The influence of QoS routing on the achievable capacity in TDMA based Ad hoc wireless networks
The issue of providing Quality of Service (QoS) guarantees in an Ad hoc wireless network is a very challenging problem. In this paper, we make the following contributions: (i) analytically derive bounds for the end-to-end call acceptance rate using existing queueing theory methods, (ii) study the impact of the routing scheme on the end-to-end call acceptance rate, and (iii) propose a differentiated services scheme for deterministically providing QoS guarantees. Unlike the existing studies which analyze the transport capacity, we focus on the end-to-end call acceptance. The framework that we assume is that of a TDMA based Ad hoc wireless network. The routing scheme employed influences the end-to-end call acceptance of the network. The metrics that we consider are the call acceptance probability and the system saturation probability (i.e., the probability that the network is in a state in which every new call is rejected). We derive general bounds on the call acceptance and the system saturation for the case of differentiated-classes of users in the network. These bounds indicate the number of calls of the highest priority class that can be admitted into the network. Simulation studies were carried out to study the effect of load, hopcount, and the influence of the routing protocol on the call acceptance. The increase in the call acceptance rate with the introduction of load-balancing highlights the importance of load-balancing in enhancing the system performance. From these studies, we arrive at the following results: (i) load-balancing leads to significant improvement in the end-to-end call acceptance rate, and is an important factor in attaining the maximum end-to-end call acceptance rate in a given network and (ii) it is indeed possible to provide deterministic QoS guarantees for a designated set of nodes which are characterized by deterministic guarantee limit.
qos routing tdma ad hoc wireless networks call acceptance probability load-balancing