- Email: [email protected]
Prediction of Broadband Spectrum Utilization Data for Spectrum Sharing
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Procediaonline Computer 00 (2018) 000–000 Available at Science www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 126 (2018) 1434–1441
22nd International Conference on Knowledge-Based and Intelligent Information & 22nd International ConferenceEngineering on Knowledge-Based Systems and Intelligent Information & Engineering Systems
Prediction of Broadband Spectrum Utilization Data for Spectrum Prediction of Broadband Spectrum Utilization Data for Spectrum Sharing Sharing Nobuo Suzuki* and Tatsuya Yoshioka Nobuo Suzuki* and Tatsuya Yoshioka
Advanced Telecommunications Research Institute International 2-2-2, Hikaridai, Seikacho, Sorakugun, Kyoto, 619-0288, Japan Advanced Telecommunications Research Institute International 2-2-2, Hikaridai, Seikacho, Sorakugun, Kyoto, 619-0288, Japan
Abstract Abstract The 5th generation mobile network system (5G) comprises the infrastructure of next generation communication services such 5th generation mobile network system (5G)time comprises the infrastructure generation communication services such as The large-capacity video delivery services and real entertainment services. Itof is next extremely important to secure more spectrum as large-capacity video delivery servicesrequirements. and real time Solutions entertainment services. It is sharing extremely importantThis to secure resources to satisfy the 5G performance include spectrum technology. paper more showsspectrum that the resources satisfy the requirements. spectrum sharing technology. paper utilization shows thatdata the sequencestoconsisting of5G theperformance channel vacancy durations Solutions follow theinclude exponential distribution by using theThis spectrum sequences of the the spectrum channel vacancy durations followThe the exponential distribution by using the spectrum utilization data in an urbanconsisting area where is used at high density. spectrum usage state could be described by using the channel in an urban area where the spectrum is usedmethod at highisdensity. The state could be described by frequent using thesequence channel vacancy periods. Furthermore, a prediction proposed of spectrum the futureusage channel vacancy period with the vacancy periods. Furthermore, a prediction method is proposed of the future channel vacancy period with thereported. frequent sequence mining technology by using short time period observation data. The result of the evaluation experiment is also mining technology by using short time period observation data. The result of the evaluation experiment is also reported. © 2018 The Authors. Published by Elsevier Ltd. © 2018 The Authors. Published by Ltd. © 2018 The Authors. by Elsevier Elsevier Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an and openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection under responsibility of KES International. Selection and peer-review under responsibility of KES International. Selection and peer-review under responsibility of KES International. Keywords: Type your keywords here, separated by semicolons ; Keywords: Type your keywords here, separated by semicolons ;
1. Introduction 1. Introduction Recently, studies have increased related to the 5th generation mobile communication system (5G). Next Recently,communication studies have services increasedlike related to the 5th generation mobile business communication systementertainment (5G). Next generation the large-capacity video delivery and real-time generation communication services like the large-capacity video delivery business and real-time entertainment services are also active. 5G services need to secure more spectrum resources to satisfy performance requirements services are also active. services to secureconnectivity more spectrum satisfy performance requirements such as extremely high 5G speed, a lot need of terminal and resources extremely tolow latency [1]. The research and such as extremely high speed, a lot of terminal connectivity and extremely low latency [1]. The research development of 5G systems include developing a high-frequency band mainly like 28 GHz band. It needsand to development systems include developing a high-frequency bandsharing mainlytechnology like 28 GHz band. It solutions needs to efficiently useofthe5G conventional frequency band under 6 GHz. Spectrum is one of the efficiently use the conventional frequency band under 6 GHz. Spectrum sharing technology is one of the solutions * Corresponding author. Tel.: +81-774-95-1520; fax: +81-774-95-1509. E-mail address:author. [email protected] * Corresponding Tel.: +81-774-95-1520; fax: +81-774-95-1509. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of KES International. This is an and openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International.
