Managed Wi-Fi OPtimization A sneak peak into the inner workings of XCellAir s Wi-Fi Radio Resource Management & Self-Organizing Network capabilities

Transcription

1 APPLICATION NOTE Managed Wi-Fi OPtimization A sneak peak into the inner workings of XCellAir s Wi-Fi Radio Resource Management & Self-Organizing Network capabilities

2 Introduction XCellAir s Wi-Fi Radio Resource Management (RRM) and Self-Organizing Network (SON) solution is designed to manage multiple uncoordinated networks that share and overlap frequencies in close proximity (e.g. apartment buildings and/or shopping malls). In general, existing Wi-Fi optimization solutions assume that all or most of the access points are controlled by the network. Given this controlled environment, it becomes relatively straightforward to design a RMM and SON approach that optimizes both throughput and coverage. Unfortunately, this assumption is invalid in many public, indoor and outdoor scenarios, and typical RRM and SON algorithms fail in dynamic and uncontrolled environments. The XCellAir approach to optimizing Wi-Fi deployment starts from the assumption of a public and diverse environment with a mix of managed and unmanaged access points. This paper describes how the system effectively mitigates interference through a combination of centralized and distributed capabilities to deliver improved coverage and throughput. Radio and Location Environment Map In an uncoordinated environment with a mix of managed and unmanaged access points, it is important to characterize both self-generated and external interference accurately. This is accomplished by creating a radio interference and location map of the geographical area that is to be managed. The radio and location environment map database or, radio map for short, is the foundation of the XCellAir RRM and SON solution. It provides a robust and accurate tool to estimate the location of unmanaged access points and clients, external interference sources, hidden nodes, and the level of signal and interference that would be seen at any virtual location. A radio and location environment map of a specific geographical area is built by periodically collecting and averaging key metrics from both the Access Points and Clients. The location of unmanaged access points and mobile devices is determined using an innovative blind localization algorithm that automatically learns the propagation environment and does not simply rely on knowing in-building walls or obstacle information 1. The solution shines in indoor, multilevel, and dense environments. The localization and propagation modeling algorithms are designed to consider both Line Of Sight (LOS) and Non-Line Of Sight (NLOS) environments both of which are common in buildings with multiple floors and where signal attenuation through obstacles occurs. The location of the unmanaged access points is determined to within a few meters of the actual location. The radio and location environment map is periodically refreshed to consider changes in the environment, and locations of both managed and unmanaged access points. Figure 1: Graphical View of the Radio and Location Environment Map 1 The approach does not rely on information about walls or physical obstacles, but if available this can be provided as an input. Wi-Fi RRM and SON 1

3 The above figure contains a wealth of information about the RF environment and AP density. The physical space is divided into pixels and each of these pixels is populated with small squares that represents an access point that can be measured (i.e. heard) at this pixel. The size of the squares within each pixel represent the relative strength of the AP compared to the other APs. Figure 2: Displaying Coverage areas and gaps through the benefit of the Radio Map The radio map database is an extremely valuable source of information to the operator and can be used for example, as shown in Figure 2, to identify weak areas of coverage, or to localize unmanaged or rogue access points. In short, the automatic generation and on-going management of the radio and location environment map for cluster of APs under the management of XCellAir s system is the foundation for the dynamic RRM and SON capabilities. It enables optimal channel, band and power level selection and tuning. In addition, it can serve as a valuable operator aid for troubleshooting network and coverage issues. Automatic Provisioning Automatic provisioning of the operating parameters of new access points is accomplished by leveraging the radio and location environment map database. The radio map provides a more accurate prediction of the interference and selection of operating characteristics than typical legacy approaches since both known (managed) and unknown (unmanaged) information is utilized. Various metrics are used to determine the optimum operating parameters of the new managed AP including location relative to both managed and unmanaged APs, adjacent channel interference and frequency overlap (i.e. in the 2.4 GHz band), hidden node situations, number of access points using the various channels, and external interference. Each channel is suitably ranked based on these characteristics, and the optimum channel is chosen. In addition, once the channel is set in the AP, a further verification step utilizes metrics gathered and validated from the AP. Power and other parameters are also set optimally. Wi-Fi RRM and SON 2

4 AP2 Weak signal...ap2 behind obstruction AP3 New AP may share channel with a lot of AP2 stations New AP New AP may share channel with a few AP3 stations Weak signal...ap3 far away Figure 3: Illustration of key RRM and SON scenarios Periodic Optimization and Rebalancing In addition to choosing the optimal initial configuration, XCellAir s solution periodically tunes the radio map which, if necessary, triggers the re-tuning of the managed APs operational parameters. The retuning can also be event driven in situations where there is an excessive amount of external interference or when one or more managed access points fail. Performance and health metrics are continuously monitored such that any degradation can be proactively mitigated through resource reallocation, and/or fault recovery mechanisms. Clustering for Scalability A key consideration in designing any RRM and SON solution is scalability. As the network gets larger the algorithms need to scale appropriately and the increased complexity and processing power required needs to be linearly bounded and minimized. Typical centralized approaches tend to scale exponentially as the number of nodes in the network increase. XCellAir s system has been designed to be bounded linearly as the number of nodes in the network increase. The operator defines and creates geographical cluster areas. Typically each cluster bounds a major city or area. Network nodes within the cluster areas belong to the cluster and are managed by an independent set of algorithm processes and databases. Data associated within each cluster is maintained in a separate collection such that the algorithm processes need not be aware that other clusters exist. Network nodes or APs that straddle between clusters and can be heard but are not managed by the same cluster are considered unmanaged access points for RRM and SON purposes. Minimal information about these border access points is shared between the clusters to improve the accuracy of the metrics. This tradeoff allows the system to scale efficiently with the growth of the network and keep overall performance optimum. Wi-Fi RRM and SON 3

5 Figure 4: Cluster Map Distribution Example Compatibility with LTE Small Cells Finally, the Wi-Fi RRM and SON solution is only one part of our comprehensive suite of radio access network optimization solutions. The LTE RRM and SON small cell solution leverages the component design and feature set of the Wi-Fi solution yet is adapted to meet the unique demands of LTE small cell infrastructure. The radio environment and location map is shared between the Wi-Fi and LTE entities to ensure scalability as the network grows. A number of value added capabilities (e.g. load balancing, capacity management, QoS enforcement, analytics) across LTE small cells and Wi-Fi access points becomes relatively straightforward via a unified network management and optimization system. Wi-Fi RRM and SON 4

6 Conclusion XCellAir s approach to Wi-Fi optimization is conceived and implemented with a carrier grade and dense deployment in mind. With this as the baseline, the solution takes into consideration the scalability needs, dynamic radio environments and diverse potential deployment locations and aims to deliver RRM and SON for the most challenging situations. This allows customers who use XCellAir s solution to focus on deploying Wi-Fi services where they are needed most and provide the greatest potential return on investment (ROI). Phone: sales@xcellair.com 6540 Lusk Blvd, San Diego, CA 92121, Suite 210 Copyright 2015 XCellAir. All Rights Reserved. AN_002_201502