Mixed Contiguous and Aggregated Spectrum Allocation Algorithm for CR based TD-LTE System

doi:10.4236/cn.2013.53B2055

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Communications and Network, 2013, 5, 298-302

http://dx.doi.org/10.4236/cn.2013.53B2055 Published Online September 2013 (http://www.scirp.org/journal/cn)

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

Email: [email protected]

Received July, 2013

ABSTRACT

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

urgent.

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

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,}Nn



. And all of these requirements compose

an array RB .





,,,

SU SUSU

RBRB RBRB (1)

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

MHz.

 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

total

Bav

Bail

avail

Bavail

B can be denoted

avail



window

1

Freque ncy

Unavailable spectrum fragmentsAvailable spectrum fragments

Figure 1. Fragment fragments and allocation schematic.

X. Y. LIU ET AL.

300

as follows.



,1,,

avail

jj j

BLL U (2)



11,2,

avail

jjj

BULj J，



(3)

where

L and

U are the lower and upper unitized

channel number of jth available spectrum, which satisfy

the following relationship.



11, 2,

jjj

LU LjJ



 ，



(4)

So the total unitized available channel and its length is

avail avail



 (5)

avail avail



 (6)

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-

lows.



1,1,2,

KSU

CRBk









N (7)

And allocated channel numbers Ccannot be larger

than the number of total availa ble chan nels. So we have

avail

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

TRBn，N

(9)

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.

window

Algorithm 1: MixCASA

Input: SUs’ bandwidth requirement vector

B, normal-

ized available channelsavail

, aggregation threshold

, and the width of sliding window

threshold

Twindow

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

TRB

y = min ( S1 )

/* y is the location of the minimum of S1 */

window

LLT





else

window threshold



Spectrum Aggregate

end if

end for

X. Y. LIU ET AL. 301

Algorithm 2: Spectrum Aggregation

Input: Slide-window width,

window

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+

window

T

S2 = find((1: 2)0

avail

Bpp



)

/* S2 stores the location of available channels within slide

window */

if length(S2) >= do

11 1

window

LLT 

else

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.

threshold

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.

allo

Nallo

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.

agg

Nagg

threshold

The data in Figure 2 and Figure 3 are converted in

following way so that we can have a comparison more

understandable and reasonable.

/max()

agg

AASA allo



agg

allo

RNN

(11)

/max()

agg

ASA allo



agg

MixC allo

RNN

(12)

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

AASA.

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-

rithms.

X. Y. LIU ET AL.

302

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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.

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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-

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Spectrum Assignment in Cognitive ad-hoc Networks,”

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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).