Clustering Based Dynamic Bandwidth Allocation in HC-RAN

A wireless network is composed of several independent nodes or gadgets that communicate mutually through a wireless link. The most destructive challenge encountered in a wireless network is bandwidth allocation because it defines the amount the network will cost and how effectively it will function. The most cutting-edge network architecture in the present wireless communication system, cluster-based heterogeneous cloud radio access networks (HC-RANs), is what powers cloud computing in heterogeneous networks. In this research, we proposed an HC-RANs that may optimize energy consumption for wireless data transfer in the multi-hop device to device scenario. The proposed scheme offers bandwidth allocation in wireless environments where there are concerns about significant user mobility over the course of a given time. The above design, we used clustering with joint beam formation for the down link of heterogeneous cloud radio access network (HC-RAN), developed design to improved amount of FBS. Result outcomes helped in calculating Critical bandwidth usage (CBU)

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster

Intelligent and Efficient Ultra-Dense Heterogeneous Networks for 5G and Beyond

Ultra-dense heterogeneous network (HetNet), in which densified small cells overlaying the conventional macro-cells, is a promising technique for the fifth-generation (5G) mobile network. The dense and multi-tier network architecture is able to support the extensive data traffic and diverse quality of service (QoS) but meanwhile arises several challenges especially on the interference coordination and resource management. In this thesis, three novel network schemes are proposed to achieve intelligent and efficient operation based on the deep learning-enabled network awareness. Both optimization and deep learning methods are developed to achieve intelligent and efficient resource allocation in these proposed network schemes. To improve the cost and energy efficiency of ultra-dense HetNets, a hotspot prediction based virtual small cell (VSC) network is proposed. A VSC is formed only when the traffic volume and user density are extremely high. We leverage the feature extraction capabilities of deep learning techniques and exploit a long-short term memory (LSTM) neural network to predict potential hotspots and form VSC. Large-scale antenna array enabled hybrid beamforming is also adaptively adjusted for highly directional transmission to cover these VSCs. Within each VSC, one user equipment (UE) is selected as a cell head (CH), which collects the intra-cell traffic using the unlicensed band and relays the aggregated traffic to the macro-cell base station (MBS) in the licensed band. The inter-cell interference can thus be reduced, and the spectrum efficiency can be improved. Numerical results show that proposed VSCs can reduce $55\%$ power consumption in comparison with traditional small cells. In addition to the smart VSCs deployment, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is also proposed to achieve stringent and diverse QoS of emerging 5G applications with disparate resource constraints. Multiple access (MA) schemes in multi-dimensional resources are adaptively scheduled to accommodate dynamic QoS requirements and network states. The MD-IMA learns the integrated-quality-of-system-experience (I-QoSE) by monitoring and predicting QoS through the LSTM neural network. The resource allocation in the MD-IMA scheme is formulated as an optimization problem to maximize the I-QoSE as well as minimize the non-orthogonality (NO) in view of implementation constraints. In order to solve this problem, both model-based optimization algorithms and model-free deep reinforcement learning (DRL) approaches are utilized. Simulation results demonstrate that the achievable I-QoSE gain of MD-IMA over traditional MA is $15\%$ - $18\%$. In the final part of the thesis, a Software-Defined Networking (SDN) enabled 5G-vehicle ad hoc networks (VANET) is designed to support the growing vehicle-generated data traffic. In this integrated architecture, to reduce the signaling overhead, vehicles are clustered under the coordination of SDN and one vehicle in each cluster is selected as a gateway to aggregate intra-cluster traffic. To ensure the capacity of the trunk-link between the gateway and macro base station, a Non-orthogonal Multiplexed Modulation (NOMM) scheme is proposed to split aggregated data stream into multi-layers and use sparse spreading code to partially superpose the modulated symbols on several resource blocks. The simulation results show that the energy efficiency performance of proposed NOMM is around 1.5-2 times than that of the typical orthogonal transmission scheme

Hybrid generalized non-orthogonal multiple access for the 5G wireless networks.

