Access network selection schemes for multiple calls in next generation wireless networks

There is an increasing demand for internet services by mobile subscribers over the wireless access networks, with limited radio resources and capacity constraints. A viable solution to this capacity crunch is the deployment of heterogeneous networks. However, in this wireless environment, the choice of the most appropriate Radio Access Technology (RAT) that can Tsustain or meet the quality of service (QoS) requirements of users' applications require careful planning and cost efficient radio resource management methods. Previous research works on access network selection have focused on selecting a suitable RAT for a user's single call request. With the present request for multiple calls over wireless access networks, where each call has different QoS requirements and the available networks exhibit dynamic channel conditions, the choice of a suitable RAT capable of providing the "Always Best Connected" (ABC) experience for the user becomes a challenge. In this thesis, the problem of selecting the suitable RAT that is capable of meeting the QoS requirements for multiple call requests by mobile users in access networks is investigated. In addressing this problem, we proposed the use of Complex PRoprtional ASsesment (COPRAS) and Consensus-based Multi-Attribute Group Decision Making (MAGDM) techniques as novel and viable RAT selection methods for a grouped-multiple call. The performance of the proposed COPRAS multi-attribute decision making approach to RAT selection for a grouped-call has been evaluated through simulations in different network scenarios. The results show that the COPRAS method, which is simple and flexible, is more efficient in the selection of appropriate RAT for group multiple calls. The COPRAS method reduces handoff frequency and is computationally inexpensive when compared with other methods such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW) and Multiplicative Exponent Weighting (MEW). The application of the proposed consensus-based algorithm in the selection of a suitable RAT for group-multiple calls, comprising of voice, video-streaming, and file-downloading has been intensively investigated. This algorithm aggregates the QoS requirement of the individual application into a collective QoS for the group calls. This new and novel approach to RAT selection for a grouped-call measures and compares the consensus degree of the collective solution and individual solution against a predefined threshold value. Using the methods of coincidence among preferences and coincidence among solutions with a predefined consensus threshold of 0.9, we evaluated the performance of the consensus-based RAT selection scheme through simulations under different network scenarios. The obtained results show that both methods of coincidences have the capability to select the most suitable RAT for a group of multiple calls. However, the method of coincidence among solutions achieves better results in terms of accuracy, it is less complex and the number of iteration before achieving the predefined consensus threshold is reduced. A utility-based RAT selection method for parallel traffic-streaming in an overlapped heterogeneous wireless network has also been developed. The RAT selection method was modeled with constraints on terminal battery power, service cost and network congestion to select a specified number of RATs that optimizes the terminal interface utility. The results obtained show an optimum RAT selection strategy that maximizes the terminal utility and selects the best RAT combinations for user's parallel-streaming for voice, video and file-download

Efficient radio resource management in next generation wireless networks

The current decade has witnessed a phenomenal growth in mobile wireless communication networks and subscribers. In 2015, mobile wireless devices and connections were reported to have grown to about 7.9 billion, exceeding human population. The explosive growth in mobile wireless communication network subscribers has created a huge demand for wireless network capacity, ubiquitous wireless network coverage, and enhanced Quality of Service (QoS). These demands have led to several challenging problems for wireless communication networks operators and designers. The Next Generation Wireless Networks (NGWNs) will support high mobility communications, such as communication in high-speed rails. Mobile users in such high mobility environment demand reliable QoS, however, such users are plagued with a poor signal-tonoise ratio, due to the high vehicular penetration loss, increased transmission outage and handover information overhead, leading to poor QoS provisioning for the networks' mobile users. Providing a reliable QoS for high mobility users remains one of the unique challenges for NGWNs. The increased wireless network capacity and coverage of NGWNs means that mobile communication users at the cell-edge should have enhanced network performance. However, due to path loss (path attenuation), interference, and radio background noise, mobile communication users at the cell-edge can experience relatively poor transmission channel qualities and subsequently forced to transmit at a low bit transmission rate, even when the wireless communication networks can support high bit transmission rate. Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed. The performance of proposed ATMA CAC scheme is investigated and compare it with the traditional CAC scheme. The ATMA scheme exploits the mobility events in the highspeed mobility communication environment and the calls (new and handoff calls) generation pattern to enhance the QoS (new call blocking and handoff call dropping probabilities) of the mobile users. The numbers of new and handoff calls in wireless communication networks are dynamic random processes that can be effectively modeled by the Continuous Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed

An Overview of Multi-Attribute Decision Making (MADM) Vertical Handover Using Systematic Mapping

