Showing papers on "Data transmission published in 2021"
TL;DR: Experimental results outwards show that the intelligent module provides energy-efficient, secured transmission with low computational time as well as a reduced bit error rate, which is a key requirement considering the intelligent manufacturing of VSNs.
Abstract: Due to technology advancement, smart visual sensing required in terms of data transfer capacity, energy-efficiency, security, and computational-efficiency. The high-quality image transmission in visual sensor networks (VSNs) consumes more space, energy, transmission delay which may experience the various security threats. Image compression is a key phase of visual sensing systems that needs to be effective. This motivates us to propose a fast and efficient intelligent image transmission module to achieve the energy-efficiency, minimum delay, and bandwidth utilization. Compressive sensing (CS) introduced to speedily compressed the image to reduces the consumption of energy, time minimization, and efficient bandwidth utilization. However, CS cannot achieve security against the different kinds of threats. Several methods introduced since the last decade to address the security challenges in the CS domain, but efficiency is a key requirement considering the intelligent manufacturing of VSNs. Furthermore, the random variables selected for the CS having the problem of recovering the image quality due to the accumulation of noise. Thus concerning the above challenges, this paper introduced a novel one-way image transmission module in multiple input multiple output that provides secure and energy-efficient with the CS model. The secured transmission in the CS domain proposed using the security matrix which is called a compressed secured matrix and perfect reconstruction with the random matrix measurement in the CS. Experimental results outwards that the intelligent module provides energy-efficient, secured transmission with low computational time as well as a reduced bit error rate.
262 citations
TL;DR: The Corvus corone module two-way image transmission is proposed that provides energy efficiency along CS model, secured transmission through a matrix of security under CS such as inbuilt method, which was named as compressed secured matrix and faultless reconstruction along that of eminent random matrix counting under CS.
Abstract: The manufacturing of intelligent and secure visual data transmission over the wireless sensor network is key requirement nowadays to many applications. The two-way transmission of image under a wireless channel needed image must compatible along channel characteristics such as band width, energy-efficient, time consumption and security because the image adopts big space under the device of storage and need a long time that easily undergoes cipher attacks. Moreover, Quizzical the problem for the additional time under compression results that, the secondary process of the compression followed through the acquisition consumes more time.,Hence, for resolving these issues, compressive sensing (CS) has emerged, which compressed the image at the time of sensing emerges as a speedy manner that reduces the time consumption and saves bandwidth utilization but fails under secured transmission. Several kinds of research paved path to resolve the security problems under CS through providing security such as the secondary process.,Thus, concerning the above issues, this paper proposed the Corvus corone module two-way image transmission that provides energy efficiency along CS model, secured transmission through a matrix of security under CS such as inbuilt method, which was named as compressed secured matrix and faultless reconstruction along that of eminent random matrix counting under CS.,Experimental outputs shows intelligent module gives energy efficient, secured transmission along lower computational timing also decreased bit error rate.
252 citations
TL;DR: This paper proposed two-way image transmission to the Corvus Coron module, which presents an energy-effective with the CS model, as an inbuilt interaction in the CS transmission through the security framework, which results in energy-efficient and conserved transmission in the form of low error rate with low computational time.
Abstract: Two-way image communication in a wireless channel needs to be viable with channel properties such as transfer speed, energy-effective, time usage, and security because image capability consumes a huge space in the gadget and is quite effective. Is required in a manner. The figure goes through attacks. In addition, the quiesical issue for additional time of pressure is that the auxiliary interaction of pressure occurs through the dewar receiving extra time. To address these issues, compressed sensing emerges, which packs the image into hours of sensing, is generated in an expedient manner that reduces time usage and saves the use of data transfer capability, however Bomb in transmission. A variety of examinations cleared a way for dealing with security issues in compressive sensing (CS) through giving security as an alternative negotiation. In addition, univariate factors opted for CS as the issue of rearranging image quality is because of the aggregation of clutter. Along these lines related to the above issues, this paper proposed two-way image transmission to the Corvus Coron module, which presents an energy-effective with the CS model, as an inbuilt interaction in the CS transmission through the security framework. Receives what was designated as the pack-protected plot. Impeccable entertainment with the famous arbitrary network conjecture in CS. The result of the test is that the practical module presents energy-efficient and conserved transmission in the form of low error rate with low computational time.
