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Channel sounding

Channel sounding is a fundamental technique in wireless communications used to measure and characterize the (RF) propagation channel by transmitting known probe signals from a transmitter and analyzing the distorted received signals at a to estimate key parameters such as the channel impulse response, multipath delays, , Doppler shift, and frequency selectivity. This process captures the effects of the physical environment on signal propagation, including , , , and , enabling accurate modeling of real-world channel behavior. The importance of channel sounding lies in its role as a cornerstone for designing and optimizing robust wireless systems, particularly in challenging environments like urban areas, high-speed railways, underground tunnels, and vehicular scenarios, where it helps mitigate signal impairments and distortions to improve coverage, reliability, and quality of service. In modern applications, it is essential for multiple-input multiple-output (MIMO) systems, where it characterizes spatial correlations, angular spreads, and angle-of-arrival distributions to support features like beamforming and spatial multiplexing, thereby enhancing spectral efficiency and capacity. For fifth-generation (5G) and beyond networks, channel sounding addresses high-frequency bands such as millimeter waves (mmWave, above 25 GHz), wide bandwidths exceeding 500 MHz, and high-mobility scenarios, facilitating high-data-rate communications up to 150 Mbps at speeds of 500 km/h while accounting for dynamic effects like rapid fading and path loss. Channel sounding techniques are broadly classified into time-domain and frequency-domain methods, each suited to different measurement needs. Time-domain approaches, such as periodic pulse-based sounders or using pseudo-noise (PN) sequences, directly probe the channel and power delay profile () to quantify multipath components and root-mean-square () delay —for instance, 106.4 ns at 950 MHz in tunnel environments. Frequency-domain methods, including swept-frequency techniques with vector network analyzers (VNAs) or simultaneous multi-tone signals, evaluate the channel , coherence , and parameters like the Q-factor (e.g., 1200 at 980 MHz near stations), providing insights into frequency-selective and Doppler effects. Recent advancements, such as (SDR)-based sounders and phased array systems with adaptive , enable real-time, high-resolution measurements for massive and (IoT) deployments. In addition to traditional RF applications, channel sounding principles have been adapted for emerging standards like 6.0, where phase-based ranging () and round-trip time (RTT) measurements achieve centimeter-level distance accuracy for secure positioning between devices without additional hardware. Overall, ongoing research focuses on multidimensional parameter estimation, including , , and time-of-arrival, to support next-generation networks in diverse, complex scenarios.

Introduction

Definition and Principles

Channel sounding is the process of actively probing a to estimate its , , or key parameters such as , Doppler shift, and angular spread, enabling the characterization of the for communication system design and analysis. In communications, the is influenced by , where the transmitted signal arrives at the receiver via multiple paths due to reflections, , and from environmental obstacles like buildings and , resulting in signal superposition. This effect causes , which refers to rapid fluctuations in the received signal and due to constructive and destructive , as well as slower variations from shadowing by large obstructors. The basic principles of channel sounding involve transmitting a known signal through the channel and processing the received signal to isolate the channel's response, thereby capturing the multipath effects without interference from data modulation. Common excitation signals include short impulses for direct time-domain probing, signals with linearly swept frequencies for improved resolution in scenarios, and pseudo-noise () sequences that exhibit impulse-like properties for robust estimation in noisy environments. Unlike passive listening techniques that rely on observing ambient or existing transmissions, channel sounding employs active probing by deliberately transmitting these dedicated signals, allowing precise measurement of the channel's time-variant behavior. A fundamental representation of the is its , which models the multipath structure as h(\tau, t) = \sum_{k=1}^{K} \alpha_k(t) \, \delta(\tau - \tau_k(t)), where K is the number of multipath components, \alpha_k(t) is the complex (incorporating magnitude and e^{j\phi_k}) of the k-th path, \tau_k(t) is the delay, \tau is the excess delay relative to the line-of-sight path, and t denotes time variation due to . From this response, key parameters are derived: quantifies the temporal dispersion of multipath arrivals, Doppler shift captures offsets from relative motion, and angular spread measures the spatial dispersion of arrival angles, all essential for understanding selectivity in time, , and .

