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.

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.

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.

With the proliferation of IoT devices across the globe, the adoption of IoT related technologies has been increasing rapidly. Newer technologies which fall in the category of Low Power Wide Area Network (LPWAN) have increased this adoption even further. A lot of research is being done in LPWAN licensed band as well as unlicensed band technologies to make them more efficient, such as in terms of power consumption and latency. In this thesis the author has focused on cellular IoT technologies (licensed spectrum LPWAN technologies), to improve the end-to-end behavior. The focus is to see and improve the effect of the device on the network behavior. We have done tests on different networks and went through the 3GPP Specification Release 14 to find possible areas of improvement. Keeping this in mind we have designed a solution to increase the number of pageable devices that can be maintained by the network compared to its original capacity (when not using our solution). This solution can be used to optimize as per the use case, whether to provide lower latency or save energy consumption of the device. To verify that the solution can be used in real life, we have tested it with Stedin critical application device in their substation.

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.

Software-Defined Networking (SDN) potentially can improve the flexibility and management of Wireless Sensor Networks (WSNs). To investigate the impact of SDN on WSN, in this thesis, we consider three Software-Defined Wireless Sensor Network (SD-WSN) frameworks, namely SDN-WISE, SDWN-ONOS, and TinySDN. After comparing these frameworks, the performance of TinySDN is evaluated in three different scenarios: homogeneous, heterogeneous, and dynamic networks. The Collection Tree Protocol (CTP) is used for the evaluations, and is subsequently compared with Rime data collection protocol in Contiki. Our performance evaluation is based on four metrics: Packet Delivery Ratio (PDR), packet duplication, duty cycle, and delay.

Our results show that the PDR for WSN and SD-WSN is relatively similar, that is, around 0.98 to 1 for homogeneous and heterogeneous networks, whereas in a dynamic network, the PDR of WSN decreases from 0.98 to 0.9. Compared to the WSN, the SD-WSN reduces both the average delay of SDN sensor node and the time the SDN sensor node is active to send packets in homogeneous and heterogeneous networks. However, implementing a centralized controller in a dynamic network may cause the SD-WSN performance decrease, which is indicated by the increase ratio of average delay from 2.03 to 2.3, whereas the increase ratio of average delay for the WSN is only around 1.6 to 1.98. The packet duplication level increases by 33% in the dynamic network when the number of SDN sensor nodes increases from 10 to 15.

The performance of SD-WSN in heterogeneous, homogeneous, and dynamic networks is relatively worse than WSN in terms of packet duplication and Rx duty cycle. SD-WSN, in addition, is not optimally implemented for dynamic conditions as the activity changes from the SDN sensor nodes will significantly affect the performance of SD-WSN. To reduce the load on the centralized controller, the clustering controllers are deployed to distribute the load on multiple controllers. The result shows that packet duplication and average delay in a dynamic network can be reduced by 13% and 57%, respectively. Clustering controllers provide more stability in terms of Rx duty cycle compared to before using clustering controllers.