10,116 research outputs found
Pilot Beam Sequence Design for Channel Estimation in Millimeter-Wave MIMO Systems: A POMDP Framework
In this paper, adaptive pilot beam sequence design for channel estimation in
large millimeter-wave (mmWave) MIMO systems is considered. By exploiting the
sparsity of mmWave MIMO channels with the virtual channel representation and
imposing a Markovian random walk assumption on the physical movement of the
line-of-sight (LOS) and reflection clusters, it is shown that the sparse
channel estimation problem in large mmWave MIMO systems reduces to a sequential
detection problem that finds the locations and values of the non-zero-valued
bins in a two-dimensional rectangular grid, and the optimal adaptive pilot
design problem can be cast into the framework of a partially observable Markov
decision process (POMDP). Under the POMDP framework, an optimal adaptive pilot
beam sequence design method is obtained to maximize the accumulated
transmission data rate for a given period of time. Numerical results are
provided to validate our pilot signal design method and they show that the
proposed method yields good performance.Comment: 6 pages, 6 figures, submitted to IEEE ICC 201
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
Many fundamental problems in natural language processing rely on determining
what entities appear in a given text. Commonly referenced as entity linking,
this step is a fundamental component of many NLP tasks such as text
understanding, automatic summarization, semantic search or machine translation.
Name ambiguity, word polysemy, context dependencies and a heavy-tailed
distribution of entities contribute to the complexity of this problem.
We here propose a probabilistic approach that makes use of an effective
graphical model to perform collective entity disambiguation. Input mentions
(i.e.,~linkable token spans) are disambiguated jointly across an entire
document by combining a document-level prior of entity co-occurrences with
local information captured from mentions and their surrounding context. The
model is based on simple sufficient statistics extracted from data, thus
relying on few parameters to be learned.
Our method does not require extensive feature engineering, nor an expensive
training procedure. We use loopy belief propagation to perform approximate
inference. The low complexity of our model makes this step sufficiently fast
for real-time usage. We demonstrate the accuracy of our approach on a wide
range of benchmark datasets, showing that it matches, and in many cases
outperforms, existing state-of-the-art methods
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