Quality of Service Oriented Access Point Selection Framework for Large Wi-Fi Networks

This paper addresses the problem of Access Point (AP) selection in large Wi-Fi networks. Unlike current solutions that rely on Received Signal Strength (RSS) to determine the best AP that could serve a wireless user’s request, we propose a novel framework that considers the Quality of Service (QoS) requirements of the user’s data flow. The proposed framework relies on a function reflecting the suitability of a Wi-Fi AP to satisfy the QoS requirements of the data flow. The framework takes advantage of the flexibility and centralised nature of Software Defined Networking (SDN). A performance comparison of this algorithm developed through an SDN-based simulator shows significant achievements against other state of the art solutions in terms of provided QoS and improved wireless network capacity

A novel multipath-transmission supported software defined wireless network architecture

The inflexible management and operation of today\u27s wireless access networks cannot meet the increasingly growing specific requirements, such as high mobility and throughput, service differentiation, and high-level programmability. In this paper, we put forward a novel multipath-transmission supported software-defined wireless network architecture (MP-SDWN), with the aim of achieving seamless handover, throughput enhancement, and flow-level wireless transmission control as well as programmable interfaces. In particular, this research addresses the following issues: 1) for high mobility and throughput, multi-connection virtual access point is proposed to enable multiple transmission paths simultaneously over a set of access points for users and 2) wireless flow transmission rules and programmable interfaces are implemented into mac80211 subsystem to enable service differentiation and flow-level wireless transmission control. Moreover, the efficiency and flexibility of MP-SDWN are demonstrated in the performance evaluations conducted on a 802.11 based-testbed, and the experimental results show that compared to regular WiFi, our proposed MP-SDWN architecture achieves seamless handover and multifold throughput improvement, and supports flow-level wireless transmission control for different applications

A dynamic access point allocation algorithm for dense wireless LANs using potential game

This work introduces an innovative Access Point (AP) allocation algorithm for dense Wi-Fi networks, which relies on a centralised potential game developed in a Software-Defined Wireless Networking (SDWN)-based framework. The proposed strategy optimises the allocation of the Wi-Fi stations (STAs) to APs and allows their dynamic reallocation according to possible changes in the capacity of the Wi-Fi network. This paper illustrates the design of the proposed framework based on SDWN and the implementation of the potential game-based algorithm, which includes two possible strategies. The main novel contribution of this work is that the algorithm allows us to efficiently reallocate the STAs by considering external interference, which can negatively affect the capacities of the APs handled by the SDWN controller. Moreover, the paper provides a detailed performance analysis of the algorithm, which describes the significant improvements achieved with respect to the state of the art. Specifically, the results have been compared against the AP selection considered by the IEEE 802.11 standards and another centralised algorithm dealing with the same problem, in terms of the data bit rate provided to the STAs, their dissatisfaction and Quality of Experience (QoE). Finally, the paper analyses the trade-off between efficient performance and the computational complexity achieved by the strategies implemented in the proposed algorithm

QoS aware radio access technology selection framework in heterogeneous networks using SDN

This paper addresses the problem of radio access technology (RAT) selection in heterogeneous networks (HetNets). Current approaches rely on signal related metrics such as signal to interference plus noise ratio (SINR) for selection of the best network for the wireless user. However, such approaches do not take into account the quality of service (QoS) requirements of wireless users and therefore often do not connect them to the most suitable network. We propose a QoS aware RAT selection framework for HetNets based on software-defined networking (SDN). The proposed framework implements a RAT selection strategy that reflects QoS requirements of downlink flows using a metric called fittingness factor (FF). The framework relies on the flexibility and centralised nature of SDN to implement monitoring and RAT capacity assessment mechanisms that help in the realisation of the selection strategy. The simulation campaign illustrates the important gains achieved by our RAT selection framework in terms of data rates assigned to the wireless users, their satisfaction, and their quality of experience (QoE) compared against other state of the art RAT selection solutions

Controlled Matching Game for Resource Allocation and User Association in WLANs

In multi-rate IEEE 802.11 WLANs, the traditional user association based on the strongest received signal and the well known anomaly of the MAC protocol can lead to overloaded Access Points (APs), and poor or heterogeneous performance. Our goal is to propose an alternative game-theoretic approach for association. We model the joint resource allocation and user association as a matching game with complementarities and peer effects consisting of selfish players solely interested in their individual throughputs. Using recent game-theoretic results we first show that various resource sharing protocols actually fall in the scope of the set of stability-inducing resource allocation schemes. The game makes an extensive use of the Nash bargaining and some of its related properties that allow to control the incentives of the players. We show that the proposed mechanism can greatly improve the efficiency of 802.11 with heterogeneous nodes and reduce the negative impact of peer effects such as its MAC anomaly. The mechanism can be implemented as a virtual connectivity management layer to achieve efficient APs-user associations without modification of the MAC layer

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

Resource Allocation for Next Generation Radio Access Networks

Driven by data hungry applications, the architecture of mobile networks is moving towards that of densely deployed cells where each cell may use a different access technology as well as a different frequency band. Next generation networks (NGNs) are essentially identified by their dramatically increased data rates and their sustainable deployment. Motivated by these requirements, in this thesis we focus on (i) capacity maximisation, (ii) energy efficient configuration of different classes of radio access networks (RANs). To fairly allocate the available resources, we consider proportional fair rate allocations. We first consider capacity maximisation in co-channel 4G (LTE) networks, then we proceed to capacity maximisation in mixed LTE (including licensed LTE small cells) and 802.11 (WiFi) networks. And finally we study energy efficient capacity maximisation of dense 3G/4G co-channel small cell networks. In each chapter we provide a network model and a scalable resource allocation approach which may be implemented in a centralised or distributed manner depending on the objective and network constraints