114,934 research outputs found
Communication over an Arbitrarily Varying Channel under a State-Myopic Encoder
We study the problem of communication over a discrete arbitrarily varying
channel (AVC) when a noisy version of the state is known non-causally at the
encoder. The state is chosen by an adversary which knows the coding scheme. A
state-myopic encoder observes this state non-causally, though imperfectly,
through a noisy discrete memoryless channel (DMC). We first characterize the
capacity of this state-dependent channel when the encoder-decoder share
randomness unknown to the adversary, i.e., the randomized coding capacity.
Next, we show that when only the encoder is allowed to randomize, the capacity
remains unchanged when positive. Interesting and well-known special cases of
the state-myopic encoder model are also presented.Comment: 16 page
On Optimal TCM Encoders
An asymptotically optimal trellis-coded modulation (TCM) encoder requires the
joint design of the encoder and the binary labeling of the constellation. Since
analytical approaches are unknown, the only available solution is to perform an
exhaustive search over the encoder and the labeling. For large constellation
sizes and/or many encoder states, however, an exhaustive search is unfeasible.
Traditional TCM designs overcome this problem by using a labeling that follows
the set-partitioning principle and by performing an exhaustive search over the
encoders. In this paper we study binary labelings for TCM and show how they can
be grouped into classes, which considerably reduces the search space in a joint
design. For 8-ary constellations, the number of different binary labelings that
must be tested is reduced from 8!=40320 to 240. For the particular case of an
8-ary pulse amplitude modulation constellation, this number is further reduced
to 120 and for 8-ary phase shift keying to only 30. An algorithm to generate
one labeling in each class is also introduced. Asymptotically optimal TCM
encoders are tabulated which are up to 0.3 dB better than the previously best
known encoders
Multiple Access Channel with States Known Noncausally at One Encoder and Only Strictly Causally at the Other Encoder
We consider a two-user state-dependent multiaccess channel in which the
states of the channel are known non-causally to one of the encoders and only
strictly causally to the other encoder. Both encoders transmit a common message
and, in addition, the encoder that knows the states non-causally transmits an
individual message. We study the capacity region of this communication model.
In the discrete memoryless case, we establish inner and outer bounds on the
capacity region. Although the encoder that sends both messages knows the states
fully, we show that the strictly causal knowledge of these states at the other
encoder can be beneficial for this encoder, and in general enlarges the
capacity region. Furthermore, we find an explicit characterization of the
capacity in the case in which the two encoders transmit only the common
message. In the Gaussian case, we characterize the capacity region for the
model with individual message as well. Our converse proof in this case shows
that, for this model, strictly causal knowledge of the state at one of the
encoders does not increase capacity if the other is informed non-causally, a
result which sheds more light on the utility of conveying a compressed version
of the state to the decoder in recent results by Lapidoth and Steinberg on a
multiacess model with only strictly causal state at both encoders and
independent messages.Comment: 5 pages, to appear in the 2011 IEEE International Symposium on
Information Theor
Distributed Quantization for Compressed Sensing
We study distributed coding of compressed sensing (CS) measurements using
vector quantizer (VQ). We develop a distributed framework for realizing
optimized quantizer that enables encoding CS measurements of correlated sparse
sources followed by joint decoding at a fusion center. The optimality of VQ
encoder-decoder pairs is addressed by minimizing the sum of mean-square errors
between the sparse sources and their reconstruction vectors at the fusion
center. We derive a lower-bound on the end-to-end performance of the studied
distributed system, and propose a practical encoder-decoder design through an
iterative algorithm.Comment: 5 Pages, Accepted for presentation in ICASSP 201
- …