We present BurstTracker, the first tool that developers can use to detect if the LTE downlink is the bottleneck for their applications. BurstTracker is driven by our discovery that the proprietary LTE downlink schedulers running on LTE base stations allocate resources to users in a way that reveals if a user's downlink queue runs empty during a download. We demonstrate that BurstTracker works across Tier-1 cellular providers and across a variety of network conditions. We also present a case study that shows how application developers can use this tool in practice. Surprisingly, with BurstTracker, we find that the LTE downlink may not be the bottleneck for video streaming on several Tier-1 providers, even during peak hours at busy locations. Rather, transparent TCP middleboxes deployed by these providers lead to downlink underutilization, because they force Slow-Start Restart. With a simple workaround, we improve video streaming bitrate on busy LTE links by 35%.

The rapid growth in mobile connectivity is enabling phones to support a wide range of societally-important applications. In this work, we show that broad classes of popular mobile applications have distinct radio resource allocation signatures. Using this insight, we design a mobile application classifier, and demonstrate that (1) an application can infer its own type solely from its resource allocation patterns, and (2) anyone can accurately infer the type of application being served by each session on a particular cell tower. We present our findings by showing the breakdown of applications being served by an LTE base station belonging to a Tier 1 US provider in downtown Palo Alto. Our work encourages an open discussion about LTE standards, and whether they might need to be enhanced to mask features that can be exploited to infer application type from signals broadcast over the air.

ldquoSpectrum holesrdquo represent the potential opportunities for noninterfering (safe) use of spectrum and can be considered as multidimensional regions within frequency, time, and space. The main challenge for secondary radio systems is to be able to robustly sense when they are within such a spectrum hole. To allow a unified discussion of the core issues in spectrum sensing, the ldquoweighted probability of area recoveredrdquo (WPAR) metric is introduced to measure the performance of a sensing strategy; and the ldquofear of harmful interferencerdquoFHImetric is introduced to measure its safety. These metrics explicitly consider the impact of asymmetric uncertainties (and misaligned incentives) in the system model. Furthermore, they allow a meaningful comparison of diverse approaches to spectrum sensing unlike the traditional triad of sensitivity, probability of false-alarmPFA, and probability of missed-detectionPMD. These new metrics are used to show that fading uncertainty forces the WPAR performance of single-radio sensing algorithms to be very low for small values ofFHI, even for ideal detectors. Cooperative sensing algorithms enable a much higher WPAR, but only if users are guaranteed to experience independent fading. Lastly, in-the-field calibration for wide-band (but uncertain) environment variables (e.g., interference and shadowing) can robustly guarantee safety (lowFHI) even in the face of potentially correlated users without sacrificing WPAR.

We consider a point to point communication system facing interference from other systems, with a particular focus on the case when this interference is undecodable. It is well known that when the interference is non-interactive, we can certainly treat it as additional noise at the receiver and thereby achieve certain rates. This paper asks whether any higher rates could be achieved by exploiting knowledge of the interferer's codebook. The main contribution of this paper is to study the converse: if the interference is undecodable, then we cannot do better than treating it as additional noise. This is proved for almost all interference codebooks when viewed under the random Gaussian codebook measure. When the interference signal is strong enough to be decodable, then codebook knowledge can be exploited at our receiver to allow higher rates to be achieved by appropriately structuring our own codebooks. Finally, we give an example of an interference codebook that cannot be completely decoded, but whose knowledge is still useful. However, this interference codebook is bad from the perspective of the interferer's own communication system. This leads us to conjecture that when the interference signal is undecodable, the only interference codebooks worth knowing are those that are not worth using from the interfering system's point of view