COGNITIVELY INSPIRED RADIO ENGINE FOR IEEE 802.22 WRAN
Lizdabel Morales-Tirado
Dr. Jeffrey H. Reed, advisor
Mobile Portable Radio Group, Virginia Tech, Blacksburg, VA 24061
Abstract
Cognitive radio (CR) is a promising tool in the field of
wireless communication research. Although algorithms
and techniques envisioned in CR research can reside in
any of the layers of the protocol stack, current
investigations in CR have been focused on the physical
layer functionality. Research in radio resource
management (RRM) of a distributed, decentralized CR
network has begun recently.
IEEE 802.22 WRAN system claims to be the first
wireless communication standard adopting cognitive
radio functionalities. However, current specifications
seem to be more like “a frequency agile radio” system.
In order to be a cognitive radio system, the system needs
to have the capabilities of recognizing its system
operating scenarios on top of its awareness of
surrounding environment, choosing intelligently efficient
strategies and algorithms under, supporting coexistence
of other CR systems, and learning from its experiences
and predefined rules.
During the last year, we investigated and developed a
cognitive engine, which is the first application of
cognitive radio concept to more efficient radio resource
management (RRM) in a centralized network. We have
designed the architecture of a cognitive engine (CE)
which is generic and flexible so that it can be applied to
general cognitive radio systems. Along with a general
framework that will allow for future design,
development, and testing of more enhanced cognitive
engine, we have implemented a cognitive engine which
provides means for 802.22 base stations to have
scenario-classification and optimization capability by
tailoring the developed CE architecture to fit 802.22 base
station operation scenarios.
the cognitive radio (CR) technologies. IEEE 802.22
will be the first worldwide CR based standard to
support the unlicensed operation in TV bands (54-862
MHz), which is to coexist with incumbent users and
provide wideband internet access to rural and suburban
areas.
According to US FCC’s recent public notice, products
refarming TV bands are scheduled to be available to the
market by February, 20091. The IEEE 802.22 Work
Group kicked off in November 2004 and approved the
functional requirement document for WRAN systems in
September 20052. Ten initial proposals were merged
into a single one in March 2006, and the draft standard
(D0.1) was developed in May 20063. The complete
802.22 standard is expected to be approved by May
2007. For 802.22 WRAN systems, the primary users
(PU), those with priority rights, mainly include
incumbent analog and digital TV stations, TV
translators, TV boosters, TV receivers, and wireless
microphones. WRAN systems, including base stations
(BS) and customer premise equipment (CPE), are
secondary users (SU), and therefore, should avoid
generating harmful interference to the PUs. In this
standard, the “intelligence” resides in the 802.22
WRAN base stations. The base station is aware of its
environment, and also makes the best decision in terms
of spectrum management and maximization, as well as,
network optimization. The CPEs help the base station
obtain current radio environment information by
scanning the spectrum in their vicinity and reporting the
results. Figure 1 shows the typical IEEE 802.22
WRAN scenario.
WRAN Base Station
Introduction
CH3
On December 19, 2005, the House of Representatives
of the United States approved legislation to complete
the country’s transition to new, higher-quality digital
television (TV) by February 17, 2009. Refarming the
analog TV spectrum promotes new wireless
communication technology developments, most notably
CH2
CH1
CH1
CPE 4
CPE 3
CPE 1
CPE 2
TV Station
(Primary User)
Figure 1: IEEE 802.22 WRAN Scenario
Morales-Tirado
1
Cognitive Radio Engine Architecture
As illustrated in Figure 2, the proposed 802.22 WRAN
BS consists of three primary entities: the spectrum
sensing module, radio environment map (REM) and the
core CE. Both the spectrum sensing module and REM
database continually run to keep information current.
The core decision-making process exists in the CE
module and relies on information stored in the REM.
The CE module acts both as a high-level spectrum
management entity - allocating television channels and
sub-carrier bands as necessary - and as a MAC/PHY
layer controller by adjusting coding, modulation, and
interleaving levels. Since the 802.22 relies on a very
strict policy it is necessary to combine all these levels of
decision in order to maximize spectrum usage and
minimize interference to primary users.
