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Channel sounding
Channel sounding
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Channel sounding is a technique that evaluates a radio environment for wireless communication, especially MIMO systems. Because of the effect of terrain and obstacles, wireless signals propagate in multiple paths (the multipath effect). To minimize or use the multipath effect, engineers use channel sounding to process the multidimensional spatial–temporal signal and estimate channel characteristics. This helps simulate and design wireless systems.

Motivation & applications

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Mobile radio communication performance is significantly affected by the radio propagation environment.[1] Blocking by buildings and natural obstacles creates multiple paths between the transmitter and the receiver, with different time variances, phases and attenuations. In a single-input, single-output (SISO) system, multiple propagation paths can create problems for signal optimization. However, based on the development of multiple input, multiple output (MIMO) systems, it can enhance channel capacity and improve QoS.[2] In order to evaluate effectiveness of these multiple antenna systems, a measurement of the radio environment is needed. Channel sounding is such a technique that can estimate the channel characteristics for the simulation and design of antenna arrays.[3]

Problem statement & basics

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MIMO sounding[4]

In a multipath system, the wireless channel is frequency dependent, time dependent, and position dependent. Therefore, the following parameters describe the channel:[2]

To characterize the propagation path between each transmitter element and each receiver element, engineers transmit a broadband multi-tone test signal. The transmitter's continuous periodic test sequence arrives at the receiver, and is correlated with the original sequence. This impulse-like auto correlation function is called channel impulse response (CIR).[5] By obtaining the transfer function of CIR, we can make an estimation of the channel environment and improve the performance.

Description of existing approaches

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MIMO vector channel sounder

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Based on multiple antennas at both transmitters and receivers, a MIMO vector channel sounder can effectively collect the propagation direction at both ends of the connection and significantly improve resolution of the multiple path parameters.[1]

KD model of wave propagation

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Planar wave model

Engineers model wave propagation as a finite sum of discrete, locally planar waves instead of a ray tracing model. This reduces computation and lowers requirements for optics knowledge. The waves are considered planar between the transmitters and the receivers. Two other important assumptions are:

  • Relative bandwidth is small enough so that the time delay can be simply transformed to a phase shift among the antennas.
  • The array aperture is small enough that there is no observable magnitude variation.

Based on such assumptions, the basic signal model is described as:

where is the TDOA (time difference of arrival) of the wave-front . are DOA at the receiver and are DOD at the transmitter, is the Doppler shift.[1]

Real-time ultra-wideband MIMO channel sounding

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A higher bandwidth for channel measurement is a goal for future sounding devices. The new real-time UWB channel sounder can measure the channel in a larger bandwidth from near zero to 5 GHz. The real time UWB MIMO channel sounding is greatly improving accuracy of localization and detection, which facilitates precisely tracking mobile devices.[6]

Excitation signal

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A multitoned signal is chosen as the excitation signal.

where is the center frequency, ( is Bandwidth, is Number of multitones) is the tone spacing, and is the phase of the tone. we can obtain by

Data post-processing

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RUSK SOUNDING. is the maximum Doppler frequency. is the maximum duration of the impulse response and S is the channel's spread (the red rectangle in the figure).[4]
  1. A DFT over K-1 (one waveform lost due to array switching) waveforms that measured in each channel is performed (K: waveforms per channel).
  2. The frequency domain samples at the multitone frequencies are picked at every sample.
  3. An estimated channel transfer function is obtained by:

where is the noise power, is a reference signal and is the samples. The scaling factor c is defined as

RUSK channel sounder

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A RUSK channel sounder excites all frequencies simultaneously, so that the frequency response of all frequencies can be measured. The test signal is periodic in time with period . The period must be longer than the duration of the channel's impulse response in order to capture all delayed multipath components at the receiver. The figure shows a typical channel impulse response (CIR) for a RUSK sounder. A secondary time variable is introduced so that the CIR is a function of the delay time and the observation time . A delay-Doppler spectrum is obtained by Fourier transformation.[4]

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
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 receiver 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 absorption, reflection, , 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 (PDP) to quantify multipath components and root-mean-square (RMS) 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 (PBR) 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 channel to estimate its , , or key parameters such as , Doppler shift, and angular spread, enabling the characterization of the channel for communication system design and analysis. In communications, the channel 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 phase due to constructive and destructive interference, as well as slower variations from shadowing by large obstructors. The basic principles of channel sounding involve transmitting a known excitation 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 (PN) 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 channel is its , which models the multipath structure as h(τ,t)=k=1Kαk(t)δ(ττk(t)),h(\tau, t) = \sum_{k=1}^{K} \alpha_k(t) \, \delta(\tau - \tau_k(t)), where KK is the number of multipath components, αk(t)\alpha_k(t) is the complex (incorporating magnitude and phase ejϕke^{j\phi_k}) of the kk-th path, τk(t)\tau_k(t) is the delay, τ\tau is the excess delay relative to the line-of-sight path, and tt denotes time variation due to mobility. 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 channel 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 sounding for modeling wide-sense stationary 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 LTE. 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 interference 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 (LOS) scenarios, predominates, incorporating a strong specular component alongside diffuse ; the distribution is parameterized by the K-factor, 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 στ\sigma_\tau, which captures the spread in arrival times of signal replicas. This dispersion determines the coherence bandwidth BcB_c, the range of frequencies over which the channel frequency response remains correlated, approximated as Bc12πστB_c \approx \frac{1}{2\pi \sigma_\tau}. Relative motion between transmitter and receiver induces Doppler spread fdf_d, the extent of frequency shifts due to path-specific velocity components, inversely related to the coherence time Tc=1fdT_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)=1.5πfd1(f/fd)2S(f) = \frac{1.5}{\pi f_d \sqrt{1 - (f/f_d)^2}}
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