Dynamic denoising studies in wideband radio channel measurement and modeling

The Journal of China Universities of Posts and Telecommunications October 2014, 21(5): 43–46 www.sciencedirect.com/science/journal/10058885

http://jcupt.xsw.bupt.cn

Dynamic denoising studies in wideband radio channel measurement and modeling COULIBALY Balla Moussa, ZHAO Xiong-wen (

), LIANG Xiao-lin, LI Shu, LI Yi-wei

School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Abstract The fixed level and dynamic denoising method was studied based on indoor-to-outdoor measured channel impulse responses (IRs) at 5.25 GHz with radio frequency (RF) 100 MHz bandwidth. It is found that the dynamic ranges, peak powers and noise floors of the IRs are with close correlations. The comparisons with different denoising methods are given by deriving the power delay profiles (PDPs), root mean square (RMS) delay spread (RMS DS), number of paths (NOPs) and Ricean K-factors. It is concluded that the traditional fixed level noise cut is under estimate of DS and NOPs. The Ricean K-factors are of little sensitive to noise cut irrespective of what kind of methods applied. The PDPs are not very sensitive to the fixed level noise cut, however, obvious changes can be found by dynamic noise cut. The dynamic noise cut is preferred when clear noise floors is observed and decided from the measured IRs, it’s of importance in data post processing for wideband radio channel measurements as well as the relevant modeling work. Keywords

impulse response, wideband channel, denoising, channel parameter, channel measurement

1 Introduction The dynamic ranges (DNRs) of measured impulse responses (IRs) depend on the measurement system setup, e.g. transmitted power, code length etc, and also strongly depend on the measurement environments. The DNRs are time evolution parameters of the channel. Therefore, it should be chosen variable DNRs to perform the noise cut for channel IRs. However, in many literatures, the common applied method for noise cut was to choose a fixed dynamic range, e.g. 20~30 dB [1–8], to derive channel parameters for simplicity or in some cases, it might be difficult to decide the noise floors due to measurement system setup, e.g. short code length by direct sequence (DS) and sliding correlator (SC) techniques for time domain channel sounder, and also due to the specific measurement environment with very lower signal power. Received date: 20-03-2014 Corresponding author: ZHAO Xiong-wen, E-mail: [email protected] DOI: 10.1016/S1005-8885(14)60329-0

In this article, based on the wideband indoor-to-outdoor measured IRs, the correlations of the peak powers, dynamic ranges, noise floors and the number of paths are studied and the comparisons on how the channel parameter statistics changes with the fixed level and the dynamic noise cut are investigated. The measurements are conducted in European Wireless World Initiative New Radio (WINNER) project with 5.25 GHz carrier frequency and 100 MHz RF bandwidth. The transmitted power is 26 dBm, and the code length is 255 chip. More than seven base stations (BSs) are located inside a building, and the mobile station (MS) moves along the outdoor routes in the campus of University of Oulu. Both multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) measurements are performed also for other purposes of channel modelling. Most of the measured routes are in non-line-of-sight (NLOS), however, some specific segments of the routes are obstructed line-of-sight (OLOS). Fig. 1 shows an example of the indoor-to-outdoor measurement routes with dotted line.

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Fig. 1 An example of the indoor-to-outdoor measurement routes (dash-line)

2

Method of dynamic denoising

For fixed level noise cut, the peak power in a PDP can first be determined by the method, then the noise will be cut by the fixed level, e.g. 20 dB downwards. The method of the dynamic noise cut applied here is shown in Fig. 2, and the steps are as follows.

