Chinese Journal of Electronics
Vol.22, No.1, Jan. 2013
Primary User QoS and Activity Concerned
Resource Allocation Algorithm in OFDM
Based Cognitive Radio System∗
LIANG Hui and ZHAO Xiaohui
(College of Communication Engineering, Jilin University, Changchun 130012, China)
Abstract — Efficient and reliable resource allocation
algorithm is one of the key problems for the realization
of cognitive radio networks. Due to the rapidly changing
characteristics of cognitive environment, water-filling algorithm is difficult to meet this environment because of the
interaction with primary users. In this paper, we propose
a primary user Quality of service (QoS) and activity concerned resource allocation algorithm in OFDM based cognitive radio system with a risk-return model which reflects
availability of subcarriers or primary user activity and date
transmission outage probability constraint to guarantee
primary users’ QoS. Taking maximization of the expected
sum rate of cognitive system as the objective function,
we solve this optimization problem by a Lagrangian dual
method under the introduced primary user outage probability constraint. Finally, the performance comparison of
water-filling algorithm and our algorithm is given. The
simulation results show that the proposed algorithm can
obtain more transmission capacity and effectively guarantee primary users’ QoS.
Key words — Cognitive radio, Resource allocation, Primary user activity, Outage probability.
I. Introduction
Nowadays, Cognitive radio (CR) has been widely accepted
to solve the radio spectrum scarcity problems[1] . Comparing with traditional communication, the biggest difference between them is the existence of the primary users. That is cognitive system must guarantee primary users’ Quality of service
(QoS) demand when it is working. Over the past decade, several solutions for the key issue have achieved significant and
considerable progress. However, among these the resource allocation is considered as one of the most important steps for
CR network from theory research towards its realization.
Many resource allocation algorithms have been proposed
in recent years, but most of the previous works often assume
that the designated spectrum for secondary usage is fixed and
available. Though this is convenient for theoretical formulation, it is not suitable for practical aspects. In order to solve
this problem, people impose another model to describe these
situations by considering reliability or availability of subcarriers or primary user activity for power allocation. Ref.[2] develops a multicast-network resource allocation algorithm with
primary user activity. Then a binary integer optimal programming method is formulated by Ref.[3]. Ref.[4] transforms the
expected capacity loss into a peak power constraint and then
proposes an efficient power allocation algorithm based on primary user activity for OFDM cognitive radio systems. A novel
model to parametrize the primary user traffic in a more efficient way by arranging first-difference filtered and correlated
primary user data into clusters is proposed in Ref.[5], as well
as other method such as Ref.[6].
Furthermore, in order to limit the interference caused
by the unlicensed or secondary user’s subcarriers to the
primary user, the previous work often uses “interference
temperature”[7] concept to restrict secondary users’ power[8,9]
and other interference mitigation method[10] . However, due to
a lack of information on a reasonable means for implementation, the interference temperature concept has been shelved
by FCC. A new metric as outage probability is proposed later,
which is used to guarantee QoS of primary user. In Ref.[11],
an equivalent outage probability constraint method was proposed in Nakagami fading channels with high signal to interference radio. The work in Ref.[12] proposes a Binary power
control (BPC) approach in considering outage probability of
PU as QoS constraint to maximize secondary user throughput. Ref.[13] utilizes the Lagrangian duality framework to
provide joint subchannel and power allocation algorithm that
improves total achievable sum rate of secondary networks subject to outage probability constraint specified at the primary
users’ receivers. The study in Ref.[14] investigates an adaptive
cooperation diversity scheme with best relay selection under
the constraint of outage probability to improve performance
of secondary transmissions.
Though primary activity based modes can make secondary
network more efficient and outage probability constraint can
practically guarantee QoS of primary user, these is no one
jointly consider primary user activity and outage probability
constraint together for the CR network resource allocation. In
∗ Manuscript Received May 2011; Accepted Apr. 2012. This work is supported by the National Natural Science Foundation of China
(No.61171079).
168
Chinese Journal of Electronics
this paper, we propose a novel resource allocation algorithm
with primary user activity consideration and outage probability constraint for OFDM based cognitive radio networks in
order to significantly improve system transmission capacity
while effectively guarantee QoS of primary user at the same
time.
