lte energy saving son using fingerprinting for identification of cells to be activated_06633528

Future Network & MobileSummit 2013 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds) Poster Paper

IIMC International Information Management Corporation, 2013 ISBN: 978-1-905824-37-3

Copyright © 2013 The authors www.FutureNetworkSummit.eu/2013 of 8

LTE Energy Saving SON Using Fingerprinting for Identification of Cells to

be Activated Elke ROTH-MANDUTZ, Andreas MITSCHELE-THIEL

Ilmenau University of Technology, Ilmenau, 98693, Germany Tel: +49 1577 2942423, Fax: +49 3677 69 4823, Email: [email protected]

Ilmenau University of Technology, Ilmenau, 98693, Germany Tel: +49 3677 69 2819, Fax: +49 3677 69 4823, Email: [email protected]

Abstract: Energy saving for future radio access networks is an increasingly significant factor for network operators not only for cost reduction but also for meeting the environmental challenges. The expected exponential rise in data traffic requires the additional deployment of a very large number of base stations (BS) mostly with small coverage areas to provide the needed capacity. This increase in the BS density causes also a sharp rise in energy consumption of cellular networks. However, a self-organized process can adapt the network capacity to the current traffic demand by activating and deactivating cells in line with the changing traffic profile. Cell deactivation is in most cases a straight forward process as a cell can easily determine when its traffic has significantly reduced. It is, however, not obvious to determine which cell to activate, when multiple cells are concurrently switched off. In this work we propose the use of the fingerprinting method solely based on UE RSRP measurements for identifying the best fitting cell to take over the upcoming traffic. In case the increasing traffic can no longer be served by the activated cells, the capacity of currently deactivated cells is needed. Current measurements of a UE demanding high data rates are matched with a number of cells deactivated at a given time. The best matching candidate to take over the upcoming traffic is selected and switched on to satisfy the growing traffic. Keywords: Energy saving, SON, fingerprinting, LTE, green communication, small cells

1. Introduction Environmental sustainability demands the implementation of efficient power reduction strategies in all industrial areas. This is especially true for mobile communication networks with an anticipated overall energy consumption rise of 40 % from 2010 to 2020 [1]. The expected exponential data traffic rise from 2010 to 2015 by a factor of 26 in mobile communication networks [2] demands the installation of a huge number of additional base stations with many of them being small cells. Base stations that consume around 80% of the energy of a typical cellular network such as LTE [3], offer a high energy saving potential. Due to the strongly rising energy prices caused by the shortage of non-renewable resources as well as the partial phasing out of nuclear power, energy saving is becoming a crucial factor in the highly competitive mobile network market. For LTE networks the fraction for energy costs of the overall operational expenditure (OPEX) is expected to increase to 30% [4]. Several approaches to run energy efficient and environmental friendly Base Stations (BS) are currently under discussion [5]. Renewable energy sources to run a BS, e.g. solar and wind energy [4] are under investigation as well as less power consuming components


[6]. A better option, applicable to already existing networks as well as to future networks is the intelligent adaptation of the traffic capacity to the current traffic demand. Cellular radio networks are setup for peak traffic hours in order to cope with the expected daily maximum data rates. However, data profiles of radio cells indicate significant periods with low and even no traffic for individual cells [7]. Common approaches of network capacity adaptation are discontinuous transmission on a per frame basis [8], [9] and the self-organized energy savings by cell deactivation and re-activation. Published literature related to the energy saving SON excludes so far details on intelligent approaches to identify cells required to be switched on. Initial studies that introduce the energy saving SON and its benefits are given in [7], [10], [11] and [12]. References [10] and [11] shortly mention several options for switching on cells, whereas [7] describes the use of cell based time schedules to switch on cells assuming regular low / no traffic time periods and [12] focuses on home eNodeB activation. In this paper we introduce an efficient concept for the energy saving SON use case [13], to control the activation and deactivation of cells in accordance with the traffic demand. The focus of this paper is on the challenge to activate the relevant cells in time to satisfy the upcoming traffic demand. We propose the use of the fingerprinting method as an intelligent and self-organized mechanism to identify the best fitting cell to be activated. In the context of this paper, fingerprinting is used to identify a cell, in contrast to the more common applications using fingerprinting for positioning as discussed in [15], [16] and [17]. A fingerprint characterizes the radio environment of a cell. Each cell fingerprint consists of all its neighbor cells and the associated range of measured signal strength values. In case any cell under high load condition detects multiple deactivated cells, the current UE measurements are matched against the deactivated cell fingerprints. The cell associated to the best matching fingerprint is activated assuming that this cell is the most appropriate cell to overtake the emerging traffic. The remaining sections of this paper are organized as follows: In section 2 we present the basic idea of the energy saving use case, followed by the fingerprinting principle in the context of cell identification in section 3. Section 4 and 5 provide details on the fingerprinting procedures and algorithms, specifically details on reference database generation in section 4 and the identification of the best fitting cell in section 5, finalized with a conclusion and outlook for future work in Section 6.