Master of Science in Computer Engineering. University of KwaZulu-Natal. Durban, 2018.The deployment of 5G networks will lead to an increase in capacity, spectral efficiency, low latency and massive connectivity for wireless networks. They will still face the challenges of resource and power optimization, increasing spectrum efficiency and energy optimization, among others. Furthermore, the standardized technologies to mitigate against the challenges need to be developed and are a challenge themselves. In the current predecessor LTE-A networks, orthogonal frequency multiple access (OFDMA) scheme is used as the baseline multiple access scheme. It allows users to be served orthogonally in either time or frequency to alleviate narrowband interference and impulse noise. Further spectrum limitations of orthogonal multiple access (OMA) schemes have resulted in the development of non-orthogonal multiple access (NOMA) schemes to enable 5G networks to achieve high spectral efficiency and high data rates. NOMA schemes unorthogonally co-multiplex different users on the same resource elements (RE) (i.e. time-frequency domain, OFDMA subcarrier, or spreading code) via power domain (PD) or code domain (CD) at the transmitter and successfully separating them at the receiver by applying multi-user detection (MUD) algorithms. The current developed NOMA schemes, refered to as generalized-NOMA (G-NOMA) technologies includes; Interleaver Division Multiple Access (IDMA, Sparse code multiple access (SCMA), Low-density spreading multiple access (LDSMA), Multi-user shared access (MUSA) scheme and the Pattern Division Multiple Access (PDMA). These protocols are currently still under refinement, their performance and applicability has not been thoroughly investigated. The first part of this work undertakes a thorough investigation and analysis of the performance of the existing G-NOMA schemes and their applicability. Generally, G-NOMA schemes perceives overloading by non-orthogonal spectrum resource allocation, which enables massive connectivity of users and devices, and offers improved system spectral efficiency. Like any other technologies, the G-NOMA schemes need to be improved to further harvest their benefits on 5G networks leading to the requirement of Hybrid G-NOMA (G-NOMA) schemes. The second part of this work develops a HG-NOMA scheme to alleviate the 5G challenges of resource allocation, inter and cross-tier interference management and energy efficiency. This work develops and investigates the performance of an Energy Efficient HG-NOMA resource allocation scheme for a two-tier heterogeneous network that alleviates the cross-tier interference and improves the system throughput via spectrum resource optimization. By considering the combinatorial problem of resource pattern assignment and power allocation, the HG-NOMA scheme will enable a new transmission policy that allows more than two macro-user equipment’s (MUEs) and femto-user equipment’s (FUEs) to be co-multiplexed on the same time-frequency RE increasing the spectral efficiency. The performance of the developed model is shown to be superior to the PD-NOMA and OFDMA schemes

Resource allocation in future green wireless networks : applications and challenges

Over the past few years, green radio communication has been an emerging topic since the footprint from the Information and Communication Technologies (ICT) is predicted to increase 7.3% annually and then exceed 14% of the global footprint by 2040. Moreover, the explosive progress of ICT, e.g., the fifth generation (5G) networks, has resulted in expectations of achieving 10-fold longer device battery lifetime, and 1000-fold higher global mobile data traffic over the fourth generation (4G) networks. Therefore, the demands for increasing the data rate and the lifetime while reducing the footprint in the next-generation wireless networks call for more efficient utilization of energy and other resources. To overcome this challenge, the concepts of small-cell, energy harvesting, and wireless information and power transfer networks can be evaluated as promising solutions for re-greening the world. In this dissertation, the technical contributions in terms of saving economical cost, protecting the environment, and guaranteeing human health are provided. More specifically, novel communication scenarios are proposed to minimize energy consumption and hence save economic costs. Further, energy harvesting (EH) techniques are applied to exploit available green resources in order to reduce carbon footprint and then protect the environment. In locations where implemented user devices might not harvest energy directly from natural resources, base stations could harvest-and-store green energy and then use such energy to power the devices wirelessly. However, wireless power transfer (WPT) techniques should be used in a wise manner to avoid electromagnetic pollution and then guarantee human health. To achieve all these aspects simultaneously, this thesis proposes promising schemes to optimally manage and allocate resources in future networks. Given this direction, in the first part, Chapter 2 mainly studies a transmission power minimization scheme for a two-tier heterogeneous network (HetNet) over frequency selective fading channels. In addition, the HetNet backhaul connection is unable to support a sufficient throughput for signaling an information exchange between two tiers. A novel idea is introduced in which the time reversal (TR) beamforming technique is used at a femtocell while zero-forcing-based beamforming is deployed at a macrocell. Thus, a downlink power minimizationscheme is proposed, and optimal closed-form solutions are provided. In the second part, Chapters 3, 4, and 5 concentrate on EH and wireless information and power transfer (WIPT) using RF signals. More specifically, Chapter 3 presents an overview of the recent progress in green radio communications and discusses potential technologies for some emerging topics on the platforms of EH and WPT. Chapter 4 develops a new integrated information and energy receiver architecture based on the direct use of alternating current (AC) for computation. It is shown that the proposed approach enhances not only the computational ability but also the energy efficiency over the conventional one. Furthermore, Chapter 5 proposes a novel resource allocation scheme in simultaneous wireless information and power transfer (SWIPT) networks where three crucial issues: power-efficient improvement, user-fairness guarantee, and non-ideal channel reciprocity effect mitigation, are jointly addressed. Hence, novel methods to derive optimal and suboptimal solutions are provided. In the third part, Chapters 6, 7, and 8 focus on simultaneous lightwave information and power transfer (SLIPT) for indoor applications, as a complementary technology to RF SWIPT. In this research, Chapter 6 investigates a hybrid RF/visible light communication (VLC) ultrasmall cell network where optical transmitters deliver information and power using the visible light, whereas an RF access point works as a complementary power transfer system. Thus, a novel resource allocation scheme exploiting RF and visible light for power transfer is devised. Chapter 7 proposes the use of lightwave power transfer to enable future sustainable Federated Learning (FL)-based wireless networks. FL is a new data privacy protection technique for training shared machine learning models in a distributed approach. However, the involvement of energy-constrained mobile devices in the construction of the shared learning models may significantly reduce their lifetime. The proposed approach can support the FL-based wireless network to overcome the issue of limited energy at mobile devices. Chapter 8 introduces a novel framework for collaborative RF and lightwave power transfer for wireless communication networks. The constraints on the transmission power set by safety regulations result in significant challenges to enhance the power transfer performance. Thus, the study of technologies complementary to conventional RF SWIPT is essential. To cope with this isue, this chapter proposes a novel collaborative RF and lightwave power transfer technology for next-generation wireless networks

