http://repository.iiitd.edu.in/xmlui/handle/123456789/1425 2026-04-10T19:53:53Z 2026-04-10T19:53:53Z Jana, Rana Kumar Srivastava, Anand (Advisor) Mitra, Abhijit (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1720 2025-04-03T22:00:15Z 2024-09-01T00:00:00Z

Towards C+L band elastic optical networks: a solution to overcome the opticalfiber capacity crunch problem Jana, Rana Kumar; Srivastava, Anand (Advisor); Mitra, Abhijit (Advisor) Over the past decade, there has been an exponential rise of high-data-rate, ultra-low-latency, and bandwidth-hungry applications at the client’s end, which has increased data traffic many-fold. Recent developments in 5G and cloud-based services have driven the compound annual growth rate (CAGR) of global Internet protocol (IP) traffic by nearly 30%. Hence, in the near future, per-month IP traffic growth will reach the zetta-bytes era. The COVID-19 pandemic has also catalyzed the usage of online services, leading to a 50% increase in data traffic compared to the pre-pandemic scenario. Recent forecasts indicate that, for the next ten years, the deployed infrastructure of the optical fiber backbone network will need to be upgraded to support this exponential growth of IP traffic to avoid the fiber-capacity-crunch problem. This has compelled network operators to seek out new strategies for enhancing network capacity while increasing minimum capital expenditure (CapEx). The use of multiband (MB) and multifiber technologies appears to be a promising alternative immediate solution in recent studies while having its own trade-o↵ to address this fiber capacity crunch problem and enhance the overall network capacity. MB technology considers the exploitation of the deployed standard single-mode fiber while enabling transmission on other bands (such as O, E, S, and L) along with the traditional C-band (1530 - 1565 nm). Although the multiband technology can use the full capacity of the existing deployed infrastructure, the challenges come from nonlinear impairments such as interchannel stimulated Raman scattering (ISRS). On the contrary, the multifiber-based solution focuses on the enlightenment of available dark fibers or the addition of additional parallel fibers. However, the CapEx for additional fiber resource deployment can play a crucial role in this context. The research in this thesis has focused on addressing the following fundamental questions in this context: • What is the cost-effective solution between MB and multifiber transmission? We justify this by performing techno-economic comparisons between these two solutions. As the L-band wavelengths provide the minimum attenuation after the C-band and as the L-band amplification appears as the most viable solution using the available commercial amplifiers, this thesis entirely focuses on C+L band transmission for analyzing the multiband scenarios. • Do the conventional resource allocation and spectrum management policies also work well for C+L band systems while considering the impact of physical layer impairments? • What will be the effective strategy to upgrade the existing C-band network towards the C+L band solution? • How can network survivability be ensured in the C+L band environment for geographically diverse networks? In the first part of this thesis, we have considered the C+L band physical layer model for estimating the quality of transmission (QoT) in multi-hop links while considering nonlinear interference (NLI) due to ISRS and other linear impairments (such as amplified spontaneous emission (ASE) noise). Consequently, we have made the techno-economic comparisons between the C+L band optical network with the multifiber C band scenario in the context of geographically diverse networks. Our results capture the variation of cost-per-bit with network traffic growth and fiber leasing cost. The numerical result indicates that the transmission over C+L bands is cost-effective compared to the multifiber C band scenario, particularly for large link-based geographies while considering the fiber leasing scenario. However, for smaller geographies, C+L band transmission becomes advantageous only if the fiber lease cost is high. As an alternate solution to fiber leasing, cable deployment scenarios are also considered separately for techno-economic comparison. The reported result shows that the C+L band system can postpone the need for extra cable deployment compared to the C band system and thereby minimizes the cost-per-bit in the long run. Furthermore, the comparison between optical cable deployment and fiber leasing is captured to minimize cost-per-bit while considering an operator’s domain knowledge about their network. After showcasing the benefits of C+L band transmission on cost-per-bit minimization, the next part of this thesis is focused on efficient spectrum management policies for the C+L band network to enhance the overall network capacity. Hitless spectrum defragmentation is mainly considered for the efficient utilization of spectral resources. The proposed work provides insights for network operators to develop the quality of service (QoS) maintenance strategy while doing spectrum defragmentation in the C + L bands. The proposed scheme prioritizes the minimization of the fragmentation index while maintaining the quality of transmission (QoT) for two different defragmentation