Cell coverage estimation by radio fingerprint data analytics
© Kim and Yeo; licensee Springer. 2014
Received: 15 January 2014
Accepted: 14 April 2014
Published: 4 May 2014
We develop a novel cell coverage estimation method based on radio fingerprints collected from practical wireless service systems. A large size radio fingerprint map that shows cell identifiers and signal strength measurements on grid segmentation is built for effective estimation of cell coverage. An essential part of cell coverage estimation is radio fingerprint data cleansing and compensation. Based on this proposed iterative fingerprint data analysis method, we detect the proper cell borderline for each cell site. By the proposed method, we can efficiently estimate the cell coverage of each cell site without difficult manual field measurements. Moreover, mobile service providers can economically plan network configuration and manage subscribers using these advances in cell coverage estimation.
Accurate estimate of cell coverage is essential for cellular network design and deployment. Cell coverage is usually defined as the maximum distance, from a mobile user to a serving cell site, while maintaining sufficient service quality. It is well known that cell coverage is related to signal attenuation or path loss. The maximum path loss determines the maximum cell coverage. The Okumura-Hata  or COST-231  models are generally utilized as path loss models. The various link budget parameters shown in  are applied as input factors to the path loss models, then useful information can be extracted for network deployment, such as the location of cell sites required to cover the target area. Some of these parameters are within the control of the designer, such as transmit (Tx) power levels, antenna orientation, or tilt . By manipulating these parameters, network operators try to optimize wireless cell coverage. The literature [5–7] describes several methods for optimizing wireless cell coverage. An optimization method focuses on coverage and antenna configuration by using a simulated annealing/tabu search . A multi-objective algorithm is used to determine a series of solutions for locating the access points of wireless local area networks (WLANs) to maximize coverage and QoS . A multi-criteria genetic algorithm has also been presented that selects cell sites from candidate sets with the goal of maximizing infrastructure, cost efficiency, and coverage while constraining pairwise cell overlap . Another focus of research on cell coverage is the inverse relationship of coverage to traffic load in a cell. An increase in the volume of active traffic in the cell causes the interference at the cell site, and effective cell coverage is consequently decreased . The coverage estimation based on traffic load estimation in a cellular network was thoroughly analyzed in . They calculated the outage rate caused by interference and then restricted the effective cell coverage within an outage rate threshold.
The exact estimation of cell coverage is applicable to cellular network management. Combined with demographics and foot traffic of a particular area, decisions of cell split, addition, or re-configuration are performed. In this article, we propose a novel cell coverage estimation method using radio fingerprint data. A radio fingerprint map is built based on the measured radio fingerprint data. A grid segmentation of the fingerprint map provides an efficient frame in which to store a large amount of fingerprint data. Each grid contains a reference cell identifier in the form of a reference Pilot Number (reference PN for 3G WCDMA) or Physical Cell ID (PCI for 4G LTE) and its signal strength measurement value. A customized data analysis method that consists of cleansing and compensation is applied to the fingerprint map. From understanding the cell shapes, outlier fingerprint data are eliminated by the iterative data cleansing method. In addition, the data compensation method can keep fingerprint data integrity and confirm the proper cell borderline for effective cell coverage estimation. This effective coverage estimation creates powerful advantages for network planning and operation. For instance, economic planning for subscriber penetration is a good example. The scheduled subscriber penetration plan has a critical impact on radio resource planning for commercial service providers.
2 Coverage estimation by radio fingerprint maps
Radio signal tracking is one of the most intricate processes in wireless network planning. It needs a specialized diagnostic machine and analysis procedure. The radio signal should be scanned during a specific time band and analyzed by a custom-designed tool. Furthermore, the entire procedure of tracking and analysis is performed manually. This complexity restricts the wide adoption of manual tracking, despite its relatively higher estimation precision. In , researchers suggest a variation of manual tracking to estimate cell coverage. A cell site could receive and determine the signal quality measurements of forward and reverse links at a particular location of a MS. The distribution of measured signal quality information could determine cell coverage.
