http://repository.iiitd.edu.in/xmlui/handle/123456789/872 2026-04-10T15:25:37Z 2026-04-10T15:25:37Z Joshi, Himani Darak, Sumit Jagdish (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/946 2021-12-15T22:00:19Z 2021-11-01T00:00:00Z

Intelligent and reconfigurable ultra-wideband spectrum characterization at sub-nyquist rate Joshi, Himani; Darak, Sumit Jagdish (Advisor) Historically, throughput is one of the key performance indicators driving the transition to next-generation cellular networks. The throughput per square kilometer depends on three factors: 1) Available spectrum, 2) Base station density, and 3) Spectrum utilization efficiency. The mmWave spectrum (24 GHz - 100GHz) is actively being explored to augment the sub-6 GHz spectrum (450 MHz6000 MHz) due to the availability of a wide spectrum and low auction cost. However, it has limited coverage and range, limiting its usefulness in indoor short-range mobile broadband services. This makes the sub-6 GHz spectrum a preferred candidate for outdoor communications and network coverage services. The high auction cost of the sub-6 GHz spectrum limits the licensed spectrum, and base station density is constrained due to infrastructure cost, handover overhead, and interference constraints. Thus, innovative ways to utilize the sub-6 GHz spectrum efficiently needs to be explored. One promising solution is dynamic spectrum sharing which is now a de facto approach in cellular networks. For instance, 5G supports the deployment in shared (2.3 GHz Europe / 3.5 GHz USA) and unlicensed (2.4 GHz / 5-7 GHz / 57-71 GHz global) spectrums along with licensed noncontiguous spectrum. Joint radar-communication systems are being explored to improve the utilization of a large section of the sub-6 GHz spectrum allocated to radar applications. Similarly, IEEE 802.15.4 for industrial internet-of-things (IIoT) networks support deployment in 250-740 MHz, 3.1 4.8 GHz and 6 - 11.6 GHz. To enhance spectrum efficiency, multi-antenna systems are being explored, allowing multiple users to communicate simultaneously over a given frequency band. This demands wideband spectrum analyzer (WSA) for the digitization of ultra-wide non-contiguous spectrum (UWNS), and capability to identify the transmission opportunities in time, frequency and spatial domains reliably. The traditional approaches need complex hardware and signal processing algorithms that question their suitability for real-time requirements. iii In this thesis, we focus on the sub-Nyquist sampling (SNS) and sparse antenna array based intelligent and reconfigurable WSA for the digitization and spatial sensing of UWNS using low-rate analog-to-digital converters (ADCs). In the first contribution, we explore reconfigurable SNS, which allows the digitization of a non-contiguous spectrum. The non-contiguous nature demands learning the occupancy of various parts of the spectrum since spectrum digitization can fail when the number of occupied bands in a digitized spectrum is higher than that of ADCs. On the other hand, high throughput requirement demands digitization of as wide spectrum as possible. We address such a trade-off via Multi-Play Multi-Armed Bandit (MPMAB) framework. The functionality of the proposed intelligent and reconfigurable WSA is validated using real radio signals via universal software radio peripheral (USRP) testbed. After successful digitization and identification of vacant spectrum, the next contribution deals with the characterization of the occupied spectrum. We extend the WSA using a multi-antenna approach to enable blind identification of carrier frequency, angle of arrival and modulation scheme. It is referred to as ultra-wideband angular spectrum sensing (UWASS). The UWASS receiver overcomes the limitation of existing methods in which the number of antennas depends on the spectrum sparsity making it computationally efficient. The performance of the UWASS receiver is analyzed for uniform and sparse antenna arrays. In the third contribution, we develop a realistic multi-antenna USRP testbed to demonstrate the functional correctness of the UWASS receiver for various parameters such as signal-to noise ratio (SNR), spectrum sparsity, antenna array, and its size. Recently, deep learning has outperformed conventional statistical and machine learning based spectrum characterization methods. In the fourth and last contribution, we explored various deep learning approaches for spectrum reconstruction and characterization. Specifically, we propose a novel non iterative wideband deep learning-based modulation classification (WDLMC) which can simultaneously identify the frequency band status and the modulation scheme of all the frequency bands in the digitized spectrum compared to existing iterative approaches. We also propose deep learning based spectrum reconstruction for UWASS as an alternative to the conventional orthogonal matching pursuit (OMP) approach. In-depth performance analysis validates the functional correctness and superiority of the proposed approach over state-of-the-art approaches in terms of computational complexity and execution time. iv to summarize, the proposed intelligent and reconfigurable WSA offer efficient and hardware friendly solutions to improve the utilization of the sub-6 GHz spectrum by identifying the spectrum opportunities in time, frequency and spatial domains.

