4,126 research outputs found
On CQI Estimation for Mobility and Correlation Properties of Gaussian Process Regression
No full textChannel quality prediction is an essential function for anticipatory and proactive radio resource allocation. In this paper, we propose a channel quality prediction method based on the concept of Gaussian Process Regression (GPR) in which the spatio-temporal correlation of the wireless channel is used for wireless channel prediction. The objective of the paper is to find the optimal channel quality prediction for non-static users. Furthermore, we propose our analytical optimization in the choice of users which enhances the spatio-temporal correlation of the wireless channel and results in performance improvements in terms of BLER and rate loss. Simulation results show the potential of our proposed method.Channel quality prediction is an essential function for anticipatory and proactive radio resource allocation. In this paper, we propose a channel quality prediction method based on the concept of Gaussian Process Regression (GPR) in which the spatio-temporal correlation of the wireless channel is used for wireless channel prediction. The objective of the paper is to find the optimal channel quality prediction for non-static users. Furthermore, we propose our analytical optimization in the choice of users which enhances the spatio-temporal correlation of the wireless channel and results in performance improvements in terms of BLER and rate loss. Simulation results show the potential of our proposed method
Optimal Resource Allocation with Flexible Numerology
No full textWe consider a multi-user system that employs mixed
numerology as defined by the 3rd Generation Partnership Project
for a new radio physical layer design. Accounting for different
channel conditions, imperfect channel knowledge and inter-band
interference as a consequence of mixed numerology, we propose
a new optimization method for resource allocation. Based on
this method, we are able to optimally distribute the available
bandwidth amongst the users, such that they achieve the same
quality of service. We show how resource allocation depends on
the channel properties such as Doppler and delay spread. In
addition, optimal resource allocation is verified by simulations of
a 5G compliant system.We consider a multi-user system that employs mixed
numerology as defined by the 3rd Generation Partnership Project
for a new radio physical layer design. Accounting for different
channel conditions, imperfect channel knowledge and inter-band
interference as a consequence of mixed numerology, we propose
a new optimization method for resource allocation. Based on
this method, we are able to optimally distribute the available
bandwidth amongst the users, such that they achieve the same
quality of service. We show how resource allocation depends on
the channel properties such as Doppler and delay spread. In
addition, optimal resource allocation is verified by simulations of
a 5G compliant system
A Novel Optimization Method for Resource Allocation based on Mixed Numerology
No full textIn this paper, we propose a novel optimization
framework for resource and numerology allocation in multi-user
scenarios. The goal of our optimization is to equalize the users´
achievable rates. Our optimization includes intersymbol and
intercarrier interference, channel estimation error and interband
interference as a result of mixed numerology. The optimization
can be formulated as an integer linear program. To reduce the
computational complexity of the optimization, we also propose a
linear relaxation of the problem. We investigate the performance
of our methods by numerical simulations, demonstrating significant
gains over an LTE-compliant scenario with fixed numerology
and a heuristic approach.In this paper, we propose a novel optimization
framework for resource and numerology allocation in multi-user
scenarios. The goal of our optimization is to equalize the users´
achievable rates. Our optimization includes intersymbol and
intercarrier interference, channel estimation error and interband
interference as a result of mixed numerology. The optimization
can be formulated as an integer linear program. To reduce the
computational complexity of the optimization, we also propose a
linear relaxation of the problem. We investigate the performance
of our methods by numerical simulations, demonstrating significant
gains over an LTE-compliant scenario with fixed numerology
and a heuristic approach
Einbände aus der Bibliothek des Markus Fugger
No full textEinbände aus der Bibliothek des Markus Fugger. - Augsburg, 1983. - 11 S
A Fractionally Spaced DFE with Subband Decorrelation
No full textIn this paper we proposed a modification of the "classic" fractionally spaced decision feedback equaliser (FS-DFE) in order to increase the slow convergence rate of the oversampled feed-forward section for LMS type algorithms. This is performed by employing a subband structure for the latter part of the FS-DFE, which results in a decorrelation of the input. We motivate why an oversampled subband decomposition is beneficial and comment on the selection of the filter banks. As an additional benefit, computational savings arise if judicious implementation of the filter banks is combined with the internal decimation and expansion operations in the FS-DFE. Simulations for a sever multipath environment are presented
Adaptive blind multiuser DS-CDMA downlink equaliser
Get PDFA robust and simple adaptive blind multiuser equaliser for downlink DS-CDMA systems is presented. The adaptation is based on forcing various user symbols onto a constant modulus, whereby no additional constraints such as the mixed cross-correlation are required. The proposed algorithm has a moderate computational complexity and shows a BER performance close to the MMSE solution
CQI Mapping Optimization in Spatial Wireless Channel Prediction
No full textIn current wireless cellular networks, the BS tries to provide the highest possible rate to the users that can reliably be decoded. The BS obtains this rate information as a feedback from the users. However, the biased CQI reporting by the users impacts the rate and hence the system performance. The bias is directly related to the estimation error of the predicted SNR obtained by the Gaussian Process Regression (GPR) method in which the spatial correlation of the wireless channel is used for wireless channel prediction, and in particular, for predictive resource allocation. In this paper, the objective is to optimize the predicted SNR-CQI mapping by introducing an offset, adapted to the estimation accuracy relative to the user density. Results show that the proposed CQI mapping enhances the system performance for different user densities.In current wireless cellular networks, the BS tries to provide the highest possible rate to the users that can reliably be decoded. The BS obtains this rate information as a feedback from the users. However, the biased CQI reporting by the users impacts the rate and hence the system performance. The bias is directly related to the estimation error of the predicted SNR obtained by the Gaussian Process Regression (GPR) method in which the spatial correlation of the wireless channel is used for wireless channel prediction, and in particular, for predictive resource allocation. In this paper, the objective is to optimize the predicted SNR-CQI mapping by introducing an offset, adapted to the estimation accuracy relative to the user density. Results show that the proposed CQI mapping enhances the system performance for different user densities
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