A QoS-Guaranteed Fuzzy Channel Allocation
Controller for Hierarchical Cellular Systems
Kuen-Rong Lo, Chung-Ju Chang, Senior Member, IEEE, Cooper Chang, and C. Bernard Shung
Abstract—This paper proposes a fuzzy channel allocation controller (FCAC) for hierarchical cellular systems. The FCAC
mainly contains a fuzzy channel allocation processor (FCAP) which is designed to be in a two-layer architecture that consists of a fuzzy admission threshold estimator in the first layer and a fuzzy channel allocator in the second layer. The FCAP chooses the handoff failure probability, defined as quality-of-service (QoS) index, and the resource availability as input linguistic variables for the fuzzy admission threshold estimator, where the Sugeno’s position gradient-type reasoning method is applied to adaptively adjust the admission threshold for the fuzzy channel allocator. And the FCAP takes the mobility of user, the channel utilization, and the resource availability as input variables for the fuzzy channel allocator so that the channel allocation is finally determined, further based upon the admission threshold. Simulation results show that FCAC can always guarantee the QoS requirement of handoff failure probability for all traffic loads. Also it improves the system utilization by 31.2% while it increases the handoff rate by 12.9% over the overflow channel allocation (OCA) scheme [7]; it enhances the system utilization by 6% and still reduces the handoff rate by 6.7% as compared to the combined channel allocation (CCA) scheme [10], under a defined QoS constraint.
I. INTRODUCTION
D
UE to the increasing demands for wireless communication services, it is essential to reconfigure the existing cellular system into a hierarchical structure for enhancing system ca-pacity and improving coverage. The hierarchical cellular system provides overlaid microcells for high-teletraffic area and over-laying macrocells for low-teletraffic region [1]–[5].In such a hierarchical cellular system, Rappaport and Hu pro-posed an overflow channel allocation (OCA) scheme that al-lows a new or handoff call which has no channel available in the overlaid microcell to overflow to use free channel in the overlaying macrocell [6], [7]. The OCA scheme can reduce both the blocking probability of new calls and the forced termination probability of handoff calls, and it is easy to implement because no elaborate coordination between microcells is needed [6]–[8]. Beraldi et al. proposed a reversible hierarchical scheme [9], which allows the presence of handoff attempts from overlaying macrocell to overlaid microcell if there is idle channel available in the overlaid microcell. The reversible hierarchical scheme im-proves channel utilization in microcells and decreases blocking probabilities of both new call and handoff call since microcells
Manuscript received November 8, 1998; revised January 13, 2000. This work was supported in part by the National Science Council, Taiwan, under Contracts NSC 86-2213-E009-006 and NSC 87-2218-E009-047.
The authors are with the Department of Communication Engineering, Na-tional Chiao Tung University, Hsinchu, Taiwan.
Publisher Item Identifier S 0018-9545(00)07882-8.
are designed to be capable of supporting high capacity and bal-ancing the traffic load.
We also proposed a combined channel allocation (CCA) mechanism for hierarchical cellular systems [10]. It combines overflow, underflow, and reversible schemes, where new or handoff calls having no idle channel to use in the overlaid microcell can overflow to use free channels in the overlaying macrocell, handoff calls from neighboring macrocell can underflow to use free channels in the overlaid microcell, and handoff attempts from macrocell-only region to a microcell in the same macrocell can be reversed to use free channels in the microcell. Simulation results showed that the CCA mechanism can attain better channel utilization by an amount of 23.7% but renders more handoff rate [11] by an amount of 19.8% than the OCA mechanism.
To guarantee QoS requirement for handoffs, these conven-tional techniques are to reserve a fixed number of guard chan-nels or to provide a queue for handoffs [1], [12]. However, these methods are unable to cope with burstiness of traffic. In other words, though these protection schemes can even guarantee the QoS requirement of the handoff failure probability, the channel utilization would suffer from fundamental limitations by unpre-dictable statistical fluctuations within new and handoff calls. And they are difficult, if not impossible, to drive an accurate mathematical model to obtain the solution.
