Resea ch A icle
Dynamic Resou ce Sha ing in 5G wi h LSA: C i e ia-Based
Managemen F amewo k
Zhaleh Sad eddini ,1Pa el Masek ,2,3 Tug ul Ca da ,1
Aleksand Ome o ,4Ji i Hosek ,2,3 I ina Gudko a,3,5 and Se gey And ee 4
1Depa men o Compu e Enginee ing, Ka adeniz Technical Uni e si y, T abzon, Tu key
2Depa men o Telecommunica ions, B no Uni e si y o Technology, B no, Czech Republic
3Applied P obabili y and In o ma ics Depa men , Peoples’ F iendship Uni e si y o Russia (RUDN Uni e si y), Moscow, Russia
4Labo a o y o Elec onics and Communica ions Enginee ing, Tampe e Uni e si y o Technology, Tampe e, Finland
5Fede al Resea ch Cen e “Compu e Science and Con ol” o he Russian Academy o Sciences, Moscow, Russia
Co espondence should be add essed o Pa el Masek; masekpa[email p o ec ed] .cz
Recei ed 27 July 2017; Re ised 18 Janua y 2018; Accep ed 5 Ma ch 2018; Published 24 May 2018
Academic Edi o : Osca Espa za
Copy igh © 2018 Zhaleh Sad eddini e al. This is an open access a icle dis ibu ed unde he C ea i e Commons A ibu ion
License, which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal wo k is p ope ly
ci ed.
Owing o a s eadily inc easing demand o e icien spec um u iliza ion as pa o he i h-gene a ion (5G) cellula concep ,
i becomes c ucial o e ise he exis ing adio spec um managemen echniques and p o ide mo e lexible solu ions o he
co esponding challenges. A new wa e o spec um policy e o ms can hus be en isaged by p oducing a pa adigm shi om s a ic
o dynamic o ches a ion o sha ed esou ces. The eme ging Licensed Sha ed Access (LSA) egula o y amewo k enables lexible
spec um sha ing be ween a limi ed numbe o use s ha access he same equency bands, while gua an eeing be e in e e ence
mi iga ion. In his wo k, an ad anced use sa is ac ion-awa e spec um managemen s a egy o dynamic LSA managemen in 5G
ne wo ks is p oposed o balance bo h he connec ed use sa is ac ion and he Mobile Ne wo k Ope a o (MNO) esou ce u iliza ion.
The app oach is based on he MNO decision policy ha combines bo h p icing and ejec ion ules in he implemen ed p ocesses.
Ou s udy o e s a classi ica ion buil o e se e al ypes o use s, di e en co esponding a ibu es, and a numbe o MNO’s decision
scena ios. Ou in es iga ions a e buil on C i e ia-Based Resou ce Managemen (CBRM) amewo k, which has been speci ically
designed o acili a e dynamic LSA managemen in 5G mobile ne wo ks. To e i y he p oposed model, he esul s (spec um
u iliza ion, es ima ed Seconda y Use p ice o he u u e connec ion, and use selec ion me hodology in case o use ejec ion
p ocess) a e alida ed nume ically as we yield impo an conclusions on he applicabili y o ou app oach, which may o e aluable
guidelines o e icien adio spec um managemen in highly dynamic and he e ogeneous 5G en i onmen s.
1. In oduc ion
1.1. Backg ound on Spec um Sca ci y and Sha ing. The
emendous g ow h in mobile da a olume has become a key
d i e o he de elopmen o eme ging i h-gene a ion (5G)
cellula sys ems. As i is o ecas ed by Cisco [1], he equi ed
capaci y will be challenged by he g owing u iliza ion o
mobile de ices (e.g., sma phones) ha access a di e se se
o se ices and applica ions in o de o manage inc easing
amoun s o da a a ic. Following his end, ce ain esea ch
and de elopmen conside a ions on he nex -gene a ion
wi eless sys ems migh be ega ded in he ligh o s eady
inc ease o he o e all demand o adio spec um as well
as he deploymen o a a ie y o echnologies ha compe e
o u ilize he sha ed equency bands [2]. The adi ional
dis inc ion be ween di e en ca ego ies o applica ions, such
as da a se ices, oice communica ions, and b oadcas ing,
becomes blu ed due o con e gence in unc ionali y o hese
se ices deli e ed by new echnologies [3, 4].
A he same ime, adio spec um has ans o med in o
a c i ical esou ce om economic, cul u al, and socie al
pe spec i es [5, 6]. I s sca ci y has become a majo limi ing
ac o in pa icula equency anges, spanning 100 MHz
o 6 GHz, wi h desi ed p opaga ion cha ac e is ics o a
Hindawi
Wi eless Communica ions and Mobile Compu ing
Volume 2018, A icle ID 7302025, 12 pages
h ps://doi.o g/10.1155/2018/7302025
2 Wi eless Communica ions and Mobile Compu ing
wide ange o s a ic spec um use s (e.g., mili a y, ada ,
TV b oadcas ing, and medical and e en p oduc ion) [7,
8]. Up o now, mobile ope a o s p e e ed o ollow he
adi ional exclusi e spec um access egime when o e ing
hei se ices. Howe e , his led o u ilizing la ge po ions
o adiospec umonlyspo adically,wi hhighspace, ime,
and equency a ia ions in usage ha anged om 15% o
85% [9]. As he needs o ex a spec um ha e been g owing
o e he pas decades, al e na i e solu ions we e p oposed,
especially o exploi ing small agmen s o spec um ha a e
seldom used by hei license holde s.
Wi h espec o spec um managemen , se e al concep s
ha e been de eloped and deployed o e he yea s [10, 11].
Acco dingly, he bene i s o spec um sha ing a e wo old.
Fi s , i allows imp o emen s in spec um u iliza ion and
second i can p o ide addi ional capaci y o he use s who
equi e mo e spec um o hei se ices. A wide ange o
spec um sha ing schemes can acili a e he u iliza ion o
di e en equency bands (bo h licensed and unlicensed),
as adop ed by a ious ca ie -g ade se ice p o ide s. The
deploymen o spec um sha ing schemes is subjec o mee -
ing a se o p ede ined egula ions and equi emen s. I
can also in ol e dissimila coo dina ion echniques. Ac i ely
conside ed by elecom ope a o s, dynamic spec um sha ing
can enable in e echnology coexis ence in licensed bands [12–
14]. Cu en ly, he e iciency o spec um sha ing mechanisms
emains limi ed by p ese p io i y cons ain s o he p ima y
license holde . He e, he b oade concep o spec um sha ing
e e s o equal spec um access igh s bu may o e look
economical aspec s and use sa is ac ion conside a ions
[15].
