Femtocell provides better coverage and higher spectrum efficiency in areas rarely covered by macrocells. However, serious twotier interference emerging from randomly deploying femtocells may create dead zones where the service is unavailable for macrousers. In this paper, we present adopting cognitive radio spectrum overlay to avoid intratier interference and incorporating spectrum underlay and overlay to coordinate crosstier interference. It is a novel centralized control strategy appropriate for both uplink and downlink transmission. We introduce the application of proper spectrum sharing strategy plus optimal power allocation to address the issue of OFDMbased femtocells interferencelimited downlink transmission, along with, a lowcomplexity suboptimal solution proposed. Simulation results illustrate the proposed optimal scheme achieves the highest transmission rate on successfully avoiding two tier interference, and outperforms the traditional spectrum underlay or spectrum overlay, via maximizing the opportunity to transmit. Moreover, the strength of our proposed schemes is further demonstrated by comparison with previous classic power allocation methods, in terms of transmission rate, computational complexity and signal peaktoaverage power ratio.
1. Introduction
F
emtocells deliver wireless broadband services to the home or office customers with low power transmission. The
femtocell base station
(FBS) often approaches very close to subscribers, resulting in signaltointerferenceplusnoise ratio (SiNR) improved
[1]
. Overall network coverage and capacity have been enhanced, because of FBS generally deployed at the edge of the macrocell. Recently, due to effective infrastructure cost and better meeting users dominant indoor communication needs, both operators and academia desire to develop femtocell rapidly. in the early days of femtocells, many technical issues should be overcome, including the plugandplay interoperability, synchronization and security, seamless handover, interference alleviation
[2]
.
Universal frequency reuse, reuse factor of one, increases the longterm evolution (LTE) system spectral efficiency, but when femtocells deployed densely, interfemtocell and femtocellmacrocell interference degrades the system performance
[3]
. Antenna sectoring and time hopping rather than spectrum splitting are employed to solve intra/crosstier interference in uplink CDMA femtocell networks
[4]
. interference management of power selfcalibration and limitation, is proposed for 3G HSPA+ networks
[5]
. Six interference scenarios are elaborated in femtocellmacrocell heterogeneous networks, furthermore, reusing faraway users spectra and scheduling information to avoid strong interference are put forward
[6]
. Orthogonal radio resource allocation, a strategic game theory, decoding techniques and the Gibbs sampler are investigated to mitigate macro/femto interference
[7]
. Considering LTE employing orthogonal frequency division multiple access (OFDMA) downlink scheme, and to avoid burst interference resulting from random femtocell deployment, a new interference elimination clever manner is needed. in the paper, we introduce spectrum overlay and spectrum underlay simultaneously accessing, which further increase spectrum efficiency
[8]
, plus power control to mitigate twotier interference.
Cognitive radio
[9]
originating from the software defined radio is deemed as an effective technique to help nodes to be smartly aware of the surrounding radio electromagnetic environment
[10]
. Cognitive radio enabled femtocells opportunely access licensed spectrum bands such as macrocell and TV broadcast networks was presented
[11]
. However, they were concerned only with the downlink spectrum overlay sharing problem. Via each femtobase station autonomously sensing channels usage of the macrocell, standalone femtocell employs the unoccupied channels, collocated femtocells exploit strategic game to randomize the utilization of these channels to provide QualityofService (QoS) guarantees transmission was proposed
[12]
. Cognitive radio inspired approaches including spectrum sensing, clusterbased femtocells dynamic frequency reuse, and cognitive relay node were presented for interference coordination
[13]
. However, the above three literature all choose distributive autonomous manner, which cannot sidestep three problems: (i) Without the locations of moving macro users, it is very hard work for femtobase station to sense the unoccupied channels. (ii) Facing one vicinal macrouser, a number of femotcells have to repeat difficult sensing tasks. (iii) Selfish femtocell individually allocating spectrum based on local information cannot avoid intratier interference completely. So FBSs provided with cognitive capability
[14]
assisted the necessary information from the
macrocell base station
(MBS) via backhaul link obeying the MBS centralized spectrum management is an alternative interferencemitigation solution.