230 citations
TL;DR: A reliable VANET routing decision scheme based on the Manhattan mobility model is proposed, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization and can support real-time planning and improve network transmission performance.
Abstract: Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.
188 citations
TL;DR: In this article, a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials is demonstrated.
Abstract: Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs. Integrated optical computing requires programmable photonic and nonlinear elements. The authors demonstrate a phase-change metasurface mode converter, which can be programmed to control the waveguide mode contrast, and build an optical convolutional neural network to perform image processing tasks.
136 citations
TL;DR: In this paper, the authors analyze the access performance, data transmission path delay, energy consumption in the NB-IoT, and large-scale devices' access in the cellular narrowband IoT based on big data analysis technology.
Abstract: The purposes are to enable large-scale Internet of Things (IoT) devices to analyze data more effectively and provide high-efficiency, low-energy, and wide-coverage technical services for terminals The channel model and energy loss model analyze the devices’ access performance, data transmission path delay, energy consumption in the IoT, and large-scale devices’ access in the cellular narrowband IoT (NB-IoT) based on big data analysis technology are also discussed The results show that in the access success rate analysis, the access success rate is the highest with an access time ( ${T}$ ) of 5 s and a preamble resource number ( ${K}$ ) of 25 The restriction factor is inversely proportional to the access success rate In the node utilization analysis, different transmission node priorities result in different node utilization, and priority 2’s node utilization is better than that of priority 1 Moreover, local data makes data analysis and transmission faster The search time is prolonged, and the corresponding energy consumption is also higher without local data In the energy consumption analysis, with the 6-generation (6G) technology, different interference thresholds lead to the different energy efficiency of data transmission The larger the interference threshold, the higher the energy efficiency Therefore, the 6G-based big data analysis technology can significantly improve large-scale IoT devices’ access success rate and enable the system to meet the requirements of low energy consumption and high access success rate, significant for research on more devices’ access data analysis
113 citations
TL;DR: An invariant feature based approach that performs low rate attack detection and improves the performance of the methods used in detecting low rate attacks for invariant network conditions.
Abstract: The problem of low rate attack detection has been well studied in different situations. However the methods suffer to achieve higher performance in low rate attack detection. The multimedia transmission is focused on transmitting video and audio which claims higher bandwidth conditions. There exists no such algorithm in detecting low rate attacks for invariant network conditions. To solve this issue, an invariant feature based approach is presented in this paper. The method maintains the network features like the routes, bandwidth conditions and traffic. Based on these features, a set of routes has been identified for each data transmission. Here, low rate attack detection is performed at the reception of any packet and the data transmission is performed using cooperative routing. From the packet features, and the route being followed, the method identifies the class of route, traffic and bandwidth conditions of the route. Using these features, the method computes Network Transmission Support measure. Based on the NTS value, the method performs low rate attack detection and improves the performance.
100 citations
TL;DR: This paper proposes to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of the IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation.
Abstract: Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the promising gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided multiuser communications with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of the IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the achievable rate of each user is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoders, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Next, for the single-user case, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors; while for the multiuser case, a new penalty dual decomposition (PDD)-based algorithm is proposed to maximize the users’ weighted sum-rate. Finally, simulation results are presented to validate the effectiveness of our proposed algorithms as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase-shift control) on the system performance under imperfect CSI.
96 citations
TL;DR: The results show that the proposed DeepBAN communication framework can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme.