II. System Model and Problem
Formulation
1. System model
Consider an OFDM based cognitive radio system of subcarriers licensed to different primary users as shown in Fig.1,
all subcarriers are divided into M logical sub-channels denoting different primary activities respectively. We take a centralized system where the CR base station can gather all information as followed. Here, the power is not allocated instantaneously according to the current channel information, instead
this power allocation will be conducted after a period of time.
Fig. 1. Spectrum model of OFDM-based CR system
In a cognitive radio environment, due to its highly dynamic feature, primary users can freely come and go in the
system. Therefore, it is possible that the present environment
information used by cognitive BS at the current time ti is actually based on the already obsolete information reliable at
the previous time ti−1 . The problem of this phenomenon may
occur when primary users come back and occupy the subcarriers once available for secondary access during delay interval
Δt = ti − ti−1 , which will make current resource allocation no
longer optimal.
In the description of the primary user activities or the
availability of OFDM subcarriers, we employ risk return model
to formulate power allocation to a subcarrier as an investment
in that spectrum band[15] . In this model, cognitive radio environment can be thought as a risky environment where the
primary users may freely occupy resources at any time. Accordingly, there is inevitably a rate loss whenever a primary
user comes back to take up the subcarriers in the cognitive
system. To represent this loss, a real valued, concave and normalized average rate loss function L(p) is proposed in Ref.[2]
determined by the secondary user power investment. And this
function involves cost of resource allocation and must meet
following requirement
L(p) > 0, for p > 0
(1)
L(p) = 0, for p = 0
where p is the allocated power to the available subcarrier. We
define probability αk for the primary user activity on the subcarrier k and pk the power allocation for kth subcarrier respectively. Then the expected rate loss involved in that subcarrier
2013
at the cognitive environment is
E[ΔRk ] = αk L(pk )
(2)
Hence, the expected rate of the k subcarrier in the cognitive
radio networks becomes
hCR pCR
E[Rk ] = log2 1 + k k
(3)
− αk L(pk )
N0 B
However, choosing a common loss function for all subcarriers which correspond to all requirements is not easy. We will
take a special case when this function is an approximate linear
expression as follows
L(p) = C · p
(4)
where C is the normalized average cost per unit power for
secondary users to utilize the resource. It could be thought
as average rate loss per unit powerand will vary with many
factors (time-delays, interference caused to primary users and
so on). Thus the expected rate in the k th subcarrier for the
secondary user is represented by
hCR pCR
− αk Cpk
(5)
E[Rk ] = log2 1 + k k
N0 B
Since any other form of rate loss function only leads to a
difference in the solution of every subcarrier problem, all other
steps in the above proposed method is the same.
2. Outage probability
In order to better fit practical environment, a different way
to efficiently protect primary system from secondary user interference is proposed, which is based on outage probability[16] .
From the information theory, outage probability is defined as
probability that the instantaneous mutual information of the
channel is below the transmitted code rate[17] , and can be represented as
(6)
pout (R) = P {I(x; y) ≤ R}
where I(x; y) is the mutual information of the channel between the transmitted vector x and the received vector y, and
R is the target data rate in (bits/s/W). Reliable communication can therefore be achieved when the mutual information
of the channel is strong enough to support the target rate R.
Therefore, in a cognitive radio networks, we can formulate a
constraint with outage probability less than a given threshold.
In our problem, we define outage probability as
⎫
⎧
⎞
⎛
K
⎬
⎨
PU
h
P
k k
⎠≤R
αk log2 ⎝ 1 + M
Pout =P
⎭
⎩
k=1
CR
ρmk gmk Pmk
+ N0 B
m=1
≤q
(7)
where αk is the probability of primary user activity on subcarrier k, hk and gmk are the power gain of primary user and
cognitive base station to the primary user on subcarrier k respectively. ρmk can only be either 1 or 0, indicating whether
subcarrier k is used by secondary user m, q is the predefined
outage probability threshold of primary transmission.
3. Problem formulation
So in order to maximum the cognitive network’s total capacity considering reliability/availability of subcarriers that is
Primary User QoS and Activity Concerned Resource Allocation Algorithm in OFDM Based Cognitive Radio System 169
primary user activity while guaranteeing the primary’s QoS of
transmission. We formulate the original problem as followed
constrains optimization problem
N
M |hi |2 pi
1 + k2 k
σ Γ
ρik log2
max
p ,ρ
i=1 k=1
⎧
Pout ≤ q
⎪
⎪
⎪
⎪
⎪
pi ≥ 0
⎪
⎨ k
ρik ∈ {0, 1}, ∀i, ∀k
s.t.
