Resource Allocation for Energy Harvesting D2D Communications Underlaying NOMA Cellular Networks
| dc.contributor.advisor | Fapojuwo, Abraham Olatunji | |
| dc.contributor.author | ., Vatsala | |
| dc.contributor.committeemember | Sesay, Abu-Bakarr B | |
| dc.contributor.committeemember | Far, Behrouz H | |
| dc.date | 2022-02 | |
| dc.date.accessioned | 2021-11-23T23:25:45Z | |
| dc.date.available | 2021-11-23T23:25:45Z | |
| dc.date.issued | 2021-11 | |
| dc.description.abstract | The fifth generation (5G) cellular networks promise higher data rates, lower latency, higher energy efficiency, and increased bandwidth as compared to the fourth generation (4G) networks. To fulfill requirements raised by 5G networks, notable technologies such as Simultaneous Wireless and Information Power Transfer (SWIPT), device to device (D2D) communications and non-orthogonal multiple access (NOMA) are being extensively researched by the academia and industry. This thesis attempts to fulfill the requirements raised by current users and thus studies these technologies in the form of resource allocation problems for two SWIPT receiver architectures, namely, time switching (TS) and power splitting (PS) enabled D2D communications underlaying a NOMA based network with the objective of maximizing the D2D throughput while the rate requirements of the cellular users are guaranteed. The performance is compared with orthogonal multiple access (OMA) scheme. The problems are solved using two approaches: conventional optimization and deep learning. The conventional optimization entails a large number of iterations and involves significant time to solve the problem. Thus, deep learning is used where neural networks can learn from a dataset provided and used to predict an output. The neural networks involve less computation time and are more efficient. Therefore, a feed forward neural network (FFNN) - a kind of Deep Neural Network (DNN) is used to predict the D2D throughput. It was found that the efficient integration of D2D with the conventional cellular networks depends upon several factors such as environment, density of the network, geographical position of the devices and the rate requirement of the cellular users. Also, deep learning gives almost same results as that of the conventional optimization algorithm but is much more time efficient. In all the scenarios, the NOMA based networks give much better performance than the OMA based networks. The significance of the project lies in adopting D2D communications equipped with TS and PS SWIPT architectures in practical scenarios efficiently by studying the various factors that impact the adoption of D2D communications. | en_US |
| dc.identifier.citation | Vatsala (2021). Resource allocation for energy harvesting D2D communications underlaying NOMA cellular networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/39400 | |
| dc.identifier.uri | http://hdl.handle.net/1880/114139 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | D2D Communications | en_US |
| dc.subject | Energy Harvesting | en_US |
| dc.subject | SWIPT | en_US |
| dc.subject | Power splitting | en_US |
| dc.subject | Time switching | en_US |
| dc.subject.classification | Artificial Intelligence | en_US |
| dc.subject.classification | Engineering | en_US |
| dc.subject.classification | Engineering--Electronics and Electrical | en_US |
| dc.title | Resource Allocation for Energy Harvesting D2D Communications Underlaying NOMA Cellular Networks | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Electrical & Computer | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |