Communications and Network, 2013, 5, 298-302 Published Online September 2013 (
Mixed Contiguous and Aggregated Spectrum Allocation
Algorithm for CR based TD-LTE System
Xiaoyan Liu, Qingyi Quan, Peng Lei, Wenbin Guo, Wenbo Wang
WSPN Lab. Key Laboratory of Universal Wireless Communication, Ministry of Education, School of Information and
Communication Engineering, Beijing University of Posts and Telecommunications, China
Received July, 2013
Cognitive Radio (CR) is a promising technology to increase the efficiency of spectrum by opportunistic access. How-
ever, in many cases the sensed spectrum is so broken that it cannot be utilized sufficiently with traditional continuous
spectrum allocation strategy. Sp ectrum aggregation can make full use of narrow spectrum fragments while the overhead
it brings may reduce the overall performance of the system. This paper proposes a Mixed Contiguous and Aggregated
Spectrum Allocation (MixCASA) algorithm for CR based TD-LTE Networks. This algorithm combines contiguous
with discontiguous spectrum allocation together in order to guarantee the feasibility and efficiency of system. Simula-
tion results show that the MixCASA algorithm can increase th e number of allocated channels, and reduce the overhead
brought by excessive spectrum aggregation as well.
Keywords: Cognitive Radio; TD-LTE; Spectrum Allocation; Spectrum Aggregation
1. Introduction
As a standard for wireless communication technology,
LTE offers considerably high data rate [1]. While the
spectrum usage efficiency has been improved, it is nec-
essary to increase the transmission bandwidth to achieve
a higher data rate according to Shannon formula. How-
ever, spectrum is one of the main obstacles that the de-
veloping wireless communication technology must
overcome. Besides, traditional spectrum allocation strat-
egy is proved to be inefficient [2], so finding new algo-
rithms to assign this scarce resource more rationally is
CR is a promising way to solve this problem. In CR
networks, available spectrum is automatically detected to
allow the access of Secondary Users (SUs) without sen-
sible interference to Primary Users (PUs) [3]. However,
available spectrum condition of CR networks has great
difference compared with that of traditional wireless
communication networks. Because the sensed spectrum
may not be stable all the time and its character also
changes. Besides, in most cases the spectrum is so frag-
mented that it is hard or impossible to be utilized effi-
ciently [3]. Owning to these facts, traditional spectrum
allocation protocols are not very applicable.
Spectrum aggregation is a feasible way to settle the
problems mentioned above. By bonding multiple spec-
trum fragments together, it allows multiple contiguous or
discrete blocks of spectrum to be treated as if they are
one large contiguous block [4]. However, the aggregation
capacity is limited to some extent and fragments that are
too scattered cannot be bonded together. Moreover, ag-
gregation also brings overhead and it has high require-
ment for hardware [5]. So excessive spectrum aggrega-
tion should be avoided to reduce unnecessary overhead
and guarantee the spectrum efficiency at the same time.
Besides, spectrum resource is similar to computer
memory resource in some aspects, for both of them may
be fragmentized during the allocation, and excessive
fragments handicap their performance. In this paper, we
are inspired by the best-fit strategy of computer memory
allocation, which is a good way to minimize fragments
[6]. Best-fit memory allocation picks out the minimum
one among all the resources that satisfy user’s demand.
And it makes the best use of resource in this way.
Considering those mentioned above, we propose a
Mixed Contiguous and Aggregated Spectrum Allocation
(MixCASA) algorithm. In this algorithm, spectrum or
channel resource is allocated in two ways. If there is suf-
ficiently available contiguous resource for a certain user
demand, best-fit spectrum allocation strategy is implied.
Otherwise spectrum aggregation is carried out to increase
the total number of channels that can be allocated as
many as possible.
The rest of this paper is organized as follows. In part 2
related works are illustrated. Then the system model and
the MixCASA algorithm are proposed in part 3. After
opyright © 2013 SciRes. CN
X. Y. LIU ET AL. 299
giving the simulation results and analyzing the perform-
ance in part 4, we make our conclusions in part 5.
2. Related Work
In order to utilize the scarce spectrum resource more ef-
fectively and efficiently, Dynamic Spectrum Allocation
(DSA) has been well studied these years. Previous works
such as [7] intend to increase spectrum efficiency and
guarantee user’s demand in a contiguous spectrum level.
However, in most cases the sensed spectrum may not be
that perfect and may have many narrow fragments [8],
thus it is difficult to make full use of this scarce resou rce
with traditional continuous spectrum allocation strategy.
