Future Wireless Communication Systems Research Articles

Decoding of polar-coded OFDM systems over multipath fading channels with interleaving and channel state information

In this paper, we investigate the decoding performance and corresponding decoding strategies for polar-coded orthogonal frequency division multiplexing (OFDM) system operating under multipath fading channels. Multipath fading channels lead to frequency selective fading of the OFDM signal, resulting in varying levels of noise interference across different subcarriers after equalization. Furthermore, frequency selective fading induces burst errors in the equalized information, thereby significantly degrading the decoding performance. To address these issues, we propose two schemes in this paper to enhance the decoding performance of polar-coded OFDM system. Firstly, we integrate the channel state information of each subcarrier into the generation of soft information for the polar codes decoder, in order to alleviate the degradation of decoding performance caused by variations in signal-to-noise ratio across subcarriers during OFDM demodulation. Secondly, we introduce a packet interleaving design after the polar codes encoder to enhance the robustness of polar codes against burst errors. Simulation results demonstrate that the combined application of these two decoding strategies significantly outperforms the conventional scheme in terms of transmission performance over multipath fading channels. The findings of this study suggest that, within the context of future wireless communication systems utilizing Polar-OFDM, the concurrent implementation of our two proposed decoding strategies is essential for achieving robust error correction performance in the OFDM system.

Near-zero TCF 11.6-GHz lamb wave resonator based on 128°Y-cut LiNbO3

In this paper, a high-frequency lamb wave resonator (LWR) with near zero temperature coefficient of frequency (TCF) is designed, fabricated, and measured. The reported resonator is of a bi-layer structure consisting of lithium niobate (LiNbO3 or LN) and silicon dioxide (SiO2), and lithographically patterned aluminum (Al) inter-digitated electrode fingers on top of the bi-layer structure. By adjusting the thickness ratio of LN and SiO2 layers, both the electromechanical coupling (k2) and the TCF, including both TCF at resonant frequency (TCFr) and TCF at anti-resonant frequency (TCFa), of the thickness shear (TS) type LWR are optimized. Experimental results, which are in excellent agreement with theoretical analysis, show that the fabricated 11.6-GHz LWR achieves a k2 of 12.2%, with TCFr and TCFa being −4.2 and −5.4 ppm/K over a temperature range from 30 °C to 85 °C, respectively, demonstrating huge potential in applications for future wireless communication systems above 10 GHz.

Physical Layer Security in RIS-NOMA-Assisted IoV Systems with Uncertain RIS Deployment

Reconfigurable intelligent surfaces (RISs), as an emerging radio technology, are widely used to expand the transmission distance and structure cascade channels to improve the performance of communication systems. However, based on the continuous development of wireless communication technology, as Internet of Vehicles (IoV) communication systems assisted with RIS and non-orthogonal multiple access (NOMA) can improve the overall transmission rate and system performance, the physical layer security (PLS) issue has gradually attracted attention and has become more and more important in the application of the system. In this paper, our aim is to investigate the potential threats for PLS, where an RIS is utilized in order to improve the security of wireless communications. In particular, we consider the non-fixed RIS location and wiretapping behavior of eavesdroppers on the data in this work, and further analyze the maximum safe-rate for above location assumptions. Numerical results reveal that RIS provides significant advantages on security performance, as well as providing a useful reference for the security design of future wireless communication systems, which verify the correctness of our analysis and the effectiveness of the proposed scheme.

Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum

Recent developments in conductive and dielectric multi-material additive manufacturing have generated significant interest for their potential to enhance the performance of electronic devices. Lights-out digital manufacturing, a sophisticated multi-material printing technique, facilitates the seamless sintering of silver nanoparticles and acrylate inks, enabling the creation of functional electronic devices. In this study, the LDM process is utilized to prototype intricate 3D metasurface designs. A comprehensive analysis of the properties of the printed materials confirms the practicality of this multi-material printing approach. The research examines various element distributions within multi-metal layer structures, including carbon, phosphorus, oxygen, sulfur, and fluorine, to investigate the interfaces between conductive and dielectric structures. In addition, the conductivities of silver nanoparticle inks at different curing temperatures are studied to identify optimal printing conditions. Both the conductive and dielectric inks exhibit precisely controlled particle sizes and exceptional stability, which are critical for accurate additive manufacturing. Using this sophisticated printing solution, the paper presents a broadband 3D metasurface engineered to independently manipulate two distinct orbital angular momentum states in the V-band (60–70 GHz) and W-band (79–90 GHz), suitable for high-speed wireless communications. The integration of deep neural networks helps reduce the phase coupling between the frequency bands, enabling independent control of the OAM states. The structure of the dual-band metasurface features four distinct silver layers yet is fabricated on a single ultra-thin planar substrate, demonstrating its potential for applications in future wireless communication systems.

