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Advanced Network Planning in 6G Smart Radio Environments

Reza Aghazadeh Ayoubi, Marouan Mizmizi, Eugenio Moro, Ilario Filippini, Umberto Spagnolini

TL;DR

An optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR is proposed, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

Abstract

The growing demand for high-speed, reliable wireless connectivity in 6G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millimeter wave (mmW) bands by enhancing signal reach in environments susceptible to blockage and severe propagation losses. In this paper, we propose an optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR. We formulate a cost-minimization optimization problem that integrates the deployment and configuration of these devices to achieve seamless coverage, particularly in dense urban scenarios. Simulation results confirm that this framework significantly reduces the network planning costs while guaranteeing full coverage, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

Advanced Network Planning in 6G Smart Radio Environments

TL;DR

An optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR is proposed, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

Abstract

The growing demand for high-speed, reliable wireless connectivity in 6G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millimeter wave (mmW) bands by enhancing signal reach in environments susceptible to blockage and severe propagation losses. In this paper, we propose an optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR. We formulate a cost-minimization optimization problem that integrates the deployment and configuration of these devices to achieve seamless coverage, particularly in dense urban scenarios. Simulation results confirm that this framework significantly reduces the network planning costs while guaranteeing full coverage, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

Paper Structure

This paper contains 12 sections, 17 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Smart Radio Environment Model
  3. Signal Model
  4. Direct Signal Model
  5. Relayed Signal Model via
  6. Relayed Signal Model via
  7. Channel Model
  8. Cost Minimization
  9. System Model
  10. Optimization Formulation
  11. Results
  12. Conclusion

Figures (5)

  • Figure 1: Illustrative scheme of different components
  • Figure 2: Optimally planned network topologies for minimizing total installation cost, for the scenario of Piazza Piola in Milan \ref{['tab:SimParam_1']}, varying the the guaranteed number of connections for each test point $K$ and threshold in dB $\Gamma$, using the default parameters in Table \ref{['tab:SimParam_1']}.
  • Figure 3: Average total cost vs / price ratio with FCMC optimization model, for fixed settings of / (see Table \ref{['tab:SimParam_1']}).
  • Figure 4: Average total cost vs dimension where the configurations and price of the is the default according to Table \ref{['tab:SimParam_1']} and the cost of the scales according to relation \ref{['eq:ris_cost']}.
  • Figure 5: Average total cost vs gain, where the configuration and price of the is the default according to Table \ref{['tab:SimParam_1']} and the cost of the scales according to relation \ref{['eq:ncr_cost']}.