The Impact of Power Control Imperfections on Call Admission Control ...

The Impact of Power Control Imperfections on Call Admission Control in HAP W-CDMA Cellular Systems Stylianos Karapantazis

Fotini-Niovi Pavlidou

Dept. of Electrical & Computer Engineering Aristotle University of Thessaloniki 54124 Thessaloniki, Greece Email: [email protected]

Dept. of Electrical & Computer Engineering Aristotle University of Thessaloniki 54124 Thessaloniki, Greece Email: [email protected]

Abstract— Satellite and terrestrial systems have been dominant in the telecommunications arena for years. In parallel with these two well established technologies, a new alternative technology has gained considerable interest in recent years, based on aerial vehicles positioned in the stratosphere, known as High Altitude Platforms (HAPs). In this paper we consider a multiservice HAP wideband-code division multiple access (W-CDMA) cellular system. The focus of this study is on the impact of power control imperfections on the performance of call admission control (CAC) schemes. In particular, we propose and evaluate a CAC algorithm that takes account of both power control impairments and user mobility in order to enhance network performance. The mechanism behind the proposed scheme that allows it to attain a better performance relies on the estimation of the system state by averaging Eb /N0 (energy per bit to noise power spectral density ratio) measurements over a specific time interval. The proposed CAC scheme is compared to a CAC scheme that is based on instantaneous Eb /N0 measurements and its positive characteristics are corroborated by ample simulation experiments, where significant gains in the performance are witnessed for all scenarios examined.

I. I NTRODUCTION Aside from terrestrial and satellite systems, a new technology has been thrown into the telecommunications arena lately which is based on stratospheric platforms, often aptly dubbed High Altitude Platforms (HAPs). Being positioned at modest altitudes between 17 and 22 km above the Earth’s surface, HAPs have the ability to provide a compelling range of communications services by virtue of the outstanding features that are endowed with [1]. Moreover, the growing exigencies for both mobility and ubiquitous access prompted the development of new generation wireless telecommunications systems. In this respect, 4G systems are expected to fulfill this vision, providing high bit rates at low cost. Although it is still doubtful whether HAPs will revolutionize the telecommunications industry as satellites did in the early 1960s, HAPs can be instrumental in the evolving telecommunications infrastructure, playing a multifaceted role [2]. Mr. Stylianos Karapantazis thanks the Bodossaki Foundation for supporting his PhD studies.

The focus of this paper is on call admission control (CAC) in multiservice HAP wideband-code division multiple access (W-CDMA) cellular networks. W-CDMA has emerged as the mainstream air interface solution for 3G networks (UMTS, IMT-2000) [3]. In order to foster the deployment of HAP systems, ITU has specifically authorized the use of some frequency bands around 2 MHz by HAPs [4]. In this type of network, as well as in any other kind of system, the role of CAC is of the greatest importance to network performance. On the whole, the aim of CAC is to decide whether to admit a new call into the network or not. A new call is accepted provided that there are adequate network resources available to guarantee the Quality of Service (QoS) of all ongoing calls and the new one. Nonetheless, unlike TDMA/FDMA systems, users in a CDMA system share the same portion of bandwidth at the same time and multiple access is achieved by assigning each user a pseudo-random code. In this kind of systems, a new user is admitted into the network as long as the signal-to-interference ratio (SIR) is adequate for processing at the receiver. As befits an issue of such importance, CAC techniques for terrestrial CDMA systems have been the subject of considerable study. The study in [5] represents one of the first works that grappled with this meaningful issue. Two SIRbased CAC schemes, which are predicated upon the notion of residual capacity, were proposed in that study. The latter is defined as the additional number of new calls that a base station can accept so that the outage probability of ongoing calls is still over a predefined threshold. Kim et al extended the previous work and proposed a new algorithm that aims to predict the additional intercell interference that a new call will induce in adjacent base stations [6]. The authors of [7], [8] developed a CAC algorithm that is based on interference measurements instead of SIR measurements. Dimitriou et al extended the techniques presented in [5], [8] for the case of a multiservice CDMA cellular system [9]. In [10] another CAC scheme tailored for CDMA systems that support multimedia services was delineated. The scheme is based on instantaneous Eb /N0 (energy per bit to noise power spectral density ratio)

