Site-Specific Radio Channel Representation - Current State and Future Applications (2024)

Thomas Zemen, Jorge Gomez-Ponce, Aniruddha Chandra, Michael Walter, Enes Aksoy, Ruisi He, David Matolak, Minseok Kim, Jun-ichi Takada, Sana Salous, Reinaldo Valenzuela, and Andreas F. MolischThe work of Thomas Zemen is funded within the Principal Scientist grant Dependable Wireless 6G Communication Systems (DEDICATE 6G) at the AIT Austrian Institute of Technology (thomas.zemen@ait.ac.at)The work of Aniruddha Chandra is funded by MeitY C2S program no. EE-9/2/2021-R&D-E, GACR project no. 23-04304L, and NCN MubaMilWave no. 2021/43/I/ST7/03294.The works of Minseok Kim and Jun-ichi Takada are supported by the MIC, Japan, through research and development for the realization of high-precision radio wave emulator in cyberspace, under Grant JPJ000254.The work of A. F. Molisch is supported by the National Science Foundation. The work of J. Gomez-Ponce is supported by the National Science Foundation and the Foreign Fulbright Ecuador SENESCYT Program.

Abstract

A site-specific radio channel representation (SSCR) considers the surroundings of the communication system through the environment geometry, such as buildings, vegetation, and mobile objects including their material and surface properties. In this article, we focus on communication technologies for 5G and beyond that are increasingly able to exploit the specific environment geometry for both communication and sensing. We present methods for a SSCR that is spatially consistent, such that mobile transmitters (TXs) and receivers (RXs) cause a correlated time-varying channel impulse response. When modelled as random, this channel impulse response has non-stationary statistical properties, i.e., a time-variant Doppler spectrum, power delay profile, K-factor and spatial correlation. A SSCR will enable research into emerging 5G and beyond technologies such as distributed multiple-input multiple-output systems, reconfigurable intelligent surfaces, multi-band communication, and joint communication and sensing. These 5G and beyond technologies will be deployed for a wide range of environments, from dense urban areas to railways, road transportation, industrial automation, and unmanned aerial vehicles.

I Introduction

An SSCR allows the accurate prediction of radio propagation to analyse the performance of communication and sensing systems for sites of general interest and for different vertical domains such as railways, road transport, manned and unmanned aircraft or robotic collaboration. In this paper, we are interested in reviewing models that can influence research, system design, and standardisation in different domains.

Radio waves are emitted by the transmitting antenna and propagate via different propagation paths to the receiving antenna where they sum up linearly, causing dispersion in delay and Doppler. The resulting radio channel can be fully described by a sampled time-varying impulse response. The impulse response depends on the TX and RX coordinates, their velocity vectors as well as all visible objects in the environment. Its effect on the transmitted signal is computed by a time-varying convolution. Fundamentally, Maxwells equations can be solved numerically to obtain the impulse response, however the numerical complexity is too high for most practical cases. Hence, SSCRs aim to achieve a compromise between accuracy and complexity.

II Motivation for future standards and use cases

In the fall of 2022, the ComSoc Standards Development Board (DSB) initiated a new group on wireless propagation channel modeling, which in 2023 was formalized as the P1944 Standard under the aegis of the Mobile Communication Networks Standards Committee, with the Project Authorization Request approved by IEEE Nescom in May 2023. The special interest group is currently facilitating the development of a number of SSCRs for 5G and beyond described in the following subsections.

It is important to distinguish the aims of P1944 from the channel modeling of 3GPP, 802.11/WiFi, and similar standards efforts. The latter aim to develop channel models best suited for the specific operating parameters and use cases of this standard. In contrast, P1944 aims to develop more generic models suitable for both academic research and industry developments, which might be used by standards organizations as basis for versions that are specialized to their purposes.

