580 research outputs found
Multiaccess Channels with State Known to Some Encoders and Independent Messages
We consider a state-dependent multiaccess channel (MAC) with state
non-causally known to some encoders. We derive an inner bound for the capacity
region in the general discrete memoryless case and specialize to a binary
noiseless case. In the case of maximum entropy channel state, we obtain the
capacity region for binary noiseless MAC with one informed encoder by deriving
a non-trivial outer bound for this case. For a Gaussian state-dependent MAC
with one encoder being informed of the channel state, we present an inner bound
by applying a slightly generalized dirty paper coding (GDPC) at the informed
encoder that allows for partial state cancellation, and a trivial outer bound
by providing channel state to the decoder also. The uninformed encoders benefit
from the state cancellation in terms of achievable rates, however, appears that
GDPC cannot completely eliminate the effect of the channel state on the
achievable rate region, in contrast to the case of all encoders being informed.
In the case of infinite state variance, we analyze how the uninformed encoder
benefits from the informed encoder's actions using the inner bound and also
provide a non-trivial outer bound for this case which is better than the
trivial outer bound.Comment: Accepted to EURASIP Journal on Wireless Communication and Networking,
Feb. 200
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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