Abstract: Wireless body area network (WBAN) has become a promising technology, which can be widely applied in health monitoring, and so on. However, the performance of a practical WBAN may severely suffer from the degradation caused by dynamic nature of wireless channels with the movements of human body. Traditional communication frameworks cannot catch up with the channel variation of dynamic WBANs, which may severely degrade the performance, so an accurate channel prediction model is necessary for developing an efficient transmission strategy. In this paper, we propose a DeepBAN communication framework for dynamic WBANs. In our proposed framework, a temporal convolution network (TCN) based deep learning approach is adopted for channel prediction, the computationally intensive task of which is processed by mobile edge computing (MEC), to reduce the response time. Given the predicted channel conditions, we propose a joint power control, time-slot allocation, and relay selection algorithm to maximize the energy efficiency of the system, taking into account the transmission reliability and end-to-end latency requirements. We evaluate the performance of DeepBAN, and the results show that it can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme.
88 citations
TL;DR: In this article, the fundamental tradeoffs between communication performance, computational complexity, and fronthaul signaling requirements are thoroughly analyzed, while open problems related to these and other resource allocation problems are reviewed.
Abstract: Imagine a coverage area where each mobile device is communicating with a preferred set of wireless access points (among many) that are selected based on its needs and cooperate to jointly serve it, instead of creating autonomous cells. This effectively leads to a user-centric post-cellular network architecture, which can resolve many of the interference issues and service-quality variations that appear in cellular networks. This concept is called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and has its roots in the intersection between three technology components: Massive MIMO, coordinated multipoint processing, and ultra-dense networks. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to enable massively large networks with many mobile devices. This monograph covers the foundations of User-centric Cell-free Massive MIMO, starting from the motivation and mathematical definition. It continues by describing the state-of-the-art signal processing algorithms for channel estimation, uplink data reception, and downlink data transmission with either centralized or distributed implementation. The achievable spectral efficiency is mathematically derived and evaluated numerically using a running example that exposes the impact of various system parameters and algorithmic choices. The fundamental tradeoffs between communication performance, computational complexity, and fronthaul signaling requirements are thoroughly analyzed. Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster formation, and power optimization are provided, while open problems related to these and other resource allocation problems are reviewed. All the numerical examples can be reproduced using the accompanying Matlab code.
87 citations
TL;DR: A learning analysis framework is developed to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL, and a unified communication-learning optimization problem is formulated to jointly optimize device selection, over- the-air transceiver design, and RIS configuration.
Abstract: To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high communication latency and privacy issues as compared to centralized ML. To improve the communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a unified communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.
TL;DR: This article considers the LEO satellite-assisted UAV data collection for the IoRT sensors and presents a heuristic algorithm for the subproblem to further reduce the complexity of large-scale networks.
Abstract: As the sixth generation (6G) network is under research, and one important issue is the aerial access network and terrestrial-space integration. The Internet of Remote Things (IoRT) sensors can access the unmanned aerial vehicles (UAVs) in the air, and low Earth orbit (LEO) satellite networks in the space help to provide lower transmission delay for delay-sensitive IoRT data. Therefore, in this article, we consider the LEO satellite-assisted UAV data collection for the IoRT sensors. Specifically, a UAV collects the data from the IoRT sensors, then two transmission modes for the collected data back to Earth: 1) the delay-tolerant data leveraging the carry-store mode of UAVs to Earth and 2) the delay-sensitive data utilizing the UAV-satellite network transmission to Earth. Considering the limited payloads of UAVs, we focus on minimizing the total energy cost (trajectory and transmission) of UAVs while satisfying the IoRT demands. Due to the intractability of direct solution, we deal with the problem using the Dantzig–Wolfe decomposition and design the column generation-based algorithms to efficiently solve the problem. Moreover, we present a heuristic algorithm for the subproblem to further reduce the complexity of large-scale networks. Finally, numerical results verify the efficiency of the proposed algorithms and the advantage of LEO satellite-assisted UAV trajectory design combined with the data transmission is also analyzed.
TL;DR: A flexible and generalized distributed dynamic event-triggered control with impulsive signal to make the investigated MASs achieve secure consensus under redundant signal and communication interference is established.
