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

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Aplicação de técnicas de aprendizado por reforço à alocação de recursos e ao escalonamento de usuários em sistemas de telecomunicações

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2014.A adaptação de enlace e o escalonamento de usuários são aspectos cruciais dos atuais sistemas de comunicação devido à demanda por alta eficiência espectral, de forma a se obter a maior vazão possível com base nos recursos espectrais disponíveis, e à grande variedade de aplicações de usuário, cada uma com diferentes requisitos de qualidade de serviço. A implantação tanto da adaptação de enlace quanto de algoritmos de seleção e escalonamento de usuários impõe certos desafios, pois as soluções atualmente utilizadas consideram modelos idealizados de terminais de transmissão e de recepção, bem como um canal de comunicação de natureza invariante explicações cujas exigências são imutáveis.Nesse contexto, técnicas de aprendizado de máquina podem ser utilizadas como uma forma de superar as limitações impostas pelas técnicas tradicionais de modelagem e solução analítica dos problemas supracitados. Este trabalho apresenta como primeira contribuição uma solução para o problema de adaptação de enlace por modulação e codificação adaptativas em sistemas multiportadora utilizando técnicas de aprendizado por reforço por estados contínuos. Comosegunda contribuição, ainda com respeito à adaptação de enlace, o trabalho propõe a utilização do aprendizado por reforço para a solução do problema de bit loading em sistemas multiportadora.Como terceira contribuição, o trabalho propõe um algoritmo de seleção e escalonamento deusuários baseado na estratégia de aprendizado por reforço multi-objetivo, como uma forma delidar com os diferentes requisitos de qualidade de serviço que são impostos pela heterogeneidadedas aplicações que trafegam nas redes de comunicação atuais. Em particular, é considerado o problema de escalonamento de tráfego sensível ao atraso. Resultados de simulação mostram que as soluções propostas, baseadas em aprendizado por reforço, são capazes de explorar a variabilidade do meio de transmissão, de forma a suplantar as perdas que são introduzidas pela modelagem idealizada dos terminais de comunicação. _______________________________________________________________________________________ ABSTRACTLink adaptation and scheduling are crucial aspects of communication systems since highspectral efficiency is required in order to obtain the highest throughput given the availablespectrum resources and base stations should be able to service a wide range of quality of servicerequirements.In this context, machine learning techniques can be used as a way to overcome thelimitations imposed by traditional modeling techniques of the aforementioned problems. The firstcontribution of this thesis is to propose a solution to the problem of link adaptation for adaptivemodulation and coding in multicarrier systems using a continuous-state reinforcement learningapproach. As a second contribution, this thesis presents a solution to the bit loading problem inmulticarrier systems by means of reinforcement learning.As a third contribution, an algorithm for user selection and scheduling based on multiobjectivereinforcement is proposed. In particular, the scheduling of delay-sensitive traffic isconsidered. Simulation results show that the proposed solutions, based on reinforcement learning,are able to exploit the variability of the transmission medium and overcome the losses that areintroduced by idealized models of communication terminals and the communication channel

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