A novel new waveform is compared with two other existing waveforms to create a radar network. In this radar network one primary radar transmits a waveform for sensing and communication and one or multiple radars receive these waveforms. The constraint for the radars is that all radars can build up a radar picture and situation awareness. What also means that the performance of the primary radar does not degrade. This new radar type is called cooperative hitchhiker with communications. In this radar network the main task is sensing, therefore additional communication signals are used to increase the performance of the sensing task. The advantages of parallel sensing and communications are reducing interference, dual use of the scarce electromagnetic (EM) spectrum in a congested EM environment and it is possible in a bistatic configuration to detect a bistatic scattering object. The three waveforms which are compared are phase coded frequency modulated continuous wave (PC FMCW) linear frequency modulated – minimum shift keying (LFM-MSK) and a new waveform time delay between radar bursts (TDBRB). TDBRB is a simple but effective communication method on top of the sensing waveform and can be used with staggered waveforms. Where the first two waveforms make use of binary phase shift keying (BPSK), TDBRB uses a time modulation with the inserted time delay between two successive radar bursts. To distinguish the two radar bursts the first burst has a linear frequency modulated (LFM) upchirp and the successive burst, after the inserted time delay, has an LFM downchirp. PC FMCW and LFM-MSK data are compared with simulations and an experiment of the TDBRB waveform. The conclusions are that PC FMCW has the highest data rate, followed by LFM-MSK, and TDBRB has the lowest data rate for line-of-sight connections. The first two make use of the information in one pulse, or FMCW chirp. This results in a lower signal-to-noise ratio (SNR) and is therefore only suited for line-of-sight connections. With a matched filter and coherent integration of the TDBRB signals, this waveform is most suited for non-line-of-sight connections and communication over an object-of-interest at the cost of a lower bit rate. TDBRB has a similar performance for the different Swerling cases as the detection probability of a regular radar. The degradation in performance with the TDBRB waveform is only a fraction of the burst time because the time delay is added to an original radar burst. These results make TDBRB most suited for non-line-of-sight communication and for communications with a low signal-to-noise ratio.

The Fifth Generation (5G) of mobile networks exploit both sub-6 GHz and millimeter-wave (mmWave) spectrum. The sub-6 GHz spectrum comprises frequencies up to 6 GHz and provides large geographical coverage for radio signal in 5G. The mm-wave spectrum on the other hand, comprises higher frequencies ranging from 24 GHz -100 GHz and plays a major role in serving high data rates for the 5G technology. The evolution of 5G plays a pivotal role in the realization of challenging applications like Social XR conferences, which requires the network to deal with heavy traffic while maintaining low end-to-end latencies. The right configuration of the radio access network becomes crucial for such applications. The introduction of Massive Multiple Input Multiple Output (MIMO) technology in the radio network, concentrates the signal energy to the target user, which significantly improves the throughput and efficiency of the system. The quality and capacity of the radio channels also depend on the downlink Channel State Information (CSI). The CSI when obtained accurately at the Base Station (BS), plays a significant role in reaping the best benets out of Multiple Input Multiple Output (MIMO) technology. This thesis explores (or assesses) the different options and configurations of CSI feedback using an indoor Social Extended Reality (XR) conference application scenario. The performance analysis of the radio downlink while using the latest 5G New Radio (NR) Types I and II CSI which use a DFT-based codebook are detailed. The impact of the codebook-related configurable parameter of Rotation Factor (RF), the performance variations while using a `fixed-RF' for all the UEs compared to the more flexible `adaptive-RF', different beamforming technologies (Single-User MIMO (SU-MIMO) and Multi-User MIMO (MU-MIMO)), transmission ranks and co-scheduling parameter values are assessed using the key performance metric of Packet Loss Ratio (PLR). The frequency bands of 3.5 GHz (sub-6 GHz spectrum) and 26 GHz (mmWave spectrum) are chosen for the thesis and performance variations between the two bands are studied. The key insight from the thesis research is that the `adaptive-RF' case gives the optimal performance for the considered Social XR scenario when we set the right co-scheduling parameters (which balance the encountered interference and frequency of co-scheduling).

According to its creators, ICN is designed to fit the way we use the internet better than IP currently does. The use of named data and distributed network layer caching may provide a more efficient utilization of network resources due to the stateful forwarding plane which allows data to be retrieved from caches close by the requester, while also providing a higher content delivery performance in terms of content retrieval delay. Since the IoT is expected to connect billions of devices to the internet, a resource-efficient network paradigm is needed to cope with the corresponding enormous traffic increase. IoT deployments also typically follow a distributed data generation and retrieval paradigm, which could benefit from ICN’s in-network caching approach and stateful forwarding logic. This thesis focuses on assessing whether ICN is advantageous for the IoT in these aspects, by comparing an ICN approach to an IP approach for IoT applications.

