Number Of Antennas Research Articles

CFNet:CSI compression feedback network based on WiFi sensing

Abstract To solve the problem of huge channel state information (CSI)data caused by a large number of antennas in large-scale MIMO systems,a CSI compressed feedback network model (CFNet)with encoder-decoder structure is proposed in this paper. Specifically, the encoder efficiently compresses the CSI data to reduce the burden of data transmission and storage. A key component, the Asymmetric Expansion Convolution (AEC) module, combines asymmetric convolution and dilated convolution to extract feature information from different directions, thereby increasing the model's receptive field and minimizing information loss. The compressed CSI is fed into a Gated Recurrent Unit (GRU), which captures long-term dependencies in the CSI sequence using the encoder's output and the hidden state of the upper window. Additionally, on the decoder side, the proposed Asymmetric Transpose Convolution reconstructs the compressed CSI data, enhancing the network's ability to capture and express CSI features through two complementary convolution operations, ultimately reducing communication costs. Thanks to the proposed encoder and decoder, CFNet can accurately identify fine-grained features of human activities recognition(HAR) while leveraging compressed sensing techniques, further improving data processing efficiency and recognition accuracy. The experiments conducted on two public datasets demonstrate that the evaluation metrics of the proposed network outperform those of advanced methods in the relevant field.

Outage Performance of User‐Assisted Energy Harvesting Full Duplex MIMO‐NOMA Networks

ABSTRACTThis work focuses on analyzing the outage probability (OP) in a downlink multi‐user multiple‐input multiple‐output non‐orthogonal multiple access (MIMO‐NOMA) system, specifically employing the maximum‐ratio transmission/receive antenna selection structure. In the investigated system, the user close to the base station (BS) acts as a full‐duplex (FD) (or half‐duplex [HD]) energy harvesting (EH) relay adopting decode‐and‐forward (DF) protocol for transmission to the far user. With and without a direct link, we derive the exact and asymptotic OP expressions in closed form for Nakagami‐ fading channels. The analytical results are validated through simulations, which show that the presence of a direct link in the system significantly enhances the performance of the far user. This improvement occurs because the direct link mitigates the performance degradation caused by the self‐interference of the near‐user. Additionally, the far user exhibits superior performance when the near user operates in the FD mode to the HD mode. Moreover, as the number of transmit antennas of the BS increases, the signal‐to‐noise ratio gains are more pronounced in FD mode than in HD mode. For both HD and FD modes, the performance is better when the number of antennas at the BS is higher than that of the users.

Joint Noniterative Beamforming Schemes for BER Minimization in Next‐Generation Wireless Sensor Networks

This research paper proposes three joint transmit and receive (TX/RX) beamforming schemes for minimizing the bit error rates of next‐generation wireless sensor networks. Based on minimizing the optimization problem of the total mean squared error between the transmitted and received data symbols, the first beamforming scheme is proposed. The second beamforming scheme is based on the projection of the effective channel on the space spanned by the channel matrix. A third practical beamforming scheme based on the strongest eigenvalues of the channel matrix is introduced. This beamforming scheme proves its optimality in its operation under the condition that the number of TX antennas is equal to or larger than the total number of RX antennas. The proposed joint TX/RX beamforming schemes are formulated in closed form. Numerous numerical results are provided using the MATLAB platform. The proposed beamforming schemes are compared with the widely adopted block diagonalization transmission scheme and linear block diagonalization. Moreover, the proposed systems are compared with state‐of‐the‐art beamforming techniques such as Tomlinson–Harashima precoding‐based MMSE. The simulation results demonstrated that the proposed beamforming schemes achieved better bit error rate performance than the block diagonalization and linear block diagonalization transmission schemes. Furthermore, the third beamforming scheme provides a gain of about 4dB compared to the cutting‐edge technology of beamforming Tomlinson–Harashima precoding‐based MMSE. Accordingly, more robust communications can be achieved for next‐generation wireless sensor networks. This research paper provides an efficient manner to meet the demanded reliability of next‐generation wireless sensor networks, which is a bit error rate of 10−9.

