Cellular Networks Positioning Performance Analysis Reliability Part 10 doc

However, the 0.6 guard capacity surpasses it by up to 2% for a handoff rate 259Mobility and QoS-Aware Service Management for Cellular Networks... This is because, with a small handoff ra

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rate (MS/10s) using different load thresholds in indoor (a,b) and outdoor (c,d) environments.

threshold gives a good coverage-capacity compromise Moreover, with a threshold of 0.75, the

outage rate at high loads is below 1% on both uplink and downlink, which is better than the

95% coverage required by ITU Thus, the remaining results are obtained with a 0.75 threshold

and the outage rate is not investigated further since it remains below 1% with this threshold

value

In what concerns the new-call/handoff admission policy, it can be seen in Fig.4 and Fig.5, that

the proposed policy, which gives incoming handoff calls a priority over new calls, results in

achieving handoff drop probabilities much lower than new-call blocking ones on both uplink

and downlink The handoff drop probability does not exceed 1% in medium loads and is

around 2% in very high loads Nevertheless, in order to assess it with respect to other possible

policies, we compare the handoff drop and new-call block probabilities when deploying the

same proposed admission conditions (for CDMA) on the same simulated environments, but

with different policies Two other policies have been tested: the guard channel (GC) approach

and the equal priority (EP) scheme With a GC policy, a certain cell capacity is reserved solely

for incoming handoff calls and the left capacity is for common use for all calls That is, the load

threshold is further decreased by a guard factor for new calls This strategy was suggested

by (Cheng & Zhuang, 2002; Kulavaratharasah & Aghvami, 1999) In contrast, with the EP

0 0.1 0.2 0.3 0.4 0.5

Note that for simplicity, from here after, the drop and block probabilities include both theuplink and downlink ones Fig.6 shows that, in indoor environment, the handoff dropprobability of our policy is below that of EP scheme by a difference that varies from 1% for ahandoff rate of 1 to about 20% for a rate of 10 This is because our module gives the priority

to handoff services compared to the EP scheme which does not differentiate handoff and newservices However, our block probability is higher than that of EP scheme by a difference thatvaries from 1% to 5% It is clear that our gain in handoff admission surpasses the loss in newservice admission

Fig.6 demonstrates also the drop/block probability for 3 guard capacities of GC scheme

We observe that our handoff admission probability has a comparable performance with the

GC scheme It outperforms that of the 0.2 and 0.4 guard capacities by up to 7% and 2.5%respectively However, the 0.6 guard capacity surpasses it by up to 2% for a handoff rate

259Mobility and QoS-Aware Service Management for Cellular Networks

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Handoff/newcall rate

proposed policy Equal priority Guard capacity=0.2 Guard capacity=0.4 Guard capacity=0.6

(d)Fig 6 Drop and block probability when varying the new/handoff rate (MS/10s) usingdifferent policies in indoor (a,b) and outdoor (c,d) environments

that varies from 2 to 8 This difference drops to 0.5% in indoors and vanishes in outdoors athigh handoff rates As for the block probability of new services, it can be seen that our schemeoutperforms all the guard capacities by up to 15% indoors and 18% outdoors for new call ratesvarying from 2 to 8 This is because, with a small handoff rate, the GC scheme results not only

in high blocking of new services but also in low resource utilization because handoff servicesare allowed to use the reserved capacity exclusively On the other hand, with a big number

of handoff MSs that exceed the guard capacity, this scheme looses its advantage because itbecomes difficult to guarantee the requirements of handoff users The same observationscan be noticed in outdoor environments However, the drop probability of our approach ismarginally better at high handoff rates with a difference of 1.4% This is due to the fact thatthe outdoor cell is less dense than the indoor cell when using our motion model, which givesthe AC module a little more capacity for admitting more handoff services

We have combined both the block and drop probabilities in order to measure the total number

of admitted services Fig.7 shows that the proposed policy outperforms both the GC schemeand the EP approach in terms of total number of accepted services in the cell, either handoff ornew ones, especially in high loads It surpasses the EP approach by 14.2% and 13% in indoorand outdoor environments respectively It outperforms the GC scheme by up to 12% and 15%