2021-11-01T00:00:00Z Kimothi, Dhananjay Biyani, Pravesh (Advisor) Hogan, James M. (Advisor) Kelly, Wayne (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/943 2021-12-15T07:06:54Z 2021-10-01T00:00:00Z

Learning representations for molecular sequences Kimothi, Dhananjay; Biyani, Pravesh (Advisor); Hogan, James M. (Advisor); Kelly, Wayne (Advisor) Learning Representations for Molecular Sequences Sequence comparison is a vital step in bioinformatics tasks such as annotation of molecules, phylogeny construction, and sequence retrieval. Methods for sequence comparison are broadly divided into alignment-based and alignment free approaches. Alignment-free methods offer some computational advantages – with some loss of sensitivity - and usually rely on high dimensional vector representations based on the bag of words model. This representation excludes contextual information from the sequences. Recent work on representation in Natural Language Processing (NLP) has gained wide popularity across a number of domains. These methods essentially work on the “distributional hypothesis” – that words that occur together frequently have some semantic relationship - and provide a way to generate low dimensional distributed representations of words or sentences while considering contextual information. Motivated by these works in NLP, in this thesis, we aim to address the limitations of alignment and alignment-free methods by developing representation learning methods for bioinformatics tasks. It is notable that for many problems in bioinformatics, the available metadata also plays a role in biological inference. Representation learning frameworks allow metadata to be accommodated along with contextual information in the process of generating sequence representations. More specifically, this research aims to develop scalable, computationally efficient and competitive alignment-free solutions for bioinformatics problems such as protein classification, retrieval, and proteinprotein interaction predictions. The main contributions of this thesis are as follows: • Seq2Vec – a new unsupervised framework for learning useful low dimensional representations of molecular sequences. • SuperVec and SuperVecX – novel approaches for fusing meta and sequence information to generate improved embeddings of molecular sequences. • H-SuperVec(X) – a hierarchical algorithm utilising learned representations for sequence comparisons, achieving performance comparable to alignment-based approaches at substantially lower computational cost. • A hybrid system that utilises these alignment-free approaches as a rapid pre-processing filter to reduce the candidate set for an alignment-based algorithm, yielding a substantial speed up in the overall process. • Demonstrated utility of these representation-learning based approaches for a variety of bioinformatics problems, e.g., protein family prediction, protein-protein interactions and homologous sequence retrieval. The representation learning and task-specific approaches proposed in this thesis are generic and can be adapted for similar problems within bioinformatics and other domains.

2021-10-01T00:00:00Z Ahmad, Rizwana Srivastava, Anand (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/942 2021-12-15T07:07:21Z 2021-05-01T00:00:00Z

Performance improvement in LiFi using advanced modulation techniques and learning-based coexistence with WiFi Ahmad, Rizwana; Srivastava, Anand (Advisor) Keywords- LiFi, WiFi, O-OFDM, PAPR, DP-OFDM, GMSK, DFT, O-GFDM, HLWN, load balancing, RL, RWP, ORWP. A significant increase in wireless communication has been observed over the past decade. The existing radio frequency (RF) based communication network is not able to cope up with this influx in connections and data requirements. In order to meet future needs, researchers have started investigating Light Fidelity (LiFi) for the indoor environment. LiFi offers various advantages over RF, such as a vast spectrum, spatial reuse, and inherent security. Furthermore, LiFi does not interfere with the devices operating in the RF spectrum. However, LiFi technology has its limitations; the major challenges of the LiFi system include the non-linearity due to LiFi front end, limited front-end bandwidth, and susceptibility to blockages. In this dissertation, we have tried to address these aforementioned challenges. Firstly, we propose an adaptive learning architecture (ALA)-based predistoter to mitigate the effect of front-end non-linearity. The proposed ALA predistoter achieved near-linear performance in terms of amplitude-amplitude (AM/AM) distortions and constellation plots for different LiFi front-ends non-linearity. Secondly, in order to support high data rates with limited front-end bandwidth, highly spectral efficient modulation schemes such as optical orthogonal frequency division multiplexing (O-OFDM) are required. Nonetheless, the major drawback of O-OFDM is that it suffers from a high peak-to-average power ratio (PAPR), which causes clipping distortion, reduces the illumination-to-communication conversion efficiency, and affects the lifetime of the LED. Therefore, in this thesis, we propose advanced spectrally efficient low PAPR modulation schemes such as double precoded optical orthogonal frequency division multiplexing (DPOOFDM) and optical-generalized frequency division multiplexing (O-GFDM). The simulation results validate that the proposed DP-OOFDM with interleaved subcarrier mapping provides PAPR as low as 2.1 dB compared to 12.7 dB for the corresponding O-OFDM counterpart. Lastly, in order to deal with the problem of blockages in LiFi, the coexistence of LiFi and WiFi has been proposed in the literature. However, an appropriate load balancing strategy plays a vital role in the overall performance of such heterogeneous LiFi WiFi networks (HLWN). Nonetheless, the problem of load balancing of HLWN is a non-convex mixed-integer nonlinear programming (MINLP) optimization problem, i.e., it is mathematically intractable. Therefore, in this thesis, we propose a reinforcement learning (RL) based load balancing technique for HLWN. Additionally, we also explore the effect of different mobility models and link aggregation in HLWN. Simulation results illustrate that the proposed RL-based method can ensure near-optimal performance at relatively low complexity. The proposed frameworks in this dissertation can be utilized in LiFi standards. It will be helpful for LiFi communication engineers to design an efficient physical layer and intelligent load balancing scheme for HLWN without performing extensive simulations.

