Recent Advances in Communication Networks and Embedded Systems

With the spread of coronavirus disease (COVID-19), office workers and school students have transitioned to online platforms for work and education, respectively. During this period, the importance of face-to-face human interactions has been highlighted. In response, in a prior study, I proposed an advanced system based on the harmony between human capabilities and information technology (IT) (SHONAN), which restricts the functions implemented by IT to a minimum and limits the range of data distribution to nearby users, preventing the unnecessary spread of data over the Internet. This system was developed as a potentially new sustainable IT system. Additionally, I had implemented a short-range message-exchange system called narrow area communication system (NAMI) as a specific application of SHONAN to implement crowd detection alerts to lower the risk of COVID-19 infection. In the current study, I propose a method to provide services to users while reducing the amount of transferred data by placing the original data on an edge server and processing the data on the cloud in a COVID-19 infection warning system. The proposed method was implemented as a prototype, and its effectiveness was experimentally verified.

Antenna beamforming is an increasingly utilized technology in various wireless systems, particularly in the realm of cellular telecommunications, such as 5G. By manipulating the spacing and phases of the antennas within an array, the direction and shape of the beam can be controlled. The principal goal of beamforming is to direct a wireless signal towards a specific receiving device instead of dispersing it in all directions from the transmitting antenna. This targeted approach enables the delivery of a higher quality signal to the intended receiver, resulting in faster and more reliable information transfer with reduced errors. For modelling a communication system, the beam pattern can be discretized into rays in angular and gain dimensions. Ray-tracing plays a crucial role in evaluating different beamforming techniques by accurately predicting signal strength, coverage, and quality at various locations within the environment. This analysis helps to select optimal beamforming parameters, including antenna placement, beam steering angles, and beamforming weights, to achieve the desired performance objectives. By effectively modeling the interaction between signals and objects/surfaces, ray-tracing aids in optimizing beamforming algorithms, improving signal quality, and maximizing overall system performance in wireless communication systems. The primary aim of this research is to determine, from a codebook, the ideal beam for each transmitter and receiver that maximizes the total channel capacity. However, due to the complexity and large number of angles, finding the best angles becomes challenging, time-consuming, and nearly impossible. Therefore, this study investigates various optimization methods to efficiently discover the optimal angles within a shorter timeframe. The accuracy and speed of different optimization techniques for identifying the best angles for achieving maximum total channel capacity are compared.

The COVID-19 pandemic has shown poor communication between people, especially the elderly living in heavily deserted and depopulated agricultural regions. This is a reality present in Portugal and all over the world, where there are technological and physical barriers that prevent these older adults, the most vulnerable people, from having easy access to essential goods, food, and medicines, among others, and are also exposed to risky situations, such as theft and fraudulent activities. Bearing in mind that the best company for older adults is television, we propose the development of a system called SVBOX (Switch Video Box), which includes the design and proof of concept of an electronic system that allows the integration of various equipment, including video intercom, designed to be an active surveillance device, with the integration of the video intercom system with the internal network of sensors in the home, for monitoring living conditions and security, detecting fire, flooding, toxic gases, among others, using the television as the main means of communication and sensors to monitor the vital signs and health status of older adults. This system has growth potential, with several applications in real security and protection contexts, namely in security environments and monitoring of health conditions.

In reality, complex systems are often represented by networks, and heterogeneous networks are more effective in describing the interaction behaviors among various elements. Evaluating the importance of nodes in a heterogeneous network is beneficial for maintaining the stability of the network. This study proposes a node importance evaluation method, named TAGCN Auto-Encoder Comprehensive Influence (TAE-CI), for heterogeneous networks, which combines graph neural networks with centrality measures. The method uses Topology Adaptive Graph Convolutional Networks (TAGCN) to improve graph autoencoders, encode different semantic subgraphs, reconstruct adjacency matrices, and optimize reconstruction loss to obtain node embedding vectors. To obtain the comprehensive influence of the nodes, the node embedding vectors are incorporated into the topological potential function to calculate the global influence, which is then combined with the local influence. The proposed method is evaluated on three real network datasets using the Susceptible Infected Recovered (SIR) model, and the results demonstrate its efficacy in evaluating node importance.

