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%.

Modern mobile networks are facing unprecedented growth in demand due to a new class of traffic from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional real-time applications such as voice-calling and video. This problem is extremely challenging because conventional traffic is highly dynamic across space and time, so its performance is significantly impacted if all IoT traffic is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic. Using 4 weeks of real network data from downtown Melbourne, Australia spanning diverse traffic patterns, we demonstrate that our RL scheduler can enable mobile networks to carry 14.7% more data with minimal impact on existing traffic, and outpeforms heuristic schedulers by more than 2x. Our work is a valuable step towards designing autonomous, "self-driving" networks that learn to manage themselves from past data.

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.

Cellular operators want to be able to deploy small cells with the same ease as WiFi access points, to quickly address traffic hotspots in dense urban areas. However, deploying small cells has not been easy. The reason is that due to scarcity of licensed spectrum, small cells need to use the same spectrum as the existing macro cells, and they need to explicitly coordinate their spectrum usage with each other to manage interference. The challenge is that this coordination needs to happen with latencies less than a millisecond, otherwise adding small cells does not help scale the overall network capacity. Implementing such tight coordination in dense urban deployments has not been easy in practice.

This paper investigates the optimal sensing-order problem for the general case of multi-user and multi-channel cognitive radio networks in which channel sensing is performed by the individual cognitive users (CUs) in a decentralized manner. The aim is to determine a sensing-order for each CU so as to maximize the cognitive throughput, which is the expected sum-throughput of all CUs. We consider two broad multi-user scenarios - first, where the network uses a contention resolution strategy whenever two or more CUs try to access the same channel simultaneously, and second, where the CUs are organized into distinct preemptive priority levels. For each scenario, we present a procedure for computing the cognitive throughput in the network. In general, the optimal sensing-orders can then be determined via a computationally expensive brute-force search. We also propose a suboptimal low complexity search algorithm for each scenario and show their proximity to the optimal performance by means of simulations.

This paper investigates the optimal sensing-order problem in multi-user, multi-channel cognitive radio networks in which channel sensing and access decisions are made sequentially by a central coordinator according to a central sensing-order. Estimates of the primary-free probabilities of the different channels are assumed to be known. Cognitive throughput, which is the sum of expected throughputs of all cognitive users (CUs), is used as the reward function. When the CUs do not use rateadaptation, we show that the optimal central sensing-order is a simple ordering of the channels based on their primary-free probabilities. When the CUs use rate-adaptation, we describe the procedure for mathematically formulating the reward function. In general, a computationally expensive brute force search can be used to find the optimal central sensing-order. We also consider a couple of simple heuristic sensing-orders and compare their performance with that of the optimal sensing-order through simulations.