An Iterative Joint Linear-Programming Decoding of LDPC Codes and Finite-State Channels

In this paper, we introduce an efficient iterative solver for the joint linear-programming (LP) decoding of low-density parity-check (LDPC) codes and finite-state channels (FSCs). In particular, we extend the approach of iterative approximate LP decoding, proposed by Vontobel and Koetter and explored by Burshtein, to this problem. By taking advantage of the dual-domain structure of the joint decoding LP, we obtain a convergent iterative algorithm for joint LP decoding whose structure is similar to BCJR-based turbo equalization (TE). The result is a joint iterative decoder whose complexity is similar to TE but whose performance is similar to joint LP decoding. The main advantage of this decoder is that it appears to provide the predictability of joint LP decoding and superior performance with the computational complexity of TE.Comment: To appear in Proc. IEEE ICC 2011, Kyoto, Japan, June 5-9, 201

Real Options Methodology Applied to the ICT Sector: A Survey

This survey focuses on the application of real options methodology to the information and communications technology (ICT) industries. It examines the development of the methodology to areas as diverse as wireless cell site investments to dynamic pricing issues. In addition to aiding the reader in understanding the breadth of the applications, it demonstrates the importance of the topic. It provides a guide to the reader who is interested in exploring the topic in greater depth.Discounted cash flow, economic methodology, information and communications technology (ICT), investment, investment under uncertainty, options, present discounted value, real options, valuations.

Message-Passing Inference on a Factor Graph for Collaborative Filtering

This paper introduces a novel message-passing (MP) framework for the collaborative filtering (CF) problem associated with recommender systems. We model the movie-rating prediction problem popularized by the Netflix Prize, using a probabilistic factor graph model and study the model by deriving generalization error bounds in terms of the training error. Based on the model, we develop a new MP algorithm, termed IMP, for learning the model. To show superiority of the IMP algorithm, we compare it with the closely related expectation-maximization (EM) based algorithm and a number of other matrix completion algorithms. Our simulation results on Netflix data show that, while the methods perform similarly with large amounts of data, the IMP algorithm is superior for small amounts of data. This improves the cold-start problem of the CF systems in practice. Another advantage of the IMP algorithm is that it can be analyzed using the technique of density evolution (DE) that was originally developed for MP decoding of error-correcting codes

Big Data: The Structure and Value of Big Data Analytics

The term Big Data is intuitively appealing and increasingly well accepted in academics as well as practices. Firms readily see the possibility of new business value from big data and future business opportunities. Although they are good understanding what Big Data captures that conventional data do not, the journey for Big Data is difficult and deeply frustrating, as widely known, because of its volume, variety, and velocity. They also get stuck how to collect and analyze Big Data because how-to advice is scarce on this subject and mostly aimed at experts. As a result, Big Data Analytics are considered difficult to implement. The paper discusses that big data have business value and develop a model for measuring its value. We also attempt to design an implementing framework for big data collection as the first step for analytics. This paper can contribute to provide a guideline for studying big data analytics