The exponential growth of data in the information era has pushed conventional optical communication technology to its limitations, including inefficient spectral utilization, slow data rate, and inherent security vulnerabilities. Here, a transformative high-speed organic spectral wireless communication (SWC) technology enabled by a flexible, miniaturized, and high-performance organic hyperspectrometer is proposed that integrates ultrahigh-speed data transmission with hardware-level encryption. By synergistically combining organic photodetector arrays with tunable responsivities and spectral-tunable organic filters, the organic hyperspectrometer achieves a broad spectral detection range of 400 to 900 nm, resolution of 1.08 nm, accuracy of 0.60 nm, and response time of 684 ns. Unlike prior optical wireless communication systems, the organic hyperspectrometer-based SWC platform uniquely decodes high-speed encrypted data at the hardware level, which is a breakthrough in secure, high-speed, and high-capacity communication. Harnessing the full visible-to-near-infrared spectrum, the SWC system achieves a theoretical data rate of 9.1 Gbits s-1, ranking as the highest-speed organic optical communication system. Furthermore, the intrinsic flexibility and bandgap-tunability of organic materials enable unparalleled portability, adaptability, and scalability of organic SWC, establishing a scalable framework for terahertz-scale data transmission. These advancements mark a pivotal leap toward secure, high-speed, and ultracompact optical networks for the future data- and AI-driven era.

We experimentally demonstrate a set of U-band discrete Raman amplifiers using backward incoherent pumping in 1 km HNLF, achieving up to 22.3 dB net gain, and 4.2-5.8 dB NF. Three different types of Raman gain fiber have been investigated, including 1 km HNLF, 0.51 km HNLDSF, 8 km and 7.6 km IDF. Using HNLF achieved the highest gain and the lowest NF, while using 8 km IDF yielded up to 16.2 dB net gain and a minimum of 6.3 dB NF due to its low Raman gain coefficient and higher fiber loss. Using 0.51 km HNLDSF gave up to 21 dB net gain, but at a cost of over 8.1 dB noise figure. These amplifiers were incorporated in a C + L + U-band coherent transmission system using 516 × 24.5GBd DP-64/256QAM channels over 50 km SSMF. We achieved a maximum decoded data rate of 123.5Tb/s across C + L + U bands, with 25.6Tb/s specifically in the U-band (1625-1650 nm).

Smart cities demand next-generation wireless communication technologies capable of supporting ultra-high data rates, low latency, and seamless connectivity. Millimeter-wave (mmWave) and Terahertz (THz) communication technologies have emerged as promising solutions to meet these requirements, offering wide bandwidth availability and high-speed data transmission. However, the propagation of high-frequency signals is significantly affected by severe path loss and complex channel characteristics. To address these challenges, this paper proposes a machine learning–assisted channel estimation framework aimed at enhancing connectivity in smart city environments. The proposed approach integrates convolutional neural networks (CNNs) with long short-term memory (LSTM) networks to accurately predict channel conditions and dynamically optimize transmission parameters. Simulation results demonstrate a 30% improvement in spectral efficiency, a 25% reduction in mean square error (MSE) for channel estimation, and an 8 dB enhancement in signal-to-noise ratio (SNR) compared to conventional methods. Furthermore, the proposed system achieves a 20% reduction in latency, ensuring reliable and efficient data transmission for smart city applications. These findings highlight the potential of combining machine learning with mmWave and THz communication technologies to enable next-generation high-capacity wireless networks in urban environments

Abstract Intracortical Brain Computer Interfaces (iBCIs) hold the potential to revolutionize neurotherapeutics, but they must overcome technological challenges such as the high data rates generated by high-channel-count neural sensors and the stringent power and volume constraints of implantable devices. In addition, the brain-wide coverage needed for a deeper understanding of brain processes challenges the synchronization between distributed neural sensors and the central neural hub. To address these challenges, we present a deterministic-latency and power-efficient serializer-deserializer (SerDes) telemetry network that effectively mitigates the synchronization issue under strict power and volume constraints. The serializer on the sensor side employs event-based sampling and a packet-based address-event representation (AER) transmission protocol, achieving a low power consumption of only 127 µW and a low latency variation < 10 µs. A crystal-free clock source is employed on the sensor side to minimize power consumption, with serialized data encoded using Manchester coding scheme. The deserializer on the hub handles the bit period uncertainty by counting and extracting the bit period of received data with a clock only ~2.2× faster than the serializer clock. The proposed counting-based Manchester decoder achieves a wide frequency coverage up to 204,000 ppm of frequency variation. The deserializer achieves a measured Manchester decoding bit error rate < 10 -6 , with a total estimated power consumption below 415 µW. The SerDes performance has been validated with in vivo pre-recorded data, demonstrating a compression ratio greater than 7, while preserving a high signal fidelity with an average RMSE < 6 µV RMS .

Abstract This paper introduces a novel underwater visible light optical communications (UVLC) system architecture employing optical Minimum Shift Keying modulation with independent pulse shaping (IPS-MSK). The system under consideration is configured with a non-line- of-sight (NLOS) channel, and its performance is evaluated numerically under various aquatic conditions, such as different water types (pure seawater, clear ocean, and coastal ocean), depths, link ranges and turbulence strengths, considering different data rates. The effect of transmitter optical power and receiver aperture diameter has also been investigated. The key parameters used are bit error rate (BER), quality factor (Q), eye diagram and signal to noise ratio (SNR). Results shown that, when combined with IPS-MSK modulator, UVLC system demonstrates enhanced capacity, extended transmission range, and improved performance, particularly, in clear water. At data rate up to 40 Gbps, an underwater distance of 102 m and 100 m is achieved in the pure seawater and clear ocean, respectively, with a target Q-factor of 6. Coastal water demonstrated shorter transmission distance of 1 m while maintaining similar Q-factor. The findings confirm that the proposed system could be advantageous choice for higher data rate UVLC system over long-range in clear water, and for short-range applications in turbid water.