II-A Joint communication and sensing

Sensing capabilities will create a disruptive and explosive growth of new 5G and beyond applications and services. These can be economically implemented at minimal or no additional cost in existing base stations and/or user equipment heralding a new 5G and beyond era of joint communications and sensing (JCAS) also known as integrated sensing and communication. This sensing capability will span a very wide range of applications as described in [1], including but not limited to: Intrusion detection, health monitoring, environmental monitoring, road traffic, daily life, industrial automation, etc. For example, a base station detects and alerts a pedestrian entering a pedestrian crossing while a car is about to enter, as shown in Fig. 1.

Site-Specific Radio Channel Representation - Current State and Future Applications (1)

Monostatic and bistatic network topologies are the simplest configuration for sensing. In monostatic sensing, a single base station implements the TX and RX functions at the same site, performing the sensing function within its radio coverage. In the bistatic case, two sites work together to achieve enhanced performance. The communication waveform may also be used opportunistically for the sensing function.The reliable and robust design of such solutions will require new propagation models that enable deployment in a variety of environments for different applications with a high probability of user satisfaction. Current communication models are excellent in the context of a TX-RX link. However, at a minimum, the return power ratios from the object being sensed and its surrounding background are critical enhancements to be added to the existing propagation modes. Other improvements required include spatial consistency and Doppler statistics.

II-B Railways

Railway communications need to evolve from 2G (GSM rail, GSM-R) to the next generation railway communication system with improved performance and intelligence. 5G and related technologies [2] are seen as a solution to support the increasing data traffic, various new services, and high safety requirements for future intelligent railways. The next generation railway communication system is expected to support speeds of up to 500 km/h, which poses significant challenges for dynamic channel modelling, including dynamic estimation, tracking, and characterisation of multi-path components, which are not well studied for 2G railway communication systems and need to be addressed in the future. Channel dispersions in the delay, Doppler, and angle domain should also be accurately characterised by a SSCR for future intelligent railways for a wide range of scenarios such as viaducts, tunnels, cuttings, stations, etc. Well-designed large and small scale models are essential for site-specific coverage prediction and link budget calculation.

II-C Street transport

Trains and vehicles are both examples of wireless channels where one or both end nodes are moving, so the channel models developed for these two use cases have many similarities, e.g., Doppler shift plays a major role and scattering objects on both sides of the track or road, such as buildings, foliage, uneven terrain, etc. must be considered. However, the movement of trains along a fixed track and the availability of utility poles at regular intervals make the case for trains generally more deterministic. In contrast, vehicle-to-everything (V2X) communication breaks down into many complex scenarios [3], ranging from static intra-vehicle or indoor parking to dynamic outdoor links between two vehicles or between a vehicle and the infrastructure. Vehicle radio channels are inherently location-specific. As an indicative example, the coherence time for a vehicular channel varies with the speed of the vehicle, the wavelength and the multipath distribution. There are more unique scenarios, such as an infrastructure-to-infrastructure channel where both TX and RX may be static, but moving vehicles acting as scatterers cause channel variations.

II-D Aircraft

As the number of manned and unmanned aircraft continues to grow, there is a need to manage airspace more efficiently. This includes all aspects of communication, navigation and surveillance technologies. For all three aspects, it is important to understand the propagation channel between aircraft (manned or unmanned) and ground infrastructure. There are several key differences between the air-ground channel and traditional terrestrial communication systems. Aircraft can travel faster than terrestrial vehicles, both within and over areas where terrestrial communications take place. This poses challenges for accurate modelling of the air-ground channel, as Doppler spectra [4] and power delay profiles can be rapidly time-varying. Thus, time-varying Doppler and delay profiles provide non-stationary channel statistics.

Site-specific air-ground channel conditions can manifest themselves when aircraft enter environments for which little modelling has been done. Examples include small aircraft conducting fly-by inspections of utilities such as large wind farms, advanced air mobility aircraft manoeuvring near rooftops in urban areas, and air-ground communications with a large swarm of aircraft. In addition, aircraft-specific effects such as airframe shadowing are not addressed in typical terrestrial channel modeling.