With the development of 5G/6G communication technology, an increasing number of communication protocols for data transmission are created and implemented. At the same time, the redundancy of packet headers has become a matter to be optimized when transferring packets across different protocol layers, which causes high resource occupancy, low transmission efficiency and high latency, especially for real-time communications like Voice over IP (VoIP). The currently standardized Robust Header Compression (RoHC) Algorithm seeks to provide an approach to compressing the protocol headers inscribed in data packets to decrease the amount of data transmission without reducing the communication quality. However, the existing RoHC algorithm is not efficient enough for the requirements of present header compression, as it's initially designed for specific headers like RTP, UDP, and IP. The packet headers must be optimized over the protocol stack for the real-time data stream and the GPRS Tunneling Protocol (GTP) tunnel sections in the 5G network. An Optimized Header (OH) Compression Algorithm based on the existing RoHC algorithm is proposed in this thesis to compress the headers over the 5G protocol stack in a coordinated fashion for real-time transmission and GTP tunnels. Besides, further development of the OH algorithm is done to optimize protocol headers by merging duplicate header fields for both multiple QoS flows and multi-layer Protocol Data Unit (PDU) flows in the same PDU session. The overall optimization is conducted in the 5G System (5GS) over F1-U and N3 reference points. The simulation results indicate that the OH algorithm improves the average header compression rate by 45% compared with the RoHC algorithm, as demonstrated via scenario-based 5GS simulations. The average number of transported bits is decreased by 30%, 15% and 26% for Opus coding audio stream, H.264-Opus coding video-audio stream and BLE Version 4.2 triple packets for smartwatch telemedicine stream, respectively, compared with the RoHC algorithm. Besides, the average algorithm execution latency is decreased by 2 μs compared with the RoHC algorithm. Moreover, the multi-stream and multi-layer optimization for the OH algorithm brings about a higher header compression rate and lower execution time in specific scenarios. The overall results indicate that the OH algorithm is capable of compressing the packet size effectively, decreasing the number of transported bits, and shortening the execution time, resulting in higher transmission efficiency and lower latency for VoIP services.

Energy-harvesting devices have enabled Internet of Things applications that were impossible before. Power failures are the norm for these battery-less devices, imposing a challenge to maintain a continued notion of time on these energy restricted devices. To address this challenge we introduce a novel time measurement architecture for energy harvesting devices. Compared to existing solutions, our solution not only improves the start-up time but also reduces the required energy to measure a duration of time.

Hybrid beamforming (HBF) architecture provides promising trade-offs between the system performance and computational/hardware complexity in practical implementations of millimeter wave (mm-Wave) massive multiple-input multiple-output (mMIMO) 5th generation (5G) mobile networks compared to its fully digital beamforming (DBF) counterpart. In this thesis, we investigate the future applicability of deploying hybrid beamforming architectures with subarray beam pattern shaping for mm-Wave 5G base stations in spatially heterogeneous user distributions and different propagation scenarios. We propose HBF structures with a cosecant-squared pattern and a flat-top pattern as well as their HBF and DBF benchmarks. In addition to the uniform user distribution, three non-uniform user distributions, i.e., the near-site distribution, the cell-center distribution, and the cell-edge distribution are proposed to represent the traffic flow and mobility of users due to festivals and holidays. We evaluate the performance in a novel 5G new radio (NR) system-level simulation (SLS) model. Numerical results show that the HBF architecture with a cosecant-squared subarray beam pattern is more robust against differences in spatially heterogeneous traffics than the flat-top HBF and benchmark HBF under the line-of-sight (LoS) propagation scenario. Under the non-line-of-sight (NLoS) propagation scenario, more deterministic environment information and radio channel modeling are required to improve the system performance of the shaped HBF beamforming technique.

In this thesis, we design and assess a multi-slice resource allocation framework that is based on machine learning techniques (subset of artificial intelligence techniques). The proposed framework employs two machine learning techniques namely, artificial neural networks and reinforcement learning for resource management in sliced RAN. Alternative multi-slice resource allocation methods that involve only artificial neural networks but not reinforcement learning are also defined.

