Relaying in the Internet of Things (IoT): A Survey

The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions

Channel estimation and parameters acquisition systems employing cooperative diversity

Doutoramento em Engenharia Eletrotécnica e TelecomunicaçõesThis work investigates new channel estimation schemes for the forthcoming and future generation of cellular systems for which cooperative techniques are regarded. The studied cooperative systems are designed to re-transmit the received information to the user terminal via the relay nodes, in order to make use of benefits such as high throughput, fairness in access and extra coverage. The cooperative scenarios rely on OFDM-based systems employing classical and pilot-based channel estimators, which were originally designed to pointto-point links. The analytical studies consider two relaying protocols, namely, the Amplifyand-Forward and the Equalise-and-Forward, both for the downlink case. The relaying channels statistics show that such channels entail specific characteristics that comply to a proper filter and equalisation designs. Therefore, adjustments in the estimation process are needed in order to obtain the relay channel estimates, refine these initial estimates via iterative processing and obtain others system parameters that are required in the equalisation. The system performance is evaluated considering standardised specifications and the International Telecommunication Union multipath channel models.Este trabalho tem por objetivo o estudo de novos esquemas de estimação de canal para sistemas de comunicação móvel das próximas gerações, para os quais técnicas cooperativa são consideradas. Os sistemas cooperativos investigados neste trabalho estão projetados para fazerem uso de terminais adicionais a fim de retransmitir a informação recebida para o utilizador final. Desta forma, pode-se usurfruir de benefícios relacionados às comunicações cooperativas tais como o aumento do rendimento do sistema, fiabilidade e extra cobertura. Os cenários são basedos em sistemas OFDM que empregam estimadores de canal que fazem uso de sinais piloto e que originalmente foram projetados para ligações ponto a ponto. Os estudos analíticos consideram dois protocolos de encaminhamento, nomeadamente, Amplify-and-Forward e Equalise-and-Forward, ambos para o caso downlink. As estatísticas dos canais em estudo mostram que tais canais ocasionam características específicas para as quais o filtro do estimador e a equalisação devem ser apropridamente projetados. Estas características requerem ajustes que são necessários no processo de estimação a fim de estimar os canais, refinar as estimativas iniciais através de processos iterativos e ainda obter outros parâmetros do sistema que são necessários na equalização. O desempenho dos esquemas propostos são avaliados tendo em consideração especificações padronizadas e modelos de canal descritos na International Telecommunication Union

Learning-based communication system design – autoencoder for (differential) block coded modulation designs and path loss predictions

Shannon’s channel coding theorem states the existence of long random codes that can make the error probability arbitrarily small. Recently, advanced error-correcting codes such as turbo and low-density parity-check codes have almost reached the theoretical Shannon limit for binary additive white Gaussian noise channels. However, designing optimal high-rate short-block codes with automatic bit-labeling for various wireless networks is still an unsolved problem. Deep-learning-based autoencoders (AE) have appeared as a potential near-optimal solution for designing wireless communications systems. We take a holistic approach that jointly optimizes all the components of the communication networks by performing data-driven end-to-end learning of the neural network-based transmitter and receiver together. Specifically, to tackle the fading channels, we show that AE frameworks can perform near-optimal block coded-modulation (BCM) and differential BCM (d-BCM) designs in the presence and absence of the channel state information knowledge. Moreover, we focus on AE-based designing of high-rate short block codes with automatic bit-labeling that are capable of outperforming conventional networks with larger margins as the rate R increases. We also investigate the BCM and d-BCM from an information-theoretic perspective. With the advent of internet-of-things (IoT) networks and the widespread use of small devices, we face the challenge of limited available bandwidth. Therefore, novel techniques need to be utilized, such as full-duplex (FD) mode transmission reception at the base station for the full utilization of the spectrum, and non-orthogonal multiple access (NOMA) at the user-end for serving multiple IoT devices while fulfilling their quality-of-service requirement. Furthermore, the deployment of relay nodes will play a pivotal role in improving network coverage, reliability, and spectral efficiency for the future 5G networks. Thus, we design and develop novel end-to-end-learning-based AE frameworks for BCM and d-BCM in various scenarios such as amplify-and-forward and decode-and-forward relaying networks, FD relaying networks, and multi-user downlink networks. We focus on interpretability and understand the AE-based BCM and d-BCM from an information-theoretic perspective, such as the AE’s estimated mutual information, convergence, loss optimization, and training principles. We also determine the distinct properties of AE-based (differential) coded-modulation designs in higher-dimensional space. Moreover, we also studied the reproducibility of the trained AE framework. In contrast, large bandwidth and worldwide spectrum availability at mm-wave bands have also shown a great potential for 5G and beyond, but the high path loss (PL) and significant scattering/absorption loss make the signal propagation challenging. Highly accurate PL prediction is fundamental for mm-wave network planning and optimization, whereas existing methods such as slope-intercept models and ray tracing fall short in capturing the large street-by-street variation seen in urban cities. We also exploited the potential benefits of AE framework-based compression capabilities in mm-wave PL prediction. Specifically, we employ extensive 28 GHz measurements from Manhattan Street canyons and model the street clutters via a LiDAR point cloud dataset and 3D-buildings by a mesh-grid building dataset. We aggressively compress 3D-building shape information using convolutional-AE frameworks to reduce overfitting and propose a machine learning (ML)-based PL prediction model for mm-wave propagation.EPSRC-UKRI fundin

