Multi-slope path loss model-based performance assessment of heterogeneous cellular network in 5G

The coverage and capacity required for fifth generation (5G) and beyond can be achieved using heterogeneous wireless networks. This exploration set up a limited number of user equipment (UEs) while taking into account the three-dimensional (3D) distance between UEs and base stations (BSs), multi-slope line of sight (LOS) and non-line of sight (n-LOS), idle mode capability (IMC), and third generation partnership projects (3GPP) path loss (PL) models. In the current work, we examine the relationship between the height and gain of the macro (M) and pico (P) base stations (BSs) antennas and the ratio of the density of the MBSs to the PBSs, indicated by the symbol $\beta$. Recent research demonstrates that the antenna height of PBSs should be kept to a minimum to get the best performance in terms of coverage and capacity for a 5G wireless network, whereas ASE smashes as $\beta$ crosses a specific value in 5G. We aim to address these issues and increased the performance of the 5G network by installing directional antennas at MBSs and omnidirectional antennas at Pico BSs while taking into consideration traditional antenna heights. The authors of this work used the multi-tier 3GPP PL model to take into account real-world scenarios and calculated SINR using average power. This study demonstrates that, when the multi-slope 3GPP PL model is used and directional antennas are installed at MBSs, coverage can be improved 10% and area spectral efficiency (ASE) can be improved 2.5 times over the course of the previous analysis. Similarly to this, the issue of an ASE crash after a base station density of 1000 has been resolved in this study. © 2013 IEEE

Distributed Resource Allocation and Performance Analysis in 5G Wireless Cellular Networks

This thesis focuses on the study of Heterogeneous Networks (HetNets), Device-to-device (D2D) communication networks, and unmanned aerial vehicle (UAV) networks in fifth generation wireless communication (5G) systems. HetNets that consist of macro-cells and small-cells have become increasingly popular in current wireless networks and 5G systems to meet the exponentially growing demand for higher data rates. Compared to conventional homogeneous cellular networks, the disparity of transmission power among different types of base stations (BSs), the relatively random deployment of SBSs, and the densifying networks, bring new challenges, such as the imbalanced load between macro and small cells and severe inter-cell interference. In the other hand, with the skyrocketing number of tablets and smart phones, the notion of caching popular content in the storage of BSs and users' devices is proposed to reduce duplicated wireless transmissions. To fulfill multi-fold communication requirements from humans, machine, and things, the 5G systems which include D2D communications, UAV communications, and so on, can improve the network performance. Among them, the performance analyses of these emerging technologies are attracting much attention and should be investigated first. This thesis focuses on these hot issues and emerging technologies in 5G systems, analyzing the network performance and conducting the allocation of available resources, such as serving BSs, spectrum resources, and storage resources. Specifically, three main research focuses are included in the thesis. The first focus of this thesis is the impact of the BS idle mode capacity (IMC) on the network performance of multi-tier and dense HCNs with both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. I consider a more practical set-up with a finite number of UEs in the analysis. Moreover, the SBSs apply a positive power bias in the cell association procedure, so that macrocell UEs are actively encouraged to use the more lightly loaded SBSs. In addition, to address the severe interference that these cell range expanded UEs may suffer, the MBSs apply enhanced inter-cell interference coordination (eICIC), in the form of almost blank subframe (ABS) mechanism. For this model, I derive the coverage probability and the rate of a typical UE in the whole network or a certain tier. The impact of the IMC on the performance of the network is shown to be significant. In particular, it is important to note that there will be a surplus of BSs when the BS density exceeds the UE density, and thus a large number of BSs switch off. As a result, the overall coverage probability, as well as the area spectral efficiency (ASE), will continuously increase with the BS density, addressing the network outage that occurs when all BSs are active and the interference becomes LoS dominated. Finally, the optimal ABS factors are investigated in different BS density regions. One of major findings is that MBSs should give up all resources in favor of the SBSs when the small cell networks go ultra-dense. This reinforces the need for orthogonal deployments, shedding new light on the design and deployment of the future 5G dense HCNs. The second focus of this thesis is the content caching in D2D communication networks. In practical deployment, D2D content caching has its own problem that is not all of the user devices are willing to share the content with others due to numerous concerns such as security, battery life, and social relationship. To solve this problem, I consider the factor of social relationship in the deployment of D2D content caching. First, I apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in the analysis to obtain the average downloading delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, I develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance, but also takes into account the social relationship between D2D users. The simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, the work sheds insights on the design of D2D caching in the practical deployment of 5G networks. The third focus of this thesis is the performance analysis for practical UAV-enabled networks. By considering both LoS and NLoS transmissions between aerial BSs and ground users, the coverage probability and the ASE are derived. Considering that there is no consensus on the path loss model for studying UAVs in the literature, in this focus, three path loss models, i.e., high-altitude model, low-altitude model, and ultra-low-altitude model, are investigated and compared. Moreover, the lower bound of the network performance is obtained assuming that UAVs are hovering randomly according to homogeneous Poisson point process (HPPP), while the upper bound is derived assuming that UAVs can instantaneously move to the positions directly overhead ground users. From the analytical and simulation results for a practical UAV height of 50 meters, I find that the network performance of the high-altitude model and the low-altitude model exhibit similar trends, while that of the ultra-low-altitude model deviates significantly from the above two models. In addition, the optimal density of UAVs to maximize the coverage probability performance has also been investigated

