Generation Wireless Communication Systems Research Articles

A compact quad element MIMO CPW fed ultra‐wideband antenna for future wireless communication using machine learning optimization

SummaryIn this article, a compact quad element coplanar waveguide (CPW) fed ultra‐wideband (UWB) multiple input multiple output (MIMO) antenna for future generation wireless communication system using a machine learning (ML) optimization approach is presented. The proposed antenna is used for 5G new radio (n46/n77/n47/n78/n48/n79), Wi‐Fi 5, Wi‐Fi 6, and dedicated short range communications (DSRC) services, vehicle to infrastructure (V2I), vehicle to vehicle (V2V), and vehicle to network (V2N) in the entire operating frequency band. It is operating from 3.2 to 11.85 GHz. The bandwidth is 8.65 GHz, and the percentage of impedance bandwidth is 115%. The comparative analysis between dual and quad elements are presented. It is optimized through the various ML model K‐nearest neighbor (KNN), extreme gradient boosting (XGB), artificial neural network (ANN), and random forest (RF). The KNN ML model achieved a higher accuracy of 93%, and it accurately predicted the S parameters of the suggested UWB antenna. The MIMO parameters are calculated and found within the acceptable limits. There is a strong correlation between the simulated and measured results. Hence, the suggested antenna is a suitable candidate for future wireless communication systems.

SIW MIMO antenna with high gain and isolation for fifth generation wireless communication systems

Abstract The possibilities for further development of mmWave fifth-generation wireless systems and their deployment in our lives are immense and will make human life simpler and faster. Fifth-generation wireless communication is opening up new possibilities for development in the near future. High data rates, low latency, and enormous bandwidth are the most essential factors. These factors are at the heart of making quality healthcare, smart education systems, and the fast and efficient distribution of energy. In account of the aforementioned benefits, 5G wireless systems are primarily made possible by high gain MIMO antenna with minimal coupling. The integration of traditional wave-guide components with planar circuits, on the other hand, is a difficult issue. SIW solves this problem by providing planar alternatives for waveguide-based devices like filters, antennas, and couplers. The SIW antenna has less interference, low radiation loss, excellent isolation, and outstanding shielding properties as compared with the conventional microstrip antenna. The proposed SISO SIW antenna has gain of 9.05 dBi with 87.54 % radiation efficiency. The −10 dB impedance bandwidth is 27.79–28.19 GHz. The SIW MIMO antenna has gain of 9.05 dBi with 81 % radiation efficiency. The antenna has an isolation 52 dB, and an ECC is 0.65 × 10−7 at 28 GHz. The proposed SIW MIMO antenna’s MEG lies below −3 to −5 dB for Gaussian and isotropic medium. The CCL for the proposed MIMO antenna is 0.29–0.36 bits/s/Hz in the operating band. This article presents a high-gain and high-isolation substrate integrated waveguide (SIW) MIMO antenna using orthogonal diversity for increasing isolation between the radiating elements. The designed MIMO antenna is operating in the 28 GHz band (27.42–28.79 GHz), which comes under the n261 (FR2 5G-New Radio frequency band) band used for 5G wireless communication.

Analysis of prospects for the use of the terahertz frequency range for special purpose wireless communication networks.

Frequencies from 100 GHz to 3 THz are promising ranges for a new generation of wireless communication systems due to wide spectrum bands. These frequencies also offer the potential for revolutionary applications made possible by new thinking and advances in devices, circuits, software, signal processing and systems. But the transition to the real use of the terahertz range as a frequency resource in the provision of communication services for applications in special purpose wireless communication networks is accompanied by many problems of spectrum management, network analysis, design and optimization of the architecture of these networks. The novelty of this work is due to the fact that, despite the fact that in recent years a number of review publications by domestic and foreign authors have been published, but in the aspects of development and specifics of possible applications of wireless communication networks of the terahertz frequency range in solving problems with power structures and forces special operations were not considered. The authors focused special attention on highlighted domestic developments in the field of wireless access systems in the terahertz range. The purpose of the work is an analytical review of the features of the transition to the real use of the terahertz frequency range as a frequency resource in the provision of communication services by resource-intensive applications by wireless communication networks for promising applications in solving special problems by power structures. In the work, a systematic analysis and classification of the main areas of promising applications of wireless communication networks of the terahertz frequency range in solving problems by special-purpose power structures is made, and scientific research tasks are given for the transition to the real use of the terahertz frequency range as a frequency resource in the provision of communication services for special purpose applications. The author's proposals developed in this work for promising applications of terahertz communication networks by special operations forces or special purpose forces are the theoretical basis for creating a methodological apparatus for substantiating recommendations for the selection of promising applications when using terahertz communication networks by special operations forces when performing special purpose tasks.

