Energy Efficient, Cooperative Communication in Low-Power Wireless Networks

The increased interest in massive deployment of wireless sensors and network densification requires more innovation in low-latency communication across multi-hop networks. Moreover, the resource constrained nature of sensor nodes calls for more energy efficient transmission protocols, in order to increase the battery life of said devices. Therefore, it is important to investigate possible technologies that would aid in improving energy efficiency and decreasing latency in wireless sensor networks (WSN) while focusing on application specific requirements. To this end, and based on state of the art Glossy, a low-power WSN flooding protocol, this dissertation introduces two energy efficient, cooperative transmission schemes for low-power communication in WSNs, with the aim of achieving performance gains in energy efficiency, latency and power consumption. These approaches apply several cooperative transmission technologies such as physical layer network coding and transmit beamforming. Moreover, mathematical tools such as convex optimization and game theory are used in order to analytically construct the proposed schemes. Then, system level simulations are performed, where the proposed schemes are evaluated based on different criteria. First, in order to improve over all latency in the network as well as energy efficiency, MF-Glossy is proposed; a communication scheme that enables the simultaneous flooding of different packets from multiple sources to all nodes in the network. Using a communication-theoretic analysis, upper bounds on the performance of Glossy and MF-Glossy are determined. Further, simulation results show that MF-Glossy has the potential to achieve several-fold improvements in goodput and latency across a wide spectrum of network configurations at lower energy costs and comparable packet reception rates. Hardware implementation challenges are discussed as a step towards harnessing the potential of MF-Glossy in real networks, while focusing on key challenges and possible solutions. Second, under the assumption of available channel state information (CSI) at all nodes, centralized and distributed beamforming and power control algorithms are proposed and their performance is evaluated. They are compared in terms of energy efficiency to standard Glossy. Numerical simulations demonstrate that a centralized power control scheme can achieve several-fold improvements in energy efficiency over Glossy across a wide spectrum of network configurations at comparable packet reception rates. Furthermore, the more realistic scenario where CSI is not available at transmitting nodes is considered. To battle CSI unavailability, cooperation is introduced on two stages. First, cooperation between receiving and transmitting nodes is proposed for the process of CSI acquisition, where the receivers provide the transmitters with quantized (e.g. imperfect) CSI. Then, cooperation within transmitting nodes is proposed for the process of multi-cast transmit beamforming. In addition to an analytical formulation of the robust multi-cast beamforming problem with imperfect CSI, its performance is evaluated, in terms of energy efficiency, through numerical simulations. It is shown that the level of cooperation, represented by the number of limited feedback bits from receivers to transmitters, greatly impacts energy efficiency. To this end, the optimization problem of finding the optimal number of feedback bits B is formulated, as a programming problem, under QoS constraints of 5% maximum outage. Numerical simulations show that there exists an optimal number of feedback bits that maximizes energy efficiency. Finally, the effect of choosing cooperating transmitters on energy efficiency is studied, where it is shown that an optimum group of cooperating transmit nodes, also known as a transmit coalition, can be formed in order to maximize energy efficiency. The investigated techniques including optimum feedback bits and transmit coalition formation can achieve a 100% increase in energy efficiency when compared to state of the art Glossy under same operation requirements in very dense networks. In summary, the two main contributions in this dissertation provide insights on the possible performance gains that can be achieved when cooperative technologies are used in low-power wireless networks