260 Cellular Networks - Positioning, Performance Analysis, Reliability

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proposed policy Equal priority

Guard capacity=0.2 Guard capacity=0.4 Guard capacity=0.6

proposed policy Equal priority

Guard capacity=0.2 Guard capacity=0.4 Guard capacity=0.6

(d)Fig 6 Drop and block probability when varying the new/handoff rate (MS/10s) using

different policies in indoor (a,b) and outdoor (c,d) environments

that varies from 2 to 8 This difference drops to 0.5% in indoors and vanishes in outdoors at

high handoff rates As for the block probability of new services, it can be seen that our scheme

outperforms all the guard capacities by up to 15% indoors and 18% outdoors for new call rates

varying from 2 to 8 This is because, with a small handoff rate, the GC scheme results not only

in high blocking of new services but also in low resource utilization because handoff services

are allowed to use the reserved capacity exclusively On the other hand, with a big number

of handoff MSs that exceed the guard capacity, this scheme looses its advantage because it

becomes difficult to guarantee the requirements of handoff users The same observations

can be noticed in outdoor environments However, the drop probability of our approach is

marginally better at high handoff rates with a difference of 1.4% This is due to the fact that

the outdoor cell is less dense than the indoor cell when using our motion model, which gives

the AC module a little more capacity for admitting more handoff services

We have combined both the block and drop probabilities in order to measure the total number

of admitted services Fig.7 shows that the proposed policy outperforms both the GC scheme

and the EP approach in terms of total number of accepted services in the cell, either handoff or

new ones, especially in high loads It surpasses the EP approach by 14.2% and 13% in indoor

and outdoor environments respectively It outperforms the GC scheme by up to 12% and 15%

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

(a)

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

(b)Fig 7 Admission probability when varying the new/handoff rate (MS/10s) using differentadmission schemes in indoor (a) and outdoor (b) environments

for 4-8 new/handoff rates As also shown in Fig.7 it is clear that, at higher rates, this differencedoes not increase, where no capacity to be managed is left

Recall that the time complexity of our AC module is O( M)where M is the cell density So,

in the worst case where the cell density is 400 and 1200 MSs/cell in indoor and outdoor

environments, the computation load is O(1)in both environments This is valid for forwardservices and reverse services that are not in soft handoff with other cells However, for reverseconnections that have, for instance, 2 soft handoff legs as in our simulations, this computingload would be multiplied by the number of handoff legs, which proves that soft handoff iscomputationally expensive as mentioned in (Kumar & Nanda, 1999)

4.2 Performance of D/I modules

Next, we evaluate the effect of deploying our D/I modules on the handoff/new admissionprobability resulting from our admission control scheme First, we study the effect of varyingRsafe on the overall drop+block probability, then, for simplicity, two values are selectedfor Rsafe in order to study in details the benefits on admission probability as well as oncell throughput Fig.8 shows the drop+block probability at a 7 new/handoff rate in indoorenvironment Note that similar results were found in outdoors as well When Rsafe=Rc, thiscorresponds to no degradation, while Rsafe=0 means that all MSs inside the cell are subject

to degradation with no preference It can be seen that as Rsafe decreases, the drop+blockprobability is reduced significantly This is because as Rsafe decreases, zone 1 becomes largerand, hence, the probability of locating MSs that can be degraded rises, giving more possibility

to acquire capacity for new and handoff calls However, below 0.3Rc, the benefit of furtherdecreasing of Rsafe on drop+block probability diminishes because the remaining safe area(zone 2) has become much smaller than zone 1 In what follows, we present results for Rsafeequal to 0.75Rc and 0.5Rc, which correspond to a safe zone of about half and quarter of thecell area respectively

At low loads, the D/I scheme has a negligible effect on the admission performance However,its contribution is manifest at high loads Fig.9 shows that, when Rsafe is 0.5Rc, the dropprobability is less than that shown in Fig.6 with 3.5% in indoor environment and 2.4% inoutdoor environment Moreover, it can be seen that, with the deployement of the Degradationmodule, the handoff admission probability surpasses the ones using guard capacities This is

