Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Jagannath, Jithin"'
The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become o
Externí odkaz:
http://arxiv.org/abs/2405.17309
RF fingerprinting is emerging as a physical layer security scheme to identify illegitimate and/or unauthorized emitters sharing the RF spectrum. However, due to the lack of publicly accessible real-world datasets, most research focuses on generating
Externí odkaz:
http://arxiv.org/abs/2303.13538
Autor:
Kafle, Swatantra, Jagannath, Jithin, Kane, Zackary, Biswas, Noor, Kumar, Prem Sagar Vasanth, Jagannath, Anu
We tackle the problem of joint frequency and power allocation while emphasizing the generalization capability of a deep reinforcement learning model. Most of the existing methods solve reinforcement learning-based wireless problems for a specific pre
Externí odkaz:
http://arxiv.org/abs/2302.02250
Autor:
Jagannath, Anu, Jagannath, Jithin
A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its generalization cap
Externí odkaz:
http://arxiv.org/abs/2210.02897
A novel cross-domain attentional multi-task architecture - xDom - for robust real-world wireless radio frequency (RF) fingerprinting is presented in this work. To the best of our knowledge, this is the first time such comprehensive attention mechanis
Externí odkaz:
http://arxiv.org/abs/2209.03142
Spectrum scarcity has been a major concern for achieving the desired quality of experience (QoE) in next-generation (5G/6G and beyond) networks supporting a massive volume of mobile and IoT devices with low-latency and seamless connectivity. Hence, s
Externí odkaz:
http://arxiv.org/abs/2206.14997
We examine the problem of transmission control, i.e., when to transmit, in distributed wireless communications networks through the lens of multi-agent reinforcement learning. Most other works using reinforcement learning to control or schedule trans
Externí odkaz:
http://arxiv.org/abs/2205.06800
Dynamic resource allocation plays a critical role in the next generation of intelligent wireless communication systems. Machine learning has been leveraged as a powerful tool to make strides in this domain. In most cases, the progress has been limite
Externí odkaz:
http://arxiv.org/abs/2204.04507
Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal processing scheme for the beyond 5G and military networks. Most existing works rely on
Externí odkaz:
http://arxiv.org/abs/2204.04390
Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by Internet of Things (IoT) networks. In this position paper, we elucidate unique cha
Externí odkaz:
http://arxiv.org/abs/2204.01950