1.2. Spec um Sha ing wi h Licensed Sha ed Access. To o e -
come he p essing e ec s o spec um agmen a ion, a
demand o no el amewo ks allowing o e icien sha -
ing o a ailable bu unde u ilized spec um eme ges. A
no el Licensed Sha ed Access (LSA) [16, 17] concep enables
ad anced spec um sha ing be ween a limi ed numbe o
end-use s wi h a leas wo en i ies: he incumben (i.e., he
cu en holde o he spec um usage igh s) and he LSA
licensee (i.e., he empo a y use o spec um, which could be
a Mobile Ne wo k Ope a o , MNO) [18]. Mo e speci ically,
he LSA amewo k [19] pe mi s o con olled spec um
sha ing be ween a P ima y Use (PU) and a Seconda y Use
(SU) bo h ha ing access o a po ion o spec um a a gi en
loca ion and ime [20, 21], as i is demons a ed in Figu e 1.
The co esponding LSA sha ing ag eemen s need o
gua an ee high uni o mi y in e ms o spec um access o
all in ol ed pa icipan s. To his end, sa is ying end-use
se ice equi emen s is a majo goal in he mobile ope a o ’s
sys em design. The MNOs should maximize he ac i e SUs
(ASUs) sa is ac ion by shi ing he emphasis om Quali y
o Se ice (QoS) o Quali y o Expe ience (QoE). The e o e,
decisions made by he MNOs play an impo an ole in
he discussed ecosys em and conside a ion o app op ia e
pa ame e s a ec s he o e all ne wo k pe o mance. To his
end, he ne wo k pa ame e s and use a ibu es should be
e alua ed join ly o he op imized MNO decision-making
[22, 23], which becomes he ocus o his wo k.
Incumben use
Mac o
base s a ion
LSA licensee
LSA
con olle Small cell
Incumben use
Mac o
base s a ion
Figu e 1: Scena io o LSA amewo k ope a ion.
The es o he pape is o ganized as ollows. Sec ion 2
p o ides an o e iew o exis ing solu ions. Nex , Sec ion 3
desc ibes he p oposed sys em model o he use expe ience-
awa e spec um managemen oge he wi h de eloped Deci-
sionBasedRejec ionPolicy algo i hm. Fu he , in Sec ion 4, he
selec ed use-cases a e discussed. In Sec ion 5, he ob ained
simula ion esul s a e desc ibed. Finally, he lessons lea ned
andconclusionsa esumma izedinSec ion6.See“Lis o
F equen ly Used Ac onyms” in Nomencla u e.
2. Rela ed Wo k and Ou Focus
The eme ging LSA amewo k equi es MNOs o de elop
e icien spec um managemen s a egies in o de o inc ease
he ne wo k capaci y and p o ide high use expe ience le els
o he PUs and SUs. As a esul , highe MNO e enues a e
expec ed o be eached. As i has been al eady demons a ed
in [24], decision-making is one o he main unc ions o he
adio spec um managemen p ocess allowing he MNOs o
de e mine he adio pa ame e s ha con ol he spec um
u iliza ion.
In [25], dynamic billing and p icing s a egy is in oduced
o he LTE ne wo ks. The calcula ed esou ce cos a ies
acco ding o he use ca ego y and conges ion h eshold
speci ied by he MNO. The conside ed pa ame e o he
end-use s is p io i y class (e.g., gold, sil e , o b onze). In
[24], he au ho s p oposed an Adap i e Decision-making
Op imiza ion Scheme (ADMS) o Cogni i e Radio Ne wo k
(CRN) wi h mul iple subca ie s. All o he a iables in
he i ness unc ion a e ela ed o he ne wo k pa ame e s.
Besides ha , he au ho s conside ed he weigh o pa ame e s
in he i ness unc ion being adap i e. Howe e , he weigh
adap i e unc ion is no de ined in addi ion o he use o ixed
weigh s in he simula ion esul s.
The wo k in [26] p o ides a connec i i y managemen
pla o m o e icien Wi-Fi o loading in he e ogeneous
ne wo ks. Technique o O de o P e e ence by Simila i y
o Ideal Solu ion (TOPSIS) wi h he conside a ion o wo
a ibu es, Recei ed Signal S eng h Indica o (RSSI) and
numbe o connec ed use s, was applied. The spec um u ili y
model was de eloped in [27] u ilizing he mul ia ibu e
u ili y heo y o o m he use ’s decision model. The co e-
sponding model allows a use o balance he a ibu es, such
Wi eless Communica ions and Mobile Compu ing 3
as channel capaci y, mone a y cos , and in e e ence po en-
ial. Fu he , in [28], he impac o end-use decision-making
on p icing and managemen o adio esou ces in CRN was
s udied.
In his wo k, C i e ia-Based Resou ce Managemen
(CBRM) amewo k o LSA is p esen ed as a ade-o
be ween he MNO’s decision on ne wo k managemen and
he QoE o use s. In ou p e ious esea ch [29], Ins an
O e booking F amewo k o Cogni i e Radio (IOFCR) was
o mula ed o enable e icien managemen o spec um in
CRN. Th ee di e en p icing policies o he booking pe iod
ha e been conside ed. In his s udy, we implemen CBRM
wi h espec o hec ucialuse andne wo ka ibu esinLSA.
We u ilize c i e ia-based p icing and ejec ion policies, whe e
he weigh s o all he pa ame e s a e upda ed pe iodically and
au oma ically based on he MNO’s decision and ollowing he
gi en QoS equi emen s as well as he spec um usage a io.
The use sa is ac ion can be maximized by manipula ing
esou ce alloca ion ia adap i e p icing and ejec ion policies
basedon heQoE.
