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

Optimization Methods for Heterogeneous Wireless Communication Networks: Planning, Configuration and Operation

With the fourth generation of wireless radio communication networks reaching maturity, the upcoming fifth generation (5G) is a major subject of current research. 5G networks are designed to achieve a multitude of performance gains and the ability to provide services dedicated to various application scenarios. These applications include those that require increased network throughput, low latency, high reliability and support for a very high number of connected devices. Since the achieved throughput on a single point-to-point transmission is already close to the theoretical optimum, more efforts need to be invested to enable further performance gains in 5G. Technology candidates for future wireless networks include using very large antenna arrays with hundreds of antenna elements or expanding the bandwidth used for transmission to the millimeter-wave spectrum. Both these and other envisioned approaches require significant changes to the network architecture and a high economic commitment from the network operator. An already well established technology for expanding the throughput of a wireless communication network is a densification of the cellular layout. This is achieved by supplementing the existing, usually high-power, macro cells with a larger number of low-power small cells, resulting in a so-called heterogeneous network (HetNet). This approach builds upon the existing network infrastructure and has been shown to support the aforementioned technologies requiring more sophisticated hardware. Network densification using small cells can therefore be considered a suitable bridging technology to path the way for 5G and subsequent generations of mobile communication networks. The most significant challenge associated with HetNets is that the densification is only beneficial for the overall network performance up to a certain density, and can be harmful beyond that point. The network throughput is limited by the additional interferences caused by the close proximity of cells, and the economic operability of the network is limited by the vastly increased energy consumption and hardware cost associated with dense cell deployment. This dissertation addresses the challenge of enabling reliable performance gains through network densification while guaranteeing quality-of-service conditions and economic operability. The proposed approach is to address the underlying problem vertically over multiple layers, which differ in the time horizon on which network optimization measures are initiated, necessary information is gathered, and an optimized solutions are found. These time horizons are classified as network planning phase, network configuration phase, and network operation phase. Optimization schemes are developed for optimizing the resource- and energy consumption that operate mostly in the network configuration phase. Since these approaches require a load-balanced network, schemes to achieve and maintain load balancing between cells are introduced for the network planning phase and operation phase, respectively. For the network planning phase, an approach is proposed for optimizing the locations of additional small cells in an existing wireless network architecture, and to schedule their activity phases in advance according to data demand forecasts. Optimizing the locations of multiple cells jointly is shown to be superior to deploying them one-by-one based on greedy heuristic approaches. Furthermore, the cell activity scheduling obtains the highest load balancing performance if the time-schedule and the durations of activity periods is jointly optimized, which is an approach originating from process engineering. Simulation results show that the load levels of overloaded cells can be effectively decreased in the network planning phase by choosing optimized deployment locations and cell activity periods. Operating the network with a high resource efficiency while ensuring quality-of-service constraints is addressed using resource optimization in the network configuration phase. An optimization problem to minimize the resource consumption of the network by operating multiple separated resource slices is designed. The originally problem, which is computationally intractable for large networks, is reformulated with a linear inner approximation, that is shown to achieve close to optimal performance. The interference is approximated with a dynamic model that achieves a closer approximation of the actual cell load than the static worst-case model established in comparable state-ot-the art approaches. In order to mitigate the increase in energy consumption associated with the increase in cell density, an energy minimization problem is proposed that jointly optimizes the transmit power and activity status of all cells in the network. An original problem formulation is designed and an inner approximation with better computational tractability is proposed. Energy consumption levels of a HetNet are simulated for multiple energy minimization approaches. The proposed method achieves lower energy consumption levels than approaches based on an exhaustive search over all cell activity configurations or heuristic power scaling. Additionally, in simulations, the likelihood of finding an energy minimized solution that satisfies quality-of-service constraints is shown to be significantly higher for the proposed approach. Finally, the problem of maintaining load balancing while the network is in operation is addressed with a decentralized scheme based on a learning system using multi-class support vector machines. Established methods often require significant information exchange between network entities and a centralized optimization of the network to achieve load balancing. In this dissertation, a decentralized learning system is proposed that globally balance the load levels close to the optimal solution while only requiring limited local information exchange

Efficient Discovery and Utilization of Radio Information in Ultra-Dense Heterogeneous 3D Wireless Networks