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0 0.2 0.4 0.6 0.8 1 0

0.05 0.1 0.15 0.2

Drop Prob.,R=0.5 Block Prob.,R=0.5 Drop Prob.,R=0.75 Block Prob.,R=0.75

(b)Fig 9 Adaptation effect on drop and block probability when varying the new/handoff rate(MS/10s) in indoor (a) and outdoor (b) environments for Rsafe=0.5Rc and 0.75Rc

because, with our design, there is no reservation of capacity for handoff services; instead, thecall drop probability is decreased by degrading the QoS levels of services located near cellboundary, which reduces interference as well As for new services, their block probabilityshows a significant improvement when compared to that shown in Fig.6; it has been reduced

by a further 9.6% in indoor environments and 11.3% in outdoor environments Furthermore,

it can be seen that the new service admission probability is comparable to that of EP schemeshown in Fig.6 with the deployment of the Degradation module at Rsafe=0.75Rc and evenbetter at 0.5Rc Note that the observed outage when deploying D/I was always below 1%.When Rsafe is set to 0.75Rc, the number of candidates for degradation decreases, whichreduces the capacity that could be acquired for admitting new/handoff services Animprovement can still be observed in Fig.9 However, it is by far less than that of 0.5Rc.Fig.10 also shows the percentage of degraded MSs for both values of Rsafe It can be seenthat this percentage, in outdoor environments, goes up to 15.6% and 10% of total number ofMSs for Rsafe of 0.5Rc and 0.75Rc respectively This percentage drops to 7.5% and to 5.2%, in

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0 0.2 0.4 0.6 0.8 1 0

0.05 0.1 0.15 0.2

Drop Prob.,R=0.5 Block Prob.,R=0.5

Drop Prob.,R=0.75 Block Prob.,R=0.75

(b)Fig 9 Adaptation effect on drop and block probability when varying the new/handoff rate

(MS/10s) in indoor (a) and outdoor (b) environments for Rsafe=0.5Rc and 0.75Rc

because, with our design, there is no reservation of capacity for handoff services; instead, the

call drop probability is decreased by degrading the QoS levels of services located near cell

boundary, which reduces interference as well As for new services, their block probability

shows a significant improvement when compared to that shown in Fig.6; it has been reduced

by a further 9.6% in indoor environments and 11.3% in outdoor environments Furthermore,

it can be seen that the new service admission probability is comparable to that of EP scheme

shown in Fig.6 with the deployment of the Degradation module at Rsafe=0.75Rc and even

better at 0.5Rc Note that the observed outage when deploying D/I was always below 1%

When Rsafe is set to 0.75Rc, the number of candidates for degradation decreases, which

reduces the capacity that could be acquired for admitting new/handoff services An

improvement can still be observed in Fig.9 However, it is by far less than that of 0.5Rc

Fig.10 also shows the percentage of degraded MSs for both values of Rsafe It can be seen

that this percentage, in outdoor environments, goes up to 15.6% and 10% of total number of

MSs for Rsafe of 0.5Rc and 0.75Rc respectively This percentage drops to 7.5% and to 5.2%, in

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Rsafe=0.5Rc Rsafe=0.75Rc

(a)

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Rsafe=0.5Rc Rsafe=0.75Rc

(b)Fig 10 Portion of degraded services in indoor (a) and outdoor (b) environments forRsafe=0.5Rc and 0.75Rc when varying the new/handoff rate (MSs/10s)

0 100 200 300 400 500

proposed policy, w/o D/I proposed policy, with D/I Equal priority Guard capacity=0.4

(a)

0 100 200 300 400 500 600 700 800

(b)

0 100 200 300 400 500

(c)

0 100 200 300 400 500 600 700 800

(d)Fig 11 Cell throughput on uplink and downlink when varying the new/handoff rate(MSs/10s) in indoor (a,b) and outdoor (c,d) environments

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indoor environments, for Rsafe of 0.5Rc and 0.75Rc respectively at high loads This explainsthe difference in the observed improvement for both kinds of environments.

