Antenna Tilt Load Balancing in Self-Organizing Networks

Antenna Tilt Load Balancing in Self-Organizing Networks Vlad-Ioan Bratu1, Claes Beckman1 1

1

1

KTH Center for Wireless Systems, Wireless@KTH , Royal Institute of Technology , Sweden ; 1 1 [email protected] ; [email protected]

ABSTRACT Base station antenna tilt is a powerful tuning parameter in traditional cellular network optimization. With the introduction of Self-Organizing Networks (SON), this parameter may now be used also in the context of self-optimization. One envisioned scenario is load balancing (LB), where the coverage shaping properties of the antenna radiation pattern can be used to control the cell borders. In this paper, a generalized framework for antenna tilt LB is presented and discussed. In order to assess the performance of antenna tilt as a tool for load balancing, simulations are performed to determine the effect of tilt angle, vertical beamwidth and handover offset. The results show that there is a direct relation between these parameters and the number of users that can be shifted towards neighboring cells. In conclusion, it is found that using antennas with narrow vertical beamwidths together with small hand over offsets is an efficient way of performing load balancing but results also in high SIR variations.

CONCLUSION In this paper we discussed the process of antenna tilt load balancing and analyzed the impact of vertical beamwidth and handover offsets. A general framework on which antenna tilt LB can be introduced in mobile networks was presented, based on a centralized SON architecture. This approach does not require additional functionalities at the base station and it is based on standard performance reports, e.g. Call Blocking Rates (CBR), Call Dropping Rates (CDR). The definition of “cell load” and “resource” can be adapted depending on Radio Access Technology (RAT) to derive specific solutions. The efficiency of antenna tilt as a tool for LB is closely related to the vertical beamwidth of the antenna and also to the HO offset values between the congested cell and the neighbors. Narrow vertical beamwidths together with small HO offsets showed the best results in respect to the considered metric. On the other hand, LB using antennas with narrower vertical beamwidths also resulted in higher SIR variations.

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Vlad-Ioan Bratu, Claes Beckman, “Antenna Tilt Load Balancing in Self-Organizing Networks,” International Journal of Research in Wireless Systems (IJRWS), Vol. 2, No. 1, pp. 21-26, March, 2013 ISSN: 2320 - 3617

International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 1, March (2013)

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ANTENNA TILT LOAD BALANCING IN SELF-ORGANIZING NETWORKS

I. INTRODUCTION Self-Organizing Networks (SON) are attracting increased attention as a solution to replace some of the tasks performed today by mobile operators as part of their daily network operations. The introduction of SON, which may control part of network configuration, optimization or planning, is expected to result in reduced operational costs, improved network performance and better resource utilization. The first high level requirements for SON in mobile networks were proposed by the Next Generation Mobile Network Alliance (NGMN) (1). They were later followed by a list of use cases, some of which were standardized by 3GPP (2). Also a detailed description of relevant SON use cases was provided by the SOCRATES project (3). One of the proposed SON use cases is load balancing (LB). LB addresses the fundamental challenge that base station resources are fixed, while traffic conditions change over time and space. In particular, it targets scenarios where users have a non-uniform spatial traffic distribution and are concentrated in hotspots. This leads to situations in which resources are either under or over utilized at different times and different locations. The aim of LB is to adapt to traffic conditions by adjusting base station parameters in such a way that an equal load distribution is achieved between serving cells. This requires users to be shifted from a congested cell to less loaded neighboring cells. The possible gains of LB could reflect in a higher Grade of Service (GoS) and improved area resource utilization. Currently, handover (HO) offsets are mainly considered for controlling the LB process (4-7). In general, decreasing or increasing the offset values will reduce or expand the virtual cell size. An alternative control parameter is the antenna tilt angle, which has a direct impact on coverage shaping and thus can be used to adjust cell borders. With the use of Remote Electrical Tilt (RET) (8) systems, antenna tilt can be integrated in a SON solution. Load balancing using base station antenna tilt and pilot power was addressed in (9,10). In (11), automatic tilt adjustments are discussed as a solution for adapting to nonuniform geographical traffic distributions. The results show average GoS increases between 20 and 30 per cent, with higher gains being observed when the traffic was unevenly distributed between cells. However, as discussed in (12), besides the effects on GoS, antenna tilt load balancing will have an effect on Quality of Service (QoS) metrics such as user bit rate and cell throughput. The ambition of this paper is to assess the performance of antenna tilt as a tool for load balancing, by considering the effect of several factors such as different vertical beamwidths

or HO offset settings and to introduce a framework for antenna tilt load balancing, based on a centralized SON architecture The concept of load balancing using antenna tilt will be presented in the next section, followed by a high level framework for an antenna tilt LB solution, which includes a cell selection process and a discussion regarding antenna tilt adjustments. The remainder of the paper is dedicated to the performance evaluation of antenna tilt for LB, with the simulation model being introduced in Section IV and the results discussed in Section V. The paper is concluded in section VI.