Effectiveness and Characteristics of Virtual Antennas in the Multiple Signal Classification Algorithm

This paper proposes a novel direction of arrival (DoA) estimation method based on the Multiple Signal Classification (MUSIC) algorithm using virtual antennas. The MUSIC method is a widely used DoA estimation technique known for its high accuracy and high resolution. However, it has a fundamental limitation: when the necessary condition of having more antenna elements at the base station than users is not met, DoA estimation becomes infeasible. To address this limitation, we introduce the concept of virtual antennas, allowing DoA estimation, even under restricted conditions. Virtual antennas are not physically present but can be virtually arranged, enabling the generation of virtual-received signals similar to those of real array antennas. Through simulation experiments, we demonstrate that our approach enables DoA estimation with the MUSIC algorithm even when its necessary conditions are not satisfied. Additionally, this paper explores the characteristics of virtual antennas in detail. We conduct simulation experiments to examine the differences in estimation accuracy between real and virtual antennas, as well as the impact of virtual antenna arrangement and count on estimation accuracy. The results show that, although virtual antennas provide lower estimation accuracy compared to real antennas, their flexible arrangement allows for improved resolution when signal sources are closely spaced by increasing the spacing between virtual antennas. Furthermore, under Additive White Gaussian Noise (AWGN) conditions, increasing the number of virtual antennas enhances estimation accuracy.

Advance in Applied Cryptography Theory: Survey and Some New Results. Part 2. Keyless Cryptography

Actuality. The current paper is the second part of the paper “Advance in Applied Cryptography Theory: Survey and Some New Results. Part 1. Key Cryptography” published in the journal PTU, n.4, 2024. It is devoted to such specific area of applied cryptography as keyless one (KC\ Actuality of the current paper consists in the fact that considered in it methods allow to provide a confidentiality of information transmission over public communication channels, either without any its encryption in advance, executing a natural properties of communication channels or executing conventional key cryptography but with the keys which are elaborated before by means of KC. The natural properties of communication channels can be the following: additive noise, multiray wave propagation, MIMO technology and existence of feedback channel.Our paper starts with a consideration of Wyner’s concept of wire-tap channels and corresponding to it encoding and decoding methods providing very reliable information transmission over the main channels and negligible amount of information leaking over the wire-tap channels to eavesdroppers. Next it is investigated scenario with a commutative encryption (CE) and corresponding protocol of message exchange over ordinary noiseless public channel that provides security of encrypted information but without any key exchange between users in advance. It is proved which of well known symmetric and asymmetric ciphers are commutative or non-commutative ones. Next model concerns a fading channels under the application of Dean-Goldsmith protocol in frames of MIMO technology. We are proving that this protocol is secure if, and only if, the number of eavesdropper antennas is less than the number of antennas at legitimate users. Next scenario executes variable directional antennas (VDA) and it is proved for which conditions on a locations of legitimate users and eavesdroppers such approach occurs secure given the number of propagation rays is at least two.. We show in the next chapter that there is an attack compromising of recently proposed EVESkey cryptosystem and hence such one is not secure in spite of the statement of its authors.Finally, we investigate several protocols intended for key sharing over noiseless constant public channels (like Internet) and established that they are mostly insecure because have all zero secret capacity. Only one protocol based on matrix channel exchange is able to provide security of key sharing but in terms of the required breaking complexity. Thus such approach can be used only for the case when legitimate users belong to low level of security requirements.At the end of the paper we formulate several fundamental problems of applied cryptography which after of their solutions could be very useful for practice.

Performance Analysis of Artificial-Noise-Based Secure Transmission in Wiretap Channel

Artificial noise (AN)-aided techniques have been considered to be promising and practical candidates for enhancing physical layer security. However, there has been a lack of analysis regarding the AN effect on the eavesdropper (EV) from the perspective of the signal-to-interference plus noise ratio (SINR) regarding the existence of the EV’s channel state information (CSI) at the legitimate transmitter. In this paper, we analyze the performance of AN-aided secure transmission from the SINR perspective when a legitimate transmitter has and does not have the EV’s CSI. Based on the analyzed EV’s SINRs for the above two cases, the secrecy gap, which is the difference between the two secrecy capacities, is defined and analyzed. Based on the derived secrecy gap, we have analyzed the asymptotic performances of the secrecy capacity and gap when the number of antennas of the legitimate transmitter and the number of antennas of the EV have large values. Through asymptotic analysis, it is demonstrated that the AN-aided secure transmission under the practical environment (i.e., the case that the EV’s CSI is not available at the legitimate transmitter) can nearly achieve an ideal performance (i.e., the performance when the EV’s CSI is available at the legitimate transmitter) in a massive antenna system.