Exploiting Diversity in Broadband Wireless Relay Networks

Fading is one of the most fundamental impairments to wireless communications. The standard approach to combating fading is by adding redundancy - or diversity - to help increase coverage and transmission speed. Motivated by the results in multiple-input multiple-output technologies, which are usually used at base stations or access points, cooperation commutation has been proposed to improve the performance of wireless networks which consist of low-cost single antenna devices. While the majority of the research in cooperative communication focuses on flat fading for its simplicity and easy analysis, in practice the underlying channels in broadband wireless communication systems such as cellular systems (UMTS/LTE) are more likely to exhibit frequency selective fading. In this dissertation, we consider a frequency selective fading channel model and explore distributed diversity techniques in broadband wireless relay networks, with consideration to practical issues such as channel estimation and complexity-performance tradeoffs. We first study a system model with one source, one destination and multiple decode-and-forward (DF) relays which share a single channel orthogonal to the source. We derive the diversity-multiplexing tradeoff (DMT) for several relaying strategies: best relay selection, random relay selection, and the case when all decoding relays participate. The best relay selection method selects the relay in the decoding set with the largest sum-squared relay-to-destination channel coefficients. This scheme can achieve the optimal DMT of the system at the expense of higher complexity, compared to the other two relaying strategies which do not always exploit the spatial diversity offered by the relays. Different from flat fading, we find special cases when the three relaying strategies have the same DMT. We further present a transceiver design and prove it can achieve the optimal DMT asymptotically. Monte Carlo simulations are presented to corroborate the theoretical analysis. We provide a detailed performance comparison of the three relaying strategies in channels encountered in practice. The work has been extended to systems with multiple amplify-and-forward relays. We propose two relay selection schemes with maximum likelihood sequential estimator and linear zero- forcing equalization at the destination respectively and both schemes can asymptotically achieve the optimal DMT. We next extend the results in the two-hop network, as previously studied, to multi-hop networks. In particular, we consider the routing problem in clustered multi-hop DF relay networks since clustered multi-hop wireless networks have attracted significant attention for their robustness to fading, hierarchical structure, and ability to exploit the broadcast nature of the wireless channel. We propose an opportunistic routing (or relay selection) algorithm for such networks. In contrast to the majority of existing approaches to routing in clustered networks, our algorithm only requires channel state information in the final hop, which is shown to be essential for reaping the diversity offered by the channel. In addition to exploiting the available diversity, our simple cross-layer algorithm has the flexibility to satisfy an additional routing objective such as maximization of network lifetime. We demonstrate through analysis and simulation that our proposed routing algorithm attains full diversity under certain conditions on the cluster sizes, and its diversity is equal to the diversity of more complicated approaches that require full channel state information. The final part of this dissertation considers channel estimation in relay networks. Channel state information is vital for exploiting diversity in cooperative networks. The existing literature on cooperative channel estimation assumes that block lengths are long and that channel estimation takes place within a fading block. However, if the forwarding delay needs to be reduced, short block lengths are preferred, and adaptive estimation through multiple blocks is required. In particular, we consider estimating the relay-to-destination channel in DF relay systems for which the presence of forwarded information is probabilistic since it is unknown whether the relay participates in the forwarding phase. A detector is used so that the update of the least mean square channel estimate is made only when the detector decides the presence of training data. We use the generalized likelihood ratio test and focus on the detector threshold for deciding whether the training sequence is present. We also propose a heuristic objective function which leads to a proper threshold to improve the convergence speed and reduce the estimation error. Extensive numerical results show the superior performance of using this threshold as opposed to fixed thresholds

Power Optimisation and Relay Selection in Cooperative Wireless Communication Networks

Cooperative communications have emerged as a significant concept to improve reliability and throughput in wireless systems. In cooperative networks, the idea is to implement a scheme in wireless systems where the nodes can harmonize their resources thereby enhancing the network performance in different aspects such as latency, BER and throughput. As cooperation spans from the basic idea of transmit diversity achieved via MIMO techniques and the relay channel, it aims to reap somewhat multiple benefits of combating fading/burst errors, increasing throughput and reducing energy use. Another major benefit of cooperation in wireless networks is that since the concept only requires neighbouring nodes to act as virtual relay antennas, the concept evades the negative impacts of deployment costs of multiple physical antennas for network operators especially in areas where they are difficult to deploy. In cooperative communications energy efficiency and long network lifetimes are very important design issues, the focus in this work is on ad hoc and sensor network varieties where the nodes integrate sensing, processing and communication such that their cooperation capabilities are subject to power optimisation. As cooperation communications leads to trade-offs in Quality of Services and transmit power, the key design issue is power optimisation to dynamically combat channel fluctuations and achieve a net reduction of transmit power with the goal of saving battery life. Recent researches in cooperative communications focus on power optimisation achieved via power control at the PHY layer, and/or scheduling mechanism at the MAC layer. The approach for this work will be to review the power control strategy at the PHY layer, identify their associated trade-offs, and use this as a basis to propose a power control strategy that offers adaptability to channel conditions, the road to novelty in this work is a channel adaptable power control algorithm that jointly optimise power allocation, modulation strategy and relay selection. Thus, a novel relay selection method is developed and implemented to improve the performance of cooperative wireless networks in terms of energy consumption. The relay selection method revolves on selection the node with minimum distance to the source and destination. The design is valid to any wireless network setting especially Ad-hoc and sensor networks where space limitations preclude the implementation of bigger capacity battery. The thesis first investigates the design of relay selection schemes in cooperative networks and the associated protocols. Besides, modulation strategy and error correction code impact on energy consumption are investigated and the optimal solution is proposed and jointly implemented with the relay selection method. The proposed algorithm is extended to cooperative networks in which multiple nodes participate in cooperation in fixed and variable rate system. Thus, multi relay selection algorithm is proposed to improve virtual MIMO performance in terms of energy consumption. Furthermore, motivated by the trend of cell size optimisation in wireless networks, the proposed relay selection method is extended to clustered wireless networks, and jointly implemented with virtual clustering technique. The work will encompass three main stages: First, the cooperative system is designed and two major protocols Decode and Forward (DF) and amplify and forward (AF) are investigated. Second, the proposed algorithm is modelled and tested under different channel conditions with emphasis on its performance using different modulation strategies for different cooperative wireless networks. Finally, the performance of the proposed algorithm is illustrated and verified via computer simulations. Simulation results show that the distance based relay selection algorithm exhibits an improved performance in terms of energy consumption compared to the conventional cooperative schemes under different cooperative communication scenarios

