NINTH WORKSHOP ON POWER LINE COMMUNICATIONS, SEPTEMBER 21-22, 2015, KLAGENFURT, AUSTRIA 1 Distributed Spectrum Occupancy Measurements in the 0.15 - 10 MHz Band for LV PLC Networks Nico Otterbach, Cornelius Kaiser, Vlad Stoica and Klaus Dostert Institute of Industrial Information Technology (IIIT), Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany {nico.otterbach, cornelius.kaiser, vlad.stoica, dostert}@kit.edu I. I NTRODUCTION T ODAY’S spectrum management policies usually follow a static approach with fixed services per frequency band. However, this approach leads to a subotimal utilization of valuable portions of the RF spectrum. Dynamic Spectrum Access (DSA) is often proposed as a solution leading to a more efficient spectrum usage in the wireless domain [1]. As PLC systems inherently interfere with wireless systems, DSA techniques are a promising approach for wire-based communication on power lines, too. Hence, first systems and standards already cover basic DSA methods. The European EN 50561-1 standard, for example, proposes a Cognitive Frequency Exclusion (CFE) to allow the usage of spare broadcasting radio bands for PLC systems [2]. To ensure an interference-free operation, unlicensed PLC systems have to be aware of their RF environment and continuously monitor the spectrum for active radio stations. If a new radio station comes up, the PLC system has to exclude the frequencies of the upcoming radio station from its own operation band. The potential of the DSA technique relies on the fact that a variety of services is not present at every location at any time. Thus, even licensed spectrum can be utilized by several services depending on time and place. This is why we focus on higher (not PLC-dedicated) frequency ranges in the 150 kHz - 10 MHz band for our cognitive PLC research. The licensed users in this band include for example broadcast radio stations, military services or ham radio bands [3]. While military services may be excluded from the PLC spectrum by default, radio bands can be easily re-used depending on the location. In this study, we will present our results gathered by a spectrum occupancy measurement campaign in Karlsruhe, Germany. II. SPECTRUM OCCUPANCY ESTIMATION METHOD As the licensed user’s signals are only partially known, we use an energy detection based method as the underlying analysis method. As noisy bands cannot be used for communication purposes either, we take them into account as occupied portions of the spectrum, too. Therefore, the discussion of spectrum occupancy is based on observations of the power spectral density (PSD). Particularly, we made use of the Welch’s method [4] to obtain an estimate of the spectral density of our measured signals. In Welch’s method, histograms are averaged while the samples are divided into blocks which are overlapped, leading to a variance reduction. Thus, Welch’s method is well-suited to analyze stationary random processes. In our PSD analysis, we took 2,500,000 samples (5 mains periods at 25 MS/s) divided into blocks of 4096 samples leading to a frequency resolution of 5 kHz. We then applied the FFT to each block with an overlap of 50 % between blocks to reduce the variance. As typical power grids show a (short-)time dependent or even cyclostationary behavior [5], we additionally analyzed the signal in the time-frequency domain. In doing so, we made use of the Short-time Fourier Transform (STFT), applying a Hanning window for the 5000 point DFT with an overlap of 50 %, leading too a frequency resolution of 5 kHz at a sample rate of 25 MS/s. Thereby, we took blocks of 1,000,000 samples (2 mains periods) leading to a time resolution of 200 μs. III. MEASUREMENT SETUP The measurement data itself was obtained by an Software Defined Radio (SDR) framework. This setup offers great flexibility concerning the desired coupling stages/antennas, bandwidth and post-processing algorithms. To obtain synchronized, distributed data sets, the following new framework based on off-the-shelf SDR hardware and open source software has been developed and implemented at our institute. A. Hardware The interface to the power grid is realized by broadband PLC coupling stages that are acting as band-pass filters with a flat passband covering 0.15 - 10 MHz. As we focused on broadcast radio stations, we additionally equipped one measurement site with an wideband active loop antenna (Emco Model 6502), covering the frequency range from 10 kHz up to 30 MHz. The data acquisition itself is realized by two USRP X310 and two USRP N210 devices.