Main Controller
Sensing
Module
Utility
REM
Core Learning
Agent
Channel
Modeler and
Predictor
Multi-objective
Optimizer
Spectrum
Manager
WRAN Cognitive Engine
Figure 2: IEEE 802.22 WRAN Cognitive Engine Architecture
Furthermore, cross-layer optimization can provide
significant performance improvements over systems
which treat each layer as a separate problem. The
drawback to this method is producing a potentially large,
complex solution space over which searching becomes
an issue. To mitigate this problem, we propose a multistep search process as a compromise between the
accuracy and complexity of searching. In the first stage
the cognition focuses on assigning an appropriate TV
channel and initial power setting to new service requests;
on the second stage the cognition focuses on assigning
the resources to the node, such as: modulation scheme,
sub-carriers, time-slots, coding, etc.
Learning algorithms in CE Design
In this section we discuss several artificial intelligence
techniques that can be applied in the design of a
cognitively inspired engine for an IEEE 802.22 WRAN
network. It is worth mentioning the key characteristics
of each technique and their major role in the cognitive
radio engine implementation.
There are several
cognitive tasks that the IEEE 802.22 WRAN base needs
to perform. The WRAN system is a secondary user in
the TV spectrum thus protecting the incumbent users is a
Morales-Tirado
primary goal for the base station. In order to accomplish
this task, periodic sensing of the radio spectrum is
performed by the CPEs in the network. The base station
gathers sensing data from the CPEs in the network, and
then manages the spectrum accordingly in order to avoid
harmful interference to primary users. The second
cognitive task is to maximize the available radio
spectrum. For this task, the base station relies on
maximizing the spectrum usage via effective radio
resource management, by predicting the detection of
incumbent users and by exploiting past experience stored
in the REM.
There are many benefits to the addition of cognitive tasks
to the WRAN base station, such as: flexibility in the
network, adaptability to the current and future situations,
increased network optimization, increased network
capacity, etc. On the other hand, the addition of these
capabilities leads to more complexity in the design and
implementation of the WRAN base station, thus creating
the need for “intelligent” agents that manage the radio
resources in the network.
In recent years, there has been lots of research focused on
merging
intelligent
agent
technology
and
telecommunication systems. Wireless technologies have
expanded to provide countless services to users, and
information is required anytime, anywhere and in any
form 4, therefore the need for communication systems
that are flexible, and adapt to the needs of the user and
the dynamically changing radio environment.
In reference 4, the author discusses how artificial
intelligence techniques can be applied to wireless
communication systems and suggests a functional
scheme for implementing an intelligent system to
manage communication networks. He also suggests that
“intelligence” can be dispersed throughout the OSI layers
in the protocol stack.
In reference 5, the authors surveyed various machine
learning techniques and discussed possible “intelligent”
implementations for the design of cognitive networks.
They also identify various cognitive networking tasks
which include: anomaly detection and fault diagnosis,
responding to intruders and worms, and rapid
configuration of networks. If we evaluate the IEEE
802.22 WRAN system it can include similar cognitive
tasks as those described above, including incumbent
detection, network optimization, spectrum radio resource
management, etc. Joseph Mitola in his dissertation6 also
describes the different cognitive tasks, and furthermore
divides the “radio world” of the cognitive radio CR1 in
“micro-worlds”, or in other words knowledge domains.
It is sometimes difficult to apply a single artificial
technique that will provide an accurate solution to all the
2
various problems in a network7, it is necessary to
determine which techniques can provide a more accurate
and quick solution given the type of domain problem and
radio scenario.
In the next paragraphs we discuss several of these
techniques and describe the characteristics that make
them suitable for cognitive radio engine development. In
the next paragraphs we survey: knowledge-based (or
rule-based) systems, case-based system, artificial neural
networks, fuzzy logic, and hidden Markov models.
These techniques are discussed in the context of “expert
systems”, and the expert system in this case has the goal
of managing the 802.22 WRAN.
The most common expert system is the Knowledgebased (or Rule-based) expert system. Knowledge-based
systems rely in a simple architecture: a set of rules
included in a module known as the knowledge base, an
inference engine and a working memory. The inference
engine processes the rules and the working memory
contains the information of the current problem. The
accuracy of this technique depends upon the available
domain knowledge. Another key issue is the amount of
rules that some systems may require, as the complexity
of the system increases so as the domain knowledge.
The 802.22 scenario is suitable for the use of knowledgebased systems, especially when complemented with
inductive learning techniques (such as case-based
reasoning) to account for the imperfect knowledge in the
system such as interference, location of incumbents, etc.
As an example, the authors in reference 8 implemented a
generic cognitive radio using a knowledge-based system.
