8 research outputs found
Communication Strategies for Low-Latency Trading
The possibility of latency arbitrage in financial markets has led to the
deployment of high-speed communication links between distant financial centers.
These links are noisy and so there is a need for coding. In this paper, we
develop a gametheoretic model of trading behavior where two traders compete to
capture latency arbitrage opportunities using binary signalling. Different
coding schemes are strategies that trade off between reliability and latency.
When one trader has a better channel, the second trader should not compete.
With statistically identical channels, we find there are two different regimes
of channel noise for which: there is a unique Nash equilibrium yielding ties;
and there are two Nash equilibria with different winners.Comment: Will appear in IEEE International Symposium on Information Theory
(ISIT), 201
Coding and Decoding Schemes for MSE and Image Transmission
In this work we explore possibilities for coding and decoding tailor-made for
mean squared error evaluation of error in contexts such as image transmission.
To do so, we introduce a loss function that expresses the overall performance
of a coding and decoding scheme for discrete channels and that exchanges the
usual goal of minimizing the error probability to that of minimizing the
expected loss. In this environment we explore the possibilities of using
ordered decoders to create a message-wise unequal error protection (UEP), where
the most valuable information is protected by placing in its proximity
information words that differ by a small valued error. We give explicit
examples, using scale-of-gray images, including small-scale performance
analysis and visual simulations for the BSMC.Comment: Submitted to IEEE Transactions on Information Theor
On Optimal Finite-length Binary Codes of Four Codewords for Binary Symmetric Channels
Finite-length binary codes of four codewords are studied for memoryless
binary symmetric channels (BSCs) with the maximum likelihood decoding. For any
block-length, best linear codes of four codewords have been explicitly
characterized, but whether linear codes are better than nonlinear codes or not
is unknown in general. In this paper, we show that for any block-length, there
exists an optimal code of four codewords that is either linear or in a subset
of nonlinear codes, called Class-I codes. Based on the analysis of Class-I
codes, we derive sufficient conditions such that linear codes are optimal. For
block-length less than or equal to 8, our analytical results show that linear
codes are optimal. For block-length up to 300, numerical evaluations show that
linear codes are optimal.Comment: accepted by ISITA 202