# A highly efficient channel sounding method based on cellular communications for high-speed railway scenarios

- Liu Liu
^{1}Email author, - Cheng Tao
^{1}, - Tao Zhou
^{1}, - Youping Zhao
^{1}, - Xuefeng Yin
^{2}and - Houjin Chen
^{1}

**2012**:307

https://doi.org/10.1186/1687-1499-2012-307

© Liu et al.; licensee Springer. 2012

**Received: **14 February 2012

**Accepted: **21 June 2012

**Published: **3 October 2012

## Abstract

### Abstract

An efficient channel sounding method using cellular communication systems is proposed for high-speed railway (HSR) propagation environments. This channel measurement technique can be used conveniently to characterize different HSR scenarios, which can significantly improve the measurement efficiency. Based on downlink signals of wideband code division multiple access (WCDMA) and the long term evolution (LTE), principles and methodologies of HSR channel sounding are presented. Using the WCDMA signal, a measurement campaign is conducted in real-world HSR scenarios and statistical characterizations are provided using a radio network analyzer. Due to the limits of the radio network analyzer, afterwards, a software defined radio (SDR)-based channel data recorder is developed allowing users to collect the signals from different wireless cellular systems. Especially, the estimation accuracies are validated in lab by the faded signals emitted from a vector signal generator. The results show that the channel data recorder provides a particularly good match to the configured fading channels. Therefore, this measurement method can be employed to investigate the HSR channel, and to establish the channel models under the various HSR scenarios.

## Introduction

In recent years, broadband wireless access under high-speed railway (HSR) scenarios becomes a hot topic in industry and academia. With the development of HSR, more and more wireless data need to be transferred between the train and the ground[1]. On one hand, increasing railway controlling information consisting of security monitoring and maintenance needs to be transmitted from the train to the ground; on the other hand, mobile users are eager to have reliable multimedia services, independent of their locations or speeds. It is estimated that the wireless communication traffic could be as high as 65 Mbps per high-speed train[2]. However, the existing communication systems cannot meet such requirements, therefore, how to offer high-quality wireless communication services to HSR has become an urgent issue for researchers, telecom operators and equipment manufacturers.

A major prerequisite of any wideband digital radio system is a thorough knowledge of the propagation characteristics of the radio channel. Channel characterization is crucial in physical layer design, as it helps engineers design countermeasures to small-scale fading, such as diversity transmission/reception, error correction coding and interleaving, and equalization algorithms. Numerous wideband channel measurement campaigns have been performed to characterize urban radio channels for cellular mobile communication by researchers worldwide[3–5]. The measurement techniques can be categorized as: (1) Field strength measurements. In the actual deployment of a wireless communication system, the actual network planning optimization, capacity, blind spots and other studies are conducted according to the radio propagation environment. Accurate large-scale channel models (path loss and shadowing fading model) can make the network deployment and the network optimization reliable and efficient, so as to enhance the performance of wireless networks; (2) Measurements based on special excitation signals. This channel identification method employs a specific sequence possessing the Dirac-delta-like autocorrelation function, and correlates the received signal at the receiver with a copy of the signal identical to the transmitted one. Consequently, the channel impulse response (CIR) can be obtained.

Many of the previous studies on the HSR propagation measurement and characterization were conducted in recent years. The channel measurement using the GSM-R signal was taken on Zhengzhou to Xi’an (ZX) HSR. The experimental system is composed of a signal power recorder (Griffrin) combining with a GPS receiver. The received power amplitude was collected for the large-scale fading parameterization[6–9]. In these published articles, the empirical path loss models under viaducts and U-shape Groove (USG) have been presented, whereas the features of small-scale fading are not included. In the pulse measurement, the characterization result of this measurement technique is mainly focused on a specified environment, which yields lower measurement efficiency. RUSK measurement campaign was conducted at 5 GHz in Germany between Siegburg and Frankfurt[10, 11]. An empirical path loss, shadow fading, *K*-factor, and other parameters related to the time delay spread as well as the angle spread were presented[11]. Propsound measurement was conducted at 2.5 GHz in Taiwan[12], and the delay spread, maximum excess delay, temporal behavior of the Doppler power spectrum and Angles of Arrival/Departure are studied. Propsound measurement and characterization were carried out at 2.35 GHz band by Beijing Jiaotong University[2]. A position-based wireless channel model for HSR viaduct scenarios was proposed. Based on the geometry-based stochastic model, the non-stationary MIMO Channel model was investigated[13].

