Mobile Netw Appl DOI 10.1007/s11036-009-0177-2

Channel Modeling and Inter-Carrier Interference Analysis for V2V Communication Systems in Frequency-Dispersive Channels Tao Jiang · Hsiao-Hwa Chen · Hsiao-Chun Wu · Youwen Yi

© Springer Science + Business Media, LLC 2009

Abstract Vehicle-to-vehicle (V2V) communication has attracted much attention recently. In V2V communications mobility plays a major role in yielding frequency dispersion of the channels, and thus accurately modeling of Doppler effect becomes a challenging issue as the two fundamental assumptions of the Jack’s model may not be valid due to time-varying channel characteristics in V2V communication systems. In this paper, we present a practical model to characterize V2V communication channel and its corresponding Doppler spread spectrum is derived. In addition, we will study the impact of inter-carrier interference (ICI) generated in an orthogonal frequency division multiplexing (OFDM) based V2V communication system. Compared with the classical Jake’s channel model, our proposed new channel model is more accurate and fits in particular well for the performance assessment of vehicle-to-vehicle communication systems.

T. Jiang · Y. Yi Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China e-mail: [email protected] H.-H. Chen (B) Department of Engineering Science, National Cheng Kung University, 1 Da-Hsueh Road, Tainan City, 70101 Taiwan e-mail: [email protected] H.-C. Wu Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, USA e-mail: [email protected]

Keywords vehicle to vehicle communication · mobile channel · Doppler spread · inter-carrier interference · OFDM

1 Introduction Recently, vehicle-to-vehicle (V2V) communications attract a lot of attention [1–4]. The V2V communication systems can offer enhanced safety, intelligent traffic management and automobile wireless access through high-speed wireless links established among vehicles or vehicles and roadside access points (APs). The importance of V2V communications has been known for its many potential applications to provide various vehicle-related networking services, including adaptive traffic light control, traffic jam alarming, collision avoidance, automatic toll collection, vehicular safety services, travel information exchange, and on-road e-commerce transactions, etc. To provide automobiles with these diverse services with fast information exchanges, V2V communication will be a necessary enabling technology. It is expected that in the future all vehicles on a road will be able to communicate with each other for data access and information sharing. Therefore, the knowledge about the V2V transmission channel is very important for the design and implementation of V2V communication systems. To provide a basis for the investigation of V2V communications, high resolution time-domain impulse responses of the physical radio channel between automobiles are needed. In addition, the understanding of Doppler characteristics of a V2V mobile channel is also of importance. Motivated by this, this paper will

Mobile Netw Appl

be focused on the discussions on a novel V2V channel model. It is well known that in the time domain a wireless channel established between two vehicles can be characterized by Doppler-shift and Doppler-spread features jointly, both of which are influenced not only by the relative movement of vehicles but also by the surrounding environments (i.e., the other moving vehicles nearby, etc.). In V2V communications, both transmitter and receiver are in motion, and thus the channel in between must be time-varying. Therefore, the Doppler shift and Doppler-spread always occur and they may severely impair the overall system performance if we do not use some proper countermeasures. Obviously, it is necessary for us to establish a reliable channel model to accurately characterize the channel dynamics in reality under the context of V2V communications. To tackle the challenging issues on V2V channel modeling, we want to make an effort in this article to investigate the channel modeling problem for V2V communications. The underlined modulation system here is orthogonal frequency division multiplexing (OFDM), which has been applied to many major wireless communication systems, including the V2V communications (i.e., IEEE802.11p standard). Many works have been reported in employing different approaches to establish some empirical and theoretical channel models. For example, the Jack’s Doppler spectrum model for a mobile channel has been widely used for performance evaluation in mobile communication systems. The derivation of the Jack’s Doppler channel model was based mainly on two important assumptions: 1) The direction of arrivals (DOA) of impinge signals are uniformly distributed over (0, 2π ]; 2) All multi-path channel (MPC) returns possess identical power, or an uniform power profile of the channel impulse response is presumed. However, these two basic assumptions may not be valid in most V2V communication scenarios due to the timevarying characteristics of the dynamic channel environment. Hence, we need to find a more accurate channel model for performance evaluation of V2V communication systems, which should take into account dynamic location changes of vehicles (with both transmitter and receiver) and their mobility parameters. In this paper, we will first study the V2V communication channels and then propose a feasible channel model in particular for V2V communication systems. Based on this newly proposed V2V channel model, we will analyze the inter-carrier interference (ICI) caused by Doppler spread, which appears to be one of the most critical

