Engineering topics
Publications and source records attributed to Kasera, Sneha.
Innovative federated learning approach enables secure and reliable wireless communication in critical infrastructure.
To achieve automation across different applications, next-generation nuclear power plants are beginning to leverage advanced wireless technologies. The deployment of these wireless technologies is a salient need for modernizing the existing nuclear plants. A “one-size-fits-all” solution cannot be adopted since wireless technologies are selected according to specific application needs, quality of service, and economic restrictions. To balance the trade-off between technical and economic requirements, a multi-band heterogeneous wireless network architecture is needed. However, the co-existence of these multi-band wireless technologies poses numerous challenges. Due to factors including dissimilarity in channel access mechanisms, transmit power levels, distance between nodes, and interference among co-existing solutions, performance can be seriously degraded, leading to very low signal-to-noise ratio. This report builds on our previous study, "Experimental Evaluation of Interference in 2.4 GHz Wireless Network," which analyzed the performance of wireless communication technologies such as Wi-Fi, Zigbee, and Bluetooth co-existing within the 2.4 GHz band. We now extend this study by developing a new upper-layer approach that uses rate control to create temporary opportunities for signal transmission while using the shared band. Unlike existing work in the literature, our approach does not require modifications at the link or physical layers of the network. Further, we extend our work to explore the co-existence of Wi-Fi and fifth-generation (5G) in the 5 GHz unlicensed spectrum, using both experiments and simulations. This report provides a technical foundation for understanding the operational designs of Wi-Fi and 5G in the unlicensed band and evaluates their performance when these networks co-exist. Our findings show that the performance of 5G can be improved by controlling the Wi-Fi’s packet rate at the upper layer.
To attain automation across different applications, nuclear power plants are beginning to leverage advancements in wireless communication technologies. A “one-size-fits-all” solution cannot be applied since wireless technologies are selected according to application needs, quality of service requirements, and economic restrictions. To balance the trade-off between technical and economic requirements, a multi-band heterogeneous wireless network architecture is needed. Numerous wireless technologies including Wi-Fi, Zigbee, and Bluetooth share the 2.4 GHz industrial, scientific, and medical band. However, due to different channel access mechanisms and transmit power levels, and very importantly, uncoordinated use, coexistence of these devices in the same vicinity can cause interference and degradation in performance. This report provides the technical basis for understanding the coexistence of these wireless technologies through an experimental evaluation of their performance. This report investigates interactions encompassing variables such as transmission power level, distance between the devices, data rates, and the utilization of co-channel or adjacent channels. The results show that the operation of both Zigbee and Bluetooth is severely compromised when coexisting with Wi-Fi within the same frequency spectrum. On the other hand, the performance of Bluetooth is not impaired by Zigbee and vice versa unless there exists any external interference from Wi-Fi.
We consider the problem of power allocation over the 6 GHz Unlicensed National Information Infrastructure (UNII)- 5 spectrum. We propose a novel deep Reinforcement Learning (DRL)-based distributed power allocation scheme which utilizes the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. In particular, we model the base stations (BSs) as DRL agents that simultaneously determine the transmit powers to their scheduled user equipment (UE) in a synchronized manner. The power decision of each BS is based on its own observation of the radio environment, which consists of several local interference measurements and a limited amount of information obtained from other BSs. One advantage of the proposed scheme is that it addresses the single-agent non-stationarity problem of RL in the multi-agent scenario by incorporating the actions and observations of other BSs into each BS’s own critic which helps it to gain a more accurate perception of the overall radio environment. A centralized-training-distributed execution framework is used to train the policies where the critics are trained over the joint actions and observations of all BSs while the actor of each BS only takes the local observation as input in order to produce the transmit power. Simulation shows that the proposed power allocation scheme can achieve better throughput performance than several state-of-the-art approaches.
We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no centralized control or explicit coordination among them. Our goal is to design efficient distributed beam scheduling and power allocation schemes such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference conditions by learning from experience and thus can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.
Talk presented in WSI ACI workshop on Sep 7, 2022.
Slides for talk to be delivered in the WSI ACI workshop on Sep 7 2022.