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Statistics of the Sum of Double Random Variables and Their Applications in Performance Analysis and Optimization of
Bui Vu Minh1, Phuong T Tran2, Thu-Ha Thi Pham3
1Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City 754000, Vietnam.
This study enhances 6G wireless networks using simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). The research improves outage probability and data rates in energy-harvesting networks, outperforming traditional systems.
Area of Science:
- Wireless Communication
- Signal Processing
- Information Theory
Background:
- Sixth-generation (6G) wireless communication demands advanced solutions for low power and seamless coverage.
- Reconfigurable intelligent surfaces (RIS) offer a promising approach, with STAR-RIS enabling simultaneous transmission and reflection.
- Performance analysis of RIS-assisted networks requires understanding the statistics of double random variables.
Purpose of the Study:
- To analyze the performance of a STAR-RIS network integrated with power beacon (PB) energy-harvesting (EH) and NOMA.
- To investigate the impact of imperfect successive interference cancellation (ipSIC) on system metrics.
- To derive closed-form expressions for outage probability (OP) and ergodic rate.
Main Methods:
- Application of sum of double random variables statistics for performance analysis.
- Development of analytical models for both imperfect and perfect successive interference cancellation (pSIC) scenarios.
- Extensive simulations to validate analytical results and optimize system parameters.
Main Results:
- Closed-form expressions for OP and ergodic rate derived for both ipSIC and pSIC.
- Identification of optimal system parameters (time-switching factor, reflecting elements, power allocation) to minimize OP.
- Demonstration of the proposed STAR-RIS framework's superiority over conventional NOMA and OMA systems.
Conclusions:
- The proposed STAR-RIS framework significantly enhances performance in 6G wireless networks.
- The statistical analysis provides valuable insights for designing efficient energy-harvesting NOMA systems.
- Optimized parameter selection is crucial for maximizing system efficiency and minimizing outage probability.
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