A Machine Learning Approach for Analysis of Spectrum Availability in Kosovo based on

Experimental Measurements Zana Limani Fazliu1, Hëna Maloku1, Mimoza Ibrani1, Myzafere Limani1,2 , Blediona Gashi1

1Faculty of Electrical and Computer Engineering, University of Prishtina, Prishtina, Kosovo 2 Academy of Sciences and Arts of Kosovo, Prishtina, Kosovo

Abstract - Establishing the utilization level of spectrum

resources is an essential step in evaluating the true

availability of the frequency spectrum which could

potentially become available for opportunistic use. Such

assessments typically require extensive measurement

campaigns, in order take into account various factors which

may affect the availability of spectrum, such as frequency

allocation at national level, and the particulars of the

environment and terrain. In this study, we focus on the bands

usually reserved for TV broadcasters, since studies show that

these bands tend to be least efficiently utilized. In addition,

we take advantage of a large set of data obtained from an

extensive spectrum measurement campaign conducted over

the entire territory of Kosovo, which enables us to attain a

comprehensive picture of spectrum availability in TV bands.

Spectrum sensing techniques using energy detectors are

applied in order to determine whether the spectrum is

utilized. However, due to the significant amount of

measurement data collected, machine learning techniques

can be leveraged to improve the accuracy of the approach, by

improving the detection and false alarm rates. As an end

result, a highly accurate spectrum availability map is

obtained which can be in turn used by cognitive radio

network to opportunistically access the spectrum.

Keywords – TV bands, wireless technologies, spectrum

measurements, machine learning

I. INTRODUCTION

Under-utilization of licensed spectrum, especially in the frequency bands traditionally allocated for TV broadcasting, is a fact that has been well established in the literature [1]. Furthermore, with the transition from analog to digital broadcasting, substantial amount of spectrum in TV bands is expected to become further available. These bands, often referred to as TV White Space (TVWS), are of great interest due to the superior signal propagation characteristics [2]. While, in most countries, these frequencies remain pre-allocated for TV broadcasting, the opportunistic use by secondary devices has been considered, and allowed, most notably in USA [3] and UK [4].

Determining the utilization level of TVWS is an essential step in evaluating the true availability of the frequency spectrum which could potentially become available for opportunistic use. Such assessments typically require extensive measurement campaigns, in order take into account various factors which may affect the availability of spectrum, such as frequency allocation at national level, and the particulars of the environment and terrain.

In this paper we present the results from spectrum measurements over TVWS conducted over a wide area of the territory of Kosovo. To our knowledge, this is the first study of this scale to be conducted in Kosovo, and one of the very few in the Western Balkans region. Similar studies have been performed in Bosnia and Herzegovina [5], Hungary [6], and Macedonia [7]. Preliminary results of this project, covering only the urban area of the capital, Prishtina, have been presented in our previous work [8, 9].

Applying comparable methodologies to other similar measurement campaigns performed in other countries [10, 11] we perform a spectrum occupancy analysis over the Ultra High Frequency (UHF) bands. The detection of activity in a specific channel was evaluated using energy detection techniques based on measured values of received power. To improve the accuracy of the detection process, an unsupervised machine learning techniques based on k-means clustering were applied, an endeavor which was made possible by the significant amount of measurement data collected.

Finally, we would like to note that the analysis presented in this work gives an overview of the current spectrum availability, which may change in the future as TV broadcasting in Kosovo has not yet fully transitioned from analog to digital broadcasting. Our goal, however, is to quantify the current levels of utilization of the TV spectrum band. The results of our measurement campaign can be used to assess the current status of the spectrum use and the availability of the spectrum for other, opportunistic, users. In addition, the outcomes of our extensive analysis can give valuable information to the national regulators about the efficiency of the current spectrum allocations. Hopefully, in the future, this data can be used for maintaining spectrum usage data and facilitate spectrum sharing in an operational environment.

The rest of the paper is organized as follows: Section II gives a brief description of the relevant literature. Section III describes the details of measurement campaign protocol. The spectrum availability analysis and the learning machine approach we apply is described in section IV. Finally, numerical results are provided in Section V.

