Pinching Discretization Efficiency

• Pinching discretization efficiency is defined as the ratio of the ergodic rate of a discrete PAS to that of a continuous PAS, capturing performance loss due to finite pinching points.
• Analytical models using dielectric-waveguide PASs yield closed-form expressions for metrics like outage probability and ergodic rate, highlighting trade-offs between antenna density and throughput.
• Design guidelines derived from the analysis recommend optimal PA counts for different spatial dimensions to balance hardware simplicity with near-continuous performance in programmable wireless environments.

Pinching discretization efficiency quantifies the loss—or efficiency—incurred when a theoretically continuous actuator (such as a pinching antenna capable of arbitrary placement along a waveguide) is constrained to operate on a discrete, finite set of positions. In programmable wireless environments (PWEs), this metric rigorously characterizes how well a practical two-state pinching-antenna system (PAS) with a finite number of fixed pinching points can approximate the ideal continuous limit in terms of achievable throughput or ergodic data rate. Pinching discretization efficiency has become a central analytic tool in evaluating and optimizing the design of PASs and in engineering trade-offs between hardware simplicity and communication performance (Tyrovolas et al., 21 Dec 2025, Tyrovolas et al., 3 Nov 2025).

1. Definition and Formalism

Pinching discretization efficiency, denoted $\overline\eta_r$ or $\eta_d$ in the literature, is defined as the ratio of the ergodic achievable data rate of a discrete (two-state) PAS to that of the ideal continuous PAS: $\overline\eta_r = \frac{\overline R}{R_c}$ where

• $\overline R$: Ergodic rate with $M$ fixed pinching-antenna (PA) locations, activating exactly one per slot.
• $R_c$: Ergodic rate when PAs can be formed at any real-valued position along the waveguide.

This definition encapsulates, in a single normalized scalar, the degree to which the discrete hardware approaches the ideal propagation control promise of continuous reconfigurability. By construction, $0 < \overline\eta_r \leq 1$, and $\overline\eta_r \rightarrow 1$ as $M \to \infty$ (i.e., as the discretization becomes vanishingly fine) (Tyrovolas et al., 21 Dec 2025, Tyrovolas et al., 3 Nov 2025).

2. Analytical Framework and Key Assumptions

The canonical analysis is based on a dielectric-waveguide PAS, where radiation is produced by selectively exciting one of $M$ pre-placed PAs along a waveguide of length $D_x$. The main modeling assumptions are:

• PAs are located at $x_k = (2k-1)\delta/2$, with grid spacing $\delta = D_x/M$.
• Each user is randomly positioned in the $xy$-plane, and the optimal PA (maximizing user SNR) is chosen in each access.
• The signal undergoes exponential attenuation along the waveguide ($e^{-\alpha x}$), with additional path-loss effects from the antenna to the user.

The received SNR for user $(x_m, y_m, 0)$ from the $k$th PA is: $$\gamma^{(k)} = \frac{\eta P_t \exp(-\alpha x_k)}{\sigma^2[(x_m - x_k)^2 + y_m^2 + h^2]}$$ where $\eta = \lambda^2/(16\pi^2)$ and $h$ is the waveguide height.

With these assumptions, the ergodic rate $\overline R$ is computed by integrating the log-rate over the user distribution and the discrete PA serving regions partitioned by proximity (Tyrovolas et al., 21 Dec 2025, Tyrovolas et al., 3 Nov 2025).

3. Closed-form Expressions, Region Partitioning, and PDE Calculation

For two-state PASs, the discrete spatial structure allows the entire axis to be partitioned into $M$ serving rectangles, each associated with a PA and width $\approx \delta$. With uniform user distribution, key outcomes include:

• Outage Probability:

$$P_o = \sum_{k=1}^M \left[\frac{L_k}{D_x} P_l(L_k) + \frac{R_k}{D_x} P_l(R_k)\right]$$

$P_l(\cdot)$ is the 1D outage probability, detailed in five piecewise expressions parameterized by $A_{0,k}$.

• Ergodic Rate:

$$\overline R = \sum_{k=1}^M \left[\frac{L_k}{D_x} C_l(L_k) + \frac{R_k}{D_x} C_l(R_k)\right]$$

$$C_l(\Delta) = \frac{2}{\Delta D_y \ln 2} \left[ I_i(C_{0,k} + h^2) + I_j(C_{0,k} + h^2) - I_i(h^2) - I_j(h^2) \right]$$

$I_i(\cdot)$ and $I_j(\cdot)$ are computed in closed form, and $C_{0,k} = C e^{-\alpha x_k}$.

