Customized Packet Scheduling Algorithm for LTE Network

Advanced mobile networks are expected to provide omnipresent broadband access to a continuously growing number of mobile users. LTE system represents 4G mobile network. The key feature thereof is the adoption of advanced Radio Resource Management procedures in order to increase the system performance up to Shannon’s limit. Packet scheduling mechanisms, in particular, play a fundamental role, because they are responsible for choosing, with fine time and frequency resolutions, how to distribute scarce radio resources among different mobile stations, taking into account channel conditions and QoS requirements. This objective should be accomplished by providing an optimal trade-off between spectral efficiency and fairness. In this context, this paper proposes customized packet scheduling algorithm designed to adaptively alter scheduling schemes considering multiple input variables in order to maximize spectral efficiency as well as overall system performance

Packet scheduling in satellite LTE networks employing MIMO technology.

Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2014.Rapid growth in the number of mobile users and ongoing demand for different types of telecommunication services from mobile networks, have driven the need for new technologies that provide high data rates and satisfy their respective Quality of Service (QoS) requirements, irrespective of their location. The satellite component will play a vital role in these new technologies, since the terrestrial component is not able to provide global coverage due to economic and technical limitations. This has led to the emergence of Satellite Long Term Evolution (LTE) networks which employ Multiple-In Multiple-Out (MIMO) technology. In order to achieve the set QoS targets, required data rates and fairness among various users with different traffic demands in the satellite LTE network, it is crucial to design an effective scheduling and a sub-channel allocation scheme that will provide an optimal balance of all these requirements. It is against this background that this study investigates packet scheduling in satellite LTE networks employing MIMO technology. One of the main foci of this study is to propose new cross-layer based packet scheduling schemes, tagged Queue Aware Fair (QAF) and Channel Based Queue Sensitive (CBQS) scheduling schemes. The proposed schemes are designed to improve both fairness and network throughput without compromising users’ QoS demands, as they provide a good trade-off between throughput, QoS demands and fairness. They also improve the performance of the network in comparison with other scheduling schemes. The comparison is determined through simulations. Due to the fact that recent schedulers provide a trade-off among major performance indices, a new performance index to evaluate the overall performance of each scheduler is derived. This index is tagged the Scheduling Performance Metric (SPM). The study also investigates the impact of the long propagation delay and different effective isotropic radiated powers on the performance of the satellite LTE network. The results show that both have a significant impact on network performance. In order to actualize an optimal scheduling scheme for the satellite LTE network, the scheduling problem is formulated as an optimization function and an optimal solution is obtained using Karush-Kuhn-Tucker multipliers. The obtained Near Optimal Scheduling Scheme (NOSS), whose aim is to maximize the network throughput without compromising users’ QoS demands and fairness, provides better throughput and spectral efficiency performance than other schedulers. The comparison is determined through simulations. Based on the new SPM, the proposed NOSS1 and NOSS2 outperform other schedulers. A stability analysis is also presented to determine whether or not the proposed scheduler will provide a stable network. A fluid limit technique is used for the stability analysis. Finally, a sub-channel allocation scheme is proposed, with the aim of providing a better sub-channel or Physical Resource Block (PRB) allocation method, tagged the Utility Auction Based (UAB) subchannel allocation scheme that will improve the system performance of the satellite LTE network. The results show that the proposed method performs better than the other scheme. The comparison is obtained through simulations

Sustainable scheduling policies for radio access networks based on LTE technology

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyIn the LTE access networks, the Radio Resource Management (RRM) is one of the most important modules which is responsible for handling the overall management of radio resources. The packet scheduler is a particular sub-module which assigns the existing radio resources to each user in order to deliver the requested services in the most efficient manner. Data packets are scheduled dynamically at every Transmission Time Interval (TTI), a time window used to take the user’s requests and to respond them accordingly. The scheduling procedure is conducted by using scheduling rules which select different users to be scheduled at each TTI based on some priority metrics. Various scheduling rules exist and they behave differently by balancing the scheduler performance in the direction imposed by one of the following objectives: increasing the system throughput, maintaining the user fairness, respecting the Guaranteed Bit Rate (GBR), Head of Line (HoL) packet delay, packet loss rate and queue stability requirements. Most of the static scheduling rules follow the sequential multi-objective optimization in the sense that when the first targeted objective is satisfied, then other objectives can be prioritized. When the targeted scheduling objective(s) can be satisfied at each TTI, the LTE scheduler is considered to be optimal or feasible. So, the scheduling performance depends on the exploited rule being focused on particular objectives. This study aims to increase the percentage of feasible TTIs for a given downlink transmission by applying a mixture of scheduling rules instead of using one discipline adopted across the entire scheduling session. Two types of optimization problems are proposed in this sense: Dynamic Scheduling Rule based Sequential Multi-Objective Optimization (DSR-SMOO) when the applied scheduling rules address the same objective and Dynamic Scheduling Rule based Concurrent Multi-Objective Optimization (DSR-CMOO) if the pool of rules addresses different scheduling objectives. The best way of solving such complex optimization problems is to adapt and to refine scheduling policies which are able to call different rules at each TTI based on the best matching scheduler conditions (states). The idea is to develop a set of non-linear functions which maps the scheduler state at each TTI in optimal distribution probabilities of selecting the best scheduling rule. Due to the multi-dimensional and continuous characteristics of the scheduler state space, the scheduling functions should be approximated. Moreover, the function approximations are learned through the interaction with the RRM environment. The Reinforcement Learning (RL) algorithms are used in this sense in order to evaluate and to refine the scheduling policies for the considered DSR-SMOO/CMOO optimization problems. The neural networks are used to train the non-linear mapping functions based on the interaction among the intelligent controller, the LTE packet scheduler and the RRM environment. In order to enhance the convergence in the feasible state and to reduce the scheduler state space dimension, meta-heuristic approaches are used for the channel statement aggregation. Simulation results show that the proposed aggregation scheme is able to outperform other heuristic methods. When the aggregation scheme of the channel statements is exploited, the proposed DSR-SMOO/CMOO problems focusing on different objectives which are solved by using various RL approaches are able to: increase the mean percentage of feasible TTIs, minimize the number of TTIs when the RL approaches punish the actions taken TTI-by-TTI, and minimize the variation of the performance indicators when different simulations are launched in parallel. This way, the obtained scheduling policies being focused on the multi-objective criteria are sustainable. Keywords: LTE, packet scheduling, scheduling rules, multi-objective optimization, reinforcement learning, channel, aggregation, scheduling policies, sustainable