Historical Development

The roots of channel sounding trace back to early 20th-century experiments, particularly those conducted by Bell Laboratories in the and 1930s to support transatlantic wireless telephony. These efforts involved measuring ionospheric reflections to understand signal paths and fading, laying foundational techniques for evaluating radio environments. Following , advancements in pulse-based methods enabled initial profiling of multipath delays, using radar-inspired techniques to capture time-domain responses in terrestrial channels. A pivotal theoretical contribution came in 1963 with Phillip A. Bello's work on characterizing randomly time-variant linear channels, which formalized the use of for modeling wide-sense uncorrelated (WSSUS) in communication systems. The 1970s and 1980s saw practical innovations in correlation sounders employing pseudo-noise (PN) sequences for high-resolution multipath resolution, notably in George L. Turin's 1972 statistical model of urban propagation, which analyzed delay spreads from vehicle-based measurements at frequencies including 1280 MHz. These analog and early digital correlators improved accuracy in resolving urban delays up to several microseconds. The 1990s marked a transition to digital tools, with vector network analyzers (VNAs) enabling precise frequency-domain measurements of channel frequency responses across broadband signals. The emergence of software-defined radios (SDRs) further democratized sounding by allowing flexible signal generation and processing on general-purpose hardware. In the 2000s, the first channel sounders appeared, such as multi-antenna systems using PN sequences to capture spatial correlations, supporting the rise of in standards like and . European Union-funded projects in the , including initiatives like mmMAGIC, advanced MIMO sounding through large-scale measurement campaigns in urban and indoor environments, validating models for / deployments with up to 64 antennas. More recently, channel sounding was integrated into 6.0 in 2024, introducing phase-based ranging for secure distance estimation with centimeter-level accuracy over 2.4 GHz. By 2025, ongoing research campaigns have focused on THz sounding, using hybrid modeling to characterize sub-THz channels for terabit-per-second links, with measurements revealing very low delay spreads in indoor scenarios.

Fundamentals

Wireless Channel Characteristics

Wireless channels exhibit due to the of multiple paths, resulting in rapid fluctuations in signal amplitude and phase. In non-line-of-sight (NLOS) conditions, where no dominant path exists, the received signal envelope follows a , modeling the magnitude of a complex Gaussian representing the sum of scattered waves with random phases and equal average power. In line-of-sight () scenarios, predominates, incorporating a strong specular component alongside diffuse ; the distribution is parameterized by the , defined as the ratio of the power in the direct path to the power in the scattered paths, which quantifies the severity of . Multipath propagation introduces temporal dispersion, characterized by the root-mean-square (RMS) delay spread \sigma_\tau, which captures the spread in arrival times of signal replicas. This dispersion determines the coherence bandwidth B_c, the range of frequencies over which the channel frequency response remains correlated, approximated as B_c \approx \frac{1}{2\pi \sigma_\tau}. Relative motion between transmitter and receiver induces Doppler spread f_d, the extent of frequency shifts due to path-specific velocity components, inversely related to the coherence time T_c = \frac{1}{f_d}, which indicates the duration over which the channel impulse response is approximately invariant. Angular spread, arising from the directional variance of incoming paths, further describes spatial selectivity, influencing beamforming efficacy in array systems. The Jakes model, assuming isotropic scattering with uniform angular distribution of arrivals, yields the Doppler power spectral density S(f) = \frac{1.5}{\pi f_d \sqrt{1 - (f/f_d)^2}} for |f| \leq f_d, providing a classic representation of time-varying fading. Channel models broadly divide into deterministic and stochastic categories to represent these effects. Deterministic approaches, such as ray-tracing, compute path-specific delays, attenuations, and phases based on geometric and environmental layouts, offering site-specific accuracy but high computational cost. Stochastic models, conversely, employ statistical abstractions like the tapped delay line structure, where the channel is a with taps at discrete delays, each with random complex gains following or Rician statistics to emulate small-scale . Geometry-based stochastic channel models (GSCMs), such as the 259 directional model, integrate geometric scatterer placements with probabilistic elements, incorporating clusters of multipath components (MPCs) and regions to capture angular and delay spreads across , , and environments. These characteristics are profoundly influenced by environmental factors. Urban settings feature elevated delay spreads (typically 0.2–3 μs) from dense reflectors like , fostering rich multipath and higher angular spreads, whereas rural areas exhibit lower spreads (often <0.5 μs) with sparser scattering and prevalent LOS paths. Indoor channels display confined delay spreads (10–100 ns) due to limited propagation distances, contrasting with broader outdoor dispersions. At sub-6 GHz frequencies, channels benefit from diffraction and penetration through obstacles, yielding moderate Doppler and angular spreads suitable for macrocell coverage. In contrast, mmWave bands (above 24 GHz) experience exacerbated path loss, reduced diffraction, and increased blockage sensitivity, resulting in narrower beams, higher angular spreads from surface scattering, and more severe fading in obstructed urban or indoor scenarios, though with potential for LOS-dominant rural links over shorter ranges.