The major contributions of this article include: 1) We first explicitly point out centralized cognitive spectrum overlay and underlay (spectrum twolay) management can effectively mitigate twotier femtocell interference. 2) We introduce an optimal power control algorithm employing cognitive spectrum twolay mode to fulfill OFDMbased femtocells effective downlink transmission, and address a lowcomplexity considerablerate suboptimal scheme based on waterfilling method. 3) We evaluate the performance of the proposed schemes by extensive simulation.
The rest of the paper is organized as follows. Section 2 details the role of cognitive spectrum twolay access in femtocell interference mitigation. Section 3 describes the model of OFDM based femtocells downlink twotier interference. in Section 4, an optimal power allocation approach based on cognitive radio spectrum management and a suboptimal solution are proposed. Section 5 presents schemes comparisons. Section 6 gives extensive simulations to evaluate the performance of our proposed schemes and the conclusion is drawn in Section 7.
2. Spectrum Twolay Models Coping with TwoTier interference
LTE is intended to operate with a universal frequency reuse geared to higher throughput, where any cells share the identical set of frequencies. Although spectral efficiency maximized, twotier femtocell interference appears. Colayer interference, i.e. intratier interference is described as the unnecessary signal sent from a femtocell and received at other femtocells. Crosstier interference is the aggressor and the victim of interference belonging to different layers such as macrocell and femtocell. Early cellular concept was the first breakthrough in obtaining higher user capacity and interference avoidance between mobile users, relying on frequency reuse. Nowadays, the macrocell overlaid with femtocells complicates the problem.
Dynamic spectrum access to cognitive radio networks is classified as two types, underlay and overlay. in the spectrum underlay, secondary users are allowed to concurrently transmit with primary users, only if the cochannel interference is limited to below the interference temperature threshold first presented by the FCC, which results in higher spectrum utilization. Secondary users temporarily occupy the licensed spectrum, only when the primary users are absent, that is the spectrum overlay mode, like time or frequency division multiplexing.
Facing the femtocell twotier interference challenge due to the lack of coordination, we present cognitive radio spectrum twolay management to solve. in short, femotcell and marcocell run in spectrum sharing mode and femotcells opportunistically access to cognitive marcocell networks. To increase spectral efficiency, LTE femotcells simultaneously operate in licensed spectrum with macrocell, so that crosstier interference is inevitable. Fortunately, in the spectrum underlay, crosstier interference is diminished under the interference temperature threshold so much so that they suffer no harmful interference, which is naturally equivalent to interference avoidance. Specifically, the interference is weak, where cochannel femtousers are far away from macrousers of the identical macrocell. Certainly, it is better macrocell and femtocell running in the spectrum overlay, interferencefree, thanks to signals orthogonalization. Femotcell is characterized by lowpower shortrange coverage, so mutual interference among femotcells usually does not occur. Even if femtocells are deployed densely, as long as femotcells operate in the spectrum overlay, intratier interference vanishes due to assigning nonoverlapping channels. in conclusion, agilely using spectrum twolay can completely remove twotier femtocell interference, as long as the cognitive radio spectrum management is powerfully implemented. Note that the interference avoidance scheme is suitable for uplink and downlink transmission, even with different multiple access technology.
3. System Model and Formulation
Femtocell networks downlink twotier interference scenarios are shown in
Fig. 1
. Macrocell coverage area is represented by Hexagon. Dashed circle shows femtocell coverage area, where the
macrocell user
(MUE) and the
femtocell user
(FUE) can coexist. The solid green line arrowhead and the dotted red line arrowhead denote intratier interference and crosstier interference, respectively.
