7225059 Admission Control In Frequency Hopping Gsm Systems

Admission Control in Frequency Hopping GSM Systems Per Beming and Magnus Frodigh Ericsson Radio Systems AB S - 164 80 Stockholm Sweden Abstract: One way to improve the ability to handle offered traffic variations in a cellular system is to use soft capacity, i.e., to have an Admission Control. algorithm that accepts new calls as long as the quality of the already ongoing calls are preserved. The increased flexibility in the use of the radio resources will increase the capacity in the system. In GSM, the operators continuously tightens their cell plans. Suggestions of going to a cluster size of 3 with the help of soft capacity exists. This paper investigates the performance of an Admission Control algorithm based on the number of active users in each cell. The purpose of the Admission Control algorithm is to block some calls in order to preserve the quality in the system. By means of simulations it is shown that a traffic load based Admission Control algorithm works well in a system with cluster size of 3. The results show that the method preserves the quality both when the traffic is uniformly and non-uniformly distributed. The system is using frequency hopping, discontinuous transmission and quality based power control. I. Introduction The fast growth of the cellular market necessitates high capacity cellular systems. One way to increase the capacity is to use a tighter frequency reuse plan. It has been shown, [ 11, that a GSM operator having 5 MHz available bandwidth can gain 190% in capacity with a frequency plan using a cluster size of 3 compared to a frequency plan using a cluster size of 12 if random frequency hopping, quality based power control and discontinuous transmission are used. However, going from a cluster size of 12 to 3 implies that one cannot occupy every available channel in every cell without introducing a severe C/I situation. Thus, a mechanism that guarantees the quality in the system by limiting the utilization of the total number of available channels must be deployed. A simple solution is to limit the utilization of the channels by not installing more transceivers than possible from a quality point of view. However, due to the local variations in the offered traffic (hot spots or just the variation in time), the blocking probability with such a solution will be unnecessary high. Hence, a solution that limits the number of active calls based on other criteria than the number of transceivers is preferable. An Admission Control method is needed. In [2-41 a new user is blocked if there is no available link

0-7803-3659-3/97 $ 1 0.00 0 1 997 IEEE

giving the new call an estimated C/I over some threshold. The methods consider power capabilities and estimated path gain of the new call plus measured interference on the free channels when deciding whether the new call shall be blocked or not. In [5-61, the method of preserving the quality has another focus, namely the handover failure rate. The methods consider the number of active calls in the cell and neighbouring region. The method in [5] blocks the new call if there are more active calls in a cluster than a threshold (there are still channels left, but they are reserved for handover calls). The method in [6] blocks a new call if the estimated probability of handover failure becomes too high. The probability is estimated from the number of active calls in the cell and its neighbour cells. The purpose of this study is to evaluate one method of Admission Control called Traffic Load Admission (TLA). TLA aims to preserve the speech quality in terms of C/I as in [2-41. TLA, however, considers the already active users, which will be disturbed by a new call, when deciding to admit a call or not. The blocking probability and the characteristics of C/I are derived for two different traffic environments. The results are compared to results obtained with hard blocking due to limited hardware capacity (less transceivers than frequencies per cell) and with a traditional hard blocking system (a system that is not using any admission control at all). The study is mainly intended for GSM, but when applicable the method may also be used for other systems. Chapter I1 defines the quality and performance criteria while Chapter III presents the TLA algorithm. In Chapter IV and V the performed simulations and the obtained results are presented and finally in Chapter VI there are some conclusions. 11. Definition of Quality and Performance Measures Admission Control shall guarantee that the active users in a system have an acceptable “quality”. The “quality” is in this paper defined by the following criterion: 90 percentile of C/I 2 X dB, i.e. 90 percent of the users shall have a C/I greater than or equal to X dB.

The probability of blocking a new call, P,, is used as a performance measure. P, shall be compared when the criterion is fulfilled and the comparison shall be done while the methods have approximately the same “quality” defined by the criterion above.

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111. Traffic Load Admission

A. Intelference Areas The basic idea in this method is to divide the cell plan into intelference areas, i.e. each cell together with the co-channel cells that generates the dominating interference (e.g. the six nearest co-channel cells) build an interference area. Consider for example the system using a cluster size of 3 in Figure 1. Here, cell number 22 build together with cell number IO, 11, 21, 23, 33 and 34 one interference area if we define an interference area as the cell itself and its six nearest co-channel neighbours. Denote this set of cells by A22. Now consider the interference area corresponding to cell number 34, A34. This interference area consists of the cells 22,23,33, 34,35,42 and 43. Note that cell number 22 is a part of AS,+ It is easy to see that cell number 22 is a part of the interference areas AI,, All, A,,, A,,, A23, A33 and A34. Call this set of interference areas where cell 22 is included by S22. Generalizing this, there is a set of interference areas coupled to each cell, i.e., Si is the set of interference areas including cell i.