- 1)
We provide an efficient channel sounding method using wireless cellular systems for HSR scenarios.

- 2)
We specify the estimation methodology using downlink signal of wideband code division multiple access (WCDMA) and LTE as the excitation signal. The time delay parameters, Doppler properties and correlation matrices can be extracted.

- 3)
Based on a radio network analyzer using WCDMA signal, a channel measurement is conducted in real-world HSR environments, and the time dispersive properties are provided under the typical terrains.

- 4)
A prototype of the channel data recorder is developed to collect different cellular probing signals based on software defined radio (SDR) platform. Meanwhile, the key technologies are addressed. By employing WCDMA and LTE signals, we perform extensive measurements in lab for the calibrations using this recorder. The measured results show a good match.

The remainder of this article is organized as follows: In Section “Challenges in HSR channel characterization”, we discuss the problems and motivations of the channel characterization in HSR radio environments. Section “Principles and methodology of HSR channel characterization based on cellular communication systems” addresses the principle and methodology of the channel sounding using WCDMA and LTE signals. In Section “Measurements on ZX and Beijing-Tianjin (BT) HSR”, the measurement campaign conducted on HSRs is described and the time delay characteristics are provided. In Section “Wireless channel data recorder”, the development of the channel data recorder and the calibrations in lab are discussed in more details. Section “Conclusions” draws the conclusions.

## Challenges in HSR channel characterization

The knowledge of the wireless channel is vital to the optimal design and performance of any HSR wireless communication systems. Most of the previous literature on the channel measurement and characterization is focused on terrestrial cellular communication systems. Features of HSR propagation channels, such as time, frequency and spatial selectivity, differ from those of cellular communications channels. These differences originate from some specific features of the HSR propagation environments such as[2] (1) multiple scenarios; (2) line of sight (LOS) dominance; (3) large Doppler shift and rapid Doppler transition.

- (1)
In Europe and China, communications between the train and railway administration control centers involving the train dispatching, marshaling shunting, maintenance repair operation and emergency in HSR systems are undertaken by GSM-R. In order to guarantee the security of this train control network, the wireless frequency bands are under surveillance by the railway administration. Any wireless signals are prohibited except the wireless communications running by the wireless operators, such as GSM, WCDMA, CDMA2000, etc. Therefore, the particular signal-based channel sounding campaign encounters a great deal of difficulties.

- (2)
In the static channel scenario, it is impossible to emulate the rapid change of the radio environment to extract the time-varying characterization of a given resolvable path component. Furthermore, the Doppler effect due to the high mobility has a considerable influence on the average fading rate of the discrete propagation wave. And the Doppler spread is resulting from different incoming waves of mobile vehicles and the scatters. For this reason, in order to extract the accurate Doppler property, achieving an adequate velocity is primary.

- (3)
Measurement efficiency is extremely low when the commonly used channel sounders are employed. If the train travels at a speed of 360 km/h and the wireless coverage of a channel sounder is 1 km, the recording time of the sounder approximates 20 s. The obtained experimental data in this short period may be not adequate to extract the statistical property of the time-varying channels. Moreover, making a reservation for the measurement in a high-speed train, as well as the coordination of the rail-line and the dispatch, are exhausting. And it will take about a month for these preparatory work. Additionally, HSR encounters numerous typical terrains. It is time-consuming and costly to establish a complete and accurate propagation channel database using the universal channel sounders.