Abstract: This paper studies a class of multi-agent systems (MASs) subject to deception signal and communication interference. The objective of the present work is to establish a flexible and generalized distributed dynamic event-triggered control (DDETC) with impulsive signal to make the investigated MASs achieve secure consensus under redundant signal and communication interference. It is shown that Zeno behavior can be precluded with such a DDETC. The challenging but valuable new designed DDETC scheme shows the trigger is developed to achieve itself away from exceeding the data transmission load through parameter adjustment, to reduce redundant triggering, to flexibly adjust the triggered frequency, and even to replace sampled-data scheme as special cases. By the impulsive DDETC, anti-deception and anti-interference techniques, the secure consensus criteria of MASs are constructed cleverly. Numerical examples with simulations are given to illustrate the effectiveness of the proposed scheme and control protocol.
TL;DR: In this paper, the authors studied efficient channel estimation and passive beamforming designs for a double-intelligent reflecting surface (IRS) aided single-user communication system, where a user communicates with an access point (AP) via the cascaded user-IRS 1IRS 2-AP double-reflection link.
Abstract: In this letter, we study efficient channel estimation and passive beamforming designs for a double-intelligent reflecting surface (IRS) aided single-user communication system, where a user communicates with an access point (AP) via the cascaded user-IRS 1-IRS 2-AP double-reflection link. First, a general channel estimation scheme is proposed for the system under any arbitrary inter-IRS channel, where all coefficients of the cascaded channel are estimated. Next, for the typical scenario with a line-of-sight (LoS)-dominant inter-IRS channel, we propose another customized scheme to estimate two signature vectors of the rank-one cascaded channel with significantly less channel training time than the first scheme. For the two proposed channel estimation schemes, we further optimize their corresponding cooperative passive beamforming for data transmission to maximize the achievable rate with the training overhead and channel estimation error taken into account. Numerical results show that deploying two cooperative IRSs with the proposed channel estimation and passive beamforming designs achieves significant rate enhancement as compared to the conventional case of single IRS deployment.
TL;DR: In this article, the authors considered a downlink mmWave MIMO system, where an LIS is deployed to assist the downlink data transmission from a base station (BS) to a user equipment (UE).
Abstract: Large intelligent surface (LIS) has recently emerged as a potential low-cost solution to reshape the wireless propagation environment for improving the spectral efficiency. In this article, we consider a downlink millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) system, where an LIS is deployed to assist the downlink data transmission from a base station (BS) to a user equipment (UE). Both the BS and the UE are equipped with a large number of antennas, and a hybrid analog/digital precoding/combining structure is used to reduce the hardware cost and energy consumption. We aim to maximize the spectral efficiency by jointly optimizing the LIS’s reflection coefficients and the hybrid precoder (combiner) at the BS (UE). To tackle this non-convex problem, we reformulate the complex optimization problem into a much more friendly optimization problem by exploiting the inherent structure of the effective (cascade) mmWave channel. A manifold optimization (MO)-based algorithm is then developed. Simulation results show that by carefully devising LIS’s reflection coefficients, our proposed method can help realize a favorable propagation environment with a small channel matrix condition number. Besides, it can achieve a performance comparable to those of state-of-the-art algorithms, while at a much lower computational complexity.
TL;DR: Simulations and numerical results validate that, compared with baseline schemes, the proposed MDP model with DRL based scheme can achieve better wireless energy and data transfer strategies in terms of the higher long-term utility of the UAV.
Abstract: As a typical scenario in future generation communication network applications, UAV-assisted communication can perform autonomous data delivery for massive machine type communication (mMTC), where the data generated from Internet of Things (IoT) devices can be carried and delivered to the corresponding locations with no direct communication channels to the IoT devices. Wireless energy transfer technique can recharge the UAV when the system is in operation, assisting the UAV to continuously collect and deliver data. In this work, we formulate a Markov decision process (MDP) model to describe the energy and data transfer optimization problem for the UAV. To maximize the long-term utility of the UAV, the MDP model is solved by value iteration algorithm to obtain the optimal strategies of the UAV to collect data, deliver data, and receive transferred energy to replenish on-device battery energy storage. Furthermore, to tackle the issues of system state uncertainties, partially observable states, and large state space in UAV-assisted communication systems, we extend the MDP model and solve it by using a ${Q}$ -learning and a deep reinforcement learning (DRL) schemes. Simulations and numerical results validate that, compared with baseline schemes, the proposed MDP model with DRL based scheme can achieve better wireless energy and data transfer strategies in terms of the higher long-term utility of the UAV.