Downlink twotier interference under universal frequency reuse
Spectrum twolay power allocation according to distribution of MUEs
The research is built on the model described in
[15]
. The entire frequency band is divided into
N
subcarriers, each interval is Δ
f
Hz, as shown in
Fig. 2
. The
Z
MUEs have occupied B
_{1}
, B
_{2}
, …, B
_{Z}
bandwidth, so spectrum underlay FUEs are allocated power forcedly below the interference temperature threshold to ensure the desired QoS to theseMUEs. Via four methods include centralized notification, server part information sensing, server blind sensing and distributed assistant sensing, the FBS knows three instantaneous Rayleigh fading channel gains
[14]
:
h_{i}^{mf}
representing the downlink channel gain from MBS to FUE,
h_{l}^{fm}
indicating the one from FBS to MUE, between the FBS and FUE, denoted as
h_{i}^{ff}
, illustrated in
Fig. 1
.
As aforementioned, femtocell intratier interference does not occur in the spectrum overlay model, the following sections focus on femtocell crosstier interference.
 3.1. CrossTier interference introduced by FBS
Given OFDM employing the rectangular nonreturntozero (NRZ) signal
[15]
, the power density spectrum (PDS) of the
i
th subcarrier is defined as
[16]
,
[17]
where
P_{i}
denotes the
i
th subcarrier transmit power.
T_{s}
is the symbol duration,
T_{s}
=1/Δ
f
+guard interval
[15]
. The FBS interfering to MUE
l
th subcarrier, denoted as
I_{F→M}
(
P_{i}, l
), the sum of all subcarriers integration of the PDS, is written as
where
d_{il}
, an integer, denotes the distance between
i
th subcarrier of FBS and
l
th subcarrier of MUE.
h_{l}^{fm}
is the FBS to MUE
l
th subcarrier channel gain. Let
O
,
L
denote the set of the total occupied subcarriers, the total number of spectrum underlay subcarriers, respectively. Then the cardinality of the set
O
is
L
, i.e., 
O
 =
L
.
Note that
the interference
I_{F→M}
(
P_{i}, l
) includes FBS both spectrum overlay and spectrum underlay transmissions impact, because it is the sum of total
N
subcarriers, either occupied by MUEs, i.e.,
i
∈
O
or unoccupied, i.e.,
i
∈
U
= {
x

x
∈ {1,2,⋯,
N
},
x
∉
O
}.
 3.2. CrossTier interference introduced by MBS
Assuming a rectangular window function used
[15]
, the PDS of MBS signal after Mfast Fourier transform (FFT) processing can be given as
[15]
,
[16]
where
ω
signifies the normalized frequency, Φ
_{MBS}
(
e^{jω}
) stands for the PDS of MBS signal before MFFT. The MBS interfering to FUE
i
th subcarrier, denoted as
I_{M→F}
(
i
), sum of all
l
th subcarrier integration of the PDS, is written as
where
L
signifies the total number of subcarriers occupied by MUEs.
h_{i}^{mf}
is the MBS to FUE
i
th subcarrier channel gain.
4. Cognitive Radio Centralized Spectrum Management for interference Mitigation
We think efficiently coexisting with macrocell to provide FUEs reliable communication without harmful interference, femtocells need cognitive radio assistance. To achieve the goal, three fundamental cognitive tasks must be addressed
[10]
:
● Radioscene analysis.
● Channelstate estimation and predictive modeling.
● Transmitpower control and dynamic spectrum management.