algorithms, namely, nonlinear-impairment (NLI)-aware defragmentation (NAD) and NLI-unaware defragmentation (NUD). We leverage machine learning (ML) techniques for QoT estimation of ongoing lightpaths during spectrum reprovisioning. The optical signal-to-noise ratio (OSNR) of a lightpath is predicted for each choice of spectrum reprovisioning, which helps to monitor the effect of defragmentation on the quality of active lightpaths (in terms of assigned modulation format). Numerical results show that, compared to a baseline algorithm (NUD), the proposed NAD algorithm provides significant capacity increment for smaller and as well as larger networks. As the C+L band network is highly vulnerable to NLI, appropriate channel allocation during lightpath provisioning also becomes crucial for boosting the achievable capacity of the overall network. As a consequence, we have developed an efficient spectrum allocation strategy for C+L band networks. Unlike conventional schemes, the proposed scheme takes the effect of physical layer impairments (PLI) before the choice of the spectrum during resource allocation to reduce the likelihood of blocking. An algorithm under this scheme, namely, OSNR adaptive first–last-fit (OA-FLF), is proposed while leveraging the heterogeneity of the C+L band elastic optical network. The proposed algorithm selectively chooses the available channels among C and L bands to achieve the maximum network capacity in the long run. However, as the network evolves from the beginning-of-life (BoL) or end-of-life (EoL) situation, the analytical method of OSNR estimation under this OA-FLF algorithm becomes computationally intensive. We have leveraged the computational advantage of ML techniques to resolve this issue and utilized a deep neural network (DNN) model to predict the OSNR of all lightpaths during provisioning. Reported results show that, compared to the baseline algorithms, the proposed OA-FLF algorithm can provide gain in terms of traffic admissibility for smaller and as well as larger networks. As the existing C-band-based network can’t adopt these emerging technologies instantaneously, strategic planning needs to be done to upgrade the existing network infrastructure. Hence, the later part of this thesis focuses on efficient network upgrade strategies. First, the advantage of resource re-provisioning via selective movement of lightpaths from C-band to L-band is explored before upgrading the C-band network to C+L-bands. Later on, a novel strategy, named C to C+L Upgrade (CLU), is proposed to upgrade links from C to C+L bands gradually. We develop a recurrent neural network (RNN)-based model to efficiently predict links for the upgrade based on network state and spectrum utilization to reduce blocking and upgrade costs. Our results show that CLU outperforms baseline strategies (which do not employ predictive decisions) by upgrading fewer links at appropriate times. As the parallel multifiber C-band-based solution is beneficial for the network operators in the presence of their own dark-fibers availability, the later section of this thesis also explored the strategic network upgrade methodologies in this context. The advantage of adaptive margin allocation on network upgrades while effectively utilizing the monitoring data from optical performance monitoring equipment is shown for multifiber-based upgrade scenarios. The proposed approach considers periodic feedback from the network to gain domain knowledge and prioritizes adequate margin allocation before network upgrade initiation. Reported results show that usage of domain knowledge-assisted adaptive margin can postpone the requirement of the network upgrade, enhance the spectrum efficiency, and minimize cost-per-bit in the long run. Although operations over the C+L band can be an immediate and cost-effective solution for minimizing network upgrade costs, the impact on overall network reliability due to component failures needs to be considered for comprehensively assessing the true potential of the C+L band solution. Hence, the last part of this thesis is focused on the network survivability for C+L band networks while considering geographically diverse networks. In this context, we consider only single-band (either C or L band) inline amplifier failure scenarios. The provisioning of the backup lightpaths is prioritized over the same route as primary lightpaths using the alternate band. If the spectrum is unavailable in the primary routes, alternate routes are explored for backup path provisioning. Our approach measures the overall protection space of the network and the quality of the allocated lightpaths in geographically diverse networks. As a final step, we show the effect of the required Fill Margin (FM) on the achievable protection space and reliability of the network. To summarize, the research in this thesis is entirely focused on the C+L band optical networks while considering the impact of the physical layer impairments. Numerous significant aspects, such as techno-economic comparisons, cost-per-bit minimization, efficient spectrum resource management, periodic network upgrades, and reliability issues, have been explored in various parts of this thesis. The research output on these perspectives will be truly essential for network operators and vendors for practically enabling C+L band transmission in the core optical network.

