Work Packages 1

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Work Package 1: iNTN Coexistence and Optimization

Overview

Work Package 1 (WP1) focuses on the design, modeling, and optimization of integrated terrestrial and non-terrestrial networks (iNTNs), especially those involving Low Earth Orbit (LEO) satellites and terrestrial cellular infrastructure. The ultimate goal is to enhance coverage, user throughput, and energy efficiency through a unified approach that bridges both network types.

1. Unified Modeling and System Enhancements

A comprehensive system model was developed to accurately represent a dual-layer network composed of terrestrial base stations (BSs) and LEO satellite-mounted BSs. This model accounts for both orthogonal and co-channel spectrum allocations and supports deployments in sub-6 GHz bands, especially the S-band (~2 GHz). It integrates 3GPP-specified channel models and considers urban and rural scenarios.

Key modeling improvements:

  • Realistic deployment of User Equipments (UEs), with 80% indoor placement to reflect building penetration losses.

  • Dynamic traffic modeling, simulating daily variations (e.g., 400 UEs at low traffic, 10,000 at peak).

  • Refined channel modeling for LEO links, incorporating clutter and scintillation losses and elevation angle effects.

Energy consumption models were introduced:

  • Terrestrial BSs include static, dynamic, and sleep modes.

  • LEO satellite BSs are assumed to be solar-powered, focusing optimization efforts on terrestrial power use.

2. Optimization Framework: BLASTER

An advanced optimization tool called BLASTER (Bandwidth SpLit, UE ASsociTion, and PowEr ContRol) was developed to jointly optimize:

  • UE association to terrestrial or satellite BSs.

  • Bandwidth partitioning between both tiers.

  • Transmit power allocation.

BLASTER’s key innovations:

  • Explicit integration of energy efficiency in the optimization objective.

  • Use of L1-L2 penalties to deactivate underutilized terrestrial BSs while maintaining coverage.

  • Two-step optimization: first for UE association and bandwidth (via Lagrangian methods), then for power (via proximal gradient methods).

  • Dynamic bandwidth adaptation based on real-time UE load.

Performance results:

  • 67% average energy reduction by selectively turning off terrestrial BSs during low traffic.

  • 53% peak-hour power savings via dynamic power control.

  • 6% improvement in sum log throughput compared to the 3GPP NTN benchmark.

  • 249% increase in mean throughput during peak hours over fixed bandwidth allocation setups.

3. Trade-Off Analyses

3.1 Terrestrial vs LEO Integration

The integration of LEO satellites extends coverage (eliminating ~7% coverage holes), but at the cost of lower throughput due to limited satellite bandwidth. BLASTER mitigates this by flexibly reallocating bandwidth in response to traffic demands, achieving better coverage without excessive performance loss.

3.2 Throughput vs Energy Efficiency

Shifting UEs to satellites during low traffic enables terrestrial BSs to enter sleep mode. This reduces energy consumption by 67%, and BLASTER still manages to increase mean throughput by 6%, proving energy efficiency does not require performance compromise.

4. UAV and Aerial Highway Analysis

Expanding beyond ground UEs, WP1 also investigates serving Unmanned Aerial Vehicles (UAVs) via terrestrial networks:

  • A UAV measurement campaign was conducted in Benidorm, Spain, using professional equipment.

  • Altitudes tested: 20m, 40m, 60m.

Key findings:

  • RSRP improved with altitude due to better line-of-sight (up to +9.3 dB).

  • SINR degraded at higher altitudes due to increased interference (more overlapping cells detected).

  • Throughput dropped significantly at 60m (e.g., NR dropped to 3.1 Mbps, below the 10 Mbps UAV control threshold).

  • Operator variability showed up to 10 dB SINR difference, pointing to trade-offs in network selection.

  • Receiver quality impacted results; high-end scanners performed better than consumer smartphones.

5. Future Work

Future efforts will focus on:

  • Extending the iNTN framework to include High-Altitude Platform Stations (HAPS).

  • Conducting more UAV-focused campaigns to refine models and trade-off evaluations.

  • Publishing further results based on current UAV and iNTN findings.

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