ARL-TR-8041 ● JUNE 2017

US Army Research Laboratory

A Cognitive Agent for Spectrum Monitoring 

and Informed Spectrum Access

by Jerry L Silvious

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ARL-TR-8041 ● JUNE 2017

US Army Research Laboratory

A Cognitive Agent for Spectrum Monitoring 

and Informed Spectrum Access

by Jerry L Silvious

Sensors and Electron Devices Directorate, ARL

Approved for public release; distribution is unlimited.
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A Cognitive Agent for Spectrum Monitoring and Informed Spectrum Access

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Jerry L Silvious

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US Army Research Laboratory

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ARL-TR-8041

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14. ABSTRACT

This report presents a cognitive approach to addressing spectrum monitoring and access. A cognitive software agent was 

written in MATLAB to demonstrate some basic capabilities of a cognitive architecture, namely, sensing, analysis, learning, 

short- and long-term memory, problem solving, decision making, and focus of attention. The goal of the cognitive agent was 

to mimic intelligent behavior with respect to spectrum access.

15. SUBJECT TERMS

cognitive agent, spectrum monitoring, spectrum access, Depth of Knowledge level, DOK level

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Jerry L Silvious

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Contents

List of Figures iv

1. Background 1

2. Introduction 1

3. Cognitive Agent Methods 2

3.1 DOK Level 1: Sensing 2

3.2 DOK Level 2: Analyzing 2

3.3 DOK Level 3: Learning and Short- and Long-Term Memories 5

3.4 DOK Level 4: Focus of Attention, Problem Solving, and Decision 

Making 5

3.5 Behaviors: Adaptation and EME Modification 6

4. Results and Discussion 9

5. Conclusions 10

6. References and Notes 11

List of Abbreviations and Acronyms 12

Distribution List 13
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List of Figures

Fig. 1 Typical VHF spectrum.......................................................................... 2

Fig. 2 Probability density of the ACF of a uniform random distribution........ 3

Fig. 3 ACF of a typical VHF spectrum........................................................... 4

Fig. 4 Median power vs. ACF of a typical VHF spectrum ............................. 4

Fig. 5 ACF vs. the ratio of a constant signal added to a uniform random 

distribution ............................................................................................ 5

Fig. 6 Barrage jamming of the VHF spectrum................................................ 7

Fig. 7 ACF of the jammed VHF spectrum...................................................... 7

Fig. 8 The ACF of a typical spectrum vs. the ACF of a jammed spectrum.... 8

Fig. 9 Median power vs. the ACF of the jammed spectrum ........................... 9
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1. Background

The US Army Research Laboratory (ARL) is investigating the characteristics of 

electromagnetic environments (EMEs) to understand what spectrum bands are 

accessed, when those bands are accessed, and how much energy is detectable in 

each band. Knowledge of how the spectrum is being accessed can assist current and 

future radio frequency (RF) systems by decreasing the probability of concurrent 

access and increasing the probability of informed shared access. Similarly, 

knowledge of spectrum usage is required if the objective is to degrade or deny 

access to the spectrum. 

This report presents a cognitive approach to addressing spectrum monitoring and 

access. A cognitive software agent was written in MATLAB to demonstrate some 

basic capabilities of a cognitive architecture, namely, sensing, analysis, learning,

short- and long-term memory, problem solving, decision making, and focus of

attention.1 The goal of the cognitive agent was to mimic intelligent behavior with 

respect to spectrum access.

2. Introduction

Many types of cognitive architectures have been investigated to model various 

aspects of human cognition.2 Some models attempt to emulate the reaction time of 

humans, while others place more emphasis on models of human memory creation 

and recall. The cognitive agent in this report uses the second approach. The 

knowledge domain of the cognitive agent is the electromagnetic spectrum.

The maturity of a cognitive agent can be characterized by the robustness of the 

agent’s Depth of Knowledge (DOK) Levels.3 DOK Level 1 includes general 

abilities to measure, tabulate, and report observables. DOK Level 2 involves 

analyzing the observables, displaying graphs, and classifying patterns. DOK Level 

3 involves learning the recognized patterns by creating short-term memories; highvalue patterns are subsequently stored into long-term memories. DOK Level 4 

involves extended reasoning by applying concepts, conducting active 

experimentation, and adapting future behaviors based on results.