Wireless radio technology such as Discontiguous Or-
thogonal Frequency Div ision Multiplexing (DOFDM) [9]
makes spectrum aggregation possible, in which many
detached spectrum holes can be joint together and then
utilized as if they were a whole block [10]. It is a prom-
ising technology for its flexibility and dynamic charac-
teristic and low interference between adjacent sub-
channels when allocating spectrum resource.
Spectrum aggregation works exactly this way [11].
Spectrum aggregation makes it possible to utilize more
than one carrier and in this way increase the overall
transmission bandwidth. Although technique has been
already quite well investigated at theoretical level, their
practical implementation is not immed iate and also raises
numerous challenges. Intuitively speaking, the aggrega-
tion capacity cannot be unlimited [12], otherwise the
corresponding hardware may not satisfy its requirement
and the overhead aggregation brings may handicap the
overall performance of the system as well.
Aggregation Aware Spectrum Assignment (AASA)
proposed in [13] is a greedy algorithm. This algorithm
gives an aggregation capacity named aggregation span,
which performs in the form of sliding window. It can
achieve maximum access number by assigning available
channels within the sliding window and then moving the
window from lower to upper frequency successively.
However this greedy algorithm is not very practical be-
cause every SU in this model is set to have the same
bandwidth demand and it does not take overhead brought
by channel aggregation into consideration.
Maximum Satisfaction Algorithm (MSA) presented in
[12] considers different bandwidth requirements of SUs.
In this algorithm SU with larger bandwidth requirement
can be allocated preferentially. While it only focus on
maximization of the total access number of SUs’ band-
width requirements, and still neglects the load aggrega-
tion brings to system.
MixCASA algorithm we propose in this paper aims to
improve spectrum efficiency, and avoid excess aggrega-
tion at the same time by combine contiguous and discrete
spectrum allocation together. So it can guarantee a rela-
tively high number of allocated channels and avoid ex-
cessive overhead brought by spectrum aggregation. Ex-
periment results show that this algorithm can achieve a
much better performance when taking both spectrum
efficiency and aggregation overhead into consideration.
3. System Model and Algorithm
3.1. Scenario Definition
We assume a CR network that has N SUs denoted by
{1, 2,}
Un N
. Considering the most common
wireless communication scenario, we make some as-
sumptions as follows.
There are enough SUs, and every channel can only
be occupied by one SU or PU.
Every SU has a bandwidth requirement SU
RB ,
{1, 2,}Nn
. And all of these requirements compose
an array RB .
where is random distributed so that it can line with
the practical situation, in which SUs have various band-
width requirements.
The condition of spectrum remains unchanged,
which means we have a stable spectrum that has already
been sensed during the allocation, and the total band-
width is
The maximum aggregation capacity is threshold
and the width of sliding window is window
T, which is
shown in Figure 1. As window moves forward, the
whole spectrum will be searched.
Assume that spectrum is unitized, so we can label
these channels in integer numbers. The total spectrum
can be denoted by . And the total available spec-
trum is denoted by . Every available spectrum
fragment and its length
B can be denoted
Freque ncy
Unavailable spectrum fragmentsAvailable spectrum fragments
Figure 1. Fragment fragments and allocation schematic.
Copyright © 2013 SciRes. CN
as follows.
jj j
BLL U (2)
BULj J
L and
U are the lower and upper unitized
channel number of jth available spectrum, which satisfy
the following relationship.
11, 2,
 
So the total unitized available channel and its length is
avail avail
avail avail
In this model, we assume that SU with larger band-
width requirement has priority to be allocated firstly. Let
C denotes the total number of channels assigned after the
whole allocation process. If K SUs’ requirement are ac-
cepted in all, the equation of C can be denoted as fol-
N (7)
And allocated channel numbers Ccannot be larger
than the number of total availa ble chan nels. So we have
CB (8)
And our target is to maximize C and avoid excessive
aggregation at the same time.
3.2. Description of Proposed Algorithm
In this paper, we propose a MixCASA algorithm to solve
the problem described above.
The key idea of MixCASA is to allocate spectrum re-
source contiguously in best-fit method if there are suffi-
ciently enough contiguous vacant channels for a SU’s
demand. Otherwise spectrum aggregation is applied to
guarantee the number of channel that can be allocated.
All SUs are allocated one by one according to their
bandwidth requirements, which are sorted decreasingly.
Steps of this algorithm can be simplified as follows.
1) Sort all the channel requirements of SUs decreas-
ingly. So SU with larger demand has a priority to use the
limited spectrum resource. For a certain SU, spectrum
assignment can be divided into two stages.
2) During the contiguous assignment, best fit strategy
is applied. And the whole sensed spectrum is searched to
pick out the smallest one among all the spectrum frag-
ments that are big enough for this SU’s requirement.
