Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

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

A guide to wireless networking by light

The lack of wireless spectrum in the radio frequency bands has led to a rapid growth in research in wireless networking using light, known as LiFi (light fidelity). In this paper an overview of the subsystems, challenges and techniques required to achieve this is presented

Neural network design for intelligent mobile network optimisation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe mobile networks users’ demands for data services are increasing exponentially, this is due to two main factors: the first is the evolution of smart phones and their application, and the second is the emerging new technologies for internet of things, smart cities…etc, which keeps pumping more data into the network; ‘though most of the data routed in the current mobile network is non-live data’. This increasing of demands arise the necessity for the mobile network operators to keep improving their network to satisfy it, this improvement takes place via adding hardware or increasing the resources or a combination of both. The radio resources are strictly limited due to spectrum licensing and availability, therefore efficient spectrum utilization is a major goal to be achieved for both network operators and developers. Simultaneous and multiple channel access,and adding more cells to the network are ways used to increase the data exchanged between the network nodes. The current 4G mobile system is based on the Orthogonal Frequency Division Multiple Access (OFDMA) for accessing the medium and the intercell interference degrades the link quality at the cell edge, with the introduction of heterogeneity concept to the LTE in Release 10 of the 3GPP the handover process became even more complex. To mitigate the intercell interference at the cell edge, coordinated multipoint and carrier aggregation techniques are utilized for dual connectivity. This work is focused on designing and proposing enhancing features to improve network performance and sustainability, these features comprises of distributing small cells for data only transmission, handover schemes performance evaluation at cell edge with dual connectivity, and Artificial Intelligence technology for balancing and prediction. In the proposed model design the data and controls of the Small eNodeB (SeNodeB) are processed at the network edge using a Mobile Edge Computing (MEC) server and the SeNodeBs are used to boost services provided to the users, also the concept of caching data has been investigated, the caching units where implemented in different network levels. The proposed system and resource management are simulated using the OPNET modeller and evaluated through multiple scenarios with and without full load, the UE is reconfigured to accommodate dual connectivity and have two separate connections for uplink and downlink, while maintaining connection to the Macro cell via uplink, the downlink is dedicated for small cells when content is requested from the cache. The results clearly show that the proposed system can decrease the latency while the total throughput delivered by the network has highly improved when SeNodeBs are deployed in the system, rising throughput will incur the rise of overall capacity which leads to better services being provided to the users or more users to join and benefit from the network. Handover improvement is also considered in this work, with the help of two Artificial Intelligence (AI) entities better handover performance are achieved. Balanced load over the SeNodeBs results in less frequent handover, the proposed load balancer is based on artificial neural network clustering model with self-organizing map as a hidden layer, it’s trained to forecast the network condition and learn to reduce the number of handovers especially for the UEs at the cell edge by performing only necessary ones, and avoid handovers to the Macro cell for the downlink direction. The examined handovers concern the downlinks when routing non live video stored at the small cell’s cache, and a reduction in the frequent handovers was achieved when running the balancer. Keep revolving in the handover orbit, another way to preserve and utilize network resources is by predicting the handovers before they occur, and allocate the required data in the target SeNodeB, the predictor entity in the proposed system architecture combines the features of Radial Basis Function Neural Network and neural network time series tool to create and update prediction list from the system’s collected data and learn to predict the next SeNodeB to associate with. The prediction entity is simulated using MATLAB, and the results shows that the system was able to deliver up to 92% correct predictions for handovers which led to overall throughput improvement of 75%