learning behaviors. There is a consensus among many researchers
that to manage these new workloads and their workloads a class of techniques
exhibiting some form of computational intelligence will be needed.

Despite some progress,  key challenges remain. One problem is that these
resource management algorithms need to be able to respond anticipatively
or preventatively to problems, since the time-bandwidth product of
these networks does not always allow for reactive behavior. A second problem
is that in some cases, decisions must be made on a very rapid (sometimes
submicrosecond) time scale  in order to optimize switching behavior.
Thirdly, while the network adapts, its traffic sources and sinks are
also adapting intelligently to the network's behavior, making for added
complexity.

Computational intelligence encompasses the  information processing
paradigms of adaptive systems such as neural networks and fuzzy logic.
Examples of Artificial intelligence include expert systems and search
techniques.  Computational intelligence paradigms have the ability to
learn from experience and to predict future behaviors. In some cases
these learning rules are explicit, but in other cases the learning
algorithms are implicit in a more general structure such as a neural
network. In particular, neural networks have been shown to have properties
that can help in managing congestion in networks, dealing with changing
workloads, etc. Analog circuits that implement neural networks have been
shown to be capable of solving optimization problems in submicrosecond
timescales, fast enough to make on-the-fly switch routing decisions.

Original papers are solicited on the applications of any technique in
computational or artificial intelligence to the following topics (but
not limited to):

Fast packet switching
Fault tolerant and dynamic routing
Multimedia source measurement and modeling
Call admission control
Traffic policing
Congestion and flow control
Estimation of quality of service parameters
Wireless and mobile networks
Disconnectible terminal management

Authors wishing to submit papers, should send six copies to Prof. Ibrahim Habib
at the address below.

The following schedule shall be applied:

Submission Deadline: January 15, 1996
Notification of acceptance: May 15, 1996
Final manuscript due: July 1, 1996
Publication: 1st AQuarter, 1997.

GUEST EDITORS:

Prof. Ibrahim Habib
Department of electrical engineering
City University of New York, City College
137 street at Convent Avenue
New York, N.Y. 10031
email: ibhcc at cunyvm.cuny.edu


Dr. Robert Morris
Manager, Data Systems Technology
IBM Almaden Research Center
San Jose, CA 95120
email: rjtm at almaden.ibm.com


Dr. Hiroshi Saito
Distinguished Technical Member
NTT Telecommunications Networks Laboratories
3-9-11, Midori-chi, Musashino-shi
Tokyo 180, Japan
email: saito at hashi.ntt.jp


Prof. Bjorn Pehrson
Royal Institute of Technology
Electrum 204
S-164 Kista, Sweden
email: bjorn at it.kth.se