NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)

NOMA based Resource Allocation and Mobility Enhancement Framework for IoT in Next generation Cellular Networks

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)

Fine-Grained Radio Resource Management to Control Interference in Dense Wi-Fi Networks

In spite of the enormous popularity of Wi-Fienabled devices, the utilisation of Wi-Fi radio resources (e.g. RF spectrum and transmission power levels) at Access Points (APs) is degraded in current decentralised Radio Resource Management (RRM) schemes. Most state of the art centralised control solutions apply configurations in which the network-wide impacts of the involved parameters and their mutual relationships are ignored. In this paper, we propose an algorithm for jointly adjusting the transmission power levels and optimising the RF channel assignment of APs by taking into account the flows’ required qualities while minimising their interference impact throughout the network. The proposed solution is tailored for an operatoragnostic and Software Defined Wireless Networking (SDWN)- based centralised RRM in dense Wi-Fi networks. Our extensive simulation results validate the performance improvements of the proposed algorithm compared to the main state of the art alternative by showing more than 25% higher spectrum efficiency, satisfying the users’ demands and further mitigating the networkwide interference through flow-based and quality-oriented power level adjustment

SDN-based channel assignment algorithm for interference management in dense Wi-Fi networks

The popularity of Wi-Fi-enabled devices alongside the growing demand for non-licensed spectrum, has made the Wi-Fi networks exceedingly congested. This endangers the efficiency of Wi-Fi and negatively affect the users' experience. The problem is especially pressing in dense areas (e.g. shopping centers) where Wi-Fi channel assignment is more likely to be uncoordinated and the working environment of Wi-Fi Access Points (APs) has become increasingly time-variant. As a result, the availability of Software-Defined Networking (SDN) and network virtualization technologies has motivated the use of centralized resource management as a solution. This paper provides an algorithm for channel assignment functionality in the context of SDN-based centralized resource management, which captures the live status of a Wi-Fi network and is capable of optimising the Radio Frequency (RF) channel assignment process. The APs' network arrangement, the current assignment of the channels and the characteristics of the RF channels in IEEE 802.11 have all been taken into account in the proposed model. The performance of the proposed model in terms of the level of the interference, the spectral efficiency at each AP and the Signal to Interference plus Noise Ratio (SINR) at the user-side is evaluated through simulation and compared against state of the art solutions

A centralised Wi-Fi management framework for D2D communications in dense Wi-Fi networks

In Wi-Fi networks, Device-to-Device (D2D) communications aim to improve the efficiency of the network by supporting direct communication between users in close proximity. However, in a congested Wi-Fi network, establishing D2D connections through a locally managed self-organising approach will intensify the congestion and reduce the scalability of the solution. Therefore, a centralised management approach must be involved in orchestrating those actions to guarantee the sufficiency of D2D communications. In this paper, we propose a novel management framework for D2D communications in dense Wi-Fi networks. The proposed framework employs a Software-Defined Networking (SDN) based centralised controller in synergy with a novel Access Point (AP) channel assignment process. This framework is designed to proactively establish and manage D2D connections in Wi-Fi networks considering the available radio resources and the effect of the subsequent interference. Thus, improving the overall performance of the network and providing users with higher data rate. Through simulation, we validate the effectiveness of the proposed framework and demonstrate how D2D deployment considerably improves the Wi-Fi network efficiency especially when the data rate requirements are high. Furthermore, we show that our proposed framework achieves better performance than the widely deployed Least Congested Channel selection strategy (LCC)

A pathway to solving the Wi-Fi tragedy of the commons in apartment blocks

Surprisingly little research has quantified the severity of Wi-Fi congestion in densely populated areas. We performed a high-fidelity 3D simulation of the performance of a realistic Wi-Fi deployment in a typical apartment block. Our results show that congestion leads to significant loss of performance, and that current channel selection procedures have only little effect. Also the strategy that is mostly applied today, i.e. to deploy additional repeaters and access points (APs), fails. As this is a typical example of the “Tragedy of the Commons”, some form of collaboration between AP operators is needed to solve the problem. New channel selection algorithms that optimize Wi-Fi performance on a system level then become possible which, for instance, minimize the mutual interference impact on all APs involved. We validate that such an algorithm indeed leads to an optimized as well as fair assignment, which is a necessary first step towards solving the Tragedy

Building students’ transferable skills through classroom activities and assessments

In engineering-related subjects, students sometimes pay more attention to subject knowledge than transferable skills, which often results in gaps where university graduates equipped with subject knowledge do not possess skills required by their employers. The recent introduction of degree apprenticeships in the UK calls for business involvements and provides standards where skill requirements are specified alongside knowledge and behavior requirements. In this paper, we report the practice at the Warwick Manufacturing Group, the University of Warwick, of how the skill requirements of the Digital and Technology Solutions degree were mapped to different modules and taught through different classroom activities and assessments. We discuss our approach, students’ responses, challenges faced, our resolutions, as well as some pedagogical considerations

Using VR in science subjects : popularity and public attitudes on Twitter

Following their surge in popularity during the height of the Covid-19 pandemic, e-learning and virtual education remain a noteworthy and rapidly developing part of the educational landscape. Still relatively immature in its development, virtual reality in particular promises extraordinary potential for positive educational outcomes. Given this potential, it is essential to deepen our understanding of the needs and attitudes of the public regarding its use. Based on Twitter data, our research focuses on the use of virtual reality in biology, chemistry and physics education, providing an analysis of popularity and other trends, along with public attitudes. Major results of our study include the finding that the application of virtual reality in chemistry was the most discussed among these three subjects between July 2016 and July 2022, and the discovery of a strong positive correlation between the frequency of tweets and the launch of hardware and software products. Our overview of the traffic on these three subjects is likely to be of particular value to software developers and virtual reality content creators

Client QoE-Oriented Segment Selection for DASH

A segment selection method controlled by Quality of Experience (QoE) factors for Dynamic Adaptive Streaming over HTTP (DASH) is presented in this paper. Current rate adaption algorithms aim to eliminate buffer underrun events by significantly reducing the code rate when experiencing pauses in replay. In reality, however, viewers may choose to accept a level of buffer underrun in order to achieve an improved level of picture fidelity or to accept the degradation in picture fidelity inorder to maintain the service continuity. The proposed rate adaption scheme in our work can maximize the user QoE in terms of both continuity and fidelity (picture quality) in DASH applications. It is shown that using this scheme a high level of quality for streaming services, especially at low packet loss rates, can be achieved. Our scheme can also maintain a best trade-off between continuity-based quality and fidelity-based quality, by determining proper threshold values for the level of quality intended by clients with different quality requirements. In addition, the integration of the rate adaptation mechanism with the scheduling process is investigated in the context of a mobile communication network and related performances are analyzed