impairing factors to the performance of an OFDM based V2V communication system. The rest of this paper is outlined as follows. In Section 2, the classical Jack’s Doppler spectrum will be briefly reviewed and the major characteristic features of the V2V communications will be studied. In Section 3, we will propose our channel model derived in particular for vehicle-to-vehicle communication systems. In Section 4, we will discuss ICI caused by Doppler spread when an OFDM modem is used for V2V communications, followed by the concluding remarks given in Section 5.

2 Jake’s channel model As shown in Fig. 1, we consider a realistic situation where the signal propagation undergoes a large number of paths, each generated by a scattering object. Thus, the complex envelope of the received signal can be written as      y(t) = ρi x t − τi exp j2π ξi t , (1) i

where the scatter index is specified by variable i, the corresponding scattering coefficient is ρi , and the Doppler frequency shift is ξi . x(t) denotes the transmitted OFDM signal. If the channel is separable, the Doppler spectrum associated with τi is the actual channel Doppler spectrum. Otherwise, we can repeat the calculation for each τi to obtain the different sections of the scattering function. In this paper, we only consider a uniformly distributed delay, namely τi = τ , and thus Eq. 1 can be rewritten as y(t) =



       ρi x t − τ exp j2π ξi t = c t x t − τ ,

i

d1 d2

V0 Rx

d0

Fig. 1 A channel propagational model with scatters

Tx

(2)

Mobile Netw Appl

 where we have c(t) = i ρi exp ( j2π ξi t). Consequently, the mean of the Doppler spectrum for the delay τ is the power spectral density of the random process c(t). Let α denote the azimuth angle, g(α) be the receiving antenna azimuthal gain, and p(α) be the average received power along the direction α. When we normalize π p(α)dα = 1, p(α) can be regarded to as the prob−π ability of receiving a scattered signal component from the direction α. Therefore, it is easy to establish the expression of the Doppler frequency shift ξ , which is caused by a scatter at angle α as ξ = Fd cos(α),

(3)

where Fd = fcc v is the maximum Doppler shift and v is the relative speed between the transmitter and receiver. Summing over the scattering index i by integrating over α equivalently, we can obtain  c(t) =

π

−π

  ρα exp j2π Fd cos αt dt,

(4)

where ρα is the received signal component at angle α. Assume that we have E[ρα ρβ∗ dα dβ] = p(α)g(α)δ(α − β)dαdβ. The autocorrelation function of c(t) can be derived as  rc ( t) = E c(t)c∗ (t − t)  π   = pα gα exp j2π Fd cos α t dα.

(5)

Hence, the Doppler spectrum can be obtained as D(ξ ) =

  rc ( t) exp − j2π ξ t d t.

 g(α) = g0

D(ξ ) =

⎪ ⎩

1  π Fd 1 − 0,

ξ2 Fd2

, |ξ | < Fd

−n

,

(8)

where g0 is a constant, n is the path-loss exponent which indicates the rate at which the path-loss increases with distance, and we normally have n = 3, 4, 5, 6. r0 is the close-in reference distance which is determined from the measurements close to the transmitters, and r is the length of the MPC. Without loss of generality, we can set g0 = 1 throughout this paper. Therefore, we are motivated to derive a more accurate channel model for vehicle-to-vehicle communication systems in this paper. In the following section, we will first introduce the theoretical analysis for the Doppler spread spectrum.