TL;DR: A data-aided channel estimation algorithm for a superimposed pilot and data transmission scheme, which can improve the spectral efficiency and coarsely estimate the channel based on the pilot symbol, followed by an iterative process which detects the data symbols and refines the channel estimates.
Abstract: The recently developed orthogonal time frequency space (OTFS) modulation has shown its capability of coping with the fast time-varying channels in high-mobility environments. In particular, OTFS modulation gives rise to the sparse representation of the delay-Doppler (DD) domain channel model. Hence, one can an enjoy accurate channel estimation by adopting only one pilot symbol. However, conventional OTFS channel estimation schemes require the deployment of guard space to avoid data-pilot interference, which inevitably sacrifices the spectral efficiency. In this letter, we develop a data-aided channel estimation algorithm for a superimposed pilot and data transmission scheme, which can improve the spectral efficiency. To accurately estimate the channel and detect the data symbols, we coarsely estimate the channel based on the pilot symbol, followed by an iterative process which detects the data symbols and refines the channel estimates. Simulation results show that the bit error rate (BER) performance based on the proposed method can approach the baseline scheme with perfect channel estimation.
TL;DR: Experimental data is presented which verifies the proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrates the capability for human activity sensing.
Abstract: Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate Orthogonal Frequency-Division Multiplexing (OFDM) signals, or periodic WiFi beacon signals while in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, while a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In this article, we present experimental data which verifies our proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrate the capability for human activity sensing.
TL;DR: This paper designs an adaptive RIS-assisted transmission protocol, in which the channel estimation, transmission strategy, and data transmission are independently implemented in a frame to maximize the overall system throughput and reduce the transmit power.
Abstract: The aerial-terrestrial communication system constitutes an efficient paradigm for supporting and complementing terrestrial communications. However, the benefits of such a system cannot be fully exploited, especially when the line-of-sight (LoS) transmissions are prone to severe deterioration due to complex propagation environments in urban areas. The emerging technology of reconfigurable intelligent surfaces (RISs) has recently become a potential solution to mitigate propagation-induced impairments and improve wireless network coverage. Motivated by these considerations, in this paper, we address the coverage and link performance problems of the aerial-terrestrial communication system by proposing an RIS-assisted transmission strategy. In particular, we design an adaptive RIS-assisted transmission protocol, in which the channel estimation, transmission strategy, and data transmission are independently implemented in a frame. On this basis, we formulate an RIS-assisted transmission strategy optimization problem as a mixed-integer non-linear program (MINLP) to maximize the overall system throughput. We then employ multi-task learning to speed up the solution to the problem. Benefiting from multi-task learning, the computation time is reduced by about four orders of magnitude. Numerical results show that the proposed RIS-assisted transmission protocol significantly improves the system throughput and reduces the transmit power.
TL;DR: The experimental result showed that the proposed routing protocol adapts to dynamic changes in the communication networks, like obstacles and shadows, and achieved better performance in data transmission in terms of throughput, packet delivery ratio, end-to-end delay, and routing overhead.
Abstract: In recent times, visible light communication is an emerging technology that supports high speed data communication for wireless communication systems. However, the performance of the visible light communication system is impaired by inter symbol interference, the time dispersive nature of the channel, and nonlinear features of the light emitting diode that significantly reduces the bit error rate performance. To address these problems, many environments offer a rich infrastructure of light sources for end-to-end communication. In this research paper, an effective routing protocol named the modified grasshopper optimization algorithm is proposed to reduce communication interruptions, and to provide alternative routes in the network without the need of previous topology knowledge. In this research paper, the proposed routing protocol is implemented and analyzed using the MATLAB environment. The experimental result showed that the proposed routing protocol adapts to dynamic changes in the communication networks, like obstacles and shadows. Hence, the proposed protocol achieved better performance in data transmission in terms of throughput, packet delivery ratio, end-to-end delay, and routing overhead. In addition, the performance is analyzed by varying the number of nodes like 50, 100, 250, and 500. From the experimental analysis, the proposed routing protocol achieved maximum of 16.69% and minimum of 2.20% improvement in packet delivery ratio, and minimized 0.80 milliseconds of end-to-end delay compared to the existing optimization algorithms.