Simultaneous wireless information and power transfer (SWIPT) in cooperative networks

2019 Spring.Includes bibliographical references.In recent years, the capacity and charging speed of batteries have become the bottleneck of mobile communications systems. Energy harvesting (EH) is regarded as a promising technology to significantly extend the lifetime of battery-powered devices. Among many EH technologies, simultaneous wireless information and power transfer (SWIPT) proposes to harvest part of the energy carried by the wireless communication signals. In particular, SWIPT has been successfully applied to energy-constrained relays that are mainly or exclusively powered by the energy harvested from the received signals. These relays are known as EH relays, which attract significant attention in both the academia and the industry. In this research, we investigate the performance of SWIPT-based EH cooperative networks and the optimization problems therein. Due to hardware limitations, the energy harvesting circuit cannot decode the signal directly. Power splitting (PS) is a popular and effective solution to this problem. Therefore, we focus on PS based SWIPT in this research. First, different from existing work that employs time-switching (TS) based SWIPT, we propose to employ PS based SWIPT for a truly full-duplex (FD) EH relay network, where the information reception and transmission take place simultaneously at the relay all the time. This more thorough exploitation of the FD feature consequently leads to a significant capacity improvement compared with existing alternatives in the literature. Secondly, when multiple relays are available in the network, we explore the relay selection (RS) and network beamforming techniques in EH relay networks. Assuming orthogonal bandwidth allocation, both single relay selection (SRS) and general relay selection (GRS) without the limit on the number of cooperating relays are investigated and the corresponding RS methods are proposed. We will show that our proposed heuristic GRS methods outperform the SRS methods and achieve very similar performance compared with the optimal RS method achieved by exhaustive search but with dramatically reduced complexity. Under the shared bandwidth assumption, network beamforming among EH relays is investigated. We propose a joint PS factor optimization method based on semidefinite relaxation. Simulations show that network beamforming achieves the best performance among all other cooperative techniques. Finally, we study the problem of power allocation and PS factor optimization for SWIPT over doubly-selective wireless channels. In contrast to existing work in the literature, we take the channel variation in both time and frequency domains into consideration and jointly optimize the power allocation and the PS factors. The objective is to maximize the achievable data rate with constraints on the delivered energy in a time window. Since the problem is difficult to solve directly due to its nonconvexity, we proposed a two-step approach, named joint power allocation and splitting (JoPAS), to solve the problem along the time and frequency dimensions sequentially. Simulations show significantly improved performance compared with the existing dynamic power splitting scheme. A suboptimal heuristic algorithm, named decoupled power allocation and splitting (DePAS), is also proposed with significantly reduced computational complexity and simulations demonstrate its near-optimum performance

Joint transceiver design and power optimization for wireless sensor networks in underground mines