3 Doppler spread spectrum in V2V communications

(6)

If the scattered signal incoming from any directions possesses an equal probability when it arrives at an omnidirectional receiving antenna, we get p(α) = 1/(2π ) and g(α) = 1, resulting in the well known Jakes’ model [5], whose Doppler spectrum is given by ⎧ ⎪ ⎨

r r0

Figure 2 illustrates an example of vehicle-to-vehicle communication channel. For analysis simplicity, let h(t, τ ) in Fig. 2 be a channel impulse response where there are two dense reflectors located along one side of

−π



tions are held: (1) DOAs are uniformly distributed in (0, 2π ], and (2) all MPCs have the same power. However, these two assumptions would not be valid in most V2V communication systems since the channel characteristics are almost always changing when the subject vehicles are mobile. For example, both theoretical and measurement-based propagation models indicated that the average received signal power level (in dB) decreases logarithmically with the distance between the transmitter and the receiver in both indoor and outdoor radio channels [6]. The average large-scale path loss for an arbitrary transceiver separation can be expressed as

d0

d2

(7)

where Fd is the maximum Doppler frequency. Obviously, it is easy to verify that the Jakes’ model is valid if and only if the following two important assump-

d 2'

Rx

r1

d1 Tx

α

d

otherwise

h(t,τ)

r2

d1'

β ' h(t,τ)

Fig. 2 A V2V communication channel with a typical multi-path channel resulting from one side of the road

Mobile Netw Appl

where ξ  = Fξd and 0 < |ξ  | < 1. We can elaborate Eq. 11 further. Let ξ  = cos(θ ). We can rewrite Eq. 11 as

2.5

2

D(ξ ) =

Jakes'model

D(ξ)

1.5

= d 0 =1.1, 2.1, 3.1, ..., 10.1 1

0.5

−0.8

−0.6

−0.4

−0.2

0

ξ

0.2

0.4

0.6

0.8

1

(12)

Fig. 3 Doppler spread spectrum for different d0 values (n = 3, d1 = d 2 = 5 and d2 = d 1 = 3)

the road. Therefore, the normalized distance r between two vehicles over the channel h(t, τ ) is equal to   d2 cos α 2 2 d2 r = r1 + r2 = . d0 − +d1 + sin α sin α Similarly, the normalized distance r  between two vehicles over the channel h  (t, τ ) is equal to   d2 cos β 2  2 d2  r = d0 − +d1 − . sin β sin β According to Eq. 5, we can write the corresponding autocorrelation function of c(t) as    1 π g(α) exp j2π Fd cos α t dα, (9) rc ( t) = K −π where we have  π  K= g(θ)dθ = −π

0

−π

(r  )−n dα +



π

(r)−n dβ.

d2

+⎣ + 1−ξ  2

2.5

(10)

2 d1 =3,d2 =5 d1 =2, d 2' =3

1 1  KFd 1 − ξ  2 ⎧⎡ ⎤−n   2 ⎨  d2 d2 × ⎣ + d0 −  1−ξ  2 +d12 ⎦ ⎩ 1−ξ  2 ξ ⎡

where d 2 = d − d2 , d 1 = d − d1 and cos(θ ) = ξ  = Fξd . Figure 3 delineates the Doppler spread spectra with d1 = d2 , d1 = d2 and d1 + d1 = d2 + d2 (a symmetric Doppler channel with a path-loss exponent n = 3). When d1 = d2 and d2 = d1 , it is an asymmetrical Doppler channel as depicted in Fig. 4 (n = 4). However, even for d1 + d1 = d2 + d2 , an asymmetric Doppler channel still arises as shown in Fig. 4 (d1 = d1 = 4, d2 = 3, and d2 = 5). As a matter of fact, the Doppler effect would be more and more prominent when the path-loss exponent n gets larger and larger, as shown in Fig. 5 (d1 = d2 = 4 and d2 = d1 = 3). It is clear that the Doppler spread spectra are different when the path-loss exponents vary. According to Fig. 5, the Doppler spread spectra are symmetric when the pathloss exponent is odd such as n = 3 and n = 5; whereas the Doppler spread spectra are asymmetric when

0

Substituting Eq. 9 into Eq. 6, we can obtain the Doppler spectrum over the vehicle-to-vehicle channels as D(ξ ) =

1 1 KFd sin(θ )  −n   2 d2 2 × + d0 − d2 tan(θ ) +d1 sin(θ ) −n    2 d2 2  d0 − d2 tan(θ ) +d1 , + + sin(θ )

d1 =2,d2 =3 d1 =5, d2' =4

1.5

D(ξ)

