5G Channel Research Articles

A Network Security Transmission Model Based on Improved Sparrow Search Algorithm

With the development of the socio-economic landscape, the demand for electricity supply is steadily increasing. The application of 5G networks in this field has gradually alleviated the supply-demand response issues associated with large-scale distributed power sources. However, the broadcast nature of 5G channels raises concerns about secure data transmission. Addressing these challenges, this paper establishes a collaborative communication model for large-scale Demand Response (DR). Using the distributed power source network as a foundation, a communication system based on Power Line Communication (PLC) and Generic State Machine (GenSM) is proposed. Building on this, a self-adaptive data transmission algorithm based on the sparrow algorithm is proposed. This algorithm uses swarm intelligence for efficient energy management and secure transmission. Simulation results show that, compared to traditional Multiple-Input Multiple-Output (MIMO) models, under specific conditions, the proposed model achieves an energy efficiency improvement of 131% and 69%.

Maritime 5G channel properties along the ferry route at the N41 band

Maritime 5G channel properties along the ferry route at the N41 band

Machine learning-based detection of the man-in-the-middle attack in the physical layer of 5G networks

Fifth generation communication networks (5G) has received a great deal of attention from academia and industry alike, which will enable a wide variety of vertical applications by connecting heterogeneous devices and machines. Assessing availability and reliability in many circumstances and environments is critical. Researchers have recently focused on investigating and analyzing new multimedia networks with artificial intelligence (AI) technologies to achieve higher data rates and secure communication traffic between parties. User information privacy and security are of vital importance and of growing concerns that present evolving challenges to overcome in preventing attacks. Man-in-the-middle (MITM) attack is considered one of the most common attacks, where an attacker can impersonate one of the parties in a communication system to steal user data or forge the malicious data. Due to the limitation of using conventional cryptographic techniques for mobile networks and similar systems, new methods have been introduced to validate and authenticate transmitted signals dynamically, depending on the physical layer. In this paper, we present the distance-time directional delay (DTDD) model to detect the MITM attack in a variety of contexts and scenario. Indoor hotspots (InH) and urban micro-cellular (UMi) propagation environments were investigated to verify the reliability of the proposed approaches using realistic 5G millimeter-wave configurations and system setups. Simulations have been constructed based on the mmWave 5G channel simulator tool NYUSIM, in conjunction with a collection of machine learning algorithms (ML) including the extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) as the core of the presented models and methodologies. Numerical simulations results produced a detection accuracy approaching 100% in the InH environment scenario, whereas for UMi environment scenario, a detection accuracy approaching 99% was attained.

5G Channel Estimation using Deep Learning

The abstract focuses on the integration of 5G channel estimation and the vulnerability of deep learning models, specifically in the context of OFDM signals, while employing a student-teacher model architecture. Channel estimation is a crucial aspect of 5G communication systems, ensuring reliable data transmission in dynamic wireless environments. Simultaneously, the advent of deep learning introduces susceptibility to adversarial attacks, where malicious inputs can deceive the model's predictions. This paper explores the intricate relationship between 5G channel estimation and deep learning vulnerabilities, emphasising the application of a student-teacher model to enhance system robustness. By delving into the nuances of OFDM signals, the study aims to provide a comprehensive understanding of how these elements intertwine, offering insights into potential security enhancements for next-generation wireless communication systems.

Enhanced voice over 5G transmission with unequal error protection and generative adversarial networks

