Simultaneous Communication Research Articles

Design a hybrid meta-heuristic algorithm for optimal multicell-MMSE to maximize the spectral efficiency in massive MIMO systems

Abstract Due to many capabilities, “massive multiple-input multiple-output (MIMO) systems” are regarded as a crucial enabling innovation. High energy economy, great spectral efficiency (SE), and simultaneous communication to many user equipments (UEs) are some of the sophisticated characteristics of massive MIMO systems. Huge MIMO, which involves installing arrays of antennas with a high amount of active components at the base station (BS) and utilizing coherent baseband processing, is a viable method for boosting the SE of cell phone networks. Massive MIMO’s spatial multiplexing and unparalleled array gain can increase the processing power of cellular networks. Since its origin, it has been assumed that when the number of radios increases infinitely, the coherent interference brought on by pilot emissions leads to a limited capacity limit. To achieve this objective, an optimal multicell MMSE is proposed for SE maximization. It is processed as the precoding or combining technique that is considered the small amount of spatial channel correlation, more capacity and more number of antennas, large-scale fading variations, and pilot contamination. It is noted that several cases for increasing the SE, thus it contain multiple antenna information. The prime novelty of this paper is introducing the hybrid heuristic algorithm, named as Fitness-condition of red deer and rat swarm algorithm (FRDRSA) for providing the best solution. Finally, the work performance that produced the extensive findings is examined. On the other hand, the suggested method produces an impressive result when measuring the system’s overall SE.

Dynamic event-based optical identification and communication.

Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range, and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic optical flow computed with spiking neurons. The system is embedded in a simulated drone and evaluated in an asset monitoring use case. It is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range.

A Study on Determining the Optimal Feedback Rate in Distributed Block Diagonalization with Limited Feedback for Dense Cellular Networks

In this study, we explore a downlink cellular network where each base station (BS) engages in simultaneous communication with multiple users through spatial division multiple access (SDMA). The positions of both BSs and users are established through independent random point processes, effectively representing the cellular network. SDMA utilizes block diagonalization (BD) at each BS, employing multiple receive antennas for each user. To implement BD, users quantize and provide feedback on their downlink channels to their respective BSs. The net spectral efficiency, measuring the effective rate accounting for both downlink and uplink resource usage, serves as a performance metric. In prior research, the optimal feedback rate in terms of maximizing net spectral efficiency has been approximated in this scenario. The corresponding approximations effectively illustrate the asymptotic behavior of the optimal number as a function of the length of the coherent channel block. However, the accuracy of the approximation diminishes when the length of the coherent channel block is relatively small. Given that the length of the coherent channel block can assume relatively small values depending on wireless environments, achieving a precise estimate across the entire range of the coherent block length holds significant importance. Consequently, this paper focuses primarily on enhancing the accuracy of the approximation for the optimal feedback rate. In order to achieve a more precise estimation, we analyze the derivative of the net spectral efficiency, which encompasses two functions that demonstrate distinct growth rates. In contrast to prior studies, both functions are rigorously approximated through mathematical analysis. As a result, the proposed approximation significantly improves the accuracy compared to previous studies, particularly when dealing with short coherent channel block lengths. Moreover, this approximation generally achieves near-optimal performance, regardless of system parameters.

Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy.

Integrated Distributed Sensing and Quantum Communication Networks.

