Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Optimizing ad-hoc on-demand distance vector (AODV) routing protocol using geographical location data

This thesis summarizes the body of research regarding location-aided routing protocols for mobile ad-hoc networks (MANET). This study focuses on the use of geographical location information to reduce the control traffic overhead caused by the route discovery process in the ad-hoc on-demand distance vector (AODV) routing protocol. During this process, AODV will flood the entire network with route request packets. This introduces significant packet-handling overhead into the network. This thesis introduces Geographical AODV (GeoAODV), which uses geographical location information to limit the search area during the route discovery process to include only promising search paths. Also, this thesis benchmarks GeoAODV\u27s performance against Location Aided Routing (LAR) and examines four mechanisms for reducing the control-packet overhead introduced by the route discovery process: LAR Distance, LAR Zone, GeoAODV, and GeoAODV Rotate. OPNET Modeler version 16.0 was used to implement each of these mechanisms and compare their performance via network simulations. The results indicate that location-aided routing can significantly reduce the aforementioned control-packet overhead

Throughput Efficient AODV for Improving QoS Routing in Energy Aware Mobile Adhoc Network

Mobile Ad hoc Networks (MANETs) is a type of wireless network that is made up of mobile nodes which coordinate themselves without the help of a central coordinator. The network topology changes as nodes are mobile. One of the major challenges of MANET is limited bandwidth which tends to mitigate the Quality of Service (QoS) of the network as users are not satisfied.  A variety of routing protocols has been employed aiming at improving the throughput of the network in order to meet user demands. This paper proposes the development of a throughput efficient Ad-hoc On demand Distance Vector (TE-AODV) routing protocol targeted towards improving the QoS of MANET by mitigating network overhead. In this work, all nodes are assumed to be transmitting while calculating their Instant Processing State (IPS) using the concept of knapsack problem. A threshold value for node IPS is set and any node below the set threshold value is not considered during data transmission. An improved Location Aided Routing (iLAR) is used for route search process which helped in reducing network overhead. Results from simulation showed that TE-AODV has improved the throughput of energy aware Ad-hoc On demand Distance Vector (E-AODV) routing protocol. TE-AODV improved the network throughput by 2.9% as a function of simulation time and 3.7% as a function of mobility of node over the E-AODV routing protocol

Multi-layer Utilization of Beamforming in Millimeter Wave MIMO Systems

mmWave frequencies ranging between (30-300GHz) have been considered the perfect solution to the scarcity of bandwidth in the traditional sub-6GHz band and to the ever increasing demand of many emerging applications in today\u27s era. 5G and beyond standards are all considering the mmWave as an essential part of there networks. Beamforming is one of the most important enabling technologies for the mmWave to compensate for the huge propagation lose of these frequencies compared to the sub-6GHz frequencies and to ensure better spatial and spectral utilization of the mmWave channel space. In this work, we tried to develop different techniques to improve the performance of the systems that use mmWave. In the physical layer, we suggested several hybrid beamforming architectures that both are relatively simple and spectrally efficient by achieving fully digital like spectral efficiency (bits/sec/Hz). For the mobility management, we derived the expected degradation that can affect the performance of a special type of beamforming that is called the Random Beamforming (RBF) and optimized the tunable parameters for such systems when working in different environments. Finally, in the networking layer, we first studied the effect of using mmWave frequencies on the routing performance comparing to the performance achieved when using sub-6 GHz frequencies. Then we developed a novel opportunistic routing protocol for Mobile Ad-Hoc Networks (MANET) that uses a modified version of the Random Beamforming (RBF) to achieve better end to end performance and to reduce the overall delay in delivering data from transmitting nodes to the intended receiving nodes. From all these designs and studies, we conclude that mmWave frequencies and their enabling technologies (i.e. Beamforming, massive MIMO, ...etc.) are indeed the future of wireless communicatons in a high demanding world of Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), and self driving cars

IEEHR: Improved Energy Efficient Honeycomb based Routing in MANET for Improving Network Performance and Longevity

In present scenario, efficient energy conservation has been the greatest focus in Mobile Adhoc Networks (MANETs). Typically, the energy consumption rate of dense networks is to be reduced by proper topological management. Honeycomb based model is an efficient parallel computing technique, which can manage the topological structures in a promising manner.  Moreover, discovering optimal routes in MANET is the most significant task, to be considered with energy efficiency. With that motive, this paper presents a model called Improved Energy Efficient Honeycomb based Routing (IEEHR) in MANET. The model combines the Honeycomb based area coverage with Location-Aided Routing (LAR), thereby reducing the broadcasting range during the process of path finding. In addition to optimal routing, energy has to be effectively utilized in MANET, since the mobile nodes have energy constraints. When the energy is effectively consumed in a network, the network performance and the network longevity will be increased in respective manner. Here, more amount of energy is preserved during the sleeping state of the mobile nodes, which are further consumed during the process of optimal routing. The designed model has been implemented and analyzed with NS-2 Network Simulator based on the performance factors such as Energy Efficiency, Transmission Delay, Packet Delivery Ratio and Network Lifetime