Sounding Objectives and Metrics

Channel sounding serves several key objectives in wireless communications, primarily to characterize the propagation channel for critical system design tasks. These include estimating link budgets to predict signal strength and coverage, assessing diversity gain for antenna array performance, and informing equalizer design to mitigate inter-symbol interference. Measurements obtained through sounding enable the validation of theoretical propagation models against real-world environments, ensuring their reliability for simulation and deployment. Additionally, sounding data contributes to standardization processes, such as the development of (e.g., ), which rely on empirical measurements to parameterize scenarios for frequencies up to 100 GHz. Key metrics derived from channel sounding provide quantitative insights into channel behavior. The root mean square (RMS) delay spread measures the temporal spread of multipath components, typically ranging from a few nanoseconds in indoor settings to tens of microseconds outdoors, influencing coherence bandwidth and symbol rate selection. Angles of arrival (AoA) and departure (AoD) capture the spatial distribution of paths, essential for beamforming in MIMO systems, while the path loss exponent quantifies signal attenuation with distance, often around 2-4 depending on the environment. The Ricean K-factor assesses fading severity by the ratio of direct to scattered path power, with values >10 dB indicating line-of-sight dominance. These metrics, extracted from the channel or , guide optimizations like scheme selection. Channel estimates from sounding measurements adapt the formula for channels using the estimated channel \mathbf{H}. The ergodic is given by C = \log_2 \det \left( \mathbf{I} + \frac{\rho}{N_t} \mathbf{H} \mathbf{H}^H \right), where \rho is the , N_t the number of transmit antennas, \mathbf{I} the , and \mathbf{H}^H the Hermitian transpose; this allows evaluation of potential throughput based on measured spatial correlations. However, error sources such as measurement noise and synchronization inaccuracies can degrade estimates, with potentially causing over 100% error in high-SNR calculations. To support applications, sounding systems require high accuracy, including time resolutions below 1 ns to resolve closely spaced multipath components in wideband scenarios.

Applications and Motivation

System Design and Optimization

Channel sounding provides essential data for wireless system design, particularly in optimizing placement and configurations. By measuring angle-of-arrival (AoA) profiles, sounding techniques enable engineers to position antennas in locations that align with dominant paths, maximizing signal reception and minimizing multipath fading effects. For instance, in antenna systems, AoA estimates derived from channel sounding inform the creation of beamforming codebooks, allowing dynamic toward key directions and achieving up to 97% accuracy in AoA estimation with large arrays. This approach enhances spatial selectivity and supports applications in dynamic environments like (V2X) communications. Path loss and signal-to-noise ratio (SNR) margins obtained from channel sounding measurements guide the selection of and coding schemes (MCS) during system design. These metrics allow designers to tailor MCS to specific environmental conditions, opting for higher-order s in low-loss areas and more robust schemes where exceeds typical thresholds, thereby balancing throughput and reliability without overprovisioning resources. In system optimization, channel sounding facilitates adaptive multiple-input multiple-output () precoding by estimating the channel matrix \mathbf{H}, which captures the spatial correlations between transmit and receive antennas. Precoding matrices are then derived using (SVD) of \mathbf{H} = \mathbf{U}_\mathbf{H} \boldsymbol{\Sigma}_\mathbf{H} \mathbf{V}_\mathbf{H}^*, aligning transmitted signals with the channel's eigenmodes to mitigate and boost ; in time-varying channels, dynamic models like \hat{\mathbf{H}} = \rho \mathbf{H}_0 + (1 - \rho) \bar{\mathbf{H}} further refine precoding based on sounding , potentially doubling at low SNR levels in 4x2 setups. For dense networks, sounding-derived (CSI) supports mitigation through spatial reuse, where optimized carrier sensing thresholds and beam directions enable concurrent transmissions; this framework, relying on periodic sounding for awareness, improves overall throughput by allowing non-interfering links to share spectrum efficiently. Notable case studies highlight these applications. In (IEEE 802.11ax) certification, channel sounding measurements contribute to validating spatial channel models, such as those for indoor and urban microcell scenarios, ensuring that and features perform as specified under realistic conditions. The benefits of incorporating channel sounding in pre-deployment surveys are substantial, including up to 30% reduction in for channel estimates, which translates to more accurate system tuning and lower outage probabilities in modeled scenarios by better accounting for metrics like . This leads to enhanced reliability, particularly when adapts to sounded channel statistics.