The integration of sensing and communication can achieve ubiquitous sensing while enabling ubiquitous communication. Within the gradually improving global communication, the integrated sensing and communication system based on optical fibers can accomplish various functionalities, such as urban structure imaging, seismic wave detection, and pipeline safety monitoring. With the development of quantum communication, quantum networks based on optical fiber are gradually being established. In this paper, we propose an integrated sensing and quantum network (ISAQN) scheme, which can achieve secure key distribution among multiple nodes and distributed sensing under the standard quantum limit. The continuous variables quantum key distribution protocol and the round-trip multiband structure are adopted to achieve the multinode secure key distribution. Meanwhile, the spectrum phase monitoring protocol is proposed to realize distributed sensing. It determines which node is vibrating by monitoring the frequency spectrum and restores the vibration waveform by monitoring the phase change. The scheme is experimentally demonstrated by simulating the vibration in a star structure network. Experimental results indicate that this multiuser quantum network can achieve a secret key rate of approximately 0.7 Mbits/s for each user under 10-km standard fiber transmission, and its network capacity is 8. In terms of distributed sensing, it can achieve a vibration response bandwidth ranging from 1 Hz to 2 kHz, a strain resolution of 0.50 , and a spatial resolution of 0.20 m under shot-noise-limited detection. The proposed ISAQN scheme enables simultaneous quantum communication and distributed sensing in a multipoint network, laying a foundation for future large-scale quantum networks and high-precision sensing networks.

Compreender as Desigualdades Sociais e os Territórios pelas Histórias de Vida: reflexões partir das experiências do Núcleo IS-UP do Museu da Pessoa

The Nucleus of the ‘Museu da Pessoa’ (People’s Museum) was created to build a digital archive of qualitative data, while also promoting exhibi-tions and debates in research contexts. Through oral testimonies, life stories are collected and dis-seminated from a diversity of social actors across the national territory, facing various inequalities or vulnerabilities. With the aim of deepening the pro-cesses of structuring social inequalities and their individual inscriptions, this project democratises access to memory and gives visibility to socially vulnerable groups.Based on the experiences from the three main initiatives developed within the scope of the IS-UP Nucleus of the ‘Museu da Pessoa’, a reflection is made on the contributions of these initiatives. Framed by a theoretical-methodological framework related to the use of life stories in sociological research and situated in three different socio-territorial contexts in the northern region of Portugal, these initiatives focus on different social issues. They allow reflec-tions and analyses on various forms of social ine-quality. It is argued that this model enables simul-taneous knowledge acquisition, participation, and communication, combining fundamental research with the transfer and co-production of knowledge.

Simultaneous upstream and inter optical network unit communication for long reach PON using single transmitter and self-phase modulation

The conventional passive optical network (PON) architecture requires optical line terminal (OLT) arbitration for communication between the optical network units (ONUs). This approach increases OLT processing load, it may also increase security hazards and causes higher communication latency. Moreover this also leads to wastage of bandwidth that can be saved if ONUs are able to communicate directly. The direct communication of ONUs without involving OLT is known as inter ONU communication (IOC). Earlier reported IOC proposals either do not support simultaneous upstream communication or require dedicated or tunable transmitters and receivers for simultaneous communication. This increases the overall cost of the system and also degrades the system performance because of additional tuning delays of the tunable transmitter and receiver. Therefore, this study proposes an IOC enabled physical layer design for PON that supports simultaneous upstream and IOC communication using single transmitter and self-phase modulation mechanism at each ONU. A 1310 nm wavelength, return to zero signal is generated by the transmitter which passes through the self-phase modulator resulting in an additional side lobe signal generation that carries the IOC data without impacting the upstream transmission. These two signals are coupled at the remote node where IOC signal gets reflected back, while the upstream signal gets transmitted through it towards the OLT at the same time. A long reach PON scenario is assumed by keeping the OLT at a distance of 80 km or more. The simulation results show that the proposed design supports a maximum reach of 118 km for upstream link between remote node and OLT with bit error rate of 1.52 × 10−12 for 25 Gbps and 90 km with bit error rate of 7.1 × 10−12 for 30 Gbps data rate. The IOC communication also works well up to a split ratio of 32 with a maximum distance of approximately 5 km between the ONUs and remote node with an end to end bit error rate of 8.60 × 10−12. for 25 Gbps and 7.1 × 10−12 for 30 Gbps. Overall, the proposed design shows acceptable performance, in compliance to ITU G 987.1 recommendations for a maximum end to end reach of 122 km.