On the other hand, fuzzy logic control has growing success in various fields of applications, such as decision support, knowl-edge base systems, and pattern recognition. It is due to the in-herent capability of fuzzy logic control to formalize control al-gorithms that can tolerate imprecision and uncertainty, emu-lating the cognitive processes that human beings use every day [13]–[15].
In addition, when applied to problems, fuzzy systems have often shown a faster and smoother response than conventional systems. It is thanks to the fact that fuzzy control rules are usually simpler and do not require great computational com-plexity. The latter aspect, along with the spread of very large scale integration (VLSI) hardware structure dedicated to fuzzy computation, makes fuzzy systems cost effective [16]. In the field of telecommunications, fuzzy systems are also begin-ning to be used in areas such as traffic control in ATM net-works and channel allocation in mobile communication sys-tems [17]–[20].
In this paper, we propose a QoS-guaranteed fuzzy channel allocation controller (FCAC) for hierarchical cellular systems. The FCAC contains a fuzzy channel allocation processor (FCAP), a resource estimator, a performance evaluator, and 0018–9545/00$10.00 © 2000 IEEE
Lo et al.: QoS-GUARANTEED FUZZY CHANNEL ALLOCATION CONTROLLER 1589
Fig. 1. The fuzzy channel allocation controller for hierarchical cellular systems. base-station interface modules. It dynamically estimates avail-able resources in macrocell and microcells, evaluates system performance, and determines whether and how to allocate resources to a call, based upon the call’s QoS requirement, re-source availability, and mobility. The FCAP is a two-layer fuzzy logic controller that contains the fuzzy admission threshold estimator in the first layer and the fuzzy channel allocator in the second layer. In the fuzzy admission threshold estimator, we apply the Sugeno’s position-gradient type reasoning method to adaptively adjust the admission threshold for the fuzzy channel allocator. In the fuzzy channel allocator, we design to achieve utilization balancing between macrocell and microcells in order to obtain a higher channel utilization. The domain knowledge is based upon CCA mechanism we proposed in [10]. Simulation results show that FCAC can guarantee QoS requirement of existing calls; also, it achieves better system utilization by an amount of 31.2% but more handoff rate by an amount of 12.9% than OCA, and it attains more system utilization by 6% and still less handoff rate by 6.7% than CCA.
The rest of the paper is organized as follows. In Section II, functions of FCAC are described. Section III presents the design of FCAP. Section IV shows simulation results and discussions. Finally concluding remarks are given in Section V.
II. FUZZYCHANNELALLOCATIONCONTROLLER(FCAC) Fig. 1 shows the functional block diagram of the fuzzy channel allocation controller (FCAC) for hierarchical cellular systems, where the hierarchical cellular system contains a large geographical region tessellated by cells, referred to as macrocells, and each of which overlays several microcells. The overlaying macrocell is denoted by cell 0 and its overlaid microcells are denoted by cell . For cell , a number
of channels is allocated, . FCAC contains
functional blocks such as base-station interface module (BIM), performance evaluator, resource estimator, and fuzzy channel allocation processors (FCAP). It is installed in either a base station controller (BSC) or mobile switching center (MSC). Note that for simplicity, FCAC is drawn to do the channel allocation for one macrocell only. Functional blocks of FCAC are described as follows.