Propagation Modeling and Research

Channel sounding plays a pivotal role in refining empirical propagation models by providing measured data to calibrate predictions and environmental parameters. For instance, the Okumura-Hata model, originally developed for urban and suburban environments at frequencies up to 1500 MHz, has been extended and refined using sounding measurements to account for higher frequencies and modern scenarios, such as mmWave bands. These refinements involve fitting parameters like the exponent through least-squares optimization against measured power delay profiles, reducing standard deviation and improving accuracy in urban macro-cell deployments. Similarly, models for wireless networks incorporate angular and delay spreads derived from channel sounding campaigns to simulate in dense deployments, enabling probabilistic analysis of coverage and while capturing real-world variability in scatterer distributions. In research applications, channel sounding facilitates the validation of deterministic models like ray-tracing against empirical data, ensuring that simulated multipath components align with measured channel impulse responses. For example, in industrial environments at 26-30 GHz, sounding measurements using vector network analyzers and the algorithm for multipath extraction have calibrated ray-tracing tools by adjusting and coefficients, achieving average errors below 1.5 in received power and 5 ns in . Such validations highlight discrepancies in diffuse , where ray-tracing often underestimates non-specular components by up to 22%, guiding hybrid model developments. Additionally, sounding studies reveal non-stationarity in vehicular channels, where time-varying scatterers like roadside structures cause rapid changes in multipath clusters. Measurements at 5.9 GHz in urban settings, such as and soundproof wall scenarios, quantify stationary intervals (e.g., as low as 10.35 ms under bridges) and Ricean K-factors (ranging from 0.67 to 3.96 ), underscoring the need for time-frequency varying models to support V2V safety applications. Channel sounding has significantly contributed to standardization efforts in propagation modeling, particularly through and frameworks. In TR 38.901, sounding campaigns informed the Urban Macro (UMa) model for , refining large-scale parameters like (e.g., mean log-normalized values of -7.49 for ) and angular spreads (e.g., azimuth spread of departure at 0.90 for ) based on measurements across 0.5-100 GHz. These data-driven adjustments, including formulas calibrated for heights of 25 m and inter-site distances of 500 m, ensure realistic evaluations of non-line-of-sight (NLOS) probabilities and cluster powers in urban environments. In the 2020s, focus has shifted to non-terrestrial networks (NTN), where sounding measurements address propagation challenges in space-air-ground integrated systems, such as Doppler spreads from UAV velocities and ionospheric in links, supporting ubiquitous coverage models. Specific measurement campaigns have advanced clustered propagation models. The EU-funded WINNER II project (2004-2007) utilized sounders operating at 2-6 GHz to collect data across scenarios like urban micro-cells (B1/B2) and suburban macro-cells (C1), deriving clustered delay line (CDL) models with intra-cluster spreads (e.g., 3 sub-clusters per main cluster) and parameters for delays, powers, and angles. These models, validated against measurements from tools like the Propsound and TUI sounders, facilitated simulations and influenced subsequent standards. More recently, THz sounding efforts for bands above 100 GHz employ vector network analyzer-based systems with frequency extenders, capturing sparse multipath in indoor settings at 220-330 GHz to model high and limited , essential for short-range applications.

