A-GPS Mobile Device Testing: “Stuck Between a Rock and a Hard Place” - Part 1
The online Dictionary.com defines the idiom “Between a Rock and a Hard Place” as being “between undesirable alternatives”. What does this have to do with Assisted GPS (A-GPS) testing? There are two prevailing approaches for testing A-GPS capability in mobile devices: (1) Conformance testing to the industry-defined certification standards and/or (2) Testing on live networks in the field – most commonly referred to as drive testing. While both approaches are useful and necessary, neither one provides a reliable method for predicting how devices will actually perform when they are used in the real world. As a result, anyone wanting to determine the true end-user experience of A-GPS-enabled mobile devices is figuratively stuck between a rock and a hard place.
We will be writing a series of posts on Spirent’s Mobile Blog exploring this problem. This first post of the series will dive into the challenge of determining real-world A-GPS performance and explore some potential solutions. Future posts will elaborate on the potential solutions, including discussions on theory, practical test scenarios and actual test results, before finally pulling together a view on what this all means. Please participate in the discussion by leaving comments and checking back every two weeks or so.
The rapidly growing location based services (LBS) application market, as well as emergency location mandates (such as E911 in the US), have resulted in a rapidly-expanding proportion of mobile devices that support A-GPS. A-GPS enables devices to obtain better accuracy and quicker fixes compared to other location technologies; A-GPS enabled devices also typically perform significantly better than their conventional GPS counterparts in challenging (yet very common) environments such as in a building, or within an urban setting.
Performance in the real world ultimately determines the way end-users perceive location based services (LBS); performance could also mean the difference between life and death in an emergency situation. For these reasons, it is important for device manufacturers and mobile operators to thoroughly test performance in a real-world setting.
Current industry-defined test methodology (typically referred to as Certification or Conformance testing) largely assumes conducted (i.e. directly connected) transmission of RF signals; in all cases, ideal GPS conditions (such as a clear sky, ideal satellite constellation positions, and low incidence of multipath) are assumed. Clearly, reliance on standardized test methods alone does not provide the necessary data to ensure the real world performance of mobile devices.
A Hard Place:
A typical alternative is field testing, most commonly drive testing. Field testing provides a trial-by-fire of sorts, as it is the ultimate test of a mobile device’s location abilities. However, field testing has a variety of limitations: it is cumbersome, time consuming and costly and most importantly the test environment is constantly changing – it cannot be held static. While field testing can provide valuable system performance data, the randomness and lack of control over environmental conditions get in the way of clearly understanding how the various components affect the performance of a location system.
So current test methodologies give a partial picture of A-GPS device performance at opposite ends of a spectrum. At one end, the rigidly-controlled standards-based test scenarios which include only limited influence of real-world conditions. At the other, field testing with its significant limitations in test environment consistency. This leaves the industry with a clear need for a middle ground that combines the advantages of these testing methods while mitigating their disadvantages.
The approaches we will discuss in this series of posts have one thing in common: they all attempt to simulate the real world in a lab environment. While it is impossible to completely replicate in the lab all the conditions that are found in the real world, it is possible to identify the key characteristics that influence device performance; these then can be simulated using appropriate software and hardware. The goal is a test methodology that offers all the advantages and convenience of lab-based testing, while effectively taking into account the vagaries of the real world.
Future postings will discuss the theoretical foundations of these methodologies, including how key real-world environment characteristics are identified and characterized, together with reviews of results obtained and recommendations for further study.
The next post in this series will look at potential solutions in more detail. Please stay tuned!