APPLICATION NOTE. XCellAir s Wi-Fi Radio Resource Optimization Solution. Features, Test Results & Methodology

Transcription

1 APPLICATION NOTE XCellAir s Wi-Fi Radio Resource Optimization Solution Features, Test Results & Methodology

2 Introduction Multi Service Operators (MSOs) and Internet service providers have been aggressively expanding their Wi-Fi networks over the last couple of years. Much of the growth has been driven by a desire to provide a variety of value-adding services over an extensive network with a high-quality user experience. The rise of community Wi-Fi networks in different parts of the world has also contributed to this growth. As Wi-Fi networks expand and densify, radio resource optimization (RRO) becomes critical to delivering a high quality of experience. In a dense Wi-Fi network, the large number of devices and access points, and a high level of user activity, can combine to cause heavy contention, resulting in congestion. This is true even in a really well-behaved Wi-Fi cluster, where devices and access points can all see one another, and defer to each other gracefully when contentions occur. Excessive medium contention results in long wait times for packet transmission opportunities, high latencies and low throughputs. And in a not-so-perfect environment, where hidden nodes might exist and not every device is able to see everyone else, collisions and interference can occur, accompanied by high packet error rates, retransmissions and related degradation in quality of experience (QoE). XCellAir s cloud-based management and optimization solution suite, XCellRAN, includes powerful radio resource optimization tools to dynamically provision and tune radio resources. XCellRAN s RRO algorithms run on a cloud server, and interact with the AP to set and change channels, power levels and other parameters. Our RRO scheme works proactively to avoid congestion and interference, and also reacts to mitigate sudden interference scenarios. Additionally, sufficient hysteresis is built in to prevent ping-pong effects from frequent resource adjustments. The end results are the optimal usage of available Wi-Fi capacity, dramatically improved latency, jitter, and throughput performance, and a significantly enhanced quality of experience. The XCellRAN solution provides multi-vendor support, and works across multiple markets and deployment scenarios (e.g. residential Wi-Fi, enterprise, hotspot and metro areas). The XCellRAN RRO scheme provides multi-tier optimization covering: Boot-up when the AP powers up, XCellRAN allocates the operating channel and power level based on its knowledge of neighboring APs, the resources being used by them including among others proximity levels, and signal strengths from the APs. Periodic environment scanning XCellRAN reallocates resources and adjusts parameters as needed, based on changing environmental conditions and observed QoE degradation. Sudden interference the algorithm reacts to sudden interference scenarios, and takes corrective action to mitigate the problem. Long-term optimization to facilitate balanced channel usage and optimal radio resource allocation to maximize QoE. Load-balancing and band-steering to mitigate situations when an AP is supporting too many clients. XCellRAN s RRO takes into account not only the access points being managed by the service provider, but also unmanaged APs belonging to individuals, enterprises and hotspots not supported by the service provider. This is critical to running a Wi-Fi network optimally in dynamic and uncoordinated environments, where multiple systems share and overlap frequencies in close proximity. The solution leverages a variety of operational metrics, including: Neighbor AP information Client QoE statistics packet losses, error rates, latencies etc. Channel utilization information. Wi-Fi Radio Resource Optimization Solution 1

3 Environmental Observations At XCellAir, we conducted a study to characterize channel usage in a real-life Wi-Fi environment. The observations were done at XCellAir s Montreal office, which is located in a busy urban environment with multistoried office buildings and dense Wi-Fi deployment. At any given time, anywhere from 1 to 25 APs are visible in the immediate vicinity of the XCellAir office. The objective of the study was to observe Wi-Fi channel availability, i.e. to see how much bandwidth headroom was available at a given time in a typically busy deployment scenario. The study gathered per-channel utilization levels over several periods of time. Channel occupancy is an important consideration, given that dynamic channel allocation is a key feature within the XCellRAN RRO toolkit. This was done for both 2.4 and 5GHz bands. Figure 1: Per-Channel Utilization Levels in 2.4GHz Band Figure 1 shows the channel utilization pattern observed for the 2.4GHz channel over a period of several hours. On the x-axis are the 2.4GHz channels 1 to 11. Channel utilization levels (.9 or 9%) are shown on the y-axis. Each vertical spike on the graph reflects utilization data for the given channel over a 15-minute time period. The horizontal trend lines running through the graph indicate the minimum (red line), average (blue) and maximum (yellow) occupancy levels per channel. Some interesting conclusions can be made from Figure 1: Spikes in channel occupancy are visible on several channels at different points in time. These reflect inflection points at which contention and congestion levels increase, and service quality starts to degrade, e.g. high packet error rates, high latencies and jitter, low throughputs etc. However, based on average utilization levels, there is bandwidth headroom available at any given time more on some channels than others. Not all channels experience high occupancy at the same time. Wi-Fi Radio Resource Optimization Solution 2

4 Considering an allocation pool of even five of the channels (e.g. channels 1, 4, 6, 8 and 11), approximately two channels worth of aggregated bandwidth is available on average. The XCellRAN RRO algorithm is able to use the middle channels (e.g. 4 and 8), but ensures that neighboring APs are not allocated adjacent channels. A dynamic channel allocation algorithm can unlock this bandwidth by moving access points from heavily loaded channels to less occupied ones. Figure 2: Per-Channel Occupancy in 5GHz Band Figure 2 shows a similar channel occupancy view observed for the 5GHz band. In general, this band is less loaded today than the 2.4GHz band. XCellRAN s band steering feature (i.e. the ability to move dual-band capable devices on a tightly loaded AP from the 2.4GHz channel to a less loaded 5GHz channel) is poised to take advantage of such a loading imbalance. A separate study conducted by XCellAir looked at access point products deployed in the Montreal downtown neighborhood, and observed channel change behavior exhibited by the APs. The conclusion was interesting less than 8% of the surveyed APs changed channels, once they powered up. Without RRO capabilities, APs stay rooted to a channel regardless of how high the congestion is, and are unable to leverage headroom available Wi-Fi Radio Resource Optimization Solution 3