0 −1

  1 D d, d1 , d2 , d0 , θ Fd

1

d1 =4,d2 =3 d1 =4, d2' =5

0.5

⎤−n⎫ 2 ⎬  d2 d0 −  1−ξ  2 +d1 2 ⎦ , ⎭ ξ

 

(11)

0 −1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

ξ

Fig. 4 Doppler spread spectrum for different (d1 , d2 ) sets with n = 4 and d0 = 5.0

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tion α are not always constant in V2V communication systems. Therefore, as mentioned previously, the ICI bounds based on the Jake’s channel model are not valid for the V2V communication systems. In this paper, we derive a more accurate ICI expression based on our proposed V2V channel model. The details are described subsequently. It is well-known that the exact ICI power P ICI can be expressed in terms of the Doppler spectral density as [11]: ! "  Fd sin( f Ts ) 2 P ICI = 1 − D( f ) df, (13) f Ts −Fd

1.2

1 n=3,4,5,6

D(ξ)

0.8

0.6

0.4

0.2

0 −1

−0.8

−0.6

−0.4

−0.2

0

ξ

0.2

0.4

0.6

0.8

1

Fig. 5 Doppler spread spectrum for different n values (d1 = d 2 = 4 and d2 = d 1 = 3)

where Ts is the OFDM symbol duration. According to Eq. 12, it yields  π P ICI = 1 − D(d, d1 , d2 , d0 , θ ) 0

 ⎤2  sin cos(θ )Fd Ts ⎦ sin(θ )dθ. ×⎣ cos(θ )Fd Ts ⎡

the path-loss exponent becomes even such as n = 4 and n = 6. From the above analysis, it is obvious that our proposed Doppler spectral model can provide deeper insights under the context of V2V communications than the classical Jake’s model, especially for the symmetry property of the Doppler channels.

4 ICI analysis for OFDM-based V2V systems The inter-symbol interference (ISI) in the V2V systems, which usually arises from a time-dispersive fading environment, can also be mitigated since the V2V systems can also be built based on OFDM technology. However, when the symbol duration gets relatively long, the inter-carrier interference (ICI) caused by Doppler spread dominates and degrades the OFDM-based V2V communication system performance [7]. Recently, the Doppler spread effect on OFDM signals has been studied in [8–12]. Particularly, the exact ICI evaluation resulting from Doppler spread for the OFDM systems has been derived in [7] and [8]. In [11], the tight and universal bounds on the ICI power have been formulated and it has been shown that such bounds depend only on the maximum Doppler frequency, the symbol duration, and the variance of Doppler spectrum. However, these bounds on the ICI have been derived based on the classical Jake’s Doppler spectrum, thus rasing the questions on possible mismatch between the real performance and those obtained therein. In reality, the receiving antenna azimuthal gain g(α) and the average receiving power p(α) at the direc-

(14)

If we define D(d, d1 , d2 , d0 , θ ) = D(d, d1 , d2 , d0 , θ ) sin(θ ),

and

!

(θ, Fd , Ts ) =

sin (cos(θ )Fd Ts ) cos(θ )Fd Ts

(15)

"2 ,

(16)

Equation 14 yields  π P ICI = 1 − D(d, d1 , d2 , d0 , θ ) (θ, Fd , Ts ) dθ. (17) 0

According to Eq. 16, we can bound (θ, Fd , Ts ) as 1 ≥ (θ, Fd , Ts ) ≥

sin2 (Fd Ts ) , (Fd Ts )2

(18)

where we have Fd Ts < π and 0 ≤ θ ≤ π . It is noted that the bounds given by Eq. 18 are tight (the upper bound approaches to the lower bound) as Fd → 0 (low mobility). Following Eqs. 15–18, we get  sin2 (Fd Ts )  I d, d1 , d2 , d0 ≤ P ICI ≤ 1 2 (Fd Ts )   − I d, d1 , d2 , d0 ,

(19)

where Fd Ts < π and  π   I (d, d1 , d2 , d0 ) = D d, d1 , d2 , d0 , θ dθ.

