Int J Wireless Inf Networks (2011) 18:171–178 DOI 10.1007/s10776-011-0148-y
Opportunistic Spectrum Access Using Fuzzy Logic for Cognitive Radio Networks Hong-Sam T. Le • Hung D. Ly • Qilian Liang
Received: 27 March 2009 / Accepted: 15 April 2011 / Published online: 15 May 2011 Springer Science+Business Media, LLC 2011
Abstract Recent studies and measurements have shown that, with the traditional spectrum access approach, the radio spectrum assigned to primary (licensed) users is vastly underutilized. While many spectrum methods have been proposed to use spectrum effectively, the opportunistic spectrum access has become the most viable approach to achieve near-optimal spectrum utilization by allowing secondary (unlicensed) users to sense and access available spectrum opportunistically. Opportunistic spectrum access approach is enabled by cognitive radios which are able to sense the unused spectrum and adapt their operating characteristics to the real-time environment. However, a naive spectrum access for secondary users can make spectrum utilization inefficient and increase interference to adjacent users. In this paper, we propose a novel approach using Fuzzy Logic System (FLS) to control the spectrum access. Three descriptors are used: spectrum utilization efficiency of the secondary user, its degree of mobility, and its distance to the primary user. The linguistic knowledge of spectrum access based on these three descriptors is obtained from a group of network experts. 27 fuzzy rules are set up based on this linguistic knowledge.
H.-S. T. Le Department of Telecommunications Engineering, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam H. D. Ly (&) Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA e-mail:
[email protected] Q. Liang Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, USA
The output of the FLS provide the possibility of accessing spectrum band for secondary users and the user with the greatest possibility will be assigned the available spectrum band. Keywords Cognitive radio Fuzzy logic system Opportunistic spectrum access
1 Introduction Recent studies and measurements have shown that, with the traditional spectrum access approach, the radio spectrum assigned to primary (licensed) users is vastly underutilized while the demand for access to the limited radio spectrum have been growing dramatically. This view is supported by actual measurements conducted by the FCC’s Spectrum Policy Task Force which has determined that, in some locations or at some times of a day, about 70% of the allocated spectrum may not be in use [1]. Measurements in [2] reveal that spectrum utilization is often heavy in unlicensed bands while low in TV bands or medium in some cellular bands. These observations on actual spectrum usage have challenged approaches to the radio spectrum management and fueled interests in the opportunistic spectrum access problem. Opportunistic spectrum access has been enabled by cognitive radios (CRs). Unlike conventional radios, CRs have the capability to sense their surroundings and actively adapt their operation mode to maximize the quality of service for secondary users while minimizing interference to primary users. Hence, CRs must carry out spectrum sensing to identify white spaces or spectrum holes which are bands of frequencies assigned to primary users, but, at a particular time and specific geographic location, these
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bands are not being utilized by those users [3]. Some methods on spectrum sensing has been proposed in [4, 5], and [14]. Once spectrum holes are identified, CRs opportunistically utilize these holes for communication without causing interference to primary users. Assume that a spectrum band is available for secondary users. If only one secondary user, in a particular location and at a specific time, can sense this available spectrum, this secondary user can use this band right after the primary user finishes the communication session. What will happen, however, and which secondary user will be chosen to use the available band if multiple secondary users try to access the spectrum? Of course, for the former question, since these secondary users have the same rights to access the spectrum, they have to compete with each other in a collaborative and fair manner. This paper will give more detail answer for these questions and propose a novel approach using Fuzzy Logic System (FLS), an artificial intelligence system which is capable of making real time decisions, to decide the suitable secondary user which will use the available band. In research literature on opportunistic spectrum access, some work uses game theoretical analysis [6] to find strategies for spectrum sharing. In [7], spectrum allocation using a graph coloring algorithm is proposed but mobility of the secondary users is not considered. Moreover, authors assumed that if two secondary users within distance of each other use the same spectrum band, they fail to access spectrum. With this approach, some secondary users will lose the rights to compete for using spectrum and monitoring secondary users conflicting in using spectrum band is also a challenging issue. In our approach, we use the rule-based FLS to assign the available spectrum to secondary users efficiently and guarantee that the secondary user using assigned band will not interfere with the primary users. To achieve these objectives, we use three descriptors which are spectrum utilization efficiency of the secondary user, its degree of mobility, and its distance to the primary user. The linguistic knowledge of spectrum access based on these three descriptors is obtained from a group of network experts. 27 fuzzy rules are set up based on this linguistic knowledge. The output of the FLS provide the possibility of each secondary user which will be assigned spectrum band and the user with the greatest possibility will be assigned the available spectrum band. The rest of this paper is organized as follows. In Sect. 2, we briefly introduce the fuzzy logic system and cognitive radios. Opportunistic spectrum access using the FLS which is based on experiences from a group of network experts is proposed in Sect. 3. In Sect. 4, we discuss the simulation results. Conclusions and future works are presented in Sect. 5.