5 on other channels. RRO Benchmarking & Impact on Quality of Experience At XCellAir, we have conducted extensive lab tests to quantify the impacts of a poor radio environment on end-toend system performance and QoE; and conversely, the impact of the XCellRAN RRO algorithms towards improving QoE. These tests were also used to benchmark the RRO algorithms and define appropriate RRO parameter settings. Described in this section is an example of some of the tests we have run and related results. 2 managed APs operating in 2.4GHz where 1 managed AP operating in controllable unmanaged AP operating in controllable different level of traffic- (64kbps, 128 kbps, etc... on managed APs) 2 controllable clients increasing traffic on unmanaged APs Increasing traffic Increasing traffic Figure 3: Example Lab Test Setup Test Setup & Methodology The test setup outlined in Figure 3 was used to create channel congestion and observe impacts on key operational metrics, e.g. latency, jitter, throughput, etc. A managed AP (AP-1) was used as an observed target, with multiple client devices connected to it. A mix of VoWiFi and video traffic was run through the target AP. Traffic was generated, and end-to-end metrics such as jitter, latency and throughput for VoWiFi and video traffic were measured using the IxChariot tool. A separate AP (AP-2) was set up on the same channel and was loaded with multiple clients sending iperf and Youtube data. The objective was to load the channel to a high utilization level, observe the impacts of rising congestion on service quality on AP-1, trigger the RRO algorithm n to effect a channel change, and measure the consequent improvement in the end-to-end metrics and QoE. Wi-Fi Radio Resource Optimization Solution 4

6 Test Results Channel % Utilization Target AP - Ubiquiti Unify Congestion builds up to threshold, RRO moves AP to change channel Channel Usage Own Channel Usage Channel utilization is lower on new, cleaner channel Figure 4: Congestion Buildup and Channel Switch Figure 4 depicts how the utilization level on the channel ramps up as more traffic is loaded on the channel via AP-2 (the time periods in these charts are 15-minute intervals). The XCellRAN RRO algorithm can be triggered by a variety of conditions (e.g. QoE degradation, interference, high channel utilization etc.). In this scenario, the trigger is channel utilization crossing a defined threshold. The target AP(AP-1) switches to a different channel (triggered by the RRO) with a lower channel occupancy level the chart depicts this beyond time period 8. 9 Jitter on Target AP on VoWifi Call Jitter (ms) >3X drop in Jitter after RRO takes effect Figure 5: Impact on VoWifi Call Jitter Wi-Fi Radio Resource Optimization Solution 5

7 Figure 5 and Figure 6 illustrate the impact of channel congestion on the latency and jitter related to the VoWiFi call in progress through AP-1. Latency increases by an order of magnitude as congestion builds up, and drops by a 5x factor after the channel switch. Jitter improves by a factor of 3x after the RRO action. One-Way Latency for VoWifi Call X improvement in latency after RRO takes effect One-Way Latency Figure 6: Impact on VoWiFi Call Latency Figure 7 shows the corresponding impact on the Mean Opinion Score (MOS) for the VoWiFi call running through the target AP (AP-1). The MOS drops to borderline levels during the bad period and settles back up to a good level (MOS = 4) after the channel switch. The accompanying increase in jitter and latency also affect interactivity for the voice call, and can introduce issues such as echo. Finally, Figure 8 indicates the dramatic drop in instantaneous throughput for the video call through AP-1 during the congested phase MOS (Mean Opinion Score) for VoWiFi Call MOS Voice MOS goes back up from ~3 (Annoying/ Unacceptable) to 4 (Good) Figure 7: Impact on MOS score for VoWiFi Call Wi-Fi Radio Resource Optimization Solution 6

8 Instantaneous Throughput (IPTV session) Throughput Throughput drops to nearzero level for IPTV during congestion conditions Figure 8: Impact on Throughput for Video Session Conclusions Radio resource optimization is a critical necessity for optimal operation of a large Wi-Fi network. With a view to offering value-adding services over Wi-Fi infrastructure, MSOs and Internet providers are rolling out large and dense networks. However, high density and vigorous usage levels create congestion and interference in Wi-Fi networks issues that can significantly degrade the quality of the services the provider offers. Radio resource optimization therefore becomes a critical cog for mitigating congestion and improving QoE. XCellAir s XCellRAN solution includes leading-edge RRO tools that provide dramatic improvements in key system metrics (e.g. latency, jitter, throughput, and so on) and ensure optimal Wi-Fi network operation. Additionally, even in dense network deployments, there is generally spare headroom that can be leveraged by a smart RRO algorithm. XCellAir s studies have shown that there is spare channel bandwidth in the system largely unutilized because most existing systems are unable to dynamically reallocate resources. An intelligent RRO scheme unlocks this free bandwidth for use by APs experiencing congestion or interference. Phone: sales@xcellair.com 654 Lusk Blvd, San Diego, CA 92121, Suite 21 Copyright 215 XCellAir. All Rights Reserved. AN_3_2151