A. Base-Station Interface Modules (BIM)
BIM is to interface with the base station of macrocell or microcell. It provides complete partitioning buffers for queueing new and handoff calls which are originated in the corresponding cell and temporarily have no free channel to use. In the BIM for cell 0 ( ), there is a new-call buffer with capacity for new calls originating in the macrocell-only region, a handoff-call buffer with capacity for handoff calls from adjacent macrocells, an overflowed handoff-call buffer with capacity for overflowed handoff calls from overlaid microcells, and an overflowed new-call buffer with capacity for overflowed new calls from overlaid microcells. In the BIM for cell ( ), , there is a new-call buffer with capacity for new call originations, an underflowed handoff-call buffer with capacity for underflowed handoff calls from the overlaying macrocell, and a handoff-call buffer with capacity for handoff calls from adjacent microcells. No buffer is provided for the reversible handoff calls. Reneging of new calls and dropping of handoff calls are considered because of new calls’ impatience and handoff calls’ moving out the handoff area.
Whenever receives a call request, , it sends the necessary calling information to the resource estimator, the performance evaluator, and the FCAP. The calling information
TABLE I
CALCULATION OFAVAILABLERESOURCE
can distinctly indicate from which cell and in what type the call is originated. The type of call is defined as: denotes a new call originating in macrocell-only region; denotes a handoff call from adjacent macrocell to macrocell-only region; denotes a handoff call from microcell to macrocell-only region; denotes new call originating in microcell; denotes handoff call from adjacent macrocell to an overlaid mi-crocell; denotes handoff call from microcell to microcell; and denotes reversible handoff. Note that the macro-cell-only region is the area inside macrocell 0 but outside all microcells. The first three types of calls are to use channels in macrocell, while other types of calls can use channels either in macrocell or in microcell.
B. Resource Estimator
The resource estimator calculates the available resources in macrocell 0 and microcell when it receives calling information of the type- call from at the time instant , denoted by
and , respectively.
Since it knows system parameters of , , , ,
, and , , , , , it can obtain
and by formulas shown in Table I.
In Table I, ( ) is the number of occupied channels in
( ) at time ; ( , , ) is the number
of waiting calls in the new-call buffer (handoff-call buffer, overflowed handoff-call buffer, overflowed new-call buffer) of
, at time ; and ( , ) is the number of
waiting calls in the new-call buffer (underflowed handoff-call buffer, handoff-call buffer) of , , at time . C. Performance Evaluator
The performance evaluator is to calculate the channel utiliza-tion and the handoff failure probability. The channel utilizautiliza-tion of macrocell 0(microcell ) at time , denoted by ( ), is defined as
(1)
where ( ) is the average number of busy channels in macrocell 0 (microcell ) at time and ( ) is the channel ca-pacity for macrocell 0 (microcell ). In order to show the channel utilization balancing between macrocell and microcells, we
fur-ther define a spatial averaging channel utilization of microcells at time , denoted by , as
(2)
Also we define the system utilization of the whole system at time , denoted by , as
(3)
The handoff failure probability in macrocell (microcells) at time , denoted by ( ), is defined as
(4) where
( ) number of blocked waiting handoff calls in macrocell 0 (microcell ) at time ;
( ) number of dropped handoff calls in macrocell 0 (microcell ) at time ; and ( ) number of handoff calls in macrocell 0
(microcell ) at time .
The handoff failure probability of the whole system at time , denoted by , is given by
(5)
D. Fuzzy Channel Allocation Processor (FCAP)
The FCAP performs the channel allocation using fuzzy logic control to attain high channel utilization and keep the QoS re-quirement guaranteed. Here, a threshold is designed for channel allocation, and it can be adaptively adjusted to cope with the input traffic fluctuation. The detailed design of an FCAP is de-scribed in the next section.
III. DESIGN OFFCAP
As Fig. 2 shows, an FCAP mainly consists of a fuzzy admis-sion threshold estimator and a fuzzy channel allocator.
A. Fuzzy Admission Threshold Estimator
The fuzzy admission threshold estimator is to adaptively de-termine the decision thresholds for the fuzzy channel allocator
Lo et al.: QoS-GUARANTEED FUZZY CHANNEL ALLOCATION CONTROLLER 1591
Fig. 2. The block diagram of fuzzy channel allocation processor.
so that the QoS requirement of handoff calls can be guaranteed and a high channel utilization can be achieved. The system QoS requirement is here defined as the maximum handoff failure probability which is denoted by . The admission thresholds for macrocell 0 and microcell during time period ,
de-noted by and , are sent to the fuzzy channel
allocator if a new call occurs in macrocell 0 or microcell at time . Note that “ ” denotes the time instant the next new call arrives, and “1” denotes one unit of a new-call interarrival time.