Figure 1. Cell deactivation and activation based on traffic demand


2. Energy Saving SON – Use Case The principle of energy saving SON by adjusting the network capacity to the current traffic load is given in Fig. 1. A small section of a heterogeneous network with one macrocell and several microcells including cell A, B, and C is assumed.

During peak hours all cells are activated, as indicated in Fig. 1(a). During off-peak hours cell A, B, and C are being deactivated (see Fig. 1(b)). All these 3 cells experienced either no or minor traffic. In case of minor traffic, active connections were handed over to the macrocell prior to being deactivated. The cell deactivation procedure is performed autonomously by each cell after a predefined period with no / low traffic. When the traffic volume increases, passing a predefined cell load threshold to activate cells, any operational neighbor cell, regardless of micro or macrocell, may initiate the activation procedure as shown in Fig. 1(c). The cell to be activated should be most appropriate to immediately take over the emerging traffic. I.e. the cell providing the best radio conditions to serve – preferably high data rate - UE(s) to take over the traffic from overloaded cells. For this it is necessary, to determine which of the 3 deactivated cells is most needed. Here we use the fingerprinting method to identify the best fitting cell to overtake the emerging traffic as detailed in section III and IV. In contrast to the deactivation procedure, the activation procedure cannot be performed autonomously by the cell to be activated, but depends on triggering by a neighbor cell via the X2 interface.

3. Fingerprinting Method for Cell Identification In the WLAN context, the fingerprinting method is a commonly used positioning method as discussed in [15], [16] and [17]. Different to positioning, we use fingerprinting for cell identification in the context of the energy saving SON. However, just as for fingerprinting in the positioning context, the cell identification requires a learning phase to generate the characteristic cell fingerprints. The operational phase starts as soon as stable fingerprints are available (Fig. 2). During the operational phase the cell fingerprints are continuously updated and the cells are deactivated and activated depending on the traffic conditions.

Figure 2. Cell fingerprinting phases


Figure 2. Cell fingerprint - data processing

Data processing for cell fingerprinting involves the user entity (UE), each activated cell and its neighbor cells as shown in Fig. 3. 1. Cell fingerprint generation & update

Each serving cell requests UEs measurements including neighbor cell identifier and the related signal strength. Using the received data, the cell a. Generates reference data – called cell fingerprint - of its cell and b. Performs periodically updates of the cell fingerprint while the cell is operational.

2. Deactivation procedure In case the criteria to deactivate a cell are met (i.e. low or no traffic for a defined time period), the cell to be deactivated transmits its fingerprint to all neighbor cells prior to deactivation.

3. Activation procedure Any active cell with deactivated neighbor cells runs a procedure supervising its cell load. In case a predefined load threshold indicating the need for additional network capacity is exceeded, this cell initiates the activation procedure. During the process, the cella. Retrieves and pre-processes UE measurements b. Calculates the matching of the UE measurements for all stored cell fingerprints, and c. Activates the cell(s) associated to the best matching fingerprint.

Table 2. UE measurements - extract

Neighbor Cell List

TCI RSRP

tci(0) rsrp(0)

tci(1) rsrp(1)

tci(2) rsrp(2)

. .

