High Data Research Articles

Intelligent reflecting surface‐aided D2D‐enabled coordinated multipoint NOMA systems over Nakagami‐m fading channels

SummaryThe future wireless communication networks (beyond 5G) are intended to provide seamless connectivity, higher data rates, and lower latency. The intelligent reflecting surface (IRS) promises extended network coverage. The non‐orthogonal multiple access (NOMA) technique provides higher spectral efficiency and supports a larger number of users. In general, the cell‐edge users far from the base station are affected by severe fading, which may result in an outage. To overcome this issue, we propose an IRS‐aided device‐to‐device (D2D)‐enabled two cells coordinated NOMA system in which a common cell‐edge user acts as D2D receiver. The cell‐edge user receives the signals from near users, IRSs, and both base stations to achieve better diversity. We analyze the outage probability of cell‐edge user, while all the fading channels are assumed Nakagami‐ and obtain a closed‐form expression at higher signal‐to‐noise ratios. We develop an algorithm to find the ergodic capacity of the cell‐edge user. We validate the mathematical expressions with simulation results and see a substantial improvement in outage performance over the existing systems.

Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO

ABSTRACT The millimetre-wave (mmWave) communication satisfies the demand for high data rates due to the characteristic of wide bandwidth. Using massive multiple-input multiple-output (MIMO) technology, a significant propagation loss of mmWave communication is effectively compensated. However, it is challenging to provide a specialised radio frequency chain for each antenna due to the constrained physical area with closely spaced antennas and prohibitive power consumption in mmWave massive MIMO systems. This paper presents novel approaches for effective channel estimation and hybrid precoding in mmWave communication systems. To address the challenges of channel estimation, a convolutional neural network (CNN) is utilised, and network parameters are optimised using enhanced whale optimization algorithm (EWOA). The proposed CNN-based channel estimation method aims to accurately estimate the channel in mmWave systems with enhanced efficiency and reduced complexity. By training CNN using EWOA optimisation algorithm, the network parameters are fine-tuned to improve accuracy and generalisation capability of channel estimation process. Furthermore, hybrid precoding is achieved using adaptive radial-basis function neural networks (adaptive RBFNNs) which enables efficient precoding while minimising complexity. Moreover, the adaptive RBFNN approach determines the optimal precoding weights based on channel state information, resulting in a improved performance and a reduced computational overhead. The performance analysis is validated using the MATLAB/Simulink software and offers to provide effectual and reliable mmWave communication systems, facilitating the realisation of high-speed and high-capacity wireless networks.

Exploring data plane updates on P4 switches with P4Runtime

The development and roll-out of new Ethernet standards increases the available bandwidths in computer networks. This growth presents significant advantages enabling novel applications. At the same time, the increase introduces new challenges; higher data rates reduce the available time budget to process each packet. This development also impacts software-defined networks. Their data planes need to keep up with the increased traffic rates. Nevertheless, the control plane must not be ignored; fast reaction times are necessary to handle the increased rates handled by data planes efficiently.In our work, we analyze the interaction of a high-performance data plane and different implementations for the control plane. We selected a P4 switching ASIC as our data plane. For the control plane, we investigate vendor-specific implementations and a standardized implementation called P4 runtime. To determine the performance of the control plane, we introduce a novel measurement methodology. This methodology allows measuring the delay between the initiation of rule updates on the control plane and their application on the data plane. We investigate the behavior of the data plane, its performance and non-atomicity of updates. Based on our findings, we apply different optimization strategies to improve control plane performance. Our measurements show that neglecting the control plane performance may impact network behavior due to delayed updates, but we also show how to minimize this delay and, thereby, its impact. We have released the experiment artifacts of our study including experiment scripts and measurement data.

Study on mode properties of GaAs-based hybrid plasmonic terahertz waveguides

Terahertz (THz) fields are increasingly being used to address the critical challenges associated with achieving high data rates and rapid communication. In this study, a hybrid plasmonic THz waveguide is designed and analysed operating in the 2.5–3.5 THz frequency range. The waveguide is constructed using gallium arsenide as the high-refractive-index core, surrounded by aluminium arsenide and silver placed on a high-density polyethylene (HDPE) substrate. Graphene is strategically positioned between the HDPE layers to enhance light confinement. The mode properties of the proposed waveguide are simulated with Comol Multiphysics using the finite-element method and show unique characteristics. Observation of the simulated results at 2.5–3.5 THz reveals a high effective refractive index of 3.79, a maximum effective mode area of 1.88 mm2, a high birefringence of 0.2, a low dispersion of 0.10 ps THz−1 cm−1, a high mode field diameter of 15.8 mm, a high beat length of 123 mm and a low confinement loss of 1.79 × 10−9 mm−1. These features make the proposed waveguide suitable for applications in photonic integrated circuits for THz communications.

