Adaptive radio resource allocation algorithm for user satisfaction maximization in multiple services wireless networks

Abstract

The enriched service scope, the steep increase in mobile traffic volume, and the ever increasing number of connected devices in mobile networks coupled with the scarcity of electromagnetic spectrum have raised the importance of designing flexible and ingenious means to guarantee high user satisfaction levels. Therefore, in order to capture and maintain a representative share of the wireless communication market, effective ways to manage the scarce physical resources of cellular networks are fundamental for cellular network operators. The Radio Resource Allo- cation (RRA) algorithms are responsible for performing such a relevant and arduous task. The efficiency of such algorithms is essential so that there exists a fair resource allocation among users and the Quality of Service (QoS) requirements of each individual user are met, thus guar- anteeing high user satisfaction levels. The recent scenarios of cellular networks are composed of a wide range of available services for mobile users, which demand conflicting QoS requirements. In order to achieve the objective of user satisfaction maximization in such networks, we formulate a utility-based cross-layer opti- mization problem targeted at maximizing the user satisfaction in multi-service cellular networks. The optimal solution of the proposed problem is very hard to be found. Thus, we mathematically manipulate the problem and derive a low complexity suboptimal solution from which we design an adaptive RRA technique. Our technique is composed of user weights and an innovative ser- vice weight that is adapted to meet the satisfaction target of the most prioritized service chosen by the network operator. Furthermore, the proposed algorithm is scalable to several classes of service and can be employed in the current and future generations of wireless systems. The performance evaluation of the proposed algorithm was conducted by means of system-level simulations in various scenarios. The evaluation was performed considering different multi- service scenarios. Then, the performance was evaluated considering imperfect Channel State Information (CSI) estimation at the transmitter. Significant gains in the overall system capacity were obtained in comparison with four benchmarking algorithms from the literature, demon- strating that the adaptability and service prioritization of the proposed algorithm are effective towards the objective of simultaneously maximizing the user satisfaction for multiple services.