This report explains in more detail how each DOK Level is implemented: how the 

agent passively senses and learns about the EME, creates short- and long-term 

memories, and behaves when presented with an objective to actively access the 

spectrum.
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3. Cognitive Agent Methods

3.1 DOK Level 1: Sensing

The agent “senses”, or acquires information about, the RF spectrum by collecting 

real-time samples of the spectrum via an antenna and digitizing hardware or stored 

samples from a previously digitized spectrum.

Figure 1 is a typical very high frequency (VHF) spectrum spanning 30 to 300 MHz. 

The darker shades represent higher power densities.

Fig. 1 Typical VHF spectrum

3.2 DOK Level 2: Analyzing

The agent analyzes how the energy is distributed within the spectrum by dividing 

the frequencies into channels. Given a priori knowledge of how channels are used

by specific users, the agent may classify these channels, for instance, as frequency 

modulated (FM) radio stations, digital television (DTV) stations, or cellphone 

channels. These persistently occupied channels may then be associated with 

primary licensed users.

For instance, the dark vertical bands between 88 and 108 MHz in Fig. 1 are 

indicative of FM radio stations. These stations are separated by 0.2 MHz. The 
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vertical bands between 174 and 216 MHz are DTV stations. Specifically, RF 

channels 7 (174 MHz), 9 (186 MHz), 13 (198 MHz), and 15 (210 MHz) are clearly 

observable. DTV stations are allocated 6 MHz of bandwidth. The pilot tone that 

enables a receiver to reliably detect the presence of a DTV station is offset from the 

channel frequency by 0.310 MHz. Broadcasting channels that are discovered can 

be further identified using Internet resources that provide call signs and geographic 

locations of licensed transmitters.4

For a modest length (N > 500) sequence of random numbers drawn from the 

standard uniform distribution on the open interval (0,1), the mean of the first lag of 

the normalized autocorrelation function (ACF) is approximately 0.75, with a 

standard deviation of 0.02. Figure 2 shows the probability density function of the 

ACF. Sequences with an ACF greater than 0.9 are indicative of low variability, 

typical of continuous tones, such as a DTV pilot tone. ACF values less than 0.6 are 

typical of FM stations and pulsed transmitters. The ACF is an effective technique 

for characterizing frequency channels as low variability, amplitude modulated, or 

random noise channels.

Fig. 2 Probability density of the ACF of a uniform random distribution

Figure 3 shows the ACF of the frequency channels of the typical VHF spectrum in 

Fig. 1. A plot of the median power versus ACF of the channel samples is shown in 

Fig. 4. The probability that a given channel is noise, intermittent, or a constant tone 
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is directly assignable. Power levels below –55 dBm in Fig. 4 are due to the lower 

gain of the antenna below 35 MHz.

Fig. 3 ACF of a typical VHF spectrum

Fig. 4 Median power vs. ACF of a typical VHF spectrum
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Note that the ACF can be used to detect signals over a broad range of power levels. 

Figure 5 shows the dependence of the ACF on the signal-to-noise ratio. A constant 

signal level was added to a uniform distribution to compute the ACF.

Fig. 5 ACF vs. the ratio of a constant signal added to a uniform random distribution

3.3 DOK Level 3: Learning and Short- and Long-Term Memories

A short-term memory of the current spectrum state and identified users can be 

created by saving the state of the agent to a temporary file. High-value, long-term 

memories written to more permanent files may consist of geo-locations of identified 

primary users, other channels that have persistence, and temporal characteristics of 

the power received in the channel.

3.4 DOK Level 4: Focus of Attention, Problem Solving, and 

Decision Making

When the agent is operating in a cooperative mode, solutions must be found that 

minimize concurrent spectrum access. When the agent is operating in a spectrum 

denial mode, efficient jamming measures must be found. The agent accomplishes 

these tasks by focusing attention on the spectral bands best suited for the objective, 

analyzing the channel for received signal strength, calculating probability of active 

users using the ACF, and adapting to temporal availability.
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Federal Communications Commission regulations assign frequencies to be used for 

specific purposes. These assignments were incorporated into the agent’s knowledge 

base for this study. The role of the cognitive RF system (Federal, amateur, radio 

navigation, etc.) will influence which frequency channels occupy the focus of

attention of the cognitive agent.

3.5 Behaviors: Adaptation and EME Modification

The cognitive agent may be embedded in a larger RF system that must change state 

to accommodate a changing EME. In a cooperative scenario, the cognitive agent 

may recommend a simple change in operating frequency by selecting a channel that 

has a low power density and noise-like ACF. The cognitive agent would then 

monitor the response of the EME to this change in frequency. If no other users are 

detected on that channel, the RF system may continue to use that frequency. The 

RF system may also actively probe the EME to see if active users vacate a channel 

in a cooperative manner.