Intelligent architecture and platforms for private edge cloud systems: A review

The development of cloud, fog, and edge computing has led to great advances in reducing latency and saving bandwidth, and these methods have therefore been broadly applied in various domains, including healthcare, transportation, and the Internet of Things (IoT). Traditional edge computing solutions have proven to be insufficient in fulfilling the demanding prerequisites of low latency and high data rates. Additionally, publicly available edge cloud solutions fail to meet the required standards for ensuring privacy protection. Consequently, Private Edge Cloud Systems (PECSs) have garnered attention as a prospective solution owing to their capacity to mitigate privacy risks and their significant computing capacity. PECS research has seen significant growth, but there is a lack of detailed review of its issues, approaches, and applications in the literature. To explore the potential application value of PECS, this paper provides a systematic review of intelligent platforms and architecture for PECSs. Specifically, an overview of the fundamental characteristics of PECSs is provided. Second, we classify intelligent platforms and architectures and analyze their implementation techniques and realization methods. Third, we discuss four specific application scenarios. Finally, promising future research directions are discussed. The findings of this research show that PECSs can effectively meet the requirements for low latency and privacy protection and are a fertile domain for further research.

Sign of slant receiver for skewed alpha-stable noise shift keying-based random communication system

Purpose In contrast to traditional communication systems, slower data rate has always remained a weak link for non-traditional random communication systems (RCSs), which use alpha-stable (a-stable) noise as a carrier. This paper aims to introduce a fast receiver for skewed a-stable noise shift keying (SkaSNSK)-based RCSs. Design/methodology/approach The introduced receiver is based on the sign of slant estimator (SoSE), which provides rapid estimation of the skewed a-stable random noise signals (RNSs) received from the additive white Gaussian noise channel. The SoSE-based receiver minimizes the number of samples required to extract the encoded information from the received RNSs. This is achieved by manipulating the antipodal properties of the slant/skewness parameter of the a-stable carrier. Hence, a high data rate with relatively low complexity is guaranteed. Findings In comparison with the previously introduced sinc, logarithmic and modified extreme value method-based receivers, the proposed SoSE-based receiver also achieves improved bit error rate (BER) along with the better covertness values so that the essence of security provided by SkaSNSK-based RCSs remains intact. Research limitations/implications Because of the selected range of the associated parameters of the a-stable noise as a carrier, the BER vs MSNR results are may lack applicability for the complete range of values. Therefore, further research is required to produce results in different ranges. Practical implications The study includes implications for the hardware development based on the proposed communication scheme. Originality/value It can be seen that the paper fulfils the desired need of a fast receiver design for RCS.

Efficient in Vivo Neural Signal Compression Using an Autoencoder-Based Neural Network.

Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio.

60 GHz millimeter-wave indoor propagation path loss models for modified indoor environments

The 60 GHz band has been selected for short-range communication systems to meet consumers’ needs for high data rates. However, this frequency is attenuated by obstacles. This study addresses the limitations of the 60 GHz band by modifying indoor environments with square loop (SL) frequency selective surfaces (FSSs) wallpaper, thereby increasing its utilization. The SL FSS wallpaper response at a 61.5 GHz frequency has been analyzed using both MATLAB and CST Studio Suite software. ‘Wireless InSite’ is also used to demonstrate enhanced wave propagation in a building modified with SL FSSs wallpaper. The demonstration is applied to multiple input multiple output system to verify the effectiveness of FSSs on such systems’ capacity, as well as the effect of the human body on capacity. Simulation results presented here show that modifying a building using SL FSS wallpaper is an attractive scheme for significantly improving the indoor 60 GHz wireless communications band. This paper also presents and compares two large-scale indoor propagation path loss models, the close-in (CI) free space reference distance model and the floating intercept (FI) model. Data obtained from ‘Wireless InSite’ over distances ranging from 4 to 14.31 m is analyzed. Results show that the CI model provides good estimation and exhibits stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared to the FI model.