A Survey of PAPR Techniques Based on Machine Learning.

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks.

Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment.

Many concepts for future generations of wireless communication systems use coherent processing of signals from many distributed antennas. The aim is to improve communication reliability, capacity, and energy efficiency and provide possibilities for new applications through integrated communication and sensing. The large bandwidths available in the higher bands have inspired much work regarding sensing in the millimeter-wave (mmWave) and sub-THz bands; however, the sub-6 GHz cellular bands will still be the main provider of wide cellular coverage due to the more favorable propagation conditions. In this paper, we present a measurement system and results of sub-6 GHz distributed multiple-input-multiple-output (MIMO) measurements performed in an industrial environment. From the measurements, we evaluated the diversity for both large-scale and small-scale fading and characterized the link reliability. We also analyzed the possibility of multistatic sensing and positioning of users in the environment, with the initial results showing a mean-square error below 20 cm on the estimated position. Further, the results clearly showed that new channel models are needed that are spatially consistent and deal with the nonstationary channel properties among the antennas.

A Non-Stationary Channel Model for 6G Multi-UAV Cooperative Communication

This paper proposes a novel three-dimensional (3D) non-stationary multiple-unmanned aerial vehicle (multi-UAV) cooperative channel model for sixth generation (6G) wireless communication systems. In the proposed multi-UAV cooperative channel model, the channel impulse response (CIR) of sub-channel between each UAV and ground station (GS) is calculated, including line-of-sight (LoS), non-LoS (NLoS), as well as ground reflection transmissions. The relative distance and transmission relationship among sub-channels between multi-UAVs and GS are further modeled and analyzed. To simultaneously capture the cooperative non-stationarity of multi-UAV cooperative channels in space and time domains, a novel cooperation-based space-time (S-T) non-stationary algorithm is developed based on the birth-death (BD) process for the first time. Meanwhile, the UAV-related parameters, e.g., the height and 3D movement of UAVs, are considered. Some important channel statistical properties, such as space-time-frequency correlation function (STF-CF), Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are obtained. Based on the simulation results, multi-UAV cooperative channel statistical properties are analyzed. Finally, the simulation results match well with the ray-tracing results, which validates the accuracy of the proposed multi-UAV channel model.

ENERGY EFFICIENT OPERATION FOR NEXT GENERATION MASSIVE MIMO NETWORK

For the next generation of wireless networks, massive multiple-input, multiple-output (MIMO) is regarded as a potential technique. Base stations (BS) are provided with a relatively large number of antenna components in massive MIMO in order to increase spectral efficiency (SE) and energy efficiency (EE). Meanwhile, EE of BSs are becoming an increasingly important concern for communications operators in order to maintain profitability and reduce the overall negative impact on the environment and economic concerns for wireless network users. In this paper, an energy-aware, load-adaptive operation approach is proposed for massive MIMO for the next generation of wireless communication systems. Energy-aware, load-adaptive network operation has been identified as a promising method for improving the energy efficiency of telecommunication networks. Our research indicates that at optimal sumSE, fully load-adaptive massive MIMO may greatly increase network EE by up to 8.666%, 43.317%, and 69.367% for high, medium, and low load factors, respectively, when compared to traditional non-load-adaptive massive MIMO.

One‐Bit Distributed Sparse Spectrum Sensing Based on the DQA‐ZA‐LMS and DQA‐RZA‐LMS Algorithms Over Adaptive Networks

In this paper, we proposed the distributed quantization and sparsity aware zero attracting least mean square (DQA‐ZA‐LMS) and its reweighted version (DQA‐RZA‐LMS) algorithms that can perform sparse spectrum sensing with the lowest power possible. The usage of the quantization aware diffusion adaptive networks has recently been proposed and they can be used in many possible mobile communicative applications. The sparsity aware feature of the proposed algorithm can help the network to track and estimate sparse random vectors that are shown to be the case with the spectrum of the new generation wireless communication systems such as 4G, 5G, 6G, and beyond. The spectrum sensing is considered in this paper to be performed by small cell eNode Bs (SC‐eNBs) for the 4th generation long term evolution (LTE) and the next generation eNB (ng‐eNB) networks for the 5th and 6th generation mobile communication systems that are scattered in an area collecting distributed quantized data from the environment and working collaboratively to estimate the sparse spectrum vectors. Our findings show that in comparison with the nonquantized version of the distributed ZA‐LMS (DZA‐LMS) and distributed regularized ZA‐LMS (DRZA‐LMS) algorithms, our proposed schemes perform considerably well using the quantized data and also reduce power consumption.