TL;DR: In this paper, a multiband optimized optical power control for BDM upgrades is proposed, which consists of setting a pre-tilt and power offset in the line amplifiers, thus achieving a considerable increase in QoT, both in average value and flatness.
Abstract: Spatial-division multiplexing (SDM) and band-division multiplexing (BDM) have emerged as solutions to expand the capacity of existing C-band wavelength-division multiplexing (WDM) optical systems and to deal with increasing traffic demands An important difference between these two approaches is that BDM solutions enable data transmission over unused spectral bands of already-deployed optical fibers, whereas SDM solutions require the availability of additional fibers to replicate C-band WDM transmission On the other hand, to properly design a multiband optical line system (OLS), the following fiber propagation effects have been taken into account in the analysis: (i) stimulated Raman scattering (SRS), which induces considerable power transfer among bands; (ii) frequency dependence of fiber parameters such as attenuation, dispersion, and nonlinear coefficients; and (iii) utilization of optical amplifiers with different doping materials, thus leading to different characteristics, eg, in terms of noise figures This work follows a two-step approach: First, we aim at maximizing and flattening the quality of transmission (QoT) when adding L- and ${\rm L} {+} {\rm S}$-bands to a traditional WDM OLS where only the C-band is deployed This is achieved by applying a multiband optimized optical power control for BDM upgrades, which consists of setting a pre-tilt and power offset in the line amplifiers, thus achieving a considerable increase in QoT, both in average value and flatness Second, the SDM approach is used as a benchmark for the BDM approach by assessing network performance on three network topologies with different geographical footprints We show that, with optical power properly optimized, BDM may enable an increase in network traffic, slightly less than an SDM upgrade but still comparable, without requiring additional fiber cables
TL;DR: In this paper, a reconfigurable intelligent surfaces (RIS)-aided wireless communication system in an inband underlay D2D communication, where the direct link between DUs is unavailable, is investigated, and analytical results for the secrecy outage probability and the probability of non-zero secrecy capacity are derived for the cellular network.
Abstract: This letter investigates a reconfigurable intelligent surfaces (RIS)-aided wireless communication system in an inband underlay Device-to-Device (D2D) communication, where the direct link between D2D users is unavailable. An RIS is used to adjust its reflecting elements to enhance the D2D communication data transmission while improving the cellular network’s secrecy performance concurrently. Specifically, analytical results for the secrecy outage probability and the probability of non-zero secrecy capacity are derived for the cellular network. Moreover, the D2D outage probability is also provided. Simulation and analytical results are presented to verify the derived expressions’ correctness and the effectiveness of the proposed scenario. Moreover, the asymptotic results are presented.
TL;DR: A method to achieve simultaneous wireless power transfer and full-duplex communication with a pair of coupling coils with a double-side LCC compensation topology and a four resonance dual-rejection structure is proposed.
Abstract: A method to achieve simultaneous wireless power transfer and full-duplex communication with a pair of coupling coils is proposed in this article. A double-side LCC compensation topology for power transfer is adopted, while the data transfer channel is constituted by a four resonance dual-rejection structure. In the process of power transfer and full-duplex communication, from the view of data transmitting and receiving, the data transmitting/receiving circuits on one side can not only transmit/receive the desired data carriers but also block the interference data carriers, and from the view of power transfer, the rated power transfer can be realized with little influence. Moreover, the parameter design method of the proposed system is given. Besides, the interference between the power wave and data carriers and the crosstalk between the data carriers are analyzed. Finally, an experimental prototype is built, which achieves an output power of 600 W and a data transmission rate of 80 kbps.