Measurement Techniques

Time-Domain Sounding

Time-domain sounding techniques probe the by directly measuring its through the transmission of short-duration signals, such as impulses or pseudo-noise () sequences, followed by processing at the to resolve multipath components. These methods exploit the 's time-variant nature, where the transmitted signal interacts with scatterers to produce delayed replicas, enabling estimation of delay spreads and path losses. sequences, often modulated using binary phase-shift keying (BPSK) on a , are preferred for their noise-like properties and ability to achieve high processing gain via . The received signal in time-domain sounding can be expressed as the of the h(\tau) with the transmitted signal s(t), plus additive n(t): r(t) = \int_{-\infty}^{\infty} h(\tau) s(t - \tau) \, d\tau + n(t) of r(t) with a delayed replica of s(t) recovers the , approximated by the R(\tau) = E[r(t) s(t - \tau)], which peaks sharply at multipath delays due to the low sidelobes of PN sequences. This , often implemented via swept time-delay methods, provides the power delay profile () for . Key techniques include direct pulse transmission, where a narrow impulse excites the , but this approach is limited by constraints, as the pulse duration cannot be shorter than the inverse of the available , restricting in systems. To overcome this, PN sequences generated from linear feedback shift registers (LFSRs), such as m-sequences or , are employed for their excellent properties—, in particular, offer low and sidelobe levels, making them suitable for robust multipath even in noisy environments. The sliding correlator enhances by introducing a small offset between transmitter and clocks, creating (factor \gamma = \alpha / (\alpha - \beta), where \alpha and \beta are chip and sliding rates), allowing practical sampling of fine delay bins without ultra-high-speed analog-to-digital conversion. These methods achieve high on the nanosecond scale, enabling precise multipath separation—for instance, 1 resolution in implementations corresponding to sub-meter spatial accuracy. However, they are sensitive to timing and errors, necessitating high-stability clocks like standards to maintain accuracy in dynamic scenarios. In practice, time-domain sounding with PN sequences has been applied in indoor (UWB) systems, such as measurements in industrial environments like automotive assembly plants, where it captures PDPs at frequencies up to 5.4 GHz with 10 resolution to model propagation in cluttered spaces.

Frequency-Domain Sounding

Frequency-domain channel sounding measures the wireless channel's frequency response, or transfer function H(f), across a range of frequencies to characterize its behavior. This approach typically involves transmitting signals at discrete frequencies and recording the received amplitude and phase, which can then be transformed into the time domain to obtain the channel impulse response h(\tau). Common methods include swept sine waves, where a continuous sine wave is frequency-modulated in a continuous sweep, and stepped frequency continuous wave (SFCW), which transmits a continuous wave at successively stepped discrete frequencies over the bandwidth of interest. Vector network analyzers (VNAs) are widely used for precise frequency-domain measurements, as they provide high accuracy in capturing H(f) by injecting test signals and measuring the S-parameters at the receiver. In SFCW implementations, the transmitter steps through N frequencies f_k (for k = 0, 1, \dots, N-1) spaced by \Delta f, with the total B = N \Delta f, dwelling at each frequency long enough to achieve a desired . Multi-tone excitation techniques transmit multiple sinusoidal tones simultaneously across the band, reducing measurement time compared to sequential stepping, though they require careful to avoid inter-tone interference. To convert the measured to the , the inverse (IFFT) is applied: h(\tau) = \mathrm{IFFT} \left\{ H(f) \right\} This yields the impulse response with time resolution \Delta \tau = 1/B, enabling estimation of multipath delays and gains. Calibration is essential to mitigate hardware impairments, such as cable losses and antenna mismatches, often performed via back-to-back connections where the transmitter and receiver are directly linked to measure and subtract system responses. Frequency-domain sounding offers advantages in implementation at high frequencies, such as millimeter-wave bands, where generating short pulses for time-domain methods is challenging due to hardware limitations. The technique benefits from through coherent averaging over multiple sweeps and is particularly suited for static or slowly varying , like those in outdoor environments, where durations of seconds to minutes are acceptable. However, it suffers from longer acquisition times in SFCW setups, as each step requires sequential and , potentially missing rapid channel variations in scenarios. Compared to time-domain approaches, frequency-domain methods provide equivalent but with simpler high-frequency at the cost of extended periods.

