Wearable Radar System Design on Semi-Flexible PCB for Visually Impaired People

This paper presents a compact and wearable frequency-modulated continuous-wave (FMCW) radar on a semi-flexible printed circuit board (PCB) for an anti-collision system. This can enable visually impaired people to perceive their environment better and more safely in their everyday lives. In the proposed design, a multiple-input multiple-output (MIMO) antenna array with four receivers (RXs) and three transmitters (TXs) has been designed to achieve obstacle-detection ability in both horizontal and vertical planes through a specific geometrical configuration. Operating at 76–81 GHz, an aperture coupled wide-beam patch antenna with two parasitic patches is proposed for each channel of RXs and TXs. The fast Fourier transform (FFT) algorithm has been implemented in the radar chip AWR1843 for intermediate frequency (IF) signals to generate a range-Doppler map and search precise target angles in high sensitivity. The complete system, which includes both the MIMO antenna array and the radar chip circuit, is utilized on a six-layer semi-flexible PCB to ensure compactness and ease in wearability. Field testing of the complete system has been performed, and an obstacle-detection range of 7 m (for humans) and 19 m (for larger objects) has been obtained. A wide angular detection range of 64-degree broadside view (±32°) has also been achieved. A voice module has also been integrated to deliver the obstacle’s range and angle information to visually impaired persons.

A Study on Propagation Models for 60 GHz Signals in Indoor Environments

The millimeter-waves band will enable multi-gigabit data transmission due to the large available bandwidth and it is a promising solution for the spectrum scarcity below 6 GHz in future generations of mobile networks. In particular, the 60 GHz band will play a crucial role in providing high-capacity data links for indoor applications. In this context, this tutorial presents a comprehensive review of indoor propagation models operating in the 60 GHz band, considering the main scenarios of interest. Propagation mechanisms such as reflection, diffraction, scattering, blockage, and material penetration, as well as large-scale path loss, are discussed in order to obtain a channel model for 60 GHz signals in indoor environments. Finally, comparisons were made using data obtained from a measurement campaign available in the literature in order to emphasize the importance of developing accurate channel models for future wireless communication systems operating in millimeter-waves bands.

IMU Sensing–Based Hopfield Neuromorphic Computing for Human Activity Recognition

Aiming at the self-association feature of the Hopfield neural network, we can reduce the need for extensive sensor training samples during human behavior recognition. For a training algorithm to obtain a general activity feature template with only one time data preprocessing, this work proposes a data preprocessing framework that is suitable for neuromorphic computing. Based on the preprocessing method of the construction matrix and feature extraction, we achieved simplification and improvement in the classification of output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons by constructing a feature matrix, which changed the weights of different categories to classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion process, which helps improve the classification accuracy and avoids falling into the local optimal value caused by single sensor data. Experimental results show that the framework has high classification accuracy with necessary robustness. Using the proposed method, the classification and recognition accuracy of the Hopfield neuromorphic algorithm on the three classes of human activities is 96.3%. Compared with traditional machine learning algorithms, the proposed framework only requires learning samples once to get the feature matrix for human activities, complementing the limited sample databases while improving the classification accuracy.

Pre-6G Graduate Education of Communications Engineering

As sixth-generation (6G) communications have been widely discussed in the past 2 years, it is now the right time to investigate the potential impacts of 6G communications on the current graduate education system of communications engineering. In this article, we articulate a set of existing problems with the graduation education of communications engineering and analyze the trends and challenges of pre-6G graduate education of communications engineering. By this article, we not only aim to diagnose the existing problems with corresponding trends and challenges but also to call for proactive measures coping with them. Besides, we would also like to use this article to encourage more and more brave undergraduates to participate in communications engineering, a fast-changing and far-reaching discipline.

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

Wireless Terrestrial Backhaul for 6G Remote Access: Challenges and Low Power Solutions

Despite developments in communication systems over the last few decades, a digital divide exists in the unconnected part of the world. The latter is characterized by large distances to internet access points, underdeveloped infrastructure, sparse populations, and low incomes. This concern of digital divide is raised in the sixth generation’s (6G) initial vision as an extremely important topic. However, it is important to understand affiliated challenges and potential solutions to achieve this vision. Motivated by the recent backhaul link forecasts that expect a dominance of the microwave technology within the backhauling market, this paper studies the potential of a low-power terrestrial microwave backhaul from the sufficient-data-rate and solar powering perspective. Competing technologies (e.g., fiber) may not be energy efficient and commercially viable for global connectivity. Since rural and remote areas may not have grid power, we look at the viability of alternative sustainable sources, in particular solar power, to power the wireless backhaul in 6G. In addition, we also explore services for the operators and users to use the system efficiently. Since the access points are connected to backhaul, we also compare the two prominent solutions based on low-power small-radius cells and a mega-cell that covers a large area and show insights on the power autonomy of the systems. In the end, we propose directions for research and deployment for an inclusive connectivity as a part of future 6G networks.

Electromagnetic Insights Into Path Loss Modelling of IRS-Assisted SISO Links: Method-Of-Moment Based Analysis

In this paper, we demonstrate the usefulness of MoM (Method-of-Moments) based methods in efficient path-loss modelling for SISO (single-input single-output) communication links assisted by IRS (Intelligent Reflecting Surfaces). Being a full-wave computational electromagnetic tool, MoM is better equipped compared to high-frequency asymptotic methods like PO (Physical Optics), to handle the crucial electromagnetic (EM) effects like: mutual coupling between IRS unit-cells or interactions with spherical wave-front in antenna near-field. Furthermore, in terms of computational speed, accuracy and reproducibility, the MoM-based MATLAB Antenna Toolbox is significantly advantageous to emulate IRS-assisted wireless channels, as compared to the in-house FDTD (finite-difference time-domain) techniques. We consider a SISO system of two half-wavelength dipoles, and use a rectangular array of circular loops loaded with lumped circuit components as IRS. The lumped circuit loading enables us to control the reactance of individual unit-cells, resulting in alteration of IRS reflection coefficient and consequent changes in channel characteristics. Using numerous numerical simulations, we highlight the impacts of various IRS-parameters like: electrical size and number of unit-cells, distance of IRS from the transmitter/receiver as well as mutual coupling, on the path-loss models (both sub-6 GHz and mm-wave).

Reconfigurable Intelligent Surface-Assisted Bluetooth Low Energy Link in Metal Enclosure

Reconfigurable intelligent surface (RIS) technology is at the forefront for its transformative role in future wireless communication systems such as wireless local area networks (WLAN), sixth-generation (6G) communication, and internet-of-things (IoT). This paper presents RIS-assisted Bluetooth low energy (BLE) communication links in neighbor discovery mode. We optimized the packet error rate (PER) performance of the BLE communication link in a highly reflecting metal enclosure environment. We used one RIS for the PER optimization of four BLE physical (PHY) modes. Then, we used two RISs simultaneously in a distributed and centralized manner to further optimize the PER of all BLE PHY modes. We found PER optimization using two RISs is better than the PER optimization using one RIS. Additionally, PER optimization using a centralized arrangement of RISs outperformed PER optimization using distributed arrangement. We found the coded BLE modes i.e., LE500K and LE125K show lower PER than the uncoded counterpart i.e., LE1M and LE2M. This is because uncoded BLE PHY modes have higher data rates than the coded BLE PHY modes. Because of additional channel power gains introduced by RIS-based passive beamforming, the PER of coded and uncoded BLE PHY modes is further reduced.