M Tech (EC) Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/6

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Subject: search.filters.subject.Adaptive Algorithms

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    Identification of Block-Sparse Systems using Adaptive Filtering Algorithms
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Sonia; Das, Rajib Lochan
    An adaptive filter is a system with a linear filter that has a transfer function controlled byvariable parameters and a means to adjust those parameters according to an optimizationalgorithm. Adaptive filters are used for linear time-variant systems where thecharacteristics of the systems keep on changing with time. Therefore, adaptive filters arerequired for some applications when some parameters of the desired processingoperation are not known in advance or are changing.In the world of adaptive algorithms, sparse system identification has received a lot ofinterest. In numerous applications, including acoustic echo cancellation, interferencereduction in industrial settings, and biomedical engineering, system identification isregularly encountered. During the last ten years, system identification has been widelyused in a variety of signal processing applications, including wireless communication,radar imaging, and echo cancellation.A sparse impulse response is one in which a significant portion of the energy orinformation is concentrated in a few number of its impulse response coefficients. Thereare few non-zero or high coefficients and numerous tap-weights with zero or tiny valuesin various cases, such as network echo cancellation, where the impulse responses aresparse. Sparse systems come in a variety of forms. The conventional one is referred to asa block-sparse system, like TV transmission channels. The non-zero coefficients ofblock-sparse systems consist of one or more clusters, and a cluster is a set of non-zero orbig coefficients, in contrast to generic impulse response sparse systems where largecoefficients are distributed at random. This thesis has taken into consideration various existing adaptive algorithms, viz, LMS, NLMS, PNLMS, ZA-NLMS, ZA-PNLMS, BS PNLMS, BS-IPNLMS to identify a block-sparse system with the help of mean squareerror and the convergence rate of the coefficients. It continues to give a proposedalgorithm with some modifcations to get a better convergence rate for the coefficients ofan unknown system which is assumed to be a block-sparse system for our research.
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    ItemOpen Access
    Channel Estimation for Orthogonal Time Frequency Space Modulation using Recursive Least Squares
    (2021) Singh, Bhavesh Amar; Das, Rajib Lochan; Vasavada, Yash
    For its capacity to provide high data rates to a wide number of users, 4G wireless communications had a huge success in the previous decade. With the Internet of Things (IoT) and high mobility scenarios such as vehicle-to-vehicle (V2X) connections on the horizon, the Orthogonal Time Frequency Space (OTFS) modulation scheme has ignited lot of attention in recent years as a viable alternative to OFDM, especially in scenarios involving high user mobility. OTFS has its specialty that it is designed in the delay-Doppler domain. OTFS modulation, when combined with an appropriate equaliser, easily leverages the whole channel variety in both time and frequency. It transforms a fading, time-varying wireless channel used by modulated communications like OFDM into a time-independent channel with a nearly complex channel gain for all symbols. This thesis makes a note on existing drawbacks of OFDM and highlights the usage of a new 2-D modulation scheme called OTFS modulation. It goes on to detail the various methods of channel estimate currently in use while installing OTFS and suggests the use of an adaptive algorithm for channel estimation in the delay-Doppler domain. The proposed algorithm, unlike widely used channel estimation methods, estimates channel gain in the time domain and Doppler taps in the delay-Doppler domain.