Performance comparison of packet scheduling algorithms in LTE-A HetNets

© 2016 IEEE. Performance comparison of various Packet Scheduling (PS) algorithms such as Proportional Fair (PF), Maximum Largest Weighted Delay First (MLWDF) and Exponential/Proportional Fair (EXP/PF) has been studied in HetNets environment. The performance indicators such as throughput, Packet Loss Ratio (PLR), delay and fairness are considered to judge the performance of the scheduling algorithms. Various strategies such as increasing the number of pico cells in the cell edge were used in the simulation for the performance evaluation study. The results achieved by various simulations show that adding the pico cells to the existing macros enhances the overall system performance in addition to various scheduling algorithms implemented in macros. Simulation results show that the overall system gain has increased by adding picos, provide better coverage in the cell edge and increase the capacity of the network to provide better Quality of Service (QoS). Furthermore, simulations show that MLWDF performs better for video traffic than compared to other with PS algorithms

Macro with Pico Cells (HetNets) System Behavior Using Well-known scheduling Algorithms

This paper demonstrates the concept of using Heterogeneous networks (HetNets) to improve Long Term Evolution (LTE) system by introducing the LTE Advance (LTE-A). The type of HetNets that has been chosen for this study is Macro with Pico cells. Comparing the system performance with and without Pico cells has clearly illustrated using three well-known scheduling algorithms (Proportional Fair PF, Maximum Largest Weighted Delay First MLWDF and Exponential/Proportional Fair EXP/PF). The system is judged based on throughput, Packet Loss Ratio PLR, delay and fairness.A simulation platform called LTE-Sim has been used to collect the data and produce the paper outcomes and graphs. The results prove that adding Pico cells enhances the overall system performance. From the simulation outcomes, the overall system performance is as follows: throughput is duplicated or tripled based on the number of users, the PLR is almost quartered, the delay is nearly reduced ten times (PF case) and changed to be a half (MLWDF/EXP cases), and the fairness stays closer to value of 1. It is considered an efficient and cost effective way to increase the throughput, coverage and reduce the latency

A study of channel and delay-based scheduling algorithms for live video streaming in the fifth generation long term evolution-advanced network

This paper investigates the performance of a number of channel and delay-based scheduling algorithms for an efficient QoS (Quality of Service) provision with more live video streaming users over the Fifth Generation Long-Term Evolution-Advanced (5G LTE-A) network. These algorithms were developed for use in legacy wireless networks and minor changes were made to enable these algorithms to perform packet scheduling in the downlink 5G LTE-A. The efficacies of the EXP and M-LWDF algorithms in maximizing the number of live video streaming users at the desired transmission reliability, minimizing the average network delay and maximizing network throughput, are shown via simulations. As the M-LWDF has a simpler mathematical equation as compared to the EXP, it is more favoured for implementation in the complex downlink 5G LTE-A

Planificador de Paquetes del enlace de bajada de LTE basado en Evolución Diferencial

This paper presents a LTE Downlink Packet Scheduler, which aims to intelligently allocate radio resources to mobile stations in an access network, supported by an evolutionary algorithm. The proposed scheduler was compared with four reference algorithms, the results show that the scheduler based in differential evolution, allocates the radio resources effectively reaching suitable values of fairness index and throughputEste  artículo presenta un Planificador de Paquetes en el Enlace de Bajada de LTE que tiene como objetivo la asignación inteligente de recursos radio a estaciones móviles en la red de acceso, soportado por un algoritmo evolutivo.  El planificador propuesto fue comparado con cuatro algoritmos de referencia, los resultados muestran que el planificador basado en evolución diferencial, asigna los recursos radio eficazmente alcanzando valores adecuados de índice de justicia y throughput

Planificador de Paquetes del enlace de bajada de LTE basado en Evolución Diferencial

This paper presents a LTE Downlink Packet Scheduler, which aims to intelligently allocate radio resources to mobile stations in an access network, supported by an evolutionary algorithm. The proposed scheduler was compared with four reference algorithms, the results show that the scheduler based in differential evolution, allocates the radio resources effectively reaching suitable values of fairness index and throughputEste  artículo presenta un Planificador de Paquetes en el Enlace de Bajada de LTE que tiene como objetivo la asignación inteligente de recursos radio a estaciones móviles en la red de acceso, soportado por un algoritmo evolutivo.  El planificador propuesto fue comparado con cuatro algoritmos de referencia, los resultados muestran que el planificador basado en evolución diferencial, asigna los recursos radio eficazmente alcanzando valores adecuados de índice de justicia y throughput

Packet Scheduling Algorithms in LTE/LTE-A cellular Networks: Multi-agent Q-learning Approach

Spectrum utilization is vital for mobile operators. It ensures an efficient use of spectrum bands, especially when obtaining their license is highly expensive. Long Term Evolution (LTE), and LTE-Advanced (LTE-A) spectrum bands license were auctioned by the Federal Communication Commission (FCC) to mobile operators with hundreds of millions of dollars. In the first part of this dissertation, we study, analyze, and compare the QoS performance of QoS-aware/Channel-aware packet scheduling algorithms while using CA over LTE, and LTE-A heterogeneous cellular networks. This included a detailed study of the LTE/LTE-A cellular network and its features, and the modification of an open source LTE simulator in order to perform these QoS performance tests. In the second part of this dissertation, we aim to solve spectrum underutilization by proposing, implementing, and testing two novel multi-agent Q-learning-based packet scheduling algorithms for LTE cellular network. The Collaborative Competitive scheduling algorithm, and the Competitive Competitive scheduling algorithm. These algorithms schedule licensed users over the available radio resources and un-licensed users over spectrum holes. In conclusion, our results show that the spectrum band could be utilized by deploying efficient packet scheduling algorithms for licensed users, and can be further utilized by allowing unlicensed users to be scheduled on spectrum holes whenever they occur

A packet scheduling algorithm using traffic policing in LTE downlink networks

Orientador: Lee Luan LingDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Neste trabalho, melhoramos o desempenho dos tradicionais algoritmos de escalonamento de pacotes na rede LTE (Long-Term Evolution) para aplicações de serviços multimídia, usando mecanismos de policiamento de tráfego conhecidas como reguladores de balde furado (do inglês, Leaky bucket). Busca-se atingir a equidade entre classes de serviços, controlando as taxas de chegada de pacotes nas filas de transmissão do escalonador. O cenário de simulação considera múltiplos usuários movimentando-se aleatoriamente a duas velocidades diferentes envolvendo os fluxos de tráfego de vídeo e VoIP. A avaliação de desempenho foi realizada em termos de parâmetros de qualidade de serviço, como atraso de pacotes, taxa de perda de pacotes e vazão média para tráfego de vídeo e VoIP. Os resultados da simulação confirmam que os escalonadores com tráfego de entrada policiado fornecem melhor desempenho para serviços em tempo real, especialmente aqueles que envolvem tráfego de vídeoAbstract: In this work, we improve the performance of traditional packet-scheduling algorithms in Long-Term Evolution (LTE) for multimedia service applications, using traffic policing mechanisms known as leaky bucket regulation. It seeks to achieve fairness between classes of services, controlling the arrival rates of packets in the transmission queues of the scheduler. The simulation scenario considers multiple users randomly moving at two different speeds using video and VoIP traffic flows. The performance evaluation was performed in terms of quality of service parameters, such as packet delay, packet loss rate and average throughput for video and VoIP traffic. Simulation results confirm that schedulers with polled input traffic provide better performance for realtime services, especially those involving video trafficMestradoTelecomunicações e TelemáticaMestra em Engenharia Elétric

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