We choose ( ) and ( ) as input
lin-guistic variables for the fuzzy admission threshold estimator to
determine ( ) for macrocell 0 (microcell ).
Since the fuzzy rule set will be commonly used by macrocell 0 and microcell , hereafter in this subsection we simply use
, and , instead of or ,
or , and or .
Term sets for input variables are designed
as Low, High , and
Enough, Not Enough . And a
function defined below is chosen to be
their membership function. It is a trapezoidal function given by
for for for otherwise
(6)
where ( ) is the left (right) edge of the trapezoidal function; ( ) is the left (right) width of the trapezoidal function.
Denote and to be the membership
functions for and in , respectively, and define
and as
(7) (8)
would be set to be , and could be the safety margin provided to tolerate the dynamic behavior of handoff failure probability and guarantee the QoS requirement, and the
edge is .
Similarly, let and be the membership
functions for and in , respectively, and define
and as
(9) (10) The maximum possible “enough” value of available resource
would be the sum of buffer size and allocation channels, would be a safety margin of available resource, would be set to be a fraction of available resource, and and are provided to tolerate the change of traffic.
Based on chosen input variables and their terms, the fuzzy ad-mission threshold estimator has fuzzy IF–THEN rules. The fuzzy rules have the following form:
Rule IF is and is
THEN
where is the term of the th linguistic variable used in rule , and is the output function of rule for the time
period , .
Here, we apply the Sugeno’s position-gradient type reasoning method [21], [22] to effectively derive and then to obtain . The inference in the Sugeno’s method has a built-in defuzzification such that can be expressed as (11) where
, , forgetting constant that can maintain the esti-mator stability,
admission threshold during last time period , and
Then is given by
(12)
where is the weighting factor for the output variable
of rule , defined as .
Since membership functions are set to be symmetrical, .
The adjustment parameter is obtained by the gra-dient decent method, where an error function at time is defined as
(13) Note that is given in the sense that needs to be
con-trolled around . Then is given by
(14) where is an adaptation gain which must be properly chosen.
Because the admission threshold could be regarded as an entry barrier to regulate new calls coming to the system,
and the change of during , denoted by
would be varied in accordance with so that can be kept at around to fulfill QoS require-ment, we heuristically make an approximation that
has a first-order relationship with . Then can be expressed as
(15) where is an experience value and is a constant value. For
example, means
that in the range of is inversely
varied with respect to in the range of . Since is designed to change gradually at a rate of , the value of could be chosen to be . However, in order to attain a better adjustment of to fulfill QoS requirement in acute traffic fluctuation, we further set to be dynamically changed according to the number of handoff calls during
. In our example, it is frequent that only few handoffs occur
during , thus is set to be , where
is the number of handoff calls during . Equation (15) can be rewritten as
(16) where denotes one unit of a new-call interarrival time. And
then we have an expression for as
(17)
where is a pole which is set to a value very close to but less than one to avoid infinite memory and marginal stability of this gradient evolution.