As a result of a global pandemic, there has been an increasing interest in tools for remote video conferencing and collaboration. One of these new innovations is social eXtended Reality (XR). By combining Virtual Reality (VR) and Augmented Reality (AR) technologies, social XR can provide a more immersive experience than any other VR application by giving users at different locations the chance to virtually gather in real-time. But such applications impose enormous requirements on computational and communication resources. 5th Generation (5G) mobile networks are targeted as solution to provide ultra-low latency and ultra-high throughput for social XR. In current research, many optimisations are aimed at VR applications such as on-demand streaming, while there is a lack of solutions for real-time user-interactive applications like social XR. In this graduation project we develop and assess cross-layer solutions for optimised scheduling of social XR applications in 5G networks. An existing framework for simulating social XR conference applications serves as the basis for our modelling approach. We devise different schedulers, that utilise cross-layer information in the form of the video frametype and frame-level End-to-End (E2E) latency budgets rather than packet-level latency budgets purely within the Radio Access Network (RAN). In contrast to previous work, we create the VR traffic based on real video data and develop tools to model the packet dispersion caused by multi-hop transmission over the internet towards the RAN. We study the effect of various system and traffic parameters on the Quality of Service (QoS) and perceived Quality of Experience (QoE) in the context of social XR applications through an extensive sensitivity analysis. Herein we also assess the performance impact of different types of cross-layer packet schedulers. Further, we gain insights into the correlation between the network QoS and perceived QoE by end users which are the key in future cross-layer implementations for social XR.

Narrowband Internet-of-Things (NB-IoT), recently introduced by 3GPP, is a relevant Radio Access Technology (RAT) solution for deployment within smart grids, the electricity grids of the future, due to the need to provide low-cost connectivity to a large number of smart meters installed in households. Outage Restoration and Management (ORM), a smart grid use case, involves the smart meter User Equipment (UE) sending a notification message to the utility upon the detection of a loss or restoration of power. ORM is an effective way for utilities to quickly detect, localise and restore a power outage. However, depending on the extent of the power outage, a near simultaneous network access by multiple UEs may occur, leading to resource congestion, particularly of the so-called ‘random access channel’. This may impact the reliability, i.e. the percentage of total notifications successfully delivered within a certain transfer delay target, and in turn, the accuracy of the power outage localisation. Consequently, the maximisation of the ORM reliability performance for a technology like NB-IoT becomes a challenge, given that such use cases, though relevant, were not considered in its design phase. The main goal of this thesis is the optimisation of the NB-IoT network configuration, with a focus on packet scheduling, in order to maximise the ORM reliability performance. To this extent, a system-level simulation model is developed and implemented, incorporating realistic characteristics of energy distribution and mobile networks in four different environments (rural, suburban, urban and dense urban), the traffic characteristics of ORM and the relevant 3GPP specifications of NB-IoT. Additionally, a set of candidate time-frequency domain packet schedulers are proposed. A sensitivity analysis of key network configuration components is performed for a set of power outage scenarios i.e. network loads, the associated optimisation trade-offs are highlighted and a near-optimal network configuration is derived. Based on the sensitivity analysis, a proposed scheduler which prioritises UEs based on a combination of the Earliest Due Date First (EDDF) and Shortest Processing Time First (SPTF) principles, and assigns each UE a single uplink subcarrier with a subcarrier spacing of 3.75 kHz, performs best amongst all the candidate schedulers. Furthermore, the achieved reliability performance is close to 100% for all the considered power outage scenarios in the rural and dense urban environments. In the suburban and urban environments, close to 100% reliability is achieved for the majority of the power outage scenarios.

The environmental policy of KPN has an objective of reducing CO2 emissions and energy consumption to contribute in making the planet more sustainable. This thesis project researches the potential for reducing energy consumption and, consequently, the operational costs of the KPN LTE network. There are various resources in a mobile network which consume energy and switching off these resources will lead to a reduced energy consumption. The trade-off in switching off these resources involves a potential impact on the capacity and the quality (throughput) offered by the mobile network. In this thesis we quantify the attainable savings in operational cost and/or energy consumption while still satisfying the quality of service requirements set by KPN. Five different operational cost saving schemes are analyzed on three different timescales in this research. The results of this research indicate that there is ample opportunity present for KPN to reduce their operational cost and/or energy consumption with minimal impact on the quality of service. The results further indicate that maximum savings are attainable in the live KPN network by effectuating the ’turning off carriers’ scheme, in which the capacity carrier is turned off at the base stations. The reduction in energy consumption and operational cost on effectuating this energy reduction scheme is in the range of 21-70% with respect to the reference scenario for each of the timescale. Furthermore, the results also indicate that of the five operational cost saving schemes investigated, the ‘turning off carriers’ scheme has the lowest impact on the experienced quality of service.