Experimental Evaluation of MIMO Channel Capacity for Wireless Communication Channels

This research evaluates the channel capacity (CC) of 5th Generation Multiple Input Multiple Output (MIMO) antennas for propagation channels used in wireless communication. In the presence of several frequency sources for the information and communication channels, the effect on the CC of one-port and multi-port antennas is observed using the Path Loss Model (PLM), which is significantly impacted due to the number of antennas used and their characteristics. The CC is also estimated using a different approach, Kronecker's Model (KM). The channel matrix using both models is estimated by maintaining 5 cm and 25 cm spacing between the transmitting and receiving antennas. At 2.4 GHz for a 1-port antenna-based channel, the CC calculated using PLM is 5.5 bps/Hz for both spacing, whereas the CC calculated using KM is 3.65 bps/Hz and 3.12 bps/Hz for 5 cm and 25 cm spacing, respectively. The CC in the PLM is found between 35 to 140 bps/Hz at a frequency of 3.5 GHz, whereas it is reported between 12 to 38 bps/Hz using the KM at the same frequency, irrespective of distances. The maximum CC calculated using PLM is 5 bps/Hz for a 1-port antenna-based channel, 34 bps/Hz for 2-port MIMO antenna-based channels, 80 bps/Hz for 4-port MIMO antenna-based channels, and 157 bps/Hz for 8-port MIMO antenna based channels in the frequency band from 3.45 GHz to 3.7 GHz. Thus, it is experimentally confirmed that the capacity enriched by approximately 4 times on increasing the number of antenna ports from 2 to 8 and operating frequency.

Transmission of Physical Layer Network Coding Based on Massive MIMO over Millimeter Wave Channel

Massive Multiple Input Multiple Output system and physical layer network coding (PNC) are two important technologies that are considered in new generation networks. In this paper we investigate the effect of increasing the number of antenna elements at user nodes and relay node in the presence of PNC. Two-way wireless relay transmission is considered in conjunction with massive number of antennas at the relay node assuming millimeter wave (mmWave) band channel environment. End-to-end transmission is considered using 3-D QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) channel model. The performance measures used are the bit error rate (BER) and throughput. Up to 100% increase in throughput of PNC coded system is obtained. The BER performance is improved only when the number of relay node antennas is much higher than that of the user node. Combining mMIMO with PNC gives better throughput without affecting the BER at high SNR. This is achieved with reasonable number of antennas at the user nodes.

Study on Selection Diversity for MIMO 3-User Interference Channel with Interference Alignment

This paper explores selection diversity in the context of MIMO 3-user interference channels with interference alignment (IA), focusing on enhancing reliability through diversity order (DO) analysis. While degrees of freedom (DoF) have traditionally been emphasized for throughput optimization in IA, limited research has addressed diversity order to improve error performance. In this work, we define a conditional DO and propose a beamforming vector selection method that achieves a conditional DO of M2/2, where M is the number of antennas per transceiver. The proposed scheme employs a two-stage decoding approach, combining zero-forcing for interference cancellation with maximum likelihood (ML) decoding for desired signal recovery, which is augmented by orthogonalization techniques. Simulations demonstrate the superiority of the proposed scheme in both error probability and conditional DO values compared to conventional IA methods, particularly in scenarios with higher antenna counts. These results provide insights into optimizing IA for enhanced reliability and form the foundation for future exploration of advanced decoding and beamforming strategies.

