Wireless energy transfer (WET) has been a promising technology to tackle the lifetime bottlenecks of energy-limited wireless devices in recent years. In this paper, we study a WET-enabled ...multiple-input multiple-output system including a base station (BS) and a user equipment (UE), which has a finite battery capacity. We consider slotted transmissions, where each slot includes two phases, namely, a downlink (DL) WET phase and an uplink (UL) wireless information transmission (WIT) phase. In the WET phase (a fraction τ of a slot), the BS transfers energy and the UE stores the received energy in the battery. In the WIT phase (a fraction 1 - τ of a slot), the UE transmits information to the BS by using the energy in the battery. Considering the power sensitivity α of the radio frequency to DC conversion circuits, the BS transfers energy only if the UE received power is larger than α, and the DL WET is formulated as a Bernoulli process. Based on the formulation, we propose an online power and time allocation algorithm to maximize the average data rate of UL WIT. We also extend the proposed algorithm to multiple user systems. The numerical results show that the proposed algorithm outperforms the existing schemes in terms of average data rate, energy efficiency, and outage probability.
Driven by the limited radio spectrum resources and the high energy consumption of wireless devices, symbiotic radio (SR) has recently been proposed to support passive Internet-of-Things (IoT) ...networks, where a primary transmitter (PT) transmits information to a primary reader (PR), while passive backscatter devices (BDs) modulate their own information on the received primary signal and backscatter the modulated signal to the same PR by adjusting their reflection coefficients. Existing works on SR have mainly studied the case of a single BD while without considering the BD's energy harvesting (EH) ability. In this paper, we aim to maximize the energy efficiency (EE) of an SR system that includes multiple BDs each being able to harvest energy while backscattering, by jointly optimizing the PT transmission power and the BDs' reflection coefficients and time division multiple access (TDMA) time slot durations for both the parasitic SR (PSR) and commensal SR (CSR) cases. To solve the formulated non-convex optimization problems, we propose a Dinkelbach-based iterative algorithm that builds on the block coordinated decent (BCD) method and the successive convex programming (SCP) technique. Simulation results show that the proposed algorithm converges fast, and the system EE is maximized when the BD that can provide the highest EE is allocated the maximum allowed time for backscattering while guaranteeing the throughput requirements for both the primary link and the other backscatter links.
The pursuit of clinical effectiveness in real-world settings is at the core of clinical practice progression. In this study, we address a long-term clinical efficacy evaluation decision-making ...problem with temporal correlation hybrid attribute characteristics. To address this problem, we propose a novel approach that combines a temporal correlation feature rough set model with machine learning techniques and nonadditive measures. Our proposed approach involves several steps. First, over the framework of granular computing, we construct a temporal correlation hybrid information system, the gradient method is employed to characterize the temporal attributes and the similarity between objects is measured using cosine similarity. Second, based on the similarity of gradient and cosine, we construct a composite binary relation of temporal correlation hybrid information, enabling effective classification of this information. Third, we develop a rough set decision model based on the Choquet integral, which describes temporal correlation decision process. We provide the ranking results of decision schemes with temporal correlation features. To demonstrate the practical applications of our approach, we conduct empirical research using an unlabeled dataset consisting of 3094 patients with chronic renal failure (CRF) and 80,139 EHRs from various clinical encounters. These findings offer valuable support for clinical decision-making. Two main innovations are obtained from this study. First, it establishes general theoretical principles and decision-making methods for temporal correlation and hybrid rough sets. Second, it integrates data-driven clinical decision paradigms with traditional medical research paradigms, laying the groundwork for exploring the feasibility of data-driven clinical decision-making in the field.
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The log-normal probability distribution has been commonly used in wireless communications to model the shadowing and, recently, the small-scale fading for indoor ultrawideband (UWB) communications. ...In this paper, a tight closed-form approximation of the ergodic capacity over log-normal fading channels is derived. This expression can be easily used to evaluate and compare the ergodic capacities of communication systems operating over log-normal fading channels. We also utilize this expression to show that the capacity of a multi-antennas UWB system operating over the IEEE 802.15.3a channel can be improved mainly through receive diversity.
Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks. In this paper we are ...motivated to propose resilient routing algorithms for wireless sensor networks. The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design. We proposed firstly a novel deep-learning based link prediction model, which jointly exploits Weisfeiler-Lehman kernel and Dual Convolutional Neural Network (WL-DCNN) for lightweight subgraph extraction and labelling. It is leveraged to enhance self-learning ability of mining topological features with strong generality. Experimental results demonstrate that WL-DCNN outperforms all the studied 9 baseline schemes over 6 open complex networks datasets. The performance of AUC (Area Under the receiver operating characteristic Curve) is improved by 16% on average. Furthermore, we apply the WL-DCNN model in the design of resilient routing for wireless sensor networks, which can adaptively capture topological features to determine the reliability of target links, especially under the situations of routing table suffering from attack with varying degrees of damage to local link community. It is observed that, compared with other classical routing baselines, the proposed routing algorithm with link reliability prediction module can effectively improve the resilience of sensor networks while reserving high-energy-efficiency.
In this letter, we model the device-to-device (D2D) content sharing problem as a labor market where the base station (BS) acts as the principal and content providers serve as agents. A ...signaling-based content-sharing incentive (SCSI) mechanism is designed to encourage candidate content providers to participate in content sharing, and the optimal strategy for each content provider is derived to maximize their utility (monetary profit) while guaranteeing a non-negative utility for the BS. Simulation results show that the proposed SCSI mechanism can increase the content provider's utility and participating enthusiasm in D2D content sharing.
Coordinated multipoint (CoMP) is corroborated to be an effective technology in mitigating cochannel interference (CCI) and enhancing system performance in picocell systems, which consist of a large ...number of pico base stations (BSs). In picocell systems, effective CoMP clustering schemes could provide significant gains of system performance such as throughput and cell-edge spectrum efficiency (SE). Moreover, an intrinsic problem of densely deployed networks is the cost of signaling overhead and data exchange between BSs in clusters. In this paper, a novel semidynamic clustering scheme based on affinity propagation for CoMP-Pico is presented to maximize user SE and throughput under the constraint of backhaul cost. Our proposed scheme consists of online and offline stages that can achieve good performance and low complexity. Simulation results show that the proposed scheme yields significant gains of SE and throughput and low running time compared with the existing clustering schemes.
Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations and to improve the spectral and energy efficiency of cellular networks. ...The success of D2D communications relies on the willingness of users to share contents. In this paper, we study an economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D-assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Txs, the operator's incentives, and the popularity of contents. The closed-form expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D-assisted offloading network can be significantly improved with respect to the conventional cellular networks, where the D2D communications are disabled.
Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and ...events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.
<p><b>OFFERS COMPREHENSIVE INSIGHT INTO THE THEORY, MODELS, AND TECHNIQUES OF ULTRA&#45;DENSE NETWORKS AND APPLICATIONS IN 5G AND OTHER EMERGING WIRELESS NETWORKS</b> ...<p>The need for speed&#151;and power&#151;in wireless communications is growing exponentially. Data rates are projected to increase by a factor of ten every five years&#151;and with the emerging Internet of Things &#40;IoT&#41; predicted to wirelessly connect trillions of devices across the globe, future mobile networks &#40;5G&#41; will grind to a halt unless more capacity is created. This book presents new research related to the theory and practice of all aspects of ultra&#45;dense networks, covering recent advances in ultra&#45;dense networks for 5G networks and beyond, including cognitive radio networks, massive multiple&#45;input multiple&#45;output &#40;MIMO&#41;, device&#45;to&#45;device &#40;D2D&#41; communications, millimeter&#45;wave communications, and energy harvesting communications. <p>Clear and concise throughout,<i> Ultra&#45;dense Networks for 5G and Beyond: Modelling, Analysis, and Applications</i> offers comprehensive coverage on such topics as network optimization; mobility, handoff control, and interference management; and load balancing schemes and energy saving techniques. It delves into the backhaul traffic aspects in ultra&#45;dense networks and studies transceiver hardware impairments and power consumption models in ultra&#45;dense networks. The book also examines new IoT, smart&#45;grid, and smart&#45;city applications, as well as novel modulation, coding, and waveform designs. <ul> <li>One of the first books to focus solely on ultra&#45;dense networks for 5G</li> <li>Covers advanced architectures, self&#45;organizing protocols, resource allocation, user&#45;base station association, synchronization, and signalling</li> <li>Examines the current state of cell&#45;free massive MIMO, distributed massive MIMO, and heterogeneous small cell architectures</li> <li>Offers network measurements, implementations, and demos</li> <li>Looks at wireless caching techniques, physical layer security, cognitive radio, energy harvesting, and D2D communications in ultra&#45;dense networks</li> </ul> <p><i>Ultra&#45;dense Networks for 5G and Beyond: Modelling, Analysis, and Applications</i> is an ideal reference for those who want to design high&#45;speed, high&#45;capacity communications in advanced networks, and will appeal to postgraduate students, researchers, and engineers in the field.
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