With the increase in the number of user devices and applications, the 5G systems (5GS) user plane is bound to be burdened. More work towards independent scaling and optimisation of the 5GS user plane has to be done. The 4G base stations (eNB) are mainly deployed as monolithic units, whereas, in 5GS, the 3GPP 5G Next Generation base station (gNodeB) can be divided into Radio Unit (RU), Distributed Unit (DU) and Centralised Unit (CU). The CU can be divided into two logical components, the Centralised Unit-Control Plane (CU-CP) and the Centralised Unit-User Plane (CU-UP), extending Control and User Plane Separation (CUPS) approach into RAN. With the introduction of CUPS into RAN, more independence will be provided to the user plane. Moreover, using Open RAN (O-RAN), all these components can be deployed at different locations as Virtual Network Functions (VNFs). We propose a 5GS architecture in this study, which optimises the user plane. The key objectives of this thesis are twofold. Firstly, to compare the impact of co-locating the CU-UP with the DU at a distributed edge cloud location against co-locating the CU-UP with the CU-CP at a centralised regional cloud location. Secondly, with the aid of functional split design, virtualisation and O-RAN, we want to explore whether dynamically deploying the CU-UP and DU on a single physical host machine at a distributed location while centralising the CU-CP can enhance RAN development. To achieve this, we consider the 3GPP architecture as a reference and propose a new architecture and enhanced communication mechanism between CU-UP and DU. An analytical model was designed to evaluate the proposed architecture’s latency gains in the IP transport network, and a simulation model was designed to evaluate the proposed architecture’s communication latency. Furthermore, flow diagrams involving signalling of PDU session establishment are also presented. We present an analysis and overall evaluation of the proposed architecture by comparing it with the reference architecture based on practical architectural aspects and PDU session signalling diagrams. The results of the calculation model and outputs of the simulation model indicated a significant latency improvement when the new architecture is employed. The new architecture found that, on average, 1.5 ms/packet of midhaul delay was reduced. And based on the flow diagram comparisons, it was found that the new architecture introduces overhead in terms of control plane signalling.

This thesis project researches the effect of the optimisation time interval on the performance of a self-optimised mobile network. The goal of the thesis is to ascertain if there exists an optimal time interval for the self-optimisation of the KPN network, and what that interval is. In order to research this question, the project uses data from the KPN network as input, and sets up a simulation study in MATLAB. Two areas in the Netherlands are considered in this study – Friesland and Purmerend. The self-optimisation of the network is carried out through the modification of three optimisation parameters – antenna tilt, RS power, and Cell Individual Offset. The scope of the study is limited to LTE in the downlink, for the 800 MHz band. The bandwidth used in this study is 10 MHz. The performance of the mobile network has been studied using KPIs such as 10th throughput percentile, coverage failure rate, call drop rate, and load. In the end, the study analyses the results for each area, for the self-optimisation carried out by modifying the three parameters over several different optimisation time intervals, and discusses their impact on the performance of the network. A comparison has also been drawn between the performance of a self-optimised network and an un-optimised network, to highlight the gains achieved with SON. Finally, recommendations are made regarding a suitable time interval, and a relative comparison between suitability of the three optimisation parameters has been drawn. The study finds that a suitable time interval for optimisation does exist, and is 240 minutes, for both the simulation areas. The study finds RS power to be the most suitable parameter for self-optimisation, in both the areas. However, the research runs into some unexpected results with respect to the optimisations using tilt angle, and has been discussed in detail in the report. Significant gains are observed with SON, as compared to the case of ‘No SON’ or an un-optimised network.  

5G, the next generation of mobile network, is expected to be launched commercially around 2020. Compared to the present generation – 4G mobile network, a significant improvement in terms of performance and reliability is considered for 5G. One of the important factor in the design of 5G is – about 10 times lower packet latency than 4G. Some of the use cases identified for 5G require packet latency as low as 1 ms. Such stringent latency targets are essential to enable new services like virtual reality streaming of live content over mobile network, automated vehicle platooning over mobile network and tactile internet where machines and tools can be controlled remotely with extreme responsiveness over the mobile network. The main goal of this thesis is to understand how packet latency is affected by the various factors observed in a realistic environment. In contrast to lab environments, where the packet latency reported would be very low, a consolidated study on the various factors affecting packet latency in a 4G (LTE) network in a realistic environment is missing. To this extent, the results of this work have enabled to identify the various factors affecting packet latency in a realistic 4G network. This further led to identifying the latency contribution of the various components to the overall packet latency. Later on, two different latency reduction techniques were evaluated to verify the possible latency reduction achievable on a 4G network, using those two techniques. To reduce packet latency to achieve the latency targets for 5G, first it was necessary to identify how packet latency is caused and affected in a 4G network. This work was aimed at achieving this goal. As the latency reduction techniques were evaluated at their best configuration in terms of latency, results from the latency reduction techniques also identifies the lower limit of latency improvement achievable in a 4G network. The inference from the results suggests that in order to achieve the latency targets specified for 5G networks, a redesigned radio access technology of 4G is essential.