Considering the particular nature of the channel sounding for HSR scenarios, we explore a novel efficient channel sounding method using the signal from a wireless cellular system. This employed signal is treated as the excitation wave, and at the receiver side, the stochastic property can be extracted through post-processing.

## Principles and methodology of HSR channel characterization based on cellular communication systems

### Channel measurement and characterization with WCDMA signal

#### Channel state estimation based on WCDMA signal

WCDMA air interface employs the direct-sequence code division multiple technology and the frequency division duplex (FDD) mode. The chip rate is 3.84 Mcps with the radio bandwidth of 5 MHz. This wideband signal possesses the large time bandwidth product, low crest factor and good correlation properties. These merits meet the requirements of a typical excitation signal[14].

*S*

_{dl,n}. Figure2 shows the CPICH modulation procedure[16].

where *K* is the number of channels in the physical layer within the frequency band, *L* denotes the number of multipath components, *c*_{
l
}(*p*) represents the discrete complex channel coefficient of *l* th path, *a*_{
k
}(*p*) stands for the discrete chip data of the *k* th channel after scrambling and spreading, *τ*_{
l
} represents the delay of the *l* th path, *T*_{
c
} is the chip duration, [*x*] means the rounding operation of *x*, and *n*_{k,l} (*p*) denotes the additive white gaussian noise (AWGN) of the *l* th multipath component in the *k* th channel.

*j*. Then (1) can be modified as

where the first term refers to CPICH, the second term stands for the multiple-access interference (MAI) caused by other physical channels, and *sc*(*p*) represents the scrambling code for a specified cell. Practical experiences reveal that both MAI and *n*_{k,l} (*p*) terms can be considered as AWGN *w*(*p*). In addition, there is some interferences in practice, which are caused by adjacent cells. This interferences are assumed to be negligible.

*N*is the size of the correlation window, and

*τ*represents the correlation time index. For the balance between the correlation window and the snapshot rate (will be discussed in Section “The uncertainty principle in the time and frequency domains”),

*N*is chosen to be 256. After some mathematic operations, the estimated CIR

*h*(

*τ*) becomes

where the first term refers to the average of the channel coefficients, and the second term is inter-path interference (IPI) generated by other path components and does not align perfectly with the scrambling code phase. Since the scrambling code yields a peak as a *δ* function at the zero shift point, the IPI is usually weak when compared with the third term.

#### The uncertainty principle in the time and frequency domains

*T*

_{rep}of the sounding pulse must satisfy

*τ*

_{max}is the maximum delay of the multipath component. Under this condition, the aliasing from echo waves can be avoided. On the other hand, in order to track instantaneous fading conditions, these repetitive signals need to be sent sufficiently frequently.

*T*

_{rep}must be smaller than the time over which the channel changes. This notion can be formalized by establishing a sampling theorem in the time domain, so there is a minimum temporal sampling rate to identify a time-variant process with a band-limited Doppler spectrum. This temporal sampling frequency must be twice the maximum Doppler frequency

*f*

_{max}as

Time-varying channels can be fundamentally unidentifiable because requirements for the design of sounding signals can become contradictory[14]. According to (5) and (6), it is intractable when we select the correlator window *N* in (3). On one side, the time width *T*_{W−rep} corresponding to *N* has to be chosen larger than the maximum excess delay *τ*_{max}; On the other side, the repetition frequency *f*_{rep} has to fulfill *T*_{W − rep} ≤ 1 / 2*f*_{max}. The snapshot rate is inversely proportional to the correlator window width *N*. Consequently, if *N* is made as small as possible, then the channel sampling rate increases. This will enhance the capability of tracking Doppler characteristics. However, this narrow window will increase the noise floor when we gather CIR, which will compress the dynamical range. It will make a significant effect on the resolvability of multipath components in lower SNR regions. In the data post-processing, therefore, the window width *N* needs to be adjusted according to the measurement scenario.

