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Indoor and Outdoor Physical Channel Modeling and Efficient Positioning for Reconfigurable Intelligent Surfaces in mmWave Bands

E. Basar, I. Yildirim, F. Kilinc

arXiv:2006.02240v3eess.SPcs.IT

TL;DR

RIS-assisted mmWave communication needs a physical and broadly applicable channel model that captures realistic propagation across indoor and outdoor environments. This paper develops such a model from 5G channel conditions, evaluates deployment and positioning scenarios, and introduces SimRIS; the results identify settings where RISs provide substantial gains or limited impact.

  • Problem

    The paper addresses the need for a physical, accurate, open-source, and widely applicable RIS channel model for mmWave systems across indoor and outdoor environments.

  • Method

    The authors integrate RIS characteristics into 5G mmWave channel modeling, provide indoor and outdoor deployment analyses, and release the tunable SimRIS Channel Simulator.

  • Results

    The study reveals deployment scenarios where RISs produce promising gains, including indoor LOS-dominated links and outdoor placements with RIS–Rx distance below 15 m.

  • Takeaways & Limitations

    RIS positioning and propagation conditions determine whether RIS-assisted communication substantially improves achievable rate, while SimRIS supports practical channel-performance studies.

Abstract

from arXiv · show

Reconfigurable intelligent surface (RIS)-assisted communication appears as one of the potential enablers for sixth generation (6G) wireless networks by providing a new degree of freedom in the system design to telecom operators. Particularly, RIS-empowered millimeter wave (mmWave) communication systems can be a remedy to provide broadband and ubiquitous connectivity. This paper aims to fill an important gap in the open literature by providing a physical, accurate, open-source, and widely applicable RIS channel model for mmWave frequencies. Our model is not only applicable in various indoor and outdoor environments but also includes the physical characteristics of wireless propagation in the presence of RISs by considering 5G radio channel conditions. Various deployment scenarios are presented for RISs and useful insights are provided for system designers from the perspective of potential RIS use-cases and their efficient positioning. The scenarios in which the use of an RIS makes a big difference or might not have a big impact on the communication system performance, are revealed. The open-source and comprehensive SimRIS Channel Simulator is also introduced in this paper.

I. INTRODUCTION

The paper addresses the need for a physical, open-source, widely applicable mmWave RIS channel model spanning indoor and outdoor environments. It contributes unified modeling, deployment insights, and the open-source SimRIS simulator.

  • The paper identifies an urgent need for a physical, open-source, widely applicable mmWave channel model for RIS-assisted systems in indoor and outdoor environments.
  • The authors formulate a baseline cascaded physical channel model using power scaling laws for RIS-assisted systems with multiple scatterers under the far-field assumption.
  • The proposed unified narrowband model incorporates 5G mmWave channel conditions, random clusters and scatterers, LOS probability, shadowing, shared clusters, realistic RIS gains, and array responses.
  • The study uses the comprehensive channel model to demonstrate RIS use-cases and gains in certain setups while providing guidelines for effective RIS use.
  • The open-source SimRIS Channel Simulator supports RIS channel modeling with tunable frequency, terminal locations, RIS element count, and environments.

II. RIS-ASSISTED COMMUNICATIONS: SYSTEM MODEL

The system model represents RIS-assisted propagation through multiple scatterers between the transmitter and RIS, followed by a LOS RIS-to-receiver link. It combines path gains, RIS responses, and direct transmission in a cascaded channel formulation.

  • The baseline system places M interacting objects between the transmitter and RIS while assuming a pure LOS link between the RIS and receiver.
  • The model assigns distances, radar cross sections, RIS-element gains, and radiation patterns to characterize scattering and RIS propagation.
  • The received signal combines the RIS-assisted channel through element responses with the direct narrowband transmitter–receiver channel.
  • Each RIS-assisted path gain is formed from the Tx-to-object, object-to-RIS-element, and RIS-element-to-Rx propagation factors, with RIS distance affecting gains and phases differently.
  • The deterministic model is suitable for static setups but does not capture environmental-object or terminal movement variations.

III. PHYSICAL CHANNEL MODELING: INDOORS

The indoor modeling framework extends the RIS signal model into a unified statistical mmWave channel model applicable across environments and frequencies. It uses clustered statistical MIMO modeling to generate the constituent subchannels.