II. LOAD BALANCING USING ANTENNA TILT The mechanism behind antenna tilt load balancing relies on the fact that tilt adjustments can directly modify the coverage area of a cell. Therefore, users can be shifted to or from a neighboring cell by either down or up-tilting (Fig. 1). Measurements also have shown that larger down-tilt angles will increase the received power closer to the base station, while smaller down-tilt angles will increase the power at larger distances (13). Fig. 1. Illustration of load balancing. By down-tilting the antenna at Cell A while up-tilting the antenna at Cell B, the coverage areas for respective cells are changed and the traffic shifted

During the process of load balancing, a user can be successfully shifted to a neighboring cell, when the received signal power from the neighboring cell (Rn) plus the variation caused by tilt adjustments (LBvar) becomes stronger than the received signal power from the serving cell (Rs) at a tilt adjustment step (n).
௡

ሺ௡ିଵሻ

+ ௩௔௥ >
௦ (௡)

ሺ௡ሻ

However, a HO offset is usually added to ensure a minimum difference between the received signals from the serving and neighboring cell, before triggering a handover. Considering an offset value (HOoff), measured in dB, the condition for a user to be shifted as a result of load balancing becomes:

International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 1, March (2013)

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ANTENNA TILT LOAD BALANCING IN SELF-ORGANIZING NETWORKS


௡

ሺ௡ିଵሻ

+ ௩௔௥ >
௦ (௡)

ሺ௡ሻ

+ ௢௙௙

In this case, the value of the LB variation should be directly proportional to the HO offset. The impact of tilt changes and thus the LB variation also depends on the vertical beamwidth of the antenna. Furthermore, the user distribution relative to the cells involved in the LB process plays an important role. Taking these factors in account, the LB efficiency can be expressed as a function of vertical beamwidth (HPBWv), HO offset (HOoff), and user location (d):

architecture does not require additional functionality at the base stations. Possible delays, introduced by using a centralized approach should be taken in consideration when performing tilt adjustments. However, even with a distributed architecture, these delays will be present to some extend since the impact of tilt changes will not be reflected instantly in the considered measurements. Fig. 2. Centralized SON Architecture for antenna tilt LB

௅஻ = (  ௩ , ௢௙௙ , )

III. FRAMEWORK FOR ANTENNA TILT LOAD BALANCING The high level requirements for a complete LB solution should include automatic and continuous data gathering, cell identification procedures, the ability to automatically send commands to the RET controllers and to evaluate the impact of tilt changes. Two input sources can be considered for the LB process. One is represented by relevant performance indicators, which are collected from the network. The other should be in the form of site configuration data e.g. antenna vertical beamwidths and azimuth orientation, existing down-tilt angles and maximum adjustment range or HO parameter settings. The first type of data can be typically obtained from network operations monitoring systems and integrated with the SON solution. Site configuration data can be maintained in a centralized database and updated according to the actions of the LB process. A. SON Architecture There are three different architectures in which a SON solution can be deployed based on the network element where the SON process resides. These can either be centralized, distributed or hybrid. The choice of architecture depends on the SON functionality that will be implemented. A centralized architecture can be used for processes that require global knowledge of the network. However, delays will be introduced by forwarding and processing all data at a central node. Also, the centralized approach creates a single point of failure in the network and thus redundancy must be considered. On the other hand a distributed architecture can be more responsive, but it loses the ability to make decisions based on an overall network status. Antenna tilt LB can be implemented in a closed loop centralized architecture (Fig. 2). A global view of the networks performance and configuration is required in order to select congested cells and neighbors, coordinate tilt changes and evaluate their impact. Furthermore, the centralized