In the common case where aircraft antennas are omnidirectional, the primary components of the channel impulse response are the line of sight and the earth surface reflection. The strength of the received line-of-sight and surface reflection also depends on the altitude of the aircraft, its bank angle or pitch during take-off and landing These components must be supplemented by additional multipath components (MPCs), which may be intermittent MPCs as the aircraft moves through a volume of space. In environments where the geometric and electrical properties of obstacles (or interacting objects) can be accurately quantified, these intermittent MPCs can be estimated and tracked in the channel model. In the case of propeller aircraft, the radio signal is additionally Doppler-shifted by the frequency of the revolutions of the rotating propeller.

II-E Large buildings and their construction

Given that $80\%$ of wireless traffic data is generated indoors, there is a strong need to model building entry loss, indoor obstruction by building structures, furniture and people. As an optional feature, channel models inside a building can include the enhancements possible with strategically placed reconfigurable intelligent surfaces (RISs). A SSCRs will also play an important role in construction site automation with wireless technologies, taking into account the constant changes in building geometry during the construction phase and subsequently in the operational phase of large infrastructures.

III Model type

A radio channel can be fully described by a sampled double directional frequency response [5], which depends on the location of TX, RX, and the geometry of all visible objects in the surrounding environment. Furthermore, material properties of all objects and their movement influence the double directional channel frequency response. It can be expressed as the sum of $P_{m}$ propagation paths.

g_{m,q}({\mathchoice{\mbox{\boldmath$\displaystyle\alpha$}}{\mbox{\boldmath$%\textstyle\alpha$}}{\mbox{\boldmath$\scriptstyle\alpha$}}{\mbox{\boldmath$%\scriptscriptstyle\alpha$}}},{\mathchoice{\mbox{\boldmath$\displaystyle\beta$}%}{\mbox{\boldmath$\textstyle\beta$}}{\mbox{\boldmath$\scriptstyle\beta$}}{%\mbox{\boldmath$\scriptscriptstyle\beta$}}})=\gamma_{q}\sum_{p=1}^{P_{m}}\eta_%{p,m}e^{-j2\pi\theta_{p,m}q}\delta({\mathchoice{\mbox{\boldmath$\displaystyle%\beta$}}{\mbox{\boldmath$\textstyle\beta$}}{\mbox{\boldmath$\scriptstyle\beta$%}}{\mbox{\boldmath$\scriptscriptstyle\beta$}}}-{\mathchoice{\mbox{\boldmath$%\displaystyle\beta$}}{\mbox{\boldmath$\textstyle\beta$}}{\mbox{\boldmath$%\scriptstyle\beta$}}{\mbox{\boldmath$\scriptscriptstyle\beta$}}}_{p,m})\delta(%{\mathchoice{\mbox{\boldmath$\displaystyle\alpha$}}{\mbox{\boldmath$\textstyle%\alpha$}}{\mbox{\boldmath$\scriptstyle\alpha$}}{\mbox{\boldmath$%\scriptscriptstyle\alpha$}}}-{\mathchoice{\mbox{\boldmath$\displaystyle\alpha$%}}{\mbox{\boldmath$\textstyle\alpha$}}{\mbox{\boldmath$\scriptstyle\alpha$}}{%\mbox{\boldmath$\scriptscriptstyle\alpha$}}}_{p,m}),

(1)

where $m$ is the time index sampled at time $T_{\text{S}}$ , $q\in\{-Q/2,\ldots,Q/2-1\}$ is the frequency index and $Q$ is the even number of samples in the frequency domain, ${\mathchoice{\mbox{\boldmath$\displaystyle\alpha$}}{\mbox{\boldmath$\textstyle%\alpha$}}{\mbox{\boldmath$\scriptstyle\alpha$}}{\mbox{\boldmath$%\scriptscriptstyle\alpha$}}}_{p,m}=[\phi_{m},\theta_{m}]^{\text{T}}$ denotes the direction of arrival vector in terms of azimuth and elevation of MPC $p$ at the TX side and ${\mathchoice{\mbox{\boldmath$\displaystyle\beta$}}{\mbox{\boldmath$\textstyle%\beta$}}{\mbox{\boldmath$\scriptstyle\beta$}}{\mbox{\boldmath$%\scriptscriptstyle\beta$}}}_{p,m}$ the corresponding direction of departure at the RX side, respectively. The combined band-limited impulse response of TX and RX is denoted by $\gamma_{q}$ . The normalised path delay is denoted by $\theta_{p,m}=\tau_{p,m}/(T_{\text{S}}Q)$ , the path weight by $\eta_{p,m}$ , and $\tau_{p,m}$ is the delay of path $p$ at time index $m$ .

