Internet access for passengers travelling in aircrafts is thought to be one of the unresolved major challenges for ubiquitous Internet provision. Vast oceanic remote regions along the busy air routes of the world require low-cost, reliable, and high-speed Internet for the aircraft. Satellite links can provide Internet coverage in such remote areas; however, their services are still costly with low bandwidth and longer delays. Fortunately, the submarine optical cables deployed across the oceans pass along the same busy air routes. These cables can be utilized as high-speed Internet backbone for wireless Internet access to the aircraft. Dedicated ships stationed along these submarine optical fiber cables can be exploited to provide Internet, security, and navigation services to aircrafts and ships. A novel architecture for such a ground/sea-to-air access network is proposed. A complete solution, design, and analysis of the proposed technique are thoroughly discussed. In contrast to the traditional land mobile radio cellular systems, the high speed of the aircraft results in reduced available handover time margins. To address the challenges related to the high-speed mobility of aircraft, an analysis for the impact of various parameters on the performance of handovers is presented. Using the proposed analytical model, a mathematical relation for the handover margin with the velocity of aircraft, direction of the aircraft's motion, and propagation environment is derived on the basis of path-loss propagation model.

Abstract:The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker mimicking, tactile sensor is presented. Unlike previous designs reported in the literature, this sensor mimics the mouse whisker not only mechanically, but it also makes macro movements just like a real mouse whisker in a natural environment. We have developed a mathematical model and performed finite element analysis using COMSOL, in order to optimise the whisker to have the same natural frequency as that of a biological whisker. Similarly, we have developed a control system that enables the whisker mimicking sensor to vibrate at variable frequencies and conducted practical experiments to validate the response of the sensor. The natural frequency of the whisker can be designed anywhere between 35 and 110 Hz, the same as a biological whisker, by choosing different materials and physical dimensions. The control system of this sensor enables the whisker to vibrate between 5 and 236 Hz.

1 Abstract-This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A nonrandom and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10 -1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size.

International audienceThis paper presents a new troubleshooting methodology for 3G Long Term Evolution (LTE) networks based on a closed-form expression between Radio Resource Management (RRM) and Key Performance Indicator (KPI) parameters, using statistical learning. This methodology aims at locally optimising the RRM parameters of the cells with poor performance in an iterative manner. The optimization engine uses the closed-form relationship to calculate the optimized RRM parameters for these cells. The main advantage of this methodolgy is the small number of iterations required to achieve convergence and the QoS objective. A troubleshooting application scenario involving mobility in LTE networks is considered. Numerical simulations illustrate the benefits of our proposed schem

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