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NINTH WORKSHOP ON POWER LINE COMMUNICATIONS, SEPTEMBER 21-22, 2015, KLAGENFURT, AUSTRIA 1
Distributed Spectrum Occupancy Measurements inthe 0.15 - 10 MHz Band for LV PLC Networks
Nico Otterbach, Cornelius Kaiser, Vlad Stoica and Klaus DostertInstitute of Industrial Information Technology (IIIT), Karlsruhe Institute of Technology (KIT)
TODAY’S spectrum management policies usually follow a static approach with fixed services per frequency band. However,this approach leads to a subotimal utilization of valuable portions of the RF spectrum. Dynamic Spectrum Access (DSA)
is often proposed as a solution leading to a more efficient spectrum usage in the wireless domain [1].As PLC systems inherently interfere with wireless systems, DSA techniques are a promising approach for wire-based
communication on power lines, too. Hence, first systems and standards already cover basic DSA methods. The European EN50561-1 standard, for example, proposes a Cognitive Frequency Exclusion (CFE) to allow the usage of spare broadcastingradio bands for PLC systems [2]. To ensure an interference-free operation, unlicensed PLC systems have to be aware of theirRF environment and continuously monitor the spectrum for active radio stations. If a new radio station comes up, the PLCsystem has to exclude the frequencies of the upcoming radio station from its own operation band.
The potential of the DSA technique relies on the fact that a variety of services is not present at every location at any time.Thus, even licensed spectrum can be utilized by several services depending on time and place. This is why we focus on higher(not PLC-dedicated) frequency ranges in the 150 kHz - 10 MHz band for our cognitive PLC research. The licensed users inthis band include for example broadcast radio stations, military services or ham radio bands [3]. While military services maybe excluded from the PLC spectrum by default, radio bands can be easily re-used depending on the location. In this study, wewill present our results gathered by a spectrum occupancy measurement campaign in Karlsruhe, Germany.
II. SPECTRUM OCCUPANCY ESTIMATION METHOD
As the licensed user’s signals are only partially known, we use an energy detection based method as the underlying analysismethod. As noisy bands cannot be used for communication purposes either, we take them into account as occupied portionsof the spectrum, too. Therefore, the discussion of spectrum occupancy is based on observations of the power spectral density(PSD).
Particularly, we made use of the Welch’s method [4] to obtain an estimate of the spectral density of our measured signals.In Welch’s method, histograms are averaged while the samples are divided into blocks which are overlapped, leading to avariance reduction. Thus, Welch’s method is well-suited to analyze stationary random processes. In our PSD analysis, we took2,500,000 samples (5 mains periods at 25 MS/s) divided into blocks of 4096 samples leading to a frequency resolution of 5kHz. We then applied the FFT to each block with an overlap of 50 % between blocks to reduce the variance.
As typical power grids show a (short-)time dependent or even cyclostationary behavior [5], we additionally analyzed thesignal in the time-frequency domain. In doing so, we made use of the Short-time Fourier Transform (STFT), applying aHanning window for the 5000 point DFT with an overlap of 50 %, leading too a frequency resolution of 5 kHz at a samplerate of 25 MS/s. Thereby, we took blocks of 1,000,000 samples (2 mains periods) leading to a time resolution of 200 µs.
III. MEASUREMENT SETUP
The measurement data itself was obtained by an Software Defined Radio (SDR) framework. This setup offers great flexibilityconcerning the desired coupling stages/antennas, bandwidth and post-processing algorithms. To obtain synchronized, distributeddata sets, the following new framework based on off-the-shelf SDR hardware and open source software has been developedand implemented at our institute.
A. Hardware
The interface to the power grid is realized by broadband PLC coupling stages that are acting as band-pass filters with a flatpassband covering 0.15 - 10 MHz. As we focused on broadcast radio stations, we additionally equipped one measurement sitewith an wideband active loop antenna (Emco Model 6502), covering the frequency range from 10 kHz up to 30 MHz.
The data acquisition itself is realized by two USRP X310 and two USRP N210 devices.
NINTH WORKSHOP ON POWER LINE COMMUNICATIONS, SEPTEMBER 21-22, 2015, KLAGENFURT, AUSTRIA 2
B. Software
We implemented a novel Automated Measurement System (AMS) that is based on the GNU Radio SDR framework [6]for our investigations. The basic data acquisition software is implemented as a GNU Radio flowgraph. This flowgraph takessamples from the USRP over an Ethernet interface and stores them on a hard disk. Therefore, each measurement stationconsists of an USRP, a coupling stage and a computer. The software is realized as a Linux system that runs without installationfrom an off-the-shelf USB stick. Single measurements and measurement campaigns are planned prior to the measurementsin a graphical tool. The measurement parameters themselves are stored in an SQL database, which can be distributed to theindividual measurement sites by a central git repository.