In mo e de ail, i he MNO is no able o p o ide
ASUs wi h adequa e QoE, he ejec ed his o y a io o he
ejec ed ASUs (RASUs) will be high. The ejec ed his o y
a io as a Compensa ion Cos (CC) has s ong impac on
he p icing policy and i could con ince RASUs o eques
spec um esou ces om he ne wo k as RSUs in he nea es
u u e. Following ou p e ious wo k [30], we u ilize wo
se e pool sys ems: hey ca ego ize RSUs based on he QoE
ia he ejec ed his o y pa ame e s. Hence, we s udy use -
cen ic QoE-based esou ce alloca ion p oblem wi h se e al
MNO’s decisions, se e al use c i e ia, and di e en classes o
se ices. Ou simula ion esul s indica e he spec um si ua-
ion in o -peak and peak hou s.
3. Sys em Model
In his sec ion, he To al Ne wo k Re enue aspec is de ailed
h ough (i) esou ce block leasing, (ii) compensa ion paid
o he ejec ed ASUs, (iii) spec um u iliza ion, (i ) numbe
o accep ed and denied RSUs, ( ) numbe o ejec ed and
inished ASUs (RASUs/FASUs), and ( i) a e age se ice ime
o bo h RASUs and FASUs.
To p o ide a comp ehensi e unde s anding o he p o-
posed CBRM amewo k o LSA, we con inue his sec ion
by de ailing he de eloped model. A en ion is speci ically
paid o se e al ypes o cus ome s, di e en use a ibu es,
and se e al MNO decision use-cases, while highligh ing he
impo an simula ion esul s and discussing he s a egies o
each highe QoE le els o ASUs.
A de ailed desc ip ion o he p oposed sys em model
in his sec ion is di ided in o 3 subsec ions. (i) Ne wo k
A chi ec u e and Scena io: in his subsec ion, he en i ies
o he p oposed LSA sys em and he in e ac ions be ween
hem a e in oduced. (ii) U ilized A ibu es:in hispa ,wi h
espec o he impac o decision-making on p icing and
managemen o URB in he CBRM amewo k o LSA,
impo an used a ibu es o he p oposed sys em model
a e de ined. A e u ilizing he a ibu es o SUs, he o al
numbe o RSUs, ASUs, FASUs, and RASUs can be shown
Unde u ilized LSA band
Incumben
Alloca ed LSA license
Compensa ion
LSA eques
LSA license
Compensa ion
Regula o
LSA licensee
S a us
Condi ions
Rules
PU1
PU2PUn
x３５1
x３５2x３５m
Figu e 2: P oposed sys em model o LSA ope a ion.
in one lis as 𝐿𝑥SU(𝑡𝑖).A imeins an 𝑡𝑖, depending on he
URB numbe , MNO is aced wi h he ou main use-cases,
which a e desc ibed in his subsec ion. (iii) C i e ia-Based
Rejec ion Model:incaseo 0≤URB(𝑡𝑖) < |LPU(𝑡𝑖)|,
MNO should ejec he app op ia e ASUs. In his subsec ion,
DecisionBasedRejec ionPolicy is implemen ed by AHP a he
side o he LSA licensee o ejec he app op ia e ASUs.
Finally, heCCiscalcula ed o he ejec edASUs.Ino de
o ob ain highe ne wo k e enue, while mo i a ing use s o
eques he spec um esou ces mo e equen ly, he expec ed
p ice o he ejec edASUsiscalcula edbasedon hede ined
RR.
3.1. Ne wo k A chi ec u e and Scena io. In o de o ep esen
he eal-wo ld use-cases, we u ilize LTE-Ad anced (Rel. 10)
ne wo k in as uc u e wi h he implemen ed LSA ame-
wo k, as de ailed in ou p e ious wo k [30]. The in e ac ion
be ween he co esponding en i ies o he LSA sys em is
depic ed in Figu e 2. The conside ed ne wo k a chi ec u e
consis s o he ollowing building blocks:
(i) Incumben : LSA is he con olled sha ed access
amewo k based on an exclusi e egime o adio
spec um sha ing among he incumben s, ha is, PUs,
ha ha e he igh o comme cially exploi a gi en
po ion o wi eless spec um (cellula band).
(ii) LSA licensee: licensed use s ha lease he incum-
ben ’s spec um band, which can po en ially be used
when a pe mission is g an ed. The leased esou ces
(i.e., esou ce blocks) a e u he o e ed o he LSA
Licensee’s SUs unde he con ol o he en i y espon-
sible o g an ing pe missions o e he cellula band
(i.e., LSA con olle ). In his wo k, we assume ou
di e en s a es o SUs (i.e., 𝑥SU1,𝑚):
(1) Reques ing Seconda y Use s (RSUs): a ime
ins an 𝑡𝑖, he ea eRSUs ha sendspec um
a ailabili y eques s o he LSA licensees (mod-
eling is de ailed in ou p e ious pape [29]). The
LSA con olle moni o s he sha ing ac i i ies
in hecellula ne wo kand heco esponding
4 Wi eless Communica ions and Mobile Compu ing
decisions (accep o ejec he RSUs) a e made
basedonspec uma ailabili yp o idedby
incumben s o he LSA con olle .
(2) Ac i e Seconda y Use s (ASUs): once he RSUs
ob ain spec um esou ces (alloca ed esou ce
blocks) om he LSA con olle , hey should
deli e a compensa ion ee based on he
eques ed esou ces. Then, he LSA licensee will
lease he unde u ilized LSA spec um esou ces
o he RSUs. A e he leasing p ocedu e is
inished success ully, he RSUs o mally change
hei s a us o ASUs.
(3) Finished ASUs (FASUs): a each ime ins an 𝑡𝑖,
some o he ASUs inish hei ope a ions and
elease he alloca ed spec um esou ces back o
he incumben .
(4) Rejec ed ASUs (RASUs): in case he incumben
eques s he leased spec um esou ces back,
he ASUs a e o ced o aca e he cellula band
immedia ely, e en i hei ac i e ansmission is
no inished.
(iii) Regula o : i is he en i y esponsible o g an ing
pe missionso e heincumben ’scellula band.I
ha monizes spec um usage and opens a pa h o
spec um op imiza ion ia con olled sha ing, which
is he key esponsibili y o he Na ional Regula o y
Au ho i y (NRA).