Stakeholder Perceptions towards the Quality of Coursera MOOCs Blended Learning in Vietnam: A Qualitative Study

Coursera MOOCs blended learning (CMBL) has been implemented at a Vietnamese Higher Education Institute (HEI) since the Fall 2019 semester. Our case study, which shows how Coursera MOOCs and the traditional classroom may work together, is unique in the context of Vietnamese higher education. In this case, students must complete the courses and earn certifications through Coursera MOOCs to qualify for the HEI’s offline final examinations. Meanwhile, students also engage in offline mentoring sessions with their classmates and lecturers (mentors). By employing the Service Quality (SERVQUAL) and 3P models, the research was conducted to explore how key factors might influence the quality of CMBL. This research conducted semi-structured interviews and employed thematic analysis with thirty interview participants, including ten administrators, eleven lecturers, three curriculum developers, and six students across four campuses of the HEI. We found that assessment, learning outcomes, learning content, Coursera staff’s responsiveness, offline mentors’ responsiveness and assurance, interaction, and student satisfaction might have considerably significant relationships with the quality of CMBL. On the other hand, Coursera instructors and offline mentors’ reliability have insignificant relationships with the quality of CMBL. This study has both theoretical and practical implications for universities and academics. Regarding the theoretical implications, this qualitative study provides critical criteria to measure the quality of the CMBL. Regarding the practical implications, it provides implications for curriculum development, teaching and learning, and assessment to improve the quality of CMBL. However, the authors could not travel across Vietnam to conduct face-to-face interviews in 2021 due to the COVID-19 pandemic. Therefore, twenty-eight online interviews were conducted via Microsoft Teams and two email interviews. A downside of an online interview is that personal qualities that are critical to a study may be amended during the interview, forcing the researcher to rely on the participant’s words. Additionally, unlike a face-to-face interview, an email interview lacks simultaneous communication between the interviewer and the interviewee.
 Keywords: Higher Education Institution (HEI), blended MOOCs, Coursera MOOCs Blended Learning (CMBL), Coursera MOOCs, offline mentoring, sustainable development

Visible light sensing based on shadow features using multi-scale region convolutional neural network.

There are various production items in the industrial internet of things (IIoT) environment, such as pedestrians, robots, automated automated guided vehicles, etc. The practice industrial environment requires simultaneous communication and sensing of production items to achieve intelligent production and control. Thus, sensing methods not only require the integration of communication but also achieve sensing tasks such as recognition and positioning. Compared with traditional sensing media, visible light sensing has the advantages of high-speed communication, high sensing accuracy, and security, low energy consumption, and has become a potential sensing technology. Based on the strong directivity of visible light spatial radiation and the consistency of light intensity and position, this paper proposes a multi-scale visible light sensing-region convolutional neural network (VLS-RCNN) framework based on shadow features for multiple target sensing. The framework enables the recognition and positioning to use shared visible light shadow features to assist each other, and the multi-scale compensation strategy of the shadow region makes the framework more robust. The simulation results show that positioning results in the sensing area improve the recognition accuracy. The recognition results also reduce the positioning error without additional overhead. Therefore, this paper provides a new perspective for the sensing technology in the future IIoT, which should be considered to sense objects of interest by utilizing the inherent characteristics of visible light.

A Bipolar and Unipolar Magnetic Channel Multiplexed WPT System With Simultaneous Full-Duplex Communication for Autonomous Underwater Vehicles

A Bipolar and Unipolar Magnetic Channel Multiplexed WPT System With Simultaneous Full-Duplex Communication for Autonomous Underwater Vehicles

Lamb-Waves-Based Sparse Distributed Penetrating Communication via Phase-Position Modulation for Enclosed Metal Structures

Sensor nodes have been widely used for mechanical equipment condition monitoring, especially for enclosed metal structures. In these cases, it is important to achieve communication between the sensor nodes inside the enclosed metal structures and the outside receiver node. Conventional wired communication using cables will affect the structural integrity, and wireless communication using electromagnetic waves will be shielded by metal structures, so neither is desirable. Motivated by these issues, this study proposes a Lamb waves-based sparse distributed penetrating communication (SDPC) system using the propagation characteristics of Lamb waves considered harmful in previous studies to accomplish wireless simultaneous data communication. Combining phase shift keying and pulse position modulation mechanisms, a phase-position modulation (P-PM) technology is proposed to improve the communication rate. To overcome the intersymbol interference (ISI) problem caused by the propagation characteristics of Lamb waves, a phase-position sparse reconstruction (P-PSR) strategy is proposed for demodulation, which is more convenient and accurate compared with the conventional cross-correlation method. It should be noted that the maximum bit rate without bit error reaches 830.48 kbps via P-PM parameter optimization, which is 8.3 times higher than that of on-off keying modulation approach under the same experimental conditions. Finally, distributed communication experiments and simulations further verify the high accuracy, efficiency and strong robustness of the proposed SDPC system, which can meet the communication requirements of enclosed metal structures.

Integrated Optical Sensing and Communication (IOSAC) System Based on Hybrid‐Waveguide Structures

AbstractAdvanced silicon photonic technologies enable integrated optical sensing and communication (IOSAC) in real time for the emerging application requirements of simultaneous sensing and communication for next‐generation networks. Herein, an IOSAC system is proposed and demonstrated on the silicon nitride (Si3N4) photonics platform. The IOSAC devices leveraging microring resonators excel in real‐time monitoring of analyte fluctuations and efficiently transmit this data to the terminal, positioning them as viable alternatives to bulk optics in applications that demand both high precision and speed. By directly integrating Si3N4 ring resonators with optical communication networks, simultaneous sensing and optical communication are demonstrated by an optical signal transmission experimental system using especially filtering amplified spontaneous emission spectra. The refractive index (RI) sensing ring with a sensitivity of 172 nm RIU−1, a figure of merit (FOM) of 1220, and a detection limit (DL) of 8.2 × 10−6 RIU is demonstrated. Simultaneously, the 1.25 Gbps optical on‐off‐keying (OOK) signal is transmitted at the concentration of different NaCl solutions, indicating the bit‐error‐ratio (BER) decreases with the increase in concentration. The novel IOSAC technology shows the potential to realize high‐performance simultaneous biosensing and communication in real time and further accelerate the development of IoT and 6G networks.

Simultaneous BPSK classical communication and continuous variable quantum key distribution with a locally local oscillator regenerated by optical injection phase locked loop

This paper proposes a scheme for simultaneous classical communication and continuous variable quantum key distribution with a true local oscillator. In this scheme, the emitter’s laser, after binary phase-shift keying (BPSK) modulation, is multiplexed in polarization with the quantum signal and sent to the receiver. After BPSK demodulation and correction, this signal is used for local oscillator regeneration by an optical injection phase-locked loop method. Comparing the effective noise sources in this scheme with typical local local oscillator schemes revealed that continuous variable quantum key distribution (CV-QKD) with a true local oscillator based on the optical injection phase-locked loop encounters lower levels of noise in comparison to the pre-existing genuine local oscillator CV-QKDs.

User group-based pilot allocation and data power optimization in cell-free massive MIMO for coexistence of UAVs and ground users

To address the pilot contamination issue in cell-free massive multi-input multi-output (MIMO) systems supporting simultaneous communication between unmanned aerial vehicles (UAVs) and ground users (GUEs), a user group-based pilot allocation algorithm is proposed. Furthermore, the system performance is enhanced by optimizing the data power. Initially, the distances between users and pilots are calculated based on the principles of maximizing spectral efficiency and utilizing the large-scale fading matrix. Ground users and UAVs are randomly divided into two groups: primary users and shared users. The contamination intensity between users is measured based on the distance between users and pilots, and shared user pairs that are allocated the same orthogonal pilots are generated. Next, a user-centric approach is employed to select a subset of access points (APs) to serve the users. The scalable fractional power optimization strategy is combined as a power optimization scheme for uplink data transmission, providing a controllable trade-off between user fairness and average spectral efficiency. Simulation results demonstrate that the user group-based pilot allocation algorithm significantly improves the system’s overall spectral efficiency, and the fractional power optimization algorithm enhances the average spectral efficiency of the system.