Avec les grands développements des technologies de communication sans fil, les réseaux de capteurs sans fil (WSN) ont attiré beaucoup d’attention dans le monde entier au cours de la dernière décennie. Les réseaux de capteurs sans fil sont maintenant utilisés pour a surveillance sanitaire, la gestion des catastrophes, la défense, les télécommunications, etc. De tels réseaux sont utilisés dans de nombreuses applications industrielles et commerciales comme la surveillance des processus industriels et de l’environnement, etc. Un réseau WSN est une collection de transducteurs spécialisés connus sous le nom de noeuds de capteurs avec une liaison de communication distribuée de manière aléatoire dans tous les emplacements pour surveiller les paramètres. Chaque noeud de capteur est équipé d’un transducteur, d’un processeur de signal, d’une unité d’alimentation et d’un émetteur-récepteur. Les WSN sont maintenant largement utilisés dans l’industrie minière souterraine pour surveiller certains paramètres environnementaux, comme la quantité de gaz, d’eau, la température, l’humidité, le niveau d’oxygène, de poussière, etc. Dans le cas de la surveillance de l’environnement, un WSN peut être remplacé de manière équivalente par un réseau à relais à entrées et sorties multiples (MIMO). Les réseaux de relais multisauts ont attiré un intérêt de recherche important ces derniers temps grâce à leur capacité à augmenter la portée de la couverture. La liaison de communication réseau d’une source vers une destination est mise en oeuvre en utilisant un schéma d’amplification/transmission (AF) ou de décodage/transfert (DF). Le relais AF reçoit des informations du relais précédent et amplifie simplement le signal reçu, puis il le transmet au relais suivant. D’autre part, le relais DF décode d’abord le signal reçu, puis il le transmet au relais suivant au deuxième étage s’il peut parfaitement décoder le signal entrant. En raison de la simplicité analytique, dans cette thèse, nous considérons le schéma de relais AF et les résultats de ce travail peuvent également être développés pour le relais DF. La conception d’un émetteur/récepteur pour le relais MIMO multisauts est très difficile. Car à l’étape de relais L, il y a 2L canaux possibles. Donc, pour un réseau à grande échelle, il n’est pas économique d’envoyer un signal par tous les liens possibles. Au lieu de cela, nous pouvons trouver le meilleur chemin de la source à la destination qui donne le rapport signal sur bruit (SNR) de bout en bout le plus élevé. Nous pouvons minimiser la fonction objectif d’erreur quadratique moyenne (MSE) ou de taux d’erreur binaire (BER) en envoyant le signal utilisant le chemin sélectionné. L’ensemble de relais dans le chemin reste actif et le reste des relais s’éteint, ce qui permet d’économiser de l’énergie afin d’améliorer la durée de vie du réseau. Le meilleur chemin de transmission de signal a été étudié dans la littérature pour un relais MIMO à deux bonds mais est plus complexe pour un ...With the great developments in wireless communication technologies, Wireless Sensor Networks (WSNs) have gained attention worldwide in the past decade and are now being used in health monitoring, disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used in the underground mining industry to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay. The transceiver design for multi-hop MIMO relay is very challenging. This is because at the L-th relay stage, there are 2L possible channels. So, for a large scale network, it is not economical to send the signal through all possible links. Instead, we can find the best path from source-to-destination that gives the highest end-to-end signal-to-noise ratio (SNR). We can minimize the mean square error (MSE) or bit error rate (BER) objective function by sending the signal using the selected path. The set of relay in the path remains active and the rest of the relays are turned off which can save power to enhance network life-time. The best path signal transmission has been carried out in the literature for 2-hop MIMO relay and for multiple relaying it becomes very complex. In the first part of this thesis, we propose an optimal best path finding algorithm at perfect channel state information (CSI). We consider a parallel multi-hop multiple-input multiple-output (MIMO) AF relay system where a linear minimum mean-squared error (MMSE) receiver is used at the destination. We simplify the parallel network into equivalent series multi-hop MIMO relay link using best relaying, where the best relay ..

Outage Analysis of Energy Harvested Relay-Aided Device-to-Device Communications in Nakagami Channel

In this paper, we obtain a low-complexity closed-form formula for the outage probability of the energy-harvested decode-and-forward (DF) relay-aided underlay Device-to-device (D2D) communications in Nakagami fading channel. By proposing a new idea which finds the power splitting factor in simultaneous wireless information and power transfer (SWIPT) energy-harvesting system such that the transmit power of the relay node in the second time slot is fixed in a pre-defined value, the obtained closed-form expression is valid for both energy-harvested and non-energy-harvested scenarios. This formula is based on n-point generalized Gauss-Laguerre and m-point Gauss-Legendre solutions. It is shown that n is more effective than m for reducing the formula complexity. In addition to a good agreement between the simulation results and numerical analysis based on normalized mean square error (NMSE), it is indicated that (n, m)=(1, 4) and (n, m)=(1, 2) are the appropriate choices, respectively for 0.5≤ µ <0.7 and µ ≥0.7, where µ is the fading factor. As shown in this investigation, increasing the average distance between D2D pairs and cellular user (lower interference), is the reason for decreasing the outage probability. Furthermore, it is clear that increasing the Nakagami fading factor is the reason for decreasing the outage probability

