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Top1. Introduction
The evolution in wireless communication and mobile computing is attributed to technological improvements in communication technology and rapid growth in handheld wireless devices. Taking ergonomic and economic factors into account and considering the new trends in the telecommunication industry to provide global information access, the population of mobile users continues to grow at a tremendous rate (Katzela & Naghshineh, 1996). The mobile communication sector has witnessed the dramatic growth with the proliferation of cellphones, smartphones, and other handheld mobile devices. An effective wireless spectrum utilization is needed against the radio spectrum scarcity and not so strong telecommunications infrastructure (Ravi & West, 2015).
Radio spectrum refers to the parts of electromagnetic frequencies used for wireless transmission. Telecommunication networks in most of the countries were monopolistically operated by the government or a regulatory industry. However, of the late, the telecommunication deregulated decision in the late 1980s (Ravi & West, 2015) was instrumental in making the wireless services quite competitive and thus allowing faster deployment of communication infrastructure with competitive services. In view of this deregulation, multiple mobile service providers operate in the same region with their bounty of services. This gives a user the freedom to opt for their service providers and also the freedom of intact roaming across various heterogeneous networks that offer both real-time and non-real time services at competitive prices.
A cellular system consists of the number of cells, each with a base station. The base station houses the radio channels to serve the mobile users. Bandwidth is an enormously valued and limited resource. Therefore, effective bandwidth management plays an important role in determining the performance of a cellular network. Various studies indicate that radio channels are yet to be utilized efficiently (Jiang, Wang, & Leng, 2013). This necessitates an effective channel allocation scheme.
Under dynamic network condition, the algorithms for channel allocation are made adaptive in control decisions to strike a well-balanced network performance. These days, with remarkable growth in the number of mobile users, mobile networking technology needs an increasing range of services. However, in spite of the advent of high network infrastructure, wireless bandwidth is still an extremely valuable and scarce resource. Therefore, effective and efficient bandwidth management is very important and hence an active area of research. Though a number of models have been suggested for better channel utilization applying a chain of techniques such as heuristics, meta-heuristics, game theory, etc. still, there is a good possibility to improve the services for better radio spectrum utilization. To serve the users’ channel requests and to maximize the revenue of the service providers are the twin important objectives for a good channel allocation algorithm.
For effective channel allocation and to enhance the utility of the services among the mobile users, cooperation among service providers is warranted. In general, cooperation can be categorized into two categories: Spectrum based cooperation and Infrastructure based cooperation (P. Lin, Jia, Zhang, & Hamdi, 2010). In spectrum-based cooperation, the Wireless Service Providers (WSPs) share their spectrum to facilitate the customers e.g. China Mobile band (885-909 MHz) and the China Unicom band (909-915 MHz) (Lin et al., 2010). Sometimes, it is possible that the demand from the end users subscribed to one WSP cannot be fully satisfied by that WSP due to infrastructural limit or bad channel conditions. If a WSP has an agreement with some other overlapping WSP, the end users of the first WSP may access the services of the cooperating WSP. This allows the first WSP to offer its services uninterrupted while the second WSP may benefit by the revenue sharing besides an opportunity for the assistance. The work proposed, in this paper, addresses the problem of radio spectrum utilization considering two objectives: minimization in call block/drop and better revenue for the service providers.