A Linearized Analog Microwave Photonic link with an Eliminated Even-order Distortions

International audienceAn improved linearized analog microwave photonic link (AMPL) with significant multioctave bandwidth performance is experimentally presented. The proposed AMPL configuration is based on a double dual-parallel Mach-Zehnder modulator and a differential balanced photodetector (BPD). Explicitly, a gallium arsenide (GaAs)-based modulators are used as opposed to the commonly known lithium niobate (LiNbO3) modulators, due to its robustness in the harsh environment. The system configuration is designed to process a carrier suppressed double-sideband signal through the link, and then at the receiver, a carrier suppressed double-sideband signal is combined with an unmodulated optical carrier, which is transmitted through a polarization maintained (PM) optical fiber. In our experiment, only PM-based optical components are used for better system stability. The developed theoretical model of the proposed system illustrates the elimination of even-order distortions and a high suppression to the third-order intermodulation distortions at the BPD. Consequently, the fundamental signal to interference ratio of 60 dB was experimentally achieved. Furthermore, experimental results, simultaneously, demonstrate a significant increase of second-order spurious-free dynamic range and third-order spurious-free dynamic range by 19.5 and 3.1dB, respectively, compared to the previously reported AMPL performances based on polarization multiplexing dual-parallel Mach-Zehnder modulator. To the best of our knowledge, this is the highest dynamic range AMPL system performance deploying GaAs electro-optic modulator which has most significant capabilities in managing RF signals and exhibits excessive performance in harsh operating environment in terms of thermal stability, power-handling, radiation resistance and longevity for aerospace, defense, and satellite-to-ground downlink communication system applications

Precoded Large Scale Multi-User-MIMO System Using Likelihood Ascent Search for Signal Detection

Multiple antennas at each user equipment (UE) and/or thousands of antennas at the base station (BS) comprise the extremely spectrum efficient large scale multi-user multiple input multiple output system (BS). Due to space constraints, the closely spaced numerous antennas at each UE may cause inter antenna interference (IAI). Furthermore, when one UE comes into contact with another UE in the same cellular network, multi-user interference (MUI) may be introduced to the received signal. To mitigate IAI, efficient precoding pre-coding is necessary at each UE, and the MUI present at the BS can be canceled by efficient Multi-user Detection (MUD) techniques. The majority of earlier literature deal with one or more of these interferences. This paper implements a joint pre-coding and MUD, Lenstra-Lovasz (LLL) based Lattice Reduction (LR) assisted likelihood accent search (LAS) (LLL-LR-LAS), to mitigate IAI and MUI simultaneously LLL-based LR pre-coding mitigates IAI at each UE, and the LAS algorithm is a neighborhood search-based MUD that cancels BS MUI. The proposed approaches' performance was evaluated using Bit Error Rate analysis, and their complexity were determined using multiplication and addition.Dr. Mohammad Alibakhshikenari acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement No. 801538. Also, this work was supported by Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional-FEDER-, European Union) under the research Grant PID2021-127409OB-C31 CONDOR. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022)

Broadband 3-D shared aperture high isolation nine-element antenna array for on-demand millimeter-wave 5G applications

The paper presents the results of a novel 3-D shared aperture 3 × 3 matrix antenna-array for 26 GHz band 5 G wireless networks. Radiation elements constituting the array are hexagonal-shaped patches that are elevated above the common dielectric substrate by 3.35 mm and excited through a metallic rod of 0.4 mm diameter. The rod protrudes through the substrate of 0.8 mm thickness. It is shown that by isolating each radiating element in the array with a wall suppresses unwanted electromagnetic (EM) wave interactions, resulting in improvement in the antenna’s impedance matching and radiation characteristics. Moreover, the results show that by embedding hexagonalshaped slots in the patches improve the antenna’s gain and radiation efficiency performance. The subwavelength length slots in the patches essentially transform the radiating elements to exhibit metasurface characteristics when the array is illuminated by EM-waves. The proposed array structure has an average gain and radiation efficiency of 20 dBi and 93%, respectively, across 24.0–28.4 GHz. The isolation between its radiation elements is greater than 22 dB. Compared to the unslotted array the improvement in isolation between radiating elements is greater than 11dB, and the gain and efficiency are better than 10.5 dBi, and 25%, respectively. The compact array has a fractional bandwidth of 16% and a form factor of 20 × 20 × 3.35 mm3.Dr. Mohammad Alibakhshikenari acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 801538. Also, this work was supported by Project RTI2018-095499-B-C31, funded by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCIU/AEI/FEDER, UE)

