Paul W. Garnett

Connecting africa using the TV white spaces: from research to real world deployments

More than 4 billion people are not connected to the Internet. This is either because there is no infrastructure or because Internet access is not affordable. This digital divide is extreme in Africa. At Microsoft, we have been investigating various technologies to bridge this divide. In this paper we describe our research around the TV White Spaces, and how we have leveraged it, and worked with our partners to connect communities in Kenya, Tanzania, Ghana, Botswana, Namibia and South Africa.

TxMiner: Identifying transmitters in real-world spectrum measurements

Knowledge about active radio transmitters is critical for multiple applications: spectrum regulators can use this information to assign spectrum, licensees can identify spectrum usage patterns and better provision their future needs, and dynamic spectrum access applications can leverage such knowledge to pick operating frequency. Despite the importance of transmitter identification the current work in this space is limited and requires prior knowledge of transmitter signatures to identify active radio transmitters. More naive approaches are limited to detecting power levels and do not identify characteristics of the active transmitter. To address these challenges we propose TxMiner; a system that identifies transmitters from raw spectrum measurements without prior knowledge of transmitter signatures. TxMiner harnesses the observation that wireless signal fading follows a Rayleigh distribution and applies a novel machine learning algorithm to mine transmitters. We evaluate TxMiner on real-world spectrum measurements between 30MHz and 6GHz. The evaluation results show that TxMiner identifies transmitters robustly. We then make use of TxMiner to map the number of active transmitters and their frequency and temporal characteristics over 30MHz-6GHz, we detect rogue transmitters and identify opportunities for dynamic spectrum access.

Characterizing spectrum goodness for dynamic spectrum access

The combination of exclusive use spectrum licensing and growing demand for voice, data, and video applications is leading to artificial spectrum scarcity. A recent approach to alleviate this artificial spectrum scarcity innovatively uses unused TV spectrum, also called the TV white spaces, through dynamic spectrum access (DSA) techniques. Wireless devices can use DSA techniques such as sensing and geo-location databases to learn about available TV channels for wireless communication. One obvious question to ask is whether the technology enabler for white space networking, i.e. dynamic spectrum access, is viable in other portions of the spectrum? This paper extends our research on networking devices in TV white spaces over the last seven years to other licensed spectrum bands between 30 MHz and 6 GHz. Typically, the goodness of licensed spectrum bands is measured using spectrum occupancy as a goodness metric, but the DSA opportunities in different bands can depend on several factors. We propose a novel DSA goodness metric to compare the opportunity of capitalizing on available spectrum using DSA techniques in various licensed bands. Further, we use these metrics to evaluate the data from the ongoing spectrum measurement campaign at Microsoft Research over one year.

Enabling a Nationwide Radio Frequency Inventory Using the Spectrum Observatory

Knowledge about active radio transmitters is critical for multiple applications: spectrum regulators can use this information to assign spectrum, licensees can identify spectrum usage patterns and provision their future needs, and dynamic spectrum access applications can efficiently pick operating frequency. To achieve these goals, we need a system that continuously senses and characterizes the radio spectrum. Current measurement systems, however, do not scale over time, frequency and space and cannot perform transmitter detection. We address these challenges with the Spectrum Observatory, an end-to-end system for spectrum measurement and characterization. This paper details the design and integration of the Spectrum Observatory, and describes and evaluates the first unsupervised method for detailed characterization of arbitrary transmitters called TxMiner. We evaluate TxMiner on real-world spectrum measurements collected by the Spectrum Observatory between 30 MHz and 6 GHz and show that it identifies transmitters robustly. Furthermore, we demonstrate the Spectrum Observatory’s capabilities to map the number of active transmitters and their frequency and temporal characteristics, to detect rogue transmitters, and identify opportunities for dynamic spectrum access.