Pinching discretization efficiency is then evaluated as $\overline\eta_r = \overline R / R_c$, with $R_c$ determined by the same formalism but with $\delta \rightarrow 0$ (continuous limit) (Tyrovolas et al., 21 Dec 2025, Tyrovolas et al., 3 Nov 2025).

4. Intuitive Interpretation and Limiting Behavior

The PDE quantifies how closely a discrete, grid-constrained PAS emulates the ideal PAS with continuous pinching. At small $M$ (larger $\delta$), the closest available PA may be far from the user-optimal $x_p^*$, incurring SNR and rate loss. As $M$ increases, the discretization penalty diminishes due to finer spatial granularity.

However, a notable artifact is that, under exponential waveguide attenuation, further densification of PAs may eventually overshoot the true optimum, so $\overline\eta_r$ plateaus below unity rather than increasing indefinitely. This characterizes a fundamental performance saturation dictated by combined grid alignment and physical channel effects (Tyrovolas et al., 21 Dec 2025).

5. Design Criteria and Performance Guidelines

A key utility of pinching discretization efficiency lies in enabling principled design of PAS topologies. Analytical forms for $\overline\eta_r$ provide explicit trade-offs between the number of PAs ($M$), the physical environment dimensions ($D_x, D_y$), and the desired proximity to the continuous optimum.

Key design recommendations, validated both numerically and by closed-form analysis, are:

• For room-scale environments ($D_x \le 10\,\text{m}$), $M=2$ suffices for $\overline\eta_r \ge 0.95$.
• For $D_x = 20\,\text{m}$, $M \simeq 3$.
• For $D_x = 30\,\text{m}$, $M \simeq 4$.
• For $D_x=50\,\text{m}$, $M=8$–$12$ achieves $\overline\eta_r \ge 0.95$.
• Further increments in $M$ yield rapidly diminishing returns, so hardware cost and complexity can be minimized by targeting $\overline\eta_r$ thresholds (e.g., $0.95$ or $0.99$) (Tyrovolas et al., 21 Dec 2025, Tyrovolas et al., 3 Nov 2025).

The penalty scales as $O(\delta^2/h^2)$, motivating design of $\delta$ (and thus $M$) such that the expected PA-user misalignment is below the characteristic vertical dimension.

6. Relationship with Energy Efficiency and Dual-Scale Resolution

Beyond raw ergodic rate, discretization efficiency interacts with broader system energy efficiency, particularly under dual-scale deployment (DSD) strategies. Here, coarse and fine grid resolutions are jointly optimized against actuation power cost, RF combining gain, and deployment protocol.

• Coarse resolution $\Delta_c$ impacts amplitude alignment and transmission efficiency. Excessive coarseness ($\Delta_c \gg 1\,\text{m}$) can cause substantial efficiency loss—up to $30\%$ in multi-PA systems.
• Fine resolution $\Delta_r$ controls phase alignment and can induce a $\sim15\%$ drop if too coarse.
• Practical energy-efficient PAS designs emerge by tuning $\Delta_c$ and $\Delta_r$ to achieve less than $1\,\text{dB}$ amplitude quantization and $<10^\circ$ phase error, respectively (Gan et al., 31 Oct 2025).
• Overall system energy efficiency (spectral efficiency per total consumed power) is maximized by balancing RF, positioning, and mechanical costs with discretization-induced rate penalties using PDE as a guiding metric.

7. Numerical Validation and Practical Implications

Simulations consistently confirm the analytic framework, indicating:

• For typical PWEs ($\alpha = 0.05$, $\gamma_t = 90\,\text{dB}$, $D_y = 10\,\text{m}$, $h=3\,\text{m}$), $\overline\eta_r$ saturates quickly with moderate $M$.
• Even with moderate $M$, near-optimal performance is accessible; e.g., for $D_x=30\,\text{m}$, $\overline\eta_r \approx 0.93$ at $M=3$, and $\approx 0.96$ at $M=4$.
• The impact of increased waveguide attenuation ($\alpha$) mildly depresses the plateau in $\overline\eta_r$ but does not shift the saturation point (Tyrovolas et al., 21 Dec 2025).

The analytic approach, validated by Monte Carlo, supports hardware-efficient deployment of PAS in practical indoor scenarios, with the ability to a priori size the number of discrete antennas for a target throughput or energy efficiency constraint.

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References:

(Tyrovolas et al., 21 Dec 2025, Tyrovolas et al., 3 Nov 2025, Gan et al., 31 Oct 2025)