1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 10.1016/j.procs.2018.08.115
2
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1435
for this issue. The spectrum sharing technology consists of two kinds of methods. One is the static spectrum sharing technology, which shares the fixed frequency bands according to the requirements. Another is the dynamic spectrum sharing technology, which can change shared frequency bands and locations flexibly. This paper proposes a method to grasp the utilization information of the shared frequency band needed for the dynamic spectrum sharing technology. The spectrum utilization data in urban areas where the spectrum is used at high density is the main 5G use case. This study tries to clarify the nature of the spectrum utilization information in urban areas based on the channel vacancy period as an index. As a result, spectrum utilization in urban areas follows the exponential distribution of the channel vacancy period. Therefore, the spectrum utilization information can be described by the channel vacancy period. This idea inspires us to propose a prediction method of the future channel vacancy period by frequent pattern mining with chronological order channel vacancy periods. This paper also reports result of an evaluation experiment with real observation data in an urban area. 2. Related Works There have been many studies on spectrum sharing technology to efficiently utilize limited spectrum resources. Spectrum sharing technology falls into two major technologies. One is static spectrum sharing technology that decides the shared frequency, the time and the place in advance and then starts sharing the frequency according to the requirements. The other is dynamic spectrum sharing technology that can flexibly change the shared frequency band, the time and the place. The typical static spectrum sharing technology includes TV white space [2]. It can share the spectrum by registering the utilized place, time and frequency band to the central database in advance with the vacant frequency band when a TV system is changed to a digital broadcasting system. On the other hand, Licensed Assisted Access (LAA), is one of the methods of dynamic spectrum sharing technology [3]. LAA is defined in 3GPP, which is the international standard organization of the mobile communication system. LAA is simultaneously uses the unlicensed spectrum bands for Wireless LAN or Radar in addition to the licensed spectrum bands like LTE originally assigned to carriers. LAA senses the carrier by using Listen Before Talk (LBT), and uses the required unlicensed bands when the band is not utilized. A large-capacity dawn link communication uses this mainly. The method that dynamically shares the spectrum at any spectrum band is desired because the sharable spectrum is limited at LAA. On the contrary, our recent studies proposed a spectrum sharing method at any place and time by grasping utilization information of some spectrum bands with wide-area sensors [4][5][6]. Dynamic spectrum sharing technologies decide on using spectrum by understanding current spectrum utilization information. Sharing users should stop using the spectrum when shared spectrum services begin. It is valid to predict the shared spectrum future utilization information and find the spectrum band in the future to solve those issues. The prediction method by Markov model is well used because of prediction of the future states by using the past spectrum utilization information [7]. Unfortunately, some of the methods were implemented on hardware devices to reduce the delay by the computing cost because the cost increased according to the numbers of states [8]. On the other hand, a future spectrum utilization information prediction method based on frequent pattern mining has been proposed. It has used the large field spectrum observation data. Some studies calculate the relation among radio services by using the spectrum observation during certain weeks [9]. They also predict the future spectrum utilization information by two-dimensional frequent pattern mining. Spectrum sharing over such a large and long period of time spectrum observation is the same as static spectrum sharing. It makes flexible spectrum sharing difficult. The 24-hour observation data is limited to sharing the spectrum in real time. This paper proposes a lowcost method based on frequent pattern mining with the 24-hour observation data. 3. Broadband Spectrum Usage Data in Urban Area This study collected 24 hours of real spectrum utilization data in a high-density radio environment, which is one of the main 5G use cases. The specifications of the observation are shown in Table 1. The location of the observation is shown in Fig. 1, and 24-hour observation point is indicated by the circle in the photo.
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1436
3
Table 1. Spectrum observation specifications Item
Description
Date and Time
2017/9/8 16:30 – 2017/9/9 16:39
Place
Front of Hachi-kou statue near Shibuya station in Tokyo
Spectrum band width
0.6GHz – 6GHz
Spectrum analyzer
N9342C
Antenna
OmniLOG 07600
RBW/VBW
300kHz
Fig. 1. Place of observation.
The observation data is collected at the range between 600 MHz and 6 GHz frequencies at 10 MHz intervals, 540 power data are collected as a result, and 43,200 data also are collected with the timeslot every 2 seconds in 24 hours. In other words, 540 MHz * 43,200 points / 2 seconds = 23,328,000 points of power data are collected. Part of the collected data is 3-D plotted in Fig. 2.
Fig. 2. 3-D view of energy level of frequency band.
4
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1437
The terms are defined to analyze those data. Channel means the 10 MHz width spectrum interval that is the minimum resolution of the observation. Channel usage state, CUS, means the channel utilization information on one timeslot at a certain channel. CUS(t,c)=1 means in use when the certain power value at time t and channel c is observed. CUS(t,c)=0 means vacancy when the certain power value at time t and channel c is not observed. Channel vacancy period, CVP, means the period of channel vacancy. It consists of the numbers of zero in CUS. Next, this section describes the procedures to divide channel utilization information into in use and vacancy. The observation data includes a steady noise. It is needed to determine in use when the value is higher than the noise level. It is also difficult to predict the noise level accurately. Therefore, this study determines in use as follows. First, the maximum, minimum and average values of all measured power values are calculated as shown in Fig.3. It can be seen that near 3 GHz is vacancy allocated to marine radar. This means they are noise data. The difference of 8 dB is obtained by subtracting the maximum value -78 dB from the minimum value -86 dB. It can be seen that the difference between the noise level, is the same as the vacancy state, and the minimum value is almost around 8 dB in all measured data. Therefore, the noise level is obtained by adding 8 dB to the minimum value of all measured data. Actually, -80 dB was set to the noise level and higher than -80 dB was in use, because the minimum value of all measured data was -88 dB.
Fig. 3. Maximum, minimum and average energy levels.
CUS value decided as in use or vacancy at those noise levels was obtained every 10 MHz width channel. Fig.4 shows part of the CUS map. The black dots depict that the channels are in use. It can also be seen that the spectrum bands in use exist without depending on the time. Next section discusses the regulation of CUS by using the channel vacancy periods as the evaluation index.
1438
Author name / Procedia Computer Science 00 (2018) 000–000 Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441
5
Fig. 4. Map of Channel Usage State.