The channel frequency response at the footpoint of the antennas, is finally obtained by integrating over the unit sphere surfaces $\mathcal{T}$ and $\mathcal{R}$ at TX and RX side, taking the antenna pattern of TX, $\xi_{\text{T}}({\mathchoice{\mbox{\boldmath$\displaystyle\alpha$}}{\mbox{%\boldmath$\textstyle\alpha$}}{\mbox{\boldmath$\scriptstyle\alpha$}}{\mbox{%\boldmath$\scriptscriptstyle\alpha$}}})$ , and RX, $\xi_{\text{R}}({\mathchoice{\mbox{\boldmath$\displaystyle\beta$}}{\mbox{%\boldmath$\textstyle\beta$}}{\mbox{\boldmath$\scriptstyle\beta$}}{\mbox{%\boldmath$\scriptscriptstyle\beta$}}})$ , into account

g_{m,q}=\oint_{\mathcal{R}}\oint_{\mathcal{T}}g_{m,q}({\mathchoice{\mbox{%\boldmath$\displaystyle\alpha$}}{\mbox{\boldmath$\textstyle\alpha$}}{\mbox{%\boldmath$\scriptstyle\alpha$}}{\mbox{\boldmath$\scriptscriptstyle\alpha$}}},{%\mathchoice{\mbox{\boldmath$\displaystyle\beta$}}{\mbox{\boldmath$\textstyle%\beta$}}{\mbox{\boldmath$\scriptstyle\beta$}}{\mbox{\boldmath$%\scriptscriptstyle\beta$}}})\xi_{\text{R}}({\mathchoice{\mbox{\boldmath$%\displaystyle\beta$}}{\mbox{\boldmath$\textstyle\beta$}}{\mbox{\boldmath$%\scriptstyle\beta$}}{\mbox{\boldmath$\scriptscriptstyle\beta$}}})\xi_{\text{T}%}({\mathchoice{\mbox{\boldmath$\displaystyle\alpha$}}{\mbox{\boldmath$%\textstyle\alpha$}}{\mbox{\boldmath$\scriptstyle\alpha$}}{\mbox{\boldmath$%\scriptscriptstyle\alpha$}}})\,\text{d}{\mathchoice{\mbox{\boldmath$%\displaystyle\beta$}}{\mbox{\boldmath$\textstyle\beta$}}{\mbox{\boldmath$%\scriptstyle\beta$}}{\mbox{\boldmath$\scriptscriptstyle\beta$}}}\,\text{d}{%\mathchoice{\mbox{\boldmath$\displaystyle\alpha$}}{\mbox{\boldmath$\textstyle%\alpha$}}{\mbox{\boldmath$\scriptstyle\alpha$}}{\mbox{\boldmath$%\scriptscriptstyle\alpha$}}}\,.

(2)

The channel impulse response is related to the channel frequency response by the discrete inverse Fourier transform. The effect of the radio channel on the transmitted signal is computed by a time-varying convolution.

SSCRs consider non-stationary radio propagation conditions. In all practical scenarios the radio channel exhibits a local wide-sense stationarity for a region in time and frequency [3]. This property allows to simplify the SSCR such that the MPC parameters are constant for a stationarity region. The computation of the MPC parameters can be done in several site-specific ways, as described below.