⎪
⎪
M
⎪
⎪
⎪
⎪
⎩
ρik ≤ 1, ∀k
− αk Cpik
probability distribution function. Here, the power gain obeys
exponential distribution. Then from (11), we have
0
M
(8)
1. Outage probability equivalent
In order to realize optimal subchannel and power allocation for cognitive radio based on primary user activity under
primary user outage probability constraint, we should transform the expression (7) into a more simple form through some
reasonable assumptions for the consideration of complexity to
practical realization.
First, we make sum rate of the primary user from multiple
subcarriers equivalent to that from one subcarrier. The specified threshold of primary user is Wmax . Accordingly, the first
constraint is
⎧
⎫
⎛
⎞
⎨
⎬
h
S Wmax
⎠≤R ≤q
Prob log2 ⎝1 + M
(9)
⎩
⎭
i i i
ρS gS pS + N0
i=1
where S represents subcarrier index occupied by primary user
and the above expression can be developed as
ρiS gSi piS ≤ (2R −1)N0
(12)
Wmax
−
(2R − 1)
N
0
θ ln
1
1−q
(13)
Replacing equivalent subcarrier index S with k, we finally get
the transformed primary user outage probability constraint as
III. Optimal Subchannel and Power
Allocation
Prob hS Wmax −(2R −1)
ρiS piS ≤
i=1
where pik is the power allocated for secondary user i over
subcarrier k, hik is user i’s channel gain over subcarrier k.
σ 2 = N0 B/K is the noise power on each subcarrier, N0 is
the power spectral density of Additive white Gaussian noise
(AWGN), B is the total idle bandwidth, K is the total number
of the subcarrier, and Γ = − ln (5BER)/1.6 is the signal-tonoise (SNR) gap[18] .
M
1 −X
e θ dX ≤ q
θ
Integrating (12), we have
i=1
N0
Wmax M
ρ i pi
R
i=1 S S
(2 −1)
N
k=1
αk
M
ρik pik =
i=1
M
N αk ρik pik
k=1 i=1
≤
Wmax
−
2R − 1
N
0
θ ln
≤ q (10)
i=1
Since cognitive radio base station is closing to the primary user
base station as common situation, we assume hS = gSi , ∀m, n,
then (10) can be approximated as
⎧
⎫
⎨
⎬
N0
Prob hS ≤
≤q
(11)
M
⎩
i i ⎭
Wmax
−
ρS pS
(2R − 1) i=1
For the analysis simplicity we suppose the channel gain is
i.i.d Rayleigh distributed. However, the results can be easily
extended to any other channel model by replacing appropriate
=Q
(14)
2. Optimal subcarrier and power allocation algorithm via dual method
Substitute first constraint in (8) by (14), the original optimization problem becomes
N
M [ρik (log 2 (1 + Hki pik ) − αk Cpik )]
max
p ,ρ
i=1 k=1
⎧
N
M ⎪
⎪
αk ρik pik ≤ Q
⎪
⎪
⎪
⎪
⎪
⎪ i=1 k=1
⎪
⎨ pi ≥ 0
k
s.t.
⎪
∀i, ∀k
⎪ ρik ∈ {0, 1},
⎪
⎪
⎪
⎪
M
⎪
⎪
⎪
⎩
ρik ≤ 1,
∀k
(15)
i=1
Hki
|hik |2 /σ 2 Γ
=
is a new quantity for convenience of
where
notation.