Note that, as the handoff rate increases, the proportion of degraded services increases till apoint where the cell begins to be highly loaded At this point, the AC module starts to decreasethe rate of admitted minimum throughput services Moreover, the Eb/No degradation ofthe near-real-time services is limited to 0.5 dB only, and degradation is only allowed if theirmeasured signal to interference ratio is not already degraded This limits the possibility

of degradation for services since they are not degraded below their minimum acceptablerequirements Thus, in highly loaded situations, the proportion of degraded services increases

as well but with a rate lower than that of lighter load situations This also demonstratesthat our design succeeds in limiting the number of degraded MSs and, hence, reducing therequired signalling messages which saves time and capacity

In order to verify the effect of D/I deployment on cell throughput, the throughput of theservices inside the cell was measured, for Rsafe = 0.5Rc, and compared to the throughput

of the admission control scheme without D/I It was also compared to the throughput of

EP policy and GC scheme having a guard capacity equal to 0.4 The cell throughput onlyincludes the bit rate of the calls that stay in the cell till termination or ongoing to anothercell without being in outage It represents the average of the instantaneous aggregated bitrate of only the calls currently served by the base station Fig.11 shows the throughput onuplink and downlink in indoor and outdoor environments when varying the new/handoffrate At moderate loads, the D/I can only enhance by around 20-30kb/s each of the uplink anddownlink throughputs Nevertheless, in high loads, this enhancement is boosted up to 62kb/sand 151kb/s in indoors, and 51kb/s and 142kb/s in outdoors, on the uplink and downlinkrespectively That is, an improvement of more than 210kb/s in the total cell throughput can

be obtained in high loads As also shown in Fig.11, the throughput of the proposed policy,with D/I, clearly outperforms those of EP and GC approaches This demonstrates that theD/I deployment can rise the cell throughput as well as increasing the admission probability

as seen above However, this is achieved at the expense of unfairness between services, sincedegrading or improving the service level is not done uniformally across services, it depends

on the MS location with respect to the safe area with aim of reducing interference

The computation load of the Improvement module is the same as the one of the AC modulewithout the soft handoff factor However, the Degradation module has higher computation

load of O( N log N)where N is the cell density So, in the worst case where the cell density

is 400 and 1200 MSs/cell in indoor and outdoor environments respectively, the computation

load is O(1)for forward and reverse services

Another factor in evaluating the performance of the D/I modules is the response time for QoSadaptation Since such QoS adjustment requires at most one signalling message per service,the time taken for a service to respond to such change is the time to send the control message

to the MS of the service and processing it

5 Conclusion and future work

In this chapter, we presented the design and evaluation of a service management schemethat is responsible for controlling the admission of new and handoff services and for serviceadaptation The results show that our admission control proposal outperforms both the GCscheme and the EP approach in terms of total number of accepted services in the cell, eitherhandoff or new ones, especially in high loads It surpasses the EP approach by 14.2% and13% and outperforms the GC scheme by 12% and 15% in indoor and outdoor environments

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indoor environments, for Rsafe of 0.5Rc and 0.75Rc respectively at high loads This explains

the difference in the observed improvement for both kinds of environments

Note that, as the handoff rate increases, the proportion of degraded services increases till a

point where the cell begins to be highly loaded At this point, the AC module starts to decrease

the rate of admitted minimum throughput services Moreover, the Eb/No degradation of

the near-real-time services is limited to 0.5 dB only, and degradation is only allowed if their

measured signal to interference ratio is not already degraded This limits the possibility

of degradation for services since they are not degraded below their minimum acceptable

requirements Thus, in highly loaded situations, the proportion of degraded services increases

as well but with a rate lower than that of lighter load situations This also demonstrates

that our design succeeds in limiting the number of degraded MSs and, hence, reducing the

required signalling messages which saves time and capacity

In order to verify the effect of D/I deployment on cell throughput, the throughput of the

services inside the cell was measured, for Rsafe = 0.5Rc, and compared to the throughput

of the admission control scheme without D/I It was also compared to the throughput of