Performance Evaluation of Ultra-Dense Networks with Applications in Internet-of-Things

The new wireless era in the next decade and beyond would be very different from our experience nowadays. The fast pace of introducing new technologies, services, and applications requires the researchers and practitioners in the field be ready by making paradigm shifts. The stringent requirements on 5G networks, in terms of throughput, latency, and connectivity, challenge traditional incremental improvement in the network performance. This urges the development of unconventional solutions such as network densification, massive multiple-input multiple-output (massive MIMO), cloud-based radio access network (C-RAN), millimeter Waves (mmWaves), non-orthogonal multiple access (NOMA), full-duplex communication, wireless network virtualization, and proactive content-caching to name a few. Ultra-Dense Network (UDN) is one of the preeminent technologies in the racetrack towards fulfilling the requirements of next generation mobile networks. Dense networks are featured by the deployment of abundant of small cells in hotspots where immense traffic is generated. In this context, the density of small cells surpasses the active users’ density providing a new wireless environment that has never been experienced in mobile communication networks. The high density of small cells brings the serving cells much closer to the end users providing a two-fold gain where better link quality is achieved and more spatial reuse is accomplished. In this thesis, we identified the distinguishing features of dense networks which include: close proximity of many cells to a given user, potential inactivity of most base stations (BSs) due to lack of users, drastic inter-cell interference in hot-spots, capacity limitation by virtue of the backhaul bottleneck, and fundamentally different propagation environments. With these features in mind, we recognized several problems associated with the performance evaluation of UDN which require a treatment different from traditional cellular networks. Using rigorous advanced mathematical techniques along with extensive Monte Carlo simulations, we modelled and analytically studied the problems in question. Consequently, we developed several mathematical frameworks providing closed-form and easy-computable mathematical instruments which network designers and operators can use to tune the networks in order to achieve the optimal performance. Moreover, the investigations performed in this thesis furnish a solid ground for addressing more problems to better understand and exploit the UDN technology for higher performance grades. In Chapter 3, we propose the multiple association in dense network environment where the BSs are equipped with idle mode capabilities. This provides the user with a “data-shower,” where the user’s traffic is split into multiple paths, which helps overcoming the capacity limitations imposed by the backhaul links. We evaluate the performance of the proposed association scheme considering general fading channel distributions. To this end, we develop a tractable framework for the computation of the average downlink rate. In Chapter 4, we study the downlink performance of UDNs considering Stretched Exponential Path-Loss (SEPL) to capture the short distances of the communication links. Considering the idle mode probability of small cells, we draw conclusions which better reflect the performance of network densification considering SEPL model. Our findings reveal that the idle mode capabilities of the BSs provide a very useful interference mitigation technique. Another interesting insight is that the system interference in idle mode capable UDNs is upper-bounded by the interference generated from the active BSs, and in turn, this is upper-bounded by the number of active users where more active users is translated to more interference in the system. This means that the interference becomes independent of the density of the small cells as this density increases. In Chapter 5, we provide the derivation of the average secrecy rate in UDNs considering their distinct traits, namely, idle mode BSs and LOS transmission. To this end, we exploit the standard moment generating function (MGF)-based approach to derive relatively simple and easily computable expressions for the average secrecy rate considering the idle mode probability and Rician fading channel. The result of this investigation avoids the system level simulations where the performance evaluation complexity can be greatly reduced with the aid of the derived analytical expressions. In Chapter 6, we model the uplink coverage of mMTC deployment scenario considering a UDN environment. The presented analysis reveals the significant and unexpected impact of the high density of small cells in UDNs on the maximum transmit power of the MTC nodes. This finding relaxes the requirements on the maximum transmit power which in turn allows for less complexity, brings more cost savings, and yields much longer battery life. This investigation provides accurate, simple, and insightful expressions which shows the impact of every single system parameter on the network performance allowing for guided tunability of the network. Moreover, the results signify the asymptotic limits of the impact of all system parameters on the network performance. This allows for the efficient operation of the network by designing the system parameters which maximizes the network performance. In Chapter 7, we address the impact of the coexistence of MTC and HTC communications on the network performance in UDNs. In this investigation, we study the downlink network performance in terms of the coverage probability and the cell load where we propose two association schemes for the MTC devices, namely, Connect-to-Closest (C2C) and Connect-to-Active (C2A). The network performance is then analyzed and compared in both association schemes. In Chapter 8, we model the uplink coverage of HTC users and MTC devices paired together in NOMA-based radio access. Closed-form and easy-computable analytical results are derived for the considered performance metrics, namely the uplink coverage and the uplink network throughput. The analytical results, which are validated by extensive Monte Carlo simulations, reveal that increasing the density of small cells and the available bandwidth significantly improves the network performance. On the other side, the power control parameters has to be tuned carefully to approach the optimal performance of both the uplink coverage and the uplink network throughput