3.2. U ilized A ibu es. The main u ilized a ibu es in he
conside ed sys em model a e linked wi h RSUs and ASUs,
since hose wos a eso heSUsin heleasingp ocessa e
c ucial o ou CBRM amewo k.Thea ibu eso RSUs
include (i) eques ed bi a e (BR) and (ii) ejec ed his o y
(RH).Themain eal- imea ibu es o heASUsa elis ed
as ollows:
(i) Recei ed Signal S eng h Indica o (RSSI): an indica-
ion o he powe le el being ecei ed by he ASUs
(ii) Usage pa e n (A ST): a e age se ice ime calcu-
la edbyu ilizing heASU’sse ices a ed ime(SST)
and he cu en ime ins an 𝑡𝑖
(iii) Ac i i y ac o (DUR): he a e age download/upload
a ioo eachASU,calcula ed om heASU’sse ice
s a ed ime up o he cu en ime ins an
(i ) Mobili y pa e n (MP): mo emen di ec ion o each
ASU wi hin he cell co e age whe e “1” ep esen s
di ec ion owa ds he eNodeB; “2” s ands o he
opposi e di ec ion [30].
The SUs send a eques as RSUs; 𝐿RSU(𝑡𝑖)is u he
upda edacco ding o he ollowing:
𝐿RSU (𝑡𝑖)
={RSU𝑗=(BR𝑗,RH𝑗)|1≤𝑗≤𝛼(𝑡𝑖),1≤𝑖≤𝑇}, (1)
whe e 𝑇is he o al ime in e al, 𝑡𝑖 ep esen s he 𝑖 h ime
ins an , and 𝛼(𝑡𝑖)shows he numbe o RSUs modeled as a
Poisson dis ibu ion a ime ins an 𝑡𝑖, ha is,RSU
ShowRa e.
The lis o ASUs, 𝐿ASU(𝑡𝑖),iscalcula edas ollows:
𝐿ASU (𝑡𝑖)={ASU𝑗|0≤𝑗≤󵄨󵄨󵄨󵄨𝐿ASU (𝑡𝑖−1)󵄨󵄨󵄨󵄨−󵄨󵄨󵄨󵄨𝐿FASU (𝑡𝑖)󵄨󵄨󵄨󵄨
−󵄨󵄨󵄨󵄨𝐿RASU (𝑡𝑖)󵄨󵄨󵄨󵄨+󵄨󵄨󵄨󵄨𝐿ASU (𝑡𝑖)󵄨󵄨󵄨󵄨,1≤𝑖≤𝑇}. (2)
The numbe o FASUs in 𝐿FASU (𝑡𝑖)isassumed obe
uppe -bounded by he numbe o ASUs om p eceding ime
in e al. Nex , he lis o FASUs is de i ed as ollows:
𝐿FASU (𝑡𝑖)
={FASU𝑗|0≤𝑗≤󵄨󵄨󵄨󵄨𝐿ASU (𝑡𝑖−1)󵄨󵄨󵄨󵄨,1≤𝑖≤𝑇}. (3)
Fu he , he numbe o RASUs in 𝐿RASU(𝑡𝑖)is also uppe -
bounded by he numbe o sensed PUs in he cu en ime
in e al 𝑡𝑖 ha exceeds he numbe o URB(𝑡𝑖):
𝐿RASU (𝑡𝑖)={RASU𝑗|0≤𝑗≤󵄨󵄨󵄨󵄨𝐿PU (𝑡𝑖)󵄨󵄨󵄨󵄨
−URB (𝑡𝑖),1≤𝑖≤𝑇}, (4)
whe e URB(𝑡𝑖)s ands o he numbe o unde u ilized blocks
(URBs) and 𝐿PU(𝑡𝑖)is he lis o PUs de i ed om [29]. I is
assumed ha a PU is cha ac e ized by an on- a e, which is
deno ed by 𝛽.I is hep obabili yo PU’sac i i ype ime
in e al (PUUsageRa e). In [31], i is s a ed ha he pa ame e
𝛽 a ies be ween 15% and 85%. In ou scena io, we conside
ha 𝛽 ollows a binomial dis ibu ion and 𝛽(𝑡𝑖)deno es he
on- a e o a PU a ime ins an 𝑡𝑖wi h 0≤𝛽(𝑡𝑖)≤1. A each
ime in e al, he size o 𝐿PU(𝑡𝑖+1)is upda ed wi h espec o
he o al numbe o esou ce blocks 𝜑and 𝛽(𝑡𝑖)as ollows:
𝐿PU (𝑡𝑖)=𝜑×𝛽(𝑡𝑖). (5)
Following he abo e, he o al numbe o RSUs, ASUs,
FASUs, and RASUs can be calcula ed as ollows:
𝐿𝑥SU (𝑡𝑖)={𝑥SU𝑚|0≤𝑚≤𝜙,1≤𝑖≤𝑇}, (6)
whe e 𝑇is he o al ime in e al and 𝑡𝑖 ep esen s he 𝑖 h ime
ins an and 𝜙co esponds o he o al numbe o 𝑥SU𝑠,by
adding henumbe o RSUs,ASUs,FASUs,andRASUs om
he ela ed lis a 𝑡𝑖as ollows:
𝜙=󵄨󵄨󵄨󵄨𝐿RSU (𝑡𝑖)󵄨󵄨󵄨󵄨+󵄨󵄨󵄨󵄨𝐿ASU (𝑡𝑖)󵄨󵄨󵄨󵄨+󵄨󵄨󵄨󵄨𝐿FASU (𝑡𝑖)󵄨󵄨󵄨󵄨
+󵄨󵄨󵄨󵄨𝐿RASU (𝑡𝑖)󵄨󵄨󵄨󵄨.(7)
Fo he sake o comple eness, we assume alloca ion o
exac lyone esou ceblock ooneuse a agi en imeins an
𝑡𝑖.
Finally, a e he URB calcula ion a a gi en ime ins an
𝑡𝑖, ou mainuse-casescanbein oduced[31]:
(i) Case 1: |𝐿RSU(𝑡𝑖)|≤URB(𝑡𝑖).
(ii) Case 2: 0≤URB(𝑡𝑖)<|𝐿RSU(𝑡𝑖)|.
(iii) Case 3: 0≤URB(𝑡𝑖)<|𝐿PU(𝑡𝑖)|.
(i ) Case 4: |𝐿PU(𝑡𝑖)|=𝜑.
Wi eless Communica ions and Mobile Compu ing 5
Table 1: Pai wise compa ison ma ix o ASU a ibu es.