5G Wireless Technology: Spectrum Sharing and Heterogeneous Networks

Abstract: This paper addresses the critical challenge of 5G network cell planning, emphasizing the essential role of estimating the number of cells or base stations required for a given area and user bandwidth. The proposed model explores four scenarios with varying network parameters to determine the optimal base station deployment. The increasing demand for 5G networks necessitates a focus on small cells or femtocells, offering advantages such as consistent coverage, power adjustment, energy efficiency, and higher data rates. However, deploying excessive femtocells may lead to unnecessary handovers, requiring optimization strategies. The study introduces an analytical model for heterogeneous cellular networks, integrating fourth and fifth-generation systems. The model, represented by a two dimensional Markov Chain, employs a novel decomposition approach for analyzing system performance measures. Validation is conducted through simulation and simultaneous equation systems. The proliferation of mobile devices and data traffic necessitates dense 5G network deployments, with small cells gaining traction due to their versatile coverage, power adaptability, energy efficiency, and higher data rates. However, an excessive deployment of small cells can lead to frequent handovers, prompting the need for enhanced handover strategies and coexistence with other technologies in the 5G landscape. The paper concludes by highlighting the impending need for practical implementation testing of 5G systems, emphasizing the significant role of small cell deployments. It discusses an initiative focusing on testing small cellenabled operator business models in real-world scenarios, anticipating the pivotal role of small cells in future 5G systems.

Overloaded Spread Spectrum OFDMA in Outdoor Environment in the More Interleaving Scenario

The advancement of mobile communication technology is driven by innovations or modifications in the latest Radio Access Techniques to meet user demands and enhance capacity. This paper focuses on improving spectral efficiency and average throughput using an OFDM-based multiple access technique. Specifically, we have chosen Overloaded Spread Spectrum OFDMA for this study. Our research findings indicate that implementing a 24% overload leads to an approximate 33% increase in spectral efficiency. This significant improvement demonstrates the potential of Overloaded Spread Spectrum OFDMA in optimizing the performance of mobile communication systems. By increasing spectral efficiency, we can support a larger number of users and higher data rates, which are crucial for the evolving demands of modern mobile networks. This study highlights the importance of adopting advanced access techniques to sustain the growth and efficiency of mobile communications

Relationship of Self-Efficacy to The Knowledge of Women of Childbearing Age in Early Detection of Cervical Cancer

Introduction: Cervical cancer, is well known in the world, and this disease is a malignant disease that attacks the reproductive organs of a woman that can no longer be tolerated and about which all women should be aware. Therefore, female self-efficacy is crucial for women of childbearing age to advance knowledge and particularly maximize the reproductive health of these women. Aim: Search for data and results from this study, the author wants to determine the significance and the possible correlation between the effectiveness of women of childbearing age and their knowledge of the performance of cervical cancer screening using the VIA method, so that cervical cancer will not be affected. Method: In this quantitative study, researchers used a correlational descriptive design where the approach used was cross-sectional. In this study, researchers used a sample of 97 respondents in the consultation area of the Harapan Raya Community Health Center in Riau Pekanbaru Province. Sampling is carried out by researchers with the greatest care, following predetermined inclusion criteria and using targeted sampling techniques. Likewise, data analysis is carried out using the chi-square test method. Results: Demographic data from this study indicate that 53.6% of respondents were young adults. The majority of 43.3% of respondents have a college degree and the highest data, mothers are only housewives, accounting for 82.5% of respondents. Through the process of univariate analysis, it was determined that a majority of respondents, specifically 62.9%, had a significantly low level of self-efficacy. Furthermore, it was found that 55.7% of the respondents had a sufficient level of knowledge. The use of the chi-square test in data analysis revealed a notable association between self-efficacy and knowledge of women of childbearing age regarding cervical cancer screening using the VIA method, which is inexpensive, effective, and immediate results are known. This association was supported by a p-value of 0.002, indicating statistical significance. Conclusion: Based on the research results, it was found by researchers that there is a remarkable correlation between self-efficacy and the level of knowledge of women of childbearing age regarding cervical cancer screening using the VIA method, which is a good result to recommend. Recommendation: Researchers recommend that this study be used as a method to increase self-efficacy and knowledge of women of childbearing age in cervical cancer screening using the VIA method.

Miniaturized high signal-to-noise ratio optical air data system using a compact multi-axis parallel transceiver for airborne application

We have developed a miniaturized multi-channel parallel optical air data system with high signal-to-noise ratio for airborne application. In the system, we designed a fiber amplifier with multi-channel high-energy output that was respectively used as the transmitting signals and a compact multi-axis transceiver with an entrance pupil diameter of 70 mm that was used to receive multi-channel signals simultaneously. We demonstrated the performance of our system both on ground and on board. On ground, the measured line-of-sight speed had an average error of 0.02 m/s and a standard deviation of 0.15 m/s. On board, the standard deviation between the true air speed, angle of attack, and angle of sideslip measured by our system and a commercial Swiss air data system was 1 kt, 0.68°, and 0.54°, respectively, and those standard deviation between our system and a system with the same design but employing multiple single-axis telescopes with entrance pupil diameter of 30 mm was 0.34 kt, 0.36°, and 0.28°, respectively. The signal-to-noise ratio of our system was 4.5 times higher than that of the system with small single-axis telescopes. Our system is very promising for airborne applications because of its small volume, high signal-to-noise ratio, and high data rate.