(20)

1−

0

It is interesting to discover that the bounds given by Eq. 19 contain two separable functions in product: one depends on the Doppler frequency shift Fd and Ts only,

Mobile Netw Appl 0

0 −10

P ICI (dB)

P ICI (dB)

Jakes' model

n=3, d0=6.8,d 1 =4 , , d 2 =3, d1 =4,d 2 =5

−20

−20 6

n=4, d0=6.8,d 1 =4 , , d 2 =3, d1 =4,d 2 =5

−30

−10

4 d0 −40

2 0

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

,

0.5

1

and the other depends on the geometrical parameters d0 , d1 , d2 , and d only. Through such a new bounding analysis, we can address the Doppler effect and the geometric lay-out effect on the ICI power separately and independently. This would be very convenient for the OFDM system performance assessment in the future. On the other hand, the ICI power can be calculated when the correlation of the time-varying channel is known in the time domain and according to the classical Jake’s model [7] it can be written as follows:  1     P ICI = 1 − 1 − |x| J0 2π fd Ts x dx, (21) −1

where Ts is the OFDM symbol duration and J0 (x) is the 0th-order Bessel function of the first kind.

0.2

0.4

0.5

fd Ts

,

n=4, d =2, d =6;d =3;d =5

fd Ts

Fig. 6 Comparisons of the ICI power for our proposed mobile channel model and the classical Jakes’ model

0.1

0

0.3

1

2

2

Fig. 8 ICI power curvature for our proposed channel model with different d0 and Fd Ts (n = 4)

To get some more insights into our newly derived ICI expression as given by Eq. 20, we vary parameters to depict the ICI power in Figs. 6, 7, 8, 9, 10, 11, 12. It is well known that we can adjust the OFDM symbol duration to make the Doppler spread negligible if we can obtain the ICI power when designing OFDM systems. As shown in Fig. 6, if OFDM symbol duration is chosen so that it is very short, then the effect of the Doppler spread is negligible when n = 4 in a V2V communication system. For example, when Ts = 160 μs, then P ICI < 0.168% (i.e., −27.7 dB) when Fd = 450 Hz. Therefore, the P ICI is much lower than the noise or co-channel interference level. Moreover, it is obvious to find that the ICI power decreases more quickly with the decline of Fd Ts when the path-lost exponent n is larger. In addition, we find that the

0

0.8

-10

PICI

PICI(dB)

0.6 0.4

0.2 -20 6 4

d0

2 0

0

,

0.1

0.2

0.3

0.4

0.5

fd Ts

,

0 8 6 4

d2

2 0

0

0.1

0.2

0.3

0.4

0.5

fd Ts

n=3, d1=2, d1=6, d2=3, d2=5

n=3, d =5, d1 =4, d=8

Fig. 7 ICI power curvature for our proposed channel model with different d0 and Fd Ts (n = 3)

Fig. 9 ICI power curvature for our proposed channel model with different d2 and Fd Ts (n = 3)

0

0.8

0.8

0.6

0.6

PICI

PICI

Mobile Netw Appl

0.4

0.2

0.4

0.2

0 8

0 8

6 4 d

2

2 0

0

0.1

0.2

0.3

0.4

0.5

6

8 6

4

d2

fd Ts

2 0 0 fd Ts =0.5 n=4, d0=5, d=8, 1

n=4, d0=5, d1 =4, d=8

Fig. 10 ICI power curvature for our proposed channel model with different d2 and Fd Ts (n = 4)

variation of P ICI is similar to that of the Jakes’ model in V2V communication systems when the path-lost exponent is set to n = 4 as seen from Fig. 6. However, the variation of P ICI is small with different Fd Ts in V2V communication systems when the path-lost exponent becomes n = 3. Figures 7 and 8 show the results of P ICI when d0 and Fd Ts are different with n = 3 and n = 4, respectively. Obviously, the effects of both d0 and Fd Ts are monotonic when the parameters d, d1 and d2 are fixed. For example, P ICI is increasing with increasing of Fd Ts and decreasing of d0 , respectively, as shown in Figs. 7 and 8. Similarly, we also can find the ways that other parameters affect the ICI power from Figs. 9 to 12. However, the relationship between the ICI power and d2