The idea of dual optimization is to solve (15) by forming
its Lagrangian dual, which is defined as[19]
L(p, ρ, λ) =
N
M [ρik (log2 (1 + Hki pik ) − αk Cpik )]
i=1 k=1
1
1−q
−λ
N
M αk ρik pik − Q
(16)
i=1 k=1
where λ ≥ 0 is a dual variable. Then, we define dual function as D(λ) = max L(p, ρ, λ), so the original problem dual
problem is
(17)
D(λ) = max L(p, ρ, λ)
p ,ρ
Due to the disjoint subcarrier constraint in OFDM-based systems, the Lagrangian dual function (17) can be decomposed
into K independent optimization problems, i.e., one for each
subcarrier k as
D(λ) =
N
k=1
Dk (λ) + λQ
(18)
Chinese Journal of Electronics
170
The basic idea of the subgradient method is to design a
step size sequence to update λ in subgradient direction. Here,
we defined update expression for λ as
where
Dk (λ) = max
p ,ρ
−λ
M
[ρik (log2 (1 + Hki pik ) − αk Cpik )]
i=1
M
αk ρik pik
(19)
i=1
The Karush-Kuhn-Tucker (KKT) conditions[20] for user i and
∀k = 1, · · · , n are
∂
M
ρik (log2 (1 + Hki pik ) − αk Cpik ) − λ
i=1
∂
M
ρik log2 (1 + Hki pik )
∂
M
i=1
−
∂pik
M
αk ρik pik
∂ λ
αk ρik pik
i=1
i=1
=
M
ρik αk Cpik
∂pik
i=1
−
(20)
∂pik
Assuming subcarrier k is used by secondary y user i, then
ρik = 1. Eq.(20) becomes
∂
M
log 2 (1 + Hki pik )
i=1
M
αk Cpik
M
αk pik
∂ λ
i=1
−
∂pik
=
∂
−
∂pik
∂pik
Hki
1
·
− αk C − αk λ
ln 2 1 + Hki pik
(21)
From KKT conditions, we can get optimal power allocation as
Hki
1
− αk C − αk λ = 0
ln 2 1 + Hki pik
1
⇒
= (αk C + αk λ) ln 2
1
i
+
p
k
Hki
+
1
1
1
·
− i
⇒ pi∗
k =
ln 2 αk C + αk λ
Hk
(22)
Taking pi∗
k into Eq.(19), we have
i=1
ρik (log2 (1 + Hki pik ) − αk Cpik ) − λ
αk ρik pik
i=1
+ Hki
=
−1
log2 1 +
αk C + αk λ
i=1
+ +
M
C
1
αk C
αk
i
−
ρk
−
− i
−λ
C +λ
Hki
C+λ
Hk
i=1
(23)
M
Let
M
ρik
+
M
N λ(i+1) = λ(i) − δ (i) QI −
αk ρik pik
(24)
k=1 i=1
where δ (i) > 0 is a sequence of scalar step size. This subgradient update will certainly converge to optimal λ∗ as long as
δ (i) is chosen to be sufficiently small.
3. Algorithm realization
There is a brief implement steps as follows:
Step 1 Initialize λ;
∗
Step 2 Calculate values of pik according to Eq.(22) for
tth iteration;
Step 3 Calculate Vki (λ) for each i, then choose i∗ such
∗
that Vki (λ) maximum. Meantime, let ρik = 1, i∗ ∈ {1, · · · , M };
N
M αk ρik pik
Step 4 Calculate subgradient as s = Q −
i=1 k=1
at current iteration t, and substitute this value into Eq.(24) to
update λ;
Step 5 Go to Step 2 until the algorithm convergences.
IV. Performance Evaluation
i=1
=0
M
2013
+ +
αk C
Hki
C
−1
−
−
αk (C + λ)
C+λ
Hki
+
αk
1
− i ,
−λ
C+λ
Hk
Vki (λ) = log 2 1 +
calculate Vki (λ), i = 1, · · · , M for each i, and then select i∗
∗
which make Vki (λ) maximum, let ρik = 1, i∗ ∈ {1, · · · , M }.
To better present the theoretical resource allocation results
of the previous sections, we consider computer simulation experiment for a cognitive system consisting of one primary user
and a total available 1MHz bandwidth for different secondary
users. This bandwidth is equally divided into N = 16 subcarriers.
In order to facilitate the observations, we assume that
the primary activity pattern or probability reoccupying the
band sometimes in current time frame on subcarriers 1 to 5
is α1 , subcarriers 6 to 9 is α2 and subcarriers 10 to 16 is
α3 . And we further assume a primary total power budget
Wmax = 1W, signal to noise ratio SN R = 38dB, and SNR
gap Γ = − ln(5 × 10−3 )/1.6 = 3.3. The system is working
over frequency-selective fading channels using a 6-ray Rayleigh
model with exponential power profile and maximal multipath
delay.
Fig.2 shows the system capacity comparison between the
proposed algorithm and water-filling algorithm under the different channel reoccupation probability of different scenarios.
Each reoccupation probability scenarios are listed in Table 1.
