Optimal Resource Allocation in Ultra-low Power Fog-computing SWIPT-based Networks

In this paper, we consider a fog computing system consisting of a multi-antenna access point (AP), an ultra-low power (ULP) single antenna device and a fog server. The ULP device is assumed to be capable of both energy harvesting (EH) and information decoding (ID) using a time-switching simultaneous wireless information and power transfer (SWIPT) scheme. The ULP device deploys the harvested energy for ID and either local computing or offloading the computations to the fog server depending on which strategy is most energy efficient. In this scenario, we optimize the time slots devoted to EH, ID and local computation as well as the time slot and power required for the offloading to minimize the energy cost of the ULP device. Numerical results are provided to study the effectiveness of the optimized fog computing system and the relevant challenges

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

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

Energy and Delay Efficient Computation Offloading Solutions for Edge Computing

This thesis collects a selective set of outcomes of a PhD course in Electronics, Telecommunications, and Information Technologies Engineering and it is focused on designing techniques to optimize computational resources in different wireless communication environments. Mobile Edge Computing (MEC) is a novel and distributed computational paradigm that has emerged to address the high users demand in 5G. In MEC, edge devices can share their resources to collaborate in terms of storage and computation. One of the computational sharing techniques is computation offloading, which brings a lot of advantages to the network edge, from lower communication, to lower energy consumption for computation. However, the communication among the devices should be managed such that the resources are exploited efficiently. To this aim, in this dissertation, computation offloading in different wireless environments with different number of users, network traffic, resource availability and devices' location are analyzed in order to optimize the resource allocation at the network edge. To better organize the dissertation, the studies are classified in four main sections. In the first section, an introduction on computational sharing technologies is given. Later, the problem of computation offloading is defined, and the challenges are introduced. In the second section, two partial offloading techniques are proposed. While in the first one, centralized and distributed architectures are proposed, in the second work, an Evolutionary Algorithm for task offloading is proposed. In the third section, the offloading problem is seen from a different perspective where the end users can harvest energy from either renewable sources of energy or through Wireless Power Transfer. In the fourth section, the MEC in vehicular environments is studied. In one work a heuristic is introduced in order to perform the computation offloading in Internet of Vehicles and in the other a learning-based approach based on bandit theory is proposed

Efficient and Secure Resource Allocation in Mobile Edge Computing Enabled Wireless Networks

To support emerging applications such as autonomous vehicles and smart homes and to build an intelligent society, the next-generation internet of things (IoT) is calling for up to 50 billion devices connected world wide. Massive devices connection, explosive data circulation, and colossal data processing demand are driving both the industry and academia to explore new solutions. Uploading this vast amount of data to the cloud center for processing will significantly increase the load on backbone networks and cause relatively long latency to time-sensitive applications. A practical solution is to deploy the computing resource closer to end-users to process the distributed data. Hence, Mobile Edge Computing (MEC) emerged as a promising solution to providing high-speed data processing service with low latency. However, the implementation of MEC networks is handicapped by various challenges. For one thing, to serve massive IoT devices, dense deployment of edge servers will consume much more energy. For another, uploading sensitive user data through a wireless link intro-duces potential risks, especially for those size-limited IoT devices that cannot implement complicated encryption techniques. This dissertation investigates problems related to Energy Efficiency (EE) and Physical Layer Security (PLS) in MEC-enabled IoT networks and how Non-Orthogonal Multiple Access (NOMA), prediction-based server coordination, and Intelligent Reflecting Surface (IRS) can be used to mitigate them. Employing a new spectrum access method can help achieve greater speed with less power consumption, therefore increasing system EE. We first investigated NOMA-assisted MEC networks and verified that the EE performance could be significantly improved. Idle servers can consume unnecessary power. Proactive server coordination can help relieve the tension of increased energy consumption in MEC systems. Our next step was to employ advanced machine learning algorithms to predict data workload at the server end and adaptively adjust the system configuration over time, thus reducing the accumulated system cost. We then introduced the PLS to our system and investigated the long-term secure EE performance of the MEC-enabled IoT network with NOMA assistance. It has shown that NOMA can improve both EE and PLS for the network. Finally, we switch from the single antenna scenario to a multiple-input single-output (MISO) system to exploit space diversity and beam forming techniques in mmWave communication. IRS can be used simultaneously to help relieve the pathloss and reconfigure multi-path links. In the final part, we first investigated the secure EE performance of IRS-assisted MISO networks and introduced a friendly jammer to block the eavesdroppers and improve the PLS rate. We then combined the IRS with the NOMA in the MEC network and showed that the IRS can further enhance the system EE

Spectral, Energy and Computation Efficiency in Future 5G Wireless Networks

Wireless technology has revolutionized the way people communicate. From first generation, or 1G, in the 1980s to current, largely deployed 4G in the 2010s, we have witnessed not only a technological leap, but also the reformation of associated applications. It is expected that 5G will become commercially available in 2020. 5G is driven by ever-increasing demands for high mobile traffic, low transmission delay, and massive numbers of connected devices. Today, with the popularity of smart phones, intelligent appliances, autonomous cars, and tablets, communication demands are higher than ever, especially when it comes to low-cost and easy-access solutions. Existing communication architecture cannot fulfill 5G’s needs. For example, 5G requires connection speeds up to 1,000 times faster than current technology can provide. Also, from transmitter side to receiver side, 5G delays should be less than 1ms, while 4G targets a 5ms delay speed. To meet these requirements, 5G will apply several disruptive techniques. We focus on two of them: new radio and new scheme. As for the former, we study the non-orthogonal multiple access (NOMA) and as for the latter, we use mobile edge computing (MEC). Traditional communication systems allow users to communicate alternatively, which clearly avoids inter-user interference, but also caps the connection speed. NOMA, on the other hand, allows multiple users to transmit simultaneously. While NOMA will inevitably cause excessive interference, we prove such interference can be mitigated by an advanced receiver side technique. NOMA has existed on the research frontier since 2013. Since that time, both academics and industry professionals have extensively studied its performance. In this dissertation, our contribution is to incorporate NOMA with several potential schemes, such as relay, IoT, and cognitive radio networks. Furthermore, we reviewed various limitations on NOMA and proposed a more practical model. In the second part, MEC is considered. MEC is a transformation from the previous cloud computing system. In particular, MEC leverages powerful devices nearby and instead of sending information to distant cloud servers, the transmission occurs in closer range, which can effectively reduce communication delay. In this work, we have proposed a new evaluation metric for MEC which can more effectively leverage the trade-off between the amount of computation and the energy consumed thereby. A practical communication system for wearable devices is proposed in the last part, which combines all the techniques discussed above. The challenges for wearable communication are inherent in its diverse needs, as some devices may require low speed but high reliability (factory sensors), while others may need low delay (medical devices). We have addressed these challenges and validated our findings through simulations