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

SummaryThe Internet of Vehicles (IoV) integrates various technologies to enable real‐time data exchange between vehicles, pedestrians, roadside units, unmanned aerial vehicles, road‐added things, and so forth. IoV is an improved internet‐enabled vehicular ad hoc network (VANET), which is similarly affected by data traffic aggravated by time‐variant network density and the network's topological and informational changes. A high density of simultaneous vehicular communication leads to channel saturation and data congestion, which leads to rapid increases in data loss, packet latency, and lower service quality. Due to the importance of real‐time data communications in high dynamic networks, we used the systematic literature review (SLR) approach to identify 54 articles up to May 2023 to discuss the ways to deal with data traffic congestion in VANETs and IoV networks as well as existing and upcoming issues in this area.

3-D Localization of Multiagent Systems Under Random Environments Based on Iterative Learning

This work addresses the localization of dynamic multi-agent systems (MASs) in three-dimensional (3-D) space under random environments (i.e., random packet loss, measurement noise and simultaneous communication noise). It is more practical yet challenging to deal with than static systems under ideal environments. Barycentric coordinates based on the relative-distance are used to characterize the positions between agents. The sign coefficients are introduced to guarantee that the sum of barycentric coordinates is equal to one, thus eliminating the constraint that the agent needs to be located in the convex hull. Using the newly designed iteration-varying gains, a robust distributed estimation method based on iterative learning is proposed. The key to accurate localization is to construct an appropriate approximator via introducing two iterative-varying gains into the localization scheme. The convergence in the sense of mathematical expectation is proved. The simulation examples and experimental results verify the effectiveness of the proposed approach.

A Graph Neural Network Learning Approach to Optimize RIS-Assisted Federated Learning

Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial intelligence paradigm, where over-the-air computation enables spectral-efficient model aggregation by achieving simultaneous communication and aggregation. However, due to limited transmit power, the performance of over-the-air FL is limited by the device with the worst channel condition toward the edge server. In this paper, we leverage reconfigurable intelligent surface (RIS) to mitigate the communication bottleneck of over-the-air FL and explicitly characterize the corresponding convergence upper bound. The convergence analysis illustrates the detrimental impact of the accumulated aggregation error over all rounds and inspires us to formulate a time-average transmission distortion minimization problem by jointly optimizing the transceiver and RIS phase-shifts. To reduce the computation complexity and enhance the model aggregation accuracy, we develop a graph neural network (GNN) based learning algorithm to directly map channel coefficients to the optimized network parameters. By exploiting permutation equivalence and invariance properties of graphs, the parameter dimension of the proposed algorithm is independent of the number of edge devices, which reduces the computational complexity and improves the algorithmic scalability. Simulations show that the proposed algorithm speeds up the computation by three orders of magnitude compared to the baselines, while achieving performance superiority and algorithmic robustness.

A Transition to Multimodal Multilingual Practice: From SimCom to Translanguaging

Historically, the field of deaf education has revolved around language planning discourse, but little research has been conducted on Deaf and Hard of Hearing (DHH) students with additional disabilities as dynamic multilingual and multimodal language users. The current study focuses on the language planning process at a school serving DHH and Deaf–Blind students with varied additional disabilities. A previous Total Communication philosophy at the school was implemented in practice as Simultaneous Communication (SimCom) and later revised as a multimodal-multilingual approach with the goal of separating American Sign Language (ASL) and English and using multimodal communication such as tactile ASL and Augmentative and Alternative Communication (AAC). To implement this philosophy without reverting back to SimCom, the school employed a language planning process using action research to reflect on cycles of improvement. A grounded theory approach was used to identify and analyze themes over a three-year period of language planning and professional development in multimodal communication. Triangulated data includes language planning artifacts and an online survey of staff perceptions—analyzed by coding concepts and categories, relating concepts to define translanguaging mechanisms and attitudes, and developing an overarching theory on how a school values translanguaging after 3 years of valuing complete access to language. In the context of a multilingual, multimodal language planning cycle, developing a shared language ideology guided by how Deaf, DeafBlind, and Deaf-Disabled (DDBDD) people use language emerged as an overarching theme that promoted dynamic languaging and understanding of strategies for effective communication.

