In this paper, we propose Multi-Rate Bandwidth Available in Real Time (MR-BART) to estimate the end-to-end Available Bandwidth (AB) of a network path. The proposed scheme is an extension of the Bandwidth Available in Real Time (BART) which employs multi-rate (MR) probe packet sequences with Kalman filtering. Comparing to BART, we show that the proposed method is more robust and converges faster than that of BART and achieves a more AB accurate estimation. Furthermore, we analyze the estimation error in MR-BART and obtain analytical formula and empirical expression for the AB estimation error based on the system parameters.Comment: 12 Pages (Two columns), 14 Figures, 4 Tables

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In this paper, we propose an efficient method to estimate the end-toend Available Bandwidth (AB) of a network path. This method employs multi-rate (MR) probe packet sequences with Kalman filtering. Our proposed method is an extension of the Bandwidth Available in Real Time (BART) method, so that the probe packet sequences are injected into the network path of interest. Using this technique, the probing rate within each probing sequence is varied. We show that by a marginal addition to the computation complexity compared to the conventional BART technique, the proposed method converges faster than that of BART and achieves a more accurate estimation. In addition, this method is more robust against inappropriate initial value of Kalman filter than the conventional BART method. Furthermore, we obtain the estimation error of MR-BART based on the parameters of the probe packet sequences.

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Localization in wireless networks is possible by measuring some characteristics of the propagating signal related to the position of the user, which is always corrupted by noise components. In this paper, a correntropy based algorithm is proposed for localization and tracking of a mobile station in wireless networks. The performance of the proposed algorithm is compared with the Least Mean Square (LMS) and Least Mean P-norm (LMP) algorithms, and its preference aspects are discussed. The results show that, using correntropy can bring robustness to localization and improve performance in many realistic scenarios such as fat-tail noise distributions, one of the serious bottlenecks of the next-generation 5G wireless communications systems. In addition, it is shown that, the Gaussian kernel of the correntropy function reduces the sensitivity of the algorithm to the learning rate.