http://localhost:8081/jspui/handle/123456789/9 Thu, 07 May 2026 18:14:29 GMT 2026-05-07T18:14:29Z http://localhost:8081/jspui/handle/123456789/20769 Title: Hybrid Index and Number Modulation Authors: Kumar, K. Naresh Abstract: Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier transmis sion technique used in many high data rate and wide bandwidth wireless communi cation systems including cellular telecommunications and Wi-Fi. Data is transmitted using parallel subcarriers with in an OFDM symbol. It has many advantages like im munity to selective fading, resilience to interference, resilient to narrow band effects, simpler channel equalization, etc. which makes it a promising candidate for future wireless systems. In this thesis, Hybrid Index and Number modulation (HIN) technique is proposed for enhancing both Energy Efficiency (EE) and Spectrum Efficiency (SE) by effectively transmitting additional data bits by changing both the number and index of activated sub carriers with in an OFDM subblock. A low complexity threshold based detector is also proposed which detects the activation pattern, decodes information bits and demodulate the pulse amplitude modulation symbols on activated subcrriers. The SE and Bit Error Rate (BER) performance analysis for the proposed OFDM-HIN technique are carried out using Monte Carlo simulation study. The proposed OFDM-HIN scheme offers better BER and throughput performance in comparison to OFDM-Index Modulation, OFDM-Subcarrier Number Modulaion, and classical OFDM with equivalent SE and power at mid to high SNR values. Thu, 01 Jul 2021 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/20769 2021-07-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/20768 Title: Quantized Feedback-based Space Shift Keying in Visible Light Communication Authors: Rao, C. Sivanjan Abstract: This work proposes a novel feedback-based space shift keying (SSK) to overcome the lim itations of conventional SSK in visible light communication (VLC). The limitations of con ventional SSK are the symmetric nature of VLC channel and fixed spatial constellation. The symmetric nature of the VLC channel makes two or more channel gains equal at many loca tions. This causes a high bit error rate (BER). In addition, the fixed spatial constellation may not follow gray mapping into real-line constellation for all locations and this leads to high BER. Due to these limitations, the BER performance of SSK is poor. To overcome all these limita tions, the proposed scheme gives unequal power allocation (PA) between LEDs and uses an adaptive spatial constellation based on the knowledge of channel gain ordering. The proposed scheme requires only a finite number of feedback bits to transfer the information of channel gain ordering at the transmitter side. Since it requires few feedback bits, hence the proposed scheme named as quantized feedback-based SSK (QFSSK) scheme. It is observed from analy sis that the proposed QFSSK scheme significantly improves BER performance at all locations compared to conventional SSK with very finite feedback bits. Tue, 01 Jun 2021 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/20768 2021-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/20767 Title: Channel Estimation in Massive MIMO considering Hardware Non-Linearities using Deep Learning Authors: Kumar, Ashish Abstract: Deep learning is getting more popular in areas like computer vision and natural lan guage processing as it is difficult to describe real-world images and languages using rigorous mathematical models. Although deep learning algorithms are easy to imple ment, it is difficult to generate robust algorithms for real systems that include non linearity. This dissertation discusses the combined effects of non-linearities present in the hardware of base station (BS) and user equipment (UE) on single cell multiple input multiple output (MIMO) uplink performance in the practical Rican fading environment. The effective channel and distortion characteristics based on Bussgang decomposition are derived from the analytical method. Two deep feed forward neural networks are trained to estimate the effective channel and distortion variance at each BS antenna used for signal detection. The performance of the proposed method is compared with the Bayesian Estimator (Linear Minimum Mean Square Error (LMMSE)) for distortion aware and unaware scenario. The proposed deep learning based estimator uses attenu ation characteristics to improve the quality of the estimate, while the LMMSE method treats distortion as noise. Efficiency for non-linearity of order three or higher with deep learning-based estimators is significantly high which can be shown using the data gen erated by derived effective channels considering both the BS and UE non-linearities of general order, and hence deep learning based provides better estimate of the channel. Tue, 01 Jun 2021 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/20767 2021-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/20766 Title: Detection of Epileptic Seizure Using Accelerometer’s Time Series Data and Hidden Markov Model Authors: Agrahri, Anshuman Abstract: As per a survey done worldwide, it is found that there are approx 50 million people living with epilepsy (0.5% to 1% of the average population), and the percentage are comparatively far more in developing countries to that of the developed countries. The count of India goes to approx 10 million which is 20% of the world epilepsy patients. Epileptic seizures happen due to brain dysfunction, which can be read by electrical activity in the brain as it assumed that there is sudden rush of electrical activity during seizures. When we visualise an automatic seizure detection system an accelerometer based device would be more practical because of its ease of wearability and usability. This project presents Hidden Markov Model probabilistic approach to detect the epileptic seizure. The accelerometer-based signal has been taken and features are extracted and then from the extracted feature relevant features are selected. Two different models for non-seizure and seizure data have been created. After the training of the module when any test data comes it passes through both the model and score is calculated depending upon the score classification of test data is decided. The result has also been compared with deep learning approach (LSTM) and it is found with the given peculiarities of data (highly unbalanced and small size) the performance of HMM is better than LSTM. It is expected that the project will become a vital tool for the implementation and research work of epileptic seizure detection. Tue, 01 Jun 2021 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/20766 2021-06-01T00:00:00Z