Advanced Methods

MIMO Channel Sounding

MIMO channel sounding extends traditional single-antenna techniques to characterize the spatial structure of channels in multiple-input multiple-output () systems, enabling the measurement of the full channel matrix \mathbf{H} with dimensions N_r \times N_t, where N_r is the number of receive antennas and N_t is the number of transmit antennas. Each element h_{ij} in \mathbf{H} represents the complex gain from the j-th transmit antenna to the i-th receive antenna, capturing spatial correlations, multipath effects, and antenna interactions essential for capacity and diversity gains. In time-division duplex (TDD) systems, channel reciprocity allows uplink sounding measurements to directly inform downlink precoding due to the symmetry of the propagation channel, whereas frequency-division duplex (FDD) systems require separate downlink sounding or feedback mechanisms, as reciprocity is limited by frequency separation. Key techniques for MIMO channel sounding include time-division multiplexing (TDM), where signals are sequentially probed from each transmit antenna using switches and pseudo-noise sequences, enabling cost-effective hardware but suffering from reduced accuracy in fast-fading environments due to temporal decorrelation. Code-division multiplexing (CDM) transmits orthogonal sequences, such as loosely synchronous codes, simultaneously across all antennas, supporting real-time measurements with high temporal resolution at the expense of increased bandwidth and processing complexity. Hybrid approaches combine TDM and CDM (or frequency-division multiplexing) to balance cost, resolution, and real-time capability, often allocating codes within time or frequency slots for scalable wideband operation. A prominent modeling framework for channels derived from sounding data is the Kronecker model, which assumes separable transmit and receive s, expressed in vectorized form as \operatorname{vec}(\mathbf{H}) = (\mathbf{A}_r \otimes \mathbf{A}_t) \operatorname{vec}(\mathbf{G}), where \mathbf{A}_r and \mathbf{A}_t are the receive and transmit spatial matrices (derived from signatures), and \mathbf{G} contains i.i.d. Gaussian entries representing uncorrelated . This model simplifies analysis but underestimates joint transmit-receive in non-separable scenarios. In contrast, the degenerate model accounts for full and rank deficiency (e.g., keyhole effects), where the exhibits low effective rank due to correlated paths, leading to reduced ; sounding measurements validate this by revealing significant losses in line-of-sight or obstructed environments compared to i.i.d. assumptions. Challenges in MIMO channel sounding include precise antenna calibration to compensate for mutual coupling and hardware imbalances, which can introduce errors exceeding 10 dB in gain estimates if unaddressed, and synchronization across antenna arrays to mitigate phase drifts from local oscillators, potentially inflating channel rank and overestimating capacity by over 100% in low-rank scenarios. For instance, measurements validating IEEE 802.11n MIMO performance in residential settings used calibrated TDM sounders at 5 GHz to confirm spatial multiplexing gains, showing capacities up to 4x single-input rates but highlighting synchronization needs for dynamic beamforming. These issues are particularly acute in large arrays, where over-the-air synchronization techniques are employed to align phases without wired connections.

Wideband and Ultra-Wideband Sounding

Wideband channel sounding addresses the requirements of high-data-rate wireless systems, such as those using (OFDM), by employing signals with bandwidths typically in the hundreds of MHz to GHz range to characterize frequency-selective fading and multipath dispersion. (UWB) sounding extends this to bandwidths exceeding 1 GHz, enabling delay resolutions finer than 1 ns (corresponding to cm-scale path separation) for applications demanding precise temporal analysis of dispersive channels. The fundamental delay resolution in these techniques is determined by the inverse of the signal , expressed as \Delta \tau = \frac{1}{B}, where B denotes the , allowing discrimination of multipath components separated by less than the resolution limit. For chirp-based sounding, a linear frequency-modulated signal is transmitted, and matched filtering at the compresses the to produce sharp peaks at the true , leveraging the properties of the . Key techniques include multi-carrier modulation, where multiple tones span the band to probe the channel transfer function in parallel, and excitation signals designed with sequences such as Zadoff-Chu, which exhibit constant amplitude and perfect periodic for minimizing and in the estimated . In scenarios, such as environments, parallel processing architectures enable rapid measurements with acquisition times under milliseconds, accommodating Doppler spreads up to 115 Hz, while post-processing steps like windowing further suppress in the power delay profile. Representative examples include UWB sounders compliant with IEEE 802.15.4a standards, which employ broadband impulses or chirp-like waveforms over 2–10 GHz for localization, achieving sub-10 cm accuracy in indoor settings by resolving fine delay structures.

