Backhaul Capacity-Intent Interference Easing for Sum Rate Improving in 6G Cellular Internet of Things-Reference-Cited by-同舟云学术

Backhaul Capacity-Intent Interference Easing for Sum Rate Improving in 6G Cellular Internet of Things

Published:2023-02-05 Issue: Volume: Page:542-552
ISSN:2395-602X
Container-title:International Journal of Scientific Research in Science and Technology
language:en
Short-container-title:IJSRST

Author:

Murakuppam Divya ¹,Dr. G. Sreenivasulu ²

Affiliation:

1. M. Tech Student, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India

2. Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India

Abstract

A mmWave-enabled integrated access backhaul (IAB) network for the 6G cellular Internet of Things (IoT) is necessary to handle the escalating wideband communications needs. A decreased sum rate and low successful transmission probability were the results of the mmWave-enabled IAB network's significant interference problems between access links and differentiated small-cell base stations' backhaul capacities. This article suggests the Q-learn JUP algorithm, which stands for joint user equipment association and power allocation. In order to establish the UA and PA problem, we first investigate how the SBSs-UE association and transmit power are related. Second, using the interference and backhaul burden as the state, UA and PA as the action, and the overall sum rate increment as the reward function of Q-learning, we determine the best joint optimization framework through off-line training. In order to lessen interference and backhaul load, a new backhaul capacity and cautious matching approach utility function has also been designed. Simulation results demonstrate that, in contrast to existing algorithms, the proposed algorithm may vastly enhance the network sum rate and successful transmission probability.

Publisher

Technoscience Academy

Subject

General Medicine

Reference26 articles.

1. H. Song, J. Bai, Y. Yi, J. Wu and L. Liu, "Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access," in IEEE Computational Intelligence Magazine, vol. 15, no. 1, pp. 44-51, Feb. 2020, doi: 10.1109/MCI.2019.2954643.

2. IMT Traffic Estimates for the Years 2020 to 2030, document ITU-R SG05, ITU, Geneva, Switzerland, Jul. 2015.

3. M. Giordani, M. Polese, M. Mezzavilla, S. Rangan and M. Zorzi, "Toward 6G Networks: Use Cases and Technologies," in IEEE Communications Magazine, vol. 58, no. 3, pp. 55-61, March 2020, doi: 10.1109/MCOM.001.1900411.

4. K. B. Letaief, W. Chen, Y. Shi, J. Zhang and Y. -J. A. Zhang, "The Roadmap to 6G: AI Empowered Wireless Networks," in IEEE Communications Magazine, vol. 57, no. 8, pp. 84-90, August 2019, doi: 10.1109/MCOM.2019.1900271.

5. B. Zong, C. Fan, X. Wang, X. Duan, B. Wang and J. Wang, "6G Technologies: Key Drivers, Core Requirements, System Architectures, and Enabling Technologies," in IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 18-27, Sept. 2019, doi: 10.1109/MVT.2019.2921398.