An Investigation and Simulation of Novel Dynamic Routing Methods

Routing in networks is a multi-objective and multi-constraint optimization problem due to the nature of current networks being highly dynamic environments. Currently implemented solutions are single metric solutions where instead an optimal multi-metric solution is needed to solve this problem. This research work investigates novel multi-metric solutions to this optimization problem. Recently, it is found that the employment of a natural optimization process called the ant colony optimization process to the routing problem, resulted in a multi-metric dynamic solution. Latest research work reported two slightly different implementations of this employment. Network agents are used to sensor the status of the network and feedback the network nodes with the necessary information. This is used to update its routing tables based on the network status. These two implementations differ in the method (philosophy) used to update the information in the routing tables held by the network nodes. This research work suggests a new method to update the routing tables held by the nodes in the network. This done by merging modified versions of the previous two methods in order to overcome the disadvantages of each. A discrete event simulation system is built to test the routing method suggested by this research work together with the previous two routing methods for companson purposes. This simulation system represents a prototype for the development of a general network simulation tool. It is capable of collecting various types of simulation statistical data and generating tracing files for detailed studies of the network and for testing purposes. An expandable structured C-pointer based implementation is used to code the system. The system is tested on various networks and the results of the simulation show improvements on the performance of the network by reducing the overall delay in the network and increasing throughput. Moreover, the use of the suggested routing method results in balancing the load in the network

Energy and Throughput Optimization for Relay Based Heterogeneous Networks

International audienceHigh data rate is a challenge for the next generation cellular networks. This objective needs a significant densification of relay nodes within macro cell. In the LTE-Advanced network, multi-hop relaying has been taken as a promising key technique to provide high throughput to the users and to improve the area coverage. Besides, minimizing the energy consumption and electromagnetic pollution is an economic challenge for the operators. This paper is focused on relay based heterogeneous cellular network like LTE-Advanced. We investigate the problem of throughput and energy consumption optimization. Our contributions are two-fold. First, we develop an optimization tool to calculate an optimal offline configuration of heterogeneous cellular network that maximizes the network capacity with low energy consumption. Second, we highlight a significant gain due to the deployment of relay nodes and we investigate the energy- capacity tradeoff

Cellular network capacity and coverage enhancement with MDT data and Deep Reinforcement Learning

Recent years witnessed a remarkable increase in the availability of data and computing resources in comm-unication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent's sample efficiency, stability and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-first search in terms of long-term reward and sample efficiency. Our results indicate that MDT -driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks

Probabilistic stability of traffic load balancing on wireless complex networks

Load balancing between adjacent base stations (BSs) is important for balancing load distributions and improving service provisioning. While load balancing between any given pair of BSs is beneficial, cascade load sharing can cause network-level instability that is hard to predict. The relationship between each BS's load balancing dynamics and the network topology is not understood. In this seminal work on stability analysis, we consider a frequency reuse network with no interference, whereby load balancing dynamics does not perturb the individual cells' capacity. Our novelty is to show an exact analytical and also a probabilistic relationship for stability, relating generalized local load balancing dynamics with a generalized network topology, as well as the uncertainty we have in load balancing parameters due to noisy channel or network sensing. We prove that the stability analysis is valid for any generalized load balancing dynamics and topological cell deployment, and we believe this general relationship can inform the joint design of both the load balancing dynamics and the neighbor list of the network. The probabilistic framework provides uncertainty quantification and stability prediction for Digital Twins of wireless infrastructur

Medium Access Control Protocols for Ad-Hoc Wireless Networks: A Survey

Studies of ad hoc wireless networks are a relatively new field gaining more popularity for various new applications. In these networks, the Medium Access Control (MAC) protocols are responsible for coordinating the access from active nodes. These protocols are of significant importance since the wireless communication channel is inherently prone to errors and unique problems such as the hidden-terminal problem, the exposed-terminal problem, and signal fading effects. Although a lot of research has been conducted on MAC protocols, the various issues involved have mostly been presented in isolation of each other. We therefore make an attempt to present a comprehensive survey of major schemes, integrating various related issues and challenges with a view to providing a big-picture outlook to this vast area. We present a classification of MAC protocols and their brief description, based on their operating principles and underlying features. In conclusion, we present a brief summary of key ideas and a general direction for future work