DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND
Raied Caromi, Michael Souryal, and Wen-Bin Yang
National Institute of Standards and Technology
Communications Technology Laboratory
Gaithersburg, Maryland, USA
ABSTRACTIn the 3.5 GHz Citizens Broadband Radio Service (CBRS),
100 MHz of spectrum will be shared between commercial
users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection
mechanisms when necessary. This paper uses field-measured
waveforms of incumbent signals in and adjacent to the band toevaluate the performance of matched-filter detectors for thesesensors. We find that the proposed detectors exceed the requirements for performance in the presence of co-channel interference from commercial long term evolution (LTE) signals, meaning that more commercial devices can use the bandin the proximity of sensors. Furthermore, the detectors arerobust to out-of-band emissions into this band from adjacentband radars, which prior studies have found can be significant.

Fig. 1: Spectrograms (dBm) of measured in-band incumbent
radar [5].
not thermal-noise-limited, as the detection threshold is 25 dB
above the thermal noise floor. The challenge for detection ispresented, rather, by co-channel interference.
There are two primary sources of co-channel interferenceat the sensor’s receiver. First, by design, the band is sharedwith commercial systems. Therefore, sensors must be able
to detect the incumbent signal in channels occupied by commercial systems. These systems are expected to be fourthgeneration LTE systems, at least initially. However, the emissions of commercial systems operating in the band can, in
principle, be controlled by treating the sensors as protected
entities in the CBRS system.
The second, more challenging, source of interference isthe out-of-band emissions of systems operating in adjacentbands. These systems are also military radars, operate at
frequencies below the CBRS band, and have been observed
to generate significant emissions into the CBRS band [4, 5].
Fig. 1 shows the first 30 s of two spectrograms of the measured in-band SPN-43 radar present at 3570 MHz, one withand one without adjacent-band emissions.
This paper is a study of the performance of a class of detectors, namely matched-filter detectors, matched to the current in-band radar system and under realistic conditions ofco-channel interference. This study leverages actual waveform recordings of both the in-band radar and the out-of-band
emissions of adjacent-band radars collected in field measurements conducted at two U.S. coastal locations [4, 5]. We

Index Terms— 3.5 GHz, CBRS, detection, environmental sensing capability, radar
1. INTRODUCTION
The CBRS in the U.S. permits commercial broadband access to the radio frequency spectrum between 3550 MHz and3700 MHz on a shared basis with incumbents in the band [1].
Among the incumbents is the U.S. military which operates
radar systems in this band, including shipborne radar off the
U.S. coasts. The CBRS rules permit dynamic access to theband in the proximity of military radar provided a sensor network detects the presence of incumbent radar and triggers interference mitigation measures when necessary. The scope of
this study is on the achievable detection performance of thissensor network.
In order to operate in the CBRS ecosystem, sensors mustbe certified to meet specific requirements. Among these requirements is the ability to detect the in-band incumbent radar
signal at a minimum received power density of −89 dBm (dBrelative to 1 mW)/MHz [2], within 60 seconds of onset, andwith a probability of detection of 99 % or better [3].1 With
this minimum required power density, the detection is clearly
1 Government requirements do not specify a maximum probability of false
alarm, although this figure of merit is naturally of interest to commercial
users.

