Programmable Wireless Environments (PWEs)

• Programmable Wireless Environments (PWEs) are engineered spaces that use reconfigurable metasurfaces to dynamically shape electromagnetic wave propagation.
• They employ advanced devices such as RIS, PAS, and zeRIS for beam steering, channel optimization, and real-time adaptation to user mobility.
• System optimization through ML, metaheuristics, and closed-loop control enhances performance in terms of coverage, security, and energy efficiency.

Programmable Wireless Environments (PWEs) refer to physical spaces in which the electromagnetic wave propagation characteristics can be dynamically and programmatically controlled via engineered metasurfaces. Unlike conventional scenarios where walls, ceilings, and objects passively scatter or absorb wireless signals, PWEs leverage reconfigurable planar arrays—typically called reconfigurable intelligent surfaces (RISs)—or other RF-wave-shaping constructs, such as pinching antenna systems (PASs), to endow the propagation medium itself with software-defined and application-specific behavior. The objective of PWEs is to render the wireless channel itself a fully tunable resource, enabling real-time optimization of coverage, throughput, interference, physical-layer security, energy efficiency, and even user localization.

1. Fundamental Concepts and Enabling Technologies

At the core of PWEs are engineered metasurfaces: planar arrangements of hundreds to thousands of subwavelength "meta-atoms" whose local electromagnetic impedance can be tuned in amplitude and phase. The two main approaches to PWE realization are:

• Reconfigurable Intelligent Surfaces (RISs): Electrically tunable metasurfaces equipped with PIN diodes, varactors, or MEMS switches, allowing real-time programming of the local phase profile. RISs support beam steering, spatial filtering, null-forming, beam splitting, wavefront shaping, and even anomalous reflection/refraction (overriding Snell’s law). Centralized or distributed control interfaces can orchestrate large arrays via codebook-based selection or adaptive optimization algorithms (Strinati et al., 2021, Liaskos et al., 2018, Liaskos et al., 2018).
• Pinching Antenna Systems (PASs): Dielectric waveguides with discrete radiating “pinching antennas” that can be selectively enabled, enabling direct reconfiguration of path-loss and impulse response by choosing the effective radiation location (Tyrovolas et al., 3 Nov 2025, Tyrovolas et al., 21 Dec 2025).
• Zero-Energy RIS (zeRIS): Metasurfaces that harvest ambient RF energy to power their control circuits, supporting energy-self-sustained operation via schemes such as power splitting, time switching, and element splitting (Tyrovolas et al., 2023).

Advanced hardware concepts include adaptive metasurfaces capable of local EM field sensing and real-time closed-loop impedance control (Yang et al., 2024), and light-emitting RIS (LeRIS) panels integrating LEDs or VCSELs for joint localization, mapping, and communication, enabling ultra-precise indoor positioning and obstacle-aware beam routing (Iqbal et al., 9 Oct 2025, Bozanis et al., 2024).

2. Mathematical Models and Channel Formulations

PWEs are characterized by multi-hop, cascaded, and programmable RF channel models:

• RIS-Enabled Baseband Model:

$y = (h_d + H_r \Phi G_t)x + n$

where $h_d$ is the direct channel, $G_t$ and $H_r$ are the Tx-to-RIS and RIS-to-Rx channels, and $\Phi = \mathrm{diag}(e^{j\phi_1},…,e^{j\phi_N})$ is the RIS phase matrix. In NLOS scenarios, Rx relies entirely on RIS-assisted paths.

• Cascaded UAV–Metasurface Channel: For $K$ UAVs transmitting via cascaded static metasurfaces,

$$H_{eq} = \prod_{i=1}^K [H_{u_i,r}\Theta_i H_{t,u_i}]$$

with $\Theta_i = e^{j\phi_i}$ reflecting the fixed phase profile per metasurface board (Mekikis et al., 2022).

• PAS Channel Model: For $M$ pinching antennas, the received signal under activation $a_k=1$ is

$$\gamma^{(k)} = \frac{\eta P_t}{\sigma^2((x_m-x_k)^2+y_m^2+h^2)}$$

with optimal selection over $k$ (Tyrovolas et al., 3 Nov 2025).

• Graph-Based Multi-Tile PWE Model: Nodes correspond to tiles and users, links encode EM connectivity. Propagation paths are constructed by applying a sequence of tile functions (steering, collimation, absorption), with the end-to-end gain being the product of individual tile gains along the propagation path (Liaskos et al., 2018).

Beam steering, focusing, and absorption are synthesized by local phase gradients and amplitude modulation, described mathematically by generalized Snell's Law and programmable impedance tensors.

3. System Optimization and Orchestration Strategies

Effective PWE operation relies on multi-layer networked control and algorithmic optimization:

• Configuration Problem Formulations: Maximize sum-rate, minimize outage probability, maximize minimum secrecy capacity, or optimize energy efficiency, under constraints such as quantized phase settings, hardware reconfiguration limits, collision avoidance (UAVs), battery or energy harvesting constraints (zeRIS), and spectral occupancy (Strinati et al., 2021, Tyrovolas et al., 2023, Mekikis et al., 2022).
• Metaheuristic and ML-Based Orchestration: Genetic Algorithms and interpretable neural networks can discover optimal or near-optimal tile state assignments in combinatorially large configuration spaces (Liaskos et al., 2019). Deep reinforcement learning, model-based RL (MuZero-style), and continual learning adapt tile states in response to user mobility, concept drift, and nonstationary human behavior (Wu et al., 20 Jul 2025).
• Graph-Based Path Selection (KpConfig): Efficiently enumerates promising multi-tile EM paths subject to user, objective, and tile reuse constraints (MaxPower, MaxSIR, eavesdropper avoidance, Doppler mitigation) (Liaskos et al., 2018).
• Codebook-Based Control for Large RISs: Precomputed sets of phase profiles facilitate low-latency adaptation to user locations, with hybrid AI approaches exploiting real-time feedback and localization for closed-loop environment programming (Tyrovolas et al., 2024, Strinati et al., 2021).

