128 research outputs found

    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    The Role of Physical Layer Security in Satellite-Based Networks

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    In the coming years, 6G will revolutionize the world with a large amount of bandwidth, high data rates, and extensive coverage in remote and rural areas. These goals can only be achieved by integrating terrestrial networks with non-terrestrial networks. On the other hand, these advancements are raising more concerns than other wireless links about malicious attacks on satellite-terrestrial links due to their openness. Over the years, physical layer security (PLS) has emerged as a good candidate to deal with security threats by exploring the randomness of wireless channels. In this direction, this paper reviews how PLS methods are implemented in satellite communications. Firstly, we discuss the ongoing research on satellite-based networks by highlighting the key points in the literature. Then, we revisit the research activities on PLS in satellite-based networks by categorizing the different system architectures. Finally, we highlight research directions and opportunities to leverage the PLS in future satellite-based networks

    Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags

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    The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals. To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP

    Komunikace na milimetrových vlnách v 5G a dalších sítích: Nové systémové modely a analýza výkonnosti

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    The dissertation investigates different network models, focusing on three important features for next generation cellular networks with respect to millimeter waves (mmWave) communications: the impact of fading and co-channel interference (CCI), energy efficiency, and spectrum efficiency. To address the first aim, the dissertation contains a study of a non-orthogonal multiple access (NOMA) technique in a multi-hop relay network which uses relays that harvest energy from power beacons (PB). This part derives the exact throughput expressions for NOMA and provides a performance analysis of three different NOMA schemes to determine the optimal parameters for the proposed system’s throughput. A self-learning clustering protocol (SLCP) in which a node learns its neighbor’s information is also proposed for determining the node density and the residual energy used to cluster head (CH) selection and improve energy efficiency, thereby prolonging sensor network lifetime and gaining higher throughput. Second, NOMA provides many opportunities for massive connectivity at lower latencies, but it may also cause co-channel interference by reusing frequencies. CCI and fading play a major role in deciding the quality of the received signal. The dissertation takes into account the presence of η and µ fading channels in a network using NOMA. The closed-form expressions of outage probability (OP) and throughput were derived with perfect successive interference cancellation (SIC) and imperfect SIC. The dissertation also addresses the integration of NOMA into a satellite communications network and evaluates its system performance under the effects of imperfect channel state information (CSI) and CCI. Finally, the dissertation presents a new model for a NOMA-based hybrid satellite-terrestrial relay network (HSTRN) using mmWave communications. The satellite deploys the NOMA scheme, whereas the ground relays are equipped with multiple antennas and employ the amplify and forward (AF) protocol. The rain attenuation coefficient is considered as the fading factor of the mmWave band to choose the best relay, and the widely applied hybrid shadowed-Rician and Nakagami-m channels characterize the transmission environment of HSTRN. The closed-form formulas for OP and ergodic capacity (EC) were derived to evaluate the system performance of the proposed model and then verified with Monte Carlo simulations.Dizertační práce zkoumala různé modely sítí a zaměřila se na tři důležité vlastnosti pro buňkové sítě příští generace s ohledem na mmW komunikace, kterými jsou: vliv útlumu a mezikanálového rušení (CCI), energetická účinnost a účinnost spektra. Co se týče prvního cíle, dizertace obsahuje studii techniky neortogonálního vícenásobného přístupu (NOMA) v bezdrátové multiskokové relay síti využívající získávání energie, kde relay uzly sbírají energii z energetických majáků (PB). Tato část přináší přesné výrazy propustnosti pro NOMA a analýzu výkonnosti se třemi různými schématy NOMA s cílem určit optimální parametry pro propustnost navrženého systému. Dále byl navržen samoučící se shlukovací protokol (SLCP), ve kterém se uzel učí informace o sousedech, aby určil hustotu uzlů a zbytkovou energii použitou k výběru hlavy shluku CH pro zlepšení energetické účinnosti, čímž může prodloužit životnost sensorové sítě a zvýšit propustnost. Za druhé, přístup NOMA poskytl mnoho příležitostí pro masivní připojení s nižší latencí, NOMA však může způsobovat mezikanálové rušení v důsledku opětovného využívání kmitočtů. CCI a útlum hrají klíčovou roli při rozhodování o kvalitě přijímaného signálu. V této dizertace je brána v úvahu přítomnost η a µ útlumových kanálů v síti užívající NOMA. Odvozeny jsou výrazy v uzavřené formě pro pravděpodobnost výpadku (OP) a propustnost s dokonalým postupným rušením rušení (SIC) a nedokonalým SIC. Dále se dizertace zabývá integrací přístupu NOMA do satelitní komunikační sítě a vyhodnocuje výkonnost systému při dopadech nedokonalé informace o stavu kanálu (CSI) a CCI. Závěrem disertační práce představuje nový model pro hybridní družicově-terestriální přenosovou síť (HSTRN) založenou na NOMA vícenásobném přístupu využívající mmWave komunikaci. Satelit využívá NOMA schéma, zatímco pozemní relay uzly jsou vybaveny více anténami a aplikují protokol zesilování a předávání (AF). Je zaveden srážkový koeficient, který je uvažován jako útlumový faktor mmWave pásma při výběru nejlepšího relay uzlu. Samotné přenosové prostředí HSTRN je charakterizováno pomocí hybridních Rician a Nakagami-m kanálů. Vztahy pro vyhodnocení výkonnosti systému navrženého modelu vyjadřující ergodickou kapacitu (EC) a pravděpodobnost ztrát (OP) byly odvozeny v uzavřené formě a následně ověřeny pomocí simulační numerické metody Monte Carlo.440 - Katedra telekomunikační technikyvyhově