III-A Ray tracing

Ray tracing is a high-frequency approximation used to solve Maxwell’s equations. It has long been used successfully for deterministic channel prediction [6], particularly for deployment planning, both indoor and outdoor. As it computes the parameters of the MPCs, including amplitude, direction of arrival, and direction of departure, it provides a true double-directional channel characterization that can be combined with (almost) arbitrary antenna patterns and array patterns in post-processing. In addition, ray tracing results are inherently spatially consistent, i.e., the change in power, angles, and delays follows the environment’s geometry, and - in a static environment - the same user equipment location is associated with the same channel. The requirements of 5G and beyond have stimulated research in four directions: distributed multiple-input multiple-output (D-MIMO), ultrahigh frequencies, and RISs.

Ray tracing is typically implemented either as (i) image-theory-based ray tracing, where potential locations of image sources according to different orders of reflection are computed and rays between these locations and the RX determined, or (ii) ray launching, where a TX sends rays into different directions and follows their interactions with environmental objects until they arrive at the RX, leave the area of interest, or become too weak to be of interest, see Fig. 2. The former method is more efficient for point-to-point simulations, while the latter has advantages for many RX locations and/or area coverage investigations.

Site-Specific Radio Channel Representation - Current State and Future Applications (2)

As the size of antenna arrays increases, wavefront curvature must be considered. Wavefront curvature implies that the RX is within the Rayleigh distance of the TX; as an important consequence, spatial multiplexing or mode multiplexing (for orbital angular momentum) becomes feasible. At the same time, operating under the assumption of hom*ogeneous plane waves, when in reality, wavefront curvature occurs, can lead to a model mismatch in channel estimation, e.g., based on the sparsity assumption. Since the rays used in ray tracing are generally hom*ogeneous plane waves, new ways of incorporating wavefront curvature are being explored. One way is to increase the number of rays representing a small angular range to satisfy the plane-wave assumption locally; however, this significantly increases the computational effort.D-MIMO systems and ultra-massive MIMO arrays are susceptible to another effect: different parts of the antenna elements/distributed radio units experience different amounts of shadowing. This effect, observed in several measurement campaigns, significantly impacts both channel capacity and transceiver implementations, such as sub-array selection in hybrid beamforming. Addressing this effect requires finer angular resolution, but this comes at the expense of efficiency.

Evaluation at higher frequencies first requires more accurate and higher-resolution databases. These can be obtained, for example, from light detection and ranging (LIDAR) scanning of the environment or artificial intelligence/machine learning (AI/ML)-based mapping of photographs onto full maps. In either case, the result is a point cloud, often with a resolution in the centimeter range. This leads to a dramatic increase in computation time for both image-based ray tracers and ray launchers.

Ways to increase efficiency include:

1.
Visibility matrix-based methods, where a pre-processing step determines which surface elements (aka tiles) can ”see” each other, so that no time is wasted tracking rays that cannot reach the RX anyway.
2.
Appropriate choice of grid size, i.e., different parts of the point cloud can be merged into a single ”effective” tile; such merging may depend on both the frequency considered and the distance of the tile from TX and RX.
3.
Treating complicated structures as a single ”effective” structure: for example, instead of scanning and modeling each leaf of a tree, the tree is treated as a large structure that has an absorption coefficient and particular backscattering characteristics from its surface.
4.
Dynamic ray tracing for mobile scenarios: Two state of the art methods for this purpose are a frame based approach, dividing the simulated time frame into different snapshots or the usage of velocity vectors for the moving objects within the scenario, to simulate the resulting ray paths at different points in time. Dynamic ray tracing can reduce complexity by (a) omitting a certain number of frames during the ray tracing simulations and interpolating the channel parameters in between; (b) predicting the resulting ray paths, based on the trajectory of the moving objects in the scenario, without additional ray tracing simulations.

Another research topic for high-frequency ray tracing is accurate models for diffuse scattering, as this phenomenon becomes more important with increasing frequency. Both, phenomenological approaches and models derived from first principles, have been investigated [7].

