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Meeting Future Mobile Traffic Needs by Peak-Throughput Design of Next-Gen RAN

Paolo Fiore, Ilario Filippini, Danilo De Donno

TL;DR

This paper tackles urban mmWave network congestion by advocating peak-throughput oriented planning for next-generation RANs. It integrates Integrated Access and Backhaul with Smart Radio Environment components (RIS and NCR) and models blockage from static, nomadic, and self-blockage sources within a MILP framework that yields two formulations: Mean-Throughput Formulation (MTF) and Peak-Throughput Formulation (PTF). The work computes SRC capacities across 16 blockage states, compares MTF and PTF through extensive Milan-area simulations, and demonstrates substantial peak-throughput gains (up to 45% DL and 70% UL) with similar average performance, guiding urban deployment strategies. It also introduces a PTF heuristic to enable scalable planning and discusses extensions to QoS, mobility, and ISAC for future 6G-like networks.

Abstract

Growing congestion in current mobile networks necessitates innovative solutions. This paper explores the potential of mmWave 5G networks in urban settings, focusing on Integrated Access and Backhaul (IAB) and the Smart Radio Environment (SRE). The mmWave traffic will be mainly made of short bursts to transfer large volumes of data and long idle periods where data are processed. This must change the way of designing mobile radio networks. To this extent, we propose network planning models leveraging the maximization of the achievable peak throughput. Results highlight the advantages of this approach during the network planning phase, providing insights into better accommodating the demands of mobile traffic without sacrificing the overall network capacity.

Meeting Future Mobile Traffic Needs by Peak-Throughput Design of Next-Gen RAN

TL;DR

This paper tackles urban mmWave network congestion by advocating peak-throughput oriented planning for next-generation RANs. It integrates Integrated Access and Backhaul with Smart Radio Environment components (RIS and NCR) and models blockage from static, nomadic, and self-blockage sources within a MILP framework that yields two formulations: Mean-Throughput Formulation (MTF) and Peak-Throughput Formulation (PTF). The work computes SRC capacities across 16 blockage states, compares MTF and PTF through extensive Milan-area simulations, and demonstrates substantial peak-throughput gains (up to 45% DL and 70% UL) with similar average performance, guiding urban deployment strategies. It also introduces a PTF heuristic to enable scalable planning and discusses extensions to QoS, mobility, and ISAC for future 6G-like networks.

Abstract

Growing congestion in current mobile networks necessitates innovative solutions. This paper explores the potential of mmWave 5G networks in urban settings, focusing on Integrated Access and Backhaul (IAB) and the Smart Radio Environment (SRE). The mmWave traffic will be mainly made of short bursts to transfer large volumes of data and long idle periods where data are processed. This must change the way of designing mobile radio networks. To this extent, we propose network planning models leveraging the maximization of the achievable peak throughput. Results highlight the advantages of this approach during the network planning phase, providing insights into better accommodating the demands of mobile traffic without sacrificing the overall network capacity.

Paper Structure

This paper contains 20 sections, 22 equations, 14 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model
  4. Blockage Model
  5. Blockages due to static obstacles
  6. Blockages due to nomadic obstacles
  7. Self-blockage
  8. Blockage probability due to non-static obstacles
  9. Optimization Models
  10. Computing SRC capacity
  11. Mean-Throughput Formulation (MTF)
  12. Peak-Throughput Formulation (PTF)
  13. Peak-Throughput Heuristics
  14. Other traffic types
  15. Results
  16. ...and 5 more sections

Figures (14)

  • Figure 1: System model: the radio devices involved in the RAN and their relationships in a urban scenario.
  • Figure 2: Static blockage modeling. The blue-shaded area is where no static obstacle is present.
  • Figure 3: Schematic representation of the self-blocking region and how it could impair radio links.
  • Figure 4: Probability distribution of the blockage states from Table \ref{['tab:16_states']}. The largest probabilities are indicated.
  • Figure 5: Number of different installed devices when varying the budget. In both formulations, the expenditure is dominated by IAB Nodes.
  • ...and 9 more figures