Another common technique used to build expert systems
is Case-based reasoning (CBR), which main focus is
experience rather than knowledge. CBR is analogous to
an old physician; the experienced physician can quickly
diagnose a patient since he has seen many similar cases
before. CBR is an area of machine learning that
concentrates on using previous similar cases to guide the
problem solving process and to achieve a solution. This
technique has been successfully applied in expert
systems in the areas of law, medicine, manufacturing,
design, education and many others. The author in
reference 9 describes in detail a case-based reasoning
approach to network management. The author divides
network management in various tasks: network design,
planning, and implementation, management of customers,
and finally operation and maintenance budgeting. CBR
is suitable for the development of a cognitive engine as it
performs very well in dynamically changing
environments, and can provide solutions even in the
absence of perfect domain knowledge.
Morales-Tirado
Another technique not frequently included in expert
systems is artificial neural networks (ANN). ANNs form
powerful generic modeling techniques, well-suited for
use within cognitive radios, particularly due to their
learning capabilities, scalability, and dynamic flexibility
in describing numerous situations7. By modeling the
biological plexus in organisms, ANNs are nothing more
than a set of nonlinear functions with adjustable
parameters to give a desired output. Each artificial
neuron produces a single output value by accumulating
that from other neurons, thus forming a network.
Networks can be classified by their neuron configuration,
interconnections, and training methods, allowing for a
multitude of applications. Because of their ability to
dynamically adapt and be trained at any time, ANNs are
able to “learn” patterns, features, and attributes of
systems they describe. For this reason they have long
been used to describe functions, processes, or classes that
are difficult to formulate analytically, particularly within
device control, but only recently have they been
proposed for use within cognitive radios7. The authors in
reference 10 implemented a cognitive radio using an
ANN. The network was used only to assist the engine in
its decision for parameters to be used in the next block of
transmissions. The engine used the network to determine
how closely a set of parameters met a set of goals, and
then chose the set of parameters which maximized the
utility functions. The engine re-trained the network
based on the decisions made and the resulting outcome.
Fuzzy logic is derived from fuzzy set theory; it refers to
the process of reasoning that is approximate rather than
precisely deduced from classical predicate logic. Fuzzy
logic uses a “qualitative” approach rather than a
“quantitative” one. Furthermore, fuzzy logic allows for
set membership values between and including 0 and 1,
and in its linguistic form, imprecise concepts like
"slightly", "quite" and "very". Specifically, it allows
partial membership in a set. It is related to fuzzy sets and
possibility theory. Fuzzy set theory has been applied
successfully to computing with words or the matching of
linguistic terms for reasoning11. In terms of our current
implementation fuzzy logic can be used together with
CBR to aid in the reasoning of non-numerical parameters
as suggested by the authors in reference 11. Also using
fuzzy set theory to measure similarities between cases
may allow for simpler comparisons, multiple indexing
schemes, and term modifiers to increase flexibility in the
case retrieval process.
Hidden Markov Models are convenient and
mathematically tractable tools, and useful to describe and
analyze the dynamic behavior of complicated random
phenomena. In general, a real world process that can or
cannot be expressed as a random process produces a
sequence of observable symbols or pattern. The symbols
3
or patterns can be discrete or continuous depending on
the process of specific applications. If we build a signal
model that explains and characterizes the occurrence of
the observed symbols or patterns, then we can use that
model later to identify or recognize other sequences of
observations by choosing most likely model close to
obtained model. In an 802.22 network the HMM is a
suitable learning technique for classification, detection
and possibly channel prediction.
In this section, we focused on the discussion of
algorithms that are suitable for the development of a
cognitive radio engine, and their key characteristics. Due
to time constraints, only a few algorithms were
implemented.
Learning Agents
In this section we discuss the learning agents
implemented in the current cognitive radio engine design
for the IEEE 802.22 WRAN. We first discuss the casebased reasoning engine and follow with a discussion of a
hybrid design which includes knowledge and case-based
engine.
Case-based Reasoning (CBR) Engine
For this initial implementation our team chose to
implement a simple case-based reasoner, as a proof of
concept that this learning technique is suitable for
cognitive radio engine implementation. Case-based
reasoning was selected since its implementation didn’t
require complete domain knowledge. Also, surveyed
literature presented various successful implementations
of CBR systems for network management 9,12 and radio
resource management in telecommunication systems
13,14
. In the next paragraphs we briefly discuss the CBL
process.
Case-based learning (CBL), also known as case-based
reasoning, focuses on using previous similar experiences;
or in other words, cases to guide the problem solving
process and to achieve a solution. In CBL, a solution to
the problem is created by selecting the cases that are
relevant to the problem, and then the best match is
chosen and adapted to fit the current case or instance.
