Algorithms for 5G physical layer

There is a great activity in the research community towards the investigations of the various aspects of 5G at different protocol layers and parts of the network. Among all, physical layer design plays a very important role to satisfy high demands in terms of data rates, latency, reliability and number of connected devices for 5G deployment. This thesis addresses he latest developments in the physical layer algorithms regarding the channel coding, signal detection, frame synchronization and multiple access technique in the light of 5G use cases. These developments are governed by the requirements of the different use case scenarios that are envisioned to be the driving force in 5G. All chapters from chapter 2 to 5 are developed around the need of physical layer algorithms dedicated to 5G use cases. In brief, this thesis focuses on design, analysis, simulation and he advancement of physical layer aspects such as 1. Reliability based decoding of short length Linear Block Codes (LBCs) with very good properties in terms of minimum hamming istance for very small latency requiring applications. In this context, we enlarge the grid of possible candidates by considering, in particular, short length LBCs (especially extended CH codes) with soft-decision decoding; 2. Efficient synchronization of preamble/postamble in a short bursty frame using modified Massey correlator; 3. Detection of Primary User activity using semiblind spectrum sensing algorithms and analysis of such algorithms under practical imperfections; 4. Design of optimal spreading matrix for a Low Density Spreading (LDS) technique in the context of non-orthogonal multiple access. In such spreading matrix, small number of elements in a spreading sequences are non zero allowing each user to spread its data over small number of chips (tones), thus simplifying the decoding procedure using Message Passing Algorithm (MPA)

An Investigation into Cognitive Radio System Performance

The objective of this thesis is to explore cognitive radio performance through an in-depth literature review and an implementation of a software-defined radio prototyping system. Specifically, this thesis investigates the spectrum-sensing aspect of cognitive radio by comparing two spectrum-sensing methods. It was found in the literature review that a system utilizing matched filter detection would provide higher probability of detection in low signal-to-noise ratio environments when compared to a system utilizing energy detection. These spectrum sensing methods were thus implemented and compared in the cognitive radio systems presented in this thesis. Additionally, experiments were conducted to determine the most efficient intervals for the spectrum sensing and cycle interval periods. Therefore, system performance was measured on the basis of probability of successful primary user signal detection and maximum throughput capabilities, quantified by bit error rate. It was found that a cognitive radio system based on matched filter detection was more robust, given that the transmitted signal of interest was previously known. However, compared to a system based on energy detection, the implementation of the matched filter required more complex algorithms and computational power. These results are consistent with the findings in the literature review

