Deep Learning Based Over-the-Air Training of Wireless Communication Systems without Feedback.

In trainable wireless communications systems, the use of deep learning for over-the-air training aims to address the discontinuity in backpropagation learning caused by the channel environment. The primary methods supporting this learning procedure either directly approximate the backpropagation gradients using techniques derived from reinforcement learning, or explicitly model the channel environment by training a generative channel model. In both cases, over-the-air training of transmitter and receiver requires a feedback channel to sound the channel environment and obtain measurements of the learning objective. The use of continuous feedback not only demands extra system resources but also makes the training process more susceptible to adversarial attacks. Conversely, opting for a feedback-free approach to train the models over the forward link, exclusively on the receiver side, could pose challenges to reliably end the training process without intermittent testing over the actual channel environment. In this article, we propose a novel method for the over-the-air training of wireless communication systems that does not require a feedback channel to train the transmitter and receiver. Random samples are transmitted through the channel environment to train a mixture density network to approximate the channel distribution on the receiver side of the network. The transmitter and receiver models are trained with the resulting channel model, and the transmitter can be deployed after training. We show that the block error rate measurements obtained with the simulated channel are suitable for monitoring as a stopping criterion during the training process. The resulting method is demonstrated to have equivalent performance to the end-to-end autoencoder training on small message sequences.

Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach.

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.

150 Gbps THz Chipscale Topological Photonic Diplexer.

Photonic diplexers are being widely investigated for high data transfer rates in on-chip communication. However, dividing the available spectrum into nonoverlapping multicarrier frequency sub-bands has remained a challenge in designing frequency-selective time-invariant channels. Here, an on-chip topological diplexer is reported exhibiting terahertz frequency band filtering through Klein tunneling of topological edge modes. The silicon topological diplexer chip facilitates two high-speed channels with quadrature amplitude modulation (QAM) over a broad bandwidth of 12.5 GHz each. These channels operate at carrier frequencies of 305 and 321.6 GHz, achieving a combined diplexer capacity of 150 Gbit s-1. To ensure minimal interference between adjacent channels, a guard band is implemented. Topologically protected edge modes suppress the frequency selective fading of the broadband signals and hold promise for diverse integrated photonic applications spanning terahertz and telecommunication realms, including the design of lossless topological multiplexers, interconnects, antennas, and modulators for the sixth to X generation (6G to XG) wireless.

A Review on the Effects of Thermal Inversions and Electromagnetic Fields on Cell Cultures and Wireless Communications.

Thermal inversions, typical in the winter season, consist of cold air at the Earth's surface being trapped under a layer of warmer air. Such an effect keeps normal convective overturning of the atmosphere from penetrating through. This phenomenon highly increases the toxicity of the atmosphere, while modifying its dielectric constant, resulting in major implications in terms of public health and wireless communications. Indeed, air pollution in large cities (related, in most cases, to particulate matter that consists of different chemical components, which can have warming or cooling effects) is primarily caused by chemical and photochemical reactions in the atmosphere. Appropriate usage of array antennas allows the effective tracking of changes in humidity (e.g., coated Yagi-Uda antennas, which do not interfere with 5G) and in the dielectric constant (e.g., optimized quasi-Yagi-Uda antennas, yielding to accurate measurements of sulfides and black carbon concentration). Remarkably, important health effects come from the combined action of electromagnetic fields with fine and coarse black carbon particles. The appearance of ducts, which are caused by thermal inversions, provokes the creation of super-refractive regions in the troposphere as well, which result in the anomalous propagation of wireless communications.

Clustering and Beamwidth Optimization for UAV-Assisted Wireless Communication.