Table 1. Channel reoccupation
probability of different scenario
Scenarios
α1
α2
α3
1
0.60
0.60
0.60
2
0.60
0.55
0.40
3
0.60
0.50
0.35
4
0.60
0.45
0.30
5
0.60
0.40
0.25
6
0.60
0.35
0.20
7
0.60
0.30
0.10
8
0.60
0.25
0.05
From Fig.2, it is clear that when every subcarrier group
reoccupation probability is very close or even same, the perfo-
Primary User QoS and Activity Concerned Resource Allocation Algorithm in OFDM Based Cognitive Radio System 171
rmance of the proposed algorithm and the
water-filling scheme is almost same. However,
the actual network environment is changing
very fast. As a result the primary activities
on different subcarriers are various hugely. In
this situation, it can be seen that our algorithm is much better than the counterpart
water-filling algorithm from the Fig.2. With
the increasing of the diversity of channel reoccupation probability, more system capacity
is obtained.
The secondary system capacity variation
with primary transmission outage probability and its occupation probability is shown in
Fig.3. From the figure we can see that when
primary transmission primary outage probability is large and its occupation probability
is small, the system has larger capacity. If
the outage probability decreases and the occupation probability increases, the capacity
gradually becomes small.
Fig.4 gives the comparison of the secondary system capacities obtained by waterfilling algorithm and the proposed algorithm
as the cost/rate loss per unit power C varies,
while keeping other parameters unchanged. It
is obvious that as C increases, the CR system
capacity drops linearly for both algorithms.
However, sum capacity of our algorithm is
much higher than that of water-filling scheme.
Fig.5 shows the secondary system capacity variation with different outage probability, and each curve denotes the capacity under the various primary user rates. We can
see that under the same data transmission
rate, the sum secondary system capacity is
higher with increase of the outage probability.
Correspondingly, when outage probability is
fixed, less primary rate means more system
capacity. That is in accordance with practical situation.
Different power allocations obtained by
using water-filling algorithm and the propo-
Fig. 2. Capacity
comparison
with traditional waterfilling scheme of different
scenarios
Fig. 4. Capacity
comparison
with traditional waterfilling scheme of different
Cost/Rate loss per unit
power
Fig. 3. Convexity of secondary system capacity with various primary outage probability and its occupation probability
Fig. 5. Capacity comparison of secondary system for different outage probability
and primary data rate
Fig. 6. Power allocation comparison in different algorithm. (a) Water filling algorithm; (b) Proposed algorithm
sed scheme are shown in Fig.6. From the Fig.6(b) we can see
that more power is allocated to the subcarriers when the primary user activity is less. Meanwhile, in each subcarrier, the
power is allocated according to different water levels, which
makes the whole secondary system capacity is much higher
than the capacity if using water-filling scheme in Fig.6(a).
V. Conclusion
In this paper, a novel cognitive radio resource allocation
algorithm is proposed which takes into account both primary
user activity and its transmission outage probability. The advantage of this algorithm is that it jointly considers primary
behavior and its transmission quality. From simulation anal-
ysis, it is shown that this algorithm can improve secondary
system capacity and guarantee QoS for primary system.
References
[1] J. Mitola, “Cognitive radio for flexible mobile multimedia communications”, 1999 IEEE International Workshop on Mobile
Multimedia Communications (MoMuC’99), Nov. 15-17, 1999,
San Diego, CA, USA., pp.3–10, 1999.
[2] D.T. Ngo, C. Tellambura, H.H. Nguyen, “Resource allocation
for OFDMA-based cognitive radio multicast networks with primary user activity consideration”, IEEE Transactions on Vehicular Technology, Vol.59, No.4, pp.1668–1679, 2010.
[3] W. Wang, T.J. Liu, T.T. Wang et al., “Primary user activity based channel allocation in cognitive radio networks”, 2010
IEEE 72nd Vehicular Technology Conference (VTC 2010-Fall),
172
Chinese Journal of Electronics
Sept. 6-9, 2010, Ottawa, ON, Canada., pp.1–5, 2010.
[4] C.H. Chen, C.L. Wang, “Power allocation for OFDM-based cognitive radio systems under primary user activity”, 2010 IEEE
71st Vehicular Technology Conference (VTC 2010-Spring),
May 16-19, Taipei, Taiwan., pp.1–5, 2010.
