49 research outputs found
Capacity Region of the Broadcast Channel with Two Deterministic Channel State Components
This paper establishes the capacity region of a class of broadcast channels
with random state in which each channel component is selected from two possible
functions and each receiver knows its state sequence. This channel model does
not fit into any class of broadcast channels for which the capacity region was
previously known and is useful in studying wireless communication channels when
the fading state is known only at the receivers. The capacity region is shown
to coincide with the UV outer bound and is achieved via Marton coding.Comment: 5 pages, 3 figures. Submitted to ISIT 201
A New Capacity Result for the Z-Gaussian Cognitive Interference Channel
This work proposes a novel outer bound for the Gaussian cognitive
interference channel in strong interference at the primary receiver based on
the capacity of a multi-antenna broadcast channel with degraded message set. It
then shows that for the Z-channel, i.e., when the secondary receiver
experiences no interference and the primary receiver experiences strong
interference, the proposed outer bound not only is the tightest among known
bounds but is actually achievable for sufficiently strong interference. The
latter is a novel capacity result that from numerical evaluations appears to be
generalizable to a larger (i.e., non-Z) class of Gaussian channels
Deterministic Capacity of MIMO Relay Networks
The deterministic capacity of a relay network is the capacity of a network
when relays are restricted to transmitting \emph{reliable} information, that
is, (asymptotically) deterministic function of the source message. In this
paper it is shown that the deterministic capacity of a number of MIMO relay
networks can be found in the low power regime where \SNR\to0. This is
accomplished through deriving single letter upper bounds and finding the limit
of these as \SNR\to0. The advantage of this technique is that it overcomes
the difficulty of finding optimum distributions for mutual information.Comment: Submitted to IEEE Transactions on Information Theor