EP policy and GC scheme having a guard capacity equal to 0.4 The cell throughput only

includes the bit rate of the calls that stay in the cell till termination or ongoing to another

cell without being in outage It represents the average of the instantaneous aggregated bit

rate of only the calls currently served by the base station Fig.11 shows the throughput on

uplink and downlink in indoor and outdoor environments when varying the new/handoff

rate At moderate loads, the D/I can only enhance by around 20-30kb/s each of the uplink and

downlink throughputs Nevertheless, in high loads, this enhancement is boosted up to 62kb/s

and 151kb/s in indoors, and 51kb/s and 142kb/s in outdoors, on the uplink and downlink

respectively That is, an improvement of more than 210kb/s in the total cell throughput can

be obtained in high loads As also shown in Fig.11, the throughput of the proposed policy,

with D/I, clearly outperforms those of EP and GC approaches This demonstrates that the

D/I deployment can rise the cell throughput as well as increasing the admission probability

as seen above However, this is achieved at the expense of unfairness between services, since

degrading or improving the service level is not done uniformally across services, it depends

on the MS location with respect to the safe area with aim of reducing interference

The computation load of the Improvement module is the same as the one of the AC module

without the soft handoff factor However, the Degradation module has higher computation

load of O( N log N)where N is the cell density So, in the worst case where the cell density

is 400 and 1200 MSs/cell in indoor and outdoor environments respectively, the computation

load is O(1)for forward and reverse services

Another factor in evaluating the performance of the D/I modules is the response time for QoS

adaptation Since such QoS adjustment requires at most one signalling message per service,

the time taken for a service to respond to such change is the time to send the control message

to the MS of the service and processing it

5 Conclusion and future work

In this chapter, we presented the design and evaluation of a service management scheme

that is responsible for controlling the admission of new and handoff services and for service

adaptation The results show that our admission control proposal outperforms both the GC

scheme and the EP approach in terms of total number of accepted services in the cell, either

handoff or new ones, especially in high loads It surpasses the EP approach by 14.2% and

13% and outperforms the GC scheme by 12% and 15% in indoor and outdoor environments

respectively Moreover, while limiting interference, signalling and computation overhead, theD/I modules succeeded in further improving the admission probability The drop probability

is lower than that when deploying the AC module only with 3.5% in indoor environment and2.4% in outdoor environments As for new services, their block probability shows a significantimprovement, it is reduced by a further 9.6% in indoor environments and 11.3% in outdoorenvironments The overall admission rate enhancement is achieved with low cost in terms

of computition time and signalling messages, however, at the expense of unfairness amongservices

In the research presented in this chapter, we did not consider automatic repeat request(ARQ) for retransmission on the radio link and forward error correction (FEC) techniques.These error correction mechanisms will be considered in a future work, since they canfurther enhance system capacity by decreasing target signal to noise ratios Another researchdirection is to further examine new procedures for service admission on multiple cells level.This requires access coordination between BSs including sharing load information amongneighbour cells, so that light loading in neighboring cells can be exploited to favor lowerdrop and block probabilities for handoff and new services respectively while still meetinginterference constraints

6 References

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11

Radio Resource Management in Heterogeneous Cellular Networks

Olabisi E Falowo and H Anthony Chan

Department of Electrical Engineering, University of Cape Town

South Africa

1 Introduction

The evolution of cellular networks from one generation to another has led to the deployment of multiple radio access technologies (such as 2G/2.5G/3G/4G) in the same geographical area This scenario is termed heterogeneous cellular networks In heterogeneous cellular networks, radio resources can be jointly or independently managed When radio resources are jointly managed, joint call admission control algorithms are needed for making radio access technology selection decisions This chapter gives an overview of joint call admission control in heterogeneous cellular networks It then presents

a model of load-based joint call admission control algorithm Four different scenarios of call admission control in heterogeneous cellular networks are analyzed and compared Simulations results are given to show the effectiveness of call admission control in the different scenarios

The coexistence of different cellular networks in the same geographical area necessitates joint radio resource management (JRRM) for enhanced QoS provisioning and efficient radio resource utilization The concept of JRRM arises in order to efficiently manage the common pool of radio resources that are available in each of the existing radio access technologies