4 2

d1

Fig. 12 ICI power curvature for our proposed channel model with different d1 and d2 (n = 4)

varies with different Fd Ts values as illustrated in Figs. 9 and 10. Figures 11 and 12 show that the ICI power decreases quickly to nearly zero when two vehicles are located close to the same side of the road. It is noted that this works focuses only on an ICI study in a generic OFDM based V2V communication system and does not take into account more details on IEEE 802.11p standard. The future works following this paper could possibly include a more detail investigation on the performance of a V2V communication systems working under IEEE 802.11p standard. Thus, the proposed channel model can be more rigorously tested under a realistic V2V communication environment.

5 Conclusion

0.8

PICI

0.6 0.4

0.2

0 8 6

8 6

4

d2

2

4 2 0 fd Ts =0.5 n=3, d =5, d=8, 1 0

d1

0

Fig. 11 ICI power curvature for our proposed channel model with different d1 and d2 (n = 3)

In this paper, we have addressed channel modeling issues for vehicle-to-vehicle communications. The more accurate Doppler spread spectrum is theoretically derived and compared with the classical Jake’s channel model. It is shown that our proposed channel model fits better the real propagation characteristics in vehicleto-vehicle communication especially when high mobility and time-varying channel are present. Besides, we derive the inter-carrier interference (ICI) expression based on our proposed channel model. We have also presented the upper and lower bounds of the derived ICI power and demonstrated that these two bounds will get closer and closer when Doppler frequency approaches to zero. The results obtained from this work are useful for performance assessment of futuristic vehicle-to-vehicle communications.

Mobile Netw Appl Acknowledgements This work was supported in parts by the NSF of China (Grant 60872008), the Program for New Century Excellent Talents in University of China (Grant NCET-08-0217), Research Fund for Doctoral Program of the Ministry of Education, China (Grant 200804871142), and Taiwan National Science Council Research Grant (NSC97-2219-E-006-004).

References 1. Richardson P, Xiang W, Stark W (2006) Modeling of ultra wide band channels within vehicles. IEEE J Sel Areas Commun 24(4):906–912 2. Xiang W (2007) A vehicular ultra-wideband channel model for future wireless intra-vehicle communications systems. In: IEEE vehicular technology conference-fall, Baltimore 3. Jiang L, Tan SY (2007) Geometrically based statistical channel models for outdoor and indoor propagation environments. IEEE Trans Veh Technol 56(6):3587–3593 4. Tran NH, Nguyen HH, Le-Ngoc T (2007) Subcarrier grouping for OFDM with linear constellation precoding over multipath fading channels. IEEE Trans Veh Technol 56(6): 3607–3613 5. Jakes WC Jr (ed) (1974) Microwave mobile communications. Wiley, New York 6. Rappaport TS (1996) Wireless communications principles and practice. Prentice Hall, Englewood Cliffs 7. Russell M, Stuer GL (1995) Interchannel interference analysis of OFDM in a mobile environment. In: Proceeding of VTC’95, pp 820–824 8. Robertson P, Kaiser S (1999) The effects of Doppler spreads in OFDM(A) mobile radio systems. In: Proceeding of VTC’99-fall, pp 329–333 9. Moose PH (1994) A technique for orthogonal frequency division multiplexing frequency offset correction. IEEE Trans Commun 42:2908–2914 10. Zhao Y, Leclercq J, Haggman S (1998) Intercarrier interference compression in OFDM communication systems by using correlative coding. IEEE Commun Lett 2:214–216 11. Li Y, Cimini LJ Jr (2001) Bounds on the interchannel interference of OFDM in time-varing impairments. IEEE Trans Commun 49(3):401–404 12. Wu H-C (2006) Analysis and characterization of intercarrier and interblock interferences for wireless mobile OFDM systems. IEEE Trans Broadcast 52(2):203–210

Tao Jiang (M’06) received the BS and MS degrees in applied geophysics from China University of Geosciences, Wuhan,

P. R. China, in 1997 and 2000, respectively, and the PhD degree in information and communication engineering from Huazhong University of Science and Technology, Wuhan, P. R. China, in April 2004. He is now with the Department of Electronics and Information Engineering, Huazhong University of Science and Technology, and Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, P. R. China. He has authored or co-authored over 50 technical papers in major journals and conferences and five books/chapters in the areas of communications. His current research interests include the areas of wireless communications and corresponding signal processing, especially for cognitive wireless access, vehicular technology, OFDM, UWB and MIMO, cooperative networks, nano networks and wireless sensor networks. Dr. Jiang is a Member of IEEE, IEEE Communication Society and IEEE Broadcasting Society.