4. Channel Vacancy Period Distribution Here, this section analyzes the statistical features of channel vacancy periods. As a result of analysis, the number of appearances of the channel vacancy periods is followed by the exponential distribution of the channel vacancy periods and has regularity. The channel vacancy period means the number of continuous zero appearance of CUS. The appearance numbers are counted as the starting point, which is the changing time from 1 to 0 of CUS sequential values. In other words, when CUS(t,c) – CUS(t+9,c) means “0010011101”, the channel vacancy period, CVP, would be “221”.
Fig. 5. Channel Vacancy Period (CVP) distribution at Specified low-power radio service.
6
Author name / Procedia Computer Science 00 (2018) 000–000 Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441
1439
Fig. 6. Channel Vacancy Period (CVP) distribution at Rural subscriber radio service.
�� � � �
�
∑��� � ��� �
(1)
�
∑��� � ����
CVPs for major radio services included in the collected data are calculated. Figure 5 shows CVP distribution at 1,215 MHz – 1,260 MHz, Specified low-power radio service. Fig.6 shows CVP distribution at 2,025 MHz – 2,110 MHz, Rural subscriber radio. The red curve means the approximate curve with the least-square regression method. The significance of the regression is obtained by the coefficient of determination r2. Yi is the sample value, �� is the average value, and fi is the approximate value. It can be seen that those CVP distributions fit well to the exponential distribution of � � � � ����� . Similar analysis was conducted for one or more radio services besides the Specified low-power radio service and the Rural subscriber radio service. Table 2 shows the result of the analysis. The significance of the regression r2 of every service is more than 0.98 and fits well to the exponential distribution. Table 2. Channel vacancy period distribution regression results. Radio service
Frequency band (MHz)
a
b
c
r2
Specified low-power radio
1215-1260
5.882
3234.523
-0.019
0.987
Rural subscriber radio
2025-2110
10.568
14504.705
-0.016
0.999
Field Pickup Unit
2330-2370
2.209
158365.076
-0.034
0.999
Wireless LAN (Japan Specific)
4900-5000
42.474
250857.764
-2.022
0.999
On the other hand, the in use or vacancy channel utilization state doesn’t mean the independent generating phenomenon. It means the phenomenon depended on former states. The channels aren’t used suddenly. Their next communication state is decided depending on the former communication states. CVP exponential distribution seems to be constructed depending on the former channel utilization state. Therefore, the following hypothesis is obtained. The sequence of CVP length also appears depending on the former state. The next section proposes the channel utilization information prediction method by using the time frequent pattern of CVP. 5. Future Channel Usage Prediction based on Frequent Pattern Mining Many methods of next state prediction based on the former state have been studied, such as the channel access method by Markov process [10]. The memory space exponentially increases by the number of states. The
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1440
7
calculation cost also increases. Therefore, in this study the prediction method of next channel state with the sequence pattern matching is carried out according to the frequent pattern mining technology. The frequent pattern mining finds the characteristic partial sequence pattern from the variety of sequence data [11]. It is the technology classified in unsupervised learning of machine learning technologies. The sequence data consists of the item set sequences arranged on time series of one or more item sets. The determination of whether it is characteristic or not is conducted by the support value defined by Eq. (2). The partial sequences are found as the characteristic sequence patterns in which the support value of given partial sequences is more than a certain value.
ݑݏሺݏሻ ൌ
்௨௦௨ௗ௧௨ௗ௦௧௦௨௦ ௨௦௨ௗ௧
.
(2)
First, CVPs for all measured data are created. The frequent patterns are extracted by the frequent pattern mining with those CVPs．PrefixSpan is used as the frequent pattern mining technology [12]. PrefixSpan extracts the frequent sequences by the depth first search. It improves efficiency by growing the sequence to longer frequent sequences from shorter ones. It is needed to specify the least frequent sequence length. PrefixSpan was conducted according to changing the least frequent sequence length from 3 to 50 in advance. The output frequent sequence length was 199 in the case where the least frequent sequence length was 3, and 2 in the case where the length was 50. The nearest frequent sequence length to the central value 37.5 among those frequent sequence lengths was 6. The least frequent sequence, therefore, was set to 6. Next, each extracted frequent pattern sequence is called n-rule and accumulated. After that, CVP of the radio services predicted is created. The partial sequences subtracted the recent items from the sequences output by PrefixSpan. CVP of next time slot is predicted by adding the last items of n-rule to CVP sequences of n-1 rule. Table 3. Prediction results of n-rules by Frequent pattern mining. No.