Enhancement of precise underwater object localization

Underwater communication applications extensively use localization services for object identification. Because of their significant impact on ocean exploration and monitoring, underwater wireless sensor networks (UWSN) are becoming increasingly popular, and acoustic communications have largely overtaken radio frequency (RF) broadcasts as the dominant means of communication. The two localization methods that are most frequently employed are those that estimate the angle of arrival (AOA) and the time difference of arrival (TDoA). The military and civilian sectors rely heavily on UWSN for object identification in the underwater environment. As a result, there is a need in UWSN for an accurate localization technique that accounts for dynamic nature of the underwater environment. Time and position data are the two key parameters to accurately define the position of an object. Moreover, due to climate change there is now a need to constrain energy consumption by UWSN to limit carbon emission to meet net-zero target by 2050. To meet these challenges, we have developed an efficient localization algorithm for determining an object position based on the angle and distance of arrival of beacon signals. We have considered the factors like sensor nodes not being in time sync with each other and the fact that the speed of sound varies in water. Our simulation results show that the proposed approach can achieve great localization accuracy while accounting for temporal synchronization inaccuracies. When compared to existing localization approaches, the mean estimation error (MEE) and energy consumption figures, the proposed approach outperforms them. The MEEs is shown to vary between 84.2154m and 93.8275m for four trials, 61.2256m and 92.7956m for eight trials, and 42.6584m and 119.5228m for twelve trials. Comparatively, the distance-based measurements show higher accuracy than the angle-based measurements

Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) ispreferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of theunknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though theoptimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linearclassifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability

An LSTM-based network slicing classification future predictive framework for optimized resource allocation in C-V2X

With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones

Review on unmanned aerial vehicle assisted sensor node localization in wireless networks: soft computing approaches

Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) is preferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of the unknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though the optimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linear classifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability

Detection of Signals in MC–CDMA Using a Novel Iterative Block Decision Feedback Equalizer

This paper presents a technique to mitigate multiple access interference (MAI) in multicarrier code division multiple access (MC-CDMA) wireless communications systems. Although under normal circumstances the MC-CDMA system can achieve high spectral efficiency and resistance towards inter symbol interference (ISI) however when exposed to substantial nonlinear distortion the issue of MAI manifests. Such distortion results when the power amplifiers are driven into saturation or when the transmit signal experiences extreme adverse channel conditions. The proposed technique uses a modified iterative block decision feedback equalizer (IB-DFE) that uses a minimal mean square error (MMSE) receiver in the feed-forward path to nullify the residual interference from the IB-DFE receiver. The received signal is re-filtered in an iterative process to significantly improve the MC-CDMA system’s performance. The effectiveness of the proposed modified IB-DFE technique in MC-CDMA systems has been analysed under various harsh nonlinear conditions, and the results of this analysis presented here confirm the effectiveness of the proposed technique to outperform conventional methodologies in terms of the bit error rate (BER) and lesser computational complexity

High gain/bandwidth off‑chip antenna loaded with metamaterial unit‑cell impedance matching circuit for sub‑terahertz near‑field electronic systems

An innovative off-chip antenna (OCA) is presented that exhibits high gain and efficiency performance at the terahertz (THz) band and has a wide operational bandwidth. The proposed OCA is implemented on stacked silicon layers and consists of an open circuit meandering line. It is shown that by loading the antenna with an array of subwavelength circular dielectric slots and terminating it with a metamaterial unit cell, its impedance bandwidth is enhanced by a factor of two and its gain on average by about 4 dB. Unlike conventional antennas, where the energy is dissipated in a resistive load, the technique proposed here significantly reduces losses. The antenna is excited from underneath the antenna by coupling RF energy from an open-circuited feedline through a slot in the ground-plane of the middle substrate layer. The feedline is shielded with another substrate layer which has a ground-plane on its opposite surface to mitigate the influence of the structure on which the antenna is mounted. The antenna has the dimensions 12.3 × 4.5 × 0.905 mm3 and operates across the 0.137–0.158 THz band corresponding to a fractional bandwidth of 14.23%. Over this frequency range the average measured gain and efficiency are 8.6 dBi and 77%, respectively. These characteristics makes the proposed antenna suitable for integration in sub-terahertz near-field electronic systems such as radio frequency identification (RFID) devices with high spatial resolution