Sustainable Load Balancing for Wireless Networks With Renewable Energy Sources

Future wireless networks powered by renewable energy sources and storage systems (e.g., batteries) require energy-aware mechanisms to ensure stability in critical and high-demand scenarios. These include large-scale user gatherings, especially during evening hours when solar generation is unavailable, and days with poor wind conditions that limit the effectiveness of wind-based energy harvesting. Maintaining network performance under such constraints, while preserving stored energy, remains a key challenge. This work proposes an enhanced Proactive-Reactive Load Balancing algorithm that integrates energy conditions into mobility management. By leveraging standardized mobility events, the algorithm optimizes traffic distribution and energy utilization (avoiding complete drainage of stored energy), thereby preventing service degradation. Simulations show improved energy sustainability and network performance under congestion and limited solar availability.

💡 Research Summary

The paper addresses a pressing challenge in future wireless networks that rely on renewable energy sources (RES) such as solar and wind, combined with battery storage, especially in dense small‑cell deployments that operate off‑grid or in hybrid power modes. While existing load‑balancing schemes (e.g., the Proactive‑Reactive Load Balancing, PRLB) effectively redistribute traffic based on radio‑resource utilization, they ignore the instantaneous energy state of the cells. Consequently, during high‑traffic events—such as large gatherings in the evening when solar generation is absent—RES‑powered cells can experience rapid battery depletion, leading to service outages.

To overcome this limitation, the authors propose an enhanced algorithm called energy‑aware PRLB (ePRLB). The core idea is to embed a continuous “sustainability score” (E_i) for each RES‑powered cell (i) that reflects three dimensions of its energy condition: (1) the state‑of‑charge (SoC) of the battery, (2) the short‑term net power balance (generation minus consumption), and (3) a mid‑range discharge attenuation that prevents over‑reaction when the battery is neither critically low nor fully charged. The SoC contribution is modeled with a logistic function (B(s_i)) that sharply penalizes SoC below a critical threshold (s_L=0.2) and rewards SoC above a sufficient threshold (s_H=0.4). The net‑power term (A(E_{g,i},E_{c,i})) uses a scaled hyperbolic tangent to keep the value bounded and numerically stable. A Gaussian attenuation (Z(s_i)) is applied around the mid‑range SoC (centered at 0.35) to smooth the response. The final score is \