Emergence of new applications, industrial automation and the explosive boost of smart concepts have led to an environment with rapidly increasing device densification and service diversification. This revolutionary upward trend has led the upcoming 6th-Generation (6G) and beyond communication systems to be globally available communication, computing and intelligent systems seamlessly connecting devices, services and infrastructure facilities. In this kind of environment, scarcity of radio resources would be upshot to an unimaginably high level compelling them to be very efficiently utilized. In this case, timely action is taken to deviate from approximate site-specific 2-Dimensional (2D) network concepts in radio resource utilization and network planning replacing them with more accurate 3-Dimensional (3D) network concepts while utilizing spatially distributed location-specific radio characteristics. Empowering this initiative, initially a framework is developed to accurately estimate the location-specific path loss parameters under dynamic environmental conditions in a 3D small cell (SC) heterogeneous networks (HetNets) facilitating efficient radio resource management schemes using crowdsensing data collection principle together with Linear Algebra (LA) and machine learning (ML) techniques. According to the results, the gradient descent technique is with the highest path loss parameter estimation accuracy which is over 98%. At a latter stage, receive signal power is calculated at a slightly extended 3D communication distances from the cluster boundaries based on already estimated propagation parameters with an accuracy of over 74% for certain distances. Coordination in both device-network and network-network interactions is also a critical factor in efficient radio resource utilization while meeting Quality of Service (QoS) requirements in heavily congested future 3D SCs HetNets. Then, overall communication performance enhancement through better utilization of spatially distributed opportunistic radio resources in a 3D SC is addressed with the device and network coordination, ML and Slotted-ALOHA principles together with scheduling, power control and access prioritization schemes. Within this solution, several communication related factors like 3D spatial positions and QoS requirements of the devices in two co-located networks operated in licensed band (LB) and unlicensed band (UB) are considered. To overcome the challenge of maintaining QoS under ongoing network densification and with limited radio resources cellular network traffic is offloaded to UB. Approximately, 70% better overall coordination efficiency is achieved at initial network access with the device network coordinated weighting factor based prioritization scheme powered with the Q-learning (QL) principle over conventional schemes. Subsequently, coverage information of nearby dense NR-Unlicensed (NR-U) base stations (BSs) is investigated for better allocation and utilization of common location-specific spatially distributed radio resources in UB. Firstly, the problem of determining the receive signal power at a given location due to a transmission done by a neighbor NR-U BS is addressed with a solution based on a deep regression neural network algorithm enabling to predict receive signal or interference power of a neighbor BS at a given location of a 3D cell. Subsequently, the problem of efficient radio resource management is considered while dynamically utilizing UB spectrum for NR-U transmissions through an algorithm based on the double Q-learning (DQL) principle and device collaboration. Over 200% faster algorithm convergence is achieved by the DQL based method over conventional solutions with estimated path loss parameters

Enabling Millimeter Wave Communications for Use Cases of 5G and Beyond Networks

The wide bandwidth requirements of the fifth generation (5G) and beyond networks are driving the move to millimeter wave (mmWave) bands where it can provide a huge increase in the available bandwidth. Increasing the bandwidth is an effective way to improve the channel capacity with limited power. Moreover, the short wavelengths of such bands enable massive number of antennas to be integrated together in small areas. With such massive number of antennas, narrow beamwidth beams can be obtained which in turn can improve the security. Furthermore, the massive number of antennas can help in mitigating the severe path-loss at mmWave frequencies, and realize high data rate communication at reasonable distances. Nevertheless, one of the main bottlenecks of mmWave communications is the signal blockage. This is due to weak diffraction ability and severe penetration losses by many common building materials such as brick, and mortar as well as the losses due to human bodies. Thus, user mobility and/or small movements of obstacles and reflectors cause rapid channel gain variations which leads to unreliable communication links. The harsh propagation environment at such high frequencies makes it hard to provide a reliable service, hence, maintaining connectivity is one key design challenge in mmWave networks. Relays represent a promising approach to improve mmWave connectivity where they can redirect the signal to avoid the obstacles existing in the propagation environment. However, routing in mmWave networks is known to be a very challenging problem due to the inherent propagation characteristics of mmWave frequencies. Furthermore, inflexible routing technique may worsen network performance and increase scheduling overhead. As such, designing an appropriate transmission routing technique for each service is a crucial issue in mmWave networks. Indeed, multiple factors must be taken into account in the routing process, such as guaranteeing the robustness of network connectivity and providing high data rates. In this thesis, we propose an analytical framework to investigate the network reliability of mmWave relaying systems for multi-hop transmissions. We also propose a flexible routing technique for mmWave networks, namely the $n^{\rm th}$ best routing technique. The performance of the proposed routing technique is investigated using tools from stochastic geometry. The obtained results provide useful insights on adjusting the signal noise ratio (SNR) threshold for decode and forward (DF) relay according to the order of the best relay, blockage and relay densities in order to improve spectral efficiency. We also propose a novel mathematical framework to investigate the performance of two appropriate routing techniques for mmWave networks, namely minimum hop count (MHC) and nearest LoS relay to the destination with MHC (NLR-MHC) to support wide range of use cases for 5G and beyond networks. Analytical models are provided to evaluate the performance of the proposed techniques using tools from stochastic geometry. In doing so, we model the distribution of hop count using phase-type distribution, and then we use this distribution to derive analytical results for the coverage probability and spectral efficiency. Capitalizing on the derived results, we introduce a comprehensive study of the effects of different system parameters on the performance of multi-hop mmWave systems. These findings provide important insights for designing multi-hop mmWave networks with better performance. Furthermore, we adapt the proposed relay selection technique for IoT devices in mmWave relaying systems to prolong the IoT device’s battery life. The obtained results reveal the trade-off between the network connectivity and the energy consumption of IoT devices. Lastly, we have exploited the enormous bandwidth available in the mmWave band to support reliable fronthaul links for cell-free (CF) massive multiple-input multiple-output (MIMO). We provide a comprehensive investigation of different system parameters on the uplink (UL) performance of mmWave fronthaul-based CF mMIMO systems. Results reveal that increasing the access point (AP) density beyond a certain limit would not achieve further improvement in the UL data rates. Also, the higher number of antennas per AP may even cause UL data rates degradation

