Modelling 5G network with network planning tool and verifying results with field measurements

Wireless communications systems aim to achieve higher throughput, smaller latency, and better reliability for ever-growing demand. This creates challenges since radio resources are limited and a need for new technologies to achieve these demands is imminent. The ability to accurately model and simulate wireless networks becomes crucial to efficient radio network planning and optimization. Precise radio network models that generate accurate simulation results can be utilized to better understand and overcome the bottlenecks of designated radio networks. In this thesis work the fifth generation (5G) Radio Access Network (RAN) model is built by using the radio network planning tool ASSET Radio and each part of the model is introduced along with RAN topology. The relevant selected system-level simulation results of the 5G RAN model are compared to field measurement results from equivalent real-life 5G RAN. The differences and the similarities of the results between system-level simulation and field measurements are analyzed and the root causes for those results are made known. The main objective of this thesis work is to better understand key factors that impact the system-level simulation and the field measurement results of the 5G network. Furthermore, the key findings of this thesis work can be used to create more accurate 5G network models and overcome the challenges related to the 5G field measurements. The results from this thesis work indicate that the radio network planning tool ASSET Radio can build an accurate 5G RAN model, which produces similar results to the results obtained from field measurements. With enough information of a designated 5G RAN, relatively precise results can be produced with ASSET Radio’s system-level simulation and depending on the required level of accuracy, potentially leaving the field measurements unnecessary

Resource Allocation, User Association, and User Scheduling for OFDMA-based Cellular Networks

Current advances in wireless communication are driven by an increased demand for more data and bandwidth, mainly due to the development of new mobile platforms and applications. Ever since then the network operators are overwhelmed by the rapid increase in mobile data traffic, which is primarily fueled by the viewing of data-intensive content. In addition, according to the statistics, the ratio of downlink and uplink data traffic demands have changed drastically over the past decade and they are increasingly asymmetric even over small time periods. In recent years, different solutions, based on topological and architectural innovations of the conventional cellular networks, have been proposed to address the issues related to the increasing data requirements and uplink/downlink traffic asymmetries. The most trivial solution is to scale the network capacity through network densification, i.e., by bringing the network nodes closer to each other through efficient spectrum sharing techniques. The resulting dense networks, also known as heterogeneous networks, can address the growing need for capacity, coverage, and uplink/downlink traffic flexibility in wireless networks by deploying numerous low power base stations overlaying the existing macro cellular coverage. However, there is a need to analyze the interplay of different network processes in this context, since, it has not been studied in detail due to complex user dynamics and interference patterns, which are known to present difficulties in their design and performance evaluation under conventional heterogeneous networks. It is expected that by centralizing some of the network processes common to different network nodes in a heterogeneous network, such as coordination between multiple nodes, it will be easier to achieve significant performance gains. In this thesis, we aim at centralizing the control of the underlying network processes through Centralized Radio Access Networks (C-RAN), to deal with the high data requirements along with the asymmetric traffic demands. We analyze both large‐scale centralized solutions and the light‐weight distributed variants to obtain practical insights on how to design and operate future heterogeneous networks

Software Defined Radio Solutions for Wireless Communications Systems

Wireless technologies have been advancing rapidly, especially in the recent years. Design, implementation, and manufacturing of devices supporting the continuously evolving technologies require great efforts. Thus, building platforms compatible with different generations of standards and technologies has gained a lot of interest. As a result, software defined radios (SDRs) are investigated to offer more flexibility and scalability, and reduce the design efforts, compared to the conventional fixed-function hardware-based solutions.This thesis mainly addresses the challenges related to SDR-based implementation of today’s wireless devices. One of the main targets of most of the wireless standards has been to improve the achievable data rates, which imposes strict requirements on the processing platforms. Realizing real-time processing of high throughput signal processing algorithms using SDR-based platforms while maintaining energy consumption close to conventional approaches is a challenging topic that is addressed in this thesis.Firstly, this thesis concentrates on the challenges of a real-time software-based implementation for the very high throughput (VHT) Institute of Electrical and Electronics Engineers (IEEE) 802.11ac amendment from the wireless local area networks (WLAN) family, where an SDR-based solution is introduced for the frequency-domain baseband processing of a multiple-input multipleoutput (MIMO) transmitter and receiver. The feasibility of the implementation is evaluated with respect to the number of clock cycles and the consumed power. Furthermore, a digital front-end (DFE) concept is developed for the IEEE 802.11ac receiver, where the 80 MHz waveform is divided to two 40 MHz signals. This is carried out through time-domain digital filtering and decimation, which is challenging due to the latency and cyclic prefix (CP) budget of the receiver. Different multi-rate channelization architectures are developed, and the software implementation is presented and evaluated in terms of execution time, number of clock cycles, power, and energy consumption on different multi-core platforms.Secondly, this thesis addresses selected advanced techniques developed to realize inband fullduplex (IBFD) systems, which aim at improving spectral efficiency in today’s congested radio spectrum. IBFD refers to concurrent transmission and reception on the same frequency band, where the main challenge to combat is the strong self-interference (SI). In this thesis, an SDRbased solution is introduced, which is capable of real-time mitigation of the SI signal. The implementation results show possibility of achieving real-time sufficient SI suppression under time-varying environments using low-power, mobile-scale multi-core processing platforms. To investigate the challenges associated with SDR implementations for mobile-scale devices with limited processing and power resources, processing platforms suitable for hand-held devices are selected in this thesis work. On the baseband processing side, a very long instruction word (VLIW) processor, optimized for wireless communication applications, is utilized. Furthermore, in the solutions presented for the DFE processing and the digital SI canceller, commercial off-the-shelf (COTS) multi-core central processing units (CPUs) and graphics processing units (GPUs) are used with the aim of investigating the performance enhancement achieved by utilizing parallel processing.Overall, this thesis provides solutions to the challenges of low-power, and real-time software-based implementation of computationally intensive signal processing algorithms for the current and future communications systems