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2 Preliminaries 2.1 Cognitive Radios Cognitive Radios have been seen as the key technology that enables cognitive radio networks or xG networks to use spectrum efficiently by allowing secondary (cognitive) users to sense and utilize available spectrum opportunistically. Cognitive radios have two intrinsic characteristics [3]: •
•
Cognitive capability: Cognitive capability implies the ability of cognitive radios to sense information from their surroundings in order to figure out spectrum portions that are unused at a specific time or location. The most suitable portion will be selected for communication without causing interference to other users. Reconfigurability: Reconfigurability enables cognitive radios to be dynamically reprogrammed according to the real environment. This means that cognitive radios can change the operating frequency, modulation scheme, transmission power, communication protocol, etc. on the fly without any modification of hardware components.
Cognitive radios, in order to use spectrum opportunistically, experience four main procedures, i.e., 1.
2.
3.
4.
Spectrum sensing: A cognitive radio monitors spectrum bands and detects unused bands, i.e., spectrum holes which are time-varying and location-dependent. Cognitive radio can use the spectrum sensing techniques such as transmitter detection, cooperative detection, and interference-based detection. Spectrum access: Assume that multiple cognitive users trying to use the spectrum coexist in an area. This procedure is used to prevent multiple users from colliding in overlapping spectrum portions. Communication: Once a cognitive radio is assigned a spectrum band for communication, it will inform its receiver about the chosen band. After the receivertransmitter handshake procedure is completed, the cognitive radio begins receiving and/or transmitting information. Spectrum mobility: A cognitive radio must move to another spectrum hole to keep doing communication once it detects the signal from the primary user. Hence, spectrum mobility occurs when a cognitive radio change its operating band.
There have been many open research problems to develop these procedures. In this paper, we use the fuzzy logic system, i.e., an optimization technique, to give solutions for the opportunistic spectrum access problem. The most suitable secondary user having the rights to access the spectrum is chosen based on three descriptors, i.e., spectrum
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Expert knowledge for selecting the best suitable secondary user to access the available band is collected based on the following three antecedents, i.e., descriptors:
FUZZY LOGIC SYSTEM
RULES CRISP OUTPUT
CRISP INPUT FUZZIFIER
DEFUZZIFIER
x X
y f (x) Y INFERENCE FUZZY INPUT SETS
FUZZY OUTPUT SETS
Fig. 1 The structure of a Fuzzy Logic System
usage efficiency of the secondary user, its degree of mobility, and its distance to the primary user. 2.2 Fuzzy Logic Systems Figrue1 shows the structure of a fuzzy logic system (FLS). When an input is applied to a FLS, the inference engine computes the output set corresponding to each rule. The defuzzifier then computes a crisp output from these rule output sets. Consider a p-input 1-output FLS, using singleton fuzzification, center-of-sets defuzzification and ‘‘IF-THEN’’ rules of the form [8] l
R : IF x1 is
Fl1
Fl2
and x2 is and . . . and xp is THEN y is Gl
Flp ;
Assuming singleton fuzzification is used, when an input x0 ¼ fx01 ; x02 ; . . .; x0p g is applied, the degree of firing corresponding to the lth rule is computed as lF1l ðx01 ÞHlF2l ðx02 ÞH HlFpl ðx0p Þ ¼ T pi¼1 lFil ðx0i Þ
1. 2. 3.
Antecedent 1: Spectrum utilization efficiency. Antecedent 2: Degree of mobility. Antecedent 3: Distance to the primary user.