Finally, a ray tracer needs a calibration step. While the geometrical positions of environmental objects can be accurately determined, this is not the case for the electromagnetic properties of materials. The permittivity and conductivity of walls, windows, etc., can vary widely and are often determined by comparing the results of measurements at sample locations with the output of the ray tracer; the resulting coefficients are then used for all buildings in the area. While trial and error were typical for this task, more recently, differentiable ray tracers have been introduced, which allow more systematic optimization [8].

III-B Quasi-deterministic channel model

Quasi-deterministic models combine deterministic predictions, e.g., from a ray tracer, with stochastic channel modeling. In one incarnation, simplified ray tracing (a small number of reflections considered) is performed on a low-resolution environmental map to deterministically find the dominant propagation paths. Smaller MPCs associated with the dominant contributions are then generated stochastically (but with their delay/angle determined by the dominant paths). The delay/angle positions and amplitudes of additional clusters of MPCs can be generated purely stochastically to (i) represent static objects such as trees, (ii) represent moving objects such as pedestrians and cars, or (iii) model diffuse reflection on rough surfaces; in the second case, the birth/death statistics of the associated MPCs are also generated stochastically. In a related approach, the cluster centers are determined from the (rough) geometry of the environment. The associated intra-cluster statistics, usually obtained from measurements, are then provided for a complete channel description. Quasi-deterministic models [9] were first proposed in the early 2000s, and their concepts were then re-used by the European METIS project and adopted as an option for 3GPP simulations.

Compared to purely stochastic models (such as 3GPP), quasi-deterministic models have the advantages of spatial consistency (since the evolution of angles and delays of clusters or dominant paths follows from the geometry) and easier incorporation of specific geometries (e.g., the impact of street width can be easily incorporated). The advantages compared to ray tracing are the significantly reduced computational effort, easier exchange of environmental databases between entities that want to compare their results, and a straightforward way of incorporating mobile scatterers.

In Fig. 3, an exemplary quasi-deterministic environment model for a vehicular scenario including buildings, vegetation, parked cars, and city lamp posts is shown. The geometry model contains open street map data augmented by objects detected via LIDAR and video data. It was validated with empirical radio channel measurements and frame error rate measurements.

Site-Specific Radio Channel Representation - Current State and Future Applications (3)

III-C AI/ML based channel modeling

A promising approach of SSCR is data-driven AI/ML-based channel modelling [11]. AI/ML-based channel models are trained to learn the mapping relationship between complex environmental features and channel characteristics. It can achieve a balance between generalisation and prediction accuracy compared to traditional channel models. SSCR requires the prediction of channel characteristics of a specific site according to environmental conditions, and for AI/ML-based modelling, the ability to extract features from environmental information determines the prediction accuracy of the model. For the extraction of environmental features, different data sources can be used, such as images, satellite maps, point cloud data, etc. The impact of data resolution on the accuracy of environmental features should be carefully considered. The developed AI network should take the environmental features as input and provide the corresponding site-specific channel parameters as output. Of course, physical radio propagation mechanisms and empirical channel models can be used and incorporated into the designed AI model to improve the site-specific channel prediction. Methods such as a regularisation strategy, an early stop strategy and ensemble learning can also be used to avoid model over-fitting for a specific scenario. In addition, the ability of the model to self-evolve for different sites should be used as an evaluation index, and its essence lies in strengthening the existing model with additional data and features. Sufficient multi-source data and prior knowledge can support AI/ML-based channel modelling for SSCR needs with low complexity.

IV Model validation

IV-A Non-stationary environment evaluation

The local scattering function (LSF) [3] $\hat{\mathcal{C}}_{s;n,p}$ can be used to evaluate non-stationary measurement data. With the time-variant frequency response estimate $\hat{g}_{m,q}$ , the LSF can be computed as

\hat{\mathcal{C}}_{s;n,p}=\frac{1}{IJ}\sum_{w=0}^{IJ-1}\Bigg{|}\sum_{m^{\prime%}=-\frac{M}{2}}^{\frac{M}{2}-1}\sum_{q=-\frac{Q}{2}}^{\frac{Q}{2}-1}\hat{g}_{m%^{\prime}+Ms,q}\cdot\\G_{w;m,q}\mathrm{e}^{-\mathrm{j}2\pi(pm-nq)}\Bigg{|}^{2},