n-rules
Frequency
Sequence length
Accuracy
1
[5 1 1 1 1 1]
6
6
0.60
2
[6 1 1 1 1 1]
6
6
0.48
3
[1 1 2 1]
9
4
0.66
4
[12 1 1]
10
3
0.28
5
[26 1 1]
6
3
0.30
6
[3 3 1]
6
3
0.36
7
[7 3 1]
6
3
0.27
8
[8 3 1]
6
3
0.27
Average accuracy
0.40
This proposed method was evaluated as follows. First, the measured data in a high-density environment as described above was divided into two parts for learning and evaluation. The different kinds of dividing method were evaluated as follows; dividing by every two seconds of data at every channel, by one-hour channel, by 12 hours as half of the whole data, and by every spectrum band. The closest approximate curve of all data is evaluated at the distribution described in section 3. As a result, the coefficient of determination by dividing every channel of one hour became the closest to the original value. All data were divided into every channel of one hour. The first half was dataset A for learning and the last half was dataset B for evaluation. Next, CVP was calculated by using dataset A for learning. Then, the frequent sequence mining of CVP by PrefixSpan was conducted. N-rules were extracted from those results. CVPs for the evaluation dataset B were also obtained and the frequent pattern mining of the CVPs was performed by PrefixSpan. N-rules of dataset A and n-1 rules of dataset B were matched, and the correct answers were obtained. Table 3 shows the extracted n-rules and the accuracy. The accuracy tends to improve as the sequence length of n-rules are long.
8
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1441
5. Conclusions This paper proposed a prediction method of the spectrum utilization information at high-density radio utilization locations. Its purpose is to understand the spectrum utilization information for spectrum sharing. 24-hour spectrum utilization information was measured at one place. Then, the spectrum utilization information features were analyzed by using the channel vacancy period as an index. It could be seen that the channel vacancy period follows the exponential distribution. Therefore, the channel vacancy period seems to follow certain rules. A method of predicting future spectrum utilization information was proposed by using frequent pattern mining. After the measured data was divided into two groups and the proposed method was evaluated, the average of the accuracy was 0.4. This low accuracy shows this method does not have a practical use. It needs further improvements. The methods to improve the accuracy include the optimization of the minimum sequence length of the frequent pattern mining, the concurrent collecting of data with running and adaptive learning, and the learning of every service. Increasing the data seems to be effective because in a past report the periodic feature every 24 hours from one-week data was observed [9]. Learning every radio service is also effective to increase the observed features of the spectrum utilization information. These improvements will be conducted in the future. Acknowledgements This research was conducted under a contract of R&D for radio resource enhancement, organized by the Ministry of Internal Affairs and Communications, Japan. References [1] 5GMF White Paper. (2016) “5G Mobile Communications Systems for 2020 and beyond.” [2] Adriana B. Flores, Ryan E. Guerra, Edward W. Knightly, Peter Ecclesine, and Santosh Pandey. (2013) “IEEE 802.11af: A Standard for TV White Space Spectrum Sharing.” IEEE Communication Magazine 51(10): 92-100. [3] 3GPP. (2015) “TR36.889: Study on Licensed-Assisted Access to Unlicensed Spectrum (Release 13).” [4] Tatsuya Yoshioka, Hiromi Matsuno, Nobuo Suzuki, Morihiko Tamai, Masaki Kitsunezuka, Kazuaki Kunihiro, Shota Yamashita, Koji Yamamoto, Yuki Koizumi, and Toru Hasegawa. (2017) “Evaluation of Functions for Dynamic Spectrum Sharing in 5th Generation Mobile Communication Systems.” IEICE Technical Report RCS2017 (181): 75-80 [5] Nobuo Suzuki, Hiromi Matsuno, and Keizo Sugiyama. (2017) “Measuring electric energy efficiency in Power Estimation by Power Contour to Monitor Sharable Frequency with Mobile Phone Sensors.” The IEEE 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET2017). [6] Nobuo Suzuki, and Hiromi Matsuno. (2017) “Radio Wave Environment Analysis at Different Locations Based on Frequent Pattern Mining.” International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES2017) [7] Xiao Shuang, Xing Tao Jing, Wei Cheng, Yan Huo, and Xiuzhen Cheng. (2013) “Spectrum prediction in cognitive radio networks.” IEEE Wireless Communications 20 (2): 90–96. [8] Z. Chen, N. Guo, Z. Hu, and R. Qiu. (2011) “Channel state prediction in cognitive radio, part II: Single-user prediction.” IEEE Southeastcon: 50-54 [9] Dawei Chen, Sixing Yin, Qian Zhang, Mingyan Liu, and Shufang Li. (2009) “Mining Spectrum Usage Data: A Large-scale Spectrum Measurement Study.” The 15th Annual International Conference on Mobile Computing and Networking (MobiCom’09). [10] Tevfik Yucek. and Huseyin Arslan. (2009) “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications.” IEEE Communications Surveys & Tutorials 11(1): 116-130 [11] Aggarwal Charu, and Han Jiawei. (2014) “Frequent Pattern Mining.” Springer. [12] J.Pei, J.Han, B.Mortazavi-Asl, H.Pinto, Q.Chen, U.Dayal, and M. C.Hsu. (2001) “PrefixSpan : Mining sequential patterns efficiently by Prefix-Projected pattern growth.” International Conference on Data Engineering: 215-224 [13] Timothy Harrold, Rafael Cepeda, and Mark Beach. (2011) “Long-term measurements of spectrum occupancy characteristics.” IEEE International Symposium on Dynamic Spectrum Access Networks (DySpan): 83-89