Enhanced Spectrum Sensing Techniques for Cognitive Radio Systems

Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources. Considering the limited radio spectrum, supporting the demand for higher capacity and higher data rates is a challenging task that requires innovative technologies capable of providing new ways of exploiting the available radio spectrum. Cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems, has received increasing attention and is considered a promising solution to the spectral crowding problem by introducing the notion of opportunistic spectrum usage. Spectrum sensing, which enables CRs to identify spectral holes, is a critical component in CR technology. Furthermore, improving the efficiency of the radio spectrum use through spectrum sensing and dynamic spectrum access (DSA) is one of the emerging trends. In this thesis, we focus on enhanced spectrum sensing techniques that provide performance gains with reduced computational complexity for realistic waveforms considering radio frequency (RF) impairments, such as noise uncertainty and power amplifier (PA) non-linearities. The first area of study is efficient energy detection (ED) methods for spectrum sensing under non-flat spectral characteristics, which deals with relatively simple methods for improving the detection performance. In realistic communication scenarios, the spectrum of the primary user (PU) is non-flat due to non-ideal frequency responses of the devices and frequency selective channel conditions. Weighting process with fast Fourier transform (FFT) and analysis filter bank (AFB) based multi-band sensing techniques are proposed for overcoming the challenge of non-flat characteristics. Furthermore, a sliding window based spectrum sensing approach is addressed to detect a re-appearing PU that is absent in one time and present in other time. Finally, the area under the receiver operating characteristics curve (AUC) is considered as a single-parameter performance metric and is derived for all the considered scenarios. The second area of study is reduced complexity energy and eigenvalue based spectrum sensing techniques utilizing frequency selectivity. More specifically, novel spectrum sensing techniques, which have relatively low computational complexity and are capable of providing accurate and robust performance in low signal-to-noise ratio (SNR) with noise uncertainty, as well as in the presence of frequency selectivity, are proposed. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due the primary signal spectrum and/or the transmission channel. The offered results indicate that the proposed methods provide quite significant saving in complexity, e.g., 78% reduction in the studied example case, whereas their detection performance is improved both in the low SNR and under noise uncertainty. Finally, a new combined spectrum sensing and resource allocation approach for multicarrier radio systems is proposed. The main contribution of this study is the evaluation of the CR performance when using wideband spectrum sensing methods in combination with water-filling and power interference (PI) based resource allocation algorithms in realistic CR scenarios. Different waveforms, such as cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM), enhanced orthogonal frequency division multiplexing (E-OFDM) and filter bank based multicarrier (FBMC), are considered with PA nonlinearity type RF impairments to see the effects of spectral leakage on the spectrum sensing and resource allocation performance. It is shown that AFB based spectrum sensing techniques and FBMC waveforms with excellent spectral containment properties have clearly better performance compared to the traditional FFT based spectrum sensing techniques with the CP-OFDM. Overall, the investigations in this thesis provide novel spectrum sensing techniques for overcoming the challenge of noise uncertainty with reduced computational complexity. The proposed methods are evaluated under realistic signal models

Cognitive radio networks : quality of service considerations and enhancements

The explosive growth of wireless and mobile networks, such as the Internet of Things and 5G, has led to a massive number of devices that primarily use wireless channels within a limited range of the radio frequency spectrum (RFS). The use of RFS is heavily regulated, both nationally and internationally, and is divided into licensed and unlicensed bands. While many of the licensed wireless bands are underutilised, useable unlicensed bands are usually overcrowded, making the efficient use of RFS one of the critical challenges faced by future wireless communication technologies. The cognitive radio (CR) concept is proposed as a promising solution for the underutilisation of useful RFS bands. Fundamentally, CR technology is based on determining the unoccupied licensed RFS bands, called spectrum white spaces or holes, and accessing them to achieve better RFS utilisation and transmission propagation. The holes are the frequencies unused by the licensed user, or primary user (PU). Based on spectrum sensing, a CR node, or secondary user (SU), senses the surrounding spectrum periodically to detect any potential PU transmission in the current channel and to identify the available spectrum holes. Under current RFS regulations, SUs may use spectrum holes as long as their transmissions do not interfere with those of the PU. However, effective spectrum sensing can introduce overheads to a CR node operation. Such overheads affect the quality of service (QoS) of the running applications. Reducing the sensing impact on the QoS is one of the key challenges to adopting CR technology, and more studies of QoS issues related to implementing CR features are needed. This thesis aims to address these QoS issues in CR while considered the enhancement of RFS utilisation. This study concentrates on the spectrum sensing function, among other CR functions, because of its major impact on QoS and spectrum utilisation. Several spectrum sensing methods are reviewed to identify potential research gaps in analysing and addressing related QoS implications. It has been found that none of the well-known sensing techniques is suitable for all the diverse QoS requirements and RFS conditions: in fact, higher accuracy sensing methods cause a significant QoS degradation, as illustrated by several simulations in this work. For instance, QoS degradation caused by high-accuracy sensing has not yet been addressed in the IEEE 802.11e QoS mechanism used in the proposed CR standard, IEEE 802.11af (or White-Fi). This study finds that most of the strategies proposed to conduct sensing are based on a fixed sensing method that is not adaptable to the changeable nature of QoS requirements. In contrast, this work confirms the necessity of using various sensing techniques and parameters during a CR node operation for better performance