1,794 research outputs found
Joint Power Control and Fronthaul Rate Allocation for Throughput Maximization in OFDMA-based Cloud Radio Access Network
The performance of cloud radio access network (C-RAN) is constrained by the
limited fronthaul link capacity under future heavy data traffic. To tackle this
problem, extensive efforts have been devoted to design efficient signal
quantization/compression techniques in the fronthaul to maximize the network
throughput. However, most of the previous results are based on
information-theoretical quantization methods, which are hard to implement due
to the extremely high complexity. In this paper, we consider using practical
uniform scalar quantization in the uplink communication of an orthogonal
frequency division multiple access (OFDMA) based C-RAN system, where the mobile
users are assigned with orthogonal sub-carriers for multiple access. In
particular, we consider joint wireless power control and fronthaul quantization
design over the sub-carriers to maximize the system end-to-end throughput.
Efficient algorithms are proposed to solve the joint optimization problem when
either information-theoretical or practical fronthaul quantization method is
applied. Interestingly, we find that the fronthaul capacity constraints have
significant impact to the optimal wireless power control policy. As a result,
the joint optimization shows significant performance gain compared with either
optimizing wireless power control or fronthaul quantization alone. Besides, we
also show that the proposed simple uniform quantization scheme performs very
close to the throughput performance upper bound, and in fact overlaps with the
upper bound when the fronthaul capacity is sufficiently large. Overall, our
results would help reveal practically achievable throughput performance of
C-RAN, and lead to more efficient deployment of C-RAN in the next-generation
wireless communication systems.Comment: submitted for possible publicatio
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
For the efficient compensation of fiber nonlinearity, one of the guiding
principles appears to be: fewer steps are better and more efficient. We
challenge this assumption and show that carefully designed multi-step
approaches can lead to better performance-complexity trade-offs than their
few-step counterparts.Comment: 4 pages, 3 figures, This is a preprint of a paper submitted to the
2019 European Conference on Optical Communicatio
Efficient end-to-end learning for quantizable representations
Embedding representation learning via neural networks is at the core
foundation of modern similarity based search. While much effort has been put in
developing algorithms for learning binary hamming code representations for
search efficiency, this still requires a linear scan of the entire dataset per
each query and trades off the search accuracy through binarization. To this
end, we consider the problem of directly learning a quantizable embedding
representation and the sparse binary hash code end-to-end which can be used to
construct an efficient hash table not only providing significant search
reduction in the number of data but also achieving the state of the art search
accuracy outperforming previous state of the art deep metric learning methods.
We also show that finding the optimal sparse binary hash code in a mini-batch
can be computed exactly in polynomial time by solving a minimum cost flow
problem. Our results on Cifar-100 and on ImageNet datasets show the state of
the art search accuracy in precision@k and NMI metrics while providing up to
98X and 478X search speedup respectively over exhaustive linear search. The
source code is available at
https://github.com/maestrojeong/Deep-Hash-Table-ICML18Comment: Accepted and to appear at ICML 2018. Camera ready versio
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