II. RELATED WORK

The opportunistic utilization of spectrum through the use of cognitive radio (CR) devices, is an idea that has been studied intensively in the last 15 years [7]. A comprehensive review on performing measurement campaigns for effective spectrum occupancy monitoring is presented in [10]. Another survey focusing on

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characterizing and modelling the availability of white spaces is provided in [2]. An important aspect when assessing activity in the surveyed spectrum band, is the detection method applied. In particular, when applying energy based techniques which rely on a pre-determined threshold to decide whether a channel is active or not, the assessment of the noise level becomes critical. In general, most studies [5-7], for sake of simplicity apply fixed thresholds against which the energy of sampled observations is compared to. In our own previous work [8], we also addressed the availability of TVWS within Prishtina city limits, using a single fixed threshold. However, as we argued in [9], a fixed predetermined threshold is not flexible enough to provide an accurate picture of the spectrum availability. To this end, more intricate methods have been studied and presented in literature. The estimation of the detection threshold without prior knowledge about the signal and noise characteristics is addressed in [11]. The use of iterative algorithms based on impulse suppression principles for spectrum sensing purposes is proposed in [12, 13]. The authors in [14] propose instead the application of adaptive double thresholds to enhance the performance of sensing process, which was also the approach taken in [9]. Finally, machine learning techniques have also found potential application in this area. The authors in [15, 16] propose the use of unsupervised and supervised machine learning techniques on observed samples to determine the activity within a specific frequency channel. A cooperative technique based on Hidden Markov Models is instead proposed [17].

Figure 1 Measurement locations (blue) and TV transmitter (orange) positions

In this work, we adopt a simple unsupervised learning technique and apply it to obtain an accurate and realistic overview of the spectrum availability in the UHF bands, at national level, within the territory of Kosovo.

III. MEASUREMENT CAMPAIGN PROTOCOL

The analog terrestrial TV broadcasting in Kosovo is allocated in the following spectrum bands: VHF 174-230 MHz (8 channels, each 7MHz wide) and UHF 470 – 860 MHz (48 channels, each 8MHz wide), according to data obtained from Kosovo Independent Media Commission [18]. A list of TV transmitters, their coordinates, antenna height and transmit power is also provided in their website [18].

The frequency bands under assessment included UHF channels over the entire 470 − 854 MHz band. The measurement campaign was conducted at 36 different outside locations across the territory of Kosovo. Out of these locations 14 (around 40%) were situated in urban/suburban areas, while 18 were situated in rural areas. The measurements were performed using the NARDA Selective Radiation Meter SRM-3006, in the spectrum analysis mode, with a frequency resolution of 100 kHz, and sweep time of ~200 ms. At each location, 10 individual measurements were performed, each lasting 1 minute. For each measurement, the minimum, maximum and average values of the power received were recorded.

In particular, 7 active transmitters were identified during the measurement campaign. The measurement locations as well as the locations of the 7 TV broadcasters are shown in Fig. 1. The locations were chosen so as to obtain a sufficient distance resolution from the transmitters. The choice of the locations was also constrained by the availability of roads and accessibility. Within the city center of Prishtina measurements were taken in several different locations within the city center as well as in the surrounding areas.

IV. SPECTRUM AVAILABILITY ANALYSIS

A. Theoretical coverage

The theoretical coverage of the TV broadcasters can be established from the official list of licensed transmitters [18], which includes detailed information on antenna locations and characteristics. In particular, we were able to confirm that 7 transmitters from the official list were currently active, in 10 TV channels. Previous studies [19], show that the propagation model that best fits Kosovo environment is the Ericsson path loss model [20]. Calculating the respective distance between each measurement location and transmitting antenna, and applying the Ericsson path loss equation, we can derive the estimated received power at each location from each transmitter.

To decide whether a location is under the theoretical coverage of a TV transmitter or not, a coverage threshold can be applied based on the expected receiver sensitivity. The value of the threshold can impact significantly the theoretical values of occupancy, however there are tools available on how to establish this threshold. In this paper we have used the FCC's FM and TV Propagation Curves online tool, which is based on its own established service contours [21]. The tool can be used to find the distance to a service or interfering contour, or the corresponding field strength at a given contour distance. Applying the Ericsson propagation model, and the values obtained from the tool, we derive that a reasonable threshold value would be in the range of -75 to -70 dBm.

B. The spectrum detection problem

In general, spectrum detection is presented as a binary hypothesis-testing problem:

𝐻0: Primary user is not present𝐻1 Primary user is present

In order to test the hypothesis, the cognitive device listens, i.e., senses, the channel and makes a decision based

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on the observed samples. Usually, once the sensing is performed the energy of sensed samples is calculated and used as a test statistic. Since the equipment used in this measurement campaign, directly measured the received power level, the latter was used as a test statistic instead. In the following, we shall formulate the spectrum detection problem for our spectrum availability analysis.