2024-09-01T00:00:00Z Garg, Tushar Roy, Sayan Basu (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1718 2025-04-02T22:00:15Z 2024-12-12T00:00:00Z

Distributed adaptive parameter estimation and control with relaxed excitation conditions Garg, Tushar; Roy, Sayan Basu (Advisor) Over the years, distributed adaptive parameter estimation/control for multi-agent systems (MASs) has gained a lot of attention in the form of dynamical systems, where the concept of cooperative persistence of excitation (C-PE) is proposed for accurate estimation of unknown parameters. The C-PE condition relaxes the traditional persistence of excitation (PE) condition in the sense that it can be satisfied by incorporating multiple system signals with each of them not necessarily being PE. However, the C-PE condition is still restrictive due to its persistent nature, which is difficult to satisfy in many practical control applications. The main objective of this dissertation is to relax the stringent C-PE condition requirement while still designing efficient distributed adaptive systems by utilizing different network topologies. The research is structured into three key sub-problems - 1 > Developing a relaxed excitation condition based distributed adaptive parameter estimation (DAPE) algorithm considering undirected connected graph network topology along with control application, 2 > Extending the framework to strongly connected directed graph network while also analyzing the effect of communication delay, 3 > Proposing a generalized relaxed excitation condition for DAPE over weakly connected digraph topology along with extremum-seeking control application. We have conceptualized a new condition, called cooperative initial excitation (C-IE), which is milder than the classical C-PE condition. We have proved that the C-IE condition is sufficient to ensure convergence for the proposed distributed adaptive algorithms using a rigorous Lyapunov analysis. Simulation results validate the efficacy of the proposed algorithms.

2024-12-12T00:00:00Z Chaturvedi, Saumya Bohara, Vivek Ashok (Advisor) Srivastava, Anand (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1700 2024-10-10T22:00:25Z 2024-07-01T00:00:00Z

Design and resource optimization of sparse code multiple access-assisted communication systems Chaturvedi, Saumya; Bohara, Vivek Ashok (Advisor); Srivastava, Anand (Advisor) The evolution of next-generation communication systems, driven by applications like smart cities, factories of future, and autonomous vehicles, has led to a surge in communication devices and applications facilitated by 5G-and-beyond networks. While legacy orthogonal multiple access (OMA) schemes struggle to meet the demands of these applications, non-orthogonal multiple access (NOMA) has gained prominence for its potential to provide more links, lower access latency, and higher spectral efficiency. This thesis delves into the disruptive sparse code multiple access (SCMA) scheme proposed by H. Nikopour and H. Baligh, exploring its performance in various scenarios. Firstly, a novel codebook (CB) is designed for SCMA-based visible light communication (VLC) systems, addressing shot noise issues. An iterative algorithm optimizes the CB, and theoretical bit error rate (BER) expressions are derived, showing superior performance in simulations compared to existing literature. The study extends to an intelligent reflecting surface (IRS)-aided downlink SCMA system for sum-rate maximization, considering constraints like minimum user data rate, total power, SCMA CB structure, and IRS channel coefficients. An alternating optimization (AO) algorithm tackles the non-convex joint optimization problem, demonstrating significant performance improvements over the IRS-aided SCMA system without IRS. In the realm of unmanned aerial vehicles (UAVs), the report shifts focus to a SCMA- assisted UAV system. The objective is to optimize resource utilization and maximize the system data rate within energy constraints. We explore UAV 3D placement optimization, optimal UAV 3D trajectory computation, and spectral resource and transmit power allocation, showcasing the benefits of SCMA in enhancing communication systems through UAV assistance. Further, the investigation extends to SCMA with multiple UAVs, addressing challenges of inter-UAV and intra-UAV interference. The complexities involve equitable user allocation, frequency reuse-based subchannel assignment, and iterative algorithms for power and bandwidth allocation, presenting a comprehensive approach to optimize system performance in multi-UAV scenarios. In conclusion, this comprehensive study demonstrates the versatility and effectiveness of SCMA across diverse scenarios, ranging from VLC to IRS-aided systems and UAV- assisted networks, showcasing its potential to revolutionize next-generation communication systems.