  • The paper develops a unified mmWave signal and channel model applicable to indoor and outdoor environments and different operating frequencies.
  • Because deterministic modeling is limited to static settings, dynamic environments require statistical channel models that capture RIS-specific fading phenomena.
  • The indoor framework applies established statistical channel models to the general RIS signal model to represent channel amplitudes and phases with adjustable RIS phase shifts.
  • The framework is built on the clustered statistical MIMO model used in 3GPP standardization and generates Tx–RIS, RIS–Rx, and Tx–Rx subchannels.

A. Tx-RIS Channel (h)

The Tx-RIS channel models clustered mmWave propagation using RIS array responses, element radiation patterns, path attenuation, and geometry-aware arrival angles under far-field assumptions.

  • The model groups M interacting objects into C clusters with Sc sub-rays and uses clustered channel coefficients with array responses and path attenuations.
  • RIS element responses use a 3D square-array geometry, with elements indexed across horizontal and vertical axes and angles defined relative to the RIS broadside.
  • The model applies a cos^q RIS element radiation pattern with energy normalization and an example element gain of Ge(0) = π (5 dBi).
  • Cluster counts follow a Poisson model whose variance can be set by scenario and frequency, with λp = 1.8 suggested at 28 GHz and λp = 1.9 at 73 GHz.
  • Scatterer distances and RIS arrival angles are obtained from 3D coordinates, while path attenuation uses a 5G close-in free-space reference model.
  • The Tx-RIS LOS component is modeled probabilistically, with indoor LOS handling conditioned on RIS height relative to the transmitter.

B. RIS-Rx Channel (g)

The RIS-Rx channel is modeled as a LOS-dominated link when the terminals are sufficiently close, using a direction-specific RIS response and path attenuation.

  • The indoor model assumes a clear RIS-Rx LOS link when the terminals are sufficiently close, with LOS probability above 50% below 4.5 m.
  • The channel recalculates the RIS array response toward the receiver and applies RIS-Rx path attenuation and element gain for the N reflected paths.
  • A random phase term is included when generating the LOS channel coefficient vector.

C. Tx-Rx Channel (hSISO)

The Tx-Rx SISO channel is retained alongside the RIS-assisted paths because RIS propagation has double-scattering structure and the direct link can remain stronger.

  • The direct Tx-Rx channel cannot be ignored because it may be relatively stronger than the RIS-assisted path even when the RIS is near the receiver.
  • For indoor settings, the model assumes the RIS and receiver share clusters when their separation is below the correlation distance.
  • The Tx-Rx channel uses SISO mmWave modeling with shared cluster parameters, path attenuation, an LOS component, and an excess phase from differing travel distances.

IV. PHYSICAL CHANNEL MODELING: OUTDOORS

The outdoor model extends the indoor framework by adapting propagation parameters and allowing the RIS-Rx link to exhibit small-scale fading with independent clusters.

  • The outdoor model retains the indoor channel-modeling strategy while adapting path-loss, shadow-fading, and small-scale parameters to the propagation environment.
  • Unlike the indoor case, the outdoor RIS-Rx link may include small-scale fading and a random number of unique clusters; the LOS-dominated model remains useful at short distances.
  • Outdoor RIS-Rx channel terms include cluster counts, complex path gains, attenuation, RIS radiation patterns, array responses, and an LOS component.
  • The outdoor LOS probability follows a distance-based 3GPP/ITU reference model for d ∈ {dT-RIS, dRIS-R, dT-R}, representing a worst-case ground-level scenario.
  • The outdoor model assumes the RIS and receiver are separated beyond the correlation distance, so their small-scale parameters use independent clusters.
  • The UMi Street Canyon example uses a 20 m transmitter and a ground-level receiver, with path-specific cluster and scatterer counts varying randomly.

V. ON RIS-ASSISTED CHANNEL MODELING WITH SimRIS

The paper summarizes a reproducible workflow for generating RIS-assisted indoor and outdoor channels and introduces SimRIS as its implementation companion.

  • SimRIS Channel Simulator implements the summarized RIS-assisted channel-modeling workflow for indoor and outdoor environments.The simulator is presented as an open-source MATLAB package accompanying the channel-generation procedure.
  • The workflow begins with Tx, Rx, and RIS coordinates, computes the Tx–RIS distance and LOS probability, and generates the LOS component of h.
  • It then determines clusters and sub-rays, generates departure angles and distances, computes scatterer–RIS geometry, and forms RIS array responses.
  • The Tx–RIS channel vector h is generated from array responses, link attenuation, and complex path gains for the specified system parameters.
  • The RIS–Rx link is modeled using its LOS distance and departure angles, with indoor and outdoor procedures differing in their treatment of clusters.
  • The direct-link coefficient hSISO is generated using shared clusters indoors and independent clusters outdoors.