The case selected by the retrieval process may not match
exactly with the current problem; the relevant case may
need to be adapted to meet the requirements of the
current situation. This process is called case adaptation.
In summary, the case-based learning approach can be
described by four action words: retrieve, reuse, revise
and retain. Figure 3 shows the basic case-based
reasoning approach.
Morales-Tirado
Retrieve
Reuse
Revise
(Relevant cases)
(useful
experience)
(adapt)
Retain
(for future
cases)
Solution
New Case
Figure 3: Case-base reasoning approach
For this implementation in Step 1 we created a simple
case memory implemented as a text file with a few
typical scenarios. Step 2 and Step 3 were executed by
performing a single nearest neighbor search and
returning the closest match to the input case. By default,
the case library search process assigns equal value to all
the parameters, but the user can easily modify the search
by changing the weight vector. For example, if the
matching has to be performed solely of the amount of
radio resource units requested, the weights vector will
have a 1 for that parameter and 0 for the rest. One
important key issue to note is that currently the nearest
neighbor algorithm returns a single case; however, in the
future the algorithm can be modified to return k cases.
The case-based reasoner then can perform further
evaluations on the relevant cases and develop a more
accurate and tailored solution. Step 4 has been
implemented by following a small set of rules as describe
in Table 1; in the future it will be necessary to include
sensing information in an attempt to uniquely describe a
case. The key issue with this approach is that the case
library can increase significantly as more cases are
gathered, thus requiring faster searching mechanisms.
Integrating Knowledge in the Reasoner
As mentioned previously, one of the key issues of casebased reasoning is that the solution is only as good as the
case library itself. When we have not compiled enough
cases or there are no cases that are relevant to our current
case, it is useful to combine both approaches: using case
and knowledge based reasoning to achieve a solution.
Multi-objective Optimizer
The multi-objective optimizer of WRAN CE is used
to maximize the system utility subject to multiple
constrains, such as the interference to PUs, and CPE
transmit power. In other words, CE needs make
decisions and/or adaptations in multiple domains, such as
frequency (channel selection), time (timeslot and frame
scheduling) and power.
Because of the high
dimensionality the problem, however, its performance
will be highly dependent upon its starting point, and
therefore will rely highly on the output of the CBL entity.
4
The general objective of WRAN BS is to meet the ToS
(type of service) and QoS requirements of CPEs with
minimal costs and risk of interference to PU, subject to
practical constraints of bandwidth (number of TV
channels supported simultaneously) and transmit power
(spectral mask specified by standard body). For 802.22
systems, to avoid/reduce harmful interference to
incumbent PUs is always the primary concern.
We have condensed the complexity of the search
problem to a linear programming search over a finite set
of values. Discretizing each parameter relaxes the
programming restrictions and simplifies the problem.
For this stage of development we are considering the
following search agents:
1.
2.
3.
4.
Exhaustive search
Local dimension search
Hill climbing search
Genetic algorithm
We have successfully implemented and verified the
exhaustive, local-dimension, and genetic algorithm
searches. We are in the process of using it to find the
optimum solution to randomly generated scenarios such
that we can populate the case library. This will provide a
benchmark against which more efficient search agents
can be tested.
Simulation Approach
A basic framework for testing algorithms for the
proposed 802.22 cognitive base station has been
developed. It has been written in C++ and provides a
basic platform for analyzing performances of the
modules proposed in this section. It is capable of
generating multiple base stations, CPEs (nodes), and
wireless microphones (Part 74 devices). Each node has a
unique service request that the cognitive base station
must satisfy before its activation. The framework has the
capability of analyzing the performance while varying
the following parameters: television channel (currently
fixed at 6MHz, but easily changeable), transmission
power, modulation scheme, FEC coding scheme, and the
number of subcarriers on both the downlink and uplink
for each CPE. The simulation starts the base station with
a certain task (such as activating links to new users or
vacating users from a television channel). The simulator
analyzes the performance of the proposed cognitive
engine in two main categories: CPU run time and
resulting performance.
Morales-Tirado
Utility Function
Performance of the cognitive engines is based upon
spectrum utilization as well as individual CPE utilities.
The global utility is defined as
utotal = 0.5ucpe + 0.35uspectrum + 0.15 min {ucpe }
where the spectrum utility is simply the ratio of candidate
channels to total channels, and the individual node
utilities are comprised of three simple sub-goals:
a. Meet the application BER requirement (50%)
b. Meet the application data rate (35%)
c. Minimize transmit power (15%)
These can be expressed mathematically as follows:
(k )
k
k
ucpe
 0.5uBER
 0.35udata
 0.15u kpower
See Figure 4 for the individual utility functions for the
CPEs.