    Simultaneous Wireless Information and Power Transfer in 5G communication

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    Green communication technology is expected to be widely adopted in future generation networks to improve energy efficiency and reliability of wireless communication network. Among the green communication technologies,simultaneous wireless information and power transfer (SWIPT) is adopted for its flexible energy harvesting technology through the radio frequency (RF) signa lthati sused for information transmission. Even though existing SWIPT techniques are flexible and adoptable for the wireless communication networks, the power and time resources of the signal need to be shared between infor- mation transmission and RF energy harvesting, and this compromises the quality of the signal. Therefore,SWIP Ttechniques need to be designed to allow an efficient resource allocation for communication and energy harvesting. The goal oft his thesisis to design SWIP Ttechniques that allow efficient,reliable and secure joint communications and power transference. A problem associated to SWIPT techniques combined with multi carrier signals is that the increased power requirements inherent to energy harvesting purposes can exacerbate nonlinear distortion effects at the transmitter. Therefore, we evaluate nonlinear distortion and present feasible solutions to mitigate the impact of nonlinear distortion effects on the performance.Another goal of the thesisis to take advantage of the energy harvesting signals in SWIP Ttechniques for channel estimation and security purposes.Theperformance of these SWIPT techniques is evaluated analytically, and those results are validated by simulations. It is shownthatthe proposed SWIPT schemes can have excellent performance, out performing conventional SWIPT schemes.Espera-se que aschamadas tecnologiasde green communications sejam amplamente ado- tadas em futuras redes de comunicação sem fios para melhorar a sua eficiência energética a fiabilidade.Entre estas,encontram-se as tecnologias SWIPT (Simultaneous Wireless Information and Power Transference), nas quais um sinal radio é usado para transferir simultaneamente potência e informações.Embora as técnicas SWIPT existentes sejam fle- xíveis e adequadas para as redes de comunicações sem fios, os recursos de energia e tempo do sinal precisam ser compartilhados entre a transmissão de informações e de energia, o que pode comprometer a qualidade do sinal. Deste modo,as técnicas SWIPT precisam ser projetadas para permitir uma alocação eficiente de recursos para comunicação e recolha de energia. O objetivo desta tese é desenvolver técnicas SWIPT que permitam transferência de energia e comunicações eficientes,fiáveis e seguras.Um problema associado às técnicas SWIPT combinadas com sinais multi-portadora são as dificuldades de amplificação ine- rentes à combinação de sinais de transmissão de energia com sinais de transferência de dados, que podem exacerbar os efeitos de distorção não-linear nos sinais transmitidos. Deste modo, um dos objectivos desta tese é avaliar o impacto da distorção não-linear em sinais SWIPT, e apresentar soluções viáveis para mitigar os efeitos da distorção não-linear no desempenho da transmissão de dados.Outro objetivo da tese é aproveitar as vantagens dos sinais de transferência de energia em técnicas SWIPT para efeitos de estimação de canal e segurança na comunicação.Os desempenhos dessas técnicas SWIPT são avaliados analiticamente,sendo os respectivos resultados validados por simulações.É mostrado que os esquemas SWIPT propostos podem ter excelente desempenho, superando esquemas SWIPT convencionais

    Performance analysis of Multiple-RIS-Based NOMA systems

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    In this paper, we present a study on a model of multirelay radio network system that utilizes reconfigurable intelligent surfaces (RISs). We investigate the use of nonorthogonal multiple access (NOMA) combined with cooperative RIS systems, using partial RIS selection (PRISs). Specifically, the RISs act as relays to forward data from the base station to the two users. The focus of this paper is to analyze the outage probabilities and throughput for the two users. Based on the results, we examine how PRISs affect the performance of the proposed NOMA scheme. The derived asymptotic expressions show that the proposed model can improve user fairness. Finally, we compare the analysis results with the simulation results and find good agreement

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

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    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

    Transceiver Optimization for Wireless Powered Time-Division Duplex MU-MIMO Systems: Non-Robust and Robust Designs

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    Wireless powered communication (WPC) has been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we study a wireless powered time-division duplex (TDD) multiuser multiple-input multiple-output (MU-MIMO) system, where the base station (BS) has its own power supply and all users can harvest radio frequency (RF) energy from the BS. We aim to maximize the users' information rates by jointly optimizing the duration of users' time slots and the signal covariance matrices of the BS and users. Different to the commonly used sum rate and max-min rate criteria, the proportional fairness of users' rates is considered in the objective function. We first study the ideal case with the perfect channel state information (CSI), and show that the non-convex proportionally fair rate optimization problem can be transformed into an equivalent convex optimization problem. Then we consider practical systems with imperfect CSI, where the CSI mismatch follows a Gaussian distribution. A chance-constrained robust system design is proposed for this scenario, where the Bernstein inequality is applied to convert the chance constraints into the convex constraints. Finally, we consider a more general case where only partial knowledge of the CSI mismatch is available. In this case, the conditional value-at-risk (CVaR) method is applied to solve the distributionally robust system rate optimization problem. Simulation results are presented to show the effectiveness of the proposed algorithms
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