Polyhedral intelligent surfaces improve wireless coverage and user rates

Polyhedral RIS Architecture with Quantum-Assisted Facet Selection for Uniform Coverage

Information Theory

Summary

Wireless signals can be bounced toward moving devices using special surfaces called reconfigurable intelligent surfaces (RIS). This study looks at a new shape for these surfaces that covers almost all directions evenly. The authors explored how small errors and limited control affect the signal strength and developed a way to pick the best surface part to use for sending signals, solving this tricky problem quickly. Their approach reduces areas with bad signal and increases the minimum connection speed more than some previous designs. They tested different algorithms and found classical methods work better than current quantum attempts for this optimization.

What this means in practice

  • For wireless network engineers: Design RIS deployments that reduce signal dead zones and improve worst-case user data rates using polyhedral surfaces with optimized facet selection.
  • For quantum algorithm developers: Benchmark quantum approximate optimization algorithms on real RIS selection problems to find limitations compared to classical heuristics.

Authors

Noha Hassan, Halim Yanikomeroglu

Abstract

Reconfigurable intelligent surfaces (RISs) direct coverage toward moving user equipment (UE), but practical deployments involve discrete phase control, uncertain facet positions, and UE mobility, which require the problem to be re-solved continually. We consider these challenges for the chamfered hexagonal polyhedral RIS, which provides nearly uniform coverage. With both incidence and departure angles, we bound in closed form the gain loss of its best facet relative to the best possible reflecting facet, include the known b-bit quantization loss, and extend the bound to joint random mounting errors of all facets. For a precoded multi-user downlink, each facet-selection subproblem of the max-min rate design is shown to be an exact quadratic unconstrained binary optimization (QUBO) problem, suited to repeated time-constrained solving. The deployment of the base station in relation to the RIS allows the polyhedral surface to bring down the percentage of dead zones in vertical planar panels from 77% to 0.3% and increase the minimum rate by 34% relative to the STAR panel. The simulated annealing algorithm finds the optimum solution in 95% of the cases. The quantum approximation algorithm fails to do so.
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