Hybrid generalized non-orthogonal multiple access for the 5G wireless networks.

Master of Science in Computer Engineering. University of KwaZulu-Natal. Durban, 2018.The deployment of 5G networks will lead to an increase in capacity, spectral efficiency, low latency and massive connectivity for wireless networks. They will still face the challenges of resource and power optimization, increasing spectrum efficiency and energy optimization, among others. Furthermore, the standardized technologies to mitigate against the challenges need to be developed and are a challenge themselves. In the current predecessor LTE-A networks, orthogonal frequency multiple access (OFDMA) scheme is used as the baseline multiple access scheme. It allows users to be served orthogonally in either time or frequency to alleviate narrowband interference and impulse noise. Further spectrum limitations of orthogonal multiple access (OMA) schemes have resulted in the development of non-orthogonal multiple access (NOMA) schemes to enable 5G networks to achieve high spectral efficiency and high data rates. NOMA schemes unorthogonally co-multiplex different users on the same resource elements (RE) (i.e. time-frequency domain, OFDMA subcarrier, or spreading code) via power domain (PD) or code domain (CD) at the transmitter and successfully separating them at the receiver by applying multi-user detection (MUD) algorithms. The current developed NOMA schemes, refered to as generalized-NOMA (G-NOMA) technologies includes; Interleaver Division Multiple Access (IDMA, Sparse code multiple access (SCMA), Low-density spreading multiple access (LDSMA), Multi-user shared access (MUSA) scheme and the Pattern Division Multiple Access (PDMA). These protocols are currently still under refinement, their performance and applicability has not been thoroughly investigated. The first part of this work undertakes a thorough investigation and analysis of the performance of the existing G-NOMA schemes and their applicability. Generally, G-NOMA schemes perceives overloading by non-orthogonal spectrum resource allocation, which enables massive connectivity of users and devices, and offers improved system spectral efficiency. Like any other technologies, the G-NOMA schemes need to be improved to further harvest their benefits on 5G networks leading to the requirement of Hybrid G-NOMA (G-NOMA) schemes. The second part of this work develops a HG-NOMA scheme to alleviate the 5G challenges of resource allocation, inter and cross-tier interference management and energy efficiency. This work develops and investigates the performance of an Energy Efficient HG-NOMA resource allocation scheme for a two-tier heterogeneous network that alleviates the cross-tier interference and improves the system throughput via spectrum resource optimization. By considering the combinatorial problem of resource pattern assignment and power allocation, the HG-NOMA scheme will enable a new transmission policy that allows more than two macro-user equipment’s (MUEs) and femto-user equipment’s (FUEs) to be co-multiplexed on the same time-frequency RE increasing the spectral efficiency. The performance of the developed model is shown to be superior to the PD-NOMA and OFDMA schemes