Generally, the secondary user with the furthest distance to the primary user or the secondary user with maximum spectrum utilization efficiency can be chosen to access spectrum under the constraint that no interference is created for the primary user. In our approach, using the rulebased FLS, we combine the above three descriptors to find optimal solutions to assign spectrum opportunistically. We see that different users will perceive different available spectrum and using spectrum efficiently is the main purpose of the opportunistic spectrum access schemes. Hence, spectrum utilization efficiency gs is introduced in our design. gs is defined as the ratio between the spectrum band which will be used by the secondary user and the available band, i.e., gs ¼
where M is the number of rules in the FLS. 3 Knowledge Processing and Opportunistic Spectrum Access 3.1 Designing the Fuzzy Logic System for Opportunistic Spectrum Access We design a fuzzy logic system to solve the opportunistic spectrum access problem in cognitive radio networks.
ð3Þ
where BWs and BWa are the spectrum band which will be used by the secondary and the available band, respectively. Mobility of the secondary user plays an important role in our design. When the secondary user is moving at a velocity v m/s, it causes the Doppler effect.
ð1Þ
where H and T both indicate the chosen t-norm. There are many kinds of defuzzifiers. In this paper, we focus, for illustrative purposes, on the center-of-sets defuzzifier. It computes a crisp output for the FLS by first computing the centroid, cGl ; of every consequent set Gl, and, then computing a weighted average of these centroids. The weight corresponding to the lth rule consequent centroid is the degree of firing associated with the lth rule, T pi¼1 lFil ðx0i Þ; so that PM p 0 l¼1 cGl T i¼1 lFil ðxi Þ 0 ycos ðx Þ ¼ PM ð2Þ p 0 l¼1 T i¼1 lFil ðxi Þ
BWs 100% BWa
fD ¼
vcosh fc c
ð4Þ
where fD is the Doppler shift, h is the arrival angle of the received signal relative to the direction of motion, c is the wave velocity, and fc is carrier frequency. Mobility can reduce capability of detecting signal from the primary users. If the secondary user is not capable of detecting the primary signal, it will incorrectly determine that the spectrum is unused; thereby leading to potential interference to adjacent users, i.e., the signal transmitted by the secondary user will interfere with the signal that the primary user is trying to decode. This situation is often referred as the hidden node problem. Besides, we also consider the distance from the secondary user to the primary user. Actually, the location of the primary users is unknown. We can consider signal-tonoise ratio (SNR) as a proxy for distance [9]. Assume the primary user at the distance R from the secondary user transmits signal at power P1 and the power gain between the primary user and secondary user, g(R), is a continuous, nonnegative, strictly decreasing function of R defined on the interval ½0; 1: SNR at the secondary user, cs, is given by
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P1 gðRÞ cs ¼ 10log r21
Int J Wireless Inf Networks (2011) 18:171–178 1.5
ð5Þ
where P1 is the transmit power of the primary user and r21 is noise power measured at the secondary user. From (5), we can derive the distance R between the primary user and the secondary user. The linguistic variances used to represent the spectrum utilization efficiency and degree of mobility are divided into three levels: low, moderate, and high while we use 3 levels, i.e., near, moderate, and far to represent the distance. The consequence, i.e., the possibility that the secondary user is chosen to access the spectrum is divided into five levels which are very low, low, medium, high and very high. We use trapezoidal membership functions (MFs) to represent near, low, far, high, very low and very high, and triangle MFs to represent moderate, low, medium and high. MFs are shown in Fig. 2. Since we have 3 antecedents and 3 fuzzy subsets, we need set up 33 = 27 rules for this FLS. Then, we design questions, which will be used in our survey, according to rules as follows: IF the spectrum utilization efficiency of the secondary user is moderate, its degree of mobility is low, and its distance to the primary user is far THEN the possibility that this user is selected to access the spectrum is . 