Fine-grained performance analysis of massive MTC networks with scheduling and data aggregation

Abstract. The Internet of Things (IoT) represents a substantial shift within wireless communication and constitutes a relevant topic of social, economic, and overall technical impact. It refers to resource-constrained devices communicating without or with low human intervention. However, communication among machines imposes several challenges compared to traditional human type communication (HTC). Moreover, as the number of devices increases exponentially, different network management techniques and technologies are needed. Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. This thesis provides an overview of the most common IoT applications and the network technologies to support them. We describe the most important challenges in machine type communication (MTC). We use a stochastic geometry (SG) tool known as the meta distribution (MD) of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the wireless nodes’ locations, to provide a fine-grained description of the per-link reliability. Specifically, we analyze the performance of two scheduling methods for data aggregation of MTC: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements. Finally, the impact on the fraction of MTDs that communicate with a target reliability when increasing the aggregators density is investigated

Ultra-Dense Networks in 5G and Beyond: Challenges and Promising Solutions

Ultra-Dense Network (UDN) is one of the promising and leading directions in Fifth Generation and beyond (5GB) networks. In UDNs, Small Cells (SCs) or Small Base Stations (SBSs) such as microcells, picocells, or femtocells are deployed in high densities where inter-site distances are within the range of few or tens of meters. UDNs also require that SCs are typically deployed in relatively large densities compared to the Human-Type Communication Users (HTCUs) such as smartphones, tablets, and/or laptops. Such SCs are characterized by their low transmission powers, small coverage areas, and low cost. Hence, the deployment of the SCs can be done either by the cellular network operators or by the customers themselves within their premises to maintain certain levels of Quality of Service (QoS). However, the randomness of the deployment of the SCs along with the small inter-site distances may degrade the achievable performance due to the uncontrolled Inter-Cell Interference (ICI). Therefore, idle mode capability is an inevitable feature in the high-density regime of SCs. In idle mode, a SC is switched off to prevent ICI when no user is associated to it. In doing so, we can imagine the UDN as a mobile network that keeps following the users to remain as close as possible to them. In 5G, different use cases are required to be supported such as enhanced Mobile Broad-Band (eMBB), Ultra-Reliable and Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). On one hand, the inevitable upcoming era of smart living requires unprecedented advances in enabling technologies to support the main building blocks of this era which are Internet of Things (IoT) devices. Machine-Type Communication (MTC), the cellular version of Machine-to-Machine (M2M) communication, constitutes the main enabling technology to support communications among such devices with minimal or even without human intervention. The massive number of these devices, Machine-Type Communication Devices (MTCDs), and the immense amount of traffic generated by them require a paramount shift in cellular and non-cellular wireless technologies to achieve the required connectivity. On the other hand, the sky-rocketing number of data hungry applications installed on human-held devices, or HTCUs, such as video conferencing and virtual reality applications require their own advances in the wireless infrastructure in terms of high capacity, enhanced reliability, and reduced latency. Throughout this thesis, we exploit the UDN infrastructure integrated with other 5G resources and enabling technologies to explore the possible opportunities in supporting both HTC and MTC, either solely or simultaneously. Given the shorter distances between transmitters and receivers encountered in UDNs, more realistic models of the path loss must be adopted such as the Stretched Exponential Path Loss (SEPL) model. We use tools from stochastic geometry to formulate novel mathematical frameworks that can be