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Published on: September 8, 2023
Energy-Efficient Resource Allocation for Near-Field MIMO Communication Networks.
Tong Lin1, Jianyue Zhu1, Junfan Zhu1
1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
This study optimizes power and antenna count for 6G wireless networks, maximizing energy efficiency in the near-field region. The proposed algorithm reduces system energy use and antenna overhead for greener communications.
Area of Science:
- Wireless Communications
- Signal Processing
- Antenna Theory
Background:
- Sixth-generation (6G) wireless networks and large-scale MIMO necessitate near-field communication models.
- Traditional far-field models are inadequate for high-precision beamforming in near-field scenarios.
Purpose of the Study:
- To jointly optimize power and antenna number for maximum energy efficiency in near-field 6G communications.
- To address the resolution constraint for negligible user interference.
Main Methods:
- Simplified near-field resolution constraint using Fresnel approximation.
- Applied Dinkelbach algorithm to transform energy efficiency maximization into a convex subproblem.
- Integral optimization of antenna number using monotonicity analysis.
Main Results:
- Significantly improved system energy efficiency.
- Reduced antenna overhead under various configurations.
- Demonstrated practical applicability for green communication systems.
Conclusions:
- The proposed low-complexity algorithm effectively optimizes power and antenna number for near-field 6G.
- The method offers a practical approach for designing energy-efficient and sustainable wireless networks.
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