Let 𝒯 be the set of the TV transmitters, i.e., primary users, ℒ be the set of measurement locations and 𝒞 the set of frequency channels under assessment. For each transmitter 𝑡 ∈ 𝒯 , we denote its transmitting power on channel 𝑐 as 𝑃𝑡(𝑡, 𝑐). We denote the power received at location 𝑙, on channel 𝑐 at time 𝑠 as 𝑃𝑟(𝑙, 𝑐, 𝑠). We define the binary variable 𝛾(𝑡, 𝑐, 𝑠) to indicate whether transmitter 𝑡 is active on channel 𝑐 at measurement time 𝑠. Our test statistic becomes:

𝑃𝑟(𝑙, 𝑐, 𝑠) = {

𝑁0 𝐻0

∑ 𝑃𝑡(𝑡, 𝑐)𝛼(𝑡, 𝑙, 𝑐)𝛾(𝑡, 𝑐, 𝑠)

𝑡∈𝒯

+ 𝑁0 𝐻1

where 𝛼(𝑡, 𝑐) represents the pathloss exponent between transmitter 𝑡 and location 𝑙 on channel 𝑐 , and 𝑁0 is the white noise power level.

In the traditional energy detection technique, the energy, i.e., the power, of the sensed samples is compared to a fixed pre-defined threshold, in order to reach a decision. In that case, the decision on whether a channel 𝑐 is active at time 𝑘 , at a location 𝑙, can be expressed as:

𝐴(𝑙, 𝑐, 𝑠) = {0 if 𝑃𝑟(𝑙, 𝑐, 𝑠) ≤ 𝛿

1 if 𝑃𝑟(𝑙, 𝑐, 𝑠) > 𝛿

where 𝛿 is the predefined threshold.

Two key metrics to be considered in these kinds of problems are the probability of misdetection defined as Pr {𝐴 = 0|𝐻1}, and the probability of false alarm defined as Pr {𝐴 = 1|𝐻0). While the energy detection technique is particularly attractive due to its simplicity and the fact that it requires no prior knowledge about the signal, its drawback is that it is very vulnerable to noise, which can lead to high rates of false or high number of misdetections depending on the threshold. On the one hand, if the threshold is set too low, the detector will be biased towards detecting the presence of a signal, even when there is only noise, leading to a false alarm. A high rate of false alarms can lead to significant underestimation of the availability of the spectrum, rendering the process useless. On the other hand, a high threshold, can underestimate the presence of the primary signal, i.e. TV signal, therefore increasing the risk of causing harmful interference to the primary user.

C. A machine learning approach

In this paper we adopt an unsupervised machine learning approach to detect the presence of a primary signal. In this approach, we again use the measured levels of received power to detect the signal, however instead of comparing them to a pre-determined threshold, we use them to construct feature vectors, which are fed to a classifier. The classifier in turn labels each vector with an inactive class, 𝐴0, or an active class, 𝐴1. Applying machine learning to the spectrum detection problem is attractive, because it is less susceptible to noise, compared to the simple threshold

technique. A similar technique was applied by the authors in [15], albeit in a cooperative context, by constructing feature vectors from samples taken by several CR devices at different locations. In this work, we do not consider cooperation between devices to reach a decision, i.e., at each location we only use the measured power levels collected there to evaluate the occupancy of the spectrum.

As already mentioned, during our measurement campaign received power levels at each location were measured. The minimum, maximum and average values of the power were recorded. Therefore, we build our feature vector, referred to as power vector, for each frequency channel, as follows:

𝐏l,c,s = [𝑃𝑟min(𝑙, 𝑐, 𝑠) 𝑃𝑟

avg(𝑙, 𝑐, 𝑠) 𝑃𝑟max(𝑙, 𝑐, 𝑠)]

However, as the name implies, in order to enable the classifier to accurately label the received power vectors, it must first undergo a learning process. Therefore, the process is separated into two phases, the learning phase and the evaluation phase, as described below.

1) Learning phase

The goal of the learning phase is to build a classifier which is able to accurately label power vectors as inactive or active. In this phase we apply an unsupervised learning technique based on k-means clustering. In order to do so, we must feed the learning algorithm with samples of feature vectors. As these samples must be different from those used during the evaluation phase, for each location we split the data collected during the measurement

campaign into two sets: the training set, 𝐏l𝑇 , and the

evaluation set, 𝐏l𝐸. The samples from the training set are

used to feed the k-means clustering learning algorithm described in Alg. 1. The k-means clustering algorithm [22], is attractive because of its simplicity, and also because it does not require the training vectors to be labeled a priori. However, the number of clusters needs to be pre-specified, which in our case is appropriate, because we already know that we are interested in two clusters only, the active and the inactive cluster.

