1. The Field of the Invention
Embodiments of the invention relate to wireless communications networks and related systems and devices. More particularly, embodiments of the invention relate to systems and methods for characterizing network performance by collecting data from multiple network devices that are associated with multiple locations of a network.
2. The Relevant Technology
As a result of advances in technology and corresponding increases in the number of wireless device uses, the size and complexity of wireless communications networks has greatly increased. From the perspective of the provider of the wireless network, the operation and maintenance of a wireless network presents several general concerns. One concern, for example, relates to the deployment of the wireless network and another concern relates to the optimization of the wireless network.
The processes of deploying and optimizing a wireless network obviously have an impact on the resulting performance of the wireless network. As a result, these processes are given considerable thought. Deploying wireless networks, for example, involves identifying the best possible locations for towers. Even though optimal tower locations might be identified, this does not suggest that these locations are available. In fact, tower locations can be severely constrained by local municipalities as well as other real world restrictions (environmental concerns, existing structures, etc.). As a result, the actual tower locations may not be optimally placed.
The process of deploying and optimizing cellular networks further involves configuring the antennae of the towers with regard to direction and power to provide the right amount of capacity for a given region. The performance or optimization of a wireless network is also dependent on other parameters such as neighbor lists, add and drop timers, CDMA parameters, and the like. Typically, these parameters are set during deployment using available information and tools.
Even though all available data is used to deploy and optimize a wireless network, actual use of the network inevitably reveals various gaps in the network's performance. In other words, differences between the assumptions made in deploying and optimizing the network and real world conditions emerge. These differences can manifest themselves as drop call metrics, increased calls to customer service, and the like.
Once a problem in a wireless network is confirmed or identified, the provider often needs to reproduce the problem in sufficient detail so as to analyze and resolve the problem. Conventional methods for deploying and/or optimizing wireless networks often includes the use of mobile probes and network simulators.
Mobile probes include, by way of example, truck based call probes, truck based RF survey probes, and autonomous probes. In truck based call probes, the truck is instrumented with multiple devices that place calls as the truck is driven to a previously identified area of interest. The performance of the calls can be collected and analyzed to diagnose the issue. In truck based RF survey probes, a radio scanner is placed in a truck. The radio scanner measures the radio performance on a frequent sampling basis and the resulting data is used to correlate the performance of network models with the real world response and to improve the design of wireless networks.
The primary drawback of truck based call probes and RF survey probes is that these probes are reactive tools. Because the issue in a given wireless network may not real or has been misidentified, the probe may be unable to detect the issue or may detect a separate unrelated issue. Detecting an unrelated issue can be detrimental to the optimization of the wireless network because it may create a false relationship between a described issue and the root cause. Further, truck probes do not reflect real customer usage or network load conditions. Truck probes, for example, are unable to go inside buildings and can only scan a limited area.
An autonomous probe uses instrumentation similar to the instrumentation used in truck based call probes and RF survey probes. In this case, the autonomous probes are typically deployed in vehicles (such as commercial vehicles) that move around a given area for various reasons. The data collected over time can be uploaded periodically and provides data over the regions covered by the vehicle without incurring the cost of a deployed vehicle. While autonomous probes are relatively inexpensive, they also do not represent real user traffic on a network.
Network simulators are tools that can calculate the performance of a network in great detail. Unfortunately, the calculations generated by network simulators often varies significantly from the real world because of inaccurate data sets. In other words, the network model is not what was actually deployed due to deployment or data entry error or due to model errors (the real world is different from a simulated environment). While RF survey probes can collect data to help tune and refine the models used by network simulators, this process is expensive and does not cover the entire radio environment being modeled.
Optimization tools use radio simulation data to optimize a radio deployment network around the simulation data. The obvious drawback to these optimization tools is that they are dependent on the accuracy of the network models, which is often suspect as described above. Further, optimization tools also depend on usage models. Predicting how and where people will be using their wireless devices is very difficult and the resulting usage models may have significant errors.
In sum, the ability to deploy and/or optimize radio networks suffers from multiple issues. Real world data that represents an entire network is difficult and expensive to collect with conventional probes. Network simulators and optimization tools are flawed or rely on data that does not accurately reflect the real world. Usage patterns and data sets used by these tools are not accurate. There is therefore a need for systems and methods for evaluating the performance of a network and for optimizing radio networks are needed.
USPTO Backgrounds
USPTO Backgrounds