2021-05-01T00:00:00Z Gupta, Rahul Hashmi, Mohammad S. (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/940 2021-12-15T07:06:32Z 2021-07-01T00:00:00Z

Innovative augmentation of impedance transformation in the design of highly flexible multi-functional RF/microwave circuits and components Gupta, Rahul; Hashmi, Mohammad S. (Advisor) The burgeoning of a multi-standard wireless communication system (WCS) with the multitude of emerging applications has been permeating the design and development of radio frequency (RF) circuits and components of the front-end subsystem. To support multiple standards, the design and development of the multi-band RF and Microwave circuits and components are highly desirable. This is due to the fact that these wireless standards are operating at multiple frequencies, and therefore, the multi-band architectures reduce system size, cost, power consumption, etc., rather than the conventional approach of using individual subsystems for the individual frequencies. Furthermore, the multi-functional RF components eliminate the redundant uni-functional components and associated interconnections between the uni-functional components of a communication system by exhibiting all the features inherently. This further reduces the system size/volume, power consumption, insertion loss, etc. However, it should be noted that the design and development of such multi-band multifunctional components are found to be very challenging. The key constraints are the achievable frequency ratios, achievable impedance transformation ratios, and increased design complexities. For example, the literature is replete with the dual-band impedance transformers, but the range of frequency ratios and impedance transformation ratios is limited. More importantly, the concurrent operation of high impedance transformation ratios at high frequency ratios is much limited. One of the possible reasons is the limited design flexibility at the expense of increased functionality. Subsequently, the requirement of multi-functional characteristics affects the design flexibility further. Furthermore, the development on the domain of multi-band multi-functional components is not explored much in the literature. Therefore, this thesis aims to investigate and address the existing lacunae on the design and development of multi-band multi-functional components. Firstly, the challenges associated with the impedance transformers for high impedance transformation ratios at high frequency ratios are discussed and addressed. Also, the concurrent high frequency ratios and high impedance ratios are accomplished. Secondly, the RF and Microwave components exhibiting multiple inherent features like impedance transformation, DC blocking, differential phase shifts, balanced-to-unbalanced signal conversion, etc., are investigated. In addition, the multi-functional architectures, inherently exhibiting more than one feature, with dual-band operations are addressed. More specifically, the developed architectures for operation at high impedance transformation ratios and high frequency ratios, even concurrently, are accomplished. Thirdly, this thesis addresses the challenges associated with the arbitrary impedance environments, varying with the design frequencies. The architectures for dual-band impedance transforming power divider with frequency-dependent complex port impedances at two arbitrary design frequencies are presented. No such feature from a dual-band power divider or combiner is presented in the literature. Finally, it should also be noted that the reported design architectures are simplified and uni-planar and are supported by sound and systematic analytical design solutions, which is rare in the literature. The closed-form equations with innovative design strategies make the designs re-configurable for the wide range of design specifications, and thus the enhanced micro strip compatibility is attained. The closed-form design equations not only make it easy to calculate the design parameters but also enables the quick prototyping of the circuits and components. Overall, the contributions are within the realm of simplifying the design strategies and rapid prototyping backed by the closed-form design equations for multi-frequency communication circuits and systems. In brief, this research work has the potential to significantly advance the current state-of-the-art that may eventually lead to a paradigm shift in the way such systems are designed. This thesis also paves a new dimension of the applications of multi-functional components, which leads to a unified PCB solution for the RF front end of a communication system. The future directions and possible improvements are also reported.

2021-07-01T00:00:00Z