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

A Comprehensive Study of Multiple Access Techniques in 6G Networks

With the proliferation of numerous burgeoning services such as ultra-reliable low-latency communication (URLLC), massive machine type communications (mMTC), enhanced mobile broadband (eMBB), among others, wireless communication systems are expected to face daunting challenges. In order to satisfy these ever-increasing traffic demands, diverse quality-of-services (QoS) requirements, and the massive connectivity accompanied by these new applications, various innovative and promising technologies, and architectures need to be developed. Novel multiple-access techniques are currently being explored in both academia and industry in order to accommodate such unprecedented requirements. Non-orthogonal multiple access (NOMA) has been deemed as one of the vital enabling multiple access techniques for the upcoming six-generation (6G) networks. This is due to its ability to enhance network spectral efficiency (NSE) and support a massive number of connected devices. Owing to its potential benefits, NOMA is recognized as a prominent member of next-generation multiple access (NGMA). Several emerging techniques such as full-duplex (FD) communication, device-to-device (D2D) communications, reconfigurable intelligent surface (RIS), coordinated multipoint (CoMP), cloud radio access networks, are being gradually developed to address fundamental problems in future wireless networks. In this thesis, and with the goal of converging toward NGMA, we investigate the synergistic integration between NOMA and other evolving physical layer technologies. Specifically, we analyze this integration aiming at improving the performance of cell-edge users (CEUs), mitigating the detrimental effect of inter-cell interference (ICI), designing energy-efficient multiple access toward ``green’’ wireless networks, guarantying reliable communication between NOMA UEs and base stations (BSs)/remote radio heads (RRHs), and maintaining the required QoS in terms of the minimum achievable data rate, especially at CEUs. Regarding the ICI mitigation in multi-cell NOMA networks and tackling the connectivity issue in traditional CoMP-based OMA networks, we first investigate the integration between location-aware CoMP transmission and NOMA in downlink heterogeneous C-RAN. In doing so, we design a novel analytical framework using tools from stochastic geometry to analyze the system performance in terms of the average achievable data rate per NOMA UE. Our results reveal that CoMP NOMA can provide a significant gain in terms of network spectral efficiency compared to the traditional CoMP OMA scheme. In addition, with the goal of further improving the performance of CEUs and user fairness, cooperative transmission with the aid of D2D communication and FD or half-duplex (HD) transmission, has been introduced to NOMA, which is commonly known as cooperative NOMA (C-NOMA). As a result, we extend our study to also investigate the potential gains of investigating CoMP and C-NOMA. In such a framework, we exploit the cooperation between the RRHs/BSs and the successive decoding strategy at NOMA UEs that are near the RRHs/BSs. Specifically, we investigate both performance analysis and resource management optimization (power control and user pairing). Our results show that the transmit power at the BS, the transmit power at the relay user, and the self-interference (SI) value at the relay user determine which multiple access technique, CoMP NOMA, CoMP HD C-NOMA, and CoMP FD C-NOMA, should be adopted at the BSs. Now, to assist in designing energy-efficient multiple access techniques and guarantying reliable communication for NOMA UEs, this thesis explores the interplay between FD/HD C-NOMA and RIS. We show that the proposed model has the best performance in terms of network power consumption compared to other multiple access techniques in the literature, which leads to ``green'' future wireless networks. Moreover, our results show that the network power consumption can be significantly reduced by increasing the number of RIS elements. A more significant finding is that the location of the RIS depends on the adopted multiple access techniques. For example, it is not recommended to deploy the RIS besides the BS if the adopted multiple access is HD C-NOMA. Another insight that has been unveiled is the FD C-NOMA with the assistance of RIS has more resistance to the residual SI effect, due to the FD transmission, and can tolerate high SI values compared to the same scheme without RIS. Although much work has been conducted to improve the network spectral efficiency of multi-cell NOMA cellular networks, the required QoS by the upcoming 6G applications, in terms of the minimum achievable rate, may not be guaranteed at CEUs. This is due to their distant locations from their serving BSs, and thus, they experience severe path-loss attenuation and high ICI. This thesis addresses this research gap by studying the synergistic integration between RIS, NOMA, and CoMP in a multi-user multi-cell scenario. Unlike the developed high-complexity optimal solutions or the low-complexity sub-optimal solutions in the literature for the power allocation problem, we derive a low-complexity optimal solution in a such challenging scenario. We also consider the interdependency between the user clustering policies in different coordinated cells, which has been ignored in the literature. Finally, we prove that this integration between RIS, NOMA, and CoMP can attain a high achievable rate for CEUs, ameliorate spectral efficiency compared to existing literature, and can form a novel paradigm for NGMA