In order to reduce the influence of co-frequency interference on the receiver in continuous wave radar remote micro motion detection, we design a new type of vibration sensor. The sensor is designed based on cylindrical resonator and TM₀₁₀ and TE₁₁₁ resonant modes. The relationship between the cavity geometry, tuning column geometry and the working frequency is studied by simulation. The results show that the sensor has good quality factor and antenna gain at two different frequencies, and can be used in the long-distance micro motion detection of continuous wave radar. Its work does not need power supply, which effectively improves the scope of its application scenarios.

Cyclic-prefix orthogonal time frequency space (CP-OTFS) systems have gained great attention recently due to achievable prominent error performance characteristics in high-mobility wireless communication scenarios and the system realizability by use of efficient Discrete Fourier transform. In this paper, a symbol decision scheme is proposed for the CP-OTFS system by virtue of iterative decision-feedback hybrid equalization (IDFHE) using time-domain linear minimum mean-square-error (LMMSE) and constrained least square (CLS) equalizers at different iterations. The proposed IDFHE scheme can exploit the advantage that intersymbol interference is suppressed progressively over iterations and noise power enhancement is avoided at the final iteration, and thereby enhance the error performance. From simulation results, the proposed IDFHE scheme is shown to outperform non-iterative LMMSE decision scheme and iterative message-passing symbol decision scheme in average error performance on time-varying Rayleigh multipath channels.

This paper presents a 0.4 V duty cycle generator based on compact pulsed modulator architecture for Micro-Electromechanical Systems (MEMS) sensing interface. It compares favorably against literature in power performance (21.6 nW) through adopting of current re-used amplifiers and Schmitt trigger based pulsed generator with self-biasing architecture in subthreshold operations. Further simulations confirm the circuit functionality which shows varying duty cycles with sensing frequencies below 1 kHz. The design only occupies active area of 0.031 mm² and is fabricated on 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) technology.

This paper discusses the shift from paper-based academic services to e-services, which has become prevalent in College of Technological Innovation (CTI), UAE. However, the usability of these e-services is a challenge due to design issues, and This paper introduces a usability assessment study focusing on the e-services available through the CTI’s academic portal. The study employs eye tracking to examine the viewing, searching, and navigation behavior of college students, along with the factors that impact their searching behavior. The study focuses on the students’ visual patterns in choosing an e-service. Eye-tracking experiments were conducted, and data were collected from a group of CTI students. According to the findings of the heatmap and eye gaze analysis, students had trouble locating the right link for the desired service and displayed intense, disoriented scattered, and erratic visual behaviors when performing assignments. We discovered several user interface design issues that hinder student productivity. Based on the results of the usability assessment study, the research recommends changes to the CTI portal’s design to better suit students’ needs, preferences, and expectations.

Low Earth Orbit (LEO) satellite Multi-Access Edge computing (MEC) is expected to realize the ubiquitous coverage for global users and provide low-delay MEC service. In LEO satellite MEC networks, although recent studies have solved the problem of requests dispatched to the appropriate edge servers, they mainly focus on the effective use of computing resources and ignore the need to pre-store a large amount of data to provide services. Due to the limited storage and computing resources of satellites, it is impossible to place all services and meet the requests of all users. Considering the significant correlation between service placement and request dispatching, we investigate the joint optimization of service placement and request dispatching in LEO satellite MEC networks, which is formulated as a Mixed Integer Linear Programming (MILP) problem. In order to avoid overloading of some satellites while other satellites are not fully utilized, the load balancing factor is introduced in this paper. Our goal is to improve the user served ratio, minimize the cost of request dispatching and improve the load balancing among satellites while ensuring the constraints of computation and storage resources. The simulation results verify that, compared with the baseline mechanism, the scheme of joint service placement and request scheduling with loading balancing (SPRDLB) that we proposed has better performance.