3) During the discontiguous allocation, available
chann els within slid ing window are s earched and bo nded
together to satisfy the SU’s demand. If no sufficient
channels can be found after these two stages, this SU’s
requirement will be refused.
As mentioned above, the proposed algorithm contains
contiguous channel assignment and discontiguous chan-
nel assignment. It is not hard to discover that steps of this
algorithm can be simplified by setting the width of slid-
ing window as follows.
During contiguous channel assignment stage,
{1, 2,}
window SU
And during aggregation stage, we have
window threshold
TT (10)
The model of sliding window is shown in Figure 1.
Different values of mean different allocation
modes. As the sliding window moves successively, all
the channels can be checked and allocated if it satisfies
any one of these two modes. Detailed procedure of
MixCASA is demonstrated in Algorithm 1 and Algo-
rithm 2.
Algorithm 1: MixCASA
Input: SUs’ bandwidth requirement vector
B, normal-
ized available channelsavail
, aggregation threshold
, and the width of sliding window
Output: Channel state after the allocation of all SUs
denoted by 0 (unallocated) and 1(allocated)
for n = 1 to N do
tag = 0
for j = 1 to avail
B do
if avail
B>0 and SU
B<= avail
B then
S1 = j
/* S1 is an temporary array to store j */
tag = 1
end if
end for
if tag = 1 then
window SU
y = min ( S1 )
/* y is the location of the minimum of S1 */
window threshold
Spectrum Aggregate
end if
end for
Copyright © 2013 SciRes. CN
X. Y. LIU ET AL. 301
Algorithm 2: Spectrum Aggregation
Input: Slide-window width,
th SU’s bandwidth
request SU
B, available channelsavail
Output: Channel state after the as signment of
th SU
/* p1, p2 are the start and stop location of slide window */
for p1 = 1 to avail window
BT do
p2 = p1+
S2 = find((1: 2)0
/* S2 stores the location of available channels within slide
window */
if length(S2) >= do
11 1
LLT 
p1 = p1+1
end if
end for
4. Simulation and Performance Analysis
We configure the simulation as shown in Table 1. Ac-
cording to current sampling speed of analog-to-digital
converters, the aggregation capacity , or aggre-
gation threshold we call here, is set at 40 MHz. Spectrum
range is fixed at VHF and UHF within broadcasting TV
band from 100 to 600 MHz. SUs’ bandwidth require-
ments are set to range from 4 MHz to 20 MHz. In the
simulation, channel fragment number changes from 1 to
100. And when it is 1, it means we have an unbroken
spectrum, which is a theoretical condition that we take as
a reference. We run these algorithms for 150 times re-
spectively and the variables are re-generated in every run.
Then we get the average values of simulation as follows.
Figure 2 shows the allocated channel number, i.e. the
number of channels that can be allocated by algorithms
mentioned above, including AASA from [12], MixCASA
proposed in this paper and traditional contiguous spec-
trum allocation. Here we denote them with array ,
and . AASA is a greedy algorithm, which
slides the window from low to high one by one without
considering the other factors. So it is a theoretical strat-
egy but not very practical. When the number of frag-
ments is not very big, i.e. the sensed spectrum is rela-
tively complete, the allocated channel number of these
algorithms nearly stay the same. As spectrum fragments
increases, performance of oontiguous spectrum allocate
strategy goes downhill quickly while the performance of
MixCASA algorithm only has a slight decrease.
Figure 3 shows the aggregation number, i.e. how
many times aggregation occurred during the allocation.
They are denoted by array and . Here we
make the assumption that there is an aggregation as long
as the width of slide-window is set at the maximum ag-
gregation capability , and channels are assigned
to certain SU successfully during this assignment. Be-
cause there is no aggregation in contiguous spectrum
assignment, we just present the aggregation number of
AASA and MixCASA algorithm here. Compare Figure
2 with Figure 3 we can see that when the sensed spec-
trum is not so fragmentary, aggregation number of
MixCASA is significantly reduced than that of greedy
algorithm. And the number of allocated channels remains
a relatively high level at the same time.
The data in Figure 2 and Figure 3 are converted in
following way so that we can have a comparison more
understandable and reasonable.
AASA allo
ASA allo
MixC allo
The results of AASA and R
ixCASA are shown in Fig-
ure 4, which in part reflect the different overhead of ag-
gregation when allocating sub-channels in different ways.
We can see that the normalized average aggregation
overhead of MixCASA is much smaller than that of
Table 1. Simulation configurations.
Parameter Name Value
Frequency range 100 MHz to 600MHz
Total available Frequency 300MHz
Bandwidth Per Sub-ca rrier 1MHz
Aggregation thresho l d 40MHz
Figure 2. Number of channels allocated of different algo-
Copyright © 2013 SciRes. CN
Copyright © 2013 SciRes. CN
[1] “LTE: The UMTS Long Term Evolution,” New York:
John Wiley & Sons, 2009.