The first two tasks can be fulfilled through a variety of approaches. in this paper, we advocate nonsubscriberterminal spectrum sensing
[14]
, and centralized coordination to assist estimating channel state. The MBS provide necessary information to the FBS through backhaul link: 1) the near MUEs' location information to help get channel gain
h_{l}^{fm}
and their occupied subcarriers, 2) crosstier interference to FUEs i.e.,
I_{M→F}
(
i
), 3) neighbor FBSs of together spectrum overlay accessing, 4) the interference threshold these MUEs can endure, according to their QoS demand. The first three jobs depending only on sensing are so difficult, that the coordination plays the important role in interference avoidance. Next, we mainly focus on the third task: multipleaccess control, i.e. transmitpower control plus dynamic spectrum management
[10]
. Our goal is to allocate maximum power to the best available frequency band to meet FUEs highrate requirement. We hope to obtain the most suitable spectrum utilization, however, if MUEs' interference threshold is strictly controlled, handy option is to allow only overlay spectrum access rather than to manage twolay spectrum bands.
 4.1. The Proposed Optimal Scheme
The objective is to adjust each subcarrier power to maximize femtocell downlink throughput
R
, on the premise of curbing twotier interference.
subject to,
where
h_{i}^{ff}
,
P_{i}
denote the FBS to FUE
i
th subcarrier channel gain, transmit power, respectively.
σ
^{2}
represents the additive white Gaussian noise.
P_{T}
is the transmission power budget.
I_{th}
signifies the interference temperature threshold. The second derivative of
R
with regards to
P_{i}
can be derived as
Hence, the problem formulation is convex, so the duality gap is zero.
Since the Δ
f
remains constant, it can be omitted in the calculation, for simplicity, so optimal power is written as
Therefore, relaxing the constraints in (6)(8), the Lagrangian is represented as
where
λ
_{1}
,
λ
_{2}
and
λ
_{3}
are Lagrange multipliers. Based on the KarushKuhnTucker (KKT) conditions
[18]
as follows
Since
P_{i}^{*}
≥0, so
λ
_{3}
^{*}
= 0, the optimal power value is yielded
where
, [·]
^{+}
= max {·,0}. Derived from subgradient method,
λ
_{1}
,
λ
_{2}
are updated, respectively, as
Note that as long as k is large enough, it guarantees the algorithm accurately converges to the optimal value. The time complexity of the core algorithm is
, where
K
,
N
,
L
are the total number of iterations, of subcarriers, of subcarriers occupied by MUEs, respectively. The variable
K
is determined by convergence precision and the initial values
λ
_{1}
^{0}
,
λ
_{2}
^{0}
,
α
^{0}
,
β
^{0}
.
In spectrum overlay, the left of (6) is always less than the right, according to the KKT conditions,
λ
_{1}
^{*}
= 0. However, the right of (7) is permanently greater than the left in spectrum underlay, so
λ
_{2}
^{*}
= 0. Therefore the algorithm accomplishes spectrum twolay optimal power allocation, combating twotier femtocell interference.
 4.2. The Proposed Suboptimal Scheme
Based on the heuristic that when interference threshold
I_{th}
is stringent, spectrum overlay outperforms spectrum twolay in total rates in nonoptimization approaches, we proposed a lowcomplexity suboptimal scheme. The key idea is that according to the model switching threshold
M_{th}
, determined by the total power budget
P_{T}
, we choose the proper classic waterfilling scheme as the suboptimal power profile, which can be expressed as
Where
P_{i}^{WF}
, denoted as waterfilling subcarrier power allocation, which is elaborated in subsection 5.3. The
U
is the set of the subcarriers unoccupied, defined in subsection 3.1.
Proposition 1.
Given P_{T}, the model switching threshold satisfies
where
,
G is derived in section 4.1. E(·) denotes the expectation operator. Proof.
Please refer to Appendix.
Note that when the channel gains of each subcarrier do not vary, the equal power allocation is a special case of the waterfilling method, so we adopt waterfilling power assignment as the core algorithm of the suboptimal scheme.
5. Schemes Comparison
In order to better show the superiority, we selected four representative algorithms to do comparison with optimal and suboptimal algorithms.