Innovative Approaches to Beam Forming Antenna Array Systems with Adaptive Partial Update NLMS Algorithms

Improvements in interference cancellation, energy economy, spectrum efficiency, and system security are among the most pressing needs for today's wireless networks. One effective technique for this is the use of a Beamforming Array Antennas (BAA). However, the complexity of the Beamforming (BF) network, the long convergence time, and the large number of adjustable weight coefficients, all work against full band BAA. The key innovation and contribution of this research was to use Partial Update (PU) instead of full band adaptive algorithms, as no previous attempt had been made to do so. A subset of the array's elements, rather than all of them, will be connected to by PU methods. This allows the system to reduce the number of active antennas across all cells while maintaining high efficiency, and low cost. In this research, a new architectural model was proposed that makes use of PU adaptive algorithms, to reduce the required number of phase shifters (PSs), and hence base station antennas. For the most part, we will be discussing PU Normalized Least Mean Square (PU NLMS) algorithms like M-max NLMS, Periodic-NLMS, and Stochastic-NLMS. Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. In other hands, these PU algorithms maintain the radiation pattern with as little change from the original (distortion-free) as possible and symmetrical of the array, while. fewer elements are needed to produce the same amount of radiation. Reducing the number of required coefficients N to M (M = 5; M: Number of coefficients to be update per iteration) compared to the full update method (N = 8), to obtain the Reduction ratio 38%.

A 400 Gbit Ethernet Core Enabling High Data Rate Streaming from FPGAs to Servers and GPUs in Radio Astronomy

Abstract The increased bandwidth coupled with the large numbers of antennas of several new radio telescope arrays has resulted in an exponential increase in the amount of data that needs to be recorded and processed. In many cases, it is necessary to process this data in real time, as the raw data volumes are too high to be recorded and stored. Due to the ability of GPUs to process data in parallel, GPUs are increasingly used for data-intensive tasks. In most radio astronomy digital instrumentation (e.g., correlators for spectral imaging, beamforming, pulsar, fast radio burst and SETI searching), the processing power of modern GPUs is limited by the input/output data rate, not by the GPU's computation ability. Techniques for streaming ultra-high-rate data to GPUs, such as those described in this paper, reduce the number of GPUs and servers needed, and make significant reductions in the cost, power consumption, size, and complexity of GPU based radio astronomy backends. In this research, we developed and tested several different techniques to stream data from network interface cards (NICs) to GPUs. We also developed an open-source UDP/IPv4 400 GbE wrapper for the AMD/Xilinx IP demonstrating high-speed data stream transfer from a field programmable gate array (FPGA) to GPU.

Leveraging Massive MIMO for Enhanced Energy Efficiency in High-Density IoT Networks

Maximizing energy efficiency (EE) in massive multiple-input multiple-output (MIMO) systems, while supporting the rapid expansion of Internet of Things (IoT) devices, is a critical challenge. In this paper, we delve into the intricate operations geared toward enhancing EE in such complex environments. To effectively support a multitude of IoT devices, we adopt a strategy of heavy reference signal (RS) reuse, and in this circumstance, we formulate the EE metrics and their corresponding inverses to determine pivotal operational parameters. These EE-centric parameters encompass factors such as the number of service antennas in the base station (BS), the number of IoT devices, and permissible coverage extents. Our objective is to calibrate these parameters to meet a predefined EE threshold, ensuring optimal system performance. Additionally, we recognize the indispensable role of Peak-to-Average Power Ratio (PAPR) reduction techniques, particularly in multicarrier systems, to further enhance EE. As such, we employ clipping-based PAPR reduction methods to mitigate signal distortions and bolster overall efficiency. Theoretical EE metrics are derived based on formulated signal-to-interference-plus-noise ratios (SINRs), yielding insightful closed-form expressions for the operational parameters. Leveraging two distinct EE metric models, we undertake parameter determinations, accounting for the levels of approximation. Intriguingly, our analysis reveals that even simplified models exhibit remarkable applicability in real-world scenarios, with a minimal margin of error. The results not only underscore the practical applicability of our theoretical constructs but also highlight the potential for significant EE enhancements in massive MIMO systems, thereby contributing to sustainable evolution in the IoT era.

Design and operation of a whole-body MRI scanner without RF shielding.