3.2 Knowledge Processing and Opportunistic Spectrum Access As pointed out in [10], ‘‘words mean different things to different people’’, and in [11], ‘‘the decision makers may have the same preferences to a particular alternative, e.g., highly preferred but with different degrees;’’ so, we created one survey for the network experts. We used rules obtained from the knowledge of 5 network experts. These experts were requested to choose a consequent, using one of the five linguistic variables. Different experts gave different answers to the questions in the survey. Table 1 summarizes the questions used in this survey. As an example, we also give an expert’s answer in this table. Since we chose a single consequent for each rule to form a rule base, we averaged the centroids of all the responses for each rule and used this average in place of the rule consequent centroid. Doing this leads to rules that have the following form: Rl: IF spectrum utilization efficiency of the secondary (x1) is F1l, and its degree of mobility(x2) is F2l, and its distance to the primary user (x3) is F3l, THEN the possibility (y) that this secondary user is chosen to access the available spectrum is clavg. where l ¼ 1; 2; . . .; 27 and clavg is defined as follows: P5 wli ci l cavg ¼ Pi¼1 ð6Þ 5 l i¼1 wi
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Moderate
Low
High
1
0.5
0 0
10
20
30
40
50
60
70
80
90
100
(a) 1.5
Low, Near
Moderate
High, Far
1
0.5
0 0
1
2
3
4
5
6
7
8
9
10
(b) 1.5
Low
Very Low
Medium
Very High
High
1
0.5
0 0
10
20
30
40
50
60
70
80
90
100
(c) Fig. 2 The membership functions (MF) used to represent the linguistic labels: a MF for antecedent 1, b MFs for other antecedents, c MF for consequence
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Table 1 Questions for opportunistic spectrum access problem in cognitive radio networks
Table 2 cavg Corresponding to each rule Rule #
cavg
Rule # Antecedent 1 Antecedent 2 Antecedent 3 Consequence 1
17.222
1
Low
Low
Near
Very low
2
23.611
2
Low
Low
Moderate
Low
3
43.333
3
Low
Low
Far
Low
4
23.611
4
Low
Moderate
Near
Very low
5
30
5
Low
Moderate
Moderate
Low
6
43.333
6
Low
Moderate
Far
Medium
7
10.833
7 8
Low Low
High High
Near Moderate
Very low Low
8
23.611
9
36.667
9
Low
High
Far
Medium
10
30.278
10
Moderate
Low
Near
Very low
11
Moderate
Low
Moderate
Medium
11 12
56.667 76.389
12
Moderate
Low
Far
High
13
23.889
13
Moderate
Moderate
Near
Very low
14
43.333
14
Moderate
Moderate
Moderate
Medium
15
63.33
15
Moderate
Moderate
Far
High
16
17.22
16
Moderate
High
Near
Very low
17
30.278
17
Moderate
High
Moderate
Low
18
56.667
18
Moderate
High
Far
High
19
50
19
High
Low
Near
Low
20
70
20
High
Low
Moderate
High
21
89.167
21
High
Low
Far
Very high
22
36.667
22 23
High High
Moderate Moderate
Near Moderate
Low High
23
63.333
24
82.778
24
High
Moderate
Far
Very high
25
30.287
25
High
High
Near
Very low
26
56.667
26
High
High
Moderate
High
27
63.333
27
High
High
Far
High
4 Simulation Results and Discussion in which wli is the number of experts choosing linguistic label i
i for the consequence of rule l and c is the centroid of the ith consequence set (i ¼ 1; 2; . . .; 5; l ¼ 1; 2; . . .; 27). Table 2 provides cavg for each rule from the completed survey. For every input (x1, x2, x3), the output y(x1, x2, x3) of the designed FLS is computed as P27 l l¼1 lF l ðx1 ÞlF2l ðx2 ÞlF3l ðx3 Þcavg yðx1 ; x2 ; x3 Þ ¼ P27 1 ð7Þ l¼1 lF1l ðx1 ÞlF2l ðx2 ÞlF3l ðx3 Þ
We recognize that (7) can be represented in a 4-D surface. Since it is impossible to plot visually, we fix one of three variables. More specifically, we fixed the distance to the primary user x3. Two cases, i.e., x3 = 1 and x3 = 9, were considered. Figure 3 represents the opportunistic spectrum access decision surface for the cognitive user for these two cases. From Fig. 3, we see clearly that, at the same spectrum utilization efficiency and mobility degree, secondary users further from the primary user have higher chance to access the spectrum.