(3)

with the Doppler index $p\in\{-M/2\ldots,M/2-1\}$ , the delay index $n\in\{0,\ldots,Q-1\}$ , and the tapers $G_{w;m,q}$ are two-dimensional discrete prolate spheroidal sequences as shown in detail in [3]. The number of tapers $IJ$ controls the bias-variance trade-off of the LSF estimate. Using the LSF $\hat{\mathcal{C}}_{s;n,p}$ we can compute the time-variant power delay profile (PDP), Doppler spectral density (DSD) and path loss as marginals with respect to Doppler, time or both.

Site-Specific Radio Channel Representation - Current State and Future Applications (4)

IV-B Frequency domain continuity

Another validation requirement for SSCR is frequency domain continuity, i.e. ensuring the applicability of channel parameters over the entire frequency range (FR) of operation. 5G NR pushes the traditional sub- $6$ GHz boundary to $7.125$ GHz (FR $1$ ) and introduces millimeter wave (mmWave) bands from $24.25$ GHz to $52.6$ GHz (FR $2$ ). The range in between, from $7$ GHz to $24$ GHz, is called the upper mid-band (FR $3$ ) and is being considered for 5G-Advanced. Experiments in the sub-THz range from $52.6$ - $114.25$ GHz (FR $4$ ) and $114.25$ - $275$ GHz (FR $5$ ), as well as in the THz range ( $>300$ GHz) have attracted interest. The validation of parameters over such different frequency ranges prompted a series of simultaneous multi-band measurements. The blocking and deviation characteristics of a scatterer become more pronounced as the carrier frequency increases. This means that small scatterers, which could be safely ignored for FR $1$ or FR $3$ bands, may need to be included in the SSCR when applied to bands for FR $2$ or above.

IV-C Joint communication and sensing

Radar cross-section parameters need to be included for path loss calculation in monostatic/bistatic sensing. If the link lengths for TX-target and target-RX are given by $d_{1}$ and $d_{2}$ , then the path loss is given by $\gamma_{\mathrm{dB}}(d_{1},d_{2},\sigma,f)=\gamma_{\mathrm{dB}}(d_{1})+\gamma_%{\mathrm{dB}}(d_{2})+10\log\frac{\lambda^{2}}{4\pi}-10\log(\sigma)$ , where $\sigma$ is the normalised reflectivity of the target. Of course, measuring the radar cross section of different targets and classifying them according to their radar cross section range are important objectives for JCAS field test campaigns. Additional SSCR validation parameters include clutter/scattering patterns and object mobility, depending on the accuracy, resolution, and refresh rate requirements of the JCAS use case.

IV-D Empirical radio channel measurements

An integral part of site-specific channel model development is model validation through extensive field measurements. Channel parameters characterized by traditional channel sounding measurements can be loosely grouped into three categories: large-scale (over an area representative of urban, sub-urban, or residential environment), medium scale (over a path or small area such as a square) and small scale (over a short distance typically a few wavelengths).

In earlier models pathloss, delay spread and Doppler spread were modeled as function of the environment.SSCRs aim to include also their dependence on distance as well as on the surrounding environment and its geometrical properties. As an example we depict the root mean square (RMS) delay spread cumulative distribution function (CDF) for two medium scale urban routes that have different orientations with respect to the TX (in Manchester, UK, at 2.1 GHz) in Fig. 5.

Site-Specific Radio Channel Representation - Current State and Future Applications (5)

New 5G and beyond inspired use cases, as described in Section II, require new empirical radio channel measurements for verification:

IV-D1 Vehicular scenarios

For rail and road applications, new measurement campaigns are aimed at capturing the interplay between higher centre frequency and highly mobile site-specific features. Challenges for such campaigns include ensuring repeatable and fault-free operation of the channel sounder under extreme environmental conditions (weather, vibration, etc.) and maintaining proper synchronization between different subsystems. The node mobility causes fluctuations in the beamwidth, and as a result, the number of MPCs varies proportionally with the beamwidth. The channel becomes highly non-stationary, and a local scattering function, as described in subsection IV-A, can be used to characterize the channel.