B. Cell Identification Cell identification has typically two steps. First cells that experience sub-optimal performance, e.g. high call blocking rates (CBR) or high call dropping rates (CDR), should be identified based on network measurements. These cells can be split in two categories, depending if congestion is an isolated event or has a repeating pattern. Besides selecting cells with high CBR or CDR, they key challenge is to determine if the values are correlated with a high traffic demand. For this, the number of connection attempts in a given time period and the cell load can be considered. Using load alone might not provide accurate results since even with a relatively low number of users the load indication can be high, for some scheduling methods. After identifying a congested cell, the second step is to identify neighboring cells to be included in LB. A first selection can be made by using existing neighbor lists. Site location and antenna azimuth orientation relative to the congest cell are used to determine if the neighbor cell is spatial located in such a way that its coverage area can be expanded towards the congested cell. Finally, load or resource availability is used to determine if the cell can support additional users. C. Antenna Tilt Adjustment Process There are several factors related to the number and granularity of tilt adjustments that need to be performed. The impact of tilt changes increases when using antennas with narrow vertical bewamidths. Therefore, low granularities can o be used, e.g. 1 increments. On the other hand, with wider vertical beawidths, a larger number of tilt changes might be required to shift users to neighboring cells. In this case, adjustments with higher granularities could help to reduce the LB time.

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A tradeoff needs to be made when multiple neighbors are selected as candidates for offloading part of the traffic. In this case, the up-tilt adjustments can be done either individually for each neighbor, or simultaneously, by up-tilting all neighbor cells at the same time. Individual up-tilting will increase accuracy by verifying a larger number of tilt configurations. On the other hand, simultaneous up-tilting will reduce the LB time at the cost of increasing the probability of missing a valid configuration. This probability will increase even more with a higher number of neighbors. Another important aspect is the time interval between consecutive tilt changes. Two methods can be used, based on instant network conditions or on longer term averages of network performance. When considering only instant network conditions, the tilt adjustments can be made during a small timeframe. A delay between consecutive changes is needed so that considered measurements reflect the current tilt settings. The minimum delay value will depend on the reporting period of the base station and the time when the tilt change was performed, as seen in Fig.3.

The maximum gain in the horizontal plane was adjusted to the vertical beamwidth using the relation: ௠௔௫ () = 10 ∗ log 

32400    ௩ ∗   ௛

where HPBWv and HPBWh are the vertical and horizontal Half Power Beamwidths of the antenna. SIMULATION PARAMETERS No. of cells Site-to Site Distance

21 800 m

BS Height

30 m

Mobile Height

1.5m

No. of users

50

Pathloss

L=120.9+37.6log(R)

Tx Power

43 dBm

Antenna Horizontal HPBW

65o

Antenna Vertical HPBW

Variable 4o-10o

Electrical Tilt

Variable 0o-10o

Tilt step size

1o

Fig. 3. Minimum time delay between consecutive tilt changes

Long term averaging will help identify cells that constantly experience sub-optimal performance. In this case, the time delay between tilt changes should be correlated to the traffic variation period. This is due to the fact that any change should be evaluated under similar network conditions, i.e. traffic load. Usually traffic in a mobile network varies with a period of 24 hours and within the same geographical region (14). In this case, a tilt change can be made every 24 hours.

The users were generated randomly in the coverage area of the target cell (Fig. 4). The process was repeated 50 times, resulting in 50 different user distributions. Also, the user positions changed randomly between consecutive trials, with a maximum distance (d) of their initial location. The movement can be made towards neighboring cells or towards the center of the serving cell. Fig. 4. Cell layout and user distribution

IV. SIMULATION MODEL The simulation model used in this paper considers the variations in received signal power caused by tilt adjustments. The model consists of 21 cells, arranged in a hexagonal pattern, with distance dependent pathloss and random uniform distributed users. The main simulation parameters are given in Table 1. The effect of down-tilting or up-tilting was simulated using the antenna model proposed in (15), where the overall antenna gain is given by: ,  =  + ()

For each user distribution five steps of down-tilting for the target cell and three steps of simultaneous up-tilting for the neighbors were performed, starting from the initial down-tilt o angle of 5 . These values are chosen, in order to have a realistic electrical tilt adjustment range, which is typically o o between 0 -10 . The changes were made iteratively, with a down-tilt adjustment being followed by an up-tilt adjustment. Finally, the results were averaged over all trials.

