Performance analysis of biological resource allocation algorithms for next generation networks.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF.Publications listed on page iii

5G NOMA user grouping using discrete particle swarm optimization approach

Non-orthogonal multiple access (NOMA) technology meets the increasing demand for high-seed cellular networks such as 5G by offering more users to be accommodated at once in accessing the cellular and wireless network. Moreover, the current demand of cellular networks for enhanced user fairness, greater spectrum efficiency and improved sum capacity further increase the need for NOMA improvement. However, the incurred interference in implementing NOMA user grouping constitutes one of the major barriers in achieving high throughput in NOMA systems. Therefore, this paper presents a computationally lower user grouping approach based on discrete particle swarm intelligence in finding the best user-pairing for 5G NOMA networks and beyond. A discrete particle swarm optimization (DPSO) algorithm is designed and proposed as a promising scheme in performing the user-grouping mechanism. The performance of this proposed approach is measured and demonstrated to have comparable result against the existing state-of-the art approach

Efficient Load Balancing Algorithm in Long Term Evolution (LTE) Heterogeneous Network Based on Dynamic Cell Range Expansion Bias

The traditional scheme for load balancing in a homogeneous Long Term Evolution (LTE) Network where User Equipment (UEs) associate to a node with the strongest received signal strength is not practical for LTE Heterogeneous Network (LTE HetNet) due to power disparity between the nodes. Therefore, dynamic Cell Range Expansion (CRE) based load-balancing schemes were employed by several scholars to address the challenges in the LTE HetNet. However, the fairness index in achieving the desired average user throughput and UE offloading effect is relatively low. In this work, an efficient load-balancing algorithm for LTE HetNet based on dynamic Cell Range Expansion (CRE) was developed to improve the fairness of the network for the desired throughput and UE offloading effect. The simulation results achieve a throughput gain improvement of up to 11%, while the fairness index improves by 6% compared to the existing algorithm. Further, the UEs offloading effect shows a significant improvement of 3% relative to the existing algorithm. Keywords: Fairness Index; Cell Range Expansion; Load Balancing; LTE Heterogeneous Network; Throughpu

Pengaruh Jumlah Partikel pada Algoritma Particle Swarm Optimization terhadap Performansi Pengalokasian Sumber Daya LTE pada Sistem MIMO-OFDM 2x2

Pada Teknologi Long Term Evolution (LTE), Third Generation Partnership Project (3GPP) merilis teknologi ini yang merupakan teknologi komunikasi akses data nirkabel yang memiliki laju data minimal 100 Mbps untuk arah downlink. Oleh karena itu teknologi ini menjanjikan kecepatan layanan informasi jauh lebih cepat dari teknologi sebelumnya. namun dalam perkembangannya standar yang telah ditetapkan belum tercapai. Salah satu hal yang menjadi masalah yang masih harus dioptimalkan adalah algoritma untukpengalokasian resource block(RB). Metode pengalokasian RB telah dilakukan dalam beberapa riset. Dalam perkembangannya ada beberapa algoritma dasar yang digunakan seperti Round Robin, namun algoritma dasar tersebut masih membutuhkan pengembangan lebih lanjut, karena belum bisa menjamin Quality of Services (QOS) yang diharapkan. Selain algoritma dasar ada beberapa algoritma lain yang digunakan untuk metode pengalokasian RB.Dalam paper ini Algoritma yang digunakan adalah algoritma particle swarm optimization(PSO) yang berdasarkan perpindahan posisi partikel. Algoritma ini memiliki beberapa parameter yang dapat mempengaruhi performansi dari algoritma ini. Pada paper ini, membahas pengaruh perubahan parameter terhadap parameter performansi dari pengalokasian resource block. Hasil simulasi dari skema yangdiusulkan, nilai throughput dan fairness pengalokasian resource block menggunakan algoritma PSO lebih tinggi jika dibandingkan dengan algoritma Round Robin