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If no interference is present, and s[n] is known, the problem
becomes the classical detection of a known complex deterministic signal in CWGN [8]. The optimal detector for thiscase is a MF (or equivalently, a replica correlator). A true MF
detector requires perfect knowledge of the signal. We assume
the pulse repetition interval and pulse duration are approximately known based on prior observation [4, 5]. Other parameters of the radar signal are simply unknown. For instance,neither the frequency nor the phase information is known apriori. Obviously, disregarding the phase information for thecoherent detector will degrade the performance. However, theissue of not knowing the center frequency of the radar signal
can be resolved by implementing a bank of MFs over the entire baseband. Prior observations [4, 5, 9] indicate that theseradars typically operate on a 10 MHz grid.
Let ŝ(t) be a template for the incumbent radar signal, defined asŝ(t) = exp(j2πf0 t)p(t),present the achievable tradeoff between detection and falsealarm rates under different interference conditions for two
variants of the matched filter (MF) detector, coherent and noncoherent. While this study is limited to detection of the current in-band radar, SPN-43, a similar analysis can be performed for future radars deployed in this band.
Detection by CBRS sensors differs from traditional radar
detection primarily because the sensor aims to identify thepresence of a radar signal rather than detecting and tracking
a target. Additionally, unlike a typical radar receiver that has
full access to the radar waveform, the sensor has only partial knowledge of radar waveform parameters. However, elements of classical radar detection can still be utilized. In particular, we use coherent and non-coherent MF detectors [6].
A comparable problem to the detection of incumbent radarin the 3.5 GHz band is the protection of radars in the 5 GHz
band, namely, dynamic frequency selection (DFS) in wirelessaccess systems. DFS systems must avoid, or vacate, a channel
identified as being occupied by the radar. However, the detection requirement is more relaxed than in CBRS; for instance,
the detection threshold for the lower-power wireless devices
is −62 dBm/18 MHz [7]. Furthermore, it is not clear thatadjacent-band emissions are as much an issue as in CBRS.

where f0 is the center frequency of the template in the baseband, and p(t) is a train of rectangular pulses. If the numberof pulses in p(t) is equal to L, then(
1, t ∈ [`Tpr , `Tpr + Tpw ], ` = 0, 1, . . . L − 1
p(t) =
0, otherwise,
where Tpw is the pulse width, and Tpr is the pulse repetitioninterval in seconds. Let ŝ[n] be the sampled version of ŝ(t).
The impulse response of the MF is h[n] = ŝ∗ [−n]. The output of the MF is given by the convolution x[n]~h[n]. Alternatively, the same result can be obtained from the deterministic
cross-correlation given by∞X
rxs [m] =x[n]ŝ∗ [n + m], −∞ < m < ∞.

2. SIGNAL MODEL
The 3.5 GHz CBRS band provides access to a total of
150 MHz bandwidth divided into 10 MHz channels. A sensor is required to detect the presence of in-band radar in any
channel of the lower 100 MHz of the band, and multiple
radar signals may be present in different channels. A capablesensor can simultaneously or sequentially acquire the signal
from all channels in order to identify the possible presence ofincumbent radar signals. Alternatively, a sensor may employ
multiple detectors in parallel, for instance, one per channel.
Therefore, we simplify the signal model by assuming asingle channel which may or may not be occupied by the incumbent in-band radar signal. In addition, we consider three
types of noise: gaussian thermal noise, co-channel commercial LTE emissions, or adjacent-band radar emissions. The
simplified model of the received baseband signal, then, isx[n] = s[n] + v[n],where s is the in-band radar signal whose presence we aretrying to detect, and v is either complex white gaussian noise(CWGN), an LTE signal, or emissions from an adjacent-bandradar.

n=−∞

In practice we compute rxs [m] for the lag values m =0, ±1, ±2, . . . , ±(N − 1), where N is the number of samplesof x[n]. Let test statistic of the detector T {rxs [m]} be afunction of the output of the cross-correlator. The decision
rule is given by
H1

T {rxs [m]} ≷ γ,
H0

where γ is the detection threshold for the MF. The probabilityof detection and the probability of false alarm are given by
PD = Pr (T {rxs [m]} > γ | H1 ) ,PF A = Pr (T {rxs [m]} > γ | H0 ) ,respectively. It is straightforward to calculate closed forms ofthese probabilities for a known signal in white gaussian noise(WGN) [6, 8]. However, we use empirical methods to estimate these probabilities since we do not have full knowledgeof the signal, and the interference is not WGN.

3. INCUMBENT RADAR DETECTION
The statistical hypothesis testing for a single radar signal detection is(
H0 : x[n] = v[n],H1 : x[n] = s[n] + v[n].