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To capture the effect of tilt changes on signal to interference (SIR) ratio, the downlink inter-cell interference is considered. The SIR for user j, which is connected to cell i, is defined as: ௜௝ ௧,௜ ∗ ௜௝ 
௝ = ௞௝ ∑ଶଵ ∗ ௞ஷ௜ ௧,௞  ௞௝

Fig. 5. LB efficiency as a function of vertical HPBW

where Pt is the transmitted power, G is the antenna gain between the user and the respective cell, and L is the distance dependant pathloss. While this type of interference modeling gives a pessimistic SIR estimation, it will still give an indication on the SIR variations caused by tilt changes, in this scenario.

V. RESULTS AND DISCUSSION The simulations show the different outcomes of the tilt adjustment process in respect to the antenna vertical beamwidths and HO offset values. The results were evaluated in terms of LB efficiency, where the efficiency is defined as the percentage of users that were shifted to neighboring cell as a result of antenna tilt adjustments (NLB) to the initial number of users in the target cell (Ninit). ௅஻ =

Fig.6. LB efficiency as a function of HO offset for different vertical HPBW

௅஻ ∗ 100 ௜௡௜௧

Figure 5 shows the variation in LB efficiency as a function of the antenna vertical HPBW. It can be seen that a narrower beamwidth will produce a higher LB variation and thus allow more users to be shifted towards neighboring cells. On the other hand, wider beamwidths will create less variation in the received signal power and thus the LB efficiency becomes lower. The considered metric was influenced also by the maximum distance (d) that users were allowed to move between consecutive trials. In Fig. 6 we can see the variation in LB efficiency as a function of HO offset values, for different vertical beamwidths and with a maximum variation in user location between trials of 50m. As mentioned in Section II, the required LB variation should be proportional to the HO offset values. Therefore, antennas with narrower vertical beamwidth will still achieve a certain level of efficiency, even for large HO offset values. An important aspect when performing load balancing is the SIR variation caused by tilt changes. Fig. 7 reveals the maximum variation, within the tilt adjustment range considered in this scenario. It can be seen that the average SIR values will decrease. Higher variations were observed for antennas with narrow vertical beamwidths in combination with large offset values.

Fig. 7. Average SIR variation as a result of antenna tilt changes

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VI. CONCLUSION In this paper we discussed the process of antenna tilt load balancing and analyzed the impact of vertical beamwidth and handover offsets. A general framework on which antenna tilt LB can be introduced in mobile networks was presented, based on a centralized SON architecture. This approach does not require additional functionalities at the base station and it is based on standard performance reports, e.g. Call Blocking Block Rates (CBR), Call Dropping Rates (CDR).. The definition of “cell load” and “resource” can be adapted depending on Radio Access Technology (RAT) to derive specific solutions. The efficiency of antenna tilt as a tool for LB is closely related to the vertical beamwidth of the antenna and also to the HO offset values between the congested cell and the neighbors. Narrow vertical beamwidths together with small HO offsets showed the best results in respect to the considered metric. On the other hand, LB using antennas with narrower vertical beamwidthss also resulted in higher SIR variations.

ACKNOWLEDGMENT The work was supported by Reverb Networks and by a Wireless@KTH Seed Project Grant entitled: “Performance Evaluation of Antenna Based SON”.

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Vlad-Ioan Ioan Bratu received a MSc in ”Communication Systems” from KTH Royal Institute of Technology, Sweden in 2012 and a B.Sc. from University Politehnica of Bucharest in 2009 in the field of Electronics and Telecommunications Engineering. Following his MSc studies he worked wo as a Research Engineer at the Center for Wireless Systems, Wireless@KTH on the topic of antenna based Self-Organizing Self Networks. Claes Beckman is the Founding Director of the Center for wireless systems, Wireless@KTH at the Royal Institute of o Technology (KTH) in Stockholm Sweden. Between 2004 and 20010 he was a Microwave and Antenna Engineering professor at the University of Gävle and also the Founding director of the Center for RF Measurement Techniques. He has spent 5 years (between 1983 and 1988) with Ericsson working as a RF design engineer, and 6 years (between 1994 and 2000) with the Swedish RF-sub-supplier RF Allgon where he held various leading research and management positions. Claes Beckman received his M.Sc (civilingenjörsexamen) from Chalmers almers University of Technology in Göteborg, Sweden in 1988 and a PhD in Applied Electron Physics from the same institute in 1994. In 1990 he was a visiting research officer at the University of Auckland, New Zealand and in 1994 he was a Post Doctoral fellow low at the university of Waterloo in Ontario, Canada. Dr. Beckman is a member of IEEE and has published more than 100 journal articles and conference reports in the areas of wireless systems, microwave engineering, antennas and optics.

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