Deep Learning Technique for Power Domain Non-Orthogonal Multiple Access Using Optimised LSTM in Cooperative Networks

Non-orthogonal Multiple Access (NOMA) is the technique proposed for multiple accesses in the fifth-generation (5G) cellular network. In NOMA, different users are allocated different power levels and are served using the same time/frequency Resource Blocks (RBs). The main challenges in existing NOMA systems are the limited channel feedback and the difficulty of merging them with advanced adaptive coding and modulation schemes. The 5G system in NOMA aims to access low latency, efficiency in superior spectra, and balanced user fairness. NOMA allows multiple users with different power levels to share resources in radio frequency time. The existing Orthogonal Multiple Access (OMA) system produces high latency, high computational complexity, and throughput complexity in modifying wireless channels. To overcome these issues, this paper proposed optimising deep learning-based power domain NOMA of Long Short-Term Memory (LSTM) with particles Swarm optimisation (PSO) technique. This proposed work (LSTM-PSO) is deployed with a Cooperative network model. The advantage of LSTM-PSO in Cooperative Non-orthogonal Multiple Access (CNOMA) is that it provides high performance, better utilisation of downlink, efficiency in sharing of resources, enhancing the activity of users, capacity of the base station and improving quality of service, estimation of channel condition. LSTM-PSO got a higher accuracy rate of 92.05%, LSTM got 86.45%, PSO got 88.13%, and the accuracy rate of ANN and DNN was 83.76% and 84.70%

Efficient radio resource management for the fifth generation slice networks

It is predicted that the IMT-2020 (5G network) will meet increasing user demands and, hence, it is therefore, expected to be as flexible as possible. The relevant standardisation bodies and academia have accepted the critical role of network slicing in the implementation of the 5G network. The network slicing paradigm allows the physical infrastructure and resources of the mobile network to be “sliced” into logical networks, which are operated by different entities, and then engineered to address the specific requirements of different verticals, business models, and individual subscribers. Network slicing offers propitious solutions to the flexibility requirements of the 5G network. The attributes and characteristics of network slicing support the multi-tenancy paradigm, which is predicted to drastically reduce the operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile network operators. Furthermore, network slices enable mobile virtual network operators to compete with one another using the same physical networks but customising their slices and network operation according to their market segment's characteristics and requirements. However, owing to scarce radio resources, the dynamic characteristics of the wireless links, and its capacity, implementing network slicing at the base stations and the access network xix becomes an uphill task. Moreover, an unplanned 5G slice network deployment results in technical challenges such as unfairness in radio resource allocation, poor quality of service provisioning, network profit maximisation challenges, and rises in energy consumption in a bid to meet QoS specifications. Therefore, there is a need to develop efficient radio resource management algorithms that address the above mentioned technical challenges. The core aim of this research is to develop and evaluate efficient radio resource management algorithms and schemes that will be implemented in 5G slice networks to guarantee the QoS of users in terms of throughput and latency while ensuring that 5G slice networks are energy efficient and economically profitable. This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. It is observed that the proposed new radio swarm-intelligent profit-aware resource allocation (NR-SiRARE) scheme outperforms the LTE-OFDMA swarm-intelligent profit-aware resource (LO-SiRARE) scheme. However, the network profit for the NR-SiRARE is greatly affected by significant degradation of the path loss associated with millimetre waves. Second, this thesis examines the resource allocation challenge in a multi-tenant multi-slice multi-tier heterogeneous network. To maximise the total utility of a multi-tenant multislice multi-tier heterogeneous network, a latency-aware dynamic resource allocation problem is formulated as an optimisation problem. Via the hierarchical decomposition method for heterogeneous networks, the formulated optimisation problem is transformed to reduce the computational complexities of the proposed solutions. Furthermore, a genetic algorithmbased latency-aware resource allocation scheme is proposed to solve the maximum utility problem by considering related constraints. It is observed that GI-LARE scheme outperforms the static slicing (SS) and an optimal resource allocation (ORA) schemes. Moreover, the GI-LARE appears to be near optimal when compared with an exact solution based on spatial branch and bound. Third, this thesis addresses a distributed resource allocation problem in a multi-slice multitier multi-domain network with different players. A three-level hierarchical business model comprising InPs, MVNOs, and service providers (SP) is examined. The radio resource allocation problem is formulated as a maximum utility optimisation problem. A multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multidomain games schemes are proposed to solve the maximum utility optimisation problem. The distributed backtracking scheme is based on the Fisher Market and Auction theory principles. The proposed multi-tier multi-domain scheme outperforms the GI-LARE and the SS schemes. This is attributed to the availability of resources from other InPs and MVNOs; and the flexibility associated with a multi-domain network. Lastly, an energy-efficient resource allocation problem for 5G slice networks in a highly dense heterogeneous environment is investigated. A mathematical formulation of energy-efficient resource allocation in 5G slice networks is developed as a mixed-integer linear fractional optimisation problem (MILFP). The method adopts hierarchical decomposition techniques to reduce complexities. Furthermore, the slice user association, QoS for different slice use cases, an adapted water filling algorithm, and stochastic geometry tools are employed to xxi model the global energy efficiency (GEE) of the 5G slice network. Besides, neither stochastic geometry nor a three-level hierarchical business model schemes have been employed to model the global energy efficiency of the 5G slice network in the literature, making it the first time such method will be applied to 5G slice network. With rigorous numerical simulations based on Monte-Carlo numerical simulation technique, the performance of the proposed algorithms and schemes was evaluated to show their adaptability, efficiency and robustness for a 5G slice network