TL;DR: An effective channel estimation and tracking scheme is proposed, which can solve the performance degradation problem caused by the unique triple delay-beam-Doppler squint effects of aeronautical terahertz UM-MIMO channels.
Abstract: The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the unique triple delay-beam-Doppler squint effects of aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designed Rotman lens -based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramer-Rao lower bounds verify the effectiveness of our solution.
TL;DR: In this article, a systematic review and survey of the latest inductive power and data transmission methods for implantable medical devices (IMDs) is presented, where the fundamental principles of remote powering through inductive links, critical parameters of design, and power transfer efficiency calculations are presented.
Abstract: Wireless power and data communication systems in implantable medical devices (IMDs) are developed to control and report acquired biological data from an implanted device to an external stage in several medical applications. Ultrasonic, capacitive, optical, radio frequency (RF), and inductive links are employed as a wireless power and data transmission technique. Inductive power transfer (IPT) is one of the most commonly used techniques due to its robustness, simplicity, safety, and capability of simultaneous and bidirectional data and power transmission. This paper presents a systematic review and survey of the latest inductive power and data transmission methods for IMDs. The fundamental principles of remote powering through inductive links, the critical parameters of design, and power transfer efficiency calculations are presented. Single and multiple inductive links, the advantages and drawbacks, optimization methods, and comparison by their performances are explored. In addition, modulation schemes, along with improvement techniques reported in the literature, the strengths and limitations, and the measured or the simulated data rate of each are also reviewed. A benchmarking table with summarized design features of systems, essential parameters for wireless powering and data communication, and categorized Figure-of-Merits (FoMs) to compare performances are also provided.
TL;DR: It has been shown via computer simulations that the GCIM-SM system has lower transmission energy, faster data transmission rate, and better error performance than DS-SS, SM, QSM, and CIM-SS systems.
Abstract: In this article, a high data rate and energy-efficient multiple-input multiple-output transmission scheme is considered by combining two popular and rational modulation techniques: spatial modulation (SM) and code index modulation-spread spectrum (CIM-SS). Since in the considered system, called generalized CIM-SM (GCIM-SM), incoming information bits determine modulated symbols, activated transmit antenna indices as well as their corresponding spreading code indices, data bits are conveyed not only by modulated symbols but also by the indices of the active antenna and spreading codes. Hence, a GCIM-SM scheme accommodates faster data rates while spending less transmission power and possessing better error performance compared to the conventional direct sequence spread spectrum (DS-SS), SM, quadrature SM (QSM), and CIM-SS systems. The mathematical expressions of the GCIM-SM system for bit error rate, throughput, energy efficiency, and the system complexity are derived to analyze the overall system performance. Besides, it has been shown via computer simulations that the GCIM-SM system has lower transmission energy, faster data transmission rate, and better error performance than DS-SS, SM, QSM, and CIM-SS systems. Performance analysis of the considered system was performed on Rayleigh block-fading channels for quadrature amplitude modulation technique.
TL;DR: In this article, a secure data aggregation scheme has been proposed, which has three phases: intra-cluster data aggregation, inter-clusters data aggregation and data transfer, and a fuzzy scheduling system is designed to adjust the appropriate data transmission rates of the cluster member nodes.
Abstract: A wireless sensor network (WSN) consists of a set of sensor nodes that are widely scattered in inaccessible areas When deployed in large areas, WSNs generate a large volume of the data Therefore, efficient methods are needed to process the data One solution to minimize traffic on large-scale wireless sensor networks is to use data aggregation schemes In this paper, a secure data aggregation method is proposed The proposed secure data aggregation scheme has three phases: intra-cluster data aggregation, inter-cluster data aggregation, and data transfer In the intra-cluster data aggregation phase, a fuzzy scheduling system is designed to adjust the appropriate data transmission rates of the cluster member nodes In the inter-cluster data aggregation phase, an aggregation tree is created between the cluster head nodes The dragonfly algorithm (DA) is used to find the optimal aggregation tree between cluster head nodes In the data transfer phase, the columnar transposition cipher method is used to establish a secure connection between cluster member nodes and their cluster head node Also, a symmetric and lightweight encryption method based on the residue number system (RNS) is utilized to provide secure communications between the cluster head nodes We modify RNS and call it RNS+ Finally, the simulation results of the proposed scheme are compared to three data aggregation methods including Sign-share, Sham-share, and RCDA The results show that the proposed data aggregation scheme outperforms other data aggregation methods in terms of network lifetime, delay and packet delivery rate
TL;DR: In this article, a dual-polarized orbital angular momentum multiple-input multiple-output (OAM-MIMO) with uniform circular arrays (UCAs) was proposed.