ScienceDirect ScienceDirect
Procediaonline Computer 00 (2018) 000–000 Available at Science www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 126 (2018) 1434–1441
22nd International Conference on Knowledge-Based and Intelligent Information & 22nd International ConferenceEngineering on Knowledge-Based Systems and Intelligent Information & Engineering Systems
Prediction of Broadband Spectrum Utilization Data for Spectrum Prediction of Broadband Spectrum Utilization Data for Spectrum Sharing Sharing Nobuo Suzuki* and Tatsuya Yoshioka Nobuo Suzuki* and Tatsuya Yoshioka
Advanced Telecommunications Research Institute International 2-2-2, Hikaridai, Seikacho, Sorakugun, Kyoto, 619-0288, Japan Advanced Telecommunications Research Institute International 2-2-2, Hikaridai, Seikacho, Sorakugun, Kyoto, 619-0288, Japan
Abstract Abstract The 5th generation mobile network system (5G) comprises the infrastructure of next generation communication services such 5th generation mobile network system (5G)time comprises the infrastructure generation communication services such as The large-capacity video delivery services and real entertainment services. Itof is next extremely important to secure more spectrum as large-capacity video delivery servicesrequirements. and real time Solutions entertainment services. It is sharing extremely importantThis to secure resources to satisfy the 5G performance include spectrum technology. paper more showsspectrum that the resources satisfy the requirements. spectrum sharing technology. paper utilization shows thatdata the sequencestoconsisting of5G theperformance channel vacancy durations Solutions follow theinclude exponential distribution by using theThis spectrum sequences of the the spectrum channel vacancy durations followThe the exponential distribution by using the spectrum utilization data in an urbanconsisting area where is used at high density. spectrum usage state could be described by using the channel in an urban area where the spectrum is usedmethod at highisdensity. The state could be described by frequent using thesequence channel vacancy periods. Furthermore, a prediction proposed of spectrum the futureusage channel vacancy period with the vacancy periods. Furthermore, a prediction method is proposed of the future channel vacancy period with thereported. frequent sequence mining technology by using short time period observation data. The result of the evaluation experiment is also mining technology by using short time period observation data. The result of the evaluation experiment is also reported. © 2018 The Authors. Published by Elsevier Ltd. © 2018 The Authors. Published by Ltd. © 2018 The Authors. by Elsevier Elsevier Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an and openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection under responsibility of KES International. Selection and peer-review under responsibility of KES International. Selection and peer-review under responsibility of KES International. Keywords: Type your keywords here, separated by semicolons ; Keywords: Type your keywords here, separated by semicolons ;
1. Introduction 1. Introduction Recently, studies have increased related to the 5th generation mobile communication system (5G). Next Recently,communication studies have services increasedlike related to the 5th generation mobile business communication systementertainment (5G). Next generation the large-capacity video delivery and real-time generation communication services like the large-capacity video delivery business and real-time entertainment services are also active. 5G services need to secure more spectrum resources to satisfy performance requirements services are also active. services to secureconnectivity more spectrum satisfy performance requirements such as extremely high 5G speed, a lot need of terminal and resources extremely tolow latency [1]. The research and such as extremely high speed, a lot of terminal connectivity and extremely low latency [1]. The research development of 5G systems include developing a high-frequency band mainly like 28 GHz band. It needsand to development systems include developing a high-frequency bandsharing mainlytechnology like 28 GHz band. It solutions needs to efficiently useofthe5G conventional frequency band under 6 GHz. Spectrum is one of the efficiently use the conventional frequency band under 6 GHz. Spectrum sharing technology is one of the solutions * Corresponding author. Tel.: +81-774-95-1520; fax: +81-774-95-1509. E-mail address:author. [email protected] * Corresponding Tel.: +81-774-95-1520; fax: +81-774-95-1509. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of KES International. This is an and openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International.