3 Radio fingerprint data analytics for grid borderline detection
In Figure 6, we can find the white ref. Cell-ID numbers that are newly compensated ref. Cell-IDs. The grids with the ‘X’ marker are grids eliminated by the cleansing procedure. The yellow line shows the final borderline of the target cell coverage. As shown in the example of cell borderline detection, the cleansing and compensation are essential analytical tools to identify the cell coverage. A logical solution for many problems is to clean the data to enhance the solution quality: exploring the data set for possible problems and endeavoring to clean the errors. Of course, for any real world data set, doing this data cleansing and/or relational data integrity analysis ‘by hand’ is completely out of the question, given the amount of person-hours required . While a data cleansing method can uncover a number of possible errors in a data set, it does not address other, more complex errors. Errors or irregularities that involve relationships among multiple fields are often very difficult to uncover. These types of errors require serious inspection and analysis to be cleaned: statistical , clustering , pattern-based , and association  techniques are used to identify patterns that can uncover the data error or irregularities. In addition, informative patterns  and ‘garbage patterns’ of meaningless or mislabeled patterns  are used to perform data cleansing. Machine learning techniques are also used to cleanse data in the written character classification problem. However, none of the mentioned researches can present an effective tool for general data cleansing. A data cleansing activity has a very domain-specific applicability . General purpose data cleansing methods, such as the Kalman filter , do not have the specific knowledge needed to effectively eliminate the outliers to detect the borderline of a cell. Thus, we developed a customized data cleansing method for effective fingerprint-based cell coverage estimation.
where gBS is the total number of grids in the analysis area. A series of circular areas from the center of the cell can be selected as the analysis area. For example, circles with a 100-, 200-, or 500-m radius can be the target analysis area. The analysis area does not have any relation to cell coverage itself. But, we obtain the antenna beamwidth by grid counting and the ratio of g c over gBS. Thus, we should set a standard circular area to obtain proper ratio of g c over gBS. The larger area contains a larger number of grids for counting and gives sufficient ref. Cell-ID data to estimate the borderlines. However, a larger area contains many erroneous ref. Cell-IDs which would be invalid for borderline detection.
4 Numerical results
To test the proposed method, we developed a cell coverage estimation program which has a map of an urban (Gangnam) area in South Korea. The test area has a total of 47,439 grids and we collected complete ref. Cell-ID and RSSI (i.e., fingerprint) data by actual wardriving. The cell coverage estimation program includes all the collected fingerprint data of the grids and the position data of cell sites. The positions of cell sites were obtained using an information database from a commercial WCDMA system. In addition, the cell coverage estimation program contains the position information of the relay stations. All the information for grids, cell sites, and relay stations are shown in a map and information window of the cell coverage estimation program. The area shown in the map window can be moved by simple mouse drag operations. The function of map panning in/out is implemented by simple menu directions.
Region A: included in both the Measured and Estimated Coverage
Region B: included in the Estimated Coverage, but not in the Measured Coverage
Region C: included in the Measured Coverage, but not in the Estimated Coverage
The primary criterion is Inclusion Ratio, defined as A/(A + C). A good estimation should contain a large amount of measured coverage within the estimated coverage. However, even a simple overestimation of the cell coverage can achieve a high inclusion ratio (i.e., small region C) but may include significant redundant space (i.e., large region B). The Conformity Ratio, defined as A/(A + B), avoids this weakness. A higher conformity ratio guarantees a tight estimation on the measured coverage (i.e., small region B). Therefore, we can evaluate the quality of the estimation using these two numerical performance criteria.
The practical processing time of proposed cell borderline detection method is very fast. All operations of method are elementary integer arithmetic and numbers of iterations are bounded to practically less than 10 times. For the tested 15 cells, total practical processing time is less than 1 second using a plain PC server. Compared to beam lobe model estimation, which needs the logarithm calculation of path loss model, the processing time of proposed method has competitive advantage. Apparently, fingerprint collection by wardriving is a time-consuming procedure. However, the beam lobe model also requires geographical map data for estimation.
In this paper, we dealt with cell coverage estimation. The problem is to find precise cell coverage information for proper network design and operation. By gathering complete radio fingerprint data in the test area, we make a grid map with reference cell identifiers and signal strengths. Based on this fingerprint map, we can estimate the cell coverage. The fingerprint data cleansing and compensation for borderline detection is an essential part of cell coverage estimation. Given our understanding of circular sector cell configurations, we developed an iterative data cleansing and compensation method. As a result of an appropriate cleansing and compensation method, we can detect a tight cell borderline. We presented the entire estimation procedure and a coverage estimation program that consists of a large amount of radio fingerprint data and a proposed procedure. The usefulness of the developed algorithmic procedure and coverage estimation program is proven by actual fingerprint data collected from a commercial wireless service provided by SK Telecom, Korea. A field engineer can now estimate cell coverage without manual operations using this simple coverage estimation program. Note that, the fine grid granularity guarantee higher performance. But it also generates higher cost for fingerprint data collection. Thus, we select single-sized grids in building a radio fingerprint map (e.g. 50 × 50). To enhance the accuracy on estimation, we can adopt variable granularity: fine granularity for cell border area and coarse granularity for cell inner area.
