11,888 research outputs found
Distributed Power Control for Cellular Networks in the Presence of Channel Uncertainties
In this paper, a novel distributed power control (DPC) scheme for cellular network in the presence of radio channel uncertainties such as path loss, shadowing, and Rayleigh fading is presented. Since these uncertainties can attenuate the received signal strength and can cause variations in the received Signal-to-Interference ratio (SIR), a new DPC scheme, which can estimate the slowly varying channel uncertainty, is proposed so that a target SIR at the receiver can be maintained. Further, the standard assumption of a constant interference during a link\u27s power update used in other works in the literature is relaxed. A CDMA-based cellular network environment has been developed to compare the proposed scheme with earlier approaches. The results show that our DPC scheme can converge faster than others by adapting to the channel variations. In the presence of channel uncertainties, our DPC scheme renders lower outage probability while consuming significantly low power per active mobile user compared with other schemes that are available in the literature
Distributed Power Control of Cellular Networks in the Presence of Channel Uncertainties
A novel distributed power control (DPC) scheme for cellular networks in the presence of radio channel uncertainties such as path loss shadowing, and Rayleigh fading is presented. Since these uncertainties can attenuate the received signal strength and can cause variations in the received Signal-to-lnterference ratio (SIR), the proposed DPC scheme maintains a target SIR at the receiver provided the uncertainty is slowly varying with time. The DPC estimates the time varying nature of the channel quickly and uses the information to arrive at a suitable transmitter power value . Further, the standard assumption of a constant interference during a link\u27s power update used in other work in the literature is relaxed. A CDMA-hased celluar network environment is used to compare the proposed scheme with earlier approaches. The results show that our DPC scheme can converge faster than others by adapting to the channel variations. The proposed DPC scheme can render outage prohability of 5 to 30% in the presence of uncertainties compared with other schemes of 50 to 90% while consuming law power per active mobile user. In other words, the proposed DPC scheme allows significant increase in network capacity while consuming low- power values even when the channel is uncertain
Spatial Wireless Channel Prediction under Location Uncertainty
Spatial wireless channel prediction is important for future wireless
networks, and in particular for proactive resource allocation at different
layers of the protocol stack. Various sources of uncertainty must be accounted
for during modeling and to provide robust predictions. We investigate two
channel prediction frameworks, classical Gaussian processes (cGP) and uncertain
Gaussian processes (uGP), and analyze the impact of location uncertainty during
learning/training and prediction/testing, for scenarios where measurements
uncertainty are dominated by large-scale fading. We observe that cGP generally
fails both in terms of learning the channel parameters and in predicting the
channel in the presence of location uncertainties.\textcolor{blue}{{} }In
contrast, uGP explicitly considers the location uncertainty. Using simulated
data, we show that uGP is able to learn and predict the wireless channel
Robust Location-Aided Beam Alignment in Millimeter Wave Massive MIMO
Location-aided beam alignment has been proposed recently as a potential
approach for fast link establishment in millimeter wave (mmWave) massive MIMO
(mMIMO) communications. However, due to mobility and other imperfections in the
estimation process, the spatial information obtained at the base station (BS)
and the user (UE) is likely to be noisy, degrading beam alignment performance.
In this paper, we introduce a robust beam alignment framework in order to
exhibit resilience with respect to this problem. We first recast beam alignment
as a decentralized coordination problem where BS and UE seek coordination on
the basis of correlated yet individual position information. We formulate the
optimum beam alignment solution as the solution of a Bayesian team decision
problem. We then propose a suite of algorithms to approach optimality with
reduced complexity. The effectiveness of the robust beam alignment procedure,
compared with classical designs, is then verified on simulation settings with
varying location information accuracies.Comment: 24 pages, 7 figures. The short version of this paper has been
accepted to IEEE Globecom 201
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