Finally, we rewrite (11) as
(18)
and we obtain by
(19)
B. Fuzzy Channel Allocator
We choose five input linguistic variables for fuzzy channel allocator: the channel utilization in macrocell 0 ( ), the channel utilization in microcell ( ), the available resource in macrocell 0 ( ), the available resource in microcell ( ), and the mobile speed ( ). and can show the traffic load intensity and the load balancing among cells; and can implicate the remaining capacity; and can show the handoff rate. The term set for both and is
de-fined as Low, High , the
term set for both and is
Enough, Not Enough , and the term set for is
Slow, Fast . Let ( )
and ( ) denote the membership functions
of terms and in ( ), respectively, and let
, , , and be
(20) (21) (22) (23) Here would be a fraction of channel utilization, and and would be a change rate of channel utilization provided to tolerate the dynamic behavior of and , respectively. The membership functions for terms of and of and have the same definition as in (9) and (10). The speed of the mobile user is hard to obtain. Usually the velocity of a mo-bile station can be estimated by the global positioning system (GPS), the Doppler effect, the elapsed time in a cell, the prop-agation time of the signal, and the number of level crossings of the average signal level [23]. The membership functions for
terms and in , denoted by and , are given by
(24) (25) where
( ) would be a fraction of slow (fast) speed of mobile user,
( ) is provided to tolerate the change of slow (fast) speed, and
would be the fastest speed.
There are two output variables, and , in the fuzzy channel allocator. The output variable represents whether the call is accepted or rejected, and indicates with which
Lo et al.: QoS-GUARANTEED FUZZY CHANNEL ALLOCATION CONTROLLER 1593
channel in either macrocell or microcell the call is allocated. In order to provide a soft channel allocation decision, not only “accept” and “reject” but also “weak accept” and “weak reject” are employed to describe the allocation decision. Thus, the term set for the output linguistic variable is
defined as Accept , Weak Accept ,
Weak Reject , Reject , and the term set for is
defined as , Macrocell Microcell .
We further define a delta function, , for the member-ship functions of the output linguistic variables, where
is characterized as and ,
; , . The membership functions
for terms , , , and in , denoted by , ,
, and , respectively, are given by
(26) (27) (28) (29) Without loss of generality, we set , , and let
, . A call
can be allocated with channel if is greater than the admission threshold . And the membership functions for terms
and in , denoted by and , respectively, are
given by
(30) (31) and are set to be positive one and negative one, re-spectively. If is greater than zero, the call is allocated with a macrocell channel, otherwise, with a microcell channel.
There are different call types in hierarchical cellular systems. For calls that can use only macrocell’s channel, only input linguistic variables of and are en-abled. For calls that could use channels in either macro-cell or micromacro-cell, input linguistic variables of ,
, , , and are enabled. The fuzzy rule
base with dimension is shown
in Table II for the former, and that with dimension is shown in Table III for the latter, where denotes the number of terms in .
The general idea of designing the fuzzy rules listed in Tables II and III is described as follows. If the available resource in either macrocell or microcell is enough, a call would have a chance of entering the system, and vice versa; if the available resource is enough in macrocell but not enough in microcell, the macrocell channel would be preferred, and vice versa; we choose a cell with low channel utilization, instead of the one with high utilization, for balancing traffic load if the available
TABLE II
INFERENCERULES FORMACROCELLONLYREGION
resource in both macrocell and microcell has same fuzzy terms in the premises of the fuzzy rule; and we also allocate calls to be biased toward macrocell if the speed is fast for lessening frequent handoff, and vice versa.
The max–min inference method is adopted. It first applies the min operator on membership values of the terms of all input linguistic variable for each rule. Assume that a call is originated in microcell and the inference rules in Table III are applied. We denote the minimal result for rule to be , , and obtain , for example, by
(32) Then the method applies the max operator to yield the overall membership value. For the output variable , there are four rules for term in Table III, which are rules 4, 8, 20, 28. Then the overall membership value for the term , denoted by , is given by
(33)
Similarly, , and are yielded as
(34)
(35) (36) Afterwards, we use the center-of-area defuzzification method to derive the defuzzification value. The defuzzification value, denoted by , is given as shown in (37) at the bottom of the page. Then the output variable is obtained by
for
for . (38)
TABLE III
INFERENCERULES FOROVERLAYREGION
Note that if the call is a new call
and is originated in macrocell-only region, and
if the call is a new call and is orig-inated in microcell . On the other hand, in order to give a good protection for handoff calls, if the call is a handoff. We similarly adopt the max–min inference method and apply the center-of-area defuzzification method for output variable
, not further described here.
