83,442 research outputs found
Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network
The limited fronthaul capacity imposes a challenge on the uplink of
centralized radio access network (C-RAN). We propose to boost the fronthaul
capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by
globally optimizing the power sharing between channel estimation and data
transmission both for the user devices (UDs) and the remote radio units (RRUs).
Intuitively, allocating more power to the channel estimation will result in
more accurate channel estimates, which increases the achievable throughput.
However, increasing the power allocated to the pilot training will reduce the
power assigned to data transmission, which reduces the achievable throughput.
In order to optimize the powers allocated to the pilot training and to the data
transmission of both the UDs and the RRUs, we assign an individual power
sharing factor to each of them and derive an asymptotic closed-form expression
of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN
consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU)
links. We then exploit the C-RAN architecture's central computing and control
capability for jointly optimizing the UDs' power sharing factors and the RRUs'
power sharing factors aiming for maximizing the fronthaul capacity. Our
simulation results show that the fronthaul capacity is significantly boosted by
the proposed global optimization of the power allocation between channel
estimation and data transmission both for the UDs and for their host RRUs. As a
specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs
deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing
factors improves 33\% compared with the one attained without optimizing power
sharing factors
A sum-of-sinusoids based simulation model for the joint shadowing process in urban peer-to-peer radio channels
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Massive MIMO: How many antennas do we need?
We consider a multicell MIMO uplink channel where each base station (BS) is
equipped with a large number of antennas N. The BSs are assumed to estimate
their channels based on pilot sequences sent by the user terminals (UTs).
Recent work has shown that, as N grows infinitely large, (i) the simplest form
of user detection, i.e., the matched filter (MF), becomes optimal, (ii) the
transmit power per UT can be made arbitrarily small, (iii) the system
performance is limited by pilot contamination. The aim of this paper is to
assess to which extent the above conclusions hold true for large, but finite N.
In particular, we derive how many antennas per UT are needed to achieve \eta %
of the ultimate performance. We then study how much can be gained through more
sophisticated minimum-mean-square-error (MMSE) detection and how many more
antennas are needed with the MF to achieve the same performance. Our analysis
relies on novel results from random matrix theory which allow us to derive
tight approximations of achievable rates with a class of linear receivers.Comment: 6 pages, 3 figures, to be presented at the Allerton Conference on
Communication, Control and Computing, Urbana-Champaign, Illinois, US, Sep.
201
Stellar calibration of L-/S-band and VHF receiving systems
STADAN reducing antenna calibration at 137, 402, and 1702 MHz using absolute flux density from Cassiopeia A or Cygnus
Wavelet Based Semi-blind Channel Estimation For Multiband OFDM
This paper introduces an expectation-maximization (EM) algorithm within a
wavelet domain Bayesian framework for semi-blind channel estimation of
multiband OFDM based UWB communications. A prior distribution is chosen for the
wavelet coefficients of the unknown channel impulse response in order to model
a sparseness property of the wavelet representation. This prior yields, in
maximum a posteriori estimation, a thresholding rule within the EM algorithm.
We particularly focus on reducing the number of estimated parameters by
iteratively discarding ``unsignificant'' wavelet coefficients from the
estimation process. Simulation results using UWB channels issued from both
models and measurements show that under sparsity conditions, the proposed
algorithm outperforms pilot based channel estimation in terms of mean square
error and bit error rate and enhances the estimation accuracy with less
computational complexity than traditional semi-blind methods
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