A Comprehensive Study of Multiple Access Techniques in 6G Networks

With the proliferation of numerous burgeoning services such as ultra-reliable low-latency communication (URLLC), massive machine type communications (mMTC), enhanced mobile broadband (eMBB), among others, wireless communication systems are expected to face daunting challenges. In order to satisfy these ever-increasing traffic demands, diverse quality-of-services (QoS) requirements, and the massive connectivity accompanied by these new applications, various innovative and promising technologies, and architectures need to be developed. Novel multiple-access techniques are currently being explored in both academia and industry in order to accommodate such unprecedented requirements. Non-orthogonal multiple access (NOMA) has been deemed as one of the vital enabling multiple access techniques for the upcoming six-generation (6G) networks. This is due to its ability to enhance network spectral efficiency (NSE) and support a massive number of connected devices. Owing to its potential benefits, NOMA is recognized as a prominent member of next-generation multiple access (NGMA). Several emerging techniques such as full-duplex (FD) communication, device-to-device (D2D) communications, reconfigurable intelligent surface (RIS), coordinated multipoint (CoMP), cloud radio access networks, are being gradually developed to address fundamental problems in future wireless networks. In this thesis, and with the goal of converging toward NGMA, we investigate the synergistic integration between NOMA and other evolving physical layer technologies. Specifically, we analyze this integration aiming at improving the performance of cell-edge users (CEUs), mitigating the detrimental effect of inter-cell interference (ICI), designing energy-efficient multiple access toward ``green’’ wireless networks, guarantying reliable communication between NOMA UEs and base stations (BSs)/remote radio heads (RRHs), and maintaining the required QoS in terms of the minimum achievable data rate, especially at CEUs. Regarding the ICI mitigation in multi-cell NOMA networks and tackling the connectivity issue in traditional CoMP-based OMA networks, we first investigate the integration between location-aware CoMP transmission and NOMA in downlink heterogeneous C-RAN. In doing so, we design a novel analytical framework using tools from stochastic geometry to analyze the system performance in terms of the average achievable data rate per NOMA UE. Our results reveal that CoMP NOMA can provide a significant gain in terms of network spectral efficiency compared to the traditional CoMP OMA scheme. In addition, with the goal of further improving the performance of CEUs and user fairness, cooperative transmission with the aid of D2D communication and FD or half-duplex (HD) transmission, has been introduced to NOMA, which is commonly known as cooperative NOMA (C-NOMA). As a result, we extend our study to also investigate the potential gains of investigating CoMP and C-NOMA. In such a framework, we exploit the cooperation between the RRHs/BSs and the successive decoding strategy at NOMA UEs that are near the RRHs/BSs. Specifically, we investigate both performance analysis and resource management optimization (power control and user pairing). Our results show that the transmit power at the BS, the transmit power at the relay user, and the self-interference (SI) value at the relay user determine which multiple access technique, CoMP NOMA, CoMP HD C-NOMA, and CoMP FD C-NOMA, should be adopted at the BSs. Now, to assist in designing energy-efficient multiple access techniques and guarantying reliable communication for NOMA UEs, this thesis explores the interplay between FD/HD C-NOMA and RIS. We show that the proposed model has the best performance in terms of network power consumption compared to other multiple access techniques in the literature, which leads to ``green'' future wireless networks. Moreover, our results show that the network power consumption can be significantly reduced by increasing the number of RIS elements. A more significant finding is that the location of the RIS depends on the adopted multiple access techniques. For example, it is not recommended to deploy the RIS besides the BS if the adopted multiple access is HD C-NOMA. Another insight that has been unveiled is the FD C-NOMA with the assistance of RIS has more resistance to the residual SI effect, due to the FD transmission, and can tolerate high SI values compared to the same scheme without RIS. Although much work has been conducted to improve the network spectral efficiency of multi-cell NOMA cellular networks, the required QoS by the upcoming 6G applications, in terms of the minimum achievable rate, may not be guaranteed at CEUs. This is due to their distant locations from their serving BSs, and thus, they experience severe path-loss attenuation and high ICI. This thesis addresses this research gap by studying the synergistic integration between RIS, NOMA, and CoMP in a multi-user multi-cell scenario. Unlike the developed high-complexity optimal solutions or the low-complexity sub-optimal solutions in the literature for the power allocation problem, we derive a low-complexity optimal solution in a such challenging scenario. We also consider the interdependency between the user clustering policies in different coordinated cells, which has been ignored in the literature. Finally, we prove that this integration between RIS, NOMA, and CoMP can attain a high achievable rate for CEUs, ameliorate spectral efficiency compared to existing literature, and can form a novel paradigm for NGMA