Qos-aware memetic-based optimal cross-layer resource allocation in mixed lte networks

Abstract

With the recent advancements and due to the rapid growth of LTE networks, Machine Type Communication (MTC) plays a vital role in the characterization of Internet of Things (IOT).Human-to-Human (H2H) communication and MTC are the two different types of communication handled by LTE-A networks. Due to the co-existence of H2H communication and MTC in LTE-A networks, a serious challenge may arise for scheduling critical MTC with H2H communication networks. To maintain the Quality of Service (QoS) requirements for H2H communication and to provide data traffic for MTC networks LTE networks faces a serious challenge for allocating the resources blocks to the users. In this paper we propose a resource allocation algorithm for optimizing the problems faced by critical MTC and H2H communication networks by maintaining the QoS requirements from a cross-layer design perspective. A novel cross layer memtic based resource allocation algorithm is presented in this paper by investigating the resource allocation problem for different combinations of Channel Quality Indicator (CQI) modes for critical MTCDs and H2H UEs. The Performance and computational complexity of the proposed algorithm in different cases of CQI is measured in terms of cell throughput and probability of delay bound violation (PBDV) is analyzed and the simulations results shows that the proposed system is more efficient compared to other resource allocation algorithms.