OPTIMASI RESOURCE ALLOCATION MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION (PSO) PADA SISTEM LONG TERM EVOLUTION (LTE) ARAH UPLINK

Kondisi perkembangan teknologi telekomunikasi yang berkembang sangat pesat dibutuhkan untuk manusia global yang setiap saat berkomunikasi untuk berbagai keperluan. Sampai pada saat ini teknologi telekomunikasi seluler yang paling mutakhir ialah 4G Long Term Evolution (LTE). Teknologi tersebut membawa keuntungan di sisi pengiriman data rate yang lebih tinggi sehingga komunikasi akan lebih terasa aktual. Dibalik keuntungan tersebut ternyata ada skema yang berpengaruh terhadap keandalan 4G standar LTE, yaitu sistem modulasi Single Carrier Orthogonal Frequency Division Multiple Access (SC-FDMA). Permasalahan pada sistem Long Term Evolution (LTE) adalah masalah pengalokasian resource allocation dan pengalokasian daya. Proses resource allocation dibutuhkan untuk mengalokasikan resource block agar kualitas layanan kepada user menjadi optimal. Sedangkan pengalokasian daya menjadi masalah karena diperlukannya daya yang optimal untuk setiap user. Pada Tugas Akhir ini algoritma yang digunakan ialah algoritma Particle Swarm Optimization (PSO) untuk pengalokasian daya kepada user dengan membandingkan teknik alokasi daya Waterfilling dengan skema Equal Power Allocation untuk meningkatkan performansi. Dari hasil simulasi yang didapat, skema yang menggunakan teknik waterfilling memiliki fairness index sistem yang lebih baik dibandingkan dengan skema equal power allocation, tetapi memiliki average user throughput dan efisiensi spektral yang lebih rendah. Pada parameter fairness index sistem, memiliki nilai perbaikan rata-rata sebesar 0,155. Parameter keluaran average user throughput dan efisiensi spektral mendapatkan nilai yang maksimum pada skema EPA tanpa menggunakan teknik alokasi daya waterfilling, dengan nilai rata-rata average user throughput sebesar 8,78 Mbps dan efisiensi spektral 3,41 bps pada semua skenario yang diujikan. Kata kunci : LTE, SC-FDMA, Waterfilling, Particle Swarm Optimization, Equal Power Allocation, Physical Resource Block