Use a ibu es RSSI A ST DUR MP
RSSI 1 1/Θ21 1/Θ31 1/Θ41
A ST Θ21 11/Θ32 1/Θ42
DUR Θ31 Θ32 11/Θ43
MP Θ41 Θ42 Θ43 1
3.3. C i e ia-Based Rejec ion Model. The p oposed CBRM
amewo k a ge s he hi d use-case men ioned in he
p e ious ex , ha is, 0≤URB(𝑡𝑖)<|𝐿PU(𝑡𝑖)|.Following his
condi ion, he LSA licensee mus aca e he leased spec um
esou ces, since he LSA con olle has o main ain he a ge
QoS and QoE pa ame e s o he incumben s (PUs).
Gene ally, he p obabili y o ASU ejec ion inc eases
du ing peak hou s. In his wo k, he ejec ion unc-
ionali y is modeled o wo scena ios: (i) inc easing
ASU’s a ge sa is ac ion, while selec ing he app op ia e
ASUs o be ejec ed, and (ii) inc easing he ne wo k
e enue p o ided o RASUs, paying he so-called CC,
which can be used du ing u he a emp s o alloca e
he unde u ilized spec um esou ces, ha is, p io i iza-
ion o ea lie ejec ed ASUs. To his end, he unc ion
𝑅𝑒𝑗𝑒𝑐𝑡(𝐿ASUs(𝑡𝑖));𝐷𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝐵𝑎𝑠𝑒𝑑𝑅𝑒𝑗𝑒𝑐𝑡𝑖𝑜𝑛𝑃𝑜𝑙𝑖𝑐𝑦 ep esen s
he gene alized o m o ejec ion p ocess men ioned as he
hi duse-caseabo e.Asanou pu ,i e u ns𝐿RASU(𝑡𝑖).
𝐷𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝐵𝑎𝑠𝑒𝑑𝑅𝑒𝑗𝑒𝑐𝑡𝑖𝑜𝑛𝑃𝑜𝑙𝑖𝑐𝑦is composed o he ollowing
s eps:
(1) Iden i ying he decision c i e ion o SUs and hei
p io i y
(2) No maliza ion p ocess o decision c i e ion
(3) Impo ance deg ee alloca ion (p io i y ec o )
(4) E alua ing he decision made
(5) Selec ion o he app op ia e ASUs ha will be ejec ed
(as s a ed in (10)).
The p inciples o 𝐷𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝐵𝑎𝑠𝑒𝑑𝑅𝑒𝑗𝑒𝑐𝑡𝑖𝑜𝑛𝑃𝑜𝑙𝑖𝑐𝑦 algo-
i hm, p oposed in his wo k, a e de ailed in Algo i hm 1. A
hebeginningo hedecisionp ocess, heMNOcollec sallo
he inpu decision c i e ia (i.e., RSSI, A ST, DUR, and MP)
om 𝐿ASU.Theob ained alue o eachASUisno malized
based on he cos /bene i ype o a pa icula c i e ion (see
line (1)in Algo i hm 1) [32]. The e o e, each c i e ion o
ASUs has o be compa ed wi h he c i e ia o o he ASUs,
conside ing he lis o c i e ia o ASUs. This compa ison
leads o s abili y among ASUs, which is he c ucial s ep in he
p ocess o inding a ejec ion a io (RR) o each and e e y
ASU.
Fu he , a nume ical weigh (p io i y) is compu ed o
each c i e ion o he ASUs by he LSA licensee; see line (7)
in Algo i hm 1. The P io i yVec o shows he impo ance
deg ee o each c i e ion as compa ed o o he c i e ia, whe e
∑|C i e ion|
𝑗=1 𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑉𝑒𝑐𝑡𝑜𝑟(𝑗)=1.Thedecisionon hep io i y
assignmen , om he LSA licensee side, can be adap i e a
each 𝑡𝑖acco ding o he peak and he o -peak hou s. In his
wo k,weha eimplemen ed heAnaly icHie a chyP ocess
(AHP) a he side o he LSA licensee. AHP is one o he
mul ic i e ia decision-making me hods ha was conside ed
in [33–35].
De eloping a single pai wise compa ison ma ix o he
c i e ia is he i s s ep when he AHP is u ilized. Table 1 shows
he pai wise compa ison ma ix composed o ou ASU’s
a ibu es men ioned in de ail wi hin Sec ion 3.2.
The alue Θ𝑖𝑗 ep esen s a se wi h espec o he scale o
absolu e judgmen in AHP me hod [22, 32, 34]. As commonly
used in AHP, he alues Θ𝑖𝑗 a y be ween 1and 9.The
ecip ocal ela ionship be ween he 𝑖 h and 𝑗 h a ibu es is
gi en by Θ𝑖𝑗 =1/Θ𝑖𝑗.
Nex , he 𝑛 h oo -o -p oduc alues in each ow is
calcula ed. The Consis ency Ra io (CR) is u he de i ed as
shown in (8), whe e consis ency o he pai wise compa ison
om he LSA licensee side is p o ided.
CR =Consis ency Index (CI)
Random Index (RI).(8)
The Consis ency Index (CI) is p oduced as
CI =(𝜆max −𝑛)
(𝑛−1),(9)
whe e 𝑛is henumbe o compa edc i e ion.In hedesc ibed
use-case, ou di e en c i e ia a e compa ed. Fu he , 𝜆max
shows he sum o he p io i y ec o alues, as men ioned a
S ep (8)in Algo i hm 1.
Con inuing ou desc ip ion o he p oposed CBRM
amewo k, he RI s ands o he di ec unc ion o he
numbe o c i e ia aken in o conside a ion [33]. Finally, he
CR (see (8)) p o ides he decision-make wi h he in o ma-
ion on how consis en was he p ocess o pai wise compa -
ison. A e he CR is calcula ed, wo main (and di e en )
condi ionsa e obechecked:
(i) CR <0.10:pai wisecompa isonsmadeby heMNOs
we e ound obe ela i elyconsis en
(ii) CR >0.10: ee alua iono hecomple edpai wise
compa ison p ocesses should be aken in o accoun
by he MNOs.