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.

Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems

SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.

Multi‐band millimetre wave indoor directional channel measurements and analysis for future wireless communication systems

AbstractSingle‐input single‐output (SISO) three‐dimensional (3D) wideband indoor directional measurements collected in a factory environment and an office environment at 38 and 70 GHz are presented. 3D single‐input multiple‐output (SIMO) dual polarised measurements with 1 × 2 antenna configurations were also carried out in a meeting room, a conference room, and an office room at the 60 GHz band. The measurements cover both azimuth and elevation by rotating the directional antenna (RDA) at the receiver side. Different statistical channel parameters such as power delay profile, power angle profile, root‐mean‐square delay spread, angular spread, and path loss were estimated for different possible antenna orientations between the transmitter and the receiver, which include line‐of‐sight, obstructed line‐of‐sight, and non‐line‐of‐sight. The polarisation effects on path loss models and the delay and angular spread models based on the surface area of the environment are studied. The results will be valuable for the design of indoor millimetre wave cellular networks.

RIS for 5G and Beyond: A Bibliometric Survey

The rapid advancement of 5G technology has sparked significant interest and investment in research and development across various domains. As the world transitions towards the era of 5G and beyond, understanding the research landscape becomes imperative for guiding future innovations and investments. This article presents a comprehensive bibliometric survey of research in the realm of reconfigurable intelligent surface (RIS) for 5G and its evolutionary successors. Through systematic analysis of scholarly publications, this survey maps the trends, key contributors, influential works, and emerging themes within the field. By synthesizing data from diverse sources, including academic journals and conference proceedings, this survey offers insights into the evolution of RIS technologies, identifies prominent research clusters, and outlines potential directions for future investigation. Furthermore, it sheds light on the interdisciplinary nature of RIS research, spanning areas such as telecommunications, signal processing, antenna design, and network optimization. This bibliometric survey serves as a valuable resource for researchers and practitioners to navigate the complex landscape of 5G innovation and shape the trajectory of future wireless communication systems.

IMPROVING 5G NETWORK PERFORMANCE WITH MIMO TECHNOLOGY USING BEAMFORMING ALGORITHM

The advent of 5G technology aims to revolutionize wireless communication by providing significantly higher data rates, ultra- reliable low latency, and enhanced connectivity. However, optimizing 5G network performance remains a challenging task, particularly in terms of Bit Error Rate (BER) and spectral efficiency. Multiple-Input Multiple-Output (MIMO) technology, coupled with beamforming algorithms, offers a promising solution to these challenges. This study investigates the application of the Fractional Least Mean Square (FLMS) algorithm for beamforming in MIMO systems to improve BER and spectral efficiency in 5G networks. The primary problem addressed is the need for effective interference mitigation and signal enhancement in densely populated environments, which traditional methods struggle to handle. The proposed method utilizes the FLMS algorithm to dynamically adjust the beamforming weights, thereby optimizing the signal reception quality. The algorithm's fractional nature allows for finer adjustments and better adaptation to varying channel conditions compared to conventional LMS algorithms. Simulation results show the efficacy of the proposed method. Specifically, the implementation of the FLMS algorithm in a 4x4 MIMO system shows a reduction in BER by 30% and an improvement in spectral efficiency by 25% compared to traditional beamforming techniques. These numerical values highlight the potential of FLMS in enhancing 5G network performance, making it a viable approach for future wireless communication systems.

An Hybrid Framework OTFS-OFDM Based on Mobile Speed Estimation

The Future wireless communication systems face the challenging task of simultaneously providing highquality service (QoS) and broadband data transmission, while also minimizing power consumption, latency, and system complexity. Although Orthogonal Frequency Division Multiplexing (OFDM) has been widely adopted in 4G and 5G systems, it struggles to cope with a significant delay and Doppler spread in high mobility scenarios. To address these challenges, a novel waveform named Orthogonal Time Frequency Space (OTFS). Designers aim to outperform OFDM by closely aligning signals with the channel behaviour. In this paper, we propose a switching strategy that empowers operators to select the most appropriate waveform based on an estimated speed of the mobile user. This strategy enables the base station to dynamically choose the waveform that best suits the mobile user’s speed. Additionally, we suggest retaining an Integrated Sensing and Communication (ISAC) radar approach for accurate Doppler estimation. This provides precise information to facilitate the waveform selection procedure. By leveraging the switching strategy and harnessing the Doppler estimation capabilities of an ISAC radar.Our proposed approach aims to enhance the performance of wireless communication systems in high mobility cases. Considering the complexity of waveform processing, we introduce an optimized hybrid system that combines OTFS and OFDM, resulting in reduced complexity while still retaining performance benefits.This hybrid system presents a promising solution for improving the performance of wireless communication systems in higher mobility.The simulation results validate the effectiveness of our approach, demonstrating its potential advantages for future wireless communication systems. The effectiveness of the proposed approach is validated by simulation results as it will be illustrated.

