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.

Efficient channel assignment method for multimedia traffic in wireless mesh networks

“…The multimedia calls need to be serviced without much delay in transmission. Various channel assignment [29][30][31][32][33][34][35][36][37][38][39][40][41] mechanisms propose efficient approaches for channel allocation and management and to improve the performance.…”

Fuzzy call admission control combined with distributed dynamic channel assignment and reassignment for cellular mobile systems

“…Therefore, most existing centralized DCA schemes are heuristic. For instance, Lima et al presented a genetic algorithm (GA)-based DCA method [10]; Kim et al proposed a DCA method that is based on the GA and minimizes inter-cell interference [11]; Misra et al developed a learning automata-based channel reservation scheme to find the optimal number of reserved channels in a system [12]; Zhao et al proposed a DCA method that is based on a noisy chaotic neural network [13]; Krishna et al presented a dynamic channel allocation scheme with efficient channel reservation for handoff calls [14]. In all of the above approaches, the DCA is centrally implemented in the mobile switching center (MSC) or in a cell with a central controller, which collects information about all of the cells in the CMS.…”

Stochastic learning automata‐based channel selection in cognitive radio/dynamic spectrum access for WiMAX networks

“…In this paper, we have considered the scenario involving the use of cognitive radio (CR) [10]/dynamic spectrum access (DSA) [11,12] for addressing the problems mentioned earlier, specifically in the context of WiMAX networks. We have considered the problem of uplink frequency channel selection [13,14] from a CR user to a particular WiMAX base station (BS). The biggest challenge in designing dynamic spectrum access (DSA), also called opportunistic spectrum allocation, or dynamic channel access (DCA) algorithms is maintaining the optimum SNR and WiMAX quality of service [15,16], irrespective of numerous channel switching [17] activities due to the licensed user requests.…”