Ayon Chakraborty

A First Look at Performance in Mobile Virtual Network Operators

Recent industry trends suggest a new phenomenon in the mobile market: mobile virtual network operators or MVNOs that operate on top of existing cellular infrastructures. While MVNOs have shown significant growth in the US and elsewhere in the past two years and have been successful in attracting customers, there is anecdotal evidence that users are concerned about cellular performance when choosing MVNOs over traditional cellular operators. In this paper, we present the first systematic measurement study to shed light on this emerging phenomenon. We study the performance of 3 key applications: web access, video streaming and voice, in 2 popular MVNO families (a total of 8 carriers) in the US, where each MVNO family consists of a major base carrier and 3 MVNOs running on top of it. We observe that some MVNOs do indeed exhibit significant performance degradation and that there are key differences between the two MVNO families.

SpecSense: Crowdsensing for efficient querying of spectrum occupancy

We describe an end-to-end platform called SpecSense to support large scale spectrum monitoring. SpecSense crowdsources spectrum monitoring to low-cost, low-power commodity SDR/embedded platforms and provides necessary analytics support in a central spectrum server. In this work, we describe SpecSense and address specific challenges related to accurately estimate spectrum occupancy on demand with low overhead. To address the accuracy question, we augment state-of-the-art spatial interpolation techniques to accommodate scenarios where RF propagation characteristics change across space. To address the overhead question, we solve the sensor selection problem to select the minimum number of spectrum sensors that can best estimate the spectrum at the requested locations.

Designing a Cloud-Based Infrastructure for Spectrum Sensing: A Case Study for Indoor Spaces

We argue that spectrum sensing on mobile clients will be both necessary and feasible if we wish to manage the white space spectrum optimally in indoor spaces. We demonstrate the necessity with a set of empirical measurements showing the need for fine grained sensing. We demonstrate the feasibility by building a spectrum sensing infrastructure that collects measurements from sensing devices to analyze and better use spectrum resources. The infrastructure consists of mobile spectrum sensors that are built using DTV receiver dongles interfaced with Android-based mobile devices and a cloud-based central server to manage such sensing devices. We also show results about resource consumption (energy, network overhead) involved in operating such sensors. The vision is ultimately creating a system where mobile devices perform part-time spectrum sensing in a coordinated fashion under the control of a central spectrum manager. We lay out the research challenges based on our initial prototyping and benchmarking experience.

Benchmarking resource usage for spectrum sensing on commodity mobile devices

Effective management of various white space spectra may require spectrum sensing at finer spatial granularity than is feasible with expensive laboratory-grade spectrum sensors. To enable this, we envision a future where commodity mobile devices would be capable of spectrum sensing as needed, possibly via crowd-sourcing. However, since mobile devices are resource limited, understanding their resource usage in this set up is important, specifically in terms of overall latency and energy usage. In this work, we carry out a comprehensive performance benchmarking study using 4 different USB-powered software radios and 2 common smartphone/ embedded computers as mobile spectrum sensing platforms. The study evaluates latency and energy usage using a suite of commonly used sensing algorithms specifically targeting TV white space spectrum. The study shows that latency due to sensing and computation and related energy usage are both modest.

LTE-Xtend: scalable support of M2M devices in cellular packet core

M2M (machine-to-machine) communications are bringing new challenges in the cellular networks as billions of such devices will need to be supported but at a fraction of the cost of todays smartphones. Analysis shows that while many of these devices generate little data load on the network, their control signaling load and memory/CPU resource consumption due to tunnel maintenance in the cellular core could still be significant. To address this scalability issue, we propose a modified packet core architecture for LTE networks called LTE-Xtend that customizes control message handling and tunnel management for M2M traffic. Evaluations using OpenAirInterface demonstrates significant scalability benefits in using LTE-Xtend.

A first look at performance of TV streaming sticks

Recent measurements show that more than half of the peak time Internet traffic is due to video streaming. Recent trends also suggest that consumers are increasingly receiving their TV content over the Internet via streaming appliances that are connected to the TV. We present the first systematic measurement study of a popular class of such devices that have the ‘stick’ form factor. The study covers streaming and network related performance using a widely used content server on the Internet (Netflix) and a local instrumented media server. On the user-end, we use three widely available mediasticks in the US – Chromecast , Amazon Fire and Roku . We observe that there are significant performance differentials across the streaming sticks. Our experiments show that Amazon Fire and Chromecast provide better user experience in the presence of varying network conditions, whereas Roku performs best at high stable bandwidth.