Improving power efficiency in 6G wireless communication networks through reconfigurable intelligent surfaces for different phase information

With increasing needs for high-bitrate, ultra-reliability, spectral efficiency, power efficiency, and reducing latency in the wireless network, global studies on the sixth generation of this network began in 2020. In this paper, we will look at intelligent reconfigurable surface structure and its application in new promising physical layer technologies, such as terahertz communications and UM-MIMO systems, to support very high-bitrate and superior network capacity in the 6G wireless communications. However, terahertz communications and UM-MIMO systems are the primary research points and confront many challenges for practical implementation. They require many RF chains and create problems in terms of cost and hardware complexity which RIS can simplify hardware and reduce cost. Therefore, we will present different modeling of wireless communication systems based on RIS for different phase information. Simulation results obtained by examining SNR performance and the error probability that shows the improvement of the received signal quality. According to results, RIS-based wireless communications can become an optimized model for future wireless communication systems.

Self‐attention convolutional neural network optimized with Remora Optimization Algorithm for energy aware resource management in Non‐orthogonal Multiple Access networks

SummaryNon‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively.

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system’s performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies.

Impact of memory on beamforming optimization for DDPG-assisted RIS-multi-user system

Recently, reconfigurable intelligent surfaces (RIS) have garnered considerable attention as indispensable components driving the evolution of future 6G wireless communication systems. This heightened interest is primarily attributed to notable advancements in programmable meta-material fabrication, which enable the creation of highly versatile and adaptable surfaces. These surfaces, often referred to as intelligent reflecting arrays, represent a significant departure from the conventional capabilities of massive multiple-input multiple-output (MIMO) systems, thereby catalyzing the emergence of intelligent radio environments characterized by enhanced flexibility and efficiency. Our study is dedicated to the comprehensive exploration of coordinated design strategies that encompass both the transmission beamforming matrix at the base station and the phase shift matrix at the RIS. Leveraging recent breakthroughs in deep reinforcement learning (DRL), our approach harnesses the power of a Long Short-Term Memory (LSTM) based DRL algorithm. This algorithm orchestrates the joint design process through iterative interactions with the environment, strategically guided by predefined rewards operating within a continuous framework of states and actions. While recent research has underscored the efficacy of LSTM-based architectures in bolstering the learning capacity of reinforcement learning (RL) algorithms and simplifying the search process, our investigation reveals a nuanced insight. Contrary to prevailing trends, we find that the integration of memory into the Deep Deterministic Policy Gradient (DDPG) with LSTM (DDPG-LSTM) algorithm yields unexpected consequences, negatively impacting the system's overall performance. Through preliminary simulation results, we empirically demonstrate the adverse effect of memory on DDPG performance when applied to RIS systems. This finding not only sheds light on the complexities of optimizing 6G wireless communication systems but also underscores the importance of careful algorithmic design and parameter tuning in achieving desired outcomes in emerging technologies.

MH-SIA: multi-objective handover using swarm intelligence algorithm for future wireless communication system

MH-SIA: multi-objective handover using swarm intelligence algorithm for future wireless communication system

Improved End-to-End Wireless Transmission Integrating NOMA and DL-Based Autoencoder

The field of wireless communication systems has experienced significant advancements in recent years, leading to the emergence of two promising technologies: non-orthogonal multiple access (NOMA) and deep learning (DL)-based autoencoders (AE). Through power allocation, NOMA enables multiple users to share a single frequency band, while AE can compress and decompress data with high precision. Integrating NOMA and AE enables end-to-end (E2E) transmission with a superior signal-to-noise (SNR) ratio. To further enhance the wireless network's block error rate (BLER) performance, the multiple-input, multiple-output (MIMO) technique is also incorporated into the newly proposed system. With the incorporation of the MIMO signal, the system is abbreviated as the MIMO-NOMA-AE system. The suggested technique for detecting MIMO-NOMA-AE signals has demonstrated a remarkable performance gain in SNR surpassing the traditional successive interference cancellation (SIC)-based NOMA system. The proposed system also performs better than previously utilized deep neural network (DNN)-based SISO-NOMA-AE communication systems. While this method shows potential for future wireless communication systems, further research and testing are necessary to evaluate its practical feasibility and effectiveness.