### Channel measurement and characterization with LTE signal

Long term evolution (LTE) of the 3rd Generation Partnership Project. Orthogonal frequency division multiplexing (OFDM) and multiple-input and multiple-output (MIMO) are the key technologies in LTE system. Like WCDMA, the OFDM signal also can be employed as the excitation signal for channel sounding[17]. The downlink signal of LTE is competent for channel sounding for HSR due to its broader bandwidth of 20 MHz.

#### Channel state estimation based on LTE signal

*ms*subframes, each of which is split into two 0.5 ms slots (as shown in Figure4[18]). Each slot comprises seven individual OFDM symbols in the case of the normal cyclic prefix length. The LTE system has been conceived to work under high-mobility assumptions. In the time-frequency plane, a uniform reference symbol (RS) grid with a diamond shape is used. The required spacing in time between the RSs can be obtained by considering the maximum Doppler spread (highest speed) to be supported, which for LTE corresponds to 500 km/h. In the frequency direction there is one RS every six subcarriers in each OFDM symbol including RS, but they are staggered so that within each Resource Block (RB) there is one RS every three subcarriers corresponding to 45 KHz. Thus it allows the feasibility of the channel estimation of the expected frequency domain.

The LTE downlink has been specifically designed to work with multiple transmit antennas, and the specific RS is orthogonal with each other antenna. For simplicity, the single antenna system is described in this section. MIMO measurement system can be easily generated with this method. Here we address the channel estimation problem over one OFDMA symbol (all the subcarriers being RSs) to exploit the frequency domain characteristics. The channel transfer function (CTF) can be estimated using a maximum likelihood approach in the frequency domain by de-correlating the constant modulus RS. Then, the CTF of the overall subcarriers can be obtained by interpolation methods[19].

*N*subcarriers in an OFDM symbol,

*X*[

*m*] is the complex-valued training data on the

*m*th subcarrier, which is actually known at the receiver. After

*N*-point IFFT, the transmitted signal

*x*(

*n*) can be expressed as

*x*(

*n*) and the CIR in time domain can be written as

*h*(

*n*,

*l*)=

*h*(

*l*). Then at the receiver, after the synchronization, CP removal and FFT operation, the received signal in frequency domain is

*H*[

*m*] and

*W*[

*m*] refer to the frequency response and AWGN at

*m*th subcarrier. Then a simple correlation processing in frequency domain is performed as

#### The spatial correlation measurement with LTE signal

where ⊗ is the Kronecker product.

*a*and

*b*in (11) define the spatial correlation between the antennas at the UE and eNB. Then the correlation coefficient is given by

where *h*_{
p
} and *h*_{
q
} denote the CIR of the *p* receiving antenna and the *q* transmitting antenna, respectively. ()^{∗}represents the conjunction operation.

## Measurements on ZX and Beijing-Tianjin (BT) HSR

### Descriptions of the measurement campaign

The R & S TSMQ Radio Network Analyzer can be employed for network analysis and optimization, and it can extract the resolvable multipath component corresponding to a specific cell[21]. The number of paths, path location (in the delay domain), and path attenuation can be derived by the supporting software. All of the behaviors, such as synchronization and channel estimation are accomplished by TSMQ. Practical testing experiences reveal that the maximum resolvable time delay is 20 *μ* s and the minimum resolvable path power is −20 dB when employing TSMQ.

BT measurement is conducted in the No. 0 high speed integrated inspection train (NHSIIT) with a speed of approximately 240 km/h using the train-mounted antenna. NHSIIT is used to check the train system administrated by the Ministry of Railways of China. The BT environment is composed of the viaduct and roadbed, with small roadside bushes and agricultural fields in between.

### Channel parameters estimation on ZX HSR

One of the primary channel statistics of interest for digital communications applications is the delay spread, which quantifies the range of delays between the first received impulse and the last, and describes the distribution of the amplitude. The probability density function (PDF) of root mean square (rms) delay spread and number of paths are shown as the basic channel parameters[23].