2024-07-01T00:00:00Z Gupta, Akshita Bohara, Vivek Ashok (Advisor) Srivastava, Anand (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1697 2024-10-10T22:00:20Z 2024-09-01T00:00:00Z

Quality-of-service aware resource allocation for integrated fiber-wireless (FiWi) access networks Gupta, Akshita; Bohara, Vivek Ashok (Advisor); Srivastava, Anand (Advisor) This thesis aims to propose an efficient resource allocation framework to ensure the quality of service (QoS) to the users. The frameworks proposed in this dissertation can be efficiently utilized to provide QoS guarantees by efficient resource allocation not only in the static FiWi network scenarios but also for the mobile vehicular networks. Specifically, this thesis is structured into four sections, namely energy resource allocation for green FiWi networks, QoS-aware high throughput FiWi network, latency reduction in FiWi-based vehicular networks, and emergency path planning for safe-driving in transportation Metaverse (TransVerse). The first section of this dissertation analyzes the techno, socio, and economic impact of energy resource allocation and provides 24/7 services to the users. In order to do so, we utilize a 3rd generation partnership project (3GPP) based model to generate the user request at the optical network unit (ONU) and access point (AP) collectively known as ONU-AP. These requests were used to calculate the load of the ONU-AP and further model the energy requirement, as well as allocate energy resources to provide 24/7 services to the users. In the second section, we extend the above work by utilizing the last 10 years’ data on available solar power to predict the amount of solar power that can be generated on a particular day. This would reduce the amount of charging and discharging cycles that are required by the battery and would improve the lifetime of the batteries. In a communication network, maintaining the latency and reliability requirements along with throughput is also crucial. Consequently, the second section of the thesis utilizes key performance indicators (KPIs) such as latency, reliability, and data rate-based classification to classify the data track at the APs into different services, such as voice, video, and best track. Based on this classification, the data traffic is assigned to different traffic containers (T-CONTs) of the PON. Depending on the KPIs, a priority-based bandwidth allocation scheme is designed to improve the QoS of the users. Moreover, during high mobility scenarios, meeting the QoS requirements becomes all the more di cult. Thus, the later part of this thesis extends the QoS guarantees for connected vehicular networks. Consequently, an integrated next-generation passive optical network 2 (NG-PON2) and IEEE 802.11p-based vehicle-to-infrastructure (V2I) network is proposed to facilitate the stringent requirements of sixth-generation (6G) vehicular networks. This work minimizes the latency of the FiWi-based V2I network by utilising a machine learning (ML) based T-CONT priority assignment wavelength allocation algorithm that minimizes the number of wavelength switching instances in the PON, sub- sequentially reducing the latency of the network. We further improve the performance of vehicular network by utilizing vehicle-to-vehicle (V2V) services along with V2I services. This requires the selection of an optimal cluster head for the offloading of V2V traffic. In order to do so, we utilize a double deep Q network (DDQN)-based cluster head selection policy to select the cluster head to minimize the energy and latency of the vehicular network. Furthermore, as a part of the 6G network, the research community has been recently focusing on emerging technologies such as digital twins (DTs), blockchain, extended reality, etc. Thus, in the last section, we exploit a transportation Metaverse (TransVerse)-enabled vehicular network to offload real-world vehicular data tra c to the vehicular cloud network and allocate physical and virtual resources in the network. We consider a pre-emptive emergency situation and formulate a utility-to-cost ratio optimization problem to minimize the collision risk along with the latency and computation cost of the network. Further, we utilize the prior information processing in the DT-enabled vehicular traffic to enhance the lane-change experience of the users. The frameworks proposed in this dissertation can be efficiently utilized to provide QoS guarantees by efficient resource allocation not only in the static FiWi network scenarios but also for the mobile vehicular networks. This will be helpful for a communication engineer to design a sustainable system that exploits new technologies, such as DT-enabled Metaverse, to guarantee the required QoS to the end users.

2024-09-01T00:00:00Z