measurements and gives priority to handoff calls over new calls. Ma et al laid particular emphasis on the impact of CAC on dropping probability, that is, the probability that an ongoing call will be forced into termination. Specifically, a CAC scheme was proposed with the aim of reducing the number of dropped calls [11]. In order to prioritize handoff calls, the concept of soft guard channel was introduced. However, despite the plethora of studies on CAC for terrestrial CDMA cellular systems, CAC issues have scarcely been addressed for HAP systems. Foo et al proposed two CAC techniques for HAP CDMA cellular systems in [12], [13]. In the former study, a CAC scheme for the uplink of this type of systems was presented. The scheme aims to predict the increase that a new call will induce in the total received power of each base station. In the latter study, a CAC algorithm that capitalizes upon the unique position of the HAP in order to dynamically allocate power to each base station was proposed. At this point it must be stressed that all the aforementioned techniques were assessed under the assumption of perfect power control. Nevertheless, perfect power control is beyond the realms of possibility. In practice, impairments in the power control loop exacerbate the network performance [2], [14]. In this paper we propose and evaluate a CAC technique tailored for the uplink of multiservice HAP W-CDMA cellular systems that bases its admission decision on Eb /N0 measurements. In addition, it aims to take power control imperfections into account. Towards this end, Eb /N0 measurement are averaged out over a specific time interval in order to diminish the impact of power control imperfections on the estimation of the current system state. The admission decision takes Eb /N0 measurements of the first tier cells into account as well, thereby accounting for user mobility. Moreover, it caters for multimedia services by setting appropriate Eb /N0 thresholds for both new and handoff calls of each service class. The proposed CAC algorithm is compared to a CAC scheme that is based on instantaneous Eb /N0 measurements. The outline of the paper is organized as follows. Section II describes the system model that is considered in this study. The details of the proposed CAC technique are spelt out in section III. Section IV deals with the simulation results of the examined schemes, while concluding remarks are drawn in section V.

II. S YSTEM M ODEL In this study we consider a HAP positioned at an altitude of 20 km above the surface of the Earth, equipped with a multibeam phased array antenna and a W-CDMA communications payload. A hexagonal cellular layout is considered and the gain of the transmitting antenna at the edge of each cell is 10 dB below the maximum gain. The radiation pattern of the employed antenna conforms to ITU recommendations [4] and is given by

 ϑ 2  ) , 34.8 − 3( 1.57   9.8, G(ϑ) =  55.95 − 60 log(ϑ),   −38.2,

for for for for

0o ≤ ϑ ≤ 4.53o 4.53o < ϑ ≤ 5.87o 5.87o < ϑ ≤ 37o 37o < ϑ ≤ 90o

(1)

where G(ϑ) denotes the gain (in dBi) at the angle ϑ from the direction toward the center of the cell. It should be noted that the drift due to wind or pressure variations and the movement of the HAP itself are considered to be compensated for by means of either beam control or an antenna steering mechanism. As in any CDMA cellular system, the capacity is limited by the uplink since the transmission onto this is asynchronous [2]. Thus, in this work we restrict ourselves to CAC for the uplink of HAP W-CDMA systems. The quality of the link is generally represented by the value of Eb /N0 which is expressed as Eb /N0 =

