Txminer: Identifying Transmitters in Real-World Spectrum Measurements

2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) TxMiner: Identifying transmitters in real-world spectrum measurements Mariya Zheleva Ranveer Chandra, Aakanksha Chowdhery, Computer Science Ashish Kapoor, Paul Garnett University at Albany, SUNY Microsoft Research [email protected] ranveer, ac, akapoor, paulgar @microsoft.com { } Abstract—Knowledge about active radio transmitters is critical for multiple applications: spectrum regulators can usethis 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 Fig. 1. Example of overlapping transmitters. 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 operate in this band. While the first question can be approached the active transmitter. To address these challenges we propose by simple estimation of power level in a given band, the other TxMiner;asystemthatidentifiestransmittersfromrawspectrum measurements without prior knowledge of transmitter signatures. two questions require more elaborate analysis of spectrum TxMiner harnesses the observation that wireless signal fading occupancy. Such analysis needs to answer questions such as are follows a Rayleigh distribution and applies a novel machine there more than one transmitters in a given band, and what are learning algorithm to mine transmitters. We evaluate TxMiner on their received powers, operating frequencies, bandwidth and real-world spectrum measurements between 30MHz and 6GHz. temporal characteristics. Learning these characteristicsfrom The evaluation results show that TxMiner identifies transmitters raw spectrum measurements is critical for improved policing robustly. We then make use of TxMiner to map the number of and technological advancements in the DSA domain. active transmitters and their frequency and temporal characteristics over 30MHz-6GHz, we detect rogue transmitters and identify Despite the need for deep understanding of spectrum opportunities for dynamic spectrum access. occupancy, there does not exist a platform to create such nationwide spectrum usage footprint. This is primarily due to I. INTRODUCTION lack of scalable infrastructure for collection and processing of RF spectrum measurements. Traditionally, spectrum occu- There is an increased demand for additional RF spectrum to pancy is analyzed via spectrum analyzers that capture large support mobile data communication. However, nearly all the amounts of data. The latter poses challenges in scalable data RF spectrum has been allocated for different purposes, e.g. storage. Furthermore, the current approaches to mining and TV, radio, cellular, radars, satellites, etc. Therefore, spectrum summarizing spectrum measurements are very limited, making regulators worldwide are investigating the use of Dynamic it hard to evaluate the collected spectrum data. Spectrum Access (DSA) techniques, such as in the TV white spaces or tiered access in 3.5 GHz of spectrum, to meet the Current approaches to spectrum summarization require additional demand. Using these techniques, mobile devices can prior knowledge of transmitter signatures and fine-grained send and receive packets over a frequency as long as they do spectrum measurements [9, 14], both of which are difficult not interfere with the licensed user of that frequency. to obtain in wide-band sweep-based spectrum sensing. Other, more general approaches, make use of measured power level To identify new spectrum for DSA, the government and in order to determine spectrum occupancy [17], however they spectrum regulators worldwide have expressed a desire to can be very inaccurate in creating a spectrum inventory. To il- create large-scale spectrum inventory in order to determine lustrate the problems related to spectrum traces summarization spectrum usage at different locations over long periods of let us consider the example in Figure 1. The top part of the time [6]. For example, in the US, the goal of the Spectrum figure plots power spectral density (PSD) measured over the Inventory Bill [2] is to create a nationwide footprint of spec- course of 90 seconds between 700 and 900MHz. The bottom trum usage over time. Based on these measurements, spectrum figure plots a maxhold of PSD in the entire frequency range, regulators can open new portions of the spectrum for DSA [5]. that is the maximum measured PSD value in each frequency Furthermore, new DSA technologies can be designed taking bin. Arguably, in some parts of the spectrum there exist more into account the characteristics of these bands. than one transmitters that occupy the same band in a time- Creating such national spectrum inventory is aimed at division fashion. Direct analysis of the data in time-frequency answering various questions including (i) how much spectrum domain is prone to errors due to the noisy nature of raw is occupied and how much is idle, (ii) how many transmitters spectrum signals. Analysis of the maxhold, on another hand, occupy a given frequency band, and (iii) are they authorized to can provide intuition of occupied fractions of this spectrum 978-1-4799-7452-8/15/$31.00 ©2015 IEEE 99 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) but hides the time-frequency characteristics of the signal;that we apply TxMiner on real world spectrum traces in Section IV is if multiple transmitters share the same frequency band they in order to demonstrate TxMiner’s capabilities to create a will be considered as a single transmitter. Furthermore, using nationwide spectrum inventory and thus benefit both regulators maxhold-based analysis, one cannot determine the temporal as well as DSA technology designers. We finalize the paper patterns of the signal. with discussion and conclusion in Section VI. To address these challenges we design a new technique, called TxMiner, that identifies transmitters from raw spec- II. TXMINER:IDENTIFYING TRANSMITTERS IN trum measurements, even when the transmitter characteristics SPECTRUM OBSERVATORY DATA are not known and the spectrum sensing resolution is low. TxMiner leverages the phenomenon that fading of non-line- Traditionally, spectrum occupancy is analyzed manually of-sight wireless signals typically follows a Rayleigh distri- by the use of tools, such as spectrograms of power spectral bution, while noise follows a Gaussian distribution [7]. Thus, density. While such tools are informative, they are not very the raw spectrum samples can be modeled as a mixture of actionable. Particularly, they do not allow automated, fine- Rayleigh distributions capturing the ongoing transmissions, grained, long-term observation of spectrum occupancy patterns and a Gaussian representing the noise. Based on this obser- that are needed to inform DSA system design and policy. We vation we design a machine learning algorithm that extracts propose TxMiner to solve the above problems by identifying Rayleigh and Gaussian sub-populations from a given RF signal transmitters in raw Spectrum Observatory data without prior population. There are two challenges associated with such knowledge of transmitter characteristics. TxMiner enablessev- approach to transmitter characterization. First, the performance eral new and exciting applications including mapping spectrum of our Rayleigh-Gaussian mixture model is dependent on the occupancy, identifying rogue transmitters, DSA beyond TV initialization of the model. To address this challenge, we white spaces and spectrum management. design a multi-scale initialization scheme, presented in detail Applications of TxMiner: The problem of spectrum map- in Section II-D. Second, in order to extract frequency and ping and management is relevant worldwide. In the US the temporal transmitter characteristics we need to perform time- FCC has been mandated by Congress to create a spectrum frequency analysis of the collected data. Towards this end, we usage map to be included in the Spectrum Inventory Bill [1]. design a post-processing technique detailed in Section II-E. In developing countries, spectrum regulators often do not know Thus, TxMiner is comprised of three critical components: (i) how spectrum is being used2.TxMinercanbeappliedin multi-scale initialization, (ii) Rayleigh-Gaussian representation both scenarios for advanced mapping of spectrum occupancy, of raw spectrum measurements and (iii) post-processing for which in turn enables effective spectrum use and regulation. actual transmitter identification. Furthermore, it can inform spectrum management by answer- We evaluate TxMiner on spectrum measurements collected ing questions such as (i) how many types of transmitters by the Spectrum Observatory1,aswellasonseveralcontrolled are using the channel? (ii) how many transmitters of each transmissions, and we have found that it can accurately iden- type are present? and (iii) what is the noise floor of the tify transmitters of different types including WiMax, TV & channel when these transmissions are not present? TxMiner FM broadcasts, as well as proprietary DSA protocols. We can also be useful in identifying rogue transmitters by detecting demonstrate TxMiner’s ability to map the number of active discrepancies between expected and detected transmitters in a transmitters

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