Modern Implementations

Bluetooth Channel Sounding

Channel Sounding is a feature introduced in the Bluetooth Core Specification version 6.0, released in September 2024 by the (SIG), extending (LE) capabilities for secure, precise distance measurement between devices. It leverages phase-based ranging () and round-trip time (RTT) techniques, utilizing Gaussian-shaped frequency pulses to estimate distances with high accuracy while maintaining low power consumption suitable for applications. This protocol enables interoperability across compliant devices, addressing limitations in prior ranging methods by providing centimeter-level precision without requiring (UWB) hardware. The core technique involves a secure fine-ranging where devices act as either an Initiator (performing the ranging ) or Reflector (compatible with any counterpart), exchanging specially formatted packets with embedded timestamps and measurements. is integrated through encrypted data exchanges using AES-128 or higher, along with mechanisms to prevent unauthorized ; resistance to relay attacks is achieved via using variations and pseudorandom challenges generated by a deterministic random bit generator (DRBG). PBR measures differences of signals transmitted at multiple frequencies to resolve distance ambiguities, while RTT uses time-of-departure (ToD) and time-of-arrival (ToA) timestamps for direct time-of-flight calculation, often combined for robust performance in multipath environments. Implementation occurs over the 2.4 GHz band, utilizing up to four 80 MHz (e.g., channel indices 2, 22, 26, 76) for packet exchanges, with Gaussian pulses modulating the carrier to minimize spectral occupancy and enable precise extraction. Devices support multiple paths (up to eight combinations) for enhanced directionality via difference of arrival (PDoA), achieving a ranging of approximately 10 within 150 m, though actual performance depends on factors like and environmental . For phase-based distance estimation in PBR, the fundamental derives the d from phase measurements at two frequencies f_1 and f_2: d = \frac{c (\phi_2 - \phi_1)}{4\pi (f_2 - f_1)} \mod \frac{c}{f_2 - f_1} where c is the , and \phi_1, \phi_2 are the measured phases; the operation accounts for phase wrapping ambiguities resolved by RTT or additional frequencies. Key applications include secure keyless entry systems for vehicles and buildings, where precise proximity verification prevents unauthorized access, and in warehouses for real-time location awareness. As of 2025, early adoption is accelerating in automotive , with chipset providers like and NXP integrating Channel Sounding into production devices for digital keys and hands-free access. In October 2025, released an open-source app for evaluating Channel Sounding on devices.

5G and mmWave/THz Sounding

In New Radio (NR), channel sounding plays a critical role in beam management, primarily through the use of Channel State Information Reference Signals (CSI-RS) as pilots for estimating channel conditions and selecting optimal beams. These signals enable () to measure (RSRP) across different transmit beams, facilitating procedures like beam refinement and tracking in millimeter-wave (mmWave) bands. CSI-RS configurations are flexible, supporting various densities and periodicities as defined in specifications, to balance overhead and accuracy in dynamic environments. mmWave channels in , operating above 24 GHz, present unique challenges for due to high , which can exceed 100 dB over short distances in urban settings, necessitating directional antennas and high-gain arrays to maintain (SNR). Additionally, these channels exhibit sparse , with typically fewer than 10 significant components within 20 dB of the strongest path, arising from limited at high frequencies and enabling techniques for efficient parameter estimation. Extensions to (THz) bands, spanning 100 GHz to 1 THz, amplify these challenges while opening opportunities for ultra-high data rates, requiring advanced sounding methods to characterize molecular and near-field effects. Photonic generation techniques, leveraging optical heterodyning for THz signal creation, enable sweeps exceeding 50 GHz bandwidth, providing high-resolution impulse responses essential for modeling delay spreads below 1 ns in indoor scenarios. Measurement campaigns informing and beyond have contributed to the TR 38.901 channel models, which incorporate empirical data from 2017 to 2025 across 0.5–100 GHz, including urban microcell and indoor scenarios with parameters like cluster decay and angular spreads derived from real-world . These models support beamforming calibration, where analog and digital are jointly optimized using sounding data to mitigate impairments in massive arrays. A key issue in mmWave is beam , where the beam direction varies across due to shifter ; the effective angle is approximated as \theta_{\text{eff}} = \theta \cdot \frac{f_c}{f}, with f_c as the carrier and f the operating , requiring compensation to preserve . Looking toward , channel is evolving to support integrated sensing and communication (ISAC), where pilots enable joint radar-like sensing for target detection and communication, exploiting shared to estimate both channel state and environmental parameters like and . This leverages THz for precise localization in dense networks, with ongoing research focusing on to minimize interference between sensing and data transmission.