When hecondi ionis eached, heMNOcanmakei s
decision and choose he app op ia e ASUs; hence, we ha e
he ollowing:
RRASU (𝑖)=|𝐿ASU(𝑡𝑖)|
∑
𝑖=1
|c i e ion|
∑
𝑗=1 𝑟(𝑖,𝑗)×𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑉𝑒𝑐𝑡𝑜𝑟𝑗,(10)

6 Wi eless Communica ions and Mobile Compu ing
(1) o 𝑖=1 o |𝐿ASU(𝑡𝑖)|do
(2) o 𝑖=1 o |c i e ion|do
(3) 𝑟(𝑖,𝑗)←󳨀No maliza ion(ASU𝑖,C i e ion𝑗)
(4) end o
(5) end o
(6) 𝑆𝑢𝑚𝑅𝑜𝑜𝑡𝑂𝑓𝑃𝑟𝑜𝑑𝑢𝑐𝑡←󳨀(𝑛
∑
𝑖=1
𝑛
∏
𝑗=1𝑃𝑎𝑖𝑟𝑊𝑖𝑠𝑒𝑀𝑎𝑡𝑟𝑖𝑥(𝑖,𝑗))1/𝑛
(7) 𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑉𝑒𝑐𝑡𝑜𝑟𝑗←󳨀 (∏𝑛
𝑗=1𝑃𝑎𝑖𝑟𝑊𝑖𝑠𝑒𝑀𝑎𝑡𝑟𝑖𝑥(𝑖,𝑗))1/𝑛
𝑆𝑢𝑚𝑅𝑜𝑜𝑡𝑂𝑓𝑃𝑟𝑜𝑑𝑢𝑐𝑡
(8) 𝜆max ←∑((𝑛
∑
𝑖=1𝑃𝑎𝑖𝑟𝑊𝑖𝑠𝑒𝑀𝑎𝑡𝑟𝑖𝑥(𝑖,𝑗))×𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑉𝑒𝑐𝑡𝑜𝑟𝑗)
(9) CI ←󳨀 (𝜆max −𝑛)
(𝑛−1)
(10) CR ←󳨀 CI
RI
(11) i C >0.1 hen
(12) go o (6)
(13) else
(14) RRASU(𝑖)←󳨀 |𝐿ASU(𝑡𝑖)|
∑
𝑖=1
|c i e ion|
∑
𝑗=1 𝑟(𝑖,𝑗)×𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑉𝑒𝑐𝑡𝑜𝑟𝑗
(15) CCRASU(𝑡𝑖)=1−RRASU(𝑖)
(16) 𝐿󸀠
ASU(𝑡𝑖)←󳨀𝐿ASU(𝑡𝑖)−𝐿RASU(𝑡𝑖)
(17) 𝐿󸀠
RASU(𝑡𝑖)←󳨀𝐿RASU(𝑡𝑖−1)+𝐿RASU(𝑡𝑖)
(18) end i
Algo i hm 1: DecisionBasedRejec ionPolicy algo i hm.
whe e |c i e ion|is conside ed as a lis o 4pa ame e s
men ionedinSec ion3.2.TheASUswillbe ejec edacco ding
o he inc easing o de o RRASU(𝑡𝑖),whe e henumbe o
RASUs can be ound as ollows:
󵄨󵄨󵄨󵄨𝐿RASU (𝑖)󵄨󵄨󵄨󵄨=󵄨󵄨󵄨󵄨𝐿ASU (𝑡𝑖)󵄨󵄨󵄨󵄨−(𝜑−󵄨󵄨󵄨󵄨𝐿PU (𝑡𝑖)󵄨󵄨󵄨󵄨).(11)
I e e y RASU is willing o eques new spec um
esou ces in he u u e as he RSU, he CC a io will be applied
o he p ice unc ion as CC𝑖in (13) and hese RSUs will
pay a lowe p ice han o he RSUs based on he CC a io.
This beha io is connec ed wi h use sa is ac ion in bo h (i)
ejec ed and (ii) spec um eques ing s eps. The CC a io is
calcula ed as ollows:
CCRASU (𝑡𝑖)=1−RRASU (𝑡𝑖). (12)
A e assigning he calcula ed CC a io o RASUs a he
ime ins an 𝑡𝑖, i should be (i) emo ed om he ASUs lis
and (ii) added o he exis ing RASUs lis . Fu he , he lis s o
ASUs and RASUs should be upda ed.
Recalling Sec ion 3, RSUs will ha e an oppo uni y o
lease unde u ilized spec um esou ces (URBs) i and only
i hey pay he eques ed esou ce block’s ee. The goal o
his app oach is o ob ain highe ne wo k e enue, while
mo i a ing use s o eques he unde u ilized spec um mo e
equen ly. The cons uc ed p icing unc ion o e s an oppo -
uni y o he LSA licensee(s) o each he men ioned goal.
The e o e, a each ime ins an 𝑡𝑖(a e he RSUs ecei e an
app o al o access he unde -u ilized spec um), he RSUs
willbecha ged o he esou cealloca ion.Thep icepaidby
he RSUs can be measu ed in mone a y uni s o al e na i ely
i canbemeasu edin i ualcu ency[27].Thep icing
unc ion is u he calcula ed as ollows:
P iceRSU (𝑡𝑖)=𝑃𝑖×(1−CCRSU),(13)
whe e 𝑃𝑖is he p ice paid by he RSUs and CC s ands o
he compensa ion cos in (12). Fo he in es iga ed p icing
unc ion, he e a e wo impo an cases:
(i) I CCRSU ==0 hen P iceRSU(𝑡𝑖)=𝑝𝑖.
(ii) I CCRSU ==1 hen P iceRSU(𝑡𝑖)=0.
The i s case akes place o he RSUs wi hou he
ejec ed his o y in he ne wo k up o he cu en ime ins an .
The e o e, he de aul p ice will be se o he RSU(s). The
second case e e s o he si ua ion whe e he RSU had 100%
ejec ion his o y in he cellula ne wo k. E en hough he
second case dec eases he ne wo k e enue a he ime ins an
𝑡𝑖, in he long- e m pe spec i e i can be expec ed o obse e
epea ed a emp s om he RSUs o use he unde u ilized
spec um. Finally, Figu e 3 summa izes he decision low
o he CBRM amewo k a ime ins an 𝑡𝑖.Fo hesakeo
comple eness, a summa y o he a iables used in ou sys em
model is gi en in “Lis o Used Va iables” in Nomencla u e.
Wi eless Communica ions and Mobile Compu ing 7
No
Yes
i
Moni o ing PU and
xSU ac i i y
Checking he LSA
band a ailabi li y
(URB)
Case 3 o 4 Alloca ing he
URB
Decision based
ejec ion
unc ionali y
(Sec ion 3.3)
Upda ing LxSU
(equa ion (6))
Figu e 3: Decision low diag am o he sys em model a 𝑡𝑖.