4. PERFORMANCE ANALYSIS
We test two types of MF detectors. The first type is coherent MF. The input to the filter for this type is the complex

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1

signal, and thus we label it complex correlator (CC). For thesecond type, non-coherent MF, the input to the MF is the signal stripped of its phase, i.e., |x[n]|. We label this filter as
magnitude correlator (MC). The test statistic for CC is the
maximum value of |rxs [m]| over all values of m. The MC, on
the other hand, benefits from a different test statistic. Specifically, T {rxs [m]} is the sum of the magnitudes of L pulses
from the output of the correlator. The sum is computed byaligning the pulses at the output of the filter.
Field-measured signals of shipborne radar are used in thesimulation for both in-band and adjacent-band radar signals.
These waveforms were originally 60 s in duration, sampled
at 225 MHz. We first decimate these waveform files to a
25 MHz sampling rate. For the in-band radar waveforms, we
shift the signals so that they are centered at zero baseband frequency. A large set of waveform files with strong in-band andadjacent-band radar signals is selected for simulations. For
each in-band radar waveform file, (8 to 15) segments are extracted for simulation. Each segment is 20 ms long, which isapproximately the time it takes for the main beam of the radar
antenna to illuminate the detector as the radar antenna rotatesin the azimuth plane. The radar segments, in addition to theinterference signals, are further filtered and downsampled to
2 MHz in order to reduce the computational time of the MF. A
total of 16 000 different radar segments with SNR higher than
30 dB of the in-band radar signals are used for performance
evaluation. Each evaluation consists of simulating both radar
present and radar absent cases.
We choose a MF template of 10 ms. Since the signal is
20 ms in duration, the template is padded with zeros for thedifference in time length. Based on the analysis provided
in [4, 5], we set Tpr = 1 ms , and Tpw = 1 µs, i.e., L = 10.
We define SNR as the peak power of the radar signal to theaverage noise power in 1 MHz. However, since the 20 ms
segments includes 20 pulses of the radar signal, we use theaverage of the peak power of these pulses for the SNR andpeak power settings. Although the original waveform captures include noise, its effect is ignored since the waveforms
have high SNR values. However, the waveform captures include channel fading effect. The in-band radar signals areadded to either WGN, LTE, or adjacent-band interference togenerate realistic scenarios similar to what an actual sensor
will observe. Radar signal power levels are adjusted to thedesired SNR or desired radar peak power values.

0.9
0.8
0.7

PD

0.6
0.5

CC, SNR=0 dB
CC, SNR=3 dB
CC, SNR=5 dB
MC, SNR=0 dB
MC, SNR=3 dB
MC, SNR=5 dB

0.4
0.3
0.2
0.1
0

0

0.1

0.2

0.3

0.4

0.5
PFA

0.6

0.7

0.8

0.9

1

Fig. 2: Measured signal as template; signal with added WGN.
1

0.8

PD

0.6
CC, SNR=3 dB
CC, SNR=5 dB
CC, SNR=7 dB
MC, SNR=3 dB
MC, SNR=5 dB
MC, SNR=7 dB

0.4

0.2

0

0

0.1

0.2

0.3

0.4

0.5
PFA

0.6

0.7

0.8

0.9

1

Fig. 3: Synthetic template; signal with added WGN.
case since the template contains the original phase information and the coherent detector performs to its fullest. In practice, a sensor will not have a priori access to the actual signal
for use as the template. Therefore, this example demonstrates
the best-case detection performance of a sensor.

4.2. Signal detection in WGN with synthetic template
For a realistic detection case, we use a synthetically generated pulse template. We use all 16 000 radar signal segmentsin the detection performance evaluation. The signals were
corrupted with noise with a fixed power level, and the radar
signal amplitudes were adjusted for specific SNR values. The
threshold values were estimated from the cumulative distribution function (CDF) of the noise for a given set of PF A
values. Fig. 3 shows the ROC curves for both CC and MC
detectors. Clearly, MC performs better than CC due to unmatched phase between CC template and the radar signal. In
addition, the difference between the performance of the perfectly matched filter, i.e., the CC detector in Fig. 2, and theMC detector in Fig. 3 is approximately 2 dB in SNR, which
represents the loss from using the synthetic template insteadof a perfectly matched detector.

