Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/13608
Title: DEVELOPMENT OF NEURAL NETWORK • BASED. ADAPTIVE BEAMFORMING ALGORITHM
Authors: Khanna, Rishi Kumar
Keywords: ELECTRONICS AND COMPUTER ENGINEERING
NEURAL NETWORK
ADAPTIVE BEAMFORMING
ANTENNAS
Issue Date: 2012
Abstract: In the last decade, the application of adaptive antennas to mobile communications has attracted considerable interest. Adaptive antennas, in a broad sense, implement spatial filtering by means of beamforming, using an antenna array with a small number of. antenna elements at the base station. In uplink, the received signals of all antenna elements are complexly, and adaptively weighted in accordance to some performance criterion, to either enhance the carrier-to-interference ratio for the reception of a single mobile's signal, or ultimately, serve many mobiles, transmitting atthe same time, and at the same frequency, but spatially separated by utilizing the spatial filter to separate the mobiles' signals, thus implementing 'spatial-domain-multiple-access, SDMA. To establish the performance criterion, adaption algorithms need a known reference for operation, either a training sequence embedded in the received signals (temporal reference), or the direction-of-arrival of the impinging signals (spatial reference), no explicit reference, except some knowledge of the impinging signal's properties (blind algorithms), or combinations thereof. Algorithm research for adaptive antennas is a vivid and ongoing discipline, as new mobile communication applications, and requirements emerge. Performance evaluation of these algorithms is possible by computer simulations to a certain extent which is carried out in this dissertation.
URI: http://hdl.handle.net/123456789/13608
Other Identifiers: M.Tech
Appears in Collections:MASTERS' DISSERTATIONS (E & C)

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