W Pc eαθk Rb Iintra + Iinter + n0

(2)

where W denotes the transmission bandwidth, Rb is the information bitrate of the service under consideration, Iintra accounts for the interfering power stemming from users in the same cell (intracell interference), Iinter is the intercell interfence, that is, interference originated from users in neighboring cells, and n0 is the thermal noise power. In addition, the nominator of the right-hand fraction denotes the power from a user that is received by a base station. In essence, Pc represents the nominal received power with ideal power control, α is equal to ln(10)/10, whereas θk accounts for variations in the received power owing to imperfections in the power control loop. Power control imperfections are considered to be lognormally distributed since log-normal imperfect power control is reckoned to be a valid assumption [15]. Hence, θk is a zero-mean Gaussian random variable with standard deviation σp . It must be stressed that all interfering signals are affected by power control impairments with the same statistics as the desired signal. In this paper we examine a system that supports two service classes whose parameters are tabulated in Table I. The link is considered to be in outage when Eb /N0 is smaller than the minimum required (Eb /N0 )min . The (Eb /N0 )min values were taken from [16]. As far as the power factor is concerned, it was calculated according to the method proposed in [9]. Specifically, the power factor is defined as the ratio of the nominal received power of the service under consideration

TABLE I: Service classes under consideration Information bitrate Minimum required Eb /N0 Power Factor Typical applications

Class-1

Class-2

12.2 kbps 5 dB 0 dB

64 kbps 2 dB 4.2 dB interactive-data interactive-audio interactive-video

voice

to the nominal received power of the lowest bitrate type of service (in our case, that is class-1) and can be expressed as Power Factor =

Ebi Rbi Eb1 Rb1

(3)

where Ebi and Rbi are the energy per bit and bitrate of the service class under consideration, while Eb1 and Rb1 are the energy per bit and bitrate of the class-1 type of service. Concerning Iintra , it is given by Iintra =

M1 X i=1

Pc1 eαθki +

M2 X

Pc2 eαθkj

(4)

j=1

where M1 and M2 are the numbers of class-1 and class-2 users in the same cell respectively. In addition, Pci , where i = 1 or 2, denotes the nominal power received from a class-i user when ideal power control is assumed. Evidently, the power received from the desired user is not taken into account in this sum. Now let us focus our attention on Iinter . The signal power PR0i received by the 0th base station BS0 from the ith user within its coverage area can be expressed (in dB) as (PR0i )dB = (PTi )dB − (L0i )dB + (GR0i )dB + (GT0i )dB (5) where (PTi )dB is the user’s transmission power and (L0i )dB denotes the losses due to free space attenuation and shadowing. Furthermore, (GR0i )dB and (GT0i )dB are the gains of the receiving and transmitting antennas respectively, evaluated at the angle under which the ith user is seen from BS0 . The interfering power (PRmi )dB that this user induces in another base station BSm is (PRmi )dB = (PTi )dB − (Lmi )dB + (GRmi )dB + (GTmi )dB (6) where (Lmi )dB indicates the losses due to free space attenuation and shadowing, while (GRmi )dB and (GTmi )dB denote the gains of the receiving and transmitting antennas respectively, evaluated at the angle under which the ith user is viewed from this base station, that is, BSm . Nevertheless, a specific feature of a HAP system is that all base stations are located on the HAP within a distance of few meters. Thus, the user’s signal traverses the same path toward all base stations, thereby L0i ' Lmi and GT0i ' GTmi . By replacing (GTmi )dB in (6) with (5) and considering that the power received by BSj is Pc eαθk , we result in ) (M1j M 2j K X X GR X GR αθkjl αθkji 0i 0l + Iinter = Pc e Pc e GRji 1 GRjl 2 j=1 i=1 l=1 (7) where K is the number of surrounding cells, M1j is the number of class-1 users in the j th cell and M2j is the number of class-2 users in the j th cell.