SummaryVoice services have progressed significantly since the launch of mobile phones and have evolved from analog technology to digital ones. Since the launch of 5G, voice over 5G (Vo5G) will slowly take over legacy mobile technologies to provide better voice services. Nevertheless, interruptions and noise will continue to affect speech communication over wireless networks, ensuing in a reduced Quality of Service (QoS). Three enhanced transmission schemes for Vo5G are proposed. The first proposal benefits from the difference in weight of the bits of internet Low Bitrate Code (iLBC) coded packets to offer them diverse degrees of protection i.e. Unequal Error Protection (UEP). UEP enhances the reception quality by allowing the more significant bits to be decoded with greater reliability during 5G transmission. The second and third schemes substitute the usual Quadrature Amplitude Modulation (QAM) constellation with a Statistical QAM (SQAM) one. The second scheme arranges the significant bits on the SQAM constellation and the remaining bits onto the conventional QAM arrangement. For the third scheme, all the bits are arranged on the SQAM constellation. Generative Adversarial Networks (GAN) were incorporated into the third scheme as the third scheme provided better gains over the first and second schemes. GAN was used to synthesize and enhance parts of the speech file that were lost during transmission over the 5G channel resulting in a gain in Segmented Signal to Noise Ratio (SSNR). The addition of GAN boosted the gain obtained by the third scheme by 1.45 dB with 16‐QAM at a code rate of 1/3.

Evaluation of Rain Attenuation Models for 5G Frequencies: Regions in Turkey

Rain attenuation in 5G satellite communications needs to be considered in planning the communication settings due to the fact that it causes significant attenuation especially above 10 GHz and in low elevation angle Earth-space connection paths. Even though various rain attenuation models have been developed by using measured meteorological data, it is important to know especially which model provides better results for 5G frequencies since direct satellite communication over 5G channels is planned to be used in the near feature. Hence, in this study, prominent rain attenuation models such as the Simple Attenuation Model, Garcia-Lopez model, and ITU-R P.618-14 model were examined. Using these models, rain attenuations occurring at elevation angles of 30°, 60°, and 85° were identified in three different cities in Turkey. Average annual precipitation data from the General Directorate of Meteorology of Turkey for the years 1930-2023 were utilized. The studies on various rain models using different parameters revealed that the attenuation in the 470-780 MHz frequency band is 2.849x10-4 dB, whereas there is a 12.63 dB attenuation in the range of 40.5-43.5 GHz. It was observed that the attenuation increases further with frequencies above 43.5 GHz. Therefore, it is evident that rain attenuation is a crucial parameter in satellite communication systems, particularly in 5G and beyond. This study has revealed that ITU-R P.618-14 model is the best suitable one for high elevation angles and at high frequencies (above 40GHz). As for lower elevation angles Garcia model is more appropriate.

Применение физического уровня 5G NR в космических коммуникациях, оценка эффективности.

The propagation channel is significantly modified in the case of 5th Generation New Radio radio access technology (5G NR) in space communication systems. In particular, large delays occur with respect to the terrestrial propagation channel, and signal attenuation increases, resulting in a lower signal-to-noise ratio (SNR). The Doppler shift of the carrier frequency is also significantly increased due to the high velocity of the spacecraft (SC) relative to the user equipment (UE). The aim of the work is to investigate the decoding capability of the physical channels and signals of the 5G NR system in case of its application in satellite communication systems (SCS), as well as to calculate the performance of such a system to assess the applicability of 5G NR in SCS. The ability to correctly decode signals and channels depending on SNR is evaluated using mathematical modelling of 5G NR signal and channel shaping in conjunction with the Quasi Deterministic Radio Channel Generator (QuaDRiGa) model. The research within the paper draws conclusions on the applicability of 5G NR radio access technology in a space communications environment.

A Wideband If Receiver Chip for Flexibly Scalable mmWave Subarray Combining and Interference Rejection

