Performance analysis of energy detection over hyper-Rayleigh fading channels

This study investigates the performance of energy detection (ED)-based spectrum sensing over two-wave with diffused power (TWDP) fading channels, which have been found to provide accurate characterisation for a variety of fading conditions. A closed-form expression for the average detection probability of ED-based spectrum sensing over TWDP fading channels is derived. This expression is then used to describe the behaviour of ED-based spectrum sensing for a variety of channels that include Rayleigh, Rician and hyper-Rayleigh fading models. Such fading scenarios present a reliable behavioural model of machine-to-machine wireless nodes operating in confined structures such as in-vehicular environments

Efficacy of Decentralized CSS Clustering Model Over TWDP Fading Scenario

Cognitive Radio technology, which lowers spectrum scarcity, is a rapidly growing wireless communication technology. CR technology detects spectrum holes or unlicensed spectrums which primary users are not using and assigns it to secondary users. The dependability of the spectrum-sensing approach is significantly impacted from two of the most critical aspects, namely fading channels and neighboring wireless users. Users of non-cooperative spectrum sensing devices face numerous difficulties, including multipath fading, masked terminals, and shadowing. This problem can be solved using a cooperative- spectrum-sensing technique. For the user, CSS enables them to detect the spectrum by using a common receiver. It has also been divided into distributed CSS and centralized CSS. This article compares both ideas by using a set of rules to find out whether a licensed user exists or not. This thought was previously used to the conventional fading channels, such as the Rician, Rayleigh and the nakagami-m models. This work focused on D-CSS using clustering approach over TWDP fading channel using two-phase hard decision algorithms with the help of OR rule as well as AND rule. The evaluation of the proposed approaches clearly depicted that the sack of achieve a detection-probability of greater than 0.8; the values SNR varies between -14 dB to -8 dB. For all two-phase hard decision algorithms using proposed approach and CSS techniques, the detection probability is essentially identical while the value of signal to noise ratio is between -12 dB to -8dB. Throughout this work, we assess performance of cluster-based cooperative spectrum-sensing over TWDP channel with the previous findings of AWGN, Rayleigh, and wei-bull fading channels. The obtained simulation results show that OR-AND decision scheme enhanced the performance of the detector for the considered range of signal to noise ratios

Spectrum sensing and occupancy prediction for cognitive machine-to-machine wireless networks

A thesis submitted to the University of Bedfordshire, in partial fulfil ment of the requirements for the degree of Doctor of Philosophy (PhD)The rapid growth of the Internet of Things (IoT) introduces an additional challenge to the existing spectrum under-utilisation problem as large scale deployments of thousands devices are expected to require wireless connectivity. Dynamic Spectrum Access (DSA) has been proposed as a means of improving the spectrum utilisation of wireless systems. Based on the Cognitive Radio (CR) paradigm, DSA enables unlicensed spectrum users to sense their spectral environment and adapt their operational parameters to opportunistically access any temporally unoccupied bands without causing interference to the primary spectrum users. In the same context, CR inspired Machine-to-Machine (M2M) communications have recently been proposed as a potential solution to the spectrum utilisation problem, which has been driven by the ever increasing number of interconnected devices. M2M communications introduce new challenges for CR in terms of operational environments and design requirements. With spectrum sensing being the key function for CR, this thesis investigates the performance of spectrum sensing and proposes novel sensing approaches and models to address the sensing problem for cognitive M2M deployments. In this thesis, the behaviour of Energy Detection (ED) spectrum sensing for cognitive M2M nodes is modelled using the two-wave with dffi use power fading model. This channel model can describe a variety of realistic fading conditions including worse than Rayleigh scenarios that are expected to occur within the operational environments of cognitive M2M communication systems. The results suggest that ED based spectrum sensing fails to meet the sensing requirements over worse than Rayleigh conditions and consequently requires the signal-to-noise ratio (SNR) to be increased by up to 137%. However, by employing appropriate diversity and node cooperation techniques, the sensing performance can be improved by up to 11.5dB in terms of the required SNR. These results are particularly useful in analysing the eff ects of severe fading in cognitive M2M systems and thus they can be used to design effi cient CR transceivers and to quantify the trade-o s between detection performance and energy e fficiency. A novel predictive spectrum sensing scheme that exploits historical data of past sensing events to predict channel occupancy is proposed and analysed. This approach allows CR terminals to sense only the channels that are predicted to be unoccupied rather than the whole band of interest. Based on this approach, a spectrum occupancy predictor is developed and experimentally validated. The proposed scheme achieves a prediction accuracy of up to 93% which in turn can lead to up to 84% reduction of the spectrum sensing cost. Furthermore, a novel probabilistic model for describing the channel availability in both the vertical and horizontal polarisations is developed. The proposed model is validated based on a measurement campaign for operational scenarios where CR terminals may change their polarisation during their operation. A Gaussian approximation is used to model the empirical channel availability data with more than 95% confi dence bounds. The proposed model can be used as a means of improving spectrum sensing performance by using statistical knowledge on the primary users occupancy pattern

Physical Layer Security in Wireless Networks: Design and Enhancement.

PhDSecurity and privacy have become increasingly significant concerns in wireless communication networks, due to the open nature of the wireless medium which makes the wireless transmission vulnerable to eavesdropping and inimical attacking. The emergence and development of decentralized and ad-hoc wireless networks pose great challenges to the implementation of higher-layer key distribution and management in practice. Against this background, physical layer security has emerged as an attractive approach for performing secure transmission in a low complexity manner. This thesis concentrates on physical layer security design and enhancement in wireless networks. First, this thesis presents a new unifying framework to analyze the average secrecy capacity and secrecy outage probability. Besides the exact average secrecy capacity and secrecy outage probability, a new approach for analyzing the asymptotic behavior is proposed to compute key performance parameters such as high signal-to-noise ratio slope, power offset, secrecy diversity order, and secrecy array gain. Typical fading environments such as two-wave with diffuse power and Nakagami-m are taken into account. Second, an analytical framework of using antenna selection schemes to achieve secrecy is provided. In particular, transmit antenna selection and generalized selection combining are considered including its special cases of selection combining and maximal-ratio combining. Third, the fundamental questions surrounding the joint impact of power constraints on the cognitive wiretap channel are addressed. Important design insights are revealed regarding the interplay between two power constraints, namely the maximum transmit at the secondary network and the peak interference power at the primary network. Fourth, secure single carrier transmission is considered in the two-hop decode-andi forward relay networks. A two-stage relay and destination selection is proposed to minimize the eavesdropping and maximize the signal power of the link between the relay and the destination. In two-hop amplify-and-forward untrusted relay networks, secrecy may not be guaranteed even in the absence of external eavesdroppers. As such, cooperative jamming with optimal power allocation is proposed to achieve non-zero secrecy rate. Fifth and last, physical layer security in large-scale wireless sensor networks is introduced. A stochastic geometry approach is adopted to model the positions of sensors, access points, sinks, and eavesdroppers. Two scenarios are considered: i) the active sensors transmit their sensing data to the access points, and ii) the active access points forward the data to the sinks. Important insights are concluded

SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks

Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions. To cope with these issues, this paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to time-varying channel conditions and data distributions. In addition, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, which makes SNN ill-suited for FL. Motivated by this, we propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models. By applying SC, SlimFL exchanges the superposition of multiple-width configurations decoded as many times as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations while avoiding inter-width interference during backpropagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also deals with non-IID data distributions and poor channel conditions, which is also corroborated by data-intensive simulations