The cognitive agent may have to adapt to a contested EME in which a jammer is 

operating. A change in operating frequency may be the easiest countermeasure for 

a jammer operating in spot jam mode, in which a single frequency channel is 

degraded. The cognitive agent may also exploit temporal vacancies in the EME if 

the jammer is discovered to be operating on a predictable time schedule.

A knowledgeable cognitive agent can mimic intelligent behavior in a frequencyswept, jammed EME by searching for a pattern in the swept frequencies. Depending 

on the capabilities and processing requirements of the RF system, the cognitive 

agent may recommend a frequency-use pattern that avoids the changing jammed 

frequencies.

In a barrage jammer EME, in which wideband jamming is occurring, the cognitive 

agent may recommend a frequency band outside the jammed frequencies. If the 

cognitive agent is purposefully acting as the jamming agent, an analysis of the 

emitters in the EME becomes indispensable to efficiently degrade the EME. 

Figure 6 shows a VHF spectrum in a barrage jammer EME. Note the elevated noise 

levels in the FM and DTV frequency bands. Figure 7 shows the ACF of the Fig. 6 

jammed spectrum. The low ACF values around 160 and 270 MHz result from the 

pulsed signals shown in Fig. 6.
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Fig. 6 Barrage jamming of the VHF spectrum

Fig. 7 ACF of the jammed VHF spectrum
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Figure 8 is a plot of the typical ACF shown in Fig. 3 versus the jammed ACF shown 

in Fig. 9. While a majority of the channels are still noise-like, many channels in the 

typical spectrum that had previously been categorized as modulated have also 

become noise-like. The frequencies that persisted with ACFs greater than 0.9, 

despite the jamming, are the DTV pilot tones.

Fig. 8 The ACF of a typical spectrum vs. the ACF of a jammed spectrum
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Fig. 9 Median power vs. the ACF of the jammed spectrum

4. Results and Discussion

A cognitive architecture approach to accessing the spectrum provides a more 

flexible means to meet the objectives of future RF systems. By incorporating 

elements that mimic human sensing, learning, memory, and decision making, a 

cognitive agent can assist in meeting those objectives.

The cognitive agent in this report has shown that, after sensing the spectrum and 

analyzing the time-history of the power levels, the ACF effectively separated 

spectrum frequencies into 3 categories: low-variability tones, modulated tones, and 

noise. These categories could aid the cognitive agent in recommending which 

frequencies to select to best meet the objectives of the RF system.

The cognitive agent also demonstrated that the ACF and median power level can 

be analyzed to learn patterns in the EME. High-value patterns, such as the 

frequency channels that are consistently noise-like, provided a long-term memory 

to assist in making dependable decisions regarding spectrum access.
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5. Conclusions

A software agent was written in MATLAB to investigate whether a cognitive-based 

approach can improve spectrum monitoring and access by RF systems. The agent 

was designed to demonstrate the ability to passively sense the EME, learn about 

channel occupancy, and create short- and long-term memories of the EME. The 

agent also made decisions about actively accessing the EME by adapting to the 

EME or attempting to modify the EME to meet objectives.

Given these cognitive capabilities, a cognitive agent should enable an RF system to 

make better informed decisions regarding access to the spectrum.
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6. References and Notes

1. Kotseruba I, Gonzalez O, Tsotsos J. A review of 40 years of cognitive 

architecture research: focus on perception, attention, learning and applications.

Ithaca (NY): Computing Research Repository; 2016. arXiv:1610.08602.

2. Butt AJ, Butt NA, Mazhar A, Khattak Z, Sheikh JA. The soar of cognitive

architectures. 2013 International Conference on Current Trends in Information 

Technology (CTIT); Dubai; 2013. 135–142.

3. Hess K. A guide for using Webb’s depth of knowledge with common core state 

standards. Chicago (IL): The Common Core Institute, Center for College and 

Career Readiness; 2013.

4. TV query broadcast station search. Washington (DC): Federal 

Communications Commission; 2015 Dec 10 [accessed 2017]. 

https://www.fcc.gov/media/television/tv-query.
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List of Abbreviations and Acronyms

ACF autocorrelation function

ARL US Army Research Laboratory

DOK Depth of Knowledge

DTV digital television

EME electromagnetic environments

FM frequency modulated

RF radio frequency

VHF very high frequency
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