Context-aware, user-driven, network-controlled RAT selection for 5G networks

Mobile data offloading using unlicensed WiFi spectrum represents one of the innovative approaches that can be adopted to cope with the huge amount of mobile traffic demand. However, the main challenge in such approach lies in the ability of the system to discover the WiFi coverage for users who may need to offload their data. For this, the access network discovery and selection function (ANDSF) has been introduced by 3GPP to provide the mobile user equipment (UE) with the discovery information and the list of available WiFi hotspots. However, the current ANDSF discovery mechanism depends on the geographical location of the UE, which need to be accurately determined and continuously transmitted to the ANDSF server. In this paper, an enhanced ANDSF WiFi discovery technique is proposed and validated. The proposed technique is designed to use the reference signal received power (RSRP) information measured at UEs from their surrounding cellular base stations to build fingerprints for various WiFi hotspot areas. Based on the constructed fingerprints, the UE's WiFi coverage state can be identified using the proposed decision tree (DT) machine learning approach. The conducted tests and generated results validate the operation and performance of the proposed machine learning-based discovery technique with accuracy reaches up to 95%.

In this paper, we propose an improved IP-based handover scheme, named the Heterogeneous Network Handover by using 3GPP ANDSF (HNH3A for short) for S-PMIPv6 where S-PMIPv6 is a MIPv6 family protocol developed in one of our previous studies, and ANDSF (standing for Access Network Discovery and Selection Function) is an entity within an evolved packet core (EPC) of the system architecture evolution (SAE) for 3GPP compliant mobile networks. The purpose of using the ANDSF is to assist user equipment (UE) to discover non-3GPP access networks, such as WiFi or WIMAX. The HNH3A can help the handover among mobile WiMAX (i.e., Worldwide Interoperability for Microwave Access), 3GPP (i.e., 3rd Generation Partnership Project) family (including 3GPP systems, 3G LTE and 4G LTE-A) and WiFi based on existing handover techniques among the three heterogeneous networks. The analytical results demonstrate that the HNH3A can effectively mitigate handover delays and handover signaling costs.

Network Offloading schemes play a vital role when there is a massive growth in the capacity of Next Generation Networks. The aggregation of LTE (Long Term Evolution) and WLAN (Wireless Local Area Network) is meant to provide service with seamless connectivity for the User Equipment (UE). There are two major issues that have been identified in existing RSSI (Received Signal Strength Indicator) based offloading scheme such as: i) it fails to provide better performance ii) it requires frequent reconnection for establishing dual connectivity. UE (User Equipment) based offloading approach requires a lot of signaling between UE and Radio access network and thus lacks in resource utilization and energy efficiency. Network based offloading approach needs a network specific entity. In the proposed LWA-SA (LTE WLAN Aggregation-SDN Assisted) which is an SDN (Software Defined Networking) based offloading scheme. In this, a controller keeps track of ongoing sessions after offloading. Here, an algorithm is used based on Access Network Discovery and Selection Function (ANDSF) offloading scheme for the purpose of selecting an efficient Wi-Fi access point which increases the throughput and reduces the delay and also provides seamless connectivity to the UE.

On UDP continuity over vertical handovers

In Long Term Evolution (LTE) system the Tracking Area (TA) is a group of cells in which User Equipment (UE) may move freely without performing location update process. The TA scheme has many limitations such as ping-pong effect and massive mobility signaling congestion. To overcome the limitations of the TA scheme, Tracking Area List (TAL) scheme is introduced. TAL is a group of TAs and if a UE is registered to one TAL it will not perform TAL update. In LTE system mobility management techniques are used to keep track of the current location of the UE which are location update and paging techniques. These techniques induce system overhead in both system signals and bandwidth consumption. In this paper we will examine the effect of TAL design in reducing this signaling overhead, taking into account TAL overlapping which mean that a cell may be included in more than one TAL. For a given TA and TAL design the overall signaling overhead was calculated. Results obtained showed that the TAL scheme gives minimum signaling overhead when compared with the TA scheme.

