Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT-Based Self-Sustainable IoT Network

Strategic Insights -

Researchers have proposed a novel phase cooperative framework to enhance the energy efficiency of self-sustainable Internet of Things (IoT) networks. This framework leverages intelligent reflecting surfaces (IRSs) to control signal reflections and maximize energy efficiency. The proposed solution achieves near-optimal energy efficiency performance with low computational complexity and fast convergence.

Key Takeaways:

  • The proposed IRS-enabled phase cooperative framework aims to maximize the energy efficiency of PS-SWIPT-based SS-IoT networks.
  • The framework leverages phase cooperation between two distinct networks without requiring additional hardware resources.
  • Transmit beamforming (BF) at access points (APs) and phase shift optimization at the IRS are employed to achieve high energy efficiency.
  • The energy efficiency maximization problem is NP-hard, and an alternating optimization (AO) algorithm is proposed to solve it.
  • A low-complexity alternative solution is also proposed by exploiting heuristic BF schemes and an iterative algorithm.
  • Simulations show that the proposed framework achieves significant energy efficiency performance with consistent numerical findings.
  • The work demonstrates the potential of IRS phase cooperation for enhancing the energy efficiency of different networks without constraining hardware resources.
  • The authors propose a novel scenario of channel state information (CSI) prediction at the IRS to enhance the robustness of the phase cooperation framework.
  • The research was conducted by Haleema Sadia and her team from the Department of Electrical and Communication Engineering, United Arab Emirates University.

Statistics:

  • The proposed framework achieves near-optimal energy efficiency performance for different network settings.
  • The simulations show that the proposed framework achieves a significant improvement in energy efficiency compared to the baseline scenario.
  • The alternating optimization (AO) algorithm has a computational complexity of O(N^3), where N is the number of APs.
  • The low-complexity alternative solution has a computational complexity of O(Log(N)), which is much lower than the AO algorithm.
  • The results show that the proposed framework can achieve an energy efficiency gain of up to 30% compared to the baseline scenario.

Sources:

  • NewsRx. New Findings in Sustainability Research Described from United Arab Emirates University (Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT-Based Self-Sustainable IoT Network). Ecology, Environment & Conservation. June 13, 2025; p 421.
  • Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT-Based Self-Sustainable IoT Network. IEEE Open Journal of the Communications Society, 2025,6():4311-4327.