Fig. 2

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estimation error is sufficient. In order to cut the noise by fixed levels, Eq. (1) is first used to estimate the DNRs of the measured IRs, and further to get the following three sets of databases: 1) Measured IRs with greater than or equal to 20 dB DNRs, which means that the measured IRs with less than 20 dB DNRs will be discarded. 2) Measured IRs with greater than or equal to 25 dB DNRs. 3) Measured IRs with greater than or equal to 30 dB DNRs. Let’s take the measured IRs with greater than or equal to 20 dB DNRs as the database to compare the dynamic ranges, peak powers, noise floors and the number of paths of the IRs. When calculating the DNRs, 3 dB estimation error is taken as indicated in Eq. (1). The number of paths is defined as to count the local maximal in a PDP. Fig. 3 shows the DNRs, peak powers, noise floors and the number of paths with respect to the number of the measured IRs. It is seen that excellent correlations among the DNRs, peak powers as well as the noise floors, e.g. the correlation coefficient is 0.93 between DNRs and peak powers.

Estimation of the dynamic range for an IR

Check the measured IRs, and decide how the delay samples (before or after the peak) can be averaged to get the noise floors, e.g. by using the last 300 delay samples as shown in Fig. 2. The noise floor is constant for a specific IR, but it changes always for different measured IRs due to the environment and multipath effect. For a specific IR, by using the constant noise floor and the amplitudes of e.g. 300 delay samples, the standard deviation (STD） of the noise can be derived. The dynamic range (in dB) of the IR is derived by (1) Dnr = Pp − N f − δ STD [ nDS (200 : 500)] − Ee where Dnr is for dynamic noise range, Pp is peak power,

N f is noise floor, nDS is the number of delay samples and Ee is for the estimation error, respectively. To add several dBs estimation errors can guarantee to completely cut the noise because in Eq. (1), the fixed STD with respect to the noise floor is applied, but the noise is fluctuated as shown in Fig. 2. As a rule of thumb, 1~3 dB

Fig. 3 Variations of the DNRs , peak powers, noise floor and the number paths using the measured IRs with greater or equal than 20 dB dynamic ranges

Lower correlation is found between the number of paths with respect to the DNRs, peak powers and the noise floors, e.g. the correlation coefficient is 0.62 between noise floors and number of paths. The correlation coefficients for all the different cases can be found in Table 1. Higher correlations between peak powers and DNRs as well as noise floors offer the possibility to find their relationships. Figs. 4 and 5 show the linear relationships between peak powers and DNRs as well as noise floors, respectively.

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COULIBALY Balla Moussa, et al. / Dynamic denoising studies in wideband radio channel measurement and modeling 45

They can be expressed in Eqs. (2) and (3): Dnr = 91.12 + 0.803Pp ; δ STD = 0.62

(2)

N f = −101.64 + 0.202 Pp ; δ STD = 0.64

(3)

Table 1 Correlation coefficient among the dynamic range, peak received power, noise floors and number of paths Paramenters Parameters DNR Peak power Noise floor Number of paths

DNR Peak power Noise floor 1.00 0.93 0.87 0.42

0.93 1.00 0.91 0.40

0.87 0.91 1.00 0.62

Number of paths 0.42 0.40 0.62 1.00

Therefore, the peak powers can be used, which can be extracted from the measured IRs easily to predict the DNRs and the noise floors.

performing dynamic level noise cut, the IRs database with greater than or equal to 20 dB DNR is used to maintain higher signal-to-noise ratios and to guarantee effectiveness of implementation of the noise cut. The window length of about 1.8 m is applied to guarantee the wide sense stationary uncorrelated scattering (WSSUS) condition to derive the PDPs and the channel parameters. The NOPs was derived by counting the local maximal in a PDP averaged by the window length [5], and Ricean factor (K in dB) was derived by moment method introduced in Ref. [6]. Fig. 6 shows the comparisons of the PDPs. It can be seen that there exist no evident changes in the PDPs by using either fixed level or dynamic noise cut. However, 15 dB fixed DNR is just a small value for the noise cut, the PDPs are almost overlapping when using more than 20 dB fixed levels. Some obvious changes of the PDP can be observed by using dynamic noise cut.

Fig. 4 Linear relationship between the peak received powers and the dynamic ranges of the IRs

Fig. 6

Comparisons of the Power delay profiles

Fig. 7 and Table 2 show the comparisons of the cumulative distribution functions (CDFs) of the RMS DS and their statistical values.