Large-scale multibeam phased array systems suffer from interbeam interference (IBI) that should be canceled either in the analog or digital domain. In wideband systems such as fifth generation (5G), interference rejection over a wide bandwidth is challenging to achieve, not only due to nonidealities of the receiver chain but also due to the properties of the radio channel. This article presents a scalable IBI cancellation (IBIC) scheme at intermediate frequency (IF) using an IF receiver (IF-RX) chip. The IF-RX provides the flexibility of not just interference rejection between the subarrays but also wideband signal combining over multiple subarrays. It also provides wideband filtering before the analog-to-digital converter (ADC) to support 5G channel bandwidths of up to 800 MHz, high linearity, and low noise figure. A calibration method is proposed to find the cancellation coefficients for the IF-RX in measurements. Furthermore, a simplified over-the-air (OTA) IBIC model for analyzing rejection bandwidth limitations is presented. Interference rejection performance is demonstrated through the OTA measurements using 5G new radio (5G NR) signals. In the OTA measurements, 34–37-dB rejection was achieved for 50–100-MHz signals, while error vector magnitude (EVM) requirements of the 5G standards were met with good margins. Finally, the interference rejection over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\ttimes 100$</tex-math> </inline-formula> MHz carrier aggregated 5G NR waveform was demonstrated.

Enhancing channel estimation accuracy in polar-coded MIMO–OFDM systems via CNN with 5G channel models

In this paper, a novel channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO–OFDM) systems in fifth generation (5G) networks is proposed, which leverages the power of convolutional neural networks (CNNs) along with the application of polar coding. By employing polar encoding at the transmitter and polar decoding at the receiver, the proposed system enhances error correction performance, reliability, and data transmission rate in 5G networks. A novel optimized CNN-based channel estimation technique, named CNN-CENet, has been integrated into the polar-coded MIMO–OFDM receiver. This integration aims to address the interference and noise challenges in 5G systems. The proposed channel estimation method utilizes multiple layers of CNN to accurately characterize the properties of the real channel by incorporating least squares (LS) estimations. Hence, the proposed CNN-CENet provides an accurate estimation of the channel coefficients, enabling efficient signal detection at the receiver. The MATLAB simulation has been conducted using deep learning and 5G toolboxes to obtain results for various channel parameters. These results are then compared with the LS and minimum mean square error (MMSE) methods for performance evaluation. The proposed model surpasses the performance of the LS and MMSE methods, achieving a significant 95.6% reduction in mean square error (MSE) compared to LS and a substantial 59.7% reduction compared to MMSE. Additionally, it delivers notable bit error rate (BER) reductions of 59.8% and 53.2% for LS and MMSE, respectively, in low mobility scenarios, and 57.4% and 48.5% reductions in high mobility scenarios. This improvement leads to enhanced reliability and overall system efficiency.

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (&gt;6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.

Investigation of the Effects of Atmospheric Attenuation and Frequency on MIMO Channel Capacity

The efficiencies of 5G channels, which are highly affected by atmospheric attenuation, are still being investigated. The effects of frequency and atmospheric attenuation parameters such as humidity, temperature, rain, and pressure were investigated in this study using the NYUSIM program. The spatial consistency mode of the NYUSIM channel simulator was turned off, and unnormalized channel capacities were calculated at 28, 45, 60, and 73 GHz frequencies. According to the research results, the rain rate was the atmospheric attenuation parameter that significantly affected MIMO channel capacity. In contrast, the humidity percentage had the slightest impact. The frequency where the channel capacity is most affected by the four determined atmospheric attenuation parameters is 60 GHz, while the frequency where it is least affected is 28 GHz. The study found that using frequencies with high atmospheric attenuation reduces communication efficiency significantly. Furthermore, rain rate has a significant impact on 5G channel performance.

SDR implementation and real-time performance evaluation of 5G channel coding techniques

This article presents a study on the real-time performance and practical implementation of several 5G channel coding candidates including low-density parity check (LDPC), polar, turbo, and convolutional channel codes. The experiment was conducted using a wireless communication transceiver system implemented on a software defined radio (SDR) platform called Universal Software Radio Peripheral (USRP). The system was implemented using Matlab software and included scrambling, channel coding, interleaving, and required synchronization and estimation blocks. Various sets of system and channel parameters were tested, including the impact of information block sizes, code lengths, and modulation orders on the performance of each channel coding technique. The study also evaluated the impact of hardware impairments and the required blocks to mitigate them. The results indicated that polar codes had the best performance, while other codes had trade-offs for various system parameters. The study concludes that polar codes with cyclic redundancy check-successive cancellation list (CRC-SCL) are the most reliable 5G channel coding candidates.

Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

AbstractThis paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination.

Deep Learning Aided 5G Channel Estimation

Abstract: Wireless communications involves the transfer of voiceand data without a cable or wires. It uses orthogonal frequencydivision multiplexing, also known as a multicarrier transmission technique. In multiple input multiple output (MIMO) wireless communication (4G and 5G technologies), channel estimation is crucial. Multiple antennas are used at both the transmit and receive sides ofa MIMO system to increase spectral efficiency and reliability. In 5G channel estimation is performed to improve the accuracy of the received signal. Least-squares estimation is a cheap method with relatively large channel estimationerrors, but it is supported in this work by using a new channel estimation method that leverages deep learning. LS (least squares) and MMSE (minimum mean squared error) are two popular traditional approaches to channel estimation, but deep learning provides much more accurate results than previous channel estimation methods.

FPLA: A Flexible Physical Layer Authentication Mechanism for Distributing Quantum Keys Securely via Wireless 5G Channels

Quantum Key Distribution (QKD) is popular for establishing a native secure quantum communication network. However, existing QKD networks are built via classical wired fiber channels; it is difficult to distribute quantum keys directly into mobile phones, and no effective candidate solution is available yet. This paper presents a novel Flexible Physical Layer Authentication (FPLA) mechanism that exploits the unique characteristic of wireless signals from mobile phones to securely distribute quantum keys via wireless 5G channels. In particular, a 5G Up-Link Sounding Reference Signal (SRS)-based transmission model is developed to capture and extract the unique characteristic, which is then used to distribute quantum keys. Moreover, the model could lose accuracy due to SRS variations introduced by 5G Multiuser Multiple-Input Multiple-Output (MU-MIMO), so a dimensional transformation residual network is designed to classify legitimate and malicious user equipment (UE). An average authentication accuracy of 96.8% is proved by FPLA in multiple experiments in a 3 dB Signal-to-Noise Ratio (SNR) test environment with a training dataset of 300 samples per malicious UE. Simulation results show that FPLA is able to adapt to antenna diversity in 5G MU-MIMO systems.

A Simple Efficient Lightweight CNN Method for LOS/NLOS Identification in Wireless Communication Systems

Line-of-sight (LOS)/Non-line-of-sight (NLOS) identification in wireless communication systems is crucial for positioning, mobile communication, and wireless sensing. Conventional LOS/NLOS identification approaches generally employ channel features, such as the Rice factor, kurtosis of the received power, etc. However, these approaches have limited identification accuracy and cannot function efficiently in a dynamic environment. The deep learning approach can show better performance; however, it has high computational complexity issues. In this study, we first demonstrate that the channel impulse response (CIR) of the 5G channel outperforms channel frequency response employing convolutional neural networks (CNNs) for LOS/NLOS identification. Then, we propose a simple efficient lightweight CNN-based LOS/NLOS identification approach. The proposed one-dimensional CNN network can effectively extract CIR features and has cross-scenario adaptability. Additionally, for the first time, 5G experimental data were employed for performance verification. The experimental results demonstrate that the proposed approach can achieve an identification accuracy of 93.31% at a computational cost of 1.35 M FLOPs and has higher identification accuracy and speed than existing MWT-CNN deep learning approaches. The code is available at https://github.com/boa2004plaust/SEL-CNN.