[2] I. F. Akyildiz, W. Y. Lee, M. C. Vuran, et al., “Next Gen-
eration/Dynamic Spectrum Access/cognitive Radio Wire-
less Networks: A Survey,” Computer Networks, Vol. 50,
No. 13, 2006, pp. 2127-2159.
[3] I. F. Akyildiz, W. Y. Lee, M. C. Vuran, et al., “A Survey
on Spectrum Management in Cognitive Radio Networks,”
Communications Magazine, IEEE, 2008, pp. 40-48.
[4] L. Chen, W. Chen, X. Zhang, et al., “Analysis and Simu-
lation for Spectrum Aggregation in LTE-advanced Sys-
tem,” Vehicular Technology Conference Fall (VTC
2009-Fall), 2009 IEEE 70th. IEEE, 2009, pp. 1-6.
Figure 3. Aggregation number of AASA and MixCASA.
[5] Z. Shen, A. Papasakellariou, J. Montojo, et al., “Over-
view of 3GPP LTE-advanced Carrier Aggregation for 4G
Wireless Communications,” Communications Magazine,
IEEE, Vol. 50, No. 2, 2012, pp. 122-130.
[6] M. Masmano, I. Ripoll, P. Balbastre, et al., “A Con-
stant-time Dynamic Storage Allocator for Real-time Sys-
tems,” Real-Time Systems, Vol. 40, No. 2, 2008, pp.
149-179. doi:10.1007/s11241-008-9052-7
[7] M. Hamid, A. Mohammed and Z. Yang, “On Spectrum
Sharing and Dynamic Spectrum Allocation: MAC Layer
Spectrum Sensing in Cognitive Radio Networks,” Com-
munications and Mobile Computing (CMC), 2010 Inter-
national Conference on IEEE, 2010, Vol. 2, pp. 183-187.
Figure 4. Converted overhead of aggregation. [8] M. Lee and T. J. Lee, “Multichannel MAC Protocol with
Discontiguous-OFDM for Cognitive Radio Networks,”
Informatics Engineering and Information Science,
Springer Berlin Heidelberg, 2011, pp. 594-603.
5. Conclusions
A MixCASA algorithm has been proposed in this paper
for CR based TD-LTE system. In this algorithm, spec-
trum resources are allocated contiguously in best-fit
method if there are sufficiently enough contiguous vacant
channels for a SU’s demand. Otherwise spectrum aggre-
gation is applied to guarantee the number of channels
that can be allocated. Simulation results have shown that
when the sensed spectrum is not extremely broken, this
algorithm can guarantee a high number of allocated
channels and reduce the overhead brought by excessive
spectrum aggregation as well.
[9] Chen Y S, Liao S H. “Spectrum-aware routing in discon-
tinuous orthogonal frequency division multiplexing-based
cognitive radio ad hoc networks,” Networks, IET, Vol. 1,
No. 1, 2012, pp. 20-33. doi:10.1049/iet-net.2011.0002
[10] Poston J D, Horne W D. “Discontiguous OFDM Consid-
erations for Dynamic Spectrum Access in Idle TV Chan-
nels,” New Frontiers in Dynamic Spectrum Access Net-
works, DySPAN, First IEEE International Symposium on.
IEEE, 2005, pp. 607-610.
[11] Y. Li, L. Zhao, C. Wang, et al., “Aggregation-based
Spectrum Allocation Algorithm in Cognitive Radio Net-
works,” Network Operations and Management Sympo-
sium (NOMS), IEEE, 2012, pp. 506-509.
Because spectrum efficiency and overhead are the
main factors that we try to balance, we don’t take the
interference SUs bring to PUs into consideration in this
paper. Our future work will focus on both spectrum effi-
ciency and power optimization.
[12] F. Huang, W. Wang, H. Luo, et al., “Prediction-Based
Spectrum Aggregation with Hardware Limitation in Cog-
nitive Radio Networks,” Vehicular Technology Confer-
ence (VTC 2010-Spring), 2010 IEEE 71st., 2010, pp. 1-5.
6. Acknowledgements [13] D. Chen, Q. Zhang and W. Jia, “Aggregation Aware
Spectrum Assignment in Cognitive ad-hoc Networks,”
Cognitive Radio Oriented Wireless Networks and Com-
munications, 2008. CrownCom 2008. 3rd International
Conference on. IEEE, 2008, pp. 1-6.
This work is supported in part by National Key Tech-
nology R&D Program of China (2012ZX03003006),
NSFC (Grant 61271181), and National 973 Program of
China (2012CB316005).