 5.1. Sequential Quadratic Programming Scheme
As we all know, (5)(8) form a Nonlinear Programming Problem (NLP). in past years, via the linearization of the actual constraints about
P_{i}
in (6)(8), the above NLP can be solved by finding the minimum value
d_{Pi}
in the Sequential Quadratic Programming (SQP) as
[19]
subject to,

C1(Pik) + ▽PiC1(Pik)dPi≤ 0,

C2(Pik) + ▽PiC2(Pik)dPi≤ 0,
where
. When setting
are chosen to satisfy the KKT optimality conditions, the SQP can be solved by Newton’ s method
[18]
. A similar optimal scheme was used in
[17]
, which is denoted as SQPS here. Although the complexity of the SQPS scheme is also
the Hessian of the Lagrangian, however, should be computed in each iterative. Furthermore, if the Newton decrement
[18]
is used as stopping criterion, the total number of iterations
K
is always larger than the one in our proposed optimal scheme.
 5.2. Ladder Fashion Proportion Scheme
Since major crosstier interference results from spectrum underlay transmission, so a reasonable strategy is to allocate more power to the idle subcarriers, taking advantage of the overlay model. The authors in
[17]
introduced Ladder Fashion Proportion scheme (LFPS) to simply fulfill power allocation. They assume equal power values are assigned to the underlay subcarriers, i.e.,
P_{i}
=
P_{underlay}
, ∀
i
∈
O
. The minimum power value
P_{overlay}
allocated among the overlay subcarriers is an integral multiple of the
P_{underlay}
. According to the minimum distance
d
between overlay subcarriers and underlay subcarriers, the power distribution of the overlay subcarriers takes a ladder fashion profile, i.e.,
P_{i}
=
d
·
P_{overlay}
, ∀
i
∈
U
in their design. Note that
O
and
U
are sets defined in Section 3.1. it is easier work to find proper
P_{underlay}
satisfying both power and interference constraints, so that the complexity of the LFPS scheme is
due to no iteration required.
 5.3. Classical Waterfilling Scheme
The wellknown waterfilling scheme can optimize the power distribution over multicarrier transmission, due to taking advantage of the better channel conditions, i.e., more power allocated to the stronger subcarriers. A linear waterfilling algorithm for delaytolerant users multimedia service allocation was presented
[20]
. Here, the precise formulation is written as
where the waterfilling level
is a constant chosen so that the power and interference threshold constraints are met. Note that when
i
∈
U
, the scheme only considers spectrum overlay model, when
i
∈ {1,2,…,
N
}, waterfilling twolay power allocation comes into being.
 5.4. Classical Equal Power
The simplest solution is equal power allocation, because it does not consider the change of channel gains. The minimum equal power value should be chosen as it satisfies both the constraints (6)(7). As above, there are two modes, i.e., spectrum overlay equal power allocation and equal power twolay model.
The complexity comparison of different schemes is concluded, as shown in
Table 1
.
Algorithm Complexity Comparison
Algorithm Complexity Comparison
The high peaktoaverage power ratio (PAPR), defined as the ratio between the maximum instantaneous power and its average power, leads to nonlinear distortion, requiring high resolution digitalanalog converter and high power amplifier to handle, which limits the OFDM applications
[21]
. So it is essential to compare the PAPR among several schemes.
6. Numerical Results
We consider the scenario that 3 MUEs are present to occupy
L
= 10 subcarriers, among total subcarriers
N
= 32; and set the OFDM symbol duration
T_{s}
= 4
μs
, subcarrier interval Δ
f
= 0.3125 MHz, the additive white Gaussian noise
σ
^{2}
=10
^{−5}
, the transmission power budget
P_{T}
= 10
^{−3}
W. We assume
h_{i}^{ff}
,
h_{l}^{fm}
Rayleigh fading channel gain average values as 6 dB, 3 dB. The average value of
I_{M→F}
(
i
) is fixed as 10
^{−4}
W, then varies from 10
^{−4}
to 9·10
^{−4}
, simplified as
I_{M→F}
in the following.