Photonics-based all-dielectric horn antenna for millimeter waves in 5G and 6G applications

This work introduces a cost-effective photonics-based approach for fast-implementing horn antennas operating in millimeter-wave frequencies (mm-waves). Instead of using conventional metallic guiding structures, we employed an all-dielectric quarter-wave stack Bragg mirror photonic design. As a proof-of-concept, we used five semi-spherical air-polylactic acid bilayers stacked with a conical (horn-like) aperture fabricated through a one-step 3D printing process. The prototype, with a bandwidth of 2.6 GHz (from 24.96 to 27.50 GHz), was fed by the WR28 standard waveguide mechanism with measured gain ranging from 10.6 to 13.9 dBi (between 25 and 27 GHz). These outcomes demonstrate our idea's suitability for alternative design of high-frequency antennas for future 5G and 6G wireless communications systems, overcoming the precision constraints of traditional manufacturing methods.

BER performance analysis of polar-coded FBMC/OQAM in the presence of AWGN and Nakagami-m fading channel

<abstract> <p>Offset quadrature amplitude modulation–based filter bank multicarrier (FBMC-OQAM) method is a promising technology for future wireless communication systems. It offers several advantages over traditional orthogonal frequency-division multiplexing (OFDM) modulation, including higher spectral efficiency, lower out-of-band emission, and improved robustness to time-frequency selective channels. Polar codes, a new class of error-correcting codes, have received much attention recently due to their ability to achieve the Shannon limit with practical decoding complexity. This paper analyzed and investigated the error rate performance of polar-coded FBMC-OQAM systems. Our results show that applying polar codes to FBMC-OQAM systems significantly improves the error rate. In addition, we found that employing random code interleavers can yield additional coding gains of up to 0.75 dB in additive white Gaussian noise (AWGN) and 2 dB in Nakagami-m fading channels. Our findings suggest that polar-coded FBMC-OQAM is a promising combination for future wireless communication systems. We also compared turbo-coded FBMC-OQAM for short code lengths, and our simulations showed that polar codes exhibit comparable error-correcting capabilities. These results will be of interest to researchers and engineers working on the advancement of future wireless communication systems.</p> </abstract>

Performance Analysis of SNR in MIMO OFDM Downlink System

The goal of future wireless communications systems is to provide a wide variety of high quality high-rate services with minimum requirements on spectrum, power consumption and hardware complexity. Toward this end, proper system structures as well as robust system designs are required to meet the challenges in wireless transmissions, such as multipath fading, limited spectrum resource, and interference. Recent research results have unveiled the multiple-input multiple-output (MIMO) system as a potential candidate to play a key role in future wireless. A MIMO wireless system is commonly deployed by using multiple transmit and receive antennas. Early work on multi-antenna systems involves the use of antenna arrays at the receiver to provide spatial diversity against the random destructive effect of fading. There is a recent rich literature on employing multiple antennas at the transmitter and achieving diversity through space-time coding when there is no channel state information at the transmitter (CSIT), or through transmit beam forming when there is perfect CSIT.

Graphene-enabled chiral metasurface for terahertz wavefront manipulation and multiplexing holographic imaging

The ability of metasurfaces to simultaneously manipulate both the spin and phase of circularly polarized (CP) incident waves has attracted considerable attention from researchers. Here, we propose a reconfigurable double circular split rings (DCSRs) chiral metasurface. By introducing graphene and changing the Fermi level of graphene, dynamic circular dichroism (CD) can be achieved. The calculation results show that when the Fermi level of graphene is increased from 0.1 eV to 1 eV, the CD decreases from 0.72 to 0.09 at 0.839 THz, which provides a CD-tunable spin-controlled method. In addition, based on the graphene-enabled DCSRs, we design two metasurface arrays combining Pancharatnam-Berry (PB) phase and coding metasurface theory to realize terahertz CP wave switchable focusing and spin-controlled multi-pencil beam generation. Furthermore, we demonstrate the application of reflective holography for multiplexing circularly polarized waves. The results of this paper are expected to play a significant role in the future terahertz wireless communication system, multi-spectral imaging, and information encryption fields.