Table 2: Simula ion pa ame e s.
Pa ame e Value
Simula ion ime 100 s
Numbe o esou ce blocks 25 RB
PUUsageRa e [0.25;0.75]%
RSUShowRa e [0.25;0.75]%
RSSI [−100;−30]dBm
Mobili y pa e n [1;2]
Downlink channel bi a e [1; 33] Mbps
Uplink/downlink a io [0.01; 1]
4. Simula ion Resul s
To adequa ely e alua e he p oposed sys em model o mu-
la ed in Sec ion 3, we decided o u ilize as ou simula ion
pa ame e s he da a ob ained om he expe imen al 3GPP
LTE-A sys em loca ed a he Depa men o Telecommunica-
ions, B no Uni e si y o Technology (BUT), Czech Republic.
I suppo s unc ionali y o LTE Release 10 communica ions
sys em. The lis o employed simula ion pa ame e s in he
de eloped amewo kisshowninTable2.
In his wo k, we assume PUUsageRa e = 0.25 and
RSUShowRa e = 0.25 o he o -peak hou s. Fu he ,
PUUsageRa e and RSUShowRa e a e se o 0.75 ep esen he peak-
hou pa ame e s. Acco ding o he LTE-A sys em design, he
RSSI is con igu ed in he ange [−100;−30]dBm and he
Table 3: Simula ion esul s o o -peak and peak hou s.
Pa ame e Values o o -peak and peak hou
O -peak Peak
To al numbe o RSUs 622 1878
To al numbe o ASUs 357 (57.39%) 166 (8.86%)
To al numbe o FASUs 321 (89.91%) 76 (45.78%)
To al numbe o RASUs 17 (4.76%) 82 (49.39%)
Numbe o emaining ASUs 19 (5.32%) 8 (4.82%)
To al ne wo k e enue 147603 78532
BR o he downlink channel is se in he ange [1; 33] Mbi /s.
The mobili y pa e n is deno ed as “1” and “2,” whe e “1”
ep esen s he di ec ion owa ds he eNodeB; “2” s ands o
heopposi edi ec ion.The o alnumbe o usable esou ce
blocks in he sys em is conside ed o be 25 (since he a ailable
sys em bandwid h is 5 MHz) o all ime in e als. The
simula ion cons uc ed in Ma lab en i onmen is se o 100
ime in e als wi h a ce ain ime du a ion. The base p ice is
se o 𝑝=500.
In his modeled scena io, he URB, PUs, and ASUs a e
in ol ed in he ne wo king. As shown in Figu e 4(a), mos
o heRSUsa egoing obechanged oASUs.Howe e , he e
a e s ill URBs a speci ic ime ins an s. Du ing he peak hou s
(see Figu e 4(b)), almos 75% o he esou ce blocks belong
o he PUs. In his case, no only is he limi ed numbe o
URBs a ailable bu also he RASUs a e encoun e ed. In mo e
de ail, PUs and RSUs exis in he ne wo k a he same ime,
which is based on PUUsageRa e and RSUShowRa e; hisbeha io
is acco ding o he men ioned use-cases; see Sec ion 3.2.
As simula ion ime p og esses, he ASUs (𝐿ASU)and he
s a us o each o hem will be changed o FASU, RASU, o
he e will s ill be ASUs a each ime ins an ; o example,
some o he ASUs will lea e he mobile ne wo k as FASUs
andsomeuse smaybe ejec ed(RASU)acco ding ocase
3: 0≤URB(𝑡𝑖)<|𝐿PU(𝑡𝑖)|; see Sec ion 3.2.
The impo an simula ion esul s a e de ailed in Table 3
o he o -peak and peak hou s, espec i ely. The Numbe o
Remaining ASUs is u he calcula ed as ollows:
NRASUs =𝐿ASU −𝐿FASU −𝐿RASU.(14)
The numbe o RSUs du ing he o -peak hou s (622) is
lowe han ha o e he peak hou s (1878). Fu he , 57.39% o
heRSUsha ebecomeASUsdu ing heo -peakhou sand
abou 8.87% did ha du ing he peak hou s. This beha io
is due o he ac ha he LTE ne wo k ope a es wi h 25 RB
(5 MHz bandwid h). Fu he , he numbe o RASUs du ing
he peak hou s (49.39%) e lec s he To al Ne wo k Re enue
(TNR), which is lowe du ing he o -peak ime. Howe e ,
o e he o -peak hou s, 89.91% o he ASUs inished hei
da a ansmission success ully (FASUs = 21) and 4.76% o
ASUs a e ejec ed (RASUs) in o al.
Table 3 shows he o al numbe o RASUs (17) du ing he
o -peak hou s and he peak hou s (82). Figu e 5 epo s he
o alnumbe o imein e alswhen heRASUswe eac i-
a ed in he ne wo k, see Figu e 5(a), and he es ima ed p ice
8 Wi eless Communica ions and Mobile Compu ing
0 20 40 60 80 100
Time cycles [—]
0
20
40
60
80
100
Spec um u iliza ion [%]
URB
PUs
ASUs
(a)
0 20406080100
Time cycles [—]
0
20
40
60
80
100
Spec um u iliza ion [%]
URB
PUs
ASUs
(b)
Figu e 4: Spec um u iliza ion du ing o -peak (a) and peak (b) hou s.
o hei nex connec ion a emp (Figu e 5(b)) du ing he
o -peak hou s. This e lec s he ac ha whene e he RASUs
a e willing o eques se ice ime, o example, a connec ion
o he mobile ne wo k, he paymen will be as shown in
Figu e 5(b) o each o hem.
Fu he , Figu e 6 de ails he si ua ion o he peak hou s:
as can be seen, i he only c i e ion o he ejec ed ASUs
was he A ST, he e should be ag eemen be ween (i) Figu es
5(a) and 5(b), as well as (ii) Figu es 6(a) and 6(b). Howe e ,
he ob ained esul s a y e en o he ASUs wi h he same
A ST. Fo example, A ST (ASU6)andA ST(ASU
7)a ese
o 9 ime in e als. On he o he hand, he es ima ed p ice
o he selec ed ASUs di e s (ASU6= 334.083;ASU
7=
320.629). This si ua ion clea ly indica es he impo ance o
ano he c i e ion o he CC calcula ion in he p oposed
CBRM model. We p oceed wi h conside ing he said c i e ion
in he ollowing sec ion.
