Explore the vast range of titles available.

Fifth-generation (5G) wireless networks are projected to bring a major transformation to the current fourth-generation network to support the billions of devices that will be connected to the Internet. 5G networks will enable new and powerful capabilities to support high-speed data rates, better connectivity and system capacity that are critical in designing applications in virtual reality, augmented reality and mobile online gaming. The infrastructure of a network that can support stringent application requirements needs to be highly dynamic and flexible. Network slicing can provide these dynamic and flexible characteristics to a network architecture. Implementing network slicing in 5G requires domain modification of the preexisting network architecture. A network slicing architecture is proposed for an existing 5G network with the aim of enhancing network dynamics and flexibility to support modern network applications. To enable network slicing in a 5G network, we established the virtualisation of the underlying physical 5G infrastructure by utilising technological advancements, such as software-defined networking and network function virtualisation. These virtual networks can fulfil the requirement of multiple use cases as required by creating slices of these virtual networks. Thus, abstracting from the physical resources to create virtual networks and then applying network slicing on these virtual networks enable the 5G network to address the increased demands for high-speed communication.

The Internet of Things (IoT) is a revolutionary advancement that automates daily tasks by interacting between digital and physical realms through a network of mostly Low-Power IoT (LP-IoT) devices. For an IoT ecosystem, reliable wireless connectivity is essential to ensure the optimal operation of LP-IoT devices, especially considering their limited resource capacity. This reliability is often achieved through channel estimation, an essential aspect for optimising signal transmission. Considering the importance of reliable channel estimation for constrained IoT devices, we developed two lightweight yet effective channel estimation models based on Random Forest Regressor (RFR). These two models are namely classified as Feature-based RFR(F) and Sequence-based RFR(S) methods and utilise Received Signal Strength Indicator (RSSI) as a fundamental channel metric to enhance efficiency for the reliability of channel estimation in constrained LP-IoT devices. The models’ performance was assessed by comparing them with the state-of-the-art and our previously developed Artificial Neural Network (ANN)-based method. The experimental results show that the RFR(F) method shows approximately 39.62% improvement in Mean Squared Error (MSE) over the Feature-based ANN(F) model and 37.86% advancement over the state-of-the-art. Similarly, the RFR(S) model shows an improvement in MSE of 24.9% compared to the Sequence-based ANN(S) model and an 80.59% improvement compared to the leading existing methods. We also evaluated the lightweight characteristics of our RFR(F) and RFR(S) methods by deploying them on Raspberry Pi 4 Model B to demonstrate their practicality for LP-IoT devices.

In the rapidly growing development of the Internet of Things (IoT) infrastructure, achieving reliable wireless communication is a challenge. IoT devices operate in diverse environments with common signal interference and fluctuating channel conditions. Accurate channel estimation helps adapt the transmission strategies to current conditions, ensuring reliable communication. Traditional methods, such as Least Squares (LS) and Minimum Mean Squared Error (MMSE) estimation techniques, often struggle to adapt to the diverse and complex environments typical of IoT networks. This research article delves into the potential of Deep Learning (DL) to enhance channel estimation, focusing on the Received Signal Strength Indicator (RSSI) metric - a critical yet challenging aspect due to its susceptibility to noise and environmental factors. This paper presents two Fully Connected Neural Networks (FCNNs)-based Low Power (LP-IoT) channel estimation models, leveraging RSSI for accurate channel estimation in LP-IoT communication. Our Model A exhibits a remarkable 99.02% reduction in Mean Squared Error (MSE), and Model B demonstrates a notable 90.03% MSE reduction compared to the benchmarks set by current studies. Additionally, the comparative studies of our model A with other DL-based techniques show significant efficiency in our estimation models.

In the expanding field of the Internet of Things (IoT), wireless channel estimation is a significant challenge. This is specifically true for low-power IoT (LP-IoT) communication, where efficiency and accuracy are extremely important. This research establishes two distinct LP-IoT wireless channel estimation models using Artificial Neural Networks (ANN): a Feature-based ANN model and a Sequence-based ANN model. Both models have been constructed to enhance LP-IoT communication by lowering the estimation error in the LP-IoT wireless channel. The Feature-based model aims to capture complex patterns of measured Received Signal Strength Indicator (RSSI) data using environmental characteristics. The Sequence-based approach utilises predetermined categorisation techniques to estimate the RSSI sequence of specifically selected environment characteristics. The findings demonstrate that our suggested approaches attain remarkable precision in channel estimation, with an improvement in MSE of \\(88.29\\%\\) of the Feature-based model and \\(97.46\\%\\) of the Sequence-based model over existing research. Additionally, the comparative analysis of these techniques with traditional and other Deep Learning (DL)-based techniques also highlights the superior performance of our developed models and their potential in real-world IoT applications.