The second part of the AMS (the online tool) is responsible for the execution of the acquiring GNU Radio flowgraph withappropriate parameters. It offers the possibility for synchronized measurements in two ways: First, the start time of the dataacquisition can be synchronized by the PC’s system time, which may be precise enough for basic investigations. The precisionof this method can be further increased by obtaining the system time from an Network Time Protocol (NTP) server. The secondsynchronization method is based on the optionally available USRP GPS extension, allowing very low deviations in time betweenseveral measurement sites. Typical deviations achieved by this method are in the range of dozens of microseconds, allowingfor more precise investigations than the first method.
The third part of the newly developed AMS is the remote control tool. It enables the user to plan, control and monitorongoing measurements at different measurement sites from one central site. The measurement sites themselves are connectedto the control computer via SSH and VPN. This setup allows the execution of distributed, synchronized measurements even inforeign networks (e.g. at customer sites) to be planned, monitored and controlled from within the campus network. To archiveand distribute the obtained measurement data, the remote tool additionally provides a feature to collect the sampled data aswell as meta data and put both into a central SQL database.
C. Campaign Description
All measurements were taken in Karlsruhe, Germany, in the summer of 2015. We have four measurement sites in total;three of them are located at the campus ”Westhochschule” of the KIT (transformer station, cellar of an office building anda depot/garage). They are part of the same low-voltage network. Additionally, there is one measurement site located at anapartment building in another district of Karlsruhe.
Each measurement consists of 5 seconds of data sampled at 25 MSamples/s. Measurements are executed every 15 minutesat each location for a period of 24 hours.
IV. ANALYSIS AND RESULTS
A. Service Survey
f / MHz1 2 3 4 5 6 7 8 9 100civil and military broadcasting services
Fig. 1: Services in the 0.15 - 10 MHz band according to [3].
Figure 1 shows a basic overview of broadcasting stationsin the 0.15 - 10 MHz band in Germany. Although future cog-nitive PLC modems may utilize unoccupied bands regardlessof the primary services, upcoming DSA standards focus onthe broadcasting radio bands. Besides wireless services, DSAPLC systems have to take into account the current frequency-
dependent noise power as well while estimating the spectrum occupancy.
B. Location Diversity
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Fig. 2: Histograms of PSDs values at four different locations.
Figure 2 shows the distribution of powervalues obtained by the PSD calculation. It canbe seen that the apartment building shows thehighest peak at much higher power densitiesthan the other ones. While the red curve(histogram obtained at the cellar) shows thebroadest and thus lowest peak of the four.
Figure 3 shows the PSDs in 0.15 - 10MHz band obtained at four different sites
synchronously. It can be easily seen that the results are hihgly location-dependent, though there are some similarities likethe spikes around 6 MHz that can be seen at all sites which are part of the same voltage network (all except ”apartmentbuilding”).
NINTH WORKSHOP ON POWER LINE COMMUNICATIONS, SEPTEMBER 21-22, 2015, KLAGENFURT, AUSTRIA 3
Fig. 3: Scaled power spectral density measured at four locations.
Additionally, it canbeen seen that thereis a lot less power athigher frequencies forthe cellar and trans-former station sites,which could be causedby the shielding effectof underground cablesthat are connected tothese sites.
C. (Long) Time Di-versity
Figure 4 shows thetemporal change ofthe PSD and the his-togram of the PSD values for two measurement sites. Especially the garage measurement shows high time dependence. Agreat drop in the PSD as well as the histogram can be seen at low frequencies between about 22:30 and 11:20. Besides almostconstant parts at 3.8 MHz, for example, a raise in the PSD can be observed between 23:00 and 05:00 for several frequencies(around 1 MHz, 7.8 MHz, 9.8 MHz).
Fig. 4: PSDs (top) and histograms (bottom) obtained at the garage (left) and the cellar (right).
The changes around 1 MHz can be observed at the cellar measurements, too. Compared to the garage measurement, thecellar measurement shows a more smooth character in both visualizations.