*μ*s, encountered for USG is approximately 60%, and for the Railway Station case, it accounts less than 10%.

On the basis of the preceding discussion, we conclude that the Plain and Hilly Terrain scenarios have similar time dispersive behaviors because the mountain in Hilly Terrain is relatively far from the rail, the reflected wave is too weak to be detected by TSMQ after the penetration loss of the carriage. Whereas, Railway Station and USG show significantly stronger multipath effects. In Railway Station scenario, radio waves will undergo the reflection many times, which induced from isolated components from reflections of strong stationary scatterers, e.g., the wall, pillar and roof of the platform. In USG environment diffuse reflections, caused by weak static scatterers such as cement slope sides, arise frequently.

**Channels for the typical terrains**

Scenario | Tap number | Relative time | Average path |
---|---|---|---|

delay [μs] | gain [dB] | ||

Plain | 1 | 0 | 0 |

2 | 0.3 | −12.9 | |

3 | 0.6 | −22.2 | |

Hilly Terrain | 1 | 0 | 0 |

2 | 0.3 | −7.6 | |

3 | 0.6 | −22 | |

USG | 1 | 0 | 0 |

2 | 0.3 | −7.3 | |

3 | 0.9 | −22.9 | |

4 | 2.1 | −24 | |

Railway Station | 1 | 0 | 0 |

2 | 0.3 | −5.2 | |

3 | 0.6 | −8.2 |

### Channel parameters estimation on BT HSR

**Delay spread characteristics on BT HSR**

Scenarios | rms=0 | Maximum number of multipath components |
---|---|---|

Terrain A | 98% | 2 |

Terrain B | 91% | 2 |

*I*

_{0}() is the 0th-order modified Bessel function of the first kind,

*r*denotes the envelope of the received signal,

*σ*

^{2}represents the variance of the diffuse components, and

*s*

^{2}is the power of the direct LOS path component. Any moment of the Ricean distribution can be given as

*Γ*(·) is the gamma function, and

*F*

_{1}(·) is the confluent hypergeometric function. Then the estimated

*K*factor can be obtained as[25]

where *μ*_{2} and *μ*_{4} are the even moments.

*K*-factor results that collected in the environments. Scenario A and B are the viaduct models, exhibiting a stronger LOS, with the

*K*factor of 4.8 and 5.2 dB, respectively. Actually, the

*K*-factor is around 5 dB in most instances in the entire measurement campaign on BT HSR.

K
**-Factor on BT HSR**

Scenarios | A | B |
---|---|---|

| 4.8 | 5.2 |

## Wireless channel data recorder

### System description

**Parameters of wireless channel data recorder**

Parameter | Value | Parameter | Value |
---|---|---|---|

Bandwidth | 100 MHz | A/D Bitwidth | 14 bits |

RF | 2.1 GHz ∼ 2.6 GHz | Interface | PCI-E Bus |

Local clock reference | GPS + Rubidium clock | Storage medium | Solid hard disk 512 GB |

Max sampling rate | 200 MSPS | Power | 12v |

- (1)
We can configure different estimation software according to different excitation signals at different frequencies to extract the channel properties owing to the wideband radio frequency (RF) interface.

- (2)
In our recorder, the signals with multiple frequencies, multiple spread codes, and various communication standard signals can be stored into the hard disk simultaneously. The extraction procedure of the target signal can be accomplished by softwares off-line. This enhances the measurement efficiency significantly. In the real measurement employing TSMQ, in order to achieve the higher sampling rate, we have to configure the parameters of a specific cell in real-time, including the central frequency, spreading code, etc. The measurement task using TSMQ is intensive.

^{−12}to 10

^{−14}s. The GPS synchronization technique can be used anywhere where there is access to the GPS network, which is justifiably outdoors with a good LOS to the sky.