B. Utilization Factor Denote the number of available channels in cell i by ci and the number of active users in cell i by ui.The utilization factor in interference area Ai,denoted by F A , , is then:

C u.i (3.1) j e A,

For example, if a system using a cluster size of 3 has 12 carriers then each cell will be assigned 4 carriers. Each carrier have 8 time slots, thus ci = 8 * 4 = 32. If the interference areas are defined as above then the denominator in Equation (3.1) will be 32 * 7 = 224.

Consider a new call in cell i. The call is admitted if

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V A j € Si

A. System Parameters In the simulations frequency hopping, discontinuoustransmission and quality based power control were used. There was no mobility. The simulator uses a cell plan with a wrap around technique to avoid border effects. Further, the system was interference limited. A 5 MHz operator was assumed, i.e., 24 available frequencies. 12 frequencies were used as BCCH carriers and not considered in the simulations. Hence, 12 frequencies remains. These 12 frequencies were divided into three groups. Since 8 time slots was simulated, ci = 8 * 4 = 32. All system parameters used in the simulations are summarized in Table I. B. TrafJic Environment

In this study, the Traffic Load Admission algorithm was simulated for two different traffic environments: Uniform and Hot spot. Uniform was realized according to the following:

C. The Algorithm

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Figure 1. Interference areas in a system using a cluster size of 3. A cell, together with its six nearest co-channel cells, build an interference area. Cell number 22, together with cell number 10, 1I, 21,23,33 and 34, build an interference area. Note that cell number 22 belongs to seven different interference areas together with the shaded cells.

(3.2)

Here, the threshold Fthreshold determines the trade-off between capacity and quality in the system. The algorithm in words: check the utilization factor in each of the interference areas where cell i is included. If the utilization factor in any of the interference areas in Siexceeds the threshold, FrhreshoLd, block the call else admit the Call. IV. Simulations The simulator tool that is used has a birth-death process with an arrival traffic according to a Poisson process and exponential distributed call duration.

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The mobiles were distributed with equal probability over the coverage area of the system. Hot spot was realized according to the following:

50% of the mobiles were distributed with equal probability over the coverage area of the system. The remaining 50% of the mobile were distributed according to a two dimensional Gaussian distribution. The standard deviations in the two dimensions of the Gaussian distribution were both set to 1500 meters. The cells that covers at least 90% of the traffic generated

Okumura-Hata (35 log d)

Propagation model Lognormal fading standard devia-

No of frequencies Cluster size Time slots used

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No of cells

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Cell radii 3 sector

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BCCH frequency TABLE I

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System parameters used in the simulations.

with the two dimensional Gaussian distribution are hereafter denoted as Hot spot cells. With this definition there was 15 Hot spot cells. The cells that are not Hot spot cells are hereafter denoted as Uniform cells. Realizations of the traffic environments with 2000 mobiles are shown in Figure 2. The shaded cells in the Hot spot case are the Hot spot cells.

b) Hot Spot Figure 2. Traffic distributions: a) Uniform. b) Hot Spot.

V. Results

C. Measure of Offered Trufic

A. Uniform Trufic Case

The offered traffic in Erlangkell was measured as the average traffic in Erlang over whole the coverage area of the system divided by the ni :ber of cells in the system. Hence, X average Erlangkell in the Hot spot case implies X/2 ErlangAJniform cell and X/2+75/15*X/2 = 3X Erlang/Hot spot cell.

In Figures 3-4, the blocking probabilities and 90 percentile C/I levels for TLA, 60% loadkell and 100% loadkell are shown for the Uniform traffic case. Here it is shown that for high loads (offered traffic > 20 Erlangkell) the performance of TLA and 60% load/cell are approximately the same. For interesting loads (offered traffic that generates a blocking around 2%), however, TLA outperforms 60% loadkell. 100% loadkell outperforms TLA for all loads (at least for blocking probabilities above 0.1%). The quality decreases with offered traffic for all methods. In the 100% loadkell case, the assumed 9 dB quality threshold is broken. TLA and 60% loadkell, however, preserve the 90 percentiles of C/I around 9 dB even at high loads. Note that there is no significant difference between TLA and 60% loadkell concerning the quality. The offered traffic can be increased from 11.9 Erlangkell to 15.7 Erlangkell and still maintain a 2% blocking with acceptable quality when TLA is used compared to 60% loadkell. This is an increase with 32%.