D. (Short) Time-Frequency AnalysisFigure 5 shows the STFT at two sites (garage and transformer station) that were synchronously taken. One can easily see
the time dependence of the spectrum occupancy. There are periodic enhancements in noise power at the garage extending upto 2 MHz. Besides the periodic parts, there are time independent parts, for example at about 3.8 MHz.
The measurement at the transformer station, on the other hand, shows narrow periodic pulses with a distance of about 10 ms(half of mains period) in time. The measurement at this site also shows fewer narrowband, time independent signals comparedto the garage measurement.
E. Wireless MeasurementsFigure 6 shows the STFT at two different times in the garage. It can be observed that are a lot more narrowband signals
visible in the antenna-based measurement, compared to the power line measurements above. Additionally, less noise powercan be observed at lower frequencies, since the noise power is mainly introduced by devices connected to the power grid.Although most of the signals show an almost constant behavior in this visualization, some 100 Hz periodic signal portions canbe observed at frequencies up to 1.5 MHz.
NINTH WORKSHOP ON POWER LINE COMMUNICATIONS, SEPTEMBER 21-22, 2015, KLAGENFURT, AUSTRIA 4
5.0 ms 10.0 ms 15.0 ms 20.0 ms 25.0 ms 30.0 ms 35.0 msTime
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Fig. 5: Spectrograms obtained at the garage (left) and the transformer station (right).
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Fig. 6: Spectrograms obtained at the garage (left) and the transformer station (right).
[h]
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Fig. 7: Histograms of PSD values obtained at by antenna and power linecoupler.
Figure 7 additionally shows the distribu-tion of power values obtained by the PSDcalculations of signals that were acquiredsynchronously at the same site (garage). Itcan be seen that the wireless measurementhas an overall flat distribution compared tothe power line measurement.
V. CONCLUSIONS
In this paper, we presented basic observa-tions of spectrum occupancy measurements towards future PLC spectrum sensing methods and dynamic spectrum access PLCsystems. Hereby, we focused on the data acquisition as well as the implications of the chosen time and location.
Our measurement campaign showed a high dependence of the measurement location. This leads to the assumption thatcooperative spectrum sensing techniques known from wireless system seem to be a promising approach for PLC systems, too.
We also observed a high time dependence regarding both the daytime (hours) and the short time behavior (milliseconds).We saw a raise in the PSD for several bands at night hours for all measurements, which is caused by the changing propagationproperties at low frequencies. Within seconds or minutes, only minor changes were observed in the spectrum occupancy.
The STFT analysis on the other side showed a short time dependence due to the 50/100 Hz character of noise sources. Theobserved time dependence leads to the assumption that future DSA PLC systems may benefit from adaptive spectrum sensingmethods in order to achieve the best results.
Finally, we compared wireless with power line-based occupancy measurements. As expected, broadcasting stations weremore visible in wireless measurements, leading to the assumption that additional wireless spectrum sensing can enhance thedetection of primary users. For modem implementations, on the other hand, wireless measurements do not necessarily need tobe taken into account, due to the reciprocity of the grid’s coupling characteristic.
REFERENCES
[1] Zhao, Qing, and Brian M. Sadler. A survey of dynamic spectrum access. Signal Processing Magazine, IEEE 24.3 (2007): 79-89.[2] European Committee for Electrotechnical Standardization (CENELEC) Final Draft FprEN 50561-1: Powerline communication apparatus used in low
voltage installations - Radio disturbance characteristics - Limits and methods of measurement - Part 1: Apparatus for in-home use., Brussels, 2012[3] Bundesnetzagentur FREQUENZPLAN gemaß § 54 TKG uber die Aufteilung des Frequenzbereichs von 9 kHz bis 275 GHz auf die Frequenznutzungen
sowie uber die Festlegungen fur diese Frequenznutzungen, Bonn, 2015[4] Oppenheim, Alan V. and Schafer, Ronald W. and Buck, John R. Discrete-time Signal Processing (2Nd Ed.), Prentice-Hall, Inc., 1999[5] Ferreira, H. C., Lampe, L., Newbury, J. and Swart, T. G. Power Line Communications: Theory and Applications for Narrowband and Broadband
Communications over Power Lines, John Wiley & Sons, Ltd, 2010[6] Official GNU Radio Website http://gnuradio.org