Reconfigurable Intelligent Surfaces (RIS) are envisioned to become a pivotal transformative technology within the realm of 6G mobile networks. In this study, we introduce three heuristic algorithms designed to optimize radio resource management, ultimately enhancing throughput within a RIS-enhanced mobile network. Our findings demonstrate that the algorithm which holistically optimizes user scheduling, RIS-UE association, cell precoding matrices, and RIS configuration matrices outperforms alternative strategies. Moreover, our investigation delves into uncovering the most effective applications of RIS. This involves a thorough performance comparison of the algorithms across diverse scenarios, encompassing varying RIS deployment configurations (position and orientation), number of users in a network, and number of RIS elements. Additionally, we model the influence of blocker loss—characterized by blocker presence probability and strength—on throughput performance. In the wake of our study, it becomes evident that RIS exhibits the most promising potential in scenarios involving MU-MIMO configurations, whether within single-cell or multi-cell layouts, and for both indoor and outdoor user settings. However, for SU-MIMO cases, RIS-induced throughput enhancement manifests exclusively in single-cell layouts, and particularly benefits outdoor users in environments marked by substantial blocker strength.

The Fifth Generation (5G) network is expected to support three main service categories namely enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC) and massive Machine Type Communications (mMTC), where each service group has a different Quality of Service (QoS) requirements i.e. the eMBB service group has a high throughput requirement, the URLLC service group require very low-latency and highly reliable transmissions and the mMTC service group do not have a strict performance requirement but have massive connection of devices in the network. 5G enables many vertical domains with these three service categories, such as, smart cities. In a smart city environment, there are applications from all three service categories such as massive connectivity of the sensors for waste managements or monitoring environmental conditions, video surveillance along the city streets and many more. This study addresses the problem of managing applications from the three service categories on the same physical network infrastructure at the Radio Access Network (RAN). Two different scenarios, one with and one without an emergency incident, are considered to find the impact of the incident in the network. In 5G networks, many new features are introduced such as, flexible numerology, mini-slot based scheduling, BandWidth Parts (BWPs) and RAN slicing. The key objective of this study is to assess the 5G RAN features in terms of achieving the performance requirements of the considered applications, simultaneously. To do so, different RAN configurations are modelled where, a RAN configuration consists of one or multiple RAN features. The evaluation is done by simulating the different possible RAN configurations. The simulations are performed using an existing 5G system-level simulator which is substantially upgraded with the 5G RAN features and is modified to the considered smart city urban macro-cellular environment and with the considered traffic models for each considered application. To evaluate the performance of each considered application, different performance metrics are defined based on the application requirements. The benefits and/or losses of different RAN features are found and then different RAN configurations are considered with the combinations of RAN features based on the evaluation of each RAN feature. For all different RAN configurations with combination of features, the performance metrics are evaluated and compared with each other to determine the best-performing configurations for the smart city environment, for the scenarios with and without an incident.

5G networks are expected to be used in many markets, one of which is the Factories of the Future (FoF). In the FoF, applications like regular monitoring and controlling of components (e.g. temperature) are introducing the need of massive deployment of sensors and actuators. Additionally, sensors which are monitoring time-critical components of the factory (e.g. pressure in power plants) should experience low delays with a high reliability. The current LTE-based technology for machine-type communications, namely Cat-M1, imposes limitations in supporting massive connectivity and application with low delay and high reliability requirements, primarily due to the so-called ‘Random Access’ (RA) procedure which is used by the devices to establish a connection with the base station, before the actual transmission of their data. The key objectives of this study are to assess the RA procedure currently used in Cat-M1 networks as a baseline, and furthermore design and evaluate a new RA procedure for 5G networks, which targets typical FoF applications and their requirements. Based on the simulation assessment of the procedures studied, it was found that the so-called ‘Two-Step RA procedure’ is performing the best regarding end-to-end delay. Specifically, it was found that e.g. in a FoF network with 3000 devices an end-to-end delay of 16 ms can be achieved with 99.99% reliability, introducing a gain of 64 ms compared to the end-to-end delay in Cat-M1 networks.