Blockage loss and shadow fading behavior of millimeter‐wave signals due to human bodies at 28 GHz

AbstractAs the millimeter‐wave (mm‐Wave) spectrum is considered to be an essential enabler to the fifth generation (5G) wireless communication systems. Human movements are one of the most significant factors that cause transient blockage in indoor mm‐wave channels. In this letter, human blockage measurements and shadow fading statistics due to human body movements in an indoor office environment are reported for the 28 GHz band. The effect of human bodies on the channel is measured for several scenarios including a variety of population and using diverse antenna heights. The reported shadow fading statistics include both the duration and the depth of the blockage fade, and accordingly, we propose several empirical models that cater for such blockage events. The findings reported in this letter could improve the modeling of indoor radio channels at 28 GHz bands by considering the presence of humans, as well as their movements.

Advanced lifting wavelet transform and V‐degree polynomial‐based channel estimation in MIMO‐GFDM

SummaryThe future generation of wireless communications systems uses the generalized frequency division multiplexing (GFDM) due to its viable candidate waveform to perform multiple user scheduling. It is a non‐orthogonal waveform susceptible to intercarrier and intersymbol interference (ISI); still, it offers flexible pulse shaping that enhances the efficiency of user scheduling. Here, to accomplish spatial diversity, the multiple‐input multiple‐output (MIMO) is incorporated with GFDM to enhance the performance; there is also an extra inter‐antenna interference problem that limits the model's performance. The issue concerning inter‐antenna interference can be resolved by adopting pilot‐based information transfer. The detection of a signal at the receiver based on the minimum mean square error has the issue of computational complexity while enhancing the estimation quality of a detector. Hence, a low‐complexity channel estimation technique is proposed in this research using the V‐degree polynomial‐based channel estimation technique, wherein the cubic order computation is reduced to square order. Also, the proposed adaptive pulse shaping technique, wherein the filter coefficient is optimized using the gazelle optimization algorithm (GOA) to provide optimal pulse shaping filter parameters by employing bit error rate (BER) as the objective function. The performance of a proposed V‐degree polynomial‐based channel estimation is analyzed based on various assessment measures like BER and MSE and acquired the minimal values of 5.75E‐05 and 2.07E‐05, respectively.

High-performance THz Metallic Axial Mode Helix Antenna with Optimised Truncated Hollow Cone Ground Plane for 6G Wireless Communication System

The Terahertz (THz) band antenna configuration operates in the 0.1–10 THz frequency range and offers a stable performance for future 6th Generation (6G) wireless communication systems. However, the available metallic axial mode helix antenna designs exhibit a peak directivity of lower than 18 dBi within 0.5–1 THz, making it inappropriate to be applied in wireless communication systems. Therefore, this study proposed a high-performance THz metallic five-turn axial mode helix antenna with an optimised truncated hollow cone ground plane for 6G wireless communication systems. Following the creation of the proposed antenna design using cost-effective copper (annealed), the truncated hollow cone ground plane of the THz axial mode helix antenna was optimised via simulation in a Computer Simulation Technology Microwave Studio (CST MWS) software and a verification of the proposed THz antenna design in Analysis System High-Frequency Structure Simulator (Ansys HFSS) software for a fair comparison. Based on the results, the proposed THz metallic axial mode helix antenna with optimised truncated hollow cone ground plane recorded an impedance bandwidth of 0.46 THz, Fractional Bandwidth (FBW) of 61.33% for |S11| ≤ -10 dB, and a maximum directivity and realised gain of 21.8 dBi and 21.5 dBi at 0.85 THz, respectively. Within the 0.5–1 THz, the proposed optimised THz antenna design achieved an outstanding performance, including an FBW of more than 50%, excellent directivity of higher than 15.8 dBi, radiation efficiency of greater than 87%, circular polarisation, and low-profile helix turns. In short, the proposed THz metallic axial mode helix antenna with optimised truncated hollow cone ground plane design is appropriate for various THz 6G wireless applications.