With the development of wireless communication technology, unmanned aerial vehicles (UAV) are now widely used in many complex communication scenarios. When a UAV serves as an aerial base station for urban and rural ground users or marine users, it is necessary to consider the clustering of ground users and the energy efficiency of the UAV since the users are usually randomly distributed. For the scenario with randomly distributed ground users and different densities of ground users in urban and rural areas, a clustering and beamwidth optimization method for UAV-assisted wireless communication is proposed. Firstly, the energy efficiency expression of a UAV serving ground users was derived in a downlink wireless communication system assisted by a UAV. Secondly, based on the geographical location information of non-uniformly distributed users, an improved k-means method is proposed to cluster ground users, ensuring that the number of users in each cluster is within an appropriate range. Then, based on the clustering results, a fixed-point iteration (FPI) algorithm was proposed to design the optimal beamwidth of UAVs and improve their energy efficiency. Finally, the superiority of the proposed algorithm in improving energy efficiency was verified through simulation analysis, and the impact of parameters such as the cluster number and transmission power on system energy efficiency was also analyzed.

Efficient Filter Design to Compensate Fabrication Imperfections in 6G Communication Systems.

In this paper, we present a consistent methodology for the reliable design of 6G-oriented filters with enhanced endurance to construction imperfections. The systematic formulation does not depend on the filter's operating frequency and employs a robust strategy for obtaining new roots and poles of the filtering function. Essentially, it requires that all the local maxima of the filtering function do not fluctuate beyond the design attenuation levels for a set of predefined roots/poles distortions. To this purpose, two novel algorithms for the derivation of the appropriate filtering functions are developed, in the prior basis, together with a versatile optimization criterion and a heuristic comparison approach that guarantee optimal outcomes. Specifically, the principal idea of the first technique is to accurately extract the roots of the new polynomial from a system of equations on condition that the maximum local peaks of the distorted (due to imperfections) initial polynomial are below a prefixed threshold, such as the unit. Conversely, the second method develops an alternative polynomial, compressed in the amplitude and frequency range, so that a similar prerequisite regarding the maximum local peaks, is satisfied. It is stressed that both methods are fully generalized and may be applied to any polynomial combination, without increasing the overall complexity. The proposed framework is successfully verified in terms of theoretical examples and the numerical simulation of realistic waveguide and mictrostrip line filters, operating at frequencies from 2GHz to 65GHz, which unveil its superiority over existing schemes and implementations.

Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications.

Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments.

Modification of Simple Antenna Pattern Models for Inter-Beam Interference Assessment in Massive Multiple-Input-Multiple-Output Systems.

The occurrence of cross-beam interference in the received signal is one of the main problems that limit the possibilities of massive multiple-input-multiple-output technology (massive-MIMO) in fifth-generation (5G) systems. Thus, the evaluation of the level of this interference is one of the most important procedures in the spatial planning of currently wireless networks. We propose a novel modification of simple antenna pattern models, which is based only on changing the directivity of real antenna system patterns. This approach is independent of the antenna system's type, structure, and analytical description. Based on the developed modification, the original methodology for assessing the signal-to-interference ratio (SIR) from adjacent beams of a common antenna system is presented. The change in the radiation direction and the accompanying change in the complex shape and parameters of the real antenna beam pattern is one of the problems that significantly hinders the evaluation of the analyzed interference. Hence, in the presented methodology, we propose using our modification. In this case, the modification is reduced to a proportional change in the directivity concerning the real antenna system, which results from a change in the beam direction. The simulation studies used a multi-ellipsoidal propagation model and a real massive MIMO antenna pattern description from 3GPP. For the SIR error analysis, the 3GPP pattern is used as a reference. The simulation results show that modifying simple antenna pattern models allows us to obtain an SIR error of no more than 3 dB and 0.1 dB under line-of-sight (LOS) and non-LOS conditions, respectively.

Study of Interference Detection of Rail Transit Wireless Communication System Based on Fourth-Order Cyclic Cumulant.

