6 research outputs found

    Simultaneous Wireless Information and Power Transfer Based on Generalized Triangular Decomposition

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    The rapidly growing number of wireless devices has raised the need for designing self-sustained wireless systems. Simultaneous wireless information and power transfer (SWIPT) has been advocated as a promising solution. Various approaches have emerged to design wireless systems that enable SWIPT. In this thesis, we propose a novel approach for spatial switching (SS) based SWIPT using the generalized triangular decomposition (GTD) for point-to-point multiple-input-multiple-output (MIMO) systems. The GTD structure allows the transmitter to use the highest gain subchannels jointly for energy and information transmissions and these joint transmissions can be separated at the receiver. We first derive the optimal GTD structure to attain optimal performance in SS based SWIPT systems. This structure is then extended to design three novel transceivers where each transceiver achieves a certain objective and meets specific constraints. The first transceiver focuses on minimizing the total transmitted power while satisfying the energy harvesting and data rate constraints at the receiver. The second transceiver targets the data rate maximization while meeting a certain amount of energy at the receiver. The third transceiver considers the energy harvesting maximization and guarantees to satisfy the required data rate constraint. The proposed transceivers are designed assuming two transmitted power constraints at the transmitter; the instantaneous total transmit power and the limited transmit power per subchannel. For each designed transceiver, optimal and/or suboptimal solutions are developed to obtain joint power allocation and subchannel assignment under a linear energy harvesting model. Additionally, a novel extension to the SS based SWIPT system is proposed considering a non-linear energy harvesting model. Thereafter, the case of maximizing the energy harvesting for a given data rate and instantaneous total transmitted power constraints is studied. A solution is developed that obtains jointly the optimal power allocation and the subchannel assignment alongside the optimal and/or suboptimal split ratios at the energy harvesters. The theoretical and simulation results show that our novel proposed GTD designs for both linear and non-linear energy harvesting models outperform the state-of-the-art singular value decomposition (SVD) based SWIPT designs

    Opportunistic Ambient Backscatter Communication in RF-Powered Cognitive Radio Networks

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    In the present contribution, we propose a novel opportunistic ambient backscatter communication (ABC) framework for radio frequency (RF)-powered cognitive radio (CR) networks. This framework considers opportunistic spectrum sensing integrated with ABC and harvest-then-transmit (HTT) operation strategies. Novel analytic expressions are derived for the average throughput, the average energy consumption and the energy efficiency in the considered set up. These expressions are represented in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. In addition, we formulate an optimization problem to maximize the energy efficiency of the CR system operating in mixed ABC - and HTT - modes, for a given set of constraints including primary interference and imperfect spectrum sensing constraints. Capitalizing on this, we determine the optimal set of parameters which in turn comprise the optimal detection threshold, the optimal degree of trade-off between the CR system operating in the ABC - and HTT - modes and the optimal data transmission time. Extensive results from respective computer simulations are also presented for corroborating the corresponding analytic results and to demonstrate the performance gain of the proposed model in terms of energy efficiency

    Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

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    The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performanceandservicetypes.Theincreasinglystringentperformancerequirementsofemergingnetworks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications and cell-free communications – tonameafew.Ourvisionfor6Gis–amassivelyconnectedcomplexnetworkcapableofrapidlyresponding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensivereviewoftherelatedstate-of-the-artinthedomainsofML(includingdeeplearning),QCand QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently,weproposeanovelQC-assistedandQML-basedframeworkfor6Gcommunicationnetworks whilearticulatingitschallengesandpotentialenablingtechnologiesatthenetwork-infrastructure,networkedge, air interface and user-end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed

    Pertanika Journal of Science & Technology

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    D13.3 Overall assessment of selected techniques on energy- and bandwidth-efficient communications

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    Deliverable D13.3 del projecte europeu NEWCOM#The report presents the outcome of the Joint Research Activities (JRA) of WP1.3 in the last year of the Newcom# project. The activities focus on the investigation of bandwidth and energy efficient techniques for current and emerging wireless systems. The JRAs are categorized in three Tasks: (i) the first deals with techniques for power efficiency and minimization at the transceiver and network level; (ii) the second deals with the handling of interference by appropriate low interference transmission techniques; (iii) the third is concentrated on Radio Resource Management (RRM) and Interference Management (IM) in selected scenarios, including HetNets and multi-tier networks.Peer ReviewedPostprint (published version

    Machine learning and energy efficient cognitive radio

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    With an explosion of wireless mobile devices and services, system designers are facing a challenge of spectrum scarcity and high energy consumption. Cognitive radio (CR) is a promising solution for fulfilling the growing demand of radio spectrum using dynamic spectrum access. It has the ability of sensing, allocating, sharing and adapting to the radio environment. In this thesis, an analytical performance evaluation of the machine learning and energy efficient cognitive radio systems has been investigated while taking some realistic conditions into account. Firstly, bio-inspired techniques, including re y algorithm (FFA), fish school search (FSS) and particle swarm optimization (PSO), have been utilized in this thesis to evaluate the optimal weighting vectors for cooperative spectrum sensing (CSS) and spectrum allocation in the cognitive radio systems. This evaluation is performed for more realistic signals that suffer from the non-linear distortions, caused by the power amplifiers. The thesis then takes the investigation further by analysing the spectrum occupancy in the cognitive radio systems using different machine learning techniques. Four machine learning algorithms, including naive bayesian classifier (NBC), decision trees (DT), support vector machine (SVM) and hidden markov model (HMM) have been studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy has been presented. In addition to this, the thesis investigates the energy efficient cognitive radio systems because energy harvesting enables the perpetual operation of the wireless networks without the need of battery change. In particular, energy can be harvested from the radio waves in the radio frequency spectrum. For ensuring reliable performance, energy prediction has been proposed as a key component for optimizing the energy harvesting because it equips the harvesting nodes with adaptation to the energy availability. Two machine learning techniques, linear regression (LR) and decision trees (DT) have been utilized to predict the harvested energy using real-time power measurements in the radio spectrum. Furthermore, the conventional energy harvesting cognitive radios do not assume any energy harvesting capability at the primary users (PUs). However, this is not the case when primary users are wirelessly powered. In this thesis, a novel framework has been proposed where PUs possess the energy harvesting capabilities and can get benefit from the presence of the secondary user (SU) without any predetermined agreement. The performances of the wireless powered PUs and the SU has also been analysed. Numerical results have been presented to show the accuracy of the analysis. First, it has been observed that bio-inspired techniques outperform the conventional algorithms used for collaborative spectrum sensing and allocation. Second, it has been noticed that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, a new SVM algorithm has been proposed by combining SVM with FFA. It has also been observed that SVM+FFA outperform all other machine leaning classifiers Third, it has been noticed in the energy predictive modelling framework that LR outperforms DT by achieving smaller prediction error. It has also been shown that optimal time and frequency attained using energy predictive model can be used for defining the scheduling policies of the harvesting nodes. Last, it has been shown that wirelessly powered PUs having energy harvesting capabilities can attain energy gain from the transmission of SU and SU can attain the throughput gain from the extra transmission time allocated for energy harvesting PUs
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