III. D ESCRIPTION OF THE P ROPOSED CAC S CHEME In this section the proposed CAC algorithm is delineated. As previously stated, this algorithm capitalizes on Eb /N0 measurements, which form part of the operational mechanism of DS-CDMA cellular systems, as is the case with UMTS networks. Moreover, it aims at taking both power control imperfections and user mobility into account and caters for multimedia services by setting appropriate thresholds to each service class. The proposed CAC scheme requires Eb /N0 measurements to be stored every 0.25 sec. In essence, for each cell two values are stored, namely the mean Eb /N0 of class-1 users and the mean Eb /N0 of class-2 users. In order to attain an accurate estimation of the current system state, the proposed CAC scheme averages out the last eight stored values for each service class upon the arrival of a new call of a handoff request. The rationale behind this approach lies in the fact that instantaneous Eb /N0 measurements fail to reflect the current system state on account of imperfections in the power control loop. The averaging of measurements over time aims to surmount this hindrance. It should be noted that averaging a sufficient number of measurements out allows capturing the system state on the one hand, however, this number should not be rather high so that stale information is not taken into account on the other hand. Upon the arrival of a new call or a call handoff request, the algorithm calculates four different values. The first two correspond to the mean Eb /N0 of class-1 and class-2 calls in the candidate cell for serving the call. The other two correspond to the mean Eb /N0 of class-1 and class-2 calls in the first tier cells. Then, these values are compared to predefined thresholds. Concerning the cells of the first tier, the algorithms necessitates all these cells satisfying all the admission criteria. In other words, the algorithm computes the minimum of the mean Eb /N0 values of first tier cells for each service class and then compares this value to the corresponding threshold. By doing so, the proposed scheme aims to ensure that the call will not be dropped at its next handoff attempt. Table II provides a tabulation of the aforementioned predefined thresholds. These thresholds have been selected via extensive simulations, where the impact of a new (or a handoff) call on the QoS of ongoing calls in the same cell as well as in contiguous cells was estimated. Essentially, in order to prioritize handoff calls over new calls smaller thresholds

TABLE II: CAC parameters Threshold for new class-1 calls - same cell new class-2 calls - same cell new class-1 calls - 1st tier cells new class-2 calls - 1st tier cells handoff class-1 calls - same cell handoff class-2 calls - same cell handoff class-1 calls - 1st tier cells handoff class-2 calls - 1st tier cells

Eb /N0

class-1 7.00 dB 7.10 dB 6.70 dB 6.80 dB 6.65 dB 6.80 dB 6.60 dB 6.65 dB

Eb /N0 class-2 2.80 dB 2.90 dB 2.60 dB 2.70 dB 2.60 dB 2.70 dB 2.55 dB 2.55 dB

apply to the former. In addition, greater values have been given to the thresholds for new and handoff class-2 calls owing to the profound effect of this type of calls on the QoS of existing calls. IV. S IMULATION R ESULTS AND D ISCUSSION The experiments conducted in this work aim at evaluating the performance of the proposed CAC algorithm, as well as comparing it to the performance of an algorithm that bases its decision on instantaneous Eb /N0 measurements [10]. The tool that was used for these experiments was custom coded by the authors in C++. The error due to imperfect power control was updated for each user independently every 0.25 sec according to the following formula θk i = aθk i−1 + bθk inew

(8)

where a2 + b2 = 1. In eq. (8), θki denotes the current error in dB, θki−1 is the error in dB at the instant of the previous measurement and θkinew is a Gaussian random variable which accounts for variations in the received power over time. In our simulations the parameters a and b were both set to a2 = b2 = 0, 5 for both class-1 and class-2 calls. It is recognized that there has been no experimental verication of the aforementioned model for HAP systems. It is, nevertheless, a reasonable supposition based on the well accepted model for terrestrial CDMA systems. In our simulations a 4 × 4 cellular system was used. However, we also had to approximate an infinite network so that each cell has neighboring cells in all directions and therefore: i) a mobile user remains within the network as he/she crosses the boundary of the simulation area; and ii) interference is experienced from all directions. To this end, the simulated cell layout was wrapped around into a torusshape. Thus, the reference cells were repeated in a regular manner. Call arrivals were generated according to a Poisson distribution. Once a call is admitted into the network, its duration is generated according to an exponential distribution, while the terminal’s position is uniformly generated over the simulation area and its direction is randomly set at a value between 0o and 360o . The time interval between two consecutive changes in the terminal’s direction is exponentially distributed with a mean value of 40 sec for class-1 calls and 80 sec for class-2 calls. The new direction is generated according to a uniform distribution over [45o , 45o ] in reference to the previous direction. Additionally, the user’s velocity is uniformly generated over [Min. user velocity, Max. user velocity] at the start of the

