Efficient Resource Allocation Algorithms for 4G-LTE Networks Open Access
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The growing demand for mobile applications, such as voice telephony, web browsing, interactive gaming and video streaming with delay and bandwidth re- quirements, poses new challenges in the design of the future generation cellular networks. As a response to this need, 3rd Generation Partnership Project (3GPP) introduced the Long Term Evolution (LTE) . LTE defines all-IP architectures for the radio access and the core networks and aims at ambitious performance goals.With radio resource management in LTE air interface being the focus this dissertation is divided into three parts. In the first part, a channel-aware scheduler is designed for commercial LTE networks in which fairness and system throughput challenges were addressed. The convergence proof of the algorithm and simulation results in terms of throughput, fairness, and delays are presented. The second part outlines the challenges of scheduling packets in LTE based public safety broadband networks (PSBNs), which are private networks with very strict performance constraints. The algorithm proposed in this section focuses on quality of service (QoS) and priority provisioning. Simulation results of different deployment scenarios are presented to support our claims. It is believed that, this is the first scheduler that considers all the QoS parameters, namely packet delay budget (PDB), packet error loss rate (PELR), guaranteed bit rate (GBR) and priority. It is also the first algorithm specifically designed for PSBN. In the third section, both a channel and QoS aware scheduler is proposed, which is based on the algorithm described in the first part. The algorithm outperforms the state-of-art scheduler in terms of QoS provisioning.The contributions of the research are QoS and priority provisioning and enhancement in fairness and spectral efficiency in the area of radio resource management. Key challenge is trade-offs between conflicting performance parameters. The proposed solutions are novel and results illustrate that they effectively contribute in addressing the challenges caused by high demand, QoS requirements, and channel conditions.