Integration of a genetic optimisation algorithm in a simulation framework for optimising femtocell networks.

The developments in mobile communication systems from 1G to 4G have increased demands on the network due to the increased number of devices and increasing volume of data and 5G is expected to significantly increase demands further. Therefore, networks need to be more efficient to deliver the expected increase in volume. An energy and cost efficient way to cope with such an anticipated increase in the demand of voice and data is the dense deployment of small cells i.e. femtocells. Femtocells are identified as a crucial way to the delivery of the increased demands for heterogeneous networks in which macrocells work in combination with femtocells to provide coverage to offices, homes and enterprise. A survey of the literature is conducted to examine the mechanisms and approaches different authors have used to optimise the network. One of the major activities in this project before the transfer was the identification of the parameters. The literature was analysed and key performance parameters were identified. Based on the identified key performance parameters, a simulation framework is used to perform the experiments and to analyse the performance of a two-tier LTE-A system having femtocell overlays. A comprehensive and easy to use graphical user interface has been set up with the desired two- tier network topologies. It estimates the throughput and path loss of all the femto and macro users for all the supported bandwidths of an LTE-A system using different modulation schemes. A series of tests are carried out using the described simulation framework for a range of scenarios. The modulation scheme that yield highest throughput for a femtocell user is identified, and path loss is found to be independent from the modulation scheme but is dependent on the distance from its base station. In another series of experiments, the effects that walls inside buildings have on connectivity are examined and positioning of the femtocells is changed for each scenario inside buildings to analyse the performance. These results are used to find the optimised location of femtocells in different room layouts of the building. The simulation framework is further developed to be able to optimise the whole femtocell network by finding the optimised positioning of femtocells using the genetic optimisation algorithm. The end user can provide the inputs of the desired network topology to the simulation framework through a graphical user interface. The throughput and path loss of all the femto users are calculated before and after optimisation. The simulation results are generated in the form of tables before and after optimisation for comparison and analysis. The layouts depicting the indoor environment of the building before and after optimisation can be seen and analysed through the graphical user interface developed as a part of this simulation framework. Two case studies are defined and described to test the capacity and capability of the developed simulation framework and to show how the simulation framework can be used to identify the optimum positions of the femtocells under different configurations of room designs and number of users that represent contrasting loads on the network. Any desired network topology can be created and analysed on the basis of throughput and path loss by using this simulation framework to optimise the femtocell networks in an indoor environment of the building. The results of the experiments are compared against the claims in other published research

Satellite-MEC Integration for 6G Internet of Things: Minimal Structures, Advances, and Prospects

The sixth-generation (6G) network is envisioned to shift its focus from the service requirements of human beings' to those of Internet-of-Things (IoT) devices'. Satellite communications are indispensable in 6G to support IoT devices operating in rural or disastrous areas. However, satellite networks face the inherent challenges of low data rate and large latency, which may not support computation-intensive and delay-sensitive IoT applications. Mobile Edge Computing (MEC) is a burgeoning paradigm by extending cloud computing capabilities to the network edge. By utilizing MEC technologies, the resource-limited IoT devices can access abundant computation resources with low latency, which enables the highly demanding applications while meeting strict delay requirements. Therefore, an integration of satellite communications and MEC technologies is necessary to better enable 6G IoT. In this survey, we provide a holistic overview of satellite-MEC integration. We first discuss the main challenges of the integrated satellite-MEC network and propose three minimal integrating structures. For each minimal structure, we summarize the current advances in terms of their research topics, after which we discuss the lessons learned and future directions of the minimal structure. Finally, we outline potential research issues to envision a more intelligent, more secure, and greener integrated satellite-MEC network

Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning

This study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods.Comment: 16, 1

Cost efficient 5G heterogeneous base station deployment using meta-heuristics

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOver the last two decades, the telecommunication industry has witnessed sustained growth in the number of mobile user devices driven by the introduction of data services, the take-off of the internet and smart user equipment. This growth, which is forecasted to continue, has continued to push the data transfer capacity requirement on mobile networks and has motivated research into the design of 5th generation (5G) mobile networks. A key concern in the design of 5G is the infrastructure and power consumption cost of the base station network which is expected to be significantly more advanced and dense than that of existing conventional mobile networks. This thesis presents an optimisation framework for the cost efficient design of 5G base station networks, based on the application of meta-heuristic algorithms. The presented optimisation framework is centred on the ability to exploit three key technologies of 5G, a heterogonous base station network with small-cells, multi-antenna spatial multiplexing MIMO and cell range extension. The framework includes mathematical integer programming models for supporting the decisions about the optimal base station topology in a 5G mobile network and provides a clear core for the application of meta-heuristics for optimising 5G base station deployment. The core optimisation framework includes the definition of solution encoding/decoding and fitness mechanisms. To increase power consumption awareness of base station network design, an independent base station deployment strategy has been presented and evaluated. Simulation results show that the strategy can improve base station network design power consumption by as much as 34%. The work in this thesis has been extensively evaluated using a simulated 5G mobile network system model. Evaluations of algorithms have been performed through empirical measurements. The main contribution of this thesis is the definition of a clear framework for application fitness based heuristic search in the design of 5G mobile networks