A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods.

Effect of Irrigation Regimes and Applied Nitrogen Levels on Growth and Physiological Responses of Ryegrass

A field experiment was conducted at the Instructional-cum-Research (ICR) Farm, Assam Agricultural University, Jorhat. The experiment was laid out in split-plot design with three replications. The treatments consisted of five levels of irrigation in main plot viz., I0:Rainfed, I1: Irrigation at critical growth stages, I2: Irrigation at IW:CPE ratio of 1.0, I3: Irrigation at IW:CPE ratio of 1.2 and I4: Irrigation at IW:CPE ratio of 1.4 along with four levels of N- N0: 0 kg N/ha, N1: 30 kg N/ha, N2: 60 kg N/ha and N3: 90 kg N/ha in sub- plots. The soil of the experimental site was sandy loam in texture, medium in organic carbon, available N and available P2O5, acidic in reaction and low in available K2O. The result revealed that the highest leaf area index (LAI) recorded in irrigation at IW:CPE ratio of 1.4 at all the three cuts respectively during both the years. The crop growth rate (CGR), relative growth rate (RGR) and net assimilation rate (NAR) of ryegrass as influenced by different irrigation regimes were found to be non-significant at 30 DAS while at later growth stages i.e. 45 DAS, 60 DAS, 90 DAS and 120 DAS were significantly influenced during both the years. The application of irrigation at IW:CPE of 1.4 produced higher value of CGR, RGR but the highest NAR was recorded in rainfed treatment. The data on LAI as influenced by different N levels was found to be significant in all three cuts. Application of 90 kg N/ha recorded the highest LAI. The CGR, RGR and NAR as influenced by different N levels were found to non significant at 30 DAS but significantly influenced at later growth stage i.e. 45 DAS, 60 DAS, 90 DAS and 120 DAS during both the years. The highest data on CGR and RGR were recorded in 90 kg N/ha but the highest NAR was found in 0 kg N/ha.

Multiband multiple‐input multiple‐output antenna design with high‐resolution circularly polarized wireless communication

SummaryA design problem of multiband multi‐input, multi‐output (MIMO) antenna arrays for maximal side lobe level lessening with constraint of a fixed beam width is dealt with. Multiband MIMO antenna is the most promising solution in a communication system, due to its maximal speed, higher data rate, and short‐range communication. Conversely, MIMO faces several complications including designing a proper antenna for a specific bandwidth and lesser antenna efficacy. In this manuscript, a high‐resolution CP multiband MIMO (HS‐CPMB‐MIMO) antenna design for wireless communication is proposed. MIMO antenna plays a vital part in the present wireless technology at MSA. A multiband microstrip line‐fed two‐port hybrid CPMIMO antenna is intended and fabricated. The proposed microstrip feed antenna contains a tapering structure, and the inverted L‐shaped slots in the radiating element are opened on both sides to present notches in sub‐6‐GHz frequencies. The inverted L‐shaped slots is positioned in the middle of asymmetric Z‐shaped radiating elements for enhancing the isolation among the two ports. The invention of a multiband MIMO antenna with four CP bands has an efficient ECC value because of a large L‐shaped slot in the ground as well as a protruding Z‐shape ground stub embedded with circular rings to have highly isolated cells. Finally, the proposed HS‐CPMB‐MIMO antenna design is implemented by utilizing simulation software CST Microwave Studio, and it is evaluated with the performance metrics. The simulation outcomes of the proposed HS‐CPMB‐MIMO antenna provide 36.34%, 57.36%, and 25.33% higher BW, 24.13%, 15.96%, and 21.22% higher gain compared with existing methods.

