12,139 research outputs found
Pilot Power Allocation Through User Grouping in Multi-Cell Massive MIMO Systems
In this paper, we propose a relative channel estimation error (RCEE) metric,
and derive closed-form expressions for its expectation and
the achievable uplink rate holding for any number of base station antennas ,
with the least squares (LS) and minimum mean squared error (MMSE) estimation
methods. It is found that RCEE and converge to the same
constant value when , resulting in the pilot power
allocation (PPA) is substantially simplified and a PPA algorithm is proposed to
minimize the average per user with a total pilot power
budget in multi-cell massive multiple-input multiple-output systems.
Numerical results show that the PPA algorithm brings considerable gains for the
LS estimation compared with equal PPA (EPPA), while the gains are only
significant with large frequency reuse factor (FRF) for the MMSE estimation.
Moreover, for large FRF and large , the performance of the LS approaches to
the performance of the MMSE, which means that simple LS estimation method is a
very viable when co-channel interference is small. For the achievable uplink
rate, the PPA scheme delivers almost the same average achievable uplink rate
and improves the minimum achievable uplink rate compared with the EPPA scheme.Comment: 30 pages, 5 figures, submitted to IEEE Transactions on Communication
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Programming language processing (similar to natural language processing) is a
hot research topic in the field of software engineering; it has also aroused
growing interest in the artificial intelligence community. However, different
from a natural language sentence, a program contains rich, explicit, and
complicated structural information. Hence, traditional NLP models may be
inappropriate for programs. In this paper, we propose a novel tree-based
convolutional neural network (TBCNN) for programming language processing, in
which a convolution kernel is designed over programs' abstract syntax trees to
capture structural information. TBCNN is a generic architecture for programming
language processing; our experiments show its effectiveness in two different
program analysis tasks: classifying programs according to functionality, and
detecting code snippets of certain patterns. TBCNN outperforms baseline
methods, including several neural models for NLP.Comment: Accepted at AAAI-1
- …