In
Fig. 3
, FUEs spectrum twolay optimal power allocation according to the distribution of MUEs is studied by varying crosstier interference threshold
I_{th}
from 2·10
^{−4}
to 10
^{−3}
, where
I_{M→F}
=10
^{−4}
.
Fig. 3
shows subcarrier power of FUEs spectrum underlay significantly reduces, as the
I_{th}
reduces, which implies the proposed optimal algorithm effectively controls crosstier interference; on the other hand, the power of spectrum overlay obviously raises in order to spend the transmission power budget. Clearly, power is loaded at every subcarrier, the higher for spectrum overlay, 3.3·10
^{−5}
<
P_{i}^{overlay}
< 5.1·10
^{−5}
, the lower for spectrum underlay, 2·10
^{−6}
<
P_{i}^{underlay}
< 2.7·10
^{−5}
, which indicates the proposed optimal performs spectrum twolay management. Since the restriction (6) controls the sum of all subcarriers interference, therefore, underlay subcarriers assigned power forms a smaller inverted triangle along the ordinate. Conversely, power distribution of spectrum overlay shows a larger triangle; in which, the nearer to MUEs subcarrier, the growth trend is smaller, the others present bigger ladder growth. Just as expected, from interferencelimited perspective,
Fig. 3
illustrates the maximum power is allocated to the best available subcarrier.
Optimal multisubcarrier power allocation according to the distribution of MUEs versus crosstier interference threshold I_{th}
Comparison of maximum rate versus crosstier interference threshold I_{th} among the proposed optimal and spectrum overlay and underlay
Fig. 4
demonstrates the maximum rate increases, as crosstier interference threshold
I_{th}
raises from 2·10
^{−4}
to 10
^{−3}
, and the growth trend is visible when
I_{M→F}
=10
^{−4}
. it reveals that as the
I_{th}
rises, i.e. the QoS demand for MUEs declines, FUEs have the opportunity to obtain a higher rate.
Fig. 4
shows that the proposed optimal scheme outperforms only spectrum overlay and only spectrum underlay, and such advantage is clearer under slighter
I_{M→F}
; because the former loads power on each subcarrier, but the latter abandons some subcarriers, which hints transmission rate maximization benefits from access opportunity maximization. As the
I_{th}
reduces, the gap between the proposal and spectrum overlay gets narrower, which implies the advantage of the former, i.e. partly using spectrum underlay, becomes puny, and the trend is distinct when
I_{M→F}
= 9·10
^{−4}
. The spectrum overlay model unlikely violates the threshold
I_{th}
, so the curve is a straight line, because
λ
_{1}
^{*}
= 0, which is explained in subsection 4.1, the power values of the subcarriers unoccupied by the MUEs do not change as the
I_{th}
changes.
Fig. 5
presents maximum transmission rate versus crosstier interference threshold
I_{th}
when
I_{M→F}
=10
^{−4}
, where proposed optimal and suboptimal schemes are compared with the SOPS, the LFPS, classic waterfilling and equal power methods. We observe that the proposed optimal and the SOPS achieve highest performance, at the expense of more time optimization calculation, and when
I_{th}
increases, the growth trends coincide. The optimal is superior to the waterfilling overlay scheme at least 1 Mbps rate increase, as shown in
Fig. 5
(a), and water filling underlay performs poorly, given interference controlled strictly, which indicates simple twolay spectrum access without optimization is not desirable.
Fig. 5
(b) shows the proposed suboptimal outperforms the LFPS scheme, especially when
I_{th}
is small enough or large enough. From two subfigures, we surprisingly find 1) although each subcarrier power values differ, classic equal power and waterfilling rate outcomes are of equivalent, and 2) their twolay power allocation results are inferior to their overlay design, when
I_{th}
is small, due to the interference threshold constraint (6) severely restricts the maximum subcarrier power, as shown in
Fig. 6
(a).