Design and stochastic analysis of emerging large-scale wireless-powered sensor networks

Premi Extraordinari de Doctorat, promoció 2016-2017. Àmbit d’Enginyeria de les TICUndeniably, the progress in wireless networks during the last two decades is extraordinary. However, the ever-increasing upward trend in the numbers of wireless devices that will overwhelm every field of our everyday life, e.g., building automation, traffic management, health-care, etc., will introduce several issues in terms of communication and energy provision that need to be handled in advance. Regarding the communication issues, it is imperative to ensure the correct operation of the vast collection of nodes, especially for life-critical applications. Two well-known metrics that can characterize sufficiently the network reliability are the coverage and the connectivity probability that are derived by taking into account the network topology, the channel conditions between every transmitter-receiver pair, and the interference from other nodes. Nevertheless, considering all those factors is not straightforward. Lately, stochastic geometry has come into prominence, which is a mathematical tool to study the average network performance over many spatial realizations, while considering all aforementioned factors. Moreover, the other crucial issue for the large-scale dense network deployments of the future is their energy supply. Traditional battery charging or swapping for the wireless devices is both inconvenient and harms the environment, especially if we take into account the enormous numbers of nodes. Therefore, novel solutions have to be found using renewable energy sources to zero down the significant electricity consumption. Wireless energy harvesting is a convenient and environmentally-friendly approach to prolong the lifetime of networks by harvesting the energy from radio-frequency (RF) signals and converting it to direct current electricity through specialized hardware. The RF energy could be harvested from signals generated in the same or other networks. However, if the amount of harvested energy is not sufficient, solar-powered dedicated transmitters could be employed. In this way, we can achieve a favorable outcome by having both a zero-energy network operation and convenience in the charging of the wireless devices. Still, extensive investigation should be done in order to ensure that the communication performance is not affected. To that end, in this thesis, we study the communication performance in large-scale networks using tools from stochastic geometry. The networks that we study comprise wireless devices that are able to harvest the energy of RF signals. In the first part of the thesis, we present the effects of wireless energy harvesting from the transmissions of the cooperative network on the coverage probability and the network lifetime. In the second part of the thesis, we first employ batteryless nodes that are powered by dedicated RF energy transmitters to study the connectivity probability. Then, we assume that the dedicated transmitters are powered by solar energy to study the connectivity in a clustered network and investigate, for the first time, the reliability of zero-energy networks. Finally, we conclude the thesis by providing insightful research challenges for future works.Innegablemente, el progreso en las redes inalámbricas durante las últimas dos décadas es extraordinario. Sin embargo, la creciente tendencia al alza en el número de dispositivos inalámbricos que abarcarán todos los ámbitos de nuestra vida cotidiana, como la automatización de edificios, la gestión del tráfico, la atención sanitaria, etc., introducirá varias cuestiones en términos de comunicación y suministro de energía que se debe tener en cuenta con antelación. Respecto a los problemas de comunicación, es imprescindible asegurar el correcto funcionamiento de la vasta colección de nodos, especialmente para las aplicaciones vitales. Dos métricas bien conocidas que pueden caracterizar suficientemente la fiabilidad de la red son la probabilidad de cobertura y la de conectividad, que se derivan teniendo en cuenta la topología de la red, las condiciones del canal entre cada par transmisor-receptor y la interferencia de otros nodos. Sin embargo, considerar todos esos factores no es sencillo. Últimamente, la geometría estocástica ha llegado a la prominencia como un metodo de análisis, que es una herramienta matemática para estudiar el rendimiento promedio de la red sobre muchas realizaciones espaciales, teniendo en cuenta todos los factores mencionados. Además, la otra cuestión crucial para los despliegues de alta densidad de las redes futuras es su suministro de energía. La carga o el intercambio de baterías para los dispositivos inalámbricos es inconveniente y daña el medio ambiente, especialmente si tenemos en cuenta el enorme número de nodos utilizados. Por lo tanto, se deben encontrar nuevas soluciones utilizando fuentes de energía renovables para reducir el consumo de electricidad. La recolección de energía inalámbrica es un método conveniente y respetuoso con el medio ambiente para prolongar la vida útil de las redes recolectando la energía de las señales de radiofrecuencia (RF) y convirtiéndola en electricidad de corriente continua mediante un hardware especializado. La energía de RF podría ser obtenida a partir de señales generadas en la misma o en otras redes. Sin embargo, si la cantidad de energía obtenida no es suficiente, podrían emplearse transmisores de energía inalambricos que la obtuvieran mediante paneles fotovoltaicos. De esta manera, podemos lograr un resultado favorable teniendo tanto una operación de red de energía cero como una conveniencia en la carga de los dispositivos inalámbricos. Por lo tanto, una investigación exhaustiva debe hacerse con el fin de garantizar que el rendimiento de la comunicación no se ve afectada. En esta tesis se estudia el rendimiento de la comunicación en redes de gran escala utilizando técnicas de geometría estocástica. Las redes que se estudian comprenden dispositivos inalámbricos capaces de recoger la energía de las señales RF. En la primera parte de la tesis, presentamos los efectos de la recolección de energía inalámbrica de las transmisiones de la red cooperativa sobre la probabilidad de cobertura y la vida útil de la red. En la segunda parte de la tesis, primero empleamos nodos sin baterías que son alimentados por transmisores de energía de RF para estudiar la probabilidad de conectividad. A continuación, asumimos que los transmisores dedicados son alimentados por energía solar para estudiar la conectividad en una red agrupada (clustered network) e investigar, por primera vez, la fiabilidad de las redes de energía cero. Finalmente, concluimos la tesis aportando nuevas lineas de investigación para trabajos futurosAward-winningPostprint (published version