IV. SIMULATIONRESULTS ANDDISCUSSIONS In the simulations, a hierarchical cellular system with microcells constructed along the Manhattan streets is assumed,
and the handoff behavior of users is characterized by a teletraffic flow matrix [4], defined as shown in the equation at the bottom of the page where , , represents the probability of a handoff call originated in cell and directed to cell ,
, and denotes the probability of this handoff call directed
to the adjacent macrocell. for , and
would be zero.
The number of mobile stations in each cell is assumed to be 550, and the new-call arrival process follows a Poisson process with calling rate per mobile station (user) . We assume that low- and high-mobility users are generated in a ratio of 7 : 3, and the cell dwell time is exponentially distributed with mean 180 s (18 s) for the high-mobility users in macrocell (microcells) and
Lo et al.: QoS-GUARANTEED FUZZY CHANNEL ALLOCATION CONTROLLER 1595
with 1440 s (144 s) for low-mobility users in macrocell (micro-cells). And the speed of mobile users is assumed to be uniformly distributed in the range of 0–40 km (40–80 km) for low- (high-) mobility users. We also assume that the mean unencumbered session duration is 100 s and the patience (dwell) time for queued new (handoff) calls is in the range of 5–20 s. There are 150 channels fixedly allocated to macrocell and microcells
with a pattern of .
If the OCA and CCA schemes are applied, the system re-serves a number of channels as guard channels for handoff calls in cell , , which are denoted by . We do some simulations and obtain
the appropriate for
OCA scheme and for
CCA schemes at . Since the reneging (dropping) process is considered, it is not necessary to provide a large buffer size for new and handoff calls [10]; all buffer sizes in macrocell and microcells are assumed to be 3. Note that in the following performance comparisons, an OCA scheme provides no buffer and the CCA scheme supports the same buffering scheme and capacity as FCAC does.
Based upon the QoS requirement and the knowledge of the CCA mechanism, parameters of membership functions for input linguistic variables in the fuzzy admission threshold estimator
are selected as follows: , , and
for and in (7) and (8);
, , and for
and with macrocell in (9) and (10);
, , and for
and with microcells in (9) and (10). In the fuzzy channel allocator, parameters of membership functions for input linguistic variables are selected as follows:
for , , , and in
(20)–(23); , , and for
and with macrocell in (9) and (10);
, , and for and
with microcells in (9) and (10); and
km, km, km, and km for
and in (24) and (25). And constant parameters are set to
be and .
Three more performance measures such as the new-call failure probability, the forced termination probability, and the handoff rate are concerned, in addition to and . The new-call failure probability at time , denoted by , is defined as
(39)
where ( ) is the number of blocked (reneging)
new calls in cell and ( ) is the number of
new calls originating in microcell (macrocell-only region), at time . Forced termination of a call occurs if a call is corrupted due to a handoff failure during its conversation time. The
Fig. 3. P (t) and P (t) for FCAC, OCA, and CCA schemes.
forced termination probability at time , denoted by , is defined as
(40)
where ( ) is the number of blocked (dropped)
handoff calls in cell and is the number of admitted new calls originated in cell , at time . The handoff rate at time
, denoted by , is defined as
(41)
Fig. 3 shows the new-call failure probability and the handoff failure probability for schemes of FCAC, OCA, and CCA versus the calling rate per user at time . It can be seen that, as varies, of FCAC remains con-stant at around , denoting the system QoS is guar-anteed, and is minimized so as to maximize the system capacity, comparing with OCA and CCA. It is because FCAC uses fuzzy logic control and considers more system information than conventional schemes to allocate channels. Fuzzy logic is a soft logic as the truth value of an entity, not restricted to ei-ther false or true, but in a continuum of [0, 1]. And softness of truth value is more appropriate to represent in determining if a given requirement constraint is complied with or violated. This in effect removes the imposition of worse case assumption from the decision making of channel allocation. It is also because the fuzzy admission threshold estimator adopts the Sugeno’s posi-tion gradient-type reasoning method to effectively estimate the
Fig. 4. U(t), U (t), and U (t) for FCAC, OCA, and CCA schemes. optimal admission threshold contained in each rule from its ob-served information; and the fuzzy channel allocator appropri-ately controls the admission of calls according to fuzzy admis-sion threshold value and allocates channels in either microcell or macrocell. While the conventional policies are inadaptive to determine the number of guard channels to maintain, but not to overprotect, the QoS requirement as the traffic load is fluctu-ating and the changing is unpredictable.