Data Processing

Signal Processing Steps

The signal processing pipeline for raw channel sounding data begins with to align timestamps between transmitter and , ensuring accurate temporal of signals. This step typically employs high-stability clocks, such as oscillators synchronized via pulse-per-second () signals, to achieve sub-nanosecond precision and mitigate drift in mobile or distributed setups. Following , corrects for imperfections, including transmitter/ imbalances and offsets, often through back-to-back measurements with known to derive a calibration factor G. The calibrated channel impulse response is then computed as h_{\text{cal}}(\tau) = \frac{r(\tau)}{s(\tau)} \times G, where r(\tau) is the received signal, s(\tau) is the known transmitted signal, and G accounts for system response. Noise suppression follows to enhance (SNR), commonly via averaging multiple measurement snapshots, which provides a processing gain proportional to the number of averages, or through bandpass filtering to attenuate artifacts. Techniques such as windowing with a are applied to the frequency-domain data before inverse , reducing and in the resulting while preserving mainlobe width for delay resolution. For finer resolution beyond the sampling grid, methods estimate sub-sample delays, enabling precise multipath timing extraction without increasing hardware sampling rates. To address non-stationarity in dynamic environments, such as vehicular scenarios, data is segmented into epochs based on channel coherence time, allowing independent processing of each segment to capture time-varying effects. Real-time implementations for mobile sounders leverage field-programmable gate arrays (FPGAs) or digital signal processors (DSPs) for on-the-fly correlation and filtering, supporting bandwidths up to 200 MHz with sampling rates exceeding 500 Msps, while offline post-processing enables more computationally intensive refinements like advanced averaging. A common challenge in this pipeline is resolving overlapping multipath components, where delays and amplitudes blend due to limited ; the space-alternating generalized expectation-maximization () algorithm addresses this by iteratively estimating individual path parameters from the composite response, improving separation in high-resolution sounders.

Parameter Extraction and Modeling

Parameter extraction from channel sounding data involves applying high-resolution algorithms to identify key multipath components, such as delays, angles of arrival (AoAs), and amplitudes, from the processed responses. Maximum () estimation is a foundational approach for jointly estimating these parameters, particularly in scenarios with closely spaced paths, by maximizing the of the observed data under a channel model. The space-alternating generalized expectation-maximization () algorithm, an iterative ML-based method, enhances resolution by alternating between subsets of parameters—such as delays and directions—while fixing others, making it suitable for multidimensional parameter extraction in channels. For super-resolution direction finding, subspace-based techniques like (multiple signal classification) and ESPRIT (estimation of signal parameters via rotational invariance techniques) outperform conventional by exploiting the eigenstructure of the of received signals. identifies AoAs by searching for spectral peaks in the noise subspace projection, achieving asymptotic unbiasedness and high resolution even for correlated sources. ESPRIT, leveraging array rotational invariance, estimates directions through a closed-form eigendecomposition, reducing compared to while maintaining comparable accuracy in uniform linear arrays. These methods are widely applied in channel sounding to resolve AoAs beyond the limit, with often preferred for its flexibility in arbitrary array geometries. Channel modeling from extracted parameters typically begins with clustering multipath components (MPCs) into groups representing dominant scatterers, using algorithms like k-means or Gaussian mixture models to group paths by proximity in delay or angle domains. Delays within clusters are commonly modeled using a to capture the symmetric spread around cluster means, reflecting physical scattering geometries in urban or indoor environments. The seminal Saleh-Valenzuela (S-V) model exemplifies this by representing indoor as clusters of rays with exponentially decaying inter-cluster delays and Laplace-distributed intra-cluster offsets, fitted to via least-squares optimization of power delay profiles. Synthetic channel realizations are then generated by sampling these distributions, enabling simulations for system design without repeated measurements. In the 2020s, advancements, particularly neural networks, have enabled non-linear mappings from raw sounding data to parameters, surpassing traditional methods in low-SNR regimes or for sparse recovery. Deep neural networks, such as convolutional architectures, learn to extract delays and AoAs directly from time-frequency representations. Recent developments as of 2025 include approaches, such as generative adversarial networks, for parameter extraction in high-frequency bands like THz, improving efficiency in channel modeling. Validation of these models relies on metrics like (MSE) between estimated and true channel impulse responses h(\tau), ensuring fidelity in regenerated traces. A core iterative step in SAGE for amplitude extraction, given fixed delays \tau_k, is: \hat{a}_k = \arg \max_a \left| \sum_m y_m e^{-j 2\pi f_m \tau_k} \right|^2 where y_m are frequency-domain observations at frequencies f_m, updated sequentially across paths until convergence. This pseudocode-level maximization approximates the ML solution, with full iterations jointly refining all parameters for robust modeling.