The evolution of infotainment industries yet the advancement of cellular gadgets such as smartphones, tablets, and laptop had increased the request on cellular traffic demands. As a result, a Heterogeneous Wireless Network (HWN) has been introduced to fulfil users requests in having seamless mobility and better Quality of Services (QoS) for the users. A lot of research works have been done in order to provide a seamless connection to the users. Even though a lot of methods have been proposed, a Multi-Attribute Decision Making (MADM) has been seemed like a promising way due to its ability to evaluate many attributes simultaneously. Previously, many reviews based on MADM methods in a Heterogeneous Wireless Network provides a details review which required researchers time in order to determine the possible potential areas to be explored. Therefore, in this study, we present an overview of the MADM method in performing vertical handover via a systematic mapping method. This will enable future researchers to identify the trends and research opportunities within this area. This mapping study analysed 30 papers. Results from the study show eight main potential research issues can be explored by researchers, including normalisation, criteria weighting, ranking abnormality, network selection, and performance comparison between MADM algorithms, network selection for a group of calls, mobility patterns and handover triggering

A source-destination based dynamic pricing scheme to optimize resource utilization in heterogeneous wireless networks

Mobile wireless resources demand is rapidly growing due to the proliferation of bandwidth-hungry mobile devices and applications. This has resulted in congestion in mobile wireless networks (MWN) especially during the peak hours when user traffic can be as high as tenfold the average traffic. Mobile network operators (MNOs) have been trying to solve this problem in various ways. First, MNOs have tried to expand the network capacity but have still been unable to meet the peak hour demand. Focus has then shifted to economic and behavioral mechanisms. The widely used of these economic mechanisms is dynamic pricing which varies the MWN resources' price according to the congestion level in the MWN. This encourages users to shift their non-critical traffic from the busy hour, when the MWN is congested, to off-peak hours when the network is under-utilized. As a result, congestion of the MWN during the peak hours is reduced. At the same time, the MWN utilization during the off-peak hours is also increased. The current dynamic pricing schemes, however, only consider the congestion level in the call-originating cell and neglect the call-destination cell when computing the dynamic price. Due to this feature, we refer the current dynamic pricing schemes as source–based dynamic pricing (SDP) schemes in this work. The main problem with these schemes is that, when the majority of the users in a congested cell are callees, dynamic pricing is ineffective because callers and not callees pay for network services, and resources used by callers and callees are the same for symmetric services. For example, application of dynamic pricing does not deter a callee located in a congested cell from receiving a call, which originates from a caller located in an uncongested cell. Also, when the distribution of prospective callees is higher than that of callers in an underutilized cell, SDP schemes are ineffective as callees do not pay for a call and therefore low discounts do not entice them to increase utilization. In this distribution, dynamic pricing entices prospective callers to make calls but since their distribution is low, the MWN resource utilization does not increase by any significant margin. To address these problems, we have developed a source-destination based dynamic pricing (SDBDP) scheme, which considers congestion levels in both the call-originating and calldestination cells to compute the dynamic price to be paid by a caller. This SDBDP scheme is integrated with a load-based joint call admission control (JCAC) algorithm for admitting incoming service requests in to the least utilized radio access technology (RAT). The load-based JCAC algorithm achieves uniform traffic distribution in the heterogeneous wireless network (HWN). To test the SDBDP scheme, we have developed an analytical model based on M/M/m/m queuing model. New or handoff service requests, arriving when all the RATs in the HWN are fully utilized, lead to call blocking for new calls and call dropping for handoff calls. The call blocking probability, call dropping probability and percentage MWN utilization are used as the performance metrics in evaluating the SDBDP scheme. An exponential demand model is used to approximate the users' response to the presented dynamic price. The exponential demand model captures both the price elasticity of demand and the demand shift constant for different users. The matrix laboratory (MATLAB) tool has been used to carry out the numerical simulations. An evaluation scenario consisting of four groups of co-located cells each with three RATs is used. Both SDP and the developed SDBDP schemes have been subjected under the evaluation scenario. Simulation results show that the developed SDBDP scheme reduces both the new call blocking and handoff call dropping probabilities during the peak hours, for all callercallee distributions. On the other hand, the current SDP scheme only reduces new call blocking and handoff call dropping probabilities only under some caller –callee distributions (When the callers were the majority in the HWN). Also, the SDBDP scheme increases the percentage MWN utilization during the off-peak for all the caller-callee distributions in the HWN. On the other hand, the SDP scheme is found to increase the percentage MWN utilization only when the distribution of callers is higher than that of callees in the HWN. From analyzing the simulations results, we conclude that the SDBDP scheme achieves better congestion control and MWN resource utilization than the existing SDP schemes, under arbitrary caller-callee distribution

Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing

Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate users’ preferences as well as service providers’ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the users’ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operators’ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the users’ and operators’ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from users’ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operators’ utility, as total utility reward obtained increases towards a defined ‘goal state’

EVEREST IST - 2002 - 00185 : D23 : final report

Deliverable públic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version

Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future