### Laboratory calibration

#### Verified results based on WCDMA signal

**Emulation channels for channel data recorder using WCMDA**

Channel | Tap number | Relative | Average path | Doppler | |
---|---|---|---|---|---|

delay [μs] | gain [dB] | spectrum | |||

Rayleigh/ | 1 | 0 | 0 | CLASS/RICE | |

Rice | 2 | 0.52 | −3 | CLASS | |

3 | 1.04 | −6 | CLASS | ||

4 | 1.56 | −9 | CLASS |

where *t*_{rep} is the snapshot repetition period, *Δτ* denotes the time delay resolution, W is the window size for averaging. It can be found that the measured path delay and relative power of PDP present a good match with the channel parameters listed in Table5.

*Δτ*=0. As can be seen in Figure14, the measured power spectrum is consistent with classical U-shape spectrum and the maximum Doppler frequency offset approximates 1110 Hz.

*K*-factor can be extracted from the CIRs. Table6 shows the measured results of

*K*-factor with a decreasing trend, from 15 to −5 dB. It is obvious that our results provide better approximations in the stronger LOS scenarios.

**Performance of**
K
**-factor estimation**

Set value K[dB] | 15 | 10 | 5 | 0 | −5 |
---|---|---|---|---|---|

Estimated value | 14.96 | 9.96 | 5.04 | 0.07 | −3.95 |

Relative error of estimated value | 0.8% | 0.1% | 1% | 1.7% | 2.1% |

#### Verified results based on LTE signal

**Parameters of wireless channel data recorder**

Parameter | Value | Parameter | Value |
---|---|---|---|

Frequency | 2.6 GHz | Duplex mode | FDD |

Bandwidth | 5 MHz | Min resolvable time | 0.22 |

Channel delay | [0, 2, 3, 5, 8, 11] × | Relative power | [0 −1 −9 −10 −15 −20] dB |

Velocity | [0, 120, 250, 350] km/h | Doppler spectrum | CLASS |

**Estimation errors [dB] of time delay characteristics using LTE signal**

Item | 0 km/h | 120 km/h | 250 km/h | 350 km/h |
---|---|---|---|---|

SNR = 0 dB | 0.1814 | 0.1956 | 0.1983 | 0.1856 |

SNR = 10 dB | 0.0633 | 0.0624 | 0.0747 | 0.0696 |

SNR = 20 dB | 0.0557 | 0.0608 | 0.0495 | 0.0630 |

**Spatial fading correlation calibration using LTE signal**

Item | Low | Medium | High |
---|---|---|---|

correlation | correlation | correlation | |

α = 0 | α = 0.3 | α = 0.9 | |

SNR = −10 dB | 0.0034 | 0.2990 | 0.7945 |

SNR = 0 dB | 0.0014 | 0.3007 | 0.8951 |

SNR = 10 dB | −0.0098 | 0.3009 | 0.8994 |

SNR = 20 dB | −0.0051 | 0.3042 | 0.8997 |

## Conclusions

This article focuses on the development of a novel and efficient channel sounding method for HSR environments, which directly exploits the radio signals of existing cellular systems. We investigate the principle and methodology of the use of WCDMA and LTE as the excitation signals. Afterwards, HSR radio channel measurements are conducted with TSMQ, which provide time-dispersive characteristics of typical HSR scenarios. Due to the shortcomings of the commercial measurement devices, a SDR-based channel data recorder is developed featuring convenient signal sampling structure and competent data processing. The experimental results are calibrated in lab compared with the configured fading models, which shows a good match. By virtue of the high efficiency of this novel method, employing this channel data recorder, our setup system should be a useful tool to characterize HSR propagation channels.

## Declarations

### Acknowledgements

The research was supported in part by the NSFC project under grant Nos. 61032002 and 61102050, the National Science and Technology Major Project under grant 2011ZX03004-006, the Beijing Natural Science Foundation project under grant No. 4122061, and the Fundamental Research Funds for the Central Universities under grant No.2012YJS005.

## Authors’ Affiliations

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