Future mobile telecommunication systems have to offer advanced multimedia services to a large number of users. The burden imposed by the signaling operations supporting these services can severely degrade the system performance and lead to congestion problems in network equipment (switches, databases, gateways, etc.). Paging operations in a mobile cellular network are responsible for a large part of the signaling load and, therefore, the issue of how to conduct them efficiently becomes of critical importance. The key to its solution lies on the application of multiple-step paging schemes and on the "proper" planning of paging areas. We devise and solve a version of the paging area planning problem. In particular, we provide a low complexity algorithm capable of reducing the paging cost, at the price of a moderate increase in the call set-up delay.

Efficient spectrum utilization in third-generation cellular systems has motivated the study of the traffic adaptive channel allocation problem: given the set of channels, the cell structure and the load to be accommodated in each cell find the optimal allocation of channels to cells, subject to the reuse distance constraints. In this paper, we extend this scheme in order to include time fluctuating traffic loads. The extended problem is formulated and solved by reduction to 0-1 (linear and quadratic) programming.

The dynamic routing problem for multiple destination networks is considered. The minimum time rather than total delay cost functional is employed. The problem is solved through an iterative link-by-link optimization. Each link capacity is optimally partitioned by examining the upper bounds for the evacuation time imposed through different capacity allocations for each origin/destination pair traffic. The computational complexity per iteration is polynomial in the number of network nodes. This is due to the examination of origin/destination pairs rather then destinations alone as in previous work where a similar approach led to exponential complexity. Sufficient conditions for the convergence of the iterative algorithm to the optimum are given. If these are not satisfied supplementary steps are described which conduct the algorithm to the desired solution. These involve exponential computational complexity.>