used to investigate the achievable performance without having to rely on extensive time-consuming Monte-Carlo simulations. Besides, the derived analytical expressions can be used to tune some system parameters or to propose some approaches/techniques that can be followed to optimize the performance of the system under certain circumstances. Tackling practical scenarios, the complexity, or sometimes in-feasibility, of providing unlimited backhaul capacity for the massive number of SCs must be considered. In this regard, we adopt multiple-association where each HTCU is allowed to associate with multiple SCs. By doing so, we carefully split the targeted traffic among several backhaul links to mitigate the bottleneck forced by limited backhaul capacities. It is noteworthy that for coexisting MTCDs with the HTCUs, activating more SCs would allow more MTCDs to be supported without introducing additional ICI towards the HTCUs. Targeting different application, multiple-association can be also adopted to tackle computation-intensive applications of HTCUs. In particular, for applications such as augmented reality and environment recognition that require heavy computations, a task is split and partially offloaded to multiple SCs with integrated Edge Computing Servers (ECSs). Then, the task partitions are processed in parallel to reduce the end-to-end processing delay. Based on relative densities between HTCUs and SCs, we use tools from stochastic geometry to develop an offline adaptive task division technique that further reduces the average end-to-end processing delay per user. With the frequent serious data breaches experienced in recent years, securing data has become more of a business risk rather than an information technology (IT) issue. Hence, we exploit the dense number of SCs found in UDN along with Physical Layer Security (PLS) protocols to secure data transfer. In particular, we again adopt multiple-association and split the data of HTCUs into multiple streams originating from different SCs to prevent illegitimate receivers from eavesdropping. To support massive number of MTCDs, we deploy the Non-Orthogonal Multiple-Access (NOMA) technique. Using power NOMA, more than one device can be supported over the same frequency/time resource and their signals are distinguished at the receiver using Successive Interference Cancellation (SIC). In the same scope, exploiting the available resources in 5G and beyond networks, we investigate a mMTC scenario in an UDN operating in the Millimeter Wave (mmWave) band and supported by wireless backhauling. In doing so, we shed lights on the possible gains of utilizing the mmWave band where the severe penetration losses of mmWave can be exploited to mitigate the significant ICI in UDNs. Also, the vast bandwidth available in the mmWave band helps to allocate more Resource Blocks (RBs) per SCs which corresponds to supporting more MTCDs

5G network deployment and the associated energy consumption in the UK: A complex systems’ exploration

Investing in the communication infrastructure transition requires significant scientific consideration of challenges, prioritisation, risks and uncertainties. To address these challenges, a bottom-up approach was used to demonstrate the future of wireless network transmission and deployment. This study developed an agent-based model to explore the future deployment of non-standalone 5G networks, synthesizing multi-dimensional data visualization. In particular, this research took the UK as an example to investigate the spatiotemporal dynamic characteristics of 5G evolution, and further analysed the energy consumption and carbon footprint of 5G networks, as well as the consequent change in the operating expenses pattern. The simulation results show that 700 MHz and 26 GHz will play an important role in 5G deployment in the UK, which allow base stations to meet short-term and long-term data traffic demands respectively. Furthermore, due to the geopolitical restrictions and embargos, telecommunications may face additional costs of £0.63bn to £1.19bn when deploying 5G radio access networks. Network densification may cause some environmental and economic problems. Take a medium demand scenario as an example, it is found that the electricity consumed by the 5G radio access network will account for more than 2.1% of the total electricity generation, and indirectly lead to 990,404 tonnes carbon emissions in 2030