D.Threshold Setting The threshold was set to 60% (Fthreshold := 0.6)which with our assumptions and system parameters resulted in a 90 percentile of C/I above 9 dB which in this paper is assumed to give an acceptable quality. An adjustment of the threshold would result in another 90 percentile of C/I. E. Reference Systems To be able to compare the obtained results, simulations were also done for a system using a cluster size of 3 with 60% load/cell hard blocking (i.e. 32*0.60 = 19 channelskell) and a system using a cluster size of 3 with 100% loadkell traditional hard blocking (i.e. 32 channels/cell).

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B. Hot Spot TrafJic Case Blocking probabilityvs offeredtraffic. Uniform

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In Figures 5 - 6, the blocking probabilities and 90 percentile C/I levels for TLA, 60% loadcell and 100% load/cell are shown in the Hot spot traffic case. The results comprise the total system regardless of cell type. Here it is shown that for high loads (offered traffic > 20 average Erlangkell), the performance for TLA and 60% loadkell are approximately the same. For the interesting loads (offered traffics that generates a blocking around 2%), the TLA and the 100%loadkell have almost identical performance. The quality decreases with average offered traffic for all methods. In the 100%loadcell case, the assumed 9 dB quality threshold is broken. TLA and 60% loadkell, however, preserve the 90 percentiles of C/I around 9 dB even at high loads. With preserved quality, the average offered traffic can be increased from 1.62 Erlangkell to 2.41 Erlangkell with TLA compared to 60% load/cell at 2% blocking. This is an increase with 49%.

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Blocking probability for uniform traffic distribution 90 percentileof C/I vs. offered traffic - Downlink, Uniform

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C. Hot Spot TrafJic Case - Hot Spot and Uniform Cells

I

For the Hot spot case, the blocking probability and 90 percentile of C/I levels are shown in Figures 7 - 8 for Uniform and Hot spot cells. Here it is shown that the blocking probability in the Uniform cells are considerable lower than in the Hot spot cells. In fact, comparing Figure 7 with Figure 5 shows that the blocking in the Hot spot cells dominates the total blocking probability. The quality decreases with the average offered traffic but it is preserved for both Uniform and Hot spot cells in the downlink. In the uplink, the quality falls below the 9 dB quality threshold for the Hot spot cells. However, antenna diversity will reduce the necessary threshold in the uplink. Thus, it is no problem to have a worse uplink than downlink.

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90 percentile of C/I vs offered traffic - Uplink, Uniform

VI. Conclusions

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Figure 4. 90 percentiles of CII for Uniform traffic distribution

The Traffic Load Admission (TLA) algorithm is shown to preserve the quality in the system. Further, TLA is shown to work well in an non uniform traffic environment. However, a system which does not have an Admission Control algorithm is shown to violate the 90 percentile quality criterion for high loads regardless of traffic environment. The TLA algorithm is shown to have better performance in terms of blocking compared to a hard blocking method where the number of installed transceivers limits the served traffic (TLA can handle 32% more traffic than 60% loadkell). The gain with the TLA algorithm increases when the traffic is not uniformly distributed (from 32% to 49%, even more if only Hot spot cells are considered). Further, TLA is shown to have almost optimum performance for a non uniformly distributed traffic scenario if the blocking probability is kept at a reasonable level (around 2%).

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Figure 7. Blocking probability for Uniform and Hot spot cells, Hot spot traffic distribution.

Figure 5. Blocking probability for Hot spot traffic distribution.

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90 percentile of Cll vs. offered traffic - Downlink, Hot spot 13

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Figure 8. 90 percentiles of C/I for Uniform and Hot spot cells, Hot spot traffic distribution.

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VII. References

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[I]H Olofsson, J Naslund, B Ritzen & J Skold, “Interference Diversity as Means for Increased Capacity in GSM’, In Proceedings of the 1st European Personal and Mobile Communications Conference, 1995, pp. 97-102.

90 percentile of CII vs offered traffic - Uplink, tiot spot

[2]Chen Nee Chuah, Roy D. Yates and David J. Goodman, “Integrated Dynamic Radio Resource Management”, In proceedings of the VTC 95, pp. 584-588. [3]Y Argyropoulos, S Jordan and S P R Kumar, “Dynamic Channel Allocation Performance under uneven Traffic Distribution Conditions”, In proceedings of the ICC 95, pp. 1855-1859. [4]M Andersin, M Frodigh and K-E Sunell, “Distributed Radio Resource Allocation in Highway Microcellular Systems”, In proceedings of the 5th Winlab workshop, April 1995, pp. 77-85.

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Figure 6. 90 percentiles of C/I for Hot spot traffic distribution

[5]M Naghshineh and A S Acampora, “Design and Control of MicroCellular Networks with QOS Provisioning for Real-Time Traffic”, In proceedings of the ICUPC 94, pp.376-381. [6]M Naghshineh and M Schwartz, “Distributed Call Admission in MobilelWireless Networks”, In proceeding of the PIMRC 95, pp. 289293.

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