To validate our approach, we randomly generated 20 secondary users over an area of 100 9 100 meters. The primary user was placed randomly in this area. Three descriptors were randomly generated for each secondary user. More specifically, the spectrum utilization efficiency of each secondary user was a random value in the interval [0,100], and its mobility degree in [0,10]. Distances to the primary users were normalized to [0,10]. di max20 i¼1 fdi g qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi di ¼ ðxi xp Þ2 þ ðyi yp Þ2
Di ¼
ð8Þ ð9Þ
where (xp, yp) and (xi, yi) represent the coordinate of the primary user and the ith secondary user ði ¼ 1; 2; . . .; 20Þ: The values of descriptors corresponding to each secondary user were passed through the FLS. The output of the FLS, i.e., the possibility that a secondary user was selected to access the available spectrum, was computed as
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Int J Wireless Inf Networks (2011) 18:171–178 Table 3 Three descriptors and possibility for four secondary users Parameters
SU1
SU2
SU3
SU4
55 50
Mobility degree
2.4966
3.0715
40
Distance to PU
8.4852
3.0036 10
35
Possibility
30
Spectrum usage efficiency (%) 88.7104 97.9340 24.2160 92.4424
y(x ,x ,1) 1 2
45
25
6.5382
0.9135 1.6437
82.2944 54.5189 45.3072 53.1432
20 15
100
10 10 8
100 80
6
x2
60
4
40
2 0
20 0
90 80
x1
70
(a)
60 50 90
40
1
y(x ,x2,9)
80
30
70
20 60
10
50
0
40 30 10 8
100 80
6
x2
60
4
40
2
20 0
x1
0
10
20
30
40
50
60
70
80
90
Fig. 4 An opportunistic spectrum access scenario in a specific space and a particular time: SU1, SU2, SU3, and SU4 are denoted using H; 5; ; and }; respectively. The primary user is denoted using h
0
(b) Guard Symbols (3 symbols)
Unique Word (40 symbols)
Payload (500 symbols)
Guard Symbols (3 symbols)
Fig. 3 The opportunistic spectrum access decision surface for the cognitive user with a fixed distance to the primary user: a when the distance to the primary user x3 = 1, b when the distance to the primary user x3 = 9
Fig. 5 Burst structure
in (7). Then, the secondary user with the highest possibility would be chosen to access the spectrum. At a particular time, values of three descriptors and possibility for four secondary users, i.e., the secondary user chosen to access the available spectrum (SU1), the secondary user with the highest spectrum utilization efficiency (SU2), the secondary user having the furthest distance to the primary user (SU3), and the secondary user with the lowest mobility degree (SU4) are listed in Table 3. Position of these users is shown in Fig. 4. We see that, in Table 3, SU2 has the highest spectrum utilization efficiency with 97.93%, SU4 with 92.4424% while SU1 only achieves 88.7104%. Although SU3 is the secondary user with the furthest distance, it has the lowest spectrum utilization efficiency and highest mobility degree. We also ran the Monte-Carlo simulation to analyze the performance of secondary users with different mobility degrees (see Table 3). In this simulation, we used the Rician flat fading channel with fading factor K = 12 dB
and Doppler shift fd = 41.6100, 51.1917, 108.9700, and 15.2250 Hz for SU1, SU2, SU3, and SU4, respectively. QPSK modulation scheme was used at transmitter side and block phase estimation algorithm proposed in [13] was applied at receiver side. Burst structure is depicted in Fig. 5 with 546 QPSK symbols (500 symbols payload, 40 symbols Unique Word, and 6 symbols guards) were used. Performance of secondary users in term of mobility degree and Eb/N0 is depicted in Fig. 6. Based on Fig. 6, we note that, at the same Eb/N0, SU4 with the lowest Doppler shift can achieve the best performance while SU3 with the highest Doppler shift has the worst performance. Performance of SU1 and SU2 is similar since there is a small difference between Doppler shifts. From above results, we can confirm that spectrum access decision is tradeoffs among three descriptors chosen to design the FLS. Therefore, the secondary user with the highest spectrum utilization or the secondary user furthest from the primary user
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177
SU1 SU2 SU3 SU4
−1
BER
10
−2
10
1
2
3
4
5
6
7
Eb/N0 [dB]
Fig. 6 Performance analysis of secondary users in term of mobility degree and Eb/N0
is not guaranteed to access the spectrum. The secondary user selected to access spectrum has the distance to PU 8.4852, spectrum utilization efficiency 88.7104% and mobility degree 2.4966. Until now, someone may have two more questions: (1) who will decide the spectrum access rights for the secondary users? (2) if there are N users competing for M spectrum bands (N [[ M), how can we control the spectrum access? Since we use the centralized spectrum sharing architecture, a centralized entity such as base stations in cognitive wireless networks or clusterheads in sensor networks collects information about three descriptors and available spectrum bands from secondary users through a common control channel and builds a spectrum map. Then, it uses our designed FLS to control the spectrum assignment and access procedures in order to prevent multiple users from colliding in overlapping spectrum portions. In case, N users competing for M spectrum bands, the centralized processor also takes advantage of our FLS for each band to allow the best secondary user to access each spectrum.