1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 10.1016/j.procs.2018.08.115
2
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1435
for this issue. The spectrum sharing technology consists of two kinds of methods. One is the static spectrum sharing technology, which shares the fixed frequency bands according to the requirements. Another is the dynamic spectrum sharing technology, which can change shared frequency bands and locations flexibly. This paper proposes a method to grasp the utilization information of the shared frequency band needed for the dynamic spectrum sharing technology. The spectrum utilization data in urban areas where the spectrum is used at high density is the main 5G use case. This study tries to clarify the nature of the spectrum utilization information in urban areas based on the channel vacancy period as an index. As a result, spectrum utilization in urban areas follows the exponential distribution of the channel vacancy period. Therefore, the spectrum utilization information can be described by the channel vacancy period. This idea inspires us to propose a prediction method of the future channel vacancy period by frequent pattern mining with chronological order channel vacancy periods. This paper also reports result of an evaluation experiment with real observation data in an urban area. 2. Related Works There have been many studies on spectrum sharing technology to efficiently utilize limited spectrum resources. Spectrum sharing technology falls into two major technologies. One is static spectrum sharing technology that decides the shared frequency, the time and the place in advance and then starts sharing the frequency according to the requirements. The other is dynamic spectrum sharing technology that can flexibly change the shared frequency band, the time and the place. The typical static spectrum sharing technology includes TV white space [2]. It can share the spectrum by registering the utilized place, time and frequency band to the central database in advance with the vacant frequency band when a TV system is changed to a digital broadcasting system. On the other hand, Licensed Assisted Access (LAA), is one of the methods of dynamic spectrum sharing technology [3]. LAA is defined in 3GPP, which is the international standard organization of the mobile communication system. LAA is simultaneously uses the unlicensed spectrum bands for Wireless LAN or Radar in addition to the licensed spectrum bands like LTE originally assigned to carriers. LAA senses the carrier by using Listen Before Talk (LBT), and uses the required unlicensed bands when the band is not utilized. A large-capacity dawn link communication uses this mainly. The method that dynamically shares the spectrum at any spectrum band is desired because the sharable spectrum is limited at LAA. On the contrary, our recent studies proposed a spectrum sharing method at any place and time by grasping utilization information of some spectrum bands with wide-area sensors [4][5][6]. Dynamic spectrum sharing technologies decide on using spectrum by understanding current spectrum utilization information. Sharing users should stop using the spectrum when shared spectrum services begin. It is valid to predict the shared spectrum future utilization information and find the spectrum band in the future to solve those issues. The prediction method by Markov model is well used because of prediction of the future states by using the past spectrum utilization information [7]. Unfortunately, some of the methods were implemented on hardware devices to reduce the delay by the computing cost because the cost increased according to the numbers of states [8]. On the other hand, a future spectrum utilization information prediction method based on frequent pattern mining has been proposed. It has used the large field spectrum observation data. Some studies calculate the relation among radio services by using the spectrum observation during certain weeks [9]. They also predict the future spectrum utilization information by two-dimensional frequent pattern mining. Spectrum sharing over such a large and long period of time spectrum observation is the same as static spectrum sharing. It makes flexible spectrum sharing difficult. The 24-hour observation data is limited to sharing the spectrum in real time. This paper proposes a lowcost method based on frequent pattern mining with the 24-hour observation data. 3. Broadband Spectrum Usage Data in Urban Area This study collected 24 hours of real spectrum utilization data in a high-density radio environment, which is one of the main 5G use cases. The specifications of the observation are shown in Table 1. The location of the observation is shown in Fig. 1, and 24-hour observation point is indicated by the circle in the photo.
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1436
3
Table 1. Spectrum observation specifications Item
Description
Date and Time
2017/9/8 16:30 – 2017/9/9 16:39
Place
Front of Hachi-kou statue near Shibuya station in Tokyo
Spectrum band width
0.6GHz – 6GHz
Spectrum analyzer
N9342C
Antenna
OmniLOG 07600
RBW/VBW
300kHz
Fig. 1. Place of observation.
The observation data is collected at the range between 600 MHz and 6 GHz frequencies at 10 MHz intervals, 540 power data are collected as a result, and 43,200 data also are collected with the timeslot every 2 seconds in 24 hours. In other words, 540 MHz * 43,200 points / 2 seconds = 23,328,000 points of power data are collected. Part of the collected data is 3-D plotted in Fig. 2.
Fig. 2. 3-D view of energy level of frequency band.
4
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1437
The terms are defined to analyze those data. Channel means the 10 MHz width spectrum interval that is the minimum resolution of the observation. Channel usage state, CUS, means the channel utilization information on one timeslot at a certain channel. CUS(t,c)=1 means in use when the certain power value at time t and channel c is observed. CUS(t,c)=0 means vacancy when the certain power value at time t and channel c is not observed. Channel vacancy period, CVP, means the period of channel vacancy. It consists of the numbers of zero in CUS. Next, this section describes the procedures to divide channel utilization information into in use and vacancy. The observation data includes a steady noise. It is needed to determine in use when the value is higher than the noise level. It is also difficult to predict the noise level accurately. Therefore, this study determines in use as follows. First, the maximum, minimum and average values of all measured power values are calculated as shown in Fig.3. It can be seen that near 3 GHz is vacancy allocated to marine radar. This means they are noise data. The difference of 8 dB is obtained by subtracting the maximum value -78 dB from the minimum value -86 dB. It can be seen that the difference between the noise level, is the same as the vacancy state, and the minimum value is almost around 8 dB in all measured data. Therefore, the noise level is obtained by adding 8 dB to the minimum value of all measured data. Actually, -80 dB was set to the noise level and higher than -80 dB was in use, because the minimum value of all measured data was -88 dB.
Fig. 3. Maximum, minimum and average energy levels.
CUS value decided as in use or vacancy at those noise levels was obtained every 10 MHz width channel. Fig.4 shows part of the CUS map. The black dots depict that the channels are in use. It can also be seen that the spectrum bands in use exist without depending on the time. Next section discusses the regulation of CUS by using the channel vacancy periods as the evaluation index.
1438
Author name / Procedia Computer Science 00 (2018) 000–000 Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441
5
Fig. 4. Map of Channel Usage State.
