Intelligent Ranking for Dynamic Restoration in Next Generation Wireless Networks

Emerging 5G and next generation 6G wireless are likely to involve myriads of connectivity, consisting of a huge number of relatively smaller cells providing ultra-dense coverage. Guaranteeing seamless connectivity and service level agreements in such a dense wireless system demands efficient network management and fast service recovery. However, restoration of a wireless network, in terms of maximizing service recovery, typically requires evaluating the service impact of every network element. Unfortunately, unavailability of real-time KPI information, during an outage, enforces most of the existing approaches to rely significantly on context-based manual evaluation. As a consequence, configuring a real-time recovery of the network nodes is almost impossible, thereby resulting in a prolonged outage duration. In this article, we explore deep learning to introduce an intelligent, proactive network recovery management scheme in anticipation of an eminent network outage. Our proposed method introduces a novel utilization-based ranking scheme of different wireless nodes to minimize the service downtime and enable a fast recovery. Efficient prediction of network KPI (Key Performance Index), based on actual wireless data demonstrates up to ~54% improvement in service outage

NexGen D-TCP: Next generation dynamic TCP congestion control algorithm

With the advancement of wireless access networks and mmWave New Radio (NR), new applications emerged, which requires a high data rate. The random packet loss due to mobility and channel conditions in a wireless network is not negligible, which degrades the significant performance of the Transmission Control Protocol (TCP). The TCP has been extensively deployed for congestion control in the communication network during the last two decades. Different variants are proposed to improve the performance of TCP in various scenarios, specifically in lossy and high bandwidth-delay product (high- BDP) networks. Implementing a new TCP congestion control algorithm whose performance is applicable over a broad range of network conditions is still a challenge. In this article, we introduce and analyze a Dynamic TCP (D-TCP) congestion control algorithm overmmWave NR and LTE-A networks. The proposed D-TCP algorithm copes up with the mmWave channel fluctuations by estimating the available channel bandwidth. The estimated bandwidth is used to derive the congestion control factor N. The congestion window is increased/decreased adaptively based on the calculated congestion control factor. We evaluated the performance of D-TCP in terms of congestion window growth, goodput, fairness and compared it with legacy and existing TCP algorithms. We performed simulations of mmWave NR during LOS \u3c-\u3e NLOS transitions and showed that D-TCP curtails the impact of under-utilization during mobility. The simulation results and live air experiment points out that D-TCP achieves 32:9% gain in goodput as compared to TCPReno and attains 118:9% gain in throughput as compared to TCP-Cubic

A framework for dynamic hybrid scheduling strategies in heterogeneous asymmetric environments

The increasing growth of wireless access networks, proliferation of the Internet and gradual deployment of broadband networks has already given birth to a set of information-centric applications based on data transmission. Efficient scheduling techniques are necessary to endow these applications with advanced data processing capability. Broadly all data transmission applications are divided into (1) push and (2) pull systems. Hybrid scheduling, resulting from an effcient combination of these two types of data delivery, often exploits the advantages of both the schemes. The objective of this dissertation is to investigate and develop a novel hybrid scheduling platform by effectively combining broadcasting (push) of popular data and dissemination (pull) of less popular data. One major advantage of this algorithm is dynamic computation of cut-of-point, used to segregate the popular and less-popular data items, without any prior knowledge or assumptions. In order to achieve a better performance, the framework is enhanced to allow a set of consecutive push and pull operations, depending on the probabilities of the data items present in the system. The framework also incorporates practical issues like clients' impatience leading to clients' departure and transmission of spurious requests. A new client's priority-based service classification scheme is proposed to provide differentiated QoS in wireless data networks. The framework proceeds further to incorporate dynamic hybrid scheduling over multiple channels. Performance modeling, analysis and simulation study points out efficiency of the entire framework

the waiting time

less and pull the current highest demanded data item to decreas

Incentive and Penalty Mechanism for Power Allocation in Cooperative D2D-Cellular Transmissions

In cellular communication systems, the introduction of device-to-device (D2D) communications provides a reasonable solution to facilitate high data rate services in short-range communication. However, it faces a challenging issue of interference management, where the cross-tier interference from D2D users to licensed cellular users (CUs) degrades their quality-of-service (QoS) requirements. D2D communications can also assist in offloading some nearby CUs to enhance the cellular operator’s benefit. To encourage the D2D transmitters (D2DT) to provide service to CUs in the dead zone, the cellular base station (CBS) needs to incentivize it with some monetary benefits. In this paper, a Stackelberg game-based joint pricing framework for interference management and data offloading is presented to illustrate the effects of cooperation between the D2D user and CBS. Specifically, a singular price is used to incentivize the D2DT to share its resources with the far-off CUs along with penalizing them for interference created at CBS. Simulation results illustrate the performance of the proposed technique in terms of the utilities of CUs and D2D users for varying distances of D2DT

Acknowledgment

Many different people provided help, support, and input that brought this thesis to fruition. If anyone is looking for an example of a good advisor, let me especially recommend mine, Maria Cristina Pinotti. Prof. Pinotti has always been an enthusiastic advisor, providing encouragement, insight, and a valuable big-picture perspective. She has been a shining catalyst for this work. In addition, she provided great moral support. She had the uncanny knack of always asking the questions I had hoped not to hear. By raising these difficult issues, she focused my attention onto critical areas. I have been fortunate and privileged to work with her. I would like to express my sincere gratitude and best regards to Prof. Sajal K. Das for his advice and encouragement. I am indebted to him for for his comments and suggestions regarding my work. My sincere regards to Prof. Kalyan Basu for his constant detail technical guidance and help to me. Thanks to all my colleagues University of Trento, Italy and in th

3B-ARA: Bandwidth, Buffer and Battery Aware Rate Adaptation for Dynamic HTTP Streaming

This letter proposes a novel rate adaptation framework for dynamic adaptive streaming where three factors-available bandwidth, buffer state, and residual battery status, jointly determine the bit rate selected. We model the rate adaptation problem as a Markov Decision Process (MDP) with a goal to optimize video streaming experience as measured by video playback quality and buffer occupancy. Using simulation study, we show that our approach, termed as 3B-ARA, reduces rebuffering rate by over 20% while delivering a comparable average video rate and maintaining an average of 93% buffer occupancy as compared to other existing rate adaptation approaches