(RATs) (Pérez-Romero et al, 2005) In heterogeneous cellular networks, the radio resource

pool consists of resources that are available in a set of cells, typically under the control of a radio network controller or a base station controller

There are a number of motivations for heterogeneous wireless networks These motivations are (1) limitation of a single radio access technology (RAT), (2) users’ demand for advanced services and complementary features of different RATs, and (3) evolution of wireless technology Every RAT is limited in one or more of the following: data rate, coverage,

security-level, type of services, and quality of service it can provide, etc (Vidales et al, 2005)

A motivation for heterogeneous cellular networks arises from the fact that no single RAT can provide ubiquitous coverage and continuous high QoS levels across multiple smart spaces, e.g home, office, public smart spaces, etc Moreover, increasing users’ demand for advanced services that consume a lot of network resources has made network researchers developed more and more spectrally efficient multiple access and modulation schemes to support these services Consequently, wireless networks have evolved from one generation

to another However, due to huge investment in existing RATs, operators do not readily discard their existing RATs when they acquire new ones This situation has led to coexistence of multiple RATs in the same geographical area

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Cellular Networks - Positioning, Performance Analysis, Reliability

268

In wireless networks, radio resource management algorithms are responsible for efficient utilization of the air interface resources in order to guarantee quality of service, maintain the planned coverage area, and offer high capacity In heterogeneous cellular networks, radio resource can be independently managed as shown in Figure 1 or jointly managed as shown

in Figure 2 However, joint management of radio resources enhances quality of service and improves overall radio resource utilization in heterogeneous cellular networks

Group-1 Subscribers

Group-J Subscribers

Group-1 Subscribers

Group-J SubscribersFig 1 Independent RRM in heterogeneous wireless networks

JRRM

Group

Group-1 Subscribers

Subscribers SubscribersGroup -J

JRRM

Group

Group-1 Subscribers

Subscribers SubscribersGroup -J Fig 2 Joint RRM in heterogeneous wireless networks

With joint radio resource management in heterogeneous cellular networks, mobile users will

be able to communicate through any of the available radio access technologies (RATs) and roam from one RAT to another, using multi-mode terminals (MTs) (Gelabert et al, 2008), (Falowo & Chan, 2007), (Falowo & Chan, 2010), (Lee et al, 2009), (Niyato & Hossain, 2008) Figure 3, adapted from (Fettweis, 2009), shows a two-RAT heterogeneous cellular network with collocated cells

LTE OFDMA

3G WCDMA

1-Mode Terminal

2-Mode Terminal

LTE OFDMA

3G WCDMA

1-Mode Terminal

2-Mode Terminal

Fig 3 A typical two-RAT heterogeneous cellular network with co-located cells

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Radio Resource Management in Heterogeneous Cellular Networks 269 Availability of multi-mode terminals is very crucial for efficient radio resource management

in heterogeneous wireless networks A mobile terminal can be single-mode or multi-mode

A single-mode terminal has just a single RAT interface, and therefore can be connected to only one RAT in the heterogeneous wireless network A multi-mode terminal has more than one RAT interface, and therefore can be connected to any of two or more RATs in the heterogeneous wireless network

As show in Figure 3, a subscriber using a two-mode terminal will be able to access network services through either of the two RATs However, a subscriber using a single-mode terminal will be confined to a single RAT, and cannot benefit from joint radio resource management in the heterogeneous wireless network

In heterogeneous cellular networks, radio resources are managed by using algorithms such

as joint call admission control algorithms, joint scheduling algorithms, joint power control algorithms, load balancing algorithms, etc This chapter focuses on joint call admission control (JCAC) algorithms in heterogeneous cellular networks

The rest of this chapter is organized as follows In Section 2, JCAC in heterogeneous cellular network is described In Section 3, we present a JCAC model and assumptions In Section 4,

we investigate the performance of the JCAC algorithm through numerical simulations