The wireless communication system is used to provide dispatching, control, communication and other services for rail transit operations. In practice, interference from other wireless communication systems will affect the normal operation of trains, so it is an urgent problem to study the interference detection algorithms for rail transit applications. In this paper, the fourth-order cyclic cumulant (FOCC) of signals with different modulation modes is analyzed for the narrow-band wireless communications system of rail transit. Based on the analysis results, an adjacent-frequency interference detection algorithm is proposed according to the FOCC of the received signal within the predetermined cyclic frequency range. To detect interference with the same carrier frequency, a same-frequency interference detection algorithm using the relationship between the FOCC and the received power is proposed. The performance of the proposed detection algorithms in terms of correct rate and computational complexity is analyzed and compared with the traditional second-order statistical methods. Simulation results show that when an interference signal coexists with the expected signal, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the signal-to-noise ratio (SNR) is higher than 2 dB and -4 dB, respectively. Under the practical rail transit wireless channel with multipath propagation and the Doppler effect, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the SNR is higher than 3 dB and 7 dB, respectively. Compared with the existing second-order statistical methods, the proposed method can detect both the adjacent-frequency and the same-frequency interference when the interference signals coexist with the expected signal. Although the computational complexity of the proposed method is increased, it is acceptable in the application of rail transit wireless communication interference detection.

Path loss modeling based on neural networks and ensemble method for future wireless networks.

In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments. These advanced models will help in deploying wireless networks with the guarantee of covering communication environments with optimum quality of service. This paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ANN), artificial recurrent neural network (RNN) based on long short-term memory (LSTM), shortly known as RNN-LSTM, and convolutional neural network (CNN). Moreover, an ensemble-method-based neural network path loss model is proposed in this paper. Finally, an extensive performance analysis of the four models is provided regarding prediction accuracy, stability, the contribution of input features, and the time needed to run the model. The data used for training and testing in this study were obtained from measurement campaigns conducted in an indoor corridor setting, covering both line-of-sight and non-line-of-sight communication scenarios. The main result of this study demonstrates that the ensemble-method-based model outperforms the other models (ANN, RNN-LSTM, and CNN) in terms of efficiency and high prediction accuracy, and could be trusted as a promising model for path loss in complex environments at high-frequency bands.

On the Effect of Imperfect Reference Signal Phase Recovery on Performance of PSK System Influenced by TWDP Fading.

We examine the effects of imperfect phase estimation of a reference signal on the bit error rate and mutual information over a communication channel influenced by fading and thermal noise. The Two-Wave Diffuse-Power (TWDP) model is utilized for statistical characterization of propagation environment where there are two dominant line-of-sight components together with diffuse ones. We derive novel analytical expression of the Fourier series for probability density function arising from the composite received signal phase. Further, the expression for the bit error rate is presented and numerically evaluated. We develop efficient analytical, numerical and simulation methods for estimating the value of the error floor and identifying the range of acceptable signal-to-noise ratio (SNR) values in cases when the floor is present during the detection of multilevel phase-shift keying (PSK) signals. In addition, we use Monte Carlo simulations in order to evaluate the mutual information for modulation orders two, four and eight, and identify its dependence on receiver hardware imperfections under the given channel conditions. Our results expose direct correspondence between bit error rate and mutual information value on one side, and the parameters of TWDP channel, SNR and phase noise standard deviation on the other side. The results illustrate that the error floor values are strongly influenced by the phase noise when signals propagate over a TWDP channel. In addition, the phase noise considerably affects the mutual information.

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

ICT Framework for Supporting Applied Behavior Analysis in the Social Inclusion of Children with Neurodevelopmental Disorders.

The applied behavior analysis (ABA) model emphasizes observable and measurable behaviors by carrying out decision making using experimental data (behavioral observation assessment strategies). In this framework, information and communication technology (ICT) becomes highly suitable for enhancing the efficiency and effectiveness of the methodology. This paper aims to delve into the potential of ICT in providing innovative solutions to support ABA applications. It focuses on how ICT can contribute to fostering social inclusion with respect to children with neurodevelopmental disorders. ICT offers advanced solutions for continuous and context-aware monitoring, as well as automatic real-time behavior assessments. Wireless sensor systems (wearable perceptual, biomedical, motion, location, and environmental sensors) facilitate real-time behavioral monitoring in various contexts, enabling the collection of behavior-related data that may not be readily evident in traditional observational studies. Moreover, the incorporation of artificial intelligence algorithms that are appropriately trained can further assist therapists throughout the different phases of ABA therapy. These algorithms can provide intervention guidelines and deliver an automatic behavioral analysis that is personalized to the child's unique profile. By leveraging the power of ICT, ABA practitioners can benefit from cutting-edge technological advancements to optimize their therapeutic interventions and outcomes for children with neurodevelopmental disorders, ultimately contributing to their social inclusion and overall wellbeing.