To restrict the estimation error (i.e., ) within ±0.05 (i.e., the error tolerance limit is 5%) under the 95% probability (i.e., ), we select approximately 384 samples to represent the total population.
This research work is supported by SK Telecom, South Korea. All data are collected using the facility of SK Telecom. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (2011-0011825).
- Medeisis A, Kajackas A: On the use of the universal Okumura-Hata propagation prediction model in rural areas, In IEEE 51st Vehicular Technology Conference Proceedings, VTC 2000 Spring. IEEE, Piscataway; 2000.Google Scholar
- Mardeni R, Priya TS: Optimised COST-231 Hata models for WiMAX path loss prediction in suburban and open urban environments. Mod. Appl. Sci. 2010, 4: 9.Google Scholar
- Holma H, Toskala A: LTE for UMTS. Wiley, New York; 2011.View ArticleGoogle Scholar
- Fagen D, Vicharelli PA, Weitzen J: Automated wireless coverage optimization with controlled overlab. IEEE Trans. VT 2008, 57: 4.Google Scholar
- Siomina I, Varbrand P, Yuan D: Automated optimization of service coverage and base station antenna configuration in UMTS networks. IEEE Wireless Commun. 2006, 13(6):16-25.View ArticleGoogle Scholar
- Jaffres-Runser K, Gorce J-M, Ubeda S: QoS-constrained wireless LAN optimization within a multiobjective framework. IEEE Wireless Commun. 2006, 13(6):26-33.View ArticleGoogle Scholar
- Whitaker R, Raisanen L, Hurley S: The infrastructure efficiency of cellular wireless networks. Comput. Netw. 2005, 48(6):941-959. 10.1016/j.comnet.2004.11.014View ArticleGoogle Scholar
- Holma H, Toskala A: WCDMA for UMTS. Wiley, New York; 2000.Google Scholar
- Jiang H, Davis CH: Cell-coverage estimation based on duration outage criterion for CDMA cellular systems. IEEE Trans. VT 2003, 52: 4.Google Scholar
- Lucent Inc: System for Determining Wireless Coverage Using Location Information for a Wireless Unit. US Patent US6,522,888. B1, 18 Feb 2003Google Scholar
- Abhayawardhana VS, Wassel IJ, Crosby D, Sellars MP, Brown MG: Comparison of empirical propagation path loss models for fixed wireless access systems. In IEEE 61st Vehicular Technology Conference, VTC 2005-Spring. IEEE, Piscataway; 2005.Google Scholar
- Yoshida H, Ito S, Kawaguchi N: Evaluation for pre-acquisition methods for position estimation system using wireless LAN. In Proceedings of the Third International Conference on Mobile Computing and Ubiquitous Networking (ICMU 2006). IEEE, Piscataway, London, UK; 2006:148-155.Google Scholar
- Maimon O, Rokach L: Data Mining and Knowledge Discovery Handbook. (Springer, New York,, 2005);Google Scholar
- Johnson RA, Wichern DW: Applied Multivariate Statistical Analysis. 4th edition. Prentice Hall, Upper Saddle River; 1998.MATHGoogle Scholar
- Knorr EM, Ng RT: A unified notion of outliers: properties and computation, in Proceedings of KDD 97. MIT Press/AAAI Press, Cambridge; 1997:219-222.Google Scholar
- Kaufman L, Rousseauw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York; 1990.View ArticleGoogle Scholar
- Guyon I, Matic N, Vapnik V: Discovering Information Patterns and Data Cleaning. In Advances in Knowledge Discovery and Data Mining. Edited by: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurasamy R. MIT Press/AAAI Press, Cambridge; 1996:181-203.Google Scholar
- Date CJ: An Introduction to Database Systems. Addison-Wesley, Reading; 1990.MATHGoogle Scholar
- Lee ML, Lu H, Ling TW, Ko YT, Lee ML, Lu H, Ling TW, Ko YT: Cleansing data for mining and warehousing, In Database and Expert Systems Applications LNIC. Volume 1677. Springer, Berlin Heidelberg; 1999. pp. 751-760Google Scholar
- Welch G, Bishop G: An Introduction to the Kalman Filter. University of North Carolina Press, Chapel Hill; 2006.Google Scholar
- Pollin S, Adams B, Bahai A: Spatial reuse for practical scenarios: iterative power adjustment from distributed contour estimation and propagation. In The International Conference of Communications (ICC). IEEE, Piscataway; 2008.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.