Accurately determining the indoor location of mobile devices has garnered great interest due to its significant challenge in locating sources due to non-line-of-sight propagation and multipath effects. To address this challenge, This paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve Accuracy. The proposed method extracts subcarrier amplitude and phase differences from CSI data to create fingerprints, which are then clustered to identify the number of groups of data and split into two sub-databases using a threshold. The ML algorithms and network architecture are used to train both sub-databases of fingerprints. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.

The 57–71 GHz millimeter-wave (mmWave) Industrial, Scientific, and Medical (ISM) band holds significant potential for enhancing the performance of next-generation industrial wireless applications. This paper first presents the design and analysis of a compact and high-performance 8-element series-fed frequency beam-scanning array designed to cover the entire 21.87% fractional bandwidth of the 57–71 GHz ISM band. Using this as a subarray, a hybrid parallel-series feed topology is designed to construct a 64-element (8 × 8) planar array with high-gain directional beams. The planar array provides a peak measured gain of 20.12 dBi at 64 GHz and maintains a flat gain of over 19.23 dBi throughout the band, with a 1 dB gain bandwidth of 13 GHz. Its narrow directional beams provide an average half-power beamwidth of 9.7° and 11.78° in the elevation and azimuth planes, facilitating point-to-point mmWave connectivity and high-resolution beam scanning. The inherent phase variation of the series-fed topology is employed to produce a beamscanning range of 40° within the 57–71 GHz band, with a scan loss of less than 1 dB. The proposed array is a low-cost, and reproducible solution for seamless integration with V-band mmWave equipment, as elucidated through practical demonstration frameworks using mmWave power sensor and EK1HMC6350 evaluation board. The proposed array is well-suited for emerging industrial wireless sensing and imaging applications, and mmWave frequency scanning radars. Its versatility extends to various 60 GHz protocols such as IEEE 802.11ay, IEEE 802.11ad, IEEE 802.15.3c/d, WirelessHD, and other customized industrial protocols such as WirelessHP.

Sixth generation (6G) internet of things (IoT) networks will modernize the applications and satisfy user demands through implementing smart and automated systems. Intelligence-based infrastructure, also called reconfigurable intelligent surfaces (RISs), have been introduced as a potential technology striving to improve system performance in terms of data rate, latency, reliability, availability, and connectivity. A huge amount of cost-effective passive components are included in RISs to interact with the impinging electromagnetic waves in a smart way. However, there are still some challenges in RIS system, such as finding the optimal configurations for a large number of RIS components. In this paper, we first provide a complete outline of the advancement of RISs along with machine learning (ML) algorithms and overview the working regulations as well as spectrum allocation in intelligent IoT systems. Also, we discuss the integration of different ML techniques in the context of RIS, including deep reinforcement learning (DRL), federated learning (FL), and FL-deep deterministic policy gradient (FL-DDPG) techniques which are utilized to design the radio propagation atmosphere without using pilot signals or channel state information (CSI). Additionally, in dynamic intelligent IoT networks, the application of existing integrated ML solutions to technical issues like user movement and random variations of wireless channels are surveyed. Finally, we present the main challenges and future directions in integrating RISs and other prominent methods to be applied in upcoming IoT networks.

Can the smart radio environment paradigm measurably enhance the performance of contemporary urban macrocells? In this study, we explore the impact of reconfigurable intelligent surfaces (RISs) on a real-world sub-6 GHz MIMO channel. A rooftop-mounted macrocell antenna has been adapted to enable frequency domain channel measurements to be ascertained. A nature-inspired beam search algorithm has been employed to maximize channel gain at user positions, revealing a potential 50% increase in channel capacity in certain circumstances. Analysis reveals, however, that the spatial characteristics of the channel can be adversely affected through the introduction of a RIS in these settings. The RIS prototype schematics, Gerber files, and source code have been made available to aid in future experimental efforts of the wireless research community.

Sign language is a mean of communication between the deaf community and hearing people, who use hand gestures, facial expressions, and body language to communicate. It has the same level of complexity as spoken language, but it does not employ the same sentence structure as English. The motions in sign language are made up of a range of distinct hand and finger articulations that are occasionally synchronized with the head, face, and body. Existing sign language recognition systems are mainly camera-based, which have fundamental limitations of poor lighting conditions, potential training challenges with longer video sequence data, and serious privacy concerns. This study presents a contact-less and privacy-preserving British sign language (BSL) Recognition system using Radar and deep learning algorithms, namely Inceptionv3, VGG16, and VGG19. The six most common emotions are considered, namely confused, depressed, happy, hate, lonely, and sad. The collected data is represented in the form of spectrograms. The deep learning models, InceptionV3, VGG19, and VGG16 then extract spatiotemporal features from the Spectrogram. Finally, the BSL emotions are accurately identified by classifying the Spectrograms, into the considered emotions signs. The simulation results demonstrate that a maximum classifying accuracy of 93.33% is obtained using VGG16.