5G Channel Estimation Based on Whale Optimization Algorithm

This paper presents a novel approach, based on the whale optimization algorithm (WOA), for channel estimation in wireless communication systems. The proposed method provides a means to accurately estimate the wireless channel, while not requiring the statistical characteristics of the channel. This method uses the WOA to search for the best channel statistical characteristics toward the ultimate goal of having the smallest bit error rate (BER). The proposed approach is aimed at enhancing the efficiency of pilot-based OFDM systems under frequency-selective fading channels used in the performance testing of 5G New Radio gNodeB. In terms of BER and mean square error (MSE), the performance of the proposed WOA-based channel estimation algorithm is evaluated and compared with the conventional least square (LS) and minimum mean square error (MMSE) algorithms. The simulation results demonstrate that the proposed algorithm provides highly competitive performance over the MMSE algorithm and significantly outperforms the LS algorithm in a variety of system configurations. Since the requirement on prior channel statistics information makes the MMSE algorithm impractical or extremely complex, the proposed WOA-based channel estimation algorithm should be a suitable and promising candidate for dealing with channel estimation problems. The simulation framework is implemented in MATLAB and available upon request.

5G Frequency Standardization, Technologies, Channel Models, and Network Deployment: Advances, Challenges, and Future Directions

The rapid increase in data traffic caused by the proliferation of smart devices has spurred the demand for extremely large-capacity wireless networks. Thus, faster data transmission rates and greater spectral efficiency have become critical requirements in modern-day networks. The ubiquitous 5G is an end-to-end network capable of accommodating billions of linked devices and offering high-performance broadcast services due to its several enabling technologies. However, the existing review works on 5G wireless systems examined only a subset of these enabling technologies by providing a limited coverage of the system model, performance analysis, technology advancements, and critical design issues, thus requiring further research directions. In order to fill this gap and fully grasp the potential of 5G, this study comprehensively examines various aspects of 5G technology. Specifically, a systematic and all-encompassing evaluation of the candidate 5G enabling technologies was conducted. The evolution of 5G, the progression of wireless mobile networks, potential use cases, channel models, applications, frequency standardization, key research issues, and prospects are discussed extensively. Key findings from the elaborate review reveal that these enabling technologies are critical to developing robust, flexible, dependable, and scalable 5G and future wireless communication systems. Overall, this review is useful as a resource for wireless communication researchers and specialists.

Analysis of 5G channel loss and its influencing factors in substation based on ray tracing algorithm

Abstract5G (fifth generation) signals have small coverage and poor stability, and 5G channels are prone to loss in substations densely populated with metal equipment. Due to the particularity of the spatial layout in the substation, traditional physical or statistical channel models cannot accurately calculate the 5G channel loss in the substation. Therefore, this paper introduces the basic idea of ray tracing algorithm for studying ray propagation. Based on the geometric optics theory of ray propagation, the effective channel path of 5G signal in substation is found. Through the electromagnetic reflection theory and consistent diffraction theory, the energy loss caused by signal reflection and diffraction is calculated. Combined with the relationship between the free space loss of 5G signal on the transceiver path and the change of electric field, the solution method of 5G channel loss in substation is deduced. According to this method, (the influence of signal receiver height, 5G antenna height and its spatial position in horizontal and vertical directions on 5G channel loss of substation is studied in turn. By optimizing the layout of communication facilities according to the influence law, the reliability of 5G channel in substation can be realized).

Analysis of MIMO Channel Capacity at 28/73 GHz with NYUSIM Channel Simulator

Analyzing the channel models in the mm-wave bandwidth is critical for 5G system performance. This study investigated the effects of 28 GHz and 73 GHz frequencies, the number of transmitting and receiving antennas, and LOS/NLOS parameters on 5G channel capacity using the NYUSIM channel simulator. As a result of the analysis, changing from a 2x2 to a 64x64 antenna structure for 28 GHz increased capacity by 29.78 times for LOS and 26.91 times for NLOS. When changing the MIMO configuration from 2x2 to 64x64 at 73 GHz, the channel capacity rises 36.88 times for LOS and 29.00 times for NLOS. With a 64x64 antenna structure, the channel capacity for 28 GHz and LOS is 8.81 times higher than for 73 GHz, and it is 12.56 times higher for NLOS. For the 28 GHz 64x64 structure and LOS condition, the channel capacity is 215.69 times higher than the NLOS condition, while this value is 307.7 times for 73 GHz.