call and remains constant throughout the duration of the call. Table III lists the rest of the simulation parameters. In addition to these parameters, Pc1 /nth = 1 dB, Pc2 /Pc1 = 4.2 dB, W = 5 MHz, whereas the radius of the cell is 1 km (the gain at the edge of the cell is 10 dB below the maximum gain). In our experiments we also made use of a hard handoff scheme due to the fact that a user is always served by the base station that illuminates the cell where the user is located. A call is dropped either due to an unsuccessful call handoff or on account of being continuously in outage for more than 5 sec for class-1 users and more than 26 sec for class-2 users [9]. For the sake of clarity, the CAC techniques that are examined in this work are described below: • IM scheme: the scheme that is based on instantaneous measurements. • AM scheme: the scheme that is based on average measurements (that is, the proposed scheme). Fig. 1 illustrates the blocking probability versus the total call arrival rate (in the whole system). Recall that 75% of the total call arrival rate corresponds to new class-1 calls, while only 25% refers to new class-2 calls. Apparently, the performance disparity between the examined CAC schemes is insignificant. Concerning service class-1, the proposed scheme attains slightly better results than the IM scheme. As far as service class-2 is concerned, albeit the AM scheme outperforms the IM scheme for low and moderate call arrival rates, its performance aggravates commensurate with the increase in call arrival rate. The next objective of our experiments was to assess the performance of the examined schemes in terms of dropping probability. Forcing a call into termination is generally considered much more disruptive and irksome than blocking a new call since it involves breaching QoS guarantees made upon call acceptance. Fig. 2 depicts the dropping probability versus the total call arrival rate. A significant amelioration in network performance is witnessed for both class-1 and class-2 calls even for low call arrival rates. Regarding service class1, the dropping probability is dropped down by half when the AM scheme is employed. Significant gains are obtained

TABLE III: Simulation Parameters Portion of new call arrivals Mean call duration Minimum user velocity Maximum user velocity σp

class-1 calls

class-2 calls

75% 180 sec 0 m/sec 20 m/sec 1 dB

25% 500 sec 0 m/sec 5 m/sec 0.5 dB

Fig. 1: Blocking Probability vs total call arrival rate

V. C ONCLUSIONS In this work, we proposed a CAC scheme for the uplink of multiservice HAP W-CDMA cellular systems. The proposed technique aims to take into account both power control errors and user mobility in the admission decision. Specifically, upon the arrival of a new call or a handoff request, it estimates the current system state by averaging out the last Eb /N0 measurements. Moreover, it caters for multimedia services by setting appropriate thresholds for each service class. The good characteristics of the proposed technique were confirmed by ample simulation studies, and significant gains in the network performance were witnessed in terms of dropping probability. Fig. 2: Dropping Probability vs total call arrival rate

Fig. 3: Outage Probability vs total call arrival rate

for class-2 type of service as well and the improvement in dropping probability can be up to 40%. One can expect that the performance would be further improved if the standard deviation of the power control error was increased. Last but not least, in order for our study to be complete, we also had to evaluate the performance of the proposed scheme in terms of outage probability, that is, the probability that the received Eb /N0 at a particular time instant is below the minimum required (Eb /N0 )min . Fig. 3 presents the outage probability of the examined CAC schemes. There do not exist any differences between the performance of these schemes regarding class-2 calls. In this case, the outage probability is always below 10−5 for any call arrival rate. Therefore, let us focus our attention to class-1 calls. On a first observation, it appears that the outage probability of the proposed scheme is slightly higher than the one of the IM scheme. However, this can be ascribed to the fact that the system serves a greater number of calls when the AM scheme is used. Besides, the outage probability is still very close to the one exhibited by the IM algorithm. Under further scrutiny, and taking into account also blocking and dropping probabilities, we are led to the conclusion that the AM scheme is the most efficacious one.

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