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

On the optimisation of practical wireless indoor and outdoor microcells subject to QOS constraints

Wireless indoor and outdoor microcells (WIOMs) have emerged as a promising means to deal with a high demand of mobile users for a variety of services. Over such heterogeneous networks, the deployment of WIOMs costs mobile/telecommunications company high capital expenditures and operating expenses. This paper aims at optimising the WIOMs taking into account various network communication environments. We first develop an optimisation problem to minimise the number of cells as well as determining their optimal locations subject to the constraints of the coverage and quality-of-service (QoS) requirements. In particular, we propose a binary-search based cell positioning (BSCP) algorithm to find the optimal number of cells given a preset candidate antenna positions. The proposed BSCP algorithm is shown to not only reduce the number of cells for saving resources but also requires a low computational complexity compared to the conventional approaches with exhaustive search over all available sites. Moreover, EDX SignalPro is exploited as a simulation platform to verify the effectiveness of the proposed BSCP for the WIOMs with respect to various propagation modes and antenna parameters of different types, including isotropic, multiple-input single-output and multiple-input multiple-output

On the optimisation of practical wireless indoor and outdoor microcells subject to QOS constraints

Wireless indoor and outdoor microcells (WIOMs) have emerged as a promising means to deal with a high demand of mobile users for a variety of services. Over such heterogeneous networks, the deployment of WIOMs costs mobile/telecommunications company high capital expenditures and operating expenses. This paper aims at optimising the WIOMs taking into account various network communication environments. We first develop an optimisation problem to minimise the number of cells as well as determining their optimal locations subject to the constraints of the coverage and quality-of-service (QoS) requirements. In particular, we propose a binary-search based cell positioning (BSCP) algorithm to find the optimal number of cells given a preset candidate antenna positions. The proposed BSCP algorithm is shown to not only reduce the number of cells for saving resources but also requires a low computational complexity compared to the conventional approaches with exhaustive search over all available sites. Moreover, EDX SignalPro is exploited as a simulation platform to verify the effectiveness of the proposed BSCP for the WIOMs with respect to various propagation modes and antenna parameters of different types, including isotropic, multiple-input single-output and multiple-input multiple-output

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

Agile gravitational search algorithm for cyber-physical path-loss modelling in 5G connected autonomous vehicular network

Based on the characteristics of the 5 G standard defined in Release 17 by 3GPP and that of the emerging Beyond 5 G (or the so-called 6 G) network, cyber-physical systems (CPSs) used in smart transport network infrastructures, such as connected autonomous vehicles (CAV), will significantly depend on the cellular networks. The 5 G and Beyond 5 G (or 6 G) will operate over millimetre-wave (mmWave) bands. These network standards require suitable path loss (PL) models to guarantee effective communication over the network standards of CAV. The existing PL models suffer heavy signal losses and interferences at mmWave bands and may not be suitable for cyber-physical (CP) signal propagation. This paper develops an Agile Gravitational Search Algorithm (AGSA) that mitigates the PL and signal interference problems in the 5G–NR network for CAV. On top of that, a modified Okumura-Hata model (OHM) suitable for deployment in CP terrestrial mobile networks is derived for the CAV-CPS application. These models are tested on the real-world 5 G infrastructure. Results from the simulated models are compared with measured data for the modified, enhanced model and four other existing models. The comparative evaluation shows that the modified OHM and AGSA performed better than existing OHM, COST, and ECC-33 models by 90%. Also, the modified OHM demonstrated reduced signal interference compared to the existing models. In terms of optimisation validation, the AGSA scheme outperforms the Genetic algorithm, Particle Swarm Optimisation, and OHM models by at least 57.43%. On top of that, the enhanced AGSA outperformed existing PL (i.e., Okumura, Egli, Ericson 999, and ECC-33 models) by at least 67%, thus presenting the potential for efficient service provisioning in 5G-NR driverless car applications