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Towards Green Connectivity: An AI-Driven Mesh Architecture for Sustainable and Scalable Wireless Networks

Muhammad Ahmed Mohsin, Muhammad Jazib, Muhammad Saad, Ayesha Mohsin

TL;DR

System-level evaluations combining propagation modeling and validated link-budget analysis demonstrate that this architecture delivers up to an 84 times improvement in useful energy delivery, reduces deployment costs by nearly 74 percent, and eliminates diesel dependence through solar-powered operations, thereby enabling sustainable, green connectivity for both rural and ultra-dense urban environments.

Abstract

Traditional macro-cell and micro-cell infrastructures suffer from severe inefficiencies, with current macro-cell networks operating at less than 5 percent energy efficiency, leading to nearly 95 percent of RF power wasted in covering vacant areas. The problem becomes particularly acute in high-density scenarios such as the Hajj, where approximately 7,000 temporary diesel-powered towers are deployed each year, consuming 56 million liters of fuel and emitting around 148,000 tons of CO2, yet still experiencing failure rates of nearly 40 percent at peak demand. To overcome these limitations, we propose an AI-driven mesh architecture based on three integrated enablers: (i) proximity-based deployment of low-power nodes within 250 to 300 meters of users, yielding a 38 dB link-budget gain and up to 6000 times efficiency improvement; (ii) spatial frequency reuse, which partitions cells into multiple non-interfering zones and achieves nearly 20 times capacity gain; and (iii) predictive network intelligence leveraging LSTMs to forecast traffic 5 seconds ahead, enabling smarter allocation and reducing congestion by about 60 percent. System-level evaluations combining propagation modeling and validated link-budget analysis demonstrate that this architecture delivers up to an 84 times improvement in useful energy delivery, reduces deployment costs by nearly 74 percent, and eliminates diesel dependence through solar-powered operations, thereby enabling sustainable, green connectivity for both rural and ultra-dense urban environments.

Towards Green Connectivity: An AI-Driven Mesh Architecture for Sustainable and Scalable Wireless Networks

TL;DR

System-level evaluations combining propagation modeling and validated link-budget analysis demonstrate that this architecture delivers up to an 84 times improvement in useful energy delivery, reduces deployment costs by nearly 74 percent, and eliminates diesel dependence through solar-powered operations, thereby enabling sustainable, green connectivity for both rural and ultra-dense urban environments.

Abstract

Traditional macro-cell and micro-cell infrastructures suffer from severe inefficiencies, with current macro-cell networks operating at less than 5 percent energy efficiency, leading to nearly 95 percent of RF power wasted in covering vacant areas. The problem becomes particularly acute in high-density scenarios such as the Hajj, where approximately 7,000 temporary diesel-powered towers are deployed each year, consuming 56 million liters of fuel and emitting around 148,000 tons of CO2, yet still experiencing failure rates of nearly 40 percent at peak demand. To overcome these limitations, we propose an AI-driven mesh architecture based on three integrated enablers: (i) proximity-based deployment of low-power nodes within 250 to 300 meters of users, yielding a 38 dB link-budget gain and up to 6000 times efficiency improvement; (ii) spatial frequency reuse, which partitions cells into multiple non-interfering zones and achieves nearly 20 times capacity gain; and (iii) predictive network intelligence leveraging LSTMs to forecast traffic 5 seconds ahead, enabling smarter allocation and reducing congestion by about 60 percent. System-level evaluations combining propagation modeling and validated link-budget analysis demonstrate that this architecture delivers up to an 84 times improvement in useful energy delivery, reduces deployment costs by nearly 74 percent, and eliminates diesel dependence through solar-powered operations, thereby enabling sustainable, green connectivity for both rural and ultra-dense urban environments.
Paper Structure (22 sections, 4 equations, 5 figures, 3 tables)

This paper contains 22 sections, 4 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction and General Description
  2. Technical Description
  3. COST-231 Radio & Channel Model
  4. Mesh Topology, Mobility & Traffic
  5. Predictive Load Forecasting
  6. Sustainability Analysis
  7. Carbon footprint and Efficiency
  8. Energy Efficiency and Network Throughput
  9. Reuse and Recycling of Nodes and Sensors
  10. Cost Analysis
  11. Results
  12. Adaptive Transmit Power Scaling
  13. Network Power Consumption
  14. Power Efficiency
  15. Expanded Coverage
  16. ...and 7 more sections

Figures (5)

  • Figure 1: A complete depiction of the working of an intelligent mesh network. Distributed low-power nodes connect to a central server that performs RL-based power control, interference mitigation, and LSTM-based traffic forecasting. The channel is modeled using COST-231 Hata and Okumura-Hata models, while adaptive coverage is delivered to an urban environment.
  • Figure 2: Annual cost comparison of traditional vs. proposed mesh network (left) and regional fuel savings in million liters (right).
  • Figure 3: Transmit Power analysis of traditional macro base station (left) with AI-powered distributed mesh nodes (right)
  • Figure 4: Power efficiency of traditional macro base station mesh nodes (left) and Total network power (right) with an increase in traffic load.
  • Figure 5: Total Network Power of traditional macro base station and mesh nodes (left) and Power efficiency per Watt (right) with an increase in traffic load.