4. Channel Vacancy Period Distribution Here, this section analyzes the statistical features of channel vacancy periods. As a result of analysis, the number of appearances of the channel vacancy periods is followed by the exponential distribution of the channel vacancy periods and has regularity. The channel vacancy period means the number of continuous zero appearance of CUS. The appearance numbers are counted as the starting point, which is the changing time from 1 to 0 of CUS sequential values. In other words, when CUS(t,c) – CUS(t+9,c) means “0010011101”, the channel vacancy period, CVP, would be “221”.
Fig. 5. Channel Vacancy Period (CVP) distribution at Specified low-power radio service.
6
Author name / Procedia Computer Science 00 (2018) 000–000 Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441
1439
Fig. 6. Channel Vacancy Period (CVP) distribution at Rural subscriber radio service.
�� � � �
�
∑��� � ��� �
(1)
�
∑��� � ����
CVPs for major radio services included in the collected data are calculated. Figure 5 shows CVP distribution at 1,215 MHz – 1,260 MHz, Specified low-power radio service. Fig.6 shows CVP distribution at 2,025 MHz – 2,110 MHz, Rural subscriber radio. The red curve means the approximate curve with the least-square regression method. The significance of the regression is obtained by the coefficient of determination r2. Yi is the sample value, �� is the average value, and fi is the approximate value. It can be seen that those CVP distributions fit well to the exponential distribution of � � � � ����� . Similar analysis was conducted for one or more radio services besides the Specified low-power radio service and the Rural subscriber radio service. Table 2 shows the result of the analysis. The significance of the regression r2 of every service is more than 0.98 and fits well to the exponential distribution. Table 2. Channel vacancy period distribution regression results. Radio service
Frequency band (MHz)
a
b
c
r2
Specified low-power radio
1215-1260
5.882
3234.523
-0.019
0.987
Rural subscriber radio
2025-2110
10.568
14504.705
-0.016
0.999
Field Pickup Unit
2330-2370
2.209
158365.076
-0.034
0.999
Wireless LAN (Japan Specific)
4900-5000
42.474
250857.764
-2.022
0.999
On the other hand, the in use or vacancy channel utilization state doesn’t mean the independent generating phenomenon. It means the phenomenon depended on former states. The channels aren’t used suddenly. Their next communication state is decided depending on the former communication states. CVP exponential distribution seems to be constructed depending on the former channel utilization state. Therefore, the following hypothesis is obtained. The sequence of CVP length also appears depending on the former state. The next section proposes the channel utilization information prediction method by using the time frequent pattern of CVP. 5. Future Channel Usage Prediction based on Frequent Pattern Mining Many methods of next state prediction based on the former state have been studied, such as the channel access method by Markov process [10]. The memory space exponentially increases by the number of states. The
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1440
7
calculation cost also increases. Therefore, in this study the prediction method of next channel state with the sequence pattern matching is carried out according to the frequent pattern mining technology. The frequent pattern mining finds the characteristic partial sequence pattern from the variety of sequence data [11]. It is the technology classified in unsupervised learning of machine learning technologies. The sequence data consists of the item set sequences arranged on time series of one or more item sets. The determination of whether it is characteristic or not is conducted by the support value defined by Eq. (2). The partial sequences are found as the characteristic sequence patterns in which the support value of given partial sequences is more than a certain value.
ݑݏሺݏሻ ൌ
்௨௦௨ௗ௧௨ௗ௦௧௦௨௦ ௨௦௨ௗ௧
.
(2)
First, CVPs for all measured data are created. The frequent patterns are extracted by the frequent pattern mining with those CVPs．PrefixSpan is used as the frequent pattern mining technology [12]. PrefixSpan extracts the frequent sequences by the depth first search. It improves efficiency by growing the sequence to longer frequent sequences from shorter ones. It is needed to specify the least frequent sequence length. PrefixSpan was conducted according to changing the least frequent sequence length from 3 to 50 in advance. The output frequent sequence length was 199 in the case where the least frequent sequence length was 3, and 2 in the case where the length was 50. The nearest frequent sequence length to the central value 37.5 among those frequent sequence lengths was 6. The least frequent sequence, therefore, was set to 6. Next, each extracted frequent pattern sequence is called n-rule and accumulated. After that, CVP of the radio services predicted is created. The partial sequences subtracted the recent items from the sequences output by PrefixSpan. CVP of next time slot is predicted by adding the last items of n-rule to CVP sequences of n-1 rule. Table 3. Prediction results of n-rules by Frequent pattern mining. No.
