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Modeling and analysis of finite-scale clustered backscatter communication networks

- Wang, Qiu, Zhou, Yong, Dai, Hong-Ning, Zhang, Guopeng, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Qiu , Zhou, Yong , Dai, Hong-Ning , Zhang, Guopeng , Imran, Muhammad , Nasser, Nidal
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Communications, ICC 2023, Rome, 28 May-1 June 2023, ICC 2023 - IEEE International Conference on Communications Vol. 2023-May, p. 1456-1461
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  • Description: Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets. © 2023 IEEE.

Federated learning based trajectory optimization for UAV enabled MEC

- Nehra, Anushka, Consul, Prakhar, Budhiraja, Ishan, Kaur, Gagandeep, Nasser, Nidal, Imran, Muhammad

  • Authors: Nehra, Anushka , Consul, Prakhar , Budhiraja, Ishan , Kaur, Gagandeep , Nasser, Nidal , Imran, Muhammad
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Communications, ICC 2023 Vol. 2023-May, p. 1640-1645
  • Full Text: false
  • Reviewed:
  • Description: We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods' while the FRL performs best among all. © 2023 IEEE.
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