Chaotic-cavity surface-emitting lasers for optical wireless communication and low-speckle illumination

Though necessary and advantageous in many fields, the high coherence of lasers is detrimental to their performance in certain applications, including illumination, imaging, and projection. This is due to the formation of coherence artifacts, commonly known as speckles, resulting from the interference of randomly scattering spatially coherent photons. It is possible to resolve this issue by increasing the number of mutually incoherent modes emitted from the laser. In vertical-cavity surface-emitting lasers (VCSELs), this can be performed by designing them to have chaotic cavities. This paves the way toward their use in simultaneous illumination and communication scenarios. Herein, we show that chaotic-cavity broad-area VCSELs can achieve significantly broader modulation bandwidths (up to 5 GHz) and higher data rates (up to 12.6 GB/s) compared to other low-coherence light sources, with a lower speckle contrast. We further report a novel technique for lowering the speckle contrast by carefully designing the AC signal used for communication. We show that the apparent spatial coherence is dramatically decreased by inserting a short chirp signal between symbols. Using this method with a chaotic-cavity VCSEL, the number of apparent modes can be up to 450, compared to 88 modes measured from a conventional broad-area VCSEL (a fivefold increase). In light of the recent advances in visible-light VCSELs, this work shows the potential of low-coherence surface-emitting lasers (LCSELs) in simultaneous illumination and optical wireless communication systems since they combine the high speed of lasers with the excellent illumination properties of light-emitting diodes.

Analisis pengaruh gaya komunikasi kepala sekolah dan kompensasi terhadap kinerja guru tidak tetap (GTT) di SD Negeri Jekulo

This research is motivated by the gap that occurs between civil servant teachers and non-civil servant teachers in the field. Communication by school principals to GTT teachers is sometimes different from PNS teachers. The same goes for the wages you receive. The importance of providing the right compensation will motivate GTT to continue working professionally and give their best performance. And accompanied by smooth communication between GTT and school principals and PNS teachers, it will have a very good impact on improving the quality of education in schools. Based on this, the formulation of the problem in this study is "How do the effects of school principal communication and multiple compensation on the performance of non-permanent teachers (GTT) in public elementary schools in Jekulo sub-district, Kudus district?". The purpose of this study, among others, is to analyze the influence of the principal's communication style and multiple compensation on the performance of non-permanent teachers (GTT) in public elementary schools in the Jekulo sub-district, Kudus district. To realize this effort, the researcher made observations in the field in the form of a questionnaire which was filled in directly by the GTT who was the research sample. From a population of 135 non-permanent teachers in the Jekulo District, 100 teachers were taken as a sample. After obtaining the data, the researchers processed it using descriptive statistical data analysis methods, instrument tests, and classical assumption tests. After that, hypothesis testing was carried out which consisted of the f test, the coefficient of determination, and multiple linear regression analysis. The results of the study show that based on the regression equation it is seen that the coefficient of the communication and compensation indicator variable is positive, this means that the increase in respondents' responses to communication and compensation can result in an increase in teacher performance. Based on the calculation of the F test, it is known that the significant value for the effect of simultaneous communication and compensation on teacher performance is 0.000 <0.05 and the calculated F value is 32.670 > F table 2.698, so it can be concluded that there is an influence of the principal's communication and simultaneous compensation on teacher performance not permanent in Jekulo District.Keywords: principal communication, compensation, teacher performance.