Design and stochastic analysis of emerging large-scale wireless-powered sensor networks

Undeniably, the progress in wireless networks during the last two decades is extraordinary. However, the ever-increasing upward trend in the numbers of wireless devices that will overwhelm every field of our everyday life, e.g., building automation, traffic management, health-care, etc., will introduce several issues in terms of communication and energy provision that need to be handled in advance. Regarding the communication issues, it is imperative to ensure the correct operation of the vast collection of nodes, especially for life-critical applications. Two well-known metrics that can characterize sufficiently the network reliability are the coverage and the connectivity probability that are derived by taking into account the network topology, the channel conditions between every transmitter-receiver pair, and the interference from other nodes. Nevertheless, considering all those factors is not straightforward. Lately, stochastic geometry has come into prominence, which is a mathematical tool to study the average network performance over many spatial realizations, while considering all aforementioned factors. Moreover, the other crucial issue for the large-scale dense network deployments of the future is their energy supply. Traditional battery charging or swapping for the wireless devices is both inconvenient and harms the environment, especially if we take into account the enormous numbers of nodes. Therefore, novel solutions have to be found using renewable energy sources to zero down the significant electricity consumption. Wireless energy harvesting is a convenient and environmentally-friendly approach to prolong the lifetime of networks by harvesting the energy from radio-frequency (RF) signals and converting it to direct current electricity through specialized hardware. The RF energy could be harvested from signals generated in the same or other networks. However, if the amount of harvested energy is not sufficient, solar-powered dedicated transmitters could be employed. In this way, we can achieve a favorable outcome by having both a zero-energy network operation and convenience in the charging of the wireless devices. Still, extensive investigation should be done in order to ensure that the communication performance is not affected. To that end, in this thesis, we study the communication performance in large-scale networks using tools from stochastic geometry. The networks that we study comprise wireless devices that are able to harvest the energy of RF signals. In the first part of the thesis, we present the effects of wireless energy harvesting from the transmissions of the cooperative network on the coverage probability and the network lifetime. In the second part of the thesis, we first employ batteryless nodes that are powered by dedicated RF energy transmitters to study the connectivity probability. Then, we assume that the dedicated transmitters are powered by solar energy to study the connectivity in a clustered network and investigate, for the first time, the reliability of zero-energy networks. Finally, we conclude the thesis by providing insightful research challenges for future works.Innegablemente, el progreso en las redes inalámbricas durante las últimas dos décadas es extraordinario. Sin embargo, la creciente tendencia al alza en el número de dispositivos inalámbricos que abarcarán todos los ámbitos de nuestra vida cotidiana, como la automatización de edificios, la gestión del tráfico, la atención sanitaria, etc., introducirá varias cuestiones en términos de comunicación y suministro de energía que se debe tener en cuenta con antelación. Respecto a los problemas de comunicación, es imprescindible asegurar el correcto funcionamiento de la vasta colección de nodos, especialmente para las aplicaciones vitales. Dos métricas bien conocidas que pueden caracterizar suficientemente la fiabilidad de la red son la probabilidad de cobertura y la de conectividad, que se derivan teniendo en cuenta la topología de la red, las condiciones del canal entre cada par transmisor-receptor y la interferencia de otros nodos. Sin embargo, considerar todos esos factores no es sencillo. Últimamente, la geometría estocástica ha llegado a la prominencia como un metodo de análisis, que es una herramienta matemática para estudiar el rendimiento promedio de la red sobre muchas realizaciones espaciales, teniendo en cuenta todos los factores mencionados. Además, la otra cuestión crucial para los despliegues de alta densidad de las redes futuras es su suministro de energía. La carga o el intercambio de baterías para los dispositivos inalámbricos es inconveniente y daña el medio ambiente, especialmente si tenemos en cuenta el enorme número de nodos utilizados. Por lo tanto, se deben encontrar nuevas soluciones utilizando fuentes de energía renovables para reducir el consumo de electricidad. La recolección de energía inalámbrica es un método conveniente y respetuoso con el medio ambiente para prolongar la vida útil de las redes recolectando la energía de las señales de radiofrecuencia (RF) y convirtiéndola en electricidad de corriente continua mediante un hardware especializado. La energía de RF podría ser obtenida a partir de señales generadas en la misma o en otras redes. Sin embargo, si la cantidad de energía obtenida no es suficiente, podrían emplearse transmisores de energía inalambricos que la obtuvieran mediante paneles fotovoltaicos. De esta manera, podemos lograr un resultado favorable teniendo tanto una operación de red de energía cero como una conveniencia en la carga de los dispositivos inalámbricos. Por lo tanto, una investigación exhaustiva debe hacerse con el fin de garantizar que el rendimiento de la comunicación no se ve afectada. En esta tesis se estudia el rendimiento de la comunicación en redes de gran escala utilizando técnicas de geometría estocástica. Las redes que se estudian comprenden dispositivos inalámbricos capaces de recoger la energía de las señales RF. En la primera parte de la tesis, presentamos los efectos de la recolección de energía inalámbrica de las transmisiones de la red cooperativa sobre la probabilidad de cobertura y la vida útil de la red. En la segunda parte de la tesis, primero empleamos nodos sin baterías que son alimentados por transmisores de energía de RF para estudiar la probabilidad de conectividad. A continuación, asumimos que los transmisores dedicados son alimentados por energía solar para estudiar la conectividad en una red agrupada (clustered network) e investigar, por primera vez, la fiabilidad de las redes de energía cero. Finalmente, concluimos la tesis aportando nuevas lineas de investigación para trabajos futuro