5 Conclusion and Future Works We propose a novel approach using the rule-based fuzzy logic system to control the opportunistic spectrum access for secondary users in cognitive radio networks. The secondary user is selected based on three descriptors, i.e., spectrum utilization efficiency of the secondary user, its degree of mobility, and its distance to the primary user. The linguistic knowledge of spectrum access is based on experiences from a group of network experts, so that an acceptable decision can be obtained. As a result, we
represent the opportunistic spectrum access decision surface. An opportunistic spectrum access scenario was analyzed and simulated to validate our approach. In our approach, moreover, we can modify the membership functions of descriptors in accordance to requirements of the primary network and the spectrum using policy. Hence, our approach is promising to be implemented practically in future cognitive radio networks. We see that it is better if the secondary user uses multiple bands simultaneously for transmission since multiband transmission provide better performance than single band transmission during the spectrum handoff [14]. This means that if a primary user returns to utilize a specific band, the secondary user must vacate this band. Since transmission is still continuing in other bands, quality of service (QoS) degradation can be mitigated. Using our scheme, multiple spectrum band decision for a secondary user is also obtained. However, some secondary users try to get as much spectrum bands as possible and they can keep some of these bands for future transmission when the spectrum handoff occurs. By this way, spectrum utilization is not efficient. So, it is important to investigate some solutions to prevent some users from using spectrum ineffectively and solve the mobility management problem in order to keep a high QoS of cognitive radio networks.
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11. Marimin, et al., Linguistic labels for expressing fuzzy preference relations in fuzzy group decisio making, IEEE Trans. on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 28, No. 2, pp. 205–218, 1998. 12. Q. Liang, Clusterhead election in mobile ad hoc networks, IEEE Proc.on Personal, Indoor and Mobile Radio Communications, Vol. 2, pp. 1623–1628, 2003. 13. A.J. Viterbi and A.M. Virterbi, Nonlinear estimation of PSKmodulated carrier phase with application to burst digital transmission, IEEE Trans. on Information Theory, Vol. 29, No. 4, pp. 543–551, 1983. 14. Ian F. Akyildiz, et al., NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey, Computer Networks Journal (Elsevier), Vol. 50, pp. 2127–2159, 2006.
Author Biographies Hong-Sam T. Le received her B.S. degree in Electronics and Telecommunications Engineering from Posts and Telecommunications Institute of Technology, Hanoi, Vietnam in 2003 and her M.S. degree in Electrical Engineering from the University of Texas at Arlington in 2007. Her research interests are in the areas of wireless communication and signal processing.
Hung D. Ly received the B.S. degree in Electronics and Telecommunications Engineering from Posts and Telecommunications Institute of Technology, Hanoi, Vietnam in 2002, and the M.S. degree in Electrical Engineering from the University of Texas at Arlington in 2007. He is currently pursuing the Ph.D. degree in Electrical and Computer Engineering at Texas A&M University. His research interests include information theory, wireless communication, and signal processing.
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Qilian Liang is a Professor at the Department of Electrical Engineering, University of Texas at Arlington. He received the B.S. degree from Wuhan University in 1993, M.S. degree from Beijing University of Posts and Telecommunications in 1996, and Ph.D degree from University of Southern California (USC) in May 2000, all in Electrical Engineering. Prior to joining the faculty of the University of Texas at Arlington in August 2002, he was a Member of Technical Staff in Hughes Network Systems Inc at San Diego, California. His research interests include compressive sensing, radar sensor networks, wireless sensor networks, wireless communications, communication system and communication theory, signal processing for communications, fuzzy logic systems and applications, etc. Dr. Liang has published more than 170 journal and conference papers, 7 book chapters, and has 6 U.S. patents pending. He received 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, 2003 U.S. Office of Naval Research (ONR) Young Investigator Award, 2005 UTA College of Engineering Outstanding Young Faculty Award, and 2007, 2009, 2010 U.S. Air Force Summer Faculty Fellowship Program Award.