Hsiao-Hwa Chen is currently a full Professor in Department of Engineering Science, National Cheng Kung University, Taiwan, and he was the founding Director of the Institute of Communications Engineering of the National Sun Yat-Sen University, Taiwan. He received BSc and MSc degrees from Zhejiang University, China, and PhD degree from University of Oulu, Finland, in 1982, 1985 and 1990, respectively, all in Electrical Engineering. He has authored or co-authored over 300 technical papers in major international journals and conferences, five books and several book chapters in the areas of communications, including the books titled “Next Generation Wireless Systems and Networks” (512 pages) and “The Next Generation CDMA Technologies” (468 pages), both published by John Wiley and Sons in 2005 and 2007, respectively. He has been an active volunteer for IEEE various technical activities for over 20 years. Currently, he is serving as the Chair of IEEE ComSoc Radio Communications Committee, and the Vice Chair of IEEE ComSoc Communications & Information Security Technical Committee. He served or is serving as symposium chair/co-chair of many major IEEE conferences, including VTC, ICC, Globecom and WCNC, etc. He served or is serving as Associate Editor or/and Guest Editor of numerous important technical journals in communications. He is serving as the Chief Editor (Asia and Pacific) for Wiley’s Wireless Communications and Mobile Computing (WCMC) Journal and Wiley’s International Journal of Communication Systems, etc. He is the founding Editor-in-Chief of Wiley’ Security and Communication Networks journal (www.interscience.wiley.com/journal/security). He is also an adjunct Professor of Zhejiang University, China, and Shanghai Jiao Tong University, China. Professor Chen is a recipient of the Best Paper Award in IEEE WCNC 2008.

Mobile Netw Appl on Broadcasting, IEEE Transactions on Vehicular Technology, International Journal of Computers and Electrical Engineering, Journal of the Franklin Institute and Journal of Information Processing Systems. Besides, he serves as the lead guest editor for a special issue in IEEE Journal of Selected Topics in Signal Processing, 2009, and another special issue in Journal of Communications, 2010. He also serves for numerous textbooks, IEEE/ACM/IET/SPIE/EURASIP conferences and journals as technical committee, symposium co-chair and track chair or reviewer in signal processing, communications, circuits and computers.

Hsiao-Chun Wu received a BSEE degree from National Cheng Kung University, Taiwan, in 1990, and the MS and PhD degrees in electrical and computer engineering from University of Florida, Gainesville, in 1993 and 1999 respectively. From March 1999 to January 2001, he had worked for Motorola Personal Communications Sector Research Labs as a Senior Electrical Engineer. Since January 2001, he has joined the faculty in Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge. In July to August 2007, Dr. Wu was a visiting assistant professor at Television and Networks Transmission Group, Communications Research Centre, Ottawa, Canada. From August to December 2008, he was a visiting associate professor at Department of Electrical Engineering, Stanford University, California. Dr. Wu has published more than 100 peer-refereed technical journal and conference articles in electrical and computer engineering. Among them, more than 85 papers are published diversely in over a dozen of different IEEE or ACM societies. His research interests include the areas of wireless communications and signal processing. Dr. Wu is an IEEE Senior Member and currently serves as an Associate Editor for IEEE Transactions

Youwen Yi (Student Member) received his BS from Harbin Institute of Technology in 2007. From Aug. 2007, he is a MS candidate in Huazhong University of Science and Technology, Wuhan, P. R. China. He is doing research under the supervision of Prof. Jiang, and his research interests include cognitive radio, dynamic spectrum access, signal process and game theory for wireless communication, and artificial intelligence.