QoS-aware radio resource allocation for ultra-dense heterogeneous networks

Recently, the heterogeneous deployment of ultra-dense small cells such as femtocells overlaid on the existing macrocells, which is referred to as ultra-dense heterogeneous networks (HetNets) is regarded as a cost-effective solution to the problem of network capacity and indoor coverage in the fifth generation (5G) mobile networks. Nonetheless, the main challenges associated with such solution are severe interference and inadequate quality-of-service (QoS) provisioning, due to the unplanned and ultra-dense deployment of femtocells and different users with diverse QoS requirements. Hence, efficient radio resource allocation (RRA) algorithms with self-organizing capabilities are crucial for interference mitigation and QoS provisioning. The majority of the existing RRA algorithms in the literature do not effectively tackle these challenges. Additionally, most of these algorithms are not scalable and so, not efficient for ultra-dense Hetnets. Motivated by these limitations, in this paper, a QoS-aware RRA (QRRA) algorithm based on cognitive radio technology with consideration for imperfect spectrum sensing and users with heterogeneous services is proposed. The QRRA problem is formulated as a mixed integer programming problem, which is solved using the Lagrangian dual decomposition (LDD) method. The evaluation of the performance of the proposed algorithm is carried out using MATLAB. Simulation results show that the proposed algorithm minimizes interference and satisfies the QoS of users in terms of throughput with fast convergence as compared with existing RRA algorithms.