4. Localization, Sensing, and Adaptive Feedback

PWEs demand ultra-precise user localization and real-time environment sensing to maintain beam alignment and maximize performance:

• Optical Anchor-Based Positioning: Embeds LEDs or VCSELs in the environment or in LeRIS panels, using RSS, AoA, and TDoA measurements to achieve sub-centimeter accuracy. Analytical frameworks solve nonlinear localization equations under constraints such as random receiver orientation and spatial anchor geometry (Iqbal et al., 9 Oct 2025, Bozanis et al., 2024, Tyrovolas et al., 2024).
• RIS-Based Sensing: RIS panels themselves act as RF anchors through beam scanning and RSS or delay-based trilateration. Cramér-Rao bounds dictate achievable localization precision as a function of RIS aperture and element count (Tyrovolas et al., 2024).
• Self-Aware Metasurfaces: Adaptive metasurfaces with in-element field sensing eliminate the need for external sensors, continuously measuring local EM field amplitude/phase and autonomously adjusting reflection coefficients to maximize cascaded channel gain (Yang et al., 2024).
• Impact of Localization Error: As RIS size increases, beamwidth narrows ($\theta_{\rm HPBW} \propto 1/(Md)$), requiring centimeter-scale localization uncertainty to prevent misalignment or severe capacity loss (Tyrovolas et al., 2024).

5. System-Level Performance, Scaling Laws, and Case Studies

PWE deployments—both simulated and experimental—demonstrate substantial gains:

• Capacity and Coverage: Example indoor scenarios yield $\approx$15–30 dB coverage improvement in NLOS zones, up to 2$\times$ the baseline throughput, and robust suppression of interference (Liaskos et al., 2018, Liaskos et al., 2018, Strinati et al., 2021).
• Physical-Layer Security: Channel steering and phase-nulling techniques mitigate eavesdropping—routing rays away from adversarial receivers or inducing destructive interference at unintended locations (Mekikis et al., 2022, Liaskos et al., 2018).
• Energy Efficiency: zeRIS concepts enable energy-self-sustained metasurfaces, with joint energy-rate outage probabilities analytically optimized by harvest-and-reflect parameters (PS, TS, ES) and strategic deployment—ideally BS-side for best harvested power (Tyrovolas et al., 2023).
• Mobility-Aware Steering: Analytical models and ray-tracing results show biased PWE steering adapts to user dynamics, sustaining near-ideal link quality despite latency-induced misalignment (Liaskos et al., 2020).
• Pinching Antenna Count Scaling: Closed-form formulas specify that as few as $M \approx 0.1 D_x$ PAs suffices to reach over 90% of continuous performance in typical indoor deployments ($D_x$ in meters) (Tyrovolas et al., 3 Nov 2025, Tyrovolas et al., 21 Dec 2025).
• Prototype Demonstrations: Real-world deployments (RISE-6G) exhibit peak data rates $>$1.4 Gbps at 77 GHz, outage probabilities below $10^{-3}$, and effective coverage in complex layouts (Strinati et al., 2021).

6. Architectural, Practical, and Implementation Considerations

PWE architectures span centralized SDN controllers coordinating tile clusters via IoT protocols, distributed edge/fog controllers, and hardware abstraction layers offering programmable APIs for environment service provisioning (Liaskos et al., 2018, Liaskos et al., 2018). Scalability considerations include:

• Hierarchical Control: As tile count surpasses $10^3$, control must scale via partitioning, clustering, or hierarchical delegation.
• Tile Networking: Wired or wireless grid networks interconnect tiles, with redundancy and multi-exit nodes for resilience.
• Hardware Constraints: Meta-atom design, phase-quantization, switch reliability, bias distribution, and waveguide attenuation must be co-optimized for target frequency bands (sub-6 GHz, mmWave, THz).
• Latency and Power: Reconfiguration cycles must remain within channel coherence times; state-preserving switches and adaptive sleep cycles are mandated for energy budget compliance.

7. Limitations, Open Challenges, and Future Research Directions

Major challenges for PWEs include:

• Nonstationarity and Concept Drift: Human mobility induces tidal-like phase and amplitude drift in RIS-covered buildings, invalidating any universal channel model. PACF analysis reveals high-order Markov dependencies and persistent nonstationarity (Wu et al., 20 Jul 2025).
• Deep Learning Generalization: Channel predictors exhibit rapid performance decay under cross-layout or cross-band transfer, necessitating continual fine-tuning and tidal-aware embedding in RL agents.
• Standardization and Security: Robust APIs, control plane security against spoofing, and interoperability among hardware vendors are needed for widespread PWE adoption (Liaskos et al., 2018).
• Joint Communication, Sensing, and Localization: Unified optimization across these dimensions—especially with adaptive metasurfaces and hybrid optical/RF anchors—is an active area (Yang et al., 2024, Tyrovolas et al., 2024, Iqbal et al., 9 Oct 2025).
• End-to-End Architectural Generation: Combining wireless performance metrics with generative layout synthesis for one-click PWE creation is advocated for next-generation networks (Wu et al., 20 Jul 2025).

A plausible implication is that PWEs will play a central role in upcoming 6G and beyond wireless systems, offering a paradigm shift from channel-oblivious propagation to fully programmable, service-oriented environments.