To investigate how a clinical MRI system can be operated without an RF cabin. Receive interference cancellation via signal processing and active transmit emission suppression are evaluated for mitigating image artifacts and achieving electromagnetic compatibility. A clinical whole-body MR scanner with B0 = 0.55 T was placed in an environment without RF shielding. The signals of 16 additional auxiliary receive antenna elements outside the scanner bore were used to remove interference from the wanted MR signal via a dedicated sidelobe cancellation algorithm. In vivo measurements with different MR sequences were performed in the presence of ambient RF noise and additional artificial interferers, covering brain and abdominal applications. To investigate the transmit electromagnetic compatibility, simulations with different auxiliary transmit antenna arrangements and body coil loading conditions were performed, and their emission suppression performance in the far field was evaluated. The MR sidelobe cancellation algorithm successfully removed interference artifacts up to more than 3 times of the wanted MR image energy; difference images carried little to no visibly removed wanted signal. According to the simulations, electromagnetic compliance can be achieved with 32 or more auxiliary transmit antennas. The number and placement of sensor points are essential. A whole-body 0.55T MR scanner can be operated without RF shielding. Image artifacts from external interference can be removed, requiring only a modest number of auxiliary antennas. Electromagnetic compliance appears to be achievable using active transmit cancellation, but the required hardware efforts may not be practical.

Electromagnetic compatibility study of trackside antenna array miniaturization in the subway tunnel

Abstract To improve the compatibility of the subway tunnel’s electromagnetic environment and reduce the radiation impact of trackside antennas on subway workers. This paper proposes a miniaturized dual-band trackside antenna array by using metamaterial units. Its operation bandwidth is 2.33 ∼ 2.56 GHz and 3.24 ∼ 3.45 GHz, which could simultaneously satisfy the signal cover demands of the communications-based train control (CBTC) and the civil 5G wireless communication system. The proposed miniaturized antenna array has a maximum gain of 14.4 dBi and a maximum channel capacity of 13.9 bps Hz−1 at a signal-to-noise ratio (SNR) of 20 dB, which can effectively improve the quality of wireless communication systems. The number of trackside antennas with a single operation frequency band is reduced, and the distance between the antennas is enlarged at the same time. Besides that, we analyze the radiation impact on the tunnel electromagnetic environment of the proposed trackside antenna array. In particular, the electromagnetic dose absorbed by the human model of a tunnel worker is quantized. The results show that the electric field strength in the tunnel reduces by 4.11% at least after antenna array miniaturization, and the specific absorption rate (SAR) absorbed by the worker model’s trunk, skull, brain, heart, and liver is reduced by a maximum of 19.02%, 33.16%, 28.27%, 41.75%, and 74.54%, respectively, further lowering the human electromagnetic exposure risk. Therefore, a miniaturized trackside antenna array could reduce the interference from other radiation sources in the tunnel while providing a new idea for electromagnetic protection for subway workers.

The Degree of Branching of Serum IgG N-glycans as a Marker of Advanced Endometriosis.

Endometriosis is a gynecological disease for which the diagnostics are difficult and often invasive; therefore, non-invasive diagnostic methods using sensitive and specific parameters present in easily available body fluid such as blood serum are needed for the detection of this disease. Our study aimed to answer the question of whether there are any differences between women with advanced endometriosis (AE), patients with gynecological diseases other than endometriosis (NE), and healthy women (control) in terms of the number of antennas of N-glycans from serum IgG. The degree of branching of IgG N-glycans was determined by a modified lectin ELISA with biotinylated lectin Con A (Canavalia ensiformis agglutinin) recognizing α-linked mannose, specifically reacting with biantennary N-glycans. The PHA-L/Con A ratio was calculated from the obtained N-glycan reactivities with Con A and PHA-L (Phaseolus vulgaris leucoagglutinin, specific to tri- and/or tetra-antennary N-linked glycans). The expression of Con A-reactive biantennary N-glycans in serum IgG was significantly lower in the control group than in the NE group (p = 0.045). The values of the PHA-L/Con A ratio were significantly higher in the NE group than in the AE and control groups (p = 0.019 and p = 0.022, respectively). The PHA-L/Con A ratio could be taken into account as a parameter helpful in the non-invasive diagnosis of advanced endometriosis, thus differentiating this disease from other gynecological diseases with an inflammatory background.

Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

SummaryWireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi‐input multi‐output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search‐based lion optimization algorithm (HRS‐LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE‐BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO‐AE‐BRNN with 13%, RSO‐AE‐BRNN with 16%, RSA‐AE‐BRNN with 17%, and LA‐AE‐BRNN with 40% respectively.