5G Millimeter Wave (mmWave) Communication between the base station (BS) and the user equipment (UE) involves a Multiple-Input Multiple-Output (MIMO) system where both BS and UE have many antennas. Initial Access (IA) in this context is the problem of establishing a directional link between the BS and UE, but finding the optimal beams can be prohibitively expensive in terms of delay and computation. Genetic Algorithms (GAs) can solve complex problems effectively, and in this case, they can be used to iteratively search for the optimal beams. We propose a dual phase GA that splits the GA process into two successive phases that uses different operations in each phase. It also navigates the search space in a smart manner, increasing the convergence rate to the optimal beamformer per iteration. We have analyzed the effect of this approach in terms of Capacity achieved vs number of transmit and receive antennas at BS and UE, total transmitted power, and number of iterations. It shows improved performance in terms of maximum Capacity achieved, reduced power consumption, and especially reduced IA delay.

Reconfigurable intelligent surface (RIS) has received wide-spread attention as a critical enabling technology for next generation wireless communications. Accurate channel state information (CSI) is fundamental for RIS to reach its full potential. Thanks to the powerful latent representation from data, generative artificial intelligence (AI) has the potential as a strong driver for the highly intelligent and digital twin of 6G. In this paper, we propose a generative diffusion model (DM)-based cascaded channel estimation (CE) algorithm through unsupervised learning for RIS-aided massive multiple-input multiple-output (MIMO) systems. The DM can effectively capture the implicit prior of the cascaded channel, and the received signal is used as conditional information to precisely guide the channel recovery. Simulation results validate that the proposed algorithm achieves superior estimation accuracy with reduced pilot overhead.

We demonstrate an efficient implementation of the Kuang and Barbeau’s Quantum Permutation pad (QPP) symmetric cryptographic algorithm with Qiskit Runtime, a new architecture offered by IBM Quantum that streamlines quantum computations. We have implemented a Python class QPP and template Jupyter notebooks with Qiskit code for encrypting and decrypting with n-qubit QPP any text file in UTF-16 format or any image file in .png format. We offer the option of running either a quantum circuit with n qubits, or an alternate one with 2ⁿ qubits which only uses swap gates and has a circuit depth of O(n). It is inherently extremely fast and could be run efficiently on currently available noisy quantum computers. Our implementation leverages the new Qiskit Sampler primitive in localized mode which dramatically improves performance. We offer a highly efficient classical implementation which performs permutation gate matrix multiplication with information state vectors. We illustrate the use with two agents Alice and Bob who exchange a text file and an image file using 2-qubit QPP and 4-qubit QPP.

This paper presents a way to integrate a Post-Quantum Cryptography key exchange mechanism based on the encryption and signature algorithms currently being standardized by the National Institute of Standards and Technology into a Quantum Key Distribution platform. In contrast to Quantum Key Distribution, the security of Post-Quantum Cryptography is based on the mathematical complexity of the cryptographic algorithms. The unique feature of the presented solution is that the Post-Quantum Cryptography key exchange mimics a Quantum Key Distribution system. As implemented, the encryption keys are continuously exchanged between the nodes that are part of the OpenQKD testbed Berlin and then stored in a secure key store. The testbed’s key management can distinguish between Quantum Key Distribution and Post-Quantum Cryptography keys based on metadata information, allowing applications to select the appropriate key type. This architecture enables interoperability between the two technologies and may also provide a means to deliver quantum-secure keys to the end user by leveraging Post-Quantum Cryptography to secure the last mile.

Given recent advances in the field of quantum cryptography, cryptographic agility is essential to protect data at rest and in transit. We consider hybrid key exchange and authentication in the framework of the TLS1.3 protocol. Traditional hybrid mode, which uses classical and quantum-safe algorithms together to secure data in the event of one of the algorithms being broken, has been a subject of research for a few years now. However, there are certain engineering challenges that come with using more than one algorithm in the framework of the TLS protocol. In this work, we propose a different hybrid mode that allows seamless negotiation between classical and quantum-safe algorithms. The proposed hybrid mode has different goals from the conventional hybrid mode and could be an excellent solution for entities not required to use standardized cryptographic algorithms and can be used in the timeframe when quantum-safe algorithms have been standardized but certain endpoints have not yet transitioned to post-quantum. We discuss conventional and proposed hybrid modes, described the proposed solution in detail, and briefly review engineering challenges associated with each hybrid mode.