[5] B. Canberk, I.F. Akyildiz, S. Oktug, “Primary user activity
modeling using first-difference filter clustering and correlation
in cognitive radio networks”, IEEE/ACM Transactions on Networking, Vol.19, No.1, pp.170–183, 2011.
[6] Y.S. Zhang, Y.F. Zhang, S.X. Sun et al., “Adaptive resource allocation with SVM-based multi-hop video packet delay bound
violation modeling”, Chinese Journal of Electronics, Vol.20,
No.2, pp.261–267, 2011.
[7] S. Haykin, “Cognitive radio: brain-empowered wireless communications”, IEEE Journal on Selected Areas, Vol.23, No.2,
pp.201–220, 2005.
[8] W. Wang, T. Peng, W.B. Wang, “Optimal power control under interference temperature constraints in cognitive radio network”, 2007 IEEE Wireless Communications and Networking
Conference (WCNC 2007), Mar. 2007, Kowloon, Hong Kong.,
pp.116–120, 2007.
[9] L.B. Le, E. Hossain, “Resource allocation for spectrum underlay
in cognitive radio networks”, IEEE Transactions on Wireless
Communications, Vol.7, No.12, pp.5306–5315, 2008.
[10] Y. Zhang, J.D. Li, L.H. Pang, “Low complexity interference
mitigation for OFDMA uplink in time and frequency selective
fading channels”, Chinese Journal of Electronics, Vol.20, No.1,
pp.125–132, 2011.
[11] C.C. Chai, Y.H. Chew, “Power control for cognitive radios
in Nakagami fading channels with outage probability requirement”, 2010 IEEE Global Telecommunications Conference
(GLOBECOM 2010), Dec. 6-10, 2010, Miami, FL, USA., pp.1–
5, 2010.
[12] K. Sohaib, Y. Choi, Y. Han, “Outage improvement in cognitive relay networks by using a generalized regional mode”, 2010
IEEE 72nd Vehicular Technology Conferenc e(VTC 2010-Fall),
Sept. 6-9, 2010, Ottawa, ON, Canada., pp.1–5, 2010.
[13] X. Qu, M. Li, J.F. Zhang et al., “Downlink joint subchannel and
power allocation in cognitive OFDM systems with QoS guarantee for primary users”, 2010 2nd International Conference on
Future Computer and Communication (ICFCC), May 21-24,
2010, Wuhan, China, V3, pp.706–711, 2010.
2013
[14] Y.L. Zou, J. Zhu, B.Y. Zheng et al., “An adaptive cooperation diversity scheme with best-relay selection in cognitive radio networks”, IEEE Transactions on Signal Processing, Vol.58,
No.10, pp.5438–5445, 2010.
[15] Z. Hasan, E. Hossain, C. Despins et al., “Power allocation for
cognitive radios based on primary user activity in an OFDM
system”, 2008 IEEE Global Telecommunications Conference
(GLOBECOM 2008), Nov. 30-Dec. 4, 2008, New Orleans, LA,
USA., pp.1–6, 2008.
[16] L.H. Ozarow, S. Shamai, A.D. Wyner, “Information theoretic
considerations for cellular mobile radio”, IEEE Transactions on
Vehicular Technology, Vol.43, No.2, pp.359–378, 1994.
[17] B. Zayen, M. Haddad, A. Hayar et al., “Binary power allocation
for cognitive radio networks with centralized and distributed
user selection strategies”, Physical Communication, Vol.1, No.3,
pp.183–193, 2008.
[18] S.T. Chung, A.J. Goldsmith, “Degrees of freedom in adaptive
modulation: a unified view”, IEEE Transactions on Communications, Vol.49, No.9, pp.1561–1571, 2001.
[19] D.P. Bertsekas, Nonlinear Programming, 2nd ed. Boston, MA:
Athena Scientific Press, 1999.
[20] S. Boyd, L. Vandenberghe, Convex Optimization. Cambridge,
UK: Cambridge University Press, 2004.
LIANG Hui
was born in 1983,
Ph.D., lecturer of College of Communication Engineering at Jilin University. His research interests include resource allocation
in cognitive radio and software defined radio. (Email: [email protected])
ZHAO Xiaohui received the Ph.D.
degree from University of Technology of
Compiègne, France, in 1993, and currently
is a Professor of College of Communication Engineering at Jilin University. His
research interests include adaptive signal
processing theory, resource allocation in
wireless communication and cooperative
communication.