Coverage Analysis and Optimization of FIRES-Assisted NOMA and OMA Systems

TL;DR

The paper develops a coverage-centric framework for FIRES-enabled full-space wireless systems serving two users with OMA or NOMA. It derives closed-form far-field LoS coverage bounds that reveal the roles of aperture, element count, energy-splitting, and phase-errors, and embeds these bounds into a bi-level optimization: an outer PSO searches the fluid-element presets under spacing constraints, while inner solvers optimize MA resource allocation (1-D for OMA, a small convex program for NOMA). The approach demonstrates substantial coverage gains of FIRES over STAR-RIS and highlights the additional benefits of NOMA when SIC is feasible, with analytical bounds closely matching simulations and showing robustness to phase-control imperfections. The resulting design provides interpretable guidance on the trade-offs among element count, mobility range, and phase quantization, and offers a practical pathway to deploy FIRES in full-space NG networks. Overall, the work bridges metasurface fluidity with network-level coverage optimization, delivering a tractable methodology for FIRES-assisted two-user downlink systems.

Abstract

Fluid integrated reflecting and emitting surfaces (FIRES) are investigated. In these metasurfaces, each subarea hosts an active element capable of simultaneous transmission and reflection, phase, and geometric positioning control within the subarea. We develop a coverage-centric system model for the two-user downlink scenario (one user per half-space) under spatially correlated Rician fading and imperfect phase control. First, we derive closed-form far-field line-of-sight (LoS) coverage bounds that reveal the effects of aperture size, base station (BS) distance, transmit power, energy-splitting (ES), and phase errors. Protocol-aware corollaries are then presented for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA), including conditions for successful successive interference cancellation (SIC). Second, we formulate coverage maximization as a bi-level optimization problem consisting of (i) an outer search over FIRES element positions, selecting one active preset per subarea under minimum-spacing constraints, and (ii) an inner resource allocation problem tailored to the multiple-access scheme, which is one-dimensional for OMA and a small convex program for NOMA. The proposed framework explicitly accounts for target rate constraints, ES conservation, power budgets, geometric placement limits, and decoding-order feasibility. Extensive simulations demonstrate that FIRES, by jointly exploiting geometric repositioning and passive energy control, substantially enlarges the coverage region compared with a conventional simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) under the same element budget. Furthermore, NOMA yields additional coverage gains when feasible. The analytical coverage bounds closely match the simulation results and quantify the robustness of FIRES to phase-control imperfections.

Figures (11)

• Figure 1: The FIRES-aided communication system.
• Figure 2: Total coverage $D_\mathrm{tot}$ versus PSO iterations $T$.
• Figure 3: Total coverage $D_\mathrm{tot}$ versus the average SNR $P/\sigma^2$.
• Figure 4: Total coverage $D_\mathrm{tot}$ versus the number of fluid elements $M$.
• Figure 5: Total coverage $D_\mathrm{tot}$ versus the target rate $R^\mathrm{tar}_u$.

Theorems & Definitions (6)

• Theorem 1: FIRES LoS Coverage Radius
• Corollary 1: OMA coverage bound
• Corollary 2: NOMA coverage bounds
• Remark 1
• Theorem 2: 1-D split for OMA with phase errors
• Theorem 3: NOMA feasibility and order with phase errors