2 Joint Call Admission Control in heterogeneous cellular networks

JCAC algorithm is one of the JRRM algorithms, which decides whether an incoming call can

be accepted or not It also decides which of the available radio access networks is most suitable to accommodate the incoming call Figure 4 shows call admission control procedure in

heterogeneous cellular networks

JCAC algorithm

RAT 1 RAT 2

RAT J

Request Response Mobile

Terminal

JCAC algorithm

RAT 1 RAT 2

RAT J

Request Response Mobile

Terminal

Fig 4 Call admission control procedure in heterogeneous cellular networks

A multi-mode mobile terminal wanting to make a call will send a service request to the JCAC algorithm The JCAC scheme, which executes the JCAC algorithm, will then select the most suitable RAT for the incoming call

Generally, the objectives of call admission control algorithm in heterogeneous cellular networks are:

1 Guarantee the QoS requirements (data rate, delay, jitter, and packet loss) of accepted calls

2 Minimize number vertical handoffs,

3 Uniformly distribute network load as much as possible,

4 Minimize call blocking/dropping probability,

5 Maximize operators’ revenue,

6 Maximize radio resource utilization

All the above objectives cannot be simultaneously realized by a single JCAC algorithm Thus, there are tradeoffs among the various objectives

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270

2.1 RAT selection approaches used in JCAC algorithms

A number of RAT selection approaches have been proposed for JCAC algorithms in heterogeneous cellular networks These approaches can be broadly classified as single-criterion or multiple-criteria Single-criterion JCAC algorithms make call admission decisions considering mainly just one criterion, such as network load, service cost, service class, random selection, path loss measurement, RAT layer, and terminal modality On the other hand, multiple-criteria JCAC algorithms make RAT selection decisions based on a combination of multiple criteria The multiple criteria are combined using computational intelligent technique (such as fuzzy logic, Fuzzy-neural, Fuzzy MADM (Multiple Attribute Decision Making, etc.) or non-computational intelligent technique (such as cost function) Figure 5 summarizes the different approaches for making RAT selection decisions by JCAC algorithms

JCAC algorithms

Single-criterion Multiple-criteria

Non computation intelligence based Computation intelligence based

Random-selection based

Network-load based Service-cost based

Service-class based Path-loss based Layer based

Terminal-modality based

Fig 5 RAT selection approaches for JCAC algorithm in heterogeneous cellular networks

2.2 Bandwidth allocation techniques

In order to give different levels of priorities to different calls in wireless networks, it may be necessary to allocate certian block of basic bandwidth units (bbu) for new and handoff calls

as well as for different classes of calls such as voice, video, etc, In this section, bandwidth allocation strategies for wireless networks are reviewed Bandwidth allocation strategies for wireless networks can be classified into four groups namely complete sharing, complete partitioning, handoff call prioritization, and service class prioritization This classification is summarized in Table 1

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Radio Resource Management in Heterogeneous Cellular Networks 271 Bandwidth

as there is enough radio resource to

accommodate it

Implementation simplicity and high radio resource utilization

High handoff call dropping probability

No differential treatment for calls with stringent QoS

requirements Complete

Partitioning Available bandwidth is partitioned into pools

and each pool is dedicated to a particular type of calls

An incoming call can only be admitted into a particular pool

Implementation simplicity Poor radio resource utilization

Handoff Call

Prioritization

Handoff calls are given more access to radio resources than new calls New calls may be blocked whereas handoff calls are still being admitted

Low handoff call dropping probability

High new call blocking probability

Service-Class

Prioritization Certain classes of calls are given preferential

treatment over some other classes of calls

For example, class-1 calls may be blocked whereas class-2 calls are still being admitted

Differential treatments of calls based on QoS requirements

Implementation complexity

Table 1 Summary of Bandwidth Allocation Strategies for Wireless Networks

2.2.1 Complete sharing

Complete sharing scheme is a first come first serve scheme and it is the simplest bandwidth allocation policy It is a non-prioritization scheme in which new and handoff calls are treated the same way An incoming call is accepted as long as there is enough radio resource

to accommodate it When the network gets to its maximum capacity, a new call will be blocked while a handoff call will be dropped Two major advantages of complete sharing CAC scheme are implementation simplicity and good radio resource utilization However, it has a high handoff call dropping probability because it does not give preference to any call Consequently, complete sharing CAC scheme has a poor QoS performance (Ho, C & Lea, C 1999) Figure 6 is the state transition diagram for complete sharing scheme where , ,

n h n and h

λ λ μ μ represent new call arrival rate, handoff call arrival rate, new call departure rate, and handoff call departure rate respectively

