The return channel in collective installations

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Abstract

This paper presents partial results of the technological ACTS project S3M1 (AC313) which provides technological solutions to offer multimedia applications and interactive digital television services to users living in collective installations. These installations known as SMATV (Satellite Master Antenna Television Systems) are a combination of typical CATV networks and individual satellite installations. The main objective of the above project is to provide innovative solutions for the physical and medium access control layers for the interaction channel in these kind of collective installations. Additionally the S3M project is developing multimedia applications to be run on the mentioned infrastructures considering both scenarios of applications Small Office Home Office (SoHo) as well as Interactive Digital TV. The S3M project have analysed the behaviour of the existing collective installations infrastructures (the new and the old installations) deriving the transfer functions of these networks and analysing the degradation suffered for the interaction channel signal when passing through the network and the installed devices. This paper presents the results obtained from simulations and measurements performed on these kind of networks when typical signals used in the return channel characteristics is passed through the network. Several type of installations (inductive, resistive, old and new) have been taken into account in the computer simulations and in the laboratory tests. The simulations and the measurements carried out have allowed to reassess the design parameters have been reassessed and has been derived that the solutions proposes by S3M project allows that the users living in collective installations can enjoy of the new emergent multimedia applications associated to the digital TV technology.

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APA

Panduro, M. A., Molina, A. M., & Rodríguez, D. (1999). The return channel in collective installations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1629, pp. 143–153). Springer Verlag. https://doi.org/10.1007/3-540-48757-3_11

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