1,723,007 research outputs found
Data set for the paper of Regularized Zero-Forcing Precoding Aided Adaptive Coding and Modulation for Large-Scale Antenna Array Based Air-to-Air Communications
No full textThis dataset supports the publication:
Zhang, Jiankang; Chen, Sheng; Robert G. Maunder, Zhang, Rong; Hanzo, Lajos.
Regularized Zero-Forcing Precoding Aided Adaptive Coding and Modulation for Large-Scale Antenna Array Based Air-to-Air Communications.
IEEE Journal on Selected Areas in Communications
This dataset contains which are used for generating Fig.2 to Fig.9. These figures are plotted using GLE (Graphics Layout Engine). The scripts of Gle are also included in the folds for each figures. In order to generate these figures, you should install Gle
http://glx.sourceforge.net/</span
Adaptive Coding and Modulation for Large-Scale Antenna Array Based Aeronautical Communications in the Presence of Co-channel Interference
No full textThis dataset supports the publication:
Zhang, Jiankang; Chen, Sheng; Maunder, Robert; Zhang, Rong; Hanzo, Lajos.
Adaptive Coding and Modulation for Large-Scale Antenna Array Based Aeronautical Communications in the Presence of Co-channel Interference.
IEEE Transactions on Wireless Communications
This dataset contains which are used for generating Fig.3 to Fig.9. These figures are plotted using GLE (Graphics Layout Engine). The scripts of Gle are also included in the folds for each figures. In order to generate these figures, you should install GLE
http://glx.sourceforge.net/
</span
Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning
No full textAn improved clustering and recursive least squares (RLS) learning algorithm for Gaussian radial basis function (RBF) networks is described for modelling and predicting nonlinear time series. Significant performance gain can be achieved with a much smaller network compared with the usual clustering and RLS method
B-spline neural network assisted space-time equalization for single-carrier multiuser nonlinear frequency-selective MIMO uplink
No full textThis paper designs a nonlinear space-time equalizer based on B-spline neural network (BSNN) for the single-carrier high-throughput multiuser frequency-selective multiple-input multiple-output (MIMO) nonlinear uplink. Specifically, based on a BSNN parametrization of the nonlinear high power amplifiers (NHPAs) at mobile terminals' transmitters, a novel nonlinear identification scheme is developed to estimate the nonlinear dispersive MIMO uplink channel, which includes the BSNN models for the NHPAs at transmitters as well as the frequency-selective MIMO channel impulse response (CIR) matrix. Furthermore, the BSNN inverse models of the NHPAs are also estimated in closed-form. This allows the base station to implement nonlinear multiuser detection effectively using the space-time equalization (STE) based on the estimated frequency-selective MIMO CIR matrix and followed by compensating for the nonlinear distortion of the transmitters' NHPAs based on the estimated BSNN inverse models. Simulation results are utilized to demonstrate the superior bit error rate performance of our nonlinear STE approach for single-carrier high-throughput multiuser nonlinear frequency-selective MIMO uplink
Orthogonal least squares regression: An efficient approach for parsimonious modelling from large data
No full textThe orthogonal least squares (OLS) algorithm, developed in the late 1980s for nonlinear system modelling, remains highly popular for nonlinear data modelling practicians, for the reason that the algorithm is simple and efficient, and is capable of producing parsimonious nonlinear models with good generalisation performance. Since its derivation, many enhanced variants of the OLS forward regression have been developed by incorporating the recent developments from machine learning. Notably, regularisation techniques, optimal experimental design methods and leave-one-out cross validation have been combined with the OLS algorithm. The resultant class of OLS algorithms offers the state-of-the-arts for parsimonious modelling from large data.Other topics discussed in this talk include effective grey-box modelling by incorporating the prior knowledge naturally to the model structure, and further efficiency enhancement for the OLS forward regression modelling by implementing the branch and bound strategy. Some very recent extensions of this unified data modelling approach will also be briefly presented
Semi-blind fast equalisation of QAM channels using concurrent gradient-Newton CMA and soft decision-directed scheme
No full textThis contribution considers semi-blind adaptive equalization for communication systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the equalizer, are first utilized to provide a rough initial least-squares estimate of the equalizer’s weight vector. A novel gradient-Newton concurrent constant modulus algorithm and soft decision-directed scheme are then applied to adapt the equalizer. The proposed semi-blind adaptive algorithm is capable of converging fast and accurately to the optimal minimum mean-square error equalization solution. Simulation results obtained demonstrate that the convergence speed of this semi-blind adaptive algorithm is close to that of the training-based recursive least-square algorithm
An efficient predistorter design for compensating nonlinear memory high power amplifier
No full textThis contribution applies digital predistorter to compensate distortions caused by memory high power amplifiers (HPAs) which exhibit true output saturation characteristics. Particle swarm optimization is first implemented to identify the Wiener HPA’s parameters. The estimated Wiener HPA model is then directly used to design the predistorter. The proposed digital predistorter solution is attractive owing to its low on-line computational complexity, small memory units required and simple VLSI hardware structure implementation. Moreover, the designed predistorter is capable of successfully compensating serious nonlinear distortions and memory effects caused by the memory HPA operating in the output saturation region. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design
Organizational Form and the Economic Impact of Corporate New Product Strategies
No full textThis paper examines the role of organizational form in explaining the economic impact of corporate new product strategies. I find that the wealth effects associated with the announcements of new product introductions are more favorable for introducing firms with focused activities than for those with diversified activities. The results hold even after controlling for other factors suggested in the literature that could affect the value of new product introductions. The findings in this study suggest that the efficient investment hypothesis dominates the internal capital markets hypothesis in terms of the net economic impact of new product introductions on the introducing firms. Copyright 2007 The Author Journal compilation (c) 2007 Blackwell Publishing Ltd.
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