An efficient approach for distributed channel allocation with learning automata-based reservation in cellular networks

Abstract

Presently, mobile and wireless networks are witnessing rapid growth and the users of these networks demand many services that require unlimited frequency spectrum. Providing unlimited frequency spectrum is expensive and difficult. So,efficient use of the available frequency spectrum will greatly satisfy the demands of various services. In this paper, we propose a learning automata (LA)-based channel reservation scheme, which determines the optimal number of reserved channels for the system that is being tested. The proposed scheme is tested on four different extended models (systems) – Single Traffic No Queues (STNQ), Single Traffic with Queues (STWQ), Multi-traffic No Queues (MTNQ), and Multi-traffic with Queues (MTWQ). These four systems employ the channel allocation procedure, which is based on the distributed dynamic allocation policies. The presented systems deal with both originating calls and handoff calls. Quality of Service (QoS) may be improved further by reserving the channels for handoff calls based on the user mobility and type of cell. The performance evaluation of the systems with the proposed LA scheme shows improvement when compared with legacy systems. At a particular instant when the system load is 100, 21%, 28%, 18%, 23%, 22%, 27%, 11%, and 18% of the originating calls are blocked and only 2.4%, 3.6%, 1.9%, 2.1%, 1.9%, 2.3%, 0.4%, and 0.55% of the handoff calls are dropped in the case of the STNQ with LA, STNQ without LA, STWQ with LA, STWQ without LA, MTNQ with LA, MTNQ without LA, MTWQ with LA, and MTWQ without LA systems respectively.