Mobile ATM Handoff and Performance Analysis

This paper extends the work of [2] to a mobile environment. In that work, the equilibrium buffer fill distribution is described by a set of differential equations assuming sources alternate asynchronously between exponentially distributed periods in “on” and “off” states. This paper extends that work by including the probabilities that mobile sources have links to that buffer. The sources represent mobile user nodes, and the buffer represents the capacity of a switch. Some familiarity with [2] is assumed. The result of this extension is an algorithm which uses mobile parameters such as speed, call rates per unit area, cell area, and call duration and determines queue fill distribution at the ATM level. This can be used to determine the effects of various ATM handoff schemes which are reviewed. 1: Introduction It is generally recognized that the next generation of mobile systems will be B-ISDN compatible. It is still at an early stage and relatively few in-depth papers have been published on such systems. The papers that have been written are very general, high level overviews of how such mobile systems might operate. As a typical example [6] discusses where features should be implemented, what hardware should run those features, and what the protocol might look like. Papers that consider the specific area of handoff are even more rare. This paper will focus on the specific topic of handoff. This is, prima facie, closely related to congestion control. As the shared access medium, available air bandwidth, becomes loaded, adding more cells1 and reducing the cell area will distribute the load and reduce interference. However, this will increase the number of handoffs which occur, thus emphasizing the need for handoffs which are as efficient as possible. It appears that there has been little work done concerning the effects of mobility on ATM. This paper will attempt to build a foundation for analyzing mobile ATM networks by extending [2]. This analysis would be useful for analysis of the base station queue fill distribution and probability of cell loss in a mobile environment. It would also be useful for simplifing mobile CBR cell simulations. The author can be contacted via e-mail at [email protected]. 1Note that there is a potential for confusion between an ATM cell and the cell referring to the area covered by a base station. The term, “cell area”, will be used for the latter. 2: Mobile Systems Analysis This section extends the work of [2] to a mobile environment. In that work, the equilibrium buffer fill distribution is described by a set of differential equations assuming sources alternate asynchronously between exponentially distributed periods in “on” and “off” states. An extension to that work will be made by including the probabilities that mobile sources have links to that buffer. The sources represent mobile user nodes, and the buffer represents a switch. In [2], the queue fill distribution is determined for multiple constant rate on-off sources. However, it assumes that the number of sources remains constant over a sufficient period of time for the equilibrium probabilities to be valid. There are at least two ways of extending [2] to a mobile CDMA cellular environment. Consider the ATM cell queue fill distribution at the base station. The simplest, but least accurate extension is to determine the average number of channels used per cell area as t ! 1. However, there is nothing to stop mobile units from concentrating in a small number of cell areas at some time. However, there is a hard limit on the number of sources a base station will accept because each base station has a limited number of CDMA codes [5]. Once this number is reached, further handoffs into such a cell will cause their connections to terminate. Thus, determining how many codes to assign to a base station is a critical design choice. Note that code assignment can be dynamic, but this will not be considered here in order to help simplify the analysis. Also, cell areas can be designed to overlap [5]. Although this can increase the probabilityof interference, it has a beneficial effect on handoff. When a mobile unit determines that a handoff is likely to occur and the cell it will enter has no channels avaliable, the mobile unit can continue to use the cell within which it currently resides, and queue the handoff to the next cell. If a channel becomes avaliable in the destination cell before the mobile leaves its current cell, the handoff can take place succesfully. It would be interesting to see the effect of queueing the handoffs. Again, in order to keep the computation simple for this paper, this will not be considered. Another possible extension of [2] to the mobile environment would be to determine the CDMA channel holding time distribution. Note that this paper makes the simplifing assumption that each mobile unit is a single ATM source multiplexed at the base station. In general, each mobile unit would be a set of sources; however, this could again be a future extension of this paper.