This chapter considers the challenges faced by network operators and service providers accommodating the increasing traffic demands in cellular networks in the most efficient yet inexpensive way. It proposes that these challenges are addressed by offloading part of the traffic to femto cell access points (FAPs) and WiFi access points (WiFiAPs). Whereas 4G micro and pico cell base stations are assumed to be managed by the network operator in terms of setup and maintenance, FAPs and WiFiAPs are normally bought and operated by the end-user. The main difference between these two solutions is that the FAPs operate on the frequency bands assigned to the network operator by national regulators, while WiFiAPs work on unlicensed spectrum. This chapter analyzes the pros and cons of such approaches, and the tradeoffs related to the different mechanisms employed in cellular and WiFi networks for interference management. Moreover, various methods for traffic offloading by the mobile network operator are discussed in detail, including local IP access (LIPA), selective IP traffic offloading (SIPTO), IP flow mobility (IFOM), access network discovery and selection function (ANDSF), and Hotspot 2.0, etc. In the experimental part of this chapter, means for traffic management from the network operator's perspective are discussed, taking into account costs and energy savings. Furthermore, a novel resource usage coordination concept in conjunction with the WiFi offloading concept is presented. Introduction The traffic served by cellular networks grows significantly on a yearly basis, making the efficient, fair, and inexpensive management of users’ traffic demands a real challenge. Various forecasts provided both by legal bodies and commercial companies show that this trend will not change in the future. One can, for example, consider the expectations provided in Table 6.1, created based on the data delivered in the Cisco Visual Networking Index [1, 2] in 2012 and 2013. It is shown that the monthly average traffic generated by mobile devices is foreseen to grow exponentially. Based on [3], it can be stated that in 2017 IP traffic originating from mobile devices will comprise more than 13% of fixed IP traffic, while in 2013 this comprised only around 4% of fixed IP traffic.

To improve the interworking between LTE and WiFi wireless technologies, an efficient handover mechanism is proposed. Dynamic and critical measures like mobile-assisted parameters are inserted to the Access Network Discovery and Selection Function (ANDSF) entity defined by 3GPP standard. Moreover, Context Aware (CA) decision algorithm is integrated within the ANDSF server so that the inserted measurements are expeditiously invested to improve the handover decision policy. The performance enhancement of the proposed model compared to the traditional one is shown by numerical simulations.

Next generation wireless networks will be heterogeneous. Multiple Radio Access Technologies (RATs) will be colocated in the same area. A challenge arising is the efficient radio resource management among overlapped RATs. The concept of Common Radio Resource Management (CRRM) has been proposed in the literature. One of the key issues of CRRM is the RAT selection algorithm. In order to support the conceptual development of Radio Access Technology (RAT) selection algorithms in heterogeneous networks, the theory of Markov model is used. This paper proposes a three-dimensional Markov model for an integrated GERAN/UTRAN/WLAN network based on the extension of existing two co-located RATs Markov models. The performance of two basic RAT selection algorithms: load balancing (LB) based and service based algorithms are evaluated in terms of call blocking probability. The numerical results obtained from our Markov model are validated by simulation results.

The concept of wireless heterogeneous networks is based on the coexistence and interoperability of different types of radio access technologies in a unified wireless heterogeneous platform. A key process in the wireless heterogeneous networks is the initiation and execution of vertical handovers. Variety of mechanisms are proposed in the literature that are tailored to optimize the choice of radio access network in the process of vertical handover initialization. This paper proposes a novel mechanism for radio access technology (RAT) selection, the Hopfield neural network Rat selection Mechanism (HRM), that utilizes the Hopfield neural networks as a powerful decision making tool. The main advantage of the proposed mechanism lays in its ability of Artificial Intelligence (AI) reasoning and the use of multi-parameter decision by exploiting the options of IEEE 802.21 protocol. This approach allows improvements in terms of service retainability, decreased packet loss, jitter, etc. The performance of the novel mechanism is compared with two other types of RAT selection mechanisms (RSS based, and Multi-criteria policy based mechanism) throughout extensive simulations. The results clearly show the benefits of the proposed new mechanism and its justified usage in wireless heterogeneous systems.

The 5-th Generation (5G) heterogeneous networks are expected to provide dense network services and a plethora of different networks for fulfilling the user requirements. They are supposed to give User Equipment (UE) the ability to connect with the appropriate Radio Access Technology (RAT). However, many parameters should be considered for the selection of RAT, which makes the problem HP-hard. Especially, Quality of Experience (QoE) is one of the important parameters for the selection of RAT in 5G wireless networks. For this reason, in this paper, we propose a fuzzy-based scheme for evaluating QoE considering three parameters: Network Capacity (NC), Experienced End-User Throughput (EEUT) and Connectivity (Cn). From simulation results, we found that when NC, EEUT and Cn are increased, the QoE parameter value is increased.