Optimization and Learning Approaches for Energy Harvesting Wireless Communication Systems

Emerging technologies such as Internet of Things (IoT) and Industry 4.0 are now possible thanks to the advances in wireless sensor networks. In such applications, the wireless communication nodes play a key role because they provide the connection between different sensors as well as the communication to the outside world. In general, these wireless communication nodes are battery operated. However, depending on the specific application, charging or replacing the batteries can be too expensive or even infeasible, e.g., when the nodes are located in remote locations or inside structures. Therefore, in order to provide sustainable service and to reduce the operation expenses, energy harvesting (EH) has been considered as a promising technology in which the nodes collect energy from the environment using natural or man-made energy sources such as solar or electromagnetic radiation. The idea behind EH is that the wireless communication nodes can recharge their batteries while in idle mode or while transmitting data to neighboring nodes. As a result, the lifetime of the wireless communication network is not limited by the availability of energy. The consideration of EH brings new challenges in the design of transmission policies. This is because in addition to the fluctuating channel conditions and data arrival processes, the variability of the amount of energy available for the communication should be taken into account. Moreover, the three processes, EH, data arrival and channel fading, should be jointly considered in order to achieve optimum performance. In this context, this dissertation contributes to the research on EH wireless communication networks by considering power allocation and resource allocation problems in four different scenarios, namely, EH point-to-point, EH two-hop, EH broadcast and EH multiple access, which are the fundamental constituents of more complicated networks. Specifically, we determine the optimal allocation policies and the corresponding upper bounds of the achievable performance by considering offline approaches in which non-causal knowledge regarding system dynamics, i.e., the EH, data arrival and channel fading processes, is assumed. Furthermore, we overcome this unrealistic assumption by developing novel learning approaches, based on reinforcement learning, under the practical assumption that only causal knowledge of the system dynamics is available. First, we focus on the EH point-to-point scenario where an EH transmitter sends data to a receiver. For this scenario, we formulate the power allocation problem for throughput maximization considering not only the transmit power, but also the energy consumed by the circuit. Adopting an offline approach, we characterize the optimum power allocation policy and exploit this analysis in the development of a learning approach. Specifically, we develop a novel learning algorithm which considers a realistic EH point-to-point scenario, i.e., only causal knowledge of the system dynamics is assumed to be available. For the proposed learning algorithm, we exploit linear function approximation to cope with the infinite number of values the harvested energy, the incoming data and the channel coefficients can take. In particular, we propose four feature functions which are inspired by the characteristics of the problem and the insights gained from the offline approach. Through numerical simulations, we show that the proposed learning approach achieves a performance close to the offline optimum without the requirement of non-causal knowledge of the system dynamics. Moreover, it can achieve a performance up to 50% higher than the performance of reference learning schemes such as Q-learning, which do not exploit the characteristics of the problem. Secondly, we investigate an EH two-hop scenario in which an EH transmitter communicates with a receiver via an EH relay. For this purpose, we consider the main relaying strategies, namely, decode-and-forward and amplify-and-forward. Furthermore, we consider both, the transmit power and the energy consumed by the circuit in each of the EH nodes. For the EH decode-and-forward relay, we formulate the power allocation problem for throughput maximization and consider an offline approach to find the optimum power allocation policy. We show that the optimal power allocation policies of both nodes, transmitter and relay, depend on each other. Additionally, following a learning