Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.16061 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910886353960960 |
|---|---|
| author | Ihsan, Asim Asif, Muhammad Safi, Hossein Tavakkolnia, Iman Haas, Harald |
| author_facet | Ihsan, Asim Asif, Muhammad Safi, Hossein Tavakkolnia, Iman Haas, Harald |
| contents | In this paper, we propose an innovative predict-and-optimize algorithm designed for hybrid WiFi/LiFi networks, aiming to achieve service differentiation while maximizing energy efficiency (EE). The proposed framework utilizes multi-access technology real-time intelligent controller (mATRIC) to dynamically predict the appropriate network slice for each user based on historically monitored key performance indicators (KPIs). This prediction is facilitated by a deep learning model trained using the resilient backpropagation algorithm, with training conducted on KPIs data at the universal non-real time RAN intelligent controller (non-RT RIC). This trained model enables real-time slice selection by mATRIC. In the subsequent phase, the algorithm focuses on optimizing EE of hybrid network as a function of precoding vectors for the predicted slices by employing techniques from sequential convex approximation and the inner approximation method. We introduce novel approximations to convert non-convex objective functions and constraints into convex forms, and develop an iterative algorithm to achieve sub-optimal solutions. Additionally, the EE maximization problem, ensures alignment with end-to-end latency requirements. It also addresses the various constraints inherent to hybrid systems, such as input signal limitations for LiFi LEDs, data rate restrictions, and power budget considerations. Simulation results validate the effectiveness of the proposed algorithm, demonstrating significant improvements in EE while ensuring service differentiation within hybrid network environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16061 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks Ihsan, Asim Asif, Muhammad Safi, Hossein Tavakkolnia, Iman Haas, Harald Optimization and Control In this paper, we propose an innovative predict-and-optimize algorithm designed for hybrid WiFi/LiFi networks, aiming to achieve service differentiation while maximizing energy efficiency (EE). The proposed framework utilizes multi-access technology real-time intelligent controller (mATRIC) to dynamically predict the appropriate network slice for each user based on historically monitored key performance indicators (KPIs). This prediction is facilitated by a deep learning model trained using the resilient backpropagation algorithm, with training conducted on KPIs data at the universal non-real time RAN intelligent controller (non-RT RIC). This trained model enables real-time slice selection by mATRIC. In the subsequent phase, the algorithm focuses on optimizing EE of hybrid network as a function of precoding vectors for the predicted slices by employing techniques from sequential convex approximation and the inner approximation method. We introduce novel approximations to convert non-convex objective functions and constraints into convex forms, and develop an iterative algorithm to achieve sub-optimal solutions. Additionally, the EE maximization problem, ensures alignment with end-to-end latency requirements. It also addresses the various constraints inherent to hybrid systems, such as input signal limitations for LiFi LEDs, data rate restrictions, and power budget considerations. Simulation results validate the effectiveness of the proposed algorithm, demonstrating significant improvements in EE while ensuring service differentiation within hybrid network environments. |
| title | Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2503.16061 |