IV-D2 Airborne scenarios

For airborne scenarios, the mobile-to-mobile (M2M) channel model validation requires at least two additional facets: first, consideration of the full three-dimensional site geometry (unlike vehicular scenarios where a 2D model is generally sufficient), and second, consideration of scattering components (along with line-of-sight and specular reflections). The validation is done in the delay-Doppler plane to account for the TX and RX’s fast and unpredictable mobility patterns. Classical site-general parameters, such as shadowing variance, are augmented with new features like shadowing correlation distances to ensure the validity of an airborne M2M channel model at different TX/RX heights.

IV-D3 Urban cellular high frequency scenarios

For example, [14] builds on the quasi deterministic channel model type described in Sec. III-B. The authors present a recipe for mmWave MPC generation for a specific urban cellular environment in Yokohama, Japan. Here, the measured data are used to estimate an exponential decay model to calibrate the ray tracer. Random clusters are generated with the measured site-specific statistical parameters of large and small scale fading parameters. This step improves the accuracy of the quasi-deterministic channel model by incorporating real-world measurement data that captures the specific characteristics of the environment under consideration.

V Channel emulation

A site-specific representation (digital twin) for a radio communication channel relies on a radio channel emulator to carry out hardware-in-the-loop tests. Transmit and receive modems are connected to the radio channel emulator to repeatedly test the communication system under well-defined propagation conditions. Channel emulators for dynamic non-stationary site-specific channel models including live objects are currently not available on the commercial market.

The challenges to realize such an emulator is the broadband communication link between the numerical channel model and the convolution unit for the sampled impulse response. The required data rate $R\sim c_{1}B^{2}T_{\text{D}}$ increases quadratically with the bandwidth of the communication system $B=1/T_{\text{S}}$ , where $c_{1}$ is a constant describing the number of bits per complex sample and $T_{\text{D}}$ is the support of the delay spread.

In [12] a novel site-specific channel emulation approach is presented where the convolution is approximated with a reduced rank subspace representation of dimension $D\ll M$ using discrete prolate spheroidal sequences. This approach avoids the quadratic increase of $R$ with $B$ and enables site-specific channel emulation. In Fig. 4 we reproduce [12, Fig. 13c], showing the time-variant PDP from the sub-space based radio channel emulator that is controlled by an SSCR. A good match with the measured radio propagation scenario in [12, Fig. 13b] is achieved.

Another approach in [15] divides the environment into a predefined grid and pre-calculates the propagation paths for each grid point via ray tracing. For a mobile device moving within a grid, complex exponential interpolation is performed between adjacent grid points to achieve a continuous channel response.

VI Conclusions and outlook

In this paper, we have reviewed the motivations for the inclusion of SSCR in future standards (e.g. 5G and beyond and WiFi7) and introduced the main use cases such as broadband communication with users in large buildings, JCAS, and reliable communication links with railways, road vehicles and aircraft. Ray tracing, quasi-deterministic and AI/ML-based models have been introduced and their specific advantages and disadvantages for SSCRs have been explored. Model validation methods for non-stationary environments, JCAS and frequency domain continuity requirements are explained. Empirical radio channel measurements for validation of path loss characteristics, vehicular, airborne and urban cellular scenarios are reviewed. Finally, low-complexity channel emulation techniques for site-specific hardware-in-the-loop testing of communications hardware are presented.

SSCRs is an emerging field with great importance for future wireless communication systems that will utilise D-MIMO, RIS, multi-band communication, and JCAS techniques. The combination of an SSCR with advanced PHY layer technologies will enable reliable and energy efficient communication systems of the future.

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Thomas Zemen is Principal Scientist at the AIT Austrian Institute of Technology and docent a TU Wien. He leads the reliable wireless communication group at AIT.