Physical layer security in 5G and beyond wireless networks enabling technologies

Information security has always been a critical concern for wireless communications due to the broadcast nature of the open wireless medium. Commonly, security relies on cryptographic encryption techniques at higher layers to ensure information security. However, traditional cryptographic methods may be inadequate or inappropriate due to novel improvements in the computational power of devices and optimization approaches. Therefore, supplementary techniques are required to secure the transmission data. Physical layer security (PLS) can improve the security of wireless communications by exploiting the characteristics of wireless channels. Therefore, we study the PLS performance in the fifth generation (5G) and beyond wireless networks enabling technologies in this thesis. The thesis consists of three main parts. In the first part, the PLS design and analysis for Device-to-Device (D2D) communication is carried out for several scenarios. More specifically, in this part, we study the underlay relay-aided D2D communications to improve the PLS of the cellular network. We propose a cooperative scheme, whereby the D2D pair, in return for being allowed to share the spectrum band of the cellular network, serves as a friendly jammer using full-duplex (FD) and half-duplex (HD) transmissions and relay selection to degrade the wiretapped signal at an eavesdropper. This part aims to show that spectrum sharing is advantageous for both D2D communications and cellular networks concerning reliability and robustness for the former and PLS enhancement for the latter. Closed-form expressions for the D2D outage probability, the secrecy outage probability (SOP), and the probability of non-zero secrecy capacity (PNSC) are derived to assess the proposed cooperative system model. The results show enhancing the robustness and reliability of D2D communication while simultaneously improving the cellular network’s PLS by generating jamming signals towards the eavesdropper. Furthermore, intensive Monte-Carlo simulations and numerical results are provided to verify the efficiency of the proposed schemes and validate the derived expressions’ accuracy. In the second part, we consider a secure underlay cognitive radio (CR) network in the presence of a primary passive eavesdropper. Herein, a secondary multi-antenna full-duplex destination node acts as a jammer to the primary eavesdropper to improve the PLS of the primary network. In return for this favor, the energy-constrained secondary source gets access to the primary network to transmit its information so long as the interference to the latter is below a certain level. As revealed in our analysis and simulation, the reliability and robustness of the CR network are improved, while the security level of the primary network is enhanced concurrently. Finally, we investigate the PLS design and analysis of reconfigurable intelligent surface (RIS)-aided wireless communication systems in an inband underlay D2D communication and the CR network. An RIS is used to adjust its reflecting elements to enhance the data transmission while improving the PLS concurrently. Furthermore, we investigate the design of active elements in RIS to overcome the double-fading problem introduced in the RISaided link in a wireless communications system. Towards this end, each active RIS element amplifies the reflected incident signal rather than only reflecting it as done in passive RIS modules. As revealed in our analysis and simulation, the use of active elements leads to a drastic reduction in the size of RIS to achieve a given performance level. Furthermore, a practical design for active RIS is proposed

Advanced DSP Techniques for High-Capacity and Energy-Efficient Optical Fiber Communications

The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well

Improved S-AF and S-DF Relaying Schemes Using Machine Learning Based Power Allocation Over Cascaded Rayleigh Fading Channels

We investigate the performance of a dual-hop intervehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called n*Rayleigh), which involves the product of n independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, n = 2); our system model considers the general cascading order n, for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.Comment: 13 pages, 10 figure