Abstract: In this letter, we present a dual-polarized orbital angular momentum multiple-input.multiple-output (OAM-MIMO) with uniform circular arrays (UCAs) that achieves a transmission rate of over 200 Gb/s. We design and evaluate the properties of microstrip antennas on 28 GHz band and then fabricate the antennas and implement them on a transmitter and receiver with four UCAs disposed concentrically. Each UCA consists of 16 microstrip antenna elements and is connected to a Butler matrix that can generate and separate five OAM modes (±2, ±1, and 0). There is also a center antenna capable of transmitting mode 0, so the total number of antenna elements is 65 and 21 streams can be transmitted simultaneously. In order to maximize the transmission capacity, we use polarization multiplexing. We conduct two data transmission experiments at a distance of 10 m. In the experiment of virtual dual-polarized OAM-MIMO transmission, we achieved approximately 110 Gb/s transmission for both polarizations. In the experiment of 21 streams, simultaneous transmission of dual-polarized OAM-MIMO, the result of 201.5 Gb/s was achieved.
TL;DR: In this paper, the authors investigated the physical layer security and data transmission for the underlay device-to-device (D2D) networks, and considered a combination of the reconfigurable intelligent surface (RIS) and full-duplex (FD) jamming receiver for the robustness and security enhancements of the system.
Abstract: This paper investigates the physical layer security and data transmission for the underlay device-to-device (D2D) networks, and considers a combination of the reconfigurable intelligent surface (RIS) and full-duplex (FD) jamming receiver for the robustness and security enhancements of the system. In the demonstrated spectrum sharing setup, the total power of the D2D networks is conceived to the transmitter and receiver to transmit a private message and emit the artificial noise (AN) signals. To prevent information leakage, a beamforming design is presented for a multi-antenna FD D2D receiver in order to suppress and inject the AN signals in the direction of legitimate users and eavesdropper, respectively. The statistical characterization of end-to-end RIS-assisted wireless channels is presented, and the achievable ergodic secrecy rate of the system is derived in novel approximate expressions. The numerical and simulation results confirm the accuracy and effectiveness of the proposed analytical framework. The results demonstrate an optimal selection of the D2D power allocations for different number of reflecting elements in terms of achievable ergodic secrecy rates of the system.
14 Jun 2021
TL;DR: Simulation results show the effectiveness of the proposed channel estimation scheme and passive beamforming design as compared to various benchmark schemes.
Abstract: In this paper, we study a new intelligent refracting surface (IRS)-assisted high-mobility communication with the IRS deployed in a high-speed moving vehicle to assist its passenger’s communication with a static base station (BS) on the roadside. The vehicle’s high Doppler frequency results in a fast fading channel between the BS and the passenger/user, which renders channel estimation for the IRS with a large number of refracting elements a more challenging task as compared to the conventional case with low-mobility users only. In order to mitigate the Doppler effect and reap the full IRS passive beamforming gain with low training overhead, we propose a new and efficient transmission protocol to execute channel estimation and IRS refraction design for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed, we first estimate the cascaded BS-IRS-user channel with the Doppler effect compensated. Then, we estimate the instantaneous BS-user fast fading channel (without IRS refraction) and tune the IRS refraction over time accordingly to align the cascaded channel with the BS-user direct channel, thus maximizing the IRS’s passive beamforming gain as well as converting their combined channel from fast to slow fading. Simulation results show the effectiveness of the proposed channel estimation scheme and passive beamforming design as compared to various benchmark schemes.