Implementation of In-Band Full-Duplex Using Software Defined Radio with Adaptive Filter-Based Self-Interference Cancellation

For next generation wireless communication systems, high throughput, low latency, and large user accommodation are popular and important required characteristics. To achieve these requirements for next generation wireless communication systems, an in-band full-duplex (IBFD) communication system is one of the possible candidate technologies. However, to realize IBFD systems, there is an essential problem that there exists a large self-interference (SI) due to the simultaneous signal transmission and reception in the IBFD systems. Therefore, to implement the IBFD system, it is necessary to realize a series of effective SI cancellation processes. In this study, we implemented a prototype of SI cancellation processes with our designed antenna, analog circuit, and digital cancellation function using an adaptive filter. For system implementation, we introduce software-defined radio (SDR) devices in this study. By using SDR devices, which can be customized by users, the evaluations of complicated wireless access systems like IBFD can be realized easily. Besides the validation stage of system practicality, the system development can be more effective by using SDR devices. Therefore, we utilize SDR devices to implement the proposed IBFD system and conduct experiments to evaluate its performance. The results show that the SI cancellation effect can reach nearly 100 dB with 10−3 order bit error rate (BER) after signal demodulation. From the experiment results, it can be seen obviously that the implemented prototype can effectively cancel the large amount of SI and obtain satisfied digital demodulation results, which validates the effectiveness of the developed system.

Model-Based Deep Learning Receiver Design for Rate-Splitting Multiple Access

Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.

An Evaluation of the Signal to Noise Ratio (SNR) of Next Generation Wireless Communication Systems using Large Intelligent Surfaces: Deep Learning Approach

The existing data rate must be greatly increased in order to support the numerous applications of the next generation communication systems. Using Large Intelligent Surfaces (LIS), which are a panel with mounted reflective components, is one way to address this problem. Their primary function is to divert the electromagnetic signal to the intended user. As a result, the received signal's strength and reception quality both improve, improving the Quality of Service (QoS). Machine Learning algorithms have been used to implement LIS in a number of ways, including channel estimation and the calculation of phase shifts (discrete), to mention a few. Here, it is suggested that the Signal to Noise Ratio (SNR) for a LIS-supported communication system be assessed using a Deep Learning (DL) model. On the DL model, the effects of the SGD, Adam RMSProp Adadelta and Adamax optimizers are investigated. The Mean Squared Error (MSE) loss function is taken into account. The Adam optimizer offers the highest level of precision, making it preferable to other optimizers. The SNR for 10 users is measured using the suggested DL model with Adam Optimizer. The outcomes of this work is contrasted with the SNR estimate by an existing technique that calculates SNR based on LIS size and the location of transmitter and receiver with an accuracy of 93.5%. The simulation results indicate that the accuracy is increased to 96% using the suggested DL model which attains an average error of 3.6.

Hypergraph-Based Joint Channel and Power Resource Allocation for Cross-Cell M2M Communication in IIoT

Industrial Internet of Things (IIoT) is the leading application scenario of the fifth generation wireless communication systems (5G) and beyond. Non-orthogonal multiple access (NOMA) has become a key technology for 5G due to its high spectrum efficiency. In this paper, a joint channel and power resource allocation problem is investigated for cross-cell IIoT networks with aim of maximizing sum rate of NOMA-based M2M pairs and cellular Machine Devices (cMDs). Since joint channel and power resource allocation problem is an NP-hard problem, the original problem is transformed into a hypergraph model to optimize channel and power resource allocation. Then, a channel allocation algorithm based on hypergraph colouring theory is proposed, and an alternative power allocation algorithm is presented. Next, some properties of hypergraph colouring and complexities are analysed. Finally, simulation results demonstrate that the proposed algorithm outperforms the graph-based algorithm in terms of sum rate, and also improves the spectrum efficiency significantly.

Higher Order MIMO Antenna Design Using Capacitively Coupled Meandered Loop Element for 5G Smartphone Applications

In this paper, a compact 10-element multiple-input multiple-output (MIMO) antenna system for fifth generation (5G) wireless communication system is presented. The antenna structure has a T-shaped feeding element and a reduced size capacitively coupled meandered loop radiating element. The proposed MIMO antenna system is operating at 3.5 GHz. The MIMO antenna element is self-isolated and it has a compact size of 13.45×6 mm2. It exhibits very good isolation of more than 15 dB without using any external decoupling structure like a neutralization line or slot technique. Furthermore, performance parameters like total efficiency are around 40-64%, envelope correlation coefficient (ECC) is below 0.02, while gain of all antennas is between 3-3.4dBi. The performance of the proposed MIMO antenna is also verified through the measurement of its fabricated prototype. In this paper, a compact 10-element multiple-input multiple-output (MIMO) antenna system for fifth generation (5G) wireless communication system is presented.

Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation.

A deep convolutional-LSTM neural network for signal detection of downlink NOMA system

Non-orthogonal multiple access (NOMA) techniques have drawn much attention for massive connectivity, heterogeneous data traffic with ultra-low latency requirements, and ultra-high bandwidth efficiency for the future generation of wireless communication systems. This paper suggests a deep convolutional-long short-term memory (ConvNet-LSTM) aided receiver for detecting signals in the downlink NOMA system. The proposed technique implicitly estimates channel state information and recovers the transmitted symbol directly. The Bayesian approach optimizes the hyperparameters for the deep ConvNet-LSTM. The proposed learning approach is significantly more efficient than the conventional signal detection method, successive interference cancellation (SIC)-based minimum mean square error (MMSE) technique, other deep learning (DL)-based approaches, long short-term memory (LSTM), and convolutional neural networks (CNNs). It simultaneously explores the characteristics of both spatial and temporal features of the received signal for increasing the detection probability of the system. The simulations discuss the advantages of the deep ConvNet-LSTM-based receiver design for downlink NOMA communication systems in terms of bit error rate (BER), achievable rate, and mean squared error (MSE). Furthermore, the robustness of the proposed deep ConvNet-LSTM approach is evaluated for its superiority to other techniques.

Design methodology for reconfigurable multi-radio platform for future generation wireless systems

Owing to the continuous development of technology, the future generation of wireless communication systems is increasingly becoming more complex to support a spectrum of new and existing services and standards. Consequently, the need for flexible and adaptable radios has become more prominent. Radio signals can be modulated using various methods. Radio modulation recognition refers to a series of processes for acquiring information regarding modulation types of non-cooperative received signals. It plays an important role as a pre-demodulation process required to ensure accuracy, safety, and effectiveness in recovering information. This study proposed a reconfigurable multi-radio platform comprising a reconfigurable antenna and receiver board. The antenna was reconfigurable to facilitate operation in different frequency bands controlled via the receiver board using bias voltages. The receiver board was programmable using custom-built software. Moreover, it can detect the modulation scheme and accurately demodulate signals that are modulated in one of three common modulation schemes: amplitude modulation, frequency modulation, and phase modulation. This solution offers the advantages of reduced size, cost, and power consumption because it uses one hardware system as opposed to the deployment of several individual radios. Moreover, the platform presents an adaptable, cost-effective solution for future-generation wireless networks and a flexible and robust terminal that can operate under changing communication environments.

Reliable hybrid deep learning technique for an effective spectrum sensing in cognitive radio

Spectrum sensing will be an essential component in developing cognitive radio networks, which will be an essential component of the subsequent generation of wireless communication systems. Over the course of several decades, a great deal of different strategies, including cyclo-stationary, energy detectors, and matching filters, have been put up as potential solutions. Obviously, each of these methods comes with a few of negatives that you have to take into consideration. When the Signal-to-Noise Ratio (SNR) changes, energy detectors work poorly; cyclo-stationary detectors are technically sophisticated; and employing matching filters needs experience with Primary User (PU) signals. Researchers have recently been devoting a great deal of attention to Machine Learning (ML) and Deep Learning (DL) algorithms as a result of the potential uses that these algorithms may have in the development of exceptionally accurate spectrum sensing models. The capacity to learn from data in a way that traditional learning algorithms are unable to has led to the rise in prominence of these types of algorithms. The Hybrid Model of Improved Long Short Term Memory with Improved Extreme Learning Models (HILSTM-IELM), to be more specific, is what is being suggested since it reduces the amount of energy that is used during data transmission as well as the range and the duty cycle. Because of this, the disadvantage in existing methodology, proposed technique reduced to a certain level in energy consumption. In the last step of this analysis, the performance of the HILSTM-IELM-based spectrum sensing is compared to that of a variety of different methods that are currently in use. According to the findings of recent studies, the spectrum sensing method that was created provides superior performance to that of technologies in terms of the accuracy, sensitivity, and specificity of data transmission systems.