4.1. Ideal signal detection in WGN
We first evaluate the detection performance when an actual
radar signal extracted from field measurements is used as the
template. The same template is then used as a signal after
corrupting it with WGN and delaying it with a random timedelay at each iteration of the simulation. Fig. 2 shows theachievable receiver operating characteristic (ROC) curves forthis case. As expected, CC performs better than MC for this243

Table 1: LTE TDD configurations
0
6/6
50/15
0

1
12/12
50/50
1

1

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6
PD

PD

TDD uplink/downlink configuration
Modulation and coding scheme index uplink/downlink2
Number of physical resource block uplink/downlink3
Downlink to uplink power ratio dB

0.5
CC, LTE @ -104 dBm/MHz
CC, LTE @ -99 dBm/MHz
CC, LTE @ -94 dBm/MHz
MC, LTE @ -104 dBm/MHz
MC, LTE @ -99 dBm/MHz
MC, LTE @ -94 dBm/MHz

0.4
0.3
0.2
0.1
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2
24/12
25/50
2

3
14/16
50/50
3

4
4/12
15/50
0

5
5/16
6/50
1

6
24/6
50/6
2

CC, INR=10-20 dB
CC, INR=20-30 dB
CC, INR > 30 dB
MC, INR=10-20 dB
MC, INR=20-30 dB
MC, INR> 30 dB

0.5
0.4
0.3
0.2
0.1
0
10-4

1

PFA

Fig. 4: Synthetic template; signal with LTE interference.

10-3

10-2
PFA

10-1

100

Fig. 5: Synthetic template; signal with measured adjacentband radar emissions.

4.3. Detection in LTE interference
Fig. 5 shows ROC curves for multiple interference levels.
We observe that, even with adjacent-band emissions at peak
INR levels of 20 dB to 30 dB, the MC detector can achieve
near perfect detection with a false alarm probability of only
10−3 . However, at higher interference levels, the false alarm
rate increases to 40 %.

We subject the detectors to a single interfering LTE time division duplex (TDD) signal, representing a nearby dominantinterferer. The LTE signals are set to be co-channel with thein-band radar signals. LTE TDD waveforms are generated
with commercial software. At each iteration of the simulation, the LTE frame structure is randomly selected from the
7 TDD configurations in Table 1. Each configuration defines
which subframes are utilized for downlink and for uplink. For
each TDD configuration, we configure the LTE waveforms asshown in the table. The peak power of the radar signals is set
at the required detection threshold of −89 dBm/MHz. Fig. 4
shows the ROC curves for multiple values of received LTE
average power per 1 MHz.
Note that sensors are required to tolerate only −109 dBm/
MHz of aggregate commercial emissions [2]. These results
indicate, therefore, that the MC detector can tolerate 10 dB
more commercial interference than required.

5. CONCLUSION
We presented an analysis of federal incumbent detectors
for the 3.5 GHz shared-spectrum CBRS band using fieldmeasured signals of the in-band incumbent radar as well asof adjacent-band emissions into this band, which prior studies have shown to be significant. While coherent detection
is shown to be superior with known signals in WGN as expected, a practical magnitude-correlator detector outperforms
the coherent detector in the absence of phase information of
the in-band radar. The magnitude detector performs well ata peak SNR of 5 dB in gaussian noise, but with interference
from commercial CBRS (LTE) devices, a peak SIR of 10 dB
is needed, suggesting that a single, dominant LTE signal is
not well modeled by gaussian noise. Nevertheless, the detector’s performance exceeds current requirements, meaningthat more commercial devices can use the band in the proximity of sensors. Finally, the MC detector is quite robust to
adjacent-band emissions having peak INR levels as high as
20 dB to 30 dB.
In ongoing work, we are examining the effectiveness ofmachine-learning algorithms for detection of the incumbent
radar in the presence of commercial LTE and adjacent-band
emissions. While this study focused on detection of the current radar in the band, future work may address the detectionof future radars entering this band.

4.4. Detection in adjacent-band interference
For this case, measured adjacent-band signals are added tothe in-band radar signals. The SNR for the in-band radar is
set to 19 dB. The generated waveforms are similar to the inband radar with adjacent-band emissions shown in Fig. 1, but
the power levels of the in-band and adjacent-band signals areset separately. We first extract strong adjacent-band bursts
from the field-measured waveforms. We divide these emissions into three sets based on the peak interference to noise
ratio (INR). The >30 dB set has a median INR of 34 dB anda maximum INR of 72 dB.
2 Table 7.1.7.1-1 [10]3 Table 7.1.7.2.1-1 [10]244

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