Results
Two simple cognitive base stations were implemented
with the framework; one which relies on knowledgebased rules to determine optimum parameter sets and
allocate resources to the nodes, and one which relies
completely upon a genetic algorithm search for the best
solution. The simulation results can be seen below in
Figure 5. Five basic scenarios were described using
external files. In each scenario, both cognitive engines
were required to add up to 40 nodes to an existing
network. Table 2 describes the scenarios under test.
Each scenario was repeated 20 times with each new
CPEs having a random location with respect to the base
station. The maximum range was set to 33km for any
node, and the application requirements were chosen
randomly among four levels with bit error rates ranging
from 10-2 to 10-6, and data rates from 10kbps to
750kbps. According to Figure 5, CE that uses genetic
algorithms produced a consistently better result in terms
of end utility, but consumed several orders of magnitude
more CPU clock cycles. Furthermore, as the number of
CPEs added increases, the end utility between the two
cognitive engines converge.
5
Data Rate Utility
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
data
1
utility u
utility u
BER
BER Utility
1
0.5
0.4
0.5
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
-2
10
-1
0
10
1
10
BER/BER0
0
2
10
10
0
0.2
0.4
0.6
0.8
1
D/D0
1.2
1.4
1.6
1.8
2
Transmit Power Utility
1
0.9
0.8
utility u
power
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-5
-4
-3
-2
-1
0
1
P - P0 [dB]
2
3
4
5
Figure 4 : Utility functions for individual CPEs
3
10
1
GA
CKL
GA
CKL
0.95
Average Adaptation Time [ms]
0.9
Average Utility
0.85
0.8
0.75
0.7
0.65
2
10
1
10
0.6
0.55
0.5
0
0
5
10
15
20
25
30
Number of CPEs Added
35
40
45
10
0
5
10
15
20
25
30
Number of CPEs Added
35
40
45
Figure 5: Preliminary Results
Morales-Tirado
6
Table 1 Rules
Problem Types
(1) Add new connection to
BS
Case Attributes
[1] CPE_profile (id, ToS,
location)
[2] RRU_Req_New
[3] RRU_Assigned
[4] MaxAvailableRRU_CH
[5] MinAvailableRRU_CH
[6] Active_CH
[7] Candidate_CH
Candidate Solutions
[1] Reject new connection
[2] Activate a Candidate Channel with
highest "channel reputation"
[3] Use the lowest_RRU_assigned_CH
[4] Use the highest_RRU_assigned_CH
[5] Use the largest_Number of CPEs
and reloacte lowRRU_CPE to other
Active channel
[6] Use the smallest_Number of CPEs
[7] Relocate to MaxAvailble_CH (to
get the max. available BW)
[8] Relocate to MinAvailble_CH (for
BE type of service)
[9] Use the most matched
Available_RRU CH
Action
[1] if Similarity >
threshold_h1 &&
Est_Utility >
threshold_h2, skip
optimizer; else, initialize
operation parameters,
and run Optimizer
[2] refer to “channel
reputation” for candidate
channel selection
[3] Use lowest frequency
Channel for CPE with
large pathloss (e.g.,
remote CPE)
[4] Use highest frequency
Channel for CPE with
low pathloss (e.g., close
to BS)
Table 2 Testing Scenarios
Scenario
# of existing CPEs
# of CPEs to add
# of initial candidate channels
3
# of initial active
channels
1
1
2
2
3
10
5
2
8
10
10
2
8
4
10
20
3
7
5
10
40
3
7
Morales-Tirado
9
7
Conclusions and Future Work
IEEE 802.22-05/0007r46, “Functional Requirements
for the 802.22 WRAN,” Sept., 2005.
2
IEEE 802.22 WRAN is the first application of cognitive
radio networks refarming the TV broadcast bands. The
standardization and deployment of cognitive WRAN
systems will have significant impact on the future
advancement of cognitive radios. In this paper, we
describe our research efforts in developing both a
cognitive radio engine framework that integrates the
spectrum management capabilities outlined in the IEEE
802.22 standard, as well as a few preliminary cognitive
engine models. We have developed a solid and generic
architecture and a general framework that will allow for
future design, development and testing of various
learning, decision-making and optimization techniques
that will provide efficient performance and accurate
solutions for the IEEE 802.22 problems.