Fig. 5 Linear relationship between the peak received powers the noise floors of the IRs

3 Comparisons of channel parameters In this section, the channel PDPs and channel parameters of the RMS DS, number of NOPs, and Ricean K-factor are extracted when using the measured IRs databases with fixed levels (15 dB, 20 dB, 25 dB and 30 dB) and dynamic noise cut. It is noted that when

Fig. 7

Comparisons of the RMS delay spread

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Table 2 Comparisons of the statistical values of the CDFs of the RMS delay spread CDF of RMS DS 10% 50% 90% Mean

15/dB 3.4 16.0 58.0 25.1

Statistical value Noise cut levels 20/dB 25/dB 30/dB 7.1 8.0 8.0 25.4 28.9 44.2 68.0 72.0 76.8 33.5 36.8 42.2

Method Dynamic 16.5 44.8 80.4 47.1

It is seen that, at most of the statistical points, the RMS DS is increased with higher fixed noise cut levels. Higher RMS DS can be seen with dynamic noise cut. This is possible because the DNRs shown in Fig. 3 are always changed, even more than 30 dB DNRs can be observed in many of the measured IRs. The median RMS DS by dynamic noise cut is almost doubled compared with 20 dB fixed level noise cut. Table 3 shows the comparisons of the CDFs of the NOPs and their statistical values. The NOPs is increased when the fixed noise cut levels are set more higher. The highest NOPs is found when performing dynamic noise cut. Table 4 shows the comparisons of the CDFs of the Ricean K-factors and their statistical values. It is seen that the statistical values of the K-factors are not sensitive to the fixed level as well as dynamic noise cut. Moreover, even if the noise is not cut at all, the K-factor statistical values can still be comparable to those by performing noise cut. However, the lowest K values can be obtained when with no noise cut. Table 3 Comparisons of the statistical values of the CDFs of the NOPS Statistical values Noise cut levels CDF of NOPs 15/dB 20/dB 25/dB 30/dB 10% 1.6 3.0 4.0 5.0 50% 4.0 6.0 9.0 16.0 90% 11.0 16.0 21.0 27.0 Mean 5.0 8.0 11.0 15.0

Method Dynamic 11.0 17.0 32.0 20.0

Table 4 Comparisons of the statistical values of the CDFs of the Ricean K-factors CDF of K 10% 50% 90% Mean

Statistical value Noise cut levels 15/dB 20/dB 25/dB 30/dB No noise cut − 2.3 − 2.2 − 2.8 − 2.2 − 2.7 3.8 3.2 4.0 3.1 2.8 9.5 8.6 8.5 7.3 7.8 3.5 3.1 3.5 3.1 2.8

Method Dynamic − 2.4 2.5 7.3 2.5

4 Conclusions The methods of fixed level and dynamic noise cut are studied by using the measured IRs in an indoor-to-outdoor environment. It is found that the dynamic ranges, peak

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powers and noise floors of the IRs have satisfied correlations, which offers a way to predict the dynamic ranges and noise floors of the IRs by their peak powers. In general, the method of the noise cut has great effection for extracting channel parameters. The fixed level noise cut is normally under estimate the RMS DS and the number of paths. The dynamic denoising is preferred if the noise floors and the DNRs can be clearly observed and derived from the measured IRs. Therefore, when setting up the measurement system, it is good to have longer code length (in case of measuring Doppler is not critical) and the higher transmitted power. The PDPs are almost overlapping when the fixed level noise cut is applied, however, obvious changes can be observed by using dynamic noise cut. The Ricean K-factors are not sensitive to the ways of noise cut even if the noise is not cut at all, the statistical values of the K-factors are still comparable to those by implementing noise cut. Acknowledgements This work were supported by the National Natural Science Foundation of China (61372051), and the Hi-Tech Research and Development

Program

of

China

(2014AA01A701).

The

Measurements Were Performed in the Framework of the IST (IST-4-027756 WINNER II).

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(Editor: WANG Xu-ying)