Algorithm 1: k-means clustering for spectrum learning

Input: 𝐏l𝑇, 𝑘 = 2

1: Choose initial cluster centroids 𝐂𝐴0, 𝐂𝐴1

1: for all 𝐏 ∈ 𝐏l𝑇

2: Calculate distance 𝑑(𝐏, 𝐂𝐴𝑖) = ‖𝐏 − 𝐂𝐴𝑖

‖from

all centroids

3: Assign P to cluster 𝐴𝑖, where min𝑖

𝑑(𝐏, 𝐂𝐴𝑖)

4: end for

5: Recalculate cluster centroids 𝐂𝐴𝑖=

1

|Ai|∑ 𝐏𝐏∈𝐴𝑖

6: for all 𝐏 ∈ 𝐏l𝑇

7: Reassign P from cluster 𝑖 to cluster 𝑗 , if

∑ ∑ ‖𝐏 − 𝐂𝐴𝑖‖

𝟐𝐏∈𝐴𝑖𝑖 is reduced

8: Recalculate cluster centroids 𝐂𝐴𝑖=

1

|Ai|∑ 𝐏𝐏∈𝐴𝑖

9: end for

10: Repeat steps 6-8 until cluster assignments do not

change, or the maximum number of iterations is

reached.

11. Return 𝐂𝐴0, 𝐂𝐴1

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The input to the algorithm are the training vectors for location 𝑙, and the number of clusters which in our case is 2. The algorithm will initially choose the cluster centroids, by selecting two random power vectors from the training set. In the first stage, it will assign each power vector to the cluster with the closest centroid. Once this stage is done, the centroids are recalculated based on the assignment. The second stage is an iterative procedure whereby the algorithm goes through each power vector, and evaluates whether changing its cluster assignment would reduce the within-cluster sum-of-squares point-to-cluster-centroid distances:

∑ ∑ ‖𝐏 − 𝐂𝐴𝑖‖

𝟐

𝐏∈𝐴𝑖𝑖

The second stage is repeated until the clusters converge, or a maximum number of iterations is reached. At the end, the algorithm returns the centroids for the two clusters. It should be noted that the algorithm is performed independently for each location. Therefore for each

location, we will have two centroid vectors denoted as 𝐂𝐴𝑖

𝒍 .

2) Evaluation phase

The second phase of the spectrum detection process is the actual evaluation of whether a primary user is active over a channel, at a specific location and time instant, or not. For each location, the power vectors from the

evaluation set 𝐏𝑙,𝑐,𝑠 ∈ 𝐏𝑙𝐸, are fed into the classifier. The

classifier evaluates each power vector, by calculating its distance from the location cluster centroids:

𝑑(𝐏𝑙,𝑐,𝑠, 𝐂𝐴𝑖

𝒍 ) = ‖𝐏 − 𝐂𝐴𝑖

𝒍 ‖, 𝑖 = 0,1

The classifier will assign to the vector, the label which corresponds to the smallest distance from the cluster centroid. It should be noted that the performance of the algorithm depends heavily on the quality of the training data. In certain locations, all training vectors may belong to the same class, which makes it difficult for the classifier to distinguish between the two clusters. A reliability indicator can be defined, by calculating the distance between the cluster centroids:

𝑅(𝑙) = ‖𝐂𝐴0𝑙 − 𝐂𝐴1

𝒍 ‖

If this reliability indicator is below a certain threshold, additional means to ensure low probability of false alarm can be taken as proposed in [15]. Namely, in cases where 𝑅(𝑙) ≤ 𝜌 , the classifier will label a power vector as inactive, 𝐴0, only if the following criteria is met:

‖𝐏 − 𝐂𝐴0‖

min𝑖

‖𝐏 − 𝐂𝐴𝑖‖

≥ 𝛽

Where 𝜌 and 𝛽 are threshold parameter to control the

tradeoff between the misdetection and the false alarm

probabilities.

V. SPECTRUM DETECTION AND ANALYSIS RESULTS

In this section we analyze the measurement data collected during the campaign described in Section II.