Fig. 4 shows the overall system utilization , the channel utilization in macrocell and in microcell versus the calling rate per user for schemes of FCAC, OCA, and CCA at time . It reveals that of FCAC gains 31.2% and 6% improvement over the OCA and CCA methods, respectively; of FCAC is increased by an amount of 8.4% over OCA but is decreased by 2.1% under CCA, while of FCAC out-performs OCA and CCA by a significant amount of 44.6% and 10%, respectively; and the pair of and for FCAC is the closest one among those for OCA and CCA schemes. The latter two phenomena justify that FCAC can achieve the highest system utilization for more balancing utilization among cells than conventional schemes. And it is because fuzzy logic is a powerful tool that allows us to qualitatively represent control rules naturally, on the basis of a simple linguistic description, to overcome some uncertainty and imprecision.
Fig. 5 shows the forced termination probability versus the calling rate per user for schemes of FCAC, OCA, and CCA at time . It is found that of FCAC has flat curve around at . This is because we have obtained the unchanged
, shown in Fig. 3.
Fig. 6 shows the handoff rate versus the calling rate per user for schemes of FCAC, OCA, and CCA at time . It re-veals that FCAC has more handoff rate by an amount of 12.9% than OCA. The reason is that the design of FCAC is based on the knowledge of CCA which combines overflow, reversible, and underflow. However, the signaling overheads for these handoffs might not cost as much as those for conventional handoffs be-tween macrocells since most of these handoffs occurred in the same macrocell. And FCAC achieves a lesser handoff rate than
Fig. 5. P (t) for FCAC, OCA, and CCA schemes.
Fig. 6. R (t) for FCAC, OCA, and CCA schemes.
CCA. It is not only because of more information such as the speed of mobile station considered in FCAC but also because of the fuzzy logic control that can provide decision support and expert system with powerful reasoning capability.
V. CONCLUDINGREMARKS
In this paper, we propose a QoS-guaranteed FCAC for hierar-chical cellular systems. The FCAC is designed to be a two-layer controller which consists of a fuzzy admission threshold es-timator in the first layer and a fuzzy channel allocator in the second layer. The fuzzy admission threshold estimator applies the Sugeno’s position-gradient type reasoning method to adap-tively adjust the admission threshold value so that the QoS con-straint can be kept. The fuzzy channel allocator uses soft logic to determine whether a call is accepted or not and which channel in macrocell or microcell will be allocated. Simulation results show that the proposed FCAC improves the overall channel uti-lization 31.2% higher than the OCA scheme and 6% better than
Lo et al.: QoS-GUARANTEED FUZZY CHANNEL ALLOCATION CONTROLLER 1597
the CCA scheme, while maintaining the QoS requirement; and it still reduces the handoff rate by an amount of 6.7% under the CCA mechanism but increases the handoff rate by an amount of 12.9% over the OCA mechanism. Since most of the hand-offs mentioned here are within the same macrocell, the signaling overheads for these handoffs are not as much as those needed in handoffs between macrocells. The FCAC would be a promising and feasible approach.
ACKNOWLEDGMENT
The authors would like to thank the anonymous reviewers for their valuable comments to improve the presentation of the paper.