. .

tci(x) rsrp(x)

Table 1. Cell fingerprint

Cell Fingerprint TCI RSRP(min) RSRP(max) tci(0) minRsrp(0) maxRsrp(0) tci(1) minRsrp(1) maxRsrp(1) tci(2) minRsrp(2) maxRsrp(2)

. . .

. . . tci(x) minRsrp(x) maxRsrp(x)


4. Cell Fingerprint Reference Data Generation The fingerprinting method requires as a pre-requisite stable reference data represented by cell fingerprints. The cell fingerprints are generated during the learning phase and continuously updated in the operational phase (Fig. 2). To ensure up-to-date reference data, continuous updates are performed compensating any network modifications related to neighbor cell setting and signal strength, such as the installation and removal of BS, network re-planning and optimization. Both procedures, generation and update, are intended to work autonomously, without any manual intervention. The intension of the fingerprinting application in context of cell identification is to identify the deactivated cell, which serves best to take over the traffic from the overloaded cell. For this, UE measurements from the active UE(s) are matched against the cell fingerprints of the deactivated cells. The cell fingerprints are based on UE measurements taken while these cells were active. The UE measurements for fingerprint generation as well as for cell identification are part of the normal mobility procedures and are also used in the Automatic Neighbor Relation SON use case [14]. The cell fingerprint is derived from the UE measurements in the form of a neighbor cell list (TABLE I.) that identifies the cell neighbors as seen by a given UE. Each UE reports for each neighbor cell the Target Cell Identifier (TCI) and the corresponding Reference Signal Received Power (RSRP). The TCI provides the unique identification of the neighbor cell while the RSRP represents the received pilot signal strength of the associated neighbor cell. The cell fingerprint of a single cell (TABLE II) consists of the list of all neighbor cell TCIs retrieved from the multiple UE measurement samples spread all over the cell area. Per neighbor cell a data record is generated including the maximum and minimum RSRP received within the cell as shown in TABLE II. The minimum RSRP, RSRP(min) and the maximum RSRP, RSRP(max) respectively represent the lowest and highest RSRP received for the given TCI for a specific cell. Any RSRP value within the range of maximum and minimum neighbor cell (x) RSRP is regarded as a valid and possible RSRP value for neighbor cell (x) within the given cell. For the generation of the reference data, RSRP outliers are ignored. Outliers are single RSRP values outside the range of frequently retrieved RSRP values, which are assumed to be valid. Thus, higher robustness against fading signals and varying RSRP values is achieved.

Figure 4. Cell activation - example


5. Cell Activation During the cell activation, the cell with the best fitting cell fingerprint is activated (see Fig. 1(c)). Any macrocell or microcell that is a neighbor cell to the deactivated cell may initiate and perform the cell activation procedure. An example of the cell activation application is given in Fig. 4, which shows a network where cells A, B, and C are initially switched off. Due to the presence of a high-data-rate user at the edge of cell G, cell G experiences high load. As cell G exceeds the load threshold, it initiates the cell activation procedure. Cell G requests a UE measurement sample as input. Samples from high-data-rate demanding UEs are preferred, raising the probability of moving that UE to the cell to be activated. The procedures involved comprise the overall cell activation (Fig. 6), the cell matching (Fig. 7) and finally the cell selection procedure (Fig. 8). Two preconditions must be fulfilled to initiate the cell activation procedure (Fig. 6). First, the measured traffic in a cell must exceed a predefined threshold indicating the need for additional capacity. Second, there should be more than one neighbor cell that is

deactivated. In case only one neighbor cell is deactivated, this cell is activated immediately.

Cell Matching for each fingerprint { // TCI matching remove fingerprint-TCIs of all deactivated cells for each fingerprint-TCI in UE sample { TciMatchPoint ++ else // missing TCI TciMatchPoint -- } for each UE-TCI not in fingerprint // surplus UE-TCI TciMatchPoint --// RSRP matching if UE-RSRP in fingerprint-RSRP range { RsrpMatchPoint ++ else // not matching RSRP range RsrpMatchPoint – } }

Figure 7. Cell matching algorithm

Figure 3. UE sample matching with fingerprints - example

Cell Activation if (cell traffic load > cell load threshold) { if exactly one cell is inactive Activate this cell else { Retrieve UE measurement sample(s)