At every location, the received power level was measured, and the minimum, average and maximum values of the power, for the measurement duration, were recorded. The received power was measured in dBm over the entire 470 − 854 MHz band, in bins of 100 kHz. The power levels detected in the individual bins within each channel band were then summed to obtain the received power level at the specific channel:

𝑃𝑙,𝑐,𝑠 = 10log10 ∑ 10𝑃𝑙,𝑏,𝑠

10

𝐵𝑐

𝑏=1

Where 𝑃𝑙,𝑏,𝑠 is the power measured at location 𝑙, on

frequency bin 𝑏, at measurement time 𝑠. 𝐵𝑐 is the number of bins in channel c, which depends on the channel bandwidth and can vary from channel to channel.

Using the list of active TV transmitters, the distance between each transmitter - location pair was calculated. This distance was used to establish whether a location falls under the theoretical coverage area of a transmitter as described in Sec. IV-A.

Figure 2. Heatmap of average measured power level in dBm

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The availability of the spectrum was assessed through processing of measurement data with MATLAB software. First, in Fig. 2, we show the average measured power at every location and channel. The max, min and average values of the measured power for each location, channel and measurement time, were stored, and separated into the training set and evaluation set, as described in Sec. IV-C. The training set was fed to the k-means clustering algorithm to obtain the cluster centroids for each location. The validation set was then submitted for classification, and an occupancy value of 0, or 1, was assigned for each (𝑙, 𝑐, 𝑠) triple.

Figure 3. Averaged occupancy over all locations and measurement times for known active channels.

Figure 4 Averaged occupancy over all locations and measurement times for known other channels with unknown activity status.

In addition, for comparative purposes we also performed the detection using a fixed threshold, and double adaptive thresholds (LAD) as described in [9]. This analysis produced availability picture for the measurement locations and frequency band under study. The average occupancy of known active channels over all measurement locations and measurement times, is shown in Fig. 3.

The results show that even known active channels appear un-occupied in more the 70% of the locations. K-means is able to detect the presence of the primary users more often that fixed and LAD methods in most locations. However, LAD is able to detect primary user presence in

some locations that k-means does not. It should be noted that LAD is an adaptive method which tunes its threshold based on the noise level, therefore it could perform better in noisy locations at higher frequencies, since noise is known to increase with frequency [11].

Figure 5. Spectrum occupancy percentage over the UHF channels, in suburban locations.

Figure 6 Spectrum occupancy percentage over the UHF channels, in rural locations.

For other channels, for which we have no confirmation about their activity, occupancy percentage varies between 0-3%, as shown in Fig. 4. LAD exhibits a near-constant occupancy rate of 0.8%, which is connected to the fact that LAD thresholds are determined based on fixed target probability of false alarm values. The fixed threshold method does not detect any activity in any of the channels, because the threshold is set too high. K-means displays variable occupancy in these channels, although the occupancy increases with frequency. This could indicate an increase in false alarms at higher frequencies due to the increase in noise levels.

Finally, we look at the spectrum occupancy at the different locations. At suburban locations the spectrum occupancy over the entire 470-860 MHz band, varies between 0-8%, as shown in Fig. 5, while in rural areas between 0.02-10%, as shown in Fig. 6. A map view of the occupancy levels is shown in Fig. 7.

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CONCLUSION

In this paper we address the utilization level of spectrum resources in the TV bands over the territory of Kosovo. To do so, we leverage a large set of data obtained from an extensive spectrum measurement campaign which enabled us to attain a comprehensive picture of spectrum availability in TV bands. The significant amount of measurement data collected, enabled us to apply machine learning techniques to improve the accuracy the spectrum detection process. As an end result, we obtained a highly accurate spectrum availability map which can be in turn used by cognitive radio network to opportunistically access the spectrum. Our analysis show that occupancy of spectrum all over the territory of Kosovo, does not surpass 10%, in any of the locations. Furthermore, we show that even known active channels are un-occupied in more than 70% percent of the measurement locations. This indicates that the under-utilization of these bands is significant. Results of our analysis will be shared with regulatory authorities in order to initiate a discussion over the liberalization of these frequency bands for potential opportunistic use.

Figure 7 A map view of the spectrum occupancy levels. Larger circles represent higher occupancy ratio

ACKNOWLEDGMENT

This work was supported by research project "Research on the Reusability Possibilities of New Frequency Bands UHF, VHF and Millimeter Waves for Wireless Communication Networks in territory of Kosovo" funded by the Kosovo Academy of Sciences and Arts.

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