VI. PRACTICAL ISSUES AND CHANNEL CORRELATION

This section examines practical RIS operation, positioning, and channel correlation. It shows that LOS-aware placement and environmental angular spread strongly influence achievable-rate gains and spatial correlation.

  • Practical RIS operation: The paper analyzes RIS operation through software-controlled phase adjustment of a uniform planar array to optimize metrics such as energy efficiency, transmit power, and achievable rate.
  • Practical RIS operation: Hardware imperfections include phase-estimation errors and quantization errors; q control bits provide 2^q discrete phases, preventing full phase alignment while retaining possible performance gains.Near-optimal phase adjustments can still improve performance under these imperfections.
  • RIS positioning and achievable rate: RISs should be placed on walls close to the Rx while preserving a Tx–RIS LOS path; side or opposite walls are identified as reasonable choices indoors and outdoors.
  • Spatial correlation: The spatial-correlation analysis models RIS-element correlation from angular distributions and distances, using correlation matrices and eigenvalue spread as analytical measures.
  • RIS positioning and achievable rate: RISs boost indoor achievable rate most effectively when both Tx–RIS and RIS–Rx links are LOS dominated.Larger Tx–Rx/RIS separations can noticeably degrade the RIS benefit in the examined scenario.
  • Spatial correlation: Indoor scatterers occupy a narrow elevation range, producing highly correlated channels with few strong eigenvalues, whereas outdoor scatterer spread yields smaller correlation coefficients.
  • Spatial correlation: Analytical and semi-analytical correlation results generally agree with simulations, with deviations emerging for higher eigenvalue numbers because of angular-distribution approximations.

VII. NUMERICAL RESULTS

Numerical evaluations examine RIS-assisted achievable rates across indoor and outdoor placements, link conditions, phase settings, and near-field operation. The results show that RIS benefits depend strongly on LOS availability, RIS–receiver distance, and positioning.

  • Indoor deployment conditions: In indoor environments, RISs effectively boost achievable rate when both Tx–RIS and RIS–Rx links are LOS dominated, but benefits are minor or degraded under weaker LOS conditions and larger separations.The reported scenarios use N = 256 and 1024 at 28 GHz under far-field conditions.
  • Indoor positioning: Around 1.6 bits/s/Hz improvement remains possible at test points 10 m from the RIS, while achievable rate varies up to 1.35 bits/s/Hz across RIS–Rx link lengths.The improvement is particularly pronounced for test points closer to the RIS, and symmetric placements can yield identical rates.
  • Outdoor receiver positioning: When the direct Tx–Rx link is blocked outdoors, RIS–Rx separation is the most critical parameter, whereas a nearby direct link can dominate achievable rate.The RIS effect diminishes when the receiver moves far from the RIS.
  • Outdoor RIS positioning: RIS placement is most effective outdoors when d_RIS-R is less than 15 m; moving the RIS away from both terminals substantially decreases achievable rate and may prevent reliable transmission.The evaluation uses N = 64, 256, and 1024 without a direct Tx–Rx path.
  • Near-field operation: Increasing RIS element count improves achievable rate under near-field conditions, but quadrupling N reduces required transmit power by approximately 10 dB versus approximately 15 dB in the far-field case.The near-field evaluation is conducted indoors at 28 GHz with the RIS placed near the receiver.

VIII. CONCLUSIONS

The paper presents SimRIS as an initial physical channel-modeling framework for RIS-enabled networks and identifies extensions needed for broader realism. Extensive measurements are specifically required to obtain more realistic RIS-assisted link parameters.

  • Conclusions: SimRIS supports Monte Carlo assessment of capacity, SNR gain, secrecy, outage, and error performance by providing cascaded channel coefficients separately.The methodology can also be extended to multicarrier cases by incorporating path delays.
  • Future research: The paper identifies delay spread, composite-channel statistics, channel time variation, and correlation as future research questions for RIS channel modeling.These questions concern wideband behavior and how RISs influence the dynamics of the composite channel.
  • Limitations: Extensive measurements are required to obtain more realistic system parameters for RIS-assisted links.This marks a scope boundary for the current modeling methodology.
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