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Antenna Identification and Power Allocation in Multicell Massive MIMO Downstream: Energy Conservation Under User Sum-Rate Constraint

Antenna Identification and Power Allocation in Multicell Massive MIMO Downstream: Energy Conservation Under User Sum-Rate Constraint

Shashi Kant Gupta, Shilpa Mehta, Arij Naser Abougreen, Prabhdeep Singh
Copyright: © 2024 |Pages: 15
ISBN13: 9798369320037|ISBN13 Softcover: 9798369345955|EISBN13: 9798369320044
DOI: 10.4018/979-8-3693-2003-7.ch001
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MLA

Gupta, Shashi Kant, et al. "Antenna Identification and Power Allocation in Multicell Massive MIMO Downstream: Energy Conservation Under User Sum-Rate Constraint." Emerging Materials, Technologies, and Solutions for Energy Harvesting, edited by Shilpa Mehta, et al., IGI Global, 2024, pp. 1-15. https://doi.org/10.4018/979-8-3693-2003-7.ch001

APA

Gupta, S. K., Mehta, S., Abougreen, A. N., & Singh, P. (2024). Antenna Identification and Power Allocation in Multicell Massive MIMO Downstream: Energy Conservation Under User Sum-Rate Constraint. In S. Mehta, A. Abougreen, & S. Gupta (Eds.), Emerging Materials, Technologies, and Solutions for Energy Harvesting (pp. 1-15). IGI Global. https://doi.org/10.4018/979-8-3693-2003-7.ch001

Chicago

Gupta, Shashi Kant, et al. "Antenna Identification and Power Allocation in Multicell Massive MIMO Downstream: Energy Conservation Under User Sum-Rate Constraint." In Emerging Materials, Technologies, and Solutions for Energy Harvesting, edited by Shilpa Mehta, Arij Naser Abougreen, and Shashi Kant Gupta, 1-15. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-2003-7.ch001

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Abstract

This chapter discusses algorithmic energy conservation in multi-cell multiple-input multiple-output (MIMO) downstream transmission. The authors propose using adaptive particle swarm optimization (APSO) to reduce the optimization issue. By framing the issue as a limited optimization assignment, they present a solid framework that allocates transmission power levels to antennas to maximize energy efficiency and meet network customers' quality of service. To evaluate the technique, they simulated various network settings with different user distributions and channel conditions. The approach outperforms previous energy-saving methods without losing the whole network sum rate, increasing energy efficiency significantly. By using optimal antenna components and the best fitness value, the proposed APSO model guarantees better power allocation schemes in wireless environments than current approaches.

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