5G IoT Networks

The abstract delves into how the advent of 5G is revolutionizing the Internet of Things (IoT) across various industries, with a particular focus on its ability to provide ultra-low latency, high data rates, and support for a massive number of connected devices, enabling real-time data transmission. It highlights the mutually beneficial relationship between 5G and IoT, which is driving innovation in sectors such as smart cities, healthcare, agriculture, and transportation. The abstract also addresses important challenges like security and interoperability, emphasizing the need for strategic deployment of 5G-enabled IoT solutions. Furthermore, it explores the potential of edge computing and AI-driven analytics to unlock new possibilities in IoT implementations. In summary, the abstract underscores the transformative power of 5G in shaping IoT ecosystems, offering unparalleled opportunities for innovation, efficiency, and value creation.

An Improved PAPR Reduction Scheme using Green OFDM

OFDM has become popular method to be applied in data communication as it has high data rate. Selective Mapping OFDM (SLM OFDM) is introduced to over-come the OFDM’s disadvantage which is high PAPR. SLM OFDM offers PAPR reduction by selecting OFDM waveform with the lowest PAPR among many copies of waveform candidates, U. As the world become more digitalized and demanding, Green OFDM shows up with more candidates, U^2/4, without making computation more complex. More candidates will promote more choices with lower PAPR values compare to the SLM OFDM. In the recent years, re-searchers have come out with a new improved Green OFDM version 2 with more waveform candidates to be chosen, U^2. The improved Green OFDM ver-sion 2 scheme will produce lower PAPR values compare to the SLM OFDM and the original Green OFDM. Thus, technologies with higher data transmission are able to be created.

Mitigation of atmospheric losses using dual‐polarized 16‐QAM MIMO technique in free‐space optical system in various weather conditions

SummaryThe technique to overcome the challenge while implementing the fiber optical link is investigated using an alternative optical link called a free‐space optical (FSO) link with advanced modulation and signal processing techniques. A dual‐polarized 16‐quadrature amplitude‐modulated signal is transmitted over single and multi‐FSO channels under various atmospheric attenuation conditions. High bandwidth and data rates (up to 10 Gbps), unlicensed spectrum (above 300 GHz), and simplicity of deployment are significant benefits of the FSO system. However, the weather conditions of haze, rain, and fog cause signal attenuation when the optical carrier wave is transmitted over free space, and atmospheric turbulence is a challenge to FSO implementation, which degrades the transmission performance of optical signals for long‐distance communication. For 120 Gbps data transmission, the performance of a multi‐input and multi‐output (MIMO) FSO system is evaluated by observing the bit error rate (BER) and received signal constellation diagram. Optical signal‐to‐noise ratio (OSNR) parameters were iterated to determine the optimal value for back‐to‐back, single‐input, single‐output (SISO), and MIMO systems. Moreover, we examined the proposed system's output with recent articles, including MIMO, multiplexing, and various modulation techniques. The comparison of results shows that the suggested MIMO‐FSO model has enhanced the performance with the lower BER as less than 3.8 × 10−3 (log of BER is −2.42) forward error correction (FEC) limit for 18.7 km link range in clear air with 0.54 dB/km attenuation, 4.8, 4.5, 1.35, and 0.57 km under haze, light fog, medium, and heavy fog with 4.6, 6.9, 28.9, and 75 dB/km attenuations.

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

A low-complexity crest factor reduction design for 5G NR sub-6 GHz digital front-end with restricted peak regrowth

In recent years, orthogonal frequency division multiplexing (OFDM) has gained widespread adoption in communication technologies, exemplified by long-term evolution (LTE), 5th generation new radio (5G NR sub-6 GHz), and others. These technologies are instrumental in meeting the increasing demands for high data rates, throughput, and reliable downlink network access. However, a notable challenge associated with OFDM is the high peak-to-average power ratio (PAPR), imposing limitations on the efficiency of power amplifiers that often operate with significant back-offs. To mitigate this issue, the widely employed crest factor reduction (CFR) technique aims to enhance efficiency. This paper introduces a novel CFR methodology utilizing mixed and multi-iterative signal distortion-based techniques. Our approach is versatile, demonstrating effectiveness for low-bandwidth signals like LTE, as well as large-bandwidth signals reaching up to 100 MHz, as seen in 5G NR FR1. Also, digital up-sampling used to achieve the required high sample rate causes the issue of peak regrowth even after the required PAPR reduction achieved by CFR processing, which defeats the purpose of CFR. Our proposed design offers a solution to this challenge, enabling operation at a low data rate before digital up-sampling while effectively mitigating peak regrowth. Importantly, the implementation significantly reduces FPGA resource utilization, alleviating the complexity associated with multiple-input multiple-output (MIMO) radio systems. Demonstrating compliance with 3rd generation partnership project (3GPP) specifications for 5G NR on a real-time testbed, our approach stands as a practical and efficient solution for contemporary communication systems.