Match UE sample against all fingerprints Select and activate best fitting fingerprint(s)

} }

Figure 6. Cell activation procedure

Cell Selection for each fingerprint { TotalMatchPoint = TciMatch + RsrpMatch }Sort fingerprints by highest TotalMatchPoint Activate cell(s) with highest TotalMatchPoint

Figure 8. Cell selection procedure


Otherwise one or multiple measurement samples are retrieved from active UEs and matched with each fingerprint. The cell(s) associated to the best matching fingerprint(s) are selected and activated. For identification of the best cell, two procedures are involved: cell matching and cell selection. The UE measurement sample(s) as well as the fingerprints of the deactivated cells are the input for the cell matching procedure. The received UE measurements are matched with each cell fingerprint. The assessed fingerprint list is used by the cell selection procedure to determine the cell(s) to be activated.

Cell Matching (Fig. 7) For each cell fingerprint the matching with the received measurement sample is calculated to determine the matching accuracy. The accuracy is based on two major factors: 1. TCI matching

The TCI matching is evaluated based on the number of matching TCIs in the UE measurement sample with the TCIs listed in each fingerprint. TCIs are determined to be not matching, if either

a UE measurement TCI (UE-TCI) is not listed in the fingerprint or ifthere are additional TCIs in the UE measurement sample – not listed in the fingerprint, see Fig. 7 – TCI matching.

2. RSRP matching Per matching TCI the RSRP derived from the UE sample is checked against the RSRP range of the cell fingerprint, see Fig. 7 – RSRP matching.Matching RSRPs are scored in case the UE RSRP is within the RSRP range of the matching TCI of the fingerprint.

Each fingerprint is assessed regarding TCI (TciMatchPoint) and RSRP (RsrpMatchPoint) matching. The TCI and RSRP matching is illustrated in Fig. 5. Within the fingerprints the deactivated cells are ignored (crossed out in Fig. 5) as they are not measureable by the UE. The cells highlighted in green indicate the cells with matching TCI.

Cell Selection (Fig. 8) After cell matching, the cell selection is performed (Fig. 8). The cell fingerprints are sorted in ascending order by the resulting total match points. The cell with the highest number of match points is determined as the best fitting cell. Input parameters control how many cells to be activated at the same time. The selected cell(s) are triggered to start the cell activation. Depending on operator setting, the selected cell(s) may even be activated in case of low or negative total match points, in order to ensure the quality of service and performance for mobile users. Other SON procedures, such as load balancing, are expected to distribute the traffic load within all operational cells.

6. Conclusion and Outlook Cell deactivation during off-peak hours will get even more important in the near future with the installation of small cells in heterogeneous networks. Our approach to identify cells to be activated is an essential contribution to support this and to exploit the high potential for energy saving, whilst at the same time minimizing the risk of performance and quality degradation caused by deactivated cells in an operational network. Our previous work [15] on the positioning of GSM mobiles using the fingerprinting method, proven useful in field trials and measurement campaigns, indicates that the presented approach will also work for the identification of the best serving cell in networks with cell overlays. Commonly to fingerprinting in this work as well as in [15], [16] and [17] the fingerprints are characteristic radio patterns exclusively based on the radio signal strength of the surrounding cells and measured by the UE. Different to this work, the


fingerprinting approaches for WLAN [16], [17] and for GSM [15] intend to determine the UE position. In respect to energy saving, however, no explicit UE position is required, but cell level accuracy only, which makes us believe that the fingerprinting method as outlined before is going to provide highly reliable results. Furthermore, the entire process is considered a distributed energy management process since all procedures are performed at cell level. Consequently, the complexity and message exchange with the limited amount of data required for its operation keeps the required signaling cost at minimum to ensure an energy efficient approach. As next steps, we plan simulations of heterogeneous network scenarios in order to analyze the energy reduction and the impact on the network quality. Based on this, the impact of other SON procedures, such as mobility load balancing and capacity and coverage optimization on the energy saving SON use case will be investigated.

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lte energy saving son using fingerprinting for identification of cells to be activated_06633528

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