5. Use Selec ion Me hodology
In his sec ion, we con inue ou desc ip ion o he con-
s uc ed model ( o mula ed in he p e ious Sec ion 4). We
ocus on selec ing he app op ia e RASUs among he ASUs.
S ep-by-s ep selec ion unc ionali y o he se ed use s u i-
lizes wo key p inciples: (i) a ailabili y o he LSA band and
(ii) decision-based ejec ion unc ionali y as de ailed in
Figu e 7. In o de o s udy pe o mance o he DecisionBase-
dRejec ionPolicy algo i hm, he second ime in e al (𝑡2)is
cap u ed. A he beginning, he LSA licensee checks whe he
he spec um esou ces a e a ailable a 𝑡2. Acco ding o he
URB (𝑡2)and|𝐿PU(𝑡2)|, henumbe o RASUsis|𝐿RASU(𝑡2)|=
2; see also he condi ion gi en in case 3: 0≤URB(𝑡𝑖)<
|𝐿PU(𝑡𝑖)|; see Sec ion 3.2.
The ollowing s ep in ou conside a ion o he ASU’s
c i e ia and he impo ance deg ee calcula ion is o employ
he AHP: he LSA licensee conside s all 6 c i e ia. The co e-
sponding impo ance deg ees a e shown in Table 4. The LSA
licensee calcula es he RR based on he condi ion o ASUs a
he ime ins an 𝑡2, wi h he co esponding pa ame e s shown
in Table 5. The A ST is he mos impo an a ibu e o bo h
sides ( he LSA licensee and he incumben (PU)). The o de
o he emaining a ibu es (c i e ia) is he ollowing: (i) RSSI,
(ii) ejec ed his o y (RH), (iii) mobili y pa e n, (i ) bi a e
(BR), and ( ) download/upload a e.
As i was discussed in Sec ion 4, a compa ison be ween
he ASUs based only on he A ST a ibu e is no easible
(see Table 5, whe e he A ST a ibu e is se o 1 o all he
ASUs). To his end, he second a ibu e (RSSI) was added
o ou conside a ion. In his case, wo ASUs nomina ed o
ejec ion will be ASUID=3 and ASUID=5 wi h RSSI = −78 dBm
and RSSI = −82 dBm, espec i ely. Ano he impo an c i e-
ion is RH, whe e ASUID=1 and ASUID=4 a e nomina ed o
be ejec ed, since hey ha e he lowes ejec ion a io. Consid-
e ing he MP, he ASUs selec ed o ejec ion will be ASUID=1
and ASUID=2. Fu he , he BR a ibu e is conside ed and,
by ollowing his c i e ion, ASUID=1 and ASUID=5 a e he
candida es o ejec ion. Finally, when DUR is aken in o
accoun , ASUID=4 and ASUID=5 a e going o be ejec ed, since
hey ha e he maximum download/upload a io a a gi en
ime ins an 𝑡2.
Con inuing he use selec ion p ocess, Table 6 p o ides
in o ma ionabou heRR, ejec edo de ,andCCo heASUs.
The LSA licensee selec s wo ASUs o ejec ion acco ding o
|𝐿RASU(𝑡2)|→ASUID=1 and ASUID=5,whichha e helowes
CC based on (12) (CC ASUID=1 = 0.1392;CCASU
ID=5 =
0.1429). Fu he , i he LSA licensee ejec ed ASUID=4 ( hi d
ASUselec ed o ejec ion,seeTable5), heLSAlicenseemus
Wi eless Communica ions and Mobile Compu ing 9
Table 4: Impo ance deg ee o each c i e ion o he DecisionBasedRejec i onPolicy based on AHP me hod.
C i e ia AHP
Impo ance o de (MNO iew) Impo ance deg ee (AHP iew)
BR 5 0.0573
RH 3 0.2005
RSSI 2 0.2526
A ST 1 0.3460
DUR 6 0.0422
MP 4 0.1010
02 4 6 8 1012141618
RASUs [ID]
1
2
3
4
5
6
7
8
9
10
Se ice ime cycle [—]
(a)
Es ima ed p ice o nex access in
0 2 4 6 8 1012141618
RASUs [ID]
200
220
240
260
280
300
320
340
360
380
o - peak hou s [—]
(b)
Figu e 5: Numbe o ime in e als ha RASUs a e ac i e (a) and es ima ed p ice o hei nex connec ion (b) du ing o -peak hou s.
0 102030405060708090
RASUs [ID]
0
1
2
3
4
5
6
7
8
9
Se ice ime cycles [—]
(a)
0 102030405060708090
RASUs [ID]
50
100
150
200
250
300
350
400
450
Es ima ed p ice o
nex access in peak hou s [—]
(b)
Figu e 6: Numbe o ime in e als ha RASUs a e ac i e (a) and es ima ed p ice o hei nex connec ion (b) du ing peak hou s.
cha ge his ASU o 336.80. Since he base p ice is assumed
o be 𝑝 = 500,ASU
ID=1 and ASUID=5 will cha ge 430.40
and428.55 o hespec um esou ces, espec i ely.Thiscon-
side a ion keeps ASUID=4 mo e sui able o he LSA licensee
(i.e., inc easing he ne wo k e enue). On he o he hand,
his decision will keep ASUID=4 sa is ied as i s DUR and BR
a e lowe han hose o o he wo ejec ed ASUs. Fu he , a
a iable spec um esou ce p ice (P iceRSU)inTable5con-
i ms ha he p ice paid o using he LSA band by e e y
accep ed RSU depends on hei ejec ion his o y (RH) ha is
calcula ed ac oss p e ious ime in e als. As an ou pu o he
decision p ocess, DecisionBasedRejec ionPolicy algo i hm has
hepo en ial obalanceall hep obabili iesandmaximize he
ne wo k pe o mance oge he wi h he use sa is ac ion.
6. Conclusion
A C i e ia-Based Resou ce Managemen amewo k o he
MNO LSA-based ope a ion was p oposed in his wo k. The
p esen ed app oach is buil on he MNO’s decisions ega ding
bo h p icing and ejec ion policies wi h se e al ypes o use s,
he co esponding a ibu es, and a numbe o he MNO
decision scena ios. The cons uc ed sys em model illus a es
he MNO decisions ac oss se e al si ua ions in e ms o