Comparison with classical schemes
Subcarrier power comparison among different schemes
Fig.6
reveals two extreme power values of
N
subcarriers, as the crosstier interference threshold
I_{th}
increases, when
I_{M→F}
=10
^{−4}
. Combine the information in two subfigures, we highlight six points. First, the curves of two classic methods in overlay model present straight line. Second, there is difference in subcarrier power allocation between waterfilling and equal power methods, no matter whether overlay or twolay. Third, the maximum and minimum power values of the proposed optimal and suboptimal schemes elastically change, when
I_{th}
raises. Fourth, the LFPS power allocation does not vary, when
I_{th}
increases over 3·10
^{−4}
. Fifth, the proposed suboptimal power profile coincides with the waterfilling scheme. Sixth, the maximum power value of the optimal scheme decreases, meanwhile, the minimum power value increases, given interference threshold grows, as shown in
Fig. 3
.
Fig. 7
demonstrates the comparison of the PAPR of OFDM signal among different schemes versus crosstier interference threshold. We observe that the LFPS has a shortcoming in the PAPR, which keeps more than 2 times higher than the others, and does not change, as
I_{th}
increases. By contrast, equal power allocation in all subcarriers naturally outcomes zero PAPR. The proposed optimal and suboptimal schemes result in moderate PAPR. in brief, the proposed suboptimal scheme characterizes lowPAPR, considerablerate, lowcomplexity and taking advantage of the better subcarriers channel condition.
PAPR comparison among different schemes
Maximum rate versus I_{th} and crosstier interference I_{M→F}
Fig. 8
illustrates the maximum rate varies, as the interference threshold
I_{th}
raises from 2·10
^{−4}
to 10
^{−3}
, and crosstier interference
I_{M→F}
declines from 9·10
^{−4}
to 10
^{−4}
. We observe that as
I_{th}
increases, where
I_{M→F}
=10
^{−4}
, the throughput increases more than 3 Mbps, which implies although the power budget
P_{T}
keeps constant, the data rate has increased, thanks to multicarrier transmission. Because there are more subcarriers using the larger power to transmit.
Fig. 8
also shows that when the
I_{M→F}
turns into serious, the transmission data rate drops greatly, from more than 24.4 Mbps to less than 5.9 Mbps, the former is four times the latter, which well illustrates crosstier interference undesired impact.
Note that the disturbing crosstier interference
I_{M→F}
is treated as noise in the proposed scheme. Predictably, a similar approach should be used inMBS to restrict the
I_{M→F}
. Therefore it becomes a more effective solution.
7. Conclusion
Femtocell twotier interference mitigation using cognitive radio spectrum twolay models has been addressed. A downlink throughput maximization algorithm fulfilling spectrum twolay power allocation under the restrictions of the crosstier interference and a lowcomplexity suboptimal scheme have been proposed. Presented extensive numerical results show the proposed optimal outperforms several classic schemes and traditional spectrum overlay or spectrum underlay in transmission rate, because of maximizing the utilization of multi subcarrier transmission. Moreover, the suboptimal approach characterizes considerablerate, lowPAPR, lowcomplexity, for being hybrid waterfilling scheme of spectrum overlay and spectrum twolay. However, these schemes all base on the perfect channel state information, i.e., the assumption of the first two steps in traditional cognitive cycle perfectly completed, here via reliable coordination between MBS and FBSs plus necessary information assisted sensing. Therefore, we consider future research includes robust spectrum sensing to eliminate femtocell twotier interference under nonideal coordination.
BIO
LengGan Yi received the M.S. degree from Chongqing University of Posts and Telecommunications, Chongqing, China in communication engineering. He is currently a Ph.D. candidate in the Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China. His research interests include cognitive radio, femtocell networks, radio resource management, game theory, and optimization theory for wireless networks and multimedia communications.
YiMin Lu is a professor, and Ph.D. supervisor in the Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China. His research interests focus on modern communication technology, multimedia communications, infrared imaging, laser detection and communication.
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