M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

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    ItemOpen Access
    Applications of deep-learning at digital communication receiver
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2020) Nanavati, Tilak Digantkumar; Vasavada, Yash
    Modulation and demodulation are fundamental modules for communication systems. The modulation techniques — Offset QPSK (OQPSK), p/2 BPSK, p/4 QPSK and GMSK — are frequently applied in the power-constrained wireless communication links (e.g., the terminal transmission links of several 2G, 3G and 4G terrestrial and satellite air-interface standards). However, their detailed numerical comparison of the performance and functional characteristics are currently lacking in the literature. The prior studies have focused on a comparison of at the most two of these four schemes (typically OQPSK versus GMSK). One of the objectives of this thesis is to bridge this gap. We provide a detailed comparison of (i) the spectral regrowth and (ii) probability of bit error Perrb versus Eb/N0 performance of these four modulation schemes in the presence ofAM/AMandAM/PM non-linearities with varying backoff (BO). We believe that our results with key observations will be beneficial in selecting an appropriate modulation technique when designing practical communication systems. Another crucial component of communication and signal processing systems is the estimation of channel parameters. In the practical communication systems, the varying channel conditions and non-linear channel impairments make the task of estimation more challenging. We propose a Deep Learning (DL) application at digital communication receiver to estimate the channel impairments that are difficult to describe with a rigid mathematical tractable model. Another objective of our research work is to develop a learned parameter estimator that effectively captures the non-linear functional mappings and produces accurate estimations. The results for Phase Offset (PO) impairment estimations obtained with our proposed approach give competitive accuracy concerning its baseline equivalent. Lastly, we demonstrate the learning-based modulation classifier that potentially solves the misclassification problem presented in an earlier study.
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    ItemOpen Access
    Symbol detection in MIMO systems
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Dhaka, Kalpana; Chakka, Vijaykumar
    Multiple input multiple output symbol detection methods are observed under frequency flat fading AWGN channel condition. The modulation method employed is Quadrature amplitude modulation (QAM). The various symbol detection techniques are compared to observe their behavior under AWGN channel condition condition. Maximum likelihood (ML) symbol detection method gives the best performance but because of its high complexity it can’t be used. Sphere decoder reduces the complexity to some extent providing similar performance as ML estimate. The other methods used are Zero forcing and Minimum mean square stimation. These two methods when used successively for interference cancellation improves performance to large extent along with reduction in the cost. The technique employed for successive detectionis devised by bell laboratory hence it is called as V-BLAST method. Maximum a posteriori when used along with V-BLAST MMSE algorithm further improves the performance. These methods even works well under Rayleigh channel condition. Finally, the simulation results for performance of V-BLAST under both the channel condition are observed for all the symbol detection techniques. To increase the diversity for improved performance space time trellis code are employed. Their performance is observed for QPSK modulation scheme.