n-rules
Frequency
Sequence length
Accuracy
1
[5 1 1 1 1 1]
6
6
0.60
2
[6 1 1 1 1 1]
6
6
0.48
3
[1 1 2 1]
9
4
0.66
4
[12 1 1]
10
3
0.28
5
[26 1 1]
6
3
0.30
6
[3 3 1]
6
3
0.36
7
[7 3 1]
6
3
0.27
8
[8 3 1]
6
3
0.27
Average accuracy
0.40
This proposed method was evaluated as follows. First, the measured data in a high-density environment as described above was divided into two parts for learning and evaluation. The different kinds of dividing method were evaluated as follows; dividing by every two seconds of data at every channel, by one-hour channel, by 12 hours as half of the whole data, and by every spectrum band. The closest approximate curve of all data is evaluated at the distribution described in section 3. As a result, the coefficient of determination by dividing every channel of one hour became the closest to the original value. All data were divided into every channel of one hour. The first half was dataset A for learning and the last half was dataset B for evaluation. Next, CVP was calculated by using dataset A for learning. Then, the frequent sequence mining of CVP by PrefixSpan was conducted. N-rules were extracted from those results. CVPs for the evaluation dataset B were also obtained and the frequent pattern mining of the CVPs was performed by PrefixSpan. N-rules of dataset A and n-1 rules of dataset B were matched, and the correct answers were obtained. Table 3 shows the extracted n-rules and the accuracy. The accuracy tends to improve as the sequence length of n-rules are long.
8
Nobuo Suzuki et al. / Procedia Computer Science 126 (2018) 1434–1441 Author name / Procedia Computer Science 00 (2018) 000–000
1441
5. Conclusions This paper proposed a prediction method of the spectrum utilization information at high-density radio utilization locations. Its purpose is to understand the spectrum utilization information for spectrum sharing. 24-hour spectrum utilization information was measured at one place. Then, the spectrum utilization information features were analyzed by using the channel vacancy period as an index. It could be seen that the channel vacancy period follows the exponential distribution. Therefore, the channel vacancy period seems to follow certain rules. A method of predicting future spectrum utilization information was proposed by using frequent pattern mining. After the measured data was divided into two groups and the proposed method was evaluated, the average of the accuracy was 0.4. This low accuracy shows this method does not have a practical use. It needs further improvements. The methods to improve the accuracy include the optimization of the minimum sequence length of the frequent pattern mining, the concurrent collecting of data with running and adaptive learning, and the learning of every service. Increasing the data seems to be effective because in a past report the periodic feature every 24 hours from one-week data was observed [9]. Learning every radio service is also effective to increase the observed features of the spectrum utilization information. These improvements will be conducted in the future. Acknowledgements This research was conducted under a contract of R&D for radio resource enhancement, organized by the Ministry of Internal Affairs and Communications, Japan. References [1] 5GMF White Paper. (2016) “5G Mobile Communications Systems for 2020 and beyond.” [2] Adriana B. Flores, Ryan E. Guerra, Edward W. Knightly, Peter Ecclesine, and Santosh Pandey. (2013) “IEEE 802.11af: A Standard for TV White Space Spectrum Sharing.” IEEE Communication Magazine 51(10): 92-100. [3] 3GPP. (2015) “TR36.889: Study on Licensed-Assisted Access to Unlicensed Spectrum (Release 13).” [4] Tatsuya Yoshioka, Hiromi Matsuno, Nobuo Suzuki, Morihiko Tamai, Masaki Kitsunezuka, Kazuaki Kunihiro, Shota Yamashita, Koji Yamamoto, Yuki Koizumi, and Toru Hasegawa. (2017) “Evaluation of Functions for Dynamic Spectrum Sharing in 5th Generation Mobile Communication Systems.” IEICE Technical Report RCS2017 (181): 75-80 [5] Nobuo Suzuki, Hiromi Matsuno, and Keizo Sugiyama. (2017) “Measuring electric energy efficiency in Power Estimation by Power Contour to Monitor Sharable Frequency with Mobile Phone Sensors.” The IEEE 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET2017). [6] Nobuo Suzuki, and Hiromi Matsuno. (2017) “Radio Wave Environment Analysis at Different Locations Based on Frequent Pattern Mining.” International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES2017) [7] Xiao Shuang, Xing Tao Jing, Wei Cheng, Yan Huo, and Xiuzhen Cheng. (2013) “Spectrum prediction in cognitive radio networks.” IEEE Wireless Communications 20 (2): 90–96. [8] Z. Chen, N. Guo, Z. Hu, and R. Qiu. (2011) “Channel state prediction in cognitive radio, part II: Single-user prediction.” IEEE Southeastcon: 50-54 [9] Dawei Chen, Sixing Yin, Qian Zhang, Mingyan Liu, and Shufang Li. (2009) “Mining Spectrum Usage Data: A Large-scale Spectrum Measurement Study.” The 15th Annual International Conference on Mobile Computing and Networking (MobiCom’09). [10] Tevfik Yucek. and Huseyin Arslan. (2009) “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications.” IEEE Communications Surveys & Tutorials 11(1): 116-130 [11] Aggarwal Charu, and Han Jiawei. (2014) “Frequent Pattern Mining.” Springer. [12] J.Pei, J.Han, B.Mortazavi-Asl, H.Pinto, Q.Chen, U.Dayal, and M. C.Hsu. (2001) “PrefixSpan : Mining sequential patterns efficiently by Prefix-Projected pattern growth.” International Conference on Data Engineering: 215-224 [13] Timothy Harrold, Rafael Cepeda, and Mark Beach. (2011) “Long-term measurements of spectrum occupancy characteristics.” IEEE International Symposium on Dynamic Spectrum Access Networks (DySpan): 83-89