Ant colony learning method for joint MCS and resource block allocation in LTE Femtocell downlink for multimedia applications with QoS guarantees

With the emergence of bandwidth-intensive online mobile multimedia applications in wireless networks, in order to make mobile users enjoy better Quality of Service (QoS) under the conditions of limited resources, efficient radio spectrum resource allocation schemes are always desirable. This paper addresses the problem of joint Resource Block (RB) allocation and Modulation-and-Coding Scheme (MCS) selection in LTE femtocell DownLink (DL) for mobile multimedia applications. We first formulate the problem as an Integer Linear Program (ILP) whose objective is to minimize the number of allocated RBs of a closed femtocell, while guaranteeing minimum throughput for each user. In view of the NP-hardness of the ILP, we then propose an intelligent optimization learning algorithm called ACO-HM algorithm with reduced polynomial time complexity. The Ant Colony Optimization (ACO) learning algorithm exhibits better performance in machine learning and supports parallel search for the RB allocation, while the Harmonic Mean (HM) method is to select a more appropriate MCS than the MINimum/MAXimum MCS selection schemes (MIN/MAX). Simulation results show that compared with the ACO-MIN algorithm and the ACO-MAX algorithm, the proposed ACO-HM learning algorithm achieves better performance with fewer RBs and better QoS guarantees.