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Kuen-Rong Lo was born in Hsinchu, Taiwan, in 1957. He received the B.S. degree in electronics engineering from National Taiwan Institute of Technology, Taipei, Taiwan, in 1984, and the M.S. degree in computer science from National Tsing Hua University, Hsinchu, in 1989. He is currently working toward the Ph.D. degree at National Chiao Tung University, Taiwan.
He is also a Project Leader in the Telecommunica-tion Laboratories Chunghwa Telecom Co., Ltd. His research interests include broad areas of mobile com-munication systems and integrated services digital networks. Presently, his re-search is focused on channel assignment schemes, traffic performance mod-eling, and analysis for mobile cellular communication systems.
Chung-Ju Chang (S’81–M’85–SM’94) was born in August, 1950. He received the B.E. and M.E. degrees in electronics engineering from the National Chiao Tung University, Hsinchu, Taiwan, in 1972 and 1976, respectively, and the Ph.D. degree in electrical engineering from the National Taiwan University, Taiwan, in 1985.
From July 1976 to August 1988, he was with the Division of Switching System Technologies, Telecommunication Labs, Directorate General of Telecommunications (DGT), Ministry of Commu-nications, Taiwan, as a Design Engineer, Supervisor, Project Manager, and then Division Director. There, he was involved in designing digital switching system, ISDN user-network interface, and ISDN service and technology trials. In the meantime, he had acted as a Science and Technical Advisor for the Ministry of Communications from 1987 to 1989 and once helped DGT in the introduction of digital switching systems in 1987. In August 1988, he joined the faculty of the Department of Communication Engineering, College of Electrical Engineering and Computer Science, National Chiao Tung University, as an Associate Professor. He has been a Professor since 1993. He was Director of the Institute of Communication Engineering from August 1993 to July 1995. His research interests include performance evaluation, wireless communica-tions networks, and broad-band networks. He had served as an Advisor for the Ministry of Education to promote the education of communication science and technologies for colleges and universities in Taiwan since 1995.
Dr. Chang is also acting as a Committee Member of the Telecommunication Deliberate Body; a Committee Member of the Technical Review Assembly, Industrial Development Bureau, a Committee Member of the Electronics and Infomatics Development Committee, Ministry of Economic Affairs; a Com-mittee Member of the Telecommunication Administration Review Board, Min-istry of Transportation and Communications; and the Chairman of IEEE Vehic-ular Technology Society, Taipei Chapter. He is a member of CIE.
Cooper Chang was born in Keelung, Taiwan. He re-ceived the B.S. degree in the Department of Elec-trophysics from the National Chiao Tung University, Hsinchu, Taiwan, in 1990, and the M.E. degree in electrical engineering from the National Tsing Hua University, Taiwan, in 1992, and the Ph.D. degree from the Institute of Electron, National Chiao Tung University, Taiwan, in 1999.
He is a System Engineer with Acer Peripherals, Inc., Taiwan. His current research topics in API are system architecture design and performance evalua-tion of wireless personal access network. His research interests include WPAN, embedded system, PCS, and 3G systems.
C. Bernard Shung received the B.S. degree in elec-trical engineering from National Taiwan University in 1981, and the M.S. and Ph.D. degrees in electrical engineering and computer science from University of California, Berkeley, in 1985 and 1988, respectively. He is currently a Design Manager at Allayer Communications. He was a Visiting Scientist of IBM Research Division, Almaden Research Center, in 1988 to 1990, and later a Research Staff Member 1998–1999. He was a Faculty Member in the Department of Electronics Engineering, National Chiao Tung University in Hsinchu, Taiwan, R.O.C. from 1990 to 1997. From 1994 to 1995, he was a Staff Engineer at Qualcomm Inc. in San Diego, CA, while taking a leave from NCTU. His research interests include VLSI architectures and integrated circuits design for communications, signal processing, and networking. He has published more than 50 technical papers in various research areas.