Characterizing optimal LPWAN access delay in massive multi-RAT smart grid deployments

The rapid increment of mobile device usage and the corresponding huge data volume generated afterwards, necessitated the utilisation of the 5G network spectrum. This is deployed today in terrestrial communication in a non‐stand‐alone (NSA) architectural mode; where 5G networks are supported by 4G LTE networks. Hence, the current 5G implementation with the gargantuan number of mobile subscribers, poses challenges to the choice of network Radio Access Technology (RAT) selection between 4G and 5G networks, among available multiple base‐stations to mobile (travelling) users, with respect to their location, bandwidth requirement, and mobility style. Hence, to address the scenario presented above, the authors record live signal measurements of 4G and 5G networks by a travelling user, that transversed multiple 5G NSA base stations. RAT selection implementations were carried out with support vector machine (SVM), deep neural network (DNN), and eXtreme Gradient Boosting (XGBoost) algorithms to select an appropriate RAT between 4G and 5G RATs, for effective resource allocation for travelling users’ requirements. Evaluation of results with standard classification metrics shows XGBoost with overall outstanding accuracy performance at 99.64%.

Future-generation radio access networks (RAN) are projected to fulfill the diverse requirements of user equipment (UE) by adopting a heterogeneous network (HetNet) environment. Necessary integration of different radio access technologies (RAT), such as 2G, 3G, 4G, wireless local area network (WLAN), and visible light communication (VLC) is inevitable. Moreover, UEs equipped with diverse requirements will be capable of accessing some or all the RATs. The complex HetNet environment with diverse requirements of UEs will present many challenges. The HetNet is likely to suffer severely from load imbalance among the base stations (BSs) from inheriting the traditional user association scheme such as max-SINR (signal-to-interference-plus-noise ratio)/max-RSSI (received signal strength indicator), unless some sophisticated schemes are invented. In this paper, a novel scheme is devised for a joint-user association for load balancing, where BSs are densely deployed and UEs typically have a certain degree of mobility. Unlike most of the present works, a dynamic network is considered where the position and channel condition of the UEs are not fixed. We develop two complex and distributed association schemes based on probability and d-choices, while carefully considering both loads of the BSs and SINR experienced by the UEs. Numerical results validate the efficiency of the proposed schemes by showing a received data-rate fairness among UEs and an improvement in the UE’s minimum received data rate.

5G is expected to operate in a wide frequency range to support new challenging use-cases. Multi- RATs (Radio Access Technologies): NR (New Radio) and evolved LTE (Long Term Evolution) will together constitute 5G. Utilizing NR at high frequencies will have a significant impact on radio propagation conditions with e.g. unfavorable higher path loss and increased outdoor-to-indoor penetration losses. In order to provide a reliable communication from the outset of 5G deployment and to minimize the standardization and implementation complexity, 5G UP (User Plane) instances of 5G AIs (Air Interface) related to evolved LTE and NR need to be aggregated on a certain layer of the protocol stack. This paper sheds light on how to integrate 5G AIs into a single 5G AI framework and explores which protocol stack layer could be used as aggregation layer. Inter-RAT hard handover is the state of the art technique to integrate multiple RATs in order to support mobility and reliability across different RATs. However, the hard handover incurs a transmission interruption which stands as an obstacle along the way of accomplishing 5G design. According to simulation results, a common PDCP (Packet Data Convergence Protocol) layer improves the hard handover functionality and stands out as a basis for tight interworking between evolved LTE and NR. By means of simulation, it is shown that the multi-RAT UP aggregation can achieve three times higher user throughput, when NR is using 28 GHz and LTE 2 GHz, compared to stand-alone NR.

The net neutrality principle, also known as Open Internet, states that users should have equal access to all Internet content and that Internet Service Providers (ISPs) should not practice differentiated treatment on any of the Internet traffic. While net neutrality aims to restrain any kind of discrimination, it also provides exemption for a certain category of Internet traffic known as specialized services (SS), by allowing the ISP to dedicate part of the resources for the latter. In this work, we shed light on this particular case by comparing five Radio Access Technology (RAT) selection policies in heterogeneous wireless networks where SS traffic and Internet Access Services (IAS) traffic are carried. The studied policies include a non-net-neutral revenue-maximizing policy used as reference policy, and four other net-neutral policies with and without exemption to SS traffic. The results show that, even though, as expected, integrating net neutrality regulation within RAT selection policies can lead to a decrease in the generated revenue, a properly designed net-neutral policy will not only be able to reduce this decrease in revenue but also can maintain a similar level of social benefit in terms of the number of users admitted to the system.