A Hybrid Network Integrating MHSA and 1D CNN–Bi-LSTM for Interference Mitigation in Faster-than-Nyquist MIMO Optical Wireless Communications

To mitigate inter-symbol interference (ISI) caused by Faster-than-Nyquist (FTN) technology in a multiple input multiple output (MIMO) optical wireless communication (OWC) system, we propose an ISI cancellation algorithm that combines multi-head self-attention (MHSA), a one-dimensional convolutional neural network (1D CNN), and bi-directional long short-term memory (Bi-LSTM). This hybrid network extracts data features using 1D CNN and captures sequential information with Bi-LSTM, while incorporating MHSA to comprehensively reduce ISI. We analyze the impact of antenna numbers, acceleration factors, wavelength, and turbulence intensity on the system’s bit error rate (BER) performance. Additionally, we compare the waveform graphs and amplitude–frequency characteristics of FTN signals before and after processing, specifically comparing sampled values of four-pulse-amplitude modulation (4PAM) signals with those obtained after ISI cancellation. The simulation results demonstrate that within the Mazo limit for selecting acceleration factors, our proposal achieves a 7 dB improvement in BER compared to the conventional systems without deep learning (DL)-based ISI cancellation algorithms. Furthermore, compared to systems employing a point-by-point elimination adaptive pre-equalization algorithm, our proposal exhibits comparable BER performance to orthogonal transmission systems while reducing computational complexity by 31.15%.

Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from O(K3) to O(K2) making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate.

Classification with electromagnetic waves

AbstractThe integration of neural networks and machine learning techniques has ushered in a revolution in various fields, including electromagnetic inversion, geophysical exploration, and microwave imaging. While these techniques have significantly improved image reconstruction and the resolution of complex inverse scattering problems, this paper explores a different question: Can near‐field electromagnetic waves be harnessed for object classification? To answer this question, we first create a dataset based on the MNIST dataset, where we transform the grayscale pixel values into relative electrical permittivity values to form scatterers and calculate the electromagnetic waves scattered from these objects using a 2D electromagnetic finite‐difference frequency‐domain solver. Then, we train various machine learning models with this dataset to classify the objects. When we compare the classification accuracy and efficiency of these models, we observe that the neural networks outperform others, achieving a 90% classification accuracy solely from the data without a need for projecting the input data into a latent space. The impacts of the training dataset size, the number of antennas, and the location of antennas on the accuracy and time spent during training are also investigated. These results demonstrate the potential for classifying objects with near‐field electromagnetic waves in a simple setup and lay the groundwork for further research in this exciting direction.

Adaptive Hybrid Deep Learning Based Effective Channel Estimation in MIMO-Noma for Millimeter-Wave Systems with an Enhanced Optimization Algorithm

The channel estimation is crucial in the “millimeter Wave (mmWave) Massive Multiple-Input MultipleOutput (MIMO) and Non-Orthogonal Multiple Access (NOMA)” devices. Hybrid beamforming techniques are employed nowadays to minimize the complexity and equipment price. However, the absence of digital beam forming in mmWave affects the dynamic range and accuracy of the channel estimation. Previous research has concentrated mainly on predicting narrow-band mmWave channels using deep learning networks, as the wideband channels of mmWave create a considerable range and noise issue. Accurate channel estimation in the MIMO system is challenging because of the increased number of antennas and radio frequency (RF) chains. MIMO system communications using mmWave are frequently chosen because of their massive spectrum resources. Therefore, it is essential to tackle the obstacles obtained in the standard channel estimation framework by developing a MIMO-NOMA network with the help of deep learning methods Hence, this paper proposes an efficient hybrid deep learning model for channel estimation in MIMO-NOMA for mmWave systems. At first, the channel estimation is carried out using the Adaptive Hybrid Deep Learning (AHDL) model, where it combines both Autoencoder and Recurrent Neural Network (RNN). Here, the parameters are optimized using the Improved Red-tailed Hawk Algorithm (IRHA). Later the hardware cost and system complexity are reduced by performing the hybrid beam-forming process. Numerical results show that the proposed channel estimation and pilot estimation process outperforms the state-of-the-art approaches.