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Figure 7 and Figure 8 are the state transition diagrams of a system where the available resource (C) is partitioned into two (C1 and C2) C1 is used for new calls (Figure 7) whereas

C2 is used for handoff calls (Figure 8)

2.2.3 Handoff call prioritization

Due to users’ mobility within the coverage of wireless networks, an accepted call that has not been completed in the current cell has to be transferred (handed over) to another cell The call may not be able to get a channel in the new cell to continue its service due to limited radio resources in wireless networks Eventually, it may be dropped However, wireless network subscribers are more intolerant to dropping a handoff call than blocking a new call Therefore, in order to ensure that handoff call dropping probability is kept below a certain level, handoff calls are usually admitted with a higher priority compared with new calls Handoff call prioritization has an advantage of low handoff call dropping probability However, the advantage of low handoff call probability is at the expense of new call blocking probability, which is high Several handoff-priority-based schemes have been proposed in the literature Some of these schemes are briefly reviewed as follows:

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Radio Resource Management in Heterogeneous Cellular Networks 273

Guard Channel

In Guard Channel scheme, some channels (referred to as guard channels) are specifically reserved in each cell to take care of handoff calls For example, if the total number of available channels in a single cell is C and the number of guard channels is C – H, a new call

is accepted if the total number of channels used by ongoing calls (i.e., busy channels) is less than the threshold H, whereas a handoff call is always accepted if there is an available channel (Hong & Rappaport, 1986) l Guard channel (GC) scheme can be divided into two categories namely static and dynamic strategies In static guard channel scheme, the value of

H is constant whereas in dynamic guard channel scheme, H varied with the arrival rates of new and handoff calls Figure 9 shows the state transition diagram for a single-class service using guard bandwidth scheme

Cμ +μ)

)(

1(C− μ +n μh

…)

Cμ +μ)

)(

1(C− μ +n μh

…)

Fig 9 State transition diagram for guard bandwidth scheme

Fractional Guard Channel

In fractional guard channel scheme, handoff calls are prioritized over new calls by accepting

an incoming new call with a certain probability that depends on the number of busy channels In other words, when the number of busy channels becomes larger, the acceptance probability for a new call becomes smaller, and vice versa This approach helps to reduce the handoff call dropping probability The policy has a threshold, H for limiting the acceptance of new calls A handoff is accepted as long as there is a channel available Before the wireless system gets to threshold, H, new calls are accepted with a probability of 1 After threshold, H, a new call is accepted with a probability of αpwhere 0≤αp≤ and H<p<C 1New calls are rejected when the system reaches the maximum capacity Figure 10 is the state transition diagram for fractional guard bandwidth policy

c λ λ

α(− )1 +

h n

α(− )2 +

)( n h

Cμ +μ)

)(

1(C− μn+μh

…)

c λ λ

α(− )1 +

h n

α(− )2 +

)( n h

Cμ +μ)

)(

1(C− μn+μh

…)

Fig 10 State transition diagram for fractional guard bandwidth policy

Queuing Priority Scheme

Queuing priority scheme accepts calls (new and handoff) whenever there are free channels When all the channels are occupied, handoff calls are queued while new calls are blocked or all incoming calls are queued with certain rearrangement in the queue When radio resource becomes available, one or some of the calls in the handoff queue are served until there is no more resource The remaining calls are queued until resource becomes available again However, a call is only queued for a certain period of time If radio resource is not available within this period, the call will be dropped

The main disadvantage of queuing priority scheme is that is needs a lot of buffers to deal with real-time multimedia traffic It also needs a sophisticated scheduling mechanism in

λh

Tiêu đề	Mobility and QoS-Aware Service Management for Cellular Networks
Trường học	University of Technology and Education
Chuyên ngành	Cellular Networks
Thể loại	Thesis

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