Toward Sub-Terahertz: Space-Time Coding Metasurface Transmitter for Wideband Wireless Communications.

A space-time coding metasurface (STCM) operating in the sub-terahertz band to construct new-architecture wireless communication systems is proposed. Specifically, a programmable STCM is designed with varactor-diode-tuned metasurface elements, enabling precise regulation of harmonic amplitudes and phases by adjusting the time delay and duty cycle of square-wave modulation signal loaded on the varactor diodes. Independent electromagnetic (EM) regulations in the space and time domains are achieved by STCM to realize flexible beam manipulations and information modulations. Based on these features, a sub-terahertz wireless communication link is constructed by employing STCM as a transmitter. Experimental results demonstrate that the STCM supports multiple modulation schemes including frequency-shift keying, phase-shift keying, and quadrature amplitude modulations in a wide frequency band. It is also shown that the STCM is capable of realizing wide-angle beam scanning in the range of ±45o , which offers an opportunity for user tracking during the communication. Thus, the STCM transmitter with high device density and low power consumption can provide low-complexity, low-cost, low-power, and low-heat solutions for building the next-generation wireless communication systems in the sub-terahertz frequency and even terahertz band.

Through-Holes Design for Ideal LiNbO3 A1 Resonators.

This paper proposes a method to realize ideal lithium niobate (LiNbO3) A1 resonators. By introducing subwavelength through-holes between the interdigital transducer (IDT) electrodes on the LiNbO3 surface, all unfavorable spurious modes of the resonators can be suppressed completely. It is convenient and valid for various IDT electrode parameters and different LiNbO3 thicknesses. Also, this method does not require additional device fabrication steps. At the same time, these through-holes can greatly reduce the suspended area of the LiNbO3 thin film, thus significantly improving the design flexibility, compactness, mechanical stability, temperature stability, and power tolerance of the resonators (and subsequent filters). It is expected to become an important means to promote the practical application of LiNbO3 A1 filters and even all Lamb waves filters.

Semantic Information Recovery in Wireless Networks.

Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and, thus, enables savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved. We consider this task as an end-to-end Information Bottleneck problem, enabling compression while preserving relevant information. As a solution approach, we propose the ML-based semantic communication system SINFONY and use it for a distributed multipoint scenario; SINFONY communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic recovery. We analyze SINFONY by processing images as message examples. Numerical results reveal a tremendous rate-normalized SNR shift up to 20 dB compared to classically designed communication systems.

Experimental Demonstration of a Visible Light Communications System Based on Binary Frequency-Shift Keying Modulation: A New Step toward Improved Noise Resilience.

Visible light communications (VLC) are an emerging technology that is increasingly demonstrating its ability to provide wireless communications in areas where radio frequency (RF) technology might have some limitations. Therefore, VLC systems offer possible answers to various applications in outdoor conditions, such as in the road traffic safety domain, or even inside large buildings, such as in indoor positioning applications for blind people. Nevertheless, several challenges must still be addressed in order to obtain a fully reliable solution. One of the most important challenges is focused on further improving the immunity to optical noise. Different from most works, where on-off keying (OOK) modulation and Manchester coding have been the preferred choices, this article proposes a prototype based on a binary frequency-shift keying (BFSK) modulation and non-return-to-zero (NRZ) coding, for which the resilience to noise is compared to that of a standard OOK VLC system. The experimental results showed an optical noise resilience improvement of 25% in direct exposure to incandescent light sources. The VLC system using BFSK modulation was able to maintain a maximum noise irradiance of 3500 µW/cm2 as compared with 2800 µW/cm2 for the OOK modulation, and an improvement of almost 20% in indirect exposure to the incandescent light sources. The VLC system with BFSK modulation was able to maintain the active link in an equivalent maximum noise irradiance of 65,000 µW/cm2, as opposed to the equivalent 54,000 µW/cm2 for the OOK modulation. Based on these results, one can see that based on a proper system design, VLC systems are able to provide impressive resilience to optical noise.

Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications.

In this paper, a compact dual-wideband fractal antenna is created for Bluetooth, WiMAX, WLAN, C, and X band applications. The proposed antenna consists of a circularly shaped resonator that contains square slots and a ground plane where a gap line is incorporated to increase the gain and bandwidth with a small volume of 40 × 34 × 1.6 mm3. The patch was supported by the FR4 dielectric, which had a permittivity of 4.4 and tan δ = 0.02. A 50 Ω microstrip line fed this antenna. The antenna was designed by the HFSS program, and after that, the simulated results were validated using the measured results. The measurement results confirm that the suggested antenna achieves dual-band frequencies ranging from 2.30 to 4.10 GHz, and from 6.10 GHz to 10.0 GHz, resonating at 2.8, 3.51, 6.53, and 9.37 GHz, respectively, for various applications including commercial, scholarly, and medical applications. Moreover, the antenna's ability to operate within the frequency range of 3.1-10.6 GHz is in accordance with the FCC guidelines for the use of UWB antennas in breast cancer detection. Over the operational bands, the gain varied between 2 and 9 dB, and an efficiency of 92% was attained. A good agreement between the simulation and the measured results was found.

Quality Indexes of the ECG Signal Transmitted Using Optical Wireless Link.

This work relates to the quality of the electrocardiogram (ECG) signal of an elderly person, transmitted using optical wireless links. The studied system uses infrared signals between an optical transmitter located on the person's wrist and optical receivers placed on the ceiling. As the elderly person moves inside a room, the optical channel is time-varying, affecting the received ECG signal. To assess the ECG quality, we use specific signal quality indexes (SQIs), allowing the evaluation of the spectral and statistical characteristics of the signal. Our main contribution is studying how the SQIs behave according to the optical transmission performance and the studied context in order to determine the conditions required to obtain excellent quality indexes. The approach is based on the simulation of the whole chain, from the raw ECG to the extraction process after transmission until the evaluation of SQIs. This technique was developed considering optical channel modeling, including the mobility of the elderly. The obtained results show the potential of optical wireless communication technologies for reliable ECG monitoring in such a context. It has been observed that excellent ECG quality can be obtained with a minimum SNR of 11 dB for on-off keying modulation.

Research on the Total Channel Capacities Pertaining to Two Coverage Layouts for Three-Dimensional, UAV-Assisted Ad Hoc Networks.

Unmanned aerial vehicles (UAVs) employed as airborne base stations (BSs) are considered the essential components in future sixth-generation wireless networks due to their mobility and line-of-sight communication links. For a UAV-assisted ad hoc network, its total channel capacity is greatly influenced by the deployment of UAV-BSs and the corresponding coverage layouts, where square and hexagonal cells are partitioned to divide the zones individual UAVs should serve. In this paper, the total channel capacities of these two kinds of coverage layouts are evaluated using our proposed novel computationally efficient channel capacity estimation scheme. The mean distance (MD) between a UAV-BS in the network and its served users as well as the MD from these users to the neighboring UAV-BSs are incorporated into the estimation of the achievable total channel capacity. We can significantly reduce the computational complexity by using a new polygon division strategy. The simulation results demonstrate that the square cell coverage layout can always lead to a superior channel capacity (with an average increase of 7.67% to be precise) to the hexagonal cell coverage layout for UAV-assisted ad hoc networks.