approach, we investigate a more realistic scenario in which the EH transmitter and the EH decode-and-forward relay have only partial and causal knowledge about the system dynamics, i.e., each node has only causal knowledge about the EH, data arrival and channel fading processes associated to it. To this aim, two novel learning algorithms are proposed which take into account whether or not the EH nodes cooperate with each other to improve their learning processes. For the cooperative case, we propose the inclusion of a signaling phase in which the EH nodes exchange their current parameters. Through numerical simulations, we show that by providing the nodes with a complete view of the system state in a signaling phase, a performance gain of up to 40% can be achieved compared to the case when no cooperation is considered. Following a similar procedure, we investigate the EH two-hop scenario with an EH amplify-and-forward relay. We show that the resulting power allocation problem for throughput maximization is non-convex. Consequently, we propose an offline approach based on a branch-and-bound algorithm tailored to the EH two-hop scenario to find the optimal power allocation policy. Additionally, a centralized learning algorithm is proposed for the realistic case in which only causal knowledge of the system dynamics is available. The proposed learning approach exploits the fact that, with an amplify-and-forward relay, the communication between the transmitter and the receiver depends on a single effective channel, which is composed of the link between the transmitter and the relay, the relay gain and the channel from the relay to the receiver. By means of numerical simulations, we show that the proposed learning algorithm achieves a performance up to two times higher than the performance achieved by reference schemes. Additionally, the extension of the proposed approaches to EH multi-hop scenarios is discussed. Thirdly, an EH broadcast scenario in which an EH transmitter sends individual data to multiple receivers is studied. We show that the power allocation problem for throughput maximization in this scenario leads to a non-convex problem when an arbitrary number of receivers is considered. However, following an offline approach we find the optimal power allocation policy for the special case when two receivers are considered. Furthermore, inspired by the offline approach for two users, a novel learning approach which does not pose any restriction on the number of receiver nodes is developed. The proposed learning approach is a two-stage learning algorithm which separates the learning task into two subtasks: determining how much power to use in each time interval and deciding how to split this selected power for the transmission of the individual data intended for each receiver. Through numerical simulations, we show that the separation of tasks leads to a performance up to 40% higher than the one achieved by standard learning techniques, specially for large numbers of receivers. Finally, an EH multiple access scenario is considered in which multiple EH transmitters communicate with a single receiver using multiple orthogonal resources. In this case, the focus is on the formulation of the resource allocation problem considering the EH processes at the different transmitters. We show that the resulting resource allocation problem falls into the category of non-linear knapsack problems which are known to be NP-hard. Therefore, we propose an offline approach based on dynamic programming to find the optimal solution. Furthermore, by exploiting the characteristics of the scenario, a novel learning approach is proposed which breaks the original resource allocation problem into smaller subproblems. As a result, it is able to handle the exponential growth of the space of possible solutions when the network size increases. Through numerical simulations, we show that in contrast to conventional reinforcement learning algorithms, the proposed learning approach is able to find the resource allocation policy that aims at maximizing the throughput when the network size is large. Furthermore, it achieves a performance up to 25% higher than the performance of the greedy policy that allocates the resources to the users with the best channel conditions. Additionally, in order to carry out a full assessment of the proposed learning algorithms, we provide convergence guarantees and a computational complexity analysis for all the developed learning approaches in the four considered scenarios