Energy efficient planning and operation models for wireless cellular networks

Prospective demands of next-generation wireless networks are ambitious and will require cellular networks to support 1000 times higher data rates and 10 times lower round-trip latency. While this data deluge is a natural outcome of the increasing number of mobile devices with data hungry applications and the internet of things (IoT), the low latency demand is required by the future interactive applications such as tactile internet , virtual and enhanced reality, and online internet gaming, etc. The motivation behind this thesis is to meet the increasing quality of service (QoS) demands in wireless communications and reduce the global carbon footprint at the same time. To achieve these goals, energy efficient planning and operations models for wireless cellular networks are proposed and analyzed. Firstly, a solution based on the overlay cognitive radio (CR) along with cooperative relaying is proposed to reduce the effect of the scarcity problem of the radio spectrum. In overlay technique, the primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. The achievable cognitive rate of two-way relaying (TWR) system assisted by multiple antennas is proposed. Compared to traditional relaying where the transmission to exchange two different messages between two sources takes place in four time slots, using TWR, the required number of transmission slots reduces to two slots. In the first slot, both sources transmit their signals simultaneously to the relay. Then, during the second slot the relay broadcasts its signal to the sources. Using an overlay CR technique, the CUs are allowed to allocate part of the PUs\u27 spectrum to perform their cognitive transmission. In return, acting as amplify-and-forward (AF) TWR, the CUs are exploited to support PUs to reach their target data rates over the remaining bandwidth. A meta-heuristic approach based on particle swarm optimization algorithm is proposed to find a near optimal resource allocation in addition to the relay amplification matrix gains. Then, we investigate a multiple relay selection scheme for energy harvesting (EH)-based on TWR system. All the relays are considered as EH nodes that harvest energy from renewable and radio frequency sources, where the relays forward the information to the sources. The power-splitting protocol, in which the receiver splits the input radio frequency signal into two components: one for information transmission and the other for energy harvesting, is adopted at the relay side. An approximate optimization framework based on geometric programming is established in a convex form to find near optimal PS ratios, the relays’ transmission power, and the selected relays in order to maximize the total rate utility over multiple time slots. Different utility metrics are considered and analyzed depending on the level of fairness. Secondly, a downlink resource and energy management approach for heterogeneous networks (HetNets) is proposed, where all base stations (BSs) are equipped to harvest energy from renewable energy (RE) sources. A hybrid power supply of green (renewable) and traditional micro-grid, such that the traditional micro-grid is not exploited as long as the BSs can meet their power demands from harvested and stored green energy. Furthermore, a dynamic BS switching ON/OFF combined with the EH model, where some BSs are turned off due to the low traffic periods and their stored energy in order to harvest more energy and help efficiently during the high traffic periods. A binary linear programming (BLP) optimization problem is formulated and solved optimally to minimize the network-wide energy consumption subject to users\u27 certain quality of service and BSs\u27 power consumption constraints. Moreover, green communication algorithms are implemented to solve the problem with low complexity time. Lastly, an energy management framework for cellular HetNets supported by dynamic drone small cells is proposed. A three-tier HetNet composed of a macrocell BS, micro cell BSs (MBSs), and solar powered drone small cell BSs are deployed to serve the networks\u27 subscribers. In addition to the RE, the drones can power their batteries via a charging station located at the macrocell BS site. Pre-planned locations are identified by the mobile operator for possible drones\u27 placement. The objective of this framework is to jointly determine the optimal locations of the drones in addition to the MBSs that can be safely turned off in order to minimize the daily energy consumption of the network. The framework takes also into account the cells\u27 capacities and the QoS level defined by the minimum required receiving power. A BLP problem is formulated to optimally determine the network status during a time-slotted horizon