3
IEEE P802.22 (D0.1) Draft Standard for Wireless
Regional Area Networks Part 22: Cognitive Wireless
RAN Medium Access Control (MAC) and Physical
Layer (PHY) specifications: Policies and procedures for
operation in the TV Bands”.
4
M. Kaiser and Z. Cucej, "Artificial intelligence in
modern telecommunication systems: analysis and
implementation," in Proceedings of Video/Image
Processing and Multimedia Communications, 2003. 4th
EURASIP Conference focused on, Volume 2, 2-5 July
2003 Page(s):805 - 810 vol.2.
5
Using this general framework we have the tools to
implement and optimize specific modules for cognitive
engines and test their performance and functionalities
further for future development. Based upon these initial
results, we can formulate which algorithms are suited for
specific scenarios. By tailoring our engine to the
strengths of each algorithm, we can optimize the
performance for an efficient cognitive engine for 802.22
wireless base stations. Therefore, to enhance the
functionality and performance of CE, we propose to
refine utility functions and testing scenarios under
possible real-world system operation scenarios, enhance
the core engine by incorporating more sophisticated case
and knowledge adaptation or decision fusion methods,
extending case library with more sophisticated radio
scenarios, and implement more multi-objective
optimizers as fine optimizers.
Acknowledgment
I would like to thank Dr. Jeffrey H. Reed, my advisor,
for his support during my graduate student career. Also,
special thanks to my colleagues Joseph Gaeddert and
Youping Zhao, who have worked on this research
problem with me. Also, thanks to our sponsor, ETRI,
for all their assistance in the development of this
Cognitive Radio Engine. This project was also funded
by the Virginia Space Grant Consortium and the Pratt
Fellowship.
References
Federal Communications Commission, “Office of
Engineering and Technology Announces Projected
Schedule for Proceeding on Unlicensed Operation in
the TV Broadcast Bands,” FCC Public Notice, ET
Docket No. 04-186, Sept., 2006
1
Morales-Tirado
T. Dietterich and P. Langley, "Machine Learning for
Cognitive Networks: Technology Assessment and
Research Challenges," Technical Report, May 2003.
6
J. Mitola, III, "An Integrated Agent Architecture for
Software Defined Radio," PhD Dissertation, Royal
Institute of Technology, Stockholm, Sweden, 2000.
7
J. Gaeddert, K. Kim, R. Menon, L. Morales, Y. Zhao,
K. K. Bae, and J. H. Reed “Applying artificial
intelligence to the development of a cognitive radio
engine”, MPRG Technical Report, June 30, 2006.
8
E. Stuntebeck, T. O'Shea, J. Hecker, and T. Clancy,
"Architecture for an Open-Source Cognitive Radio,"
SDR Forum Technical Conference, November 2006.
9
L. Lewis, "Managing Computer Networks: A CaseBased Reasoning Approach," Artech House, 2005.
10
C. R. Aguayo, J. Gaeddert, K. Kim, K. K. Bae, L.
Morales, M. Robert, Y. Zhao, C. Dietrich, and J. H.
Reed, "Desing and implementation of an open-source
software-defined cognitive radio testbed," unpublished
paper.
11
S. K. Pal and S. C. K. Shiu, "Foundations of Soft
Case-Based Reasoning," John Wiley and Sons, Inc.,
2004.
12
M. Stadler, "Case-based Reasoning for Network
Management," in Proceedings from Topics in CaseBased Reasoning, First European Workshop, EWCBR93, pp. 414-423, 1993.
13
S. Chantaraskul and L. Cuthbert,"Using Case-Based
Reasoning in Traffic Pattern Recognition for Best
Resource Management in 3G Networks," in
8
Proceedings from 7th ACM international symposium
on Modeling, analysis and simulation of wireless and
mobile systems, pp. 252-259, 2004.
14
N. Yao and L. Cuthbert, "Resource Management in
3G Networks using Case-Based Reasoning," in
Proceedings of the 8th ACM international symposium
on Modeling, analysis and simulation of wireless and
mobile systems, pp.307-312, 2005.
15
J. Mitola, III, "An Integrated Agent Architecture for
Software Defined Radio," PhD Dissertation, Royal
Institute of Technology, Stockholm, Sweden, 2000.
16
J. Gaeddert, K. Kim, R. Menon, L. Morales, Y. Zhao,
K. K. Bae, and J. H. Reed “Applying artificial
intelligence to the development of a cognitive radio
engine”, MPRG Technical Report, June 30, 2006.
Morales-Tirado
9