Abstract Visible light communication (VLC) has gained significant attention as a next-generation wireless technology due to its unlicensed bandwidth, high security, and seamless integration with LED illumination. To support high data rates and user scalability, power-domain non-orthogonal multiple access (NOMA) with 1024-QAM modulation enhances spectral efficiency; however, this combination introduces high peak-to-average power ratio (PAPR), resulting in nonlinear signal distortion and degraded bit error rate (BER) performance in intensity-modulated optical systems. To overcome these limitations, this paper proposes a deep learning-based nonlinear companding framework for waveform optimization in Optical NOMA. The proposed technique significantly reduces PAPR, achieving 3.2 dB, 4.4 dB, and 5.3 dB at a CCDF of 10 −3 for 256, 512, and 1024 subcarriers, respectively, outperforming A-Law, μ-Law, and PTS by up to 7.5 dB improvement. BER analysis further confirms its superiority, achieving a BER of 10 −3 at only 8.9 dB SNR, compared to 15.2 dB (A-Law), 13.1 dB (μ-Law), 11.4 dB (PTS), and 17.8 dB for the unprocessed Optical NOMA waveform. These results demonstrate improved power efficiency, reduced nonlinear distortion, and enhanced detection reliability, making the proposed method a strong candidate for high-speed VLC-based NOMA systems.

Free-space optical (FSO) communication is widely employed in critical sectors owing to its advantages such as high data rate and high reliability. However, signal attenuation and leakage during transmission pose significant information security challenges. In contrast to the visible and infrared (IR) spectral regions, which are susceptible to background light interference, solar-blind ultraviolet (UV) communication offers an inherent advantage of a high signal-to-noise ratio (SNR). However, novel cryptographic photodetectors operating in this waveband have been insufficiently explored. In this work, a self-powered ultraviolet (UV) photodetector based on an Al/p-GaN/In:Ga2O3/SnO2 structure was designed and fabricated, featuring synergistic regulation by a heterojunction and a Schottky junction. The photodetector exhibits a unique wavelength-selective bipolar photoresponse, generating a negative photocurrent under 254 nm illumination and a positive photocurrent under 365 nm illumination. Under zero bias, the detector achieves responsivities of -5 mA/W and 0.52 mA/W, and specific detectivities of up to -7.2 × 1011 Jones and 7.47 × 1010 Jones, for 254 and 365 nm illumination, respectively. Leveraging the unique physical properties of the photodetector, a secure optical communication (SOC) system is demonstrated. This system utilizes the positive and negative photocurrents, generated by distinct UV wavelengths, as binary signals ("1" and "0") to encrypt information, while decryption is performed at the receiving end using a preset key file. The experimental results demonstrate that the system successfully achieved the encrypted transmission and accurate decryption of the string "CUST" at zero bias. This work not only presents a new strategy for developing high-performance, multifunctional bipolar UV photodetectors but also offers an innovative and viable technological pathway for fabricating next-generation, high-security, and low-power optical communication systems.

The sixth generation of wireless communication technology, or 6G technology, was created to replace 5G. Compared to its predecessors, it promises much faster speeds, more capacity, and reduced latency, opening up new applications and advancing a number of industries. Terabits per second (Tbps) is the target data rate for 6G, which is substantially faster than 5G's gigabits per second (Gbps). In order to facilitate real-time applications and instantaneous data transfer, 6G aims for nearly zero latency, possibly as low as the microsecond level. Compared to 5G's 1 million connected devices per square kilometer, 6G will allow for a potentially 10 million more. With the help of AI and machine learning, 6G will be able to manage resources intelligently, perform better, and add new features. It is anticipated that 6G will facilitate developments in fields such as imaging, location awareness, presence technology, and the Internet of Things (IoT). A review of earlier work is presented in this paper.

The MIMO antenna design is specifically engineered to support optimized performance in emerging 6G networks. Utilizing advanced techniques such as metamaterials and machine learning algorithms, the antenna system achieves high data rates, improved diversity, and robust signal reliability, making it ideal for next-generation ultra-fast and intelligent wireless communication technologies. Our advanced metamaterial configuration demonstrates high gain and bandwidth. A low ECC value 0.0004 shows minimal correlation, ensuring better signal diversity and improved system performance. Similarly, a high diversity gain confirms the antenna’s efficiency in maintaining robust signal reception under varying conditions. The CCL values of 0.0916 bits/Hz bits/Hz provide insight into the information-carrying capacity of the MIMO configuration. The MIMO antenna design achieves a maximum gain of 8.9 dBi and a wide bandwidth of 30 THz. This performance is attained through a combination of parametric optimization and machine learning techniques, enhancing both efficiency and operational range. The machine learning algorithms used for optimization yield a high R² value of 0.99, indicating excellent prediction accuracy. The proposed antenna, featuring metamaterial characteristics, demonstrates strong potential for next-generation 6G networks, offering enhanced performance, efficiency, and compact design integration.

The integration of high-quality organic solid-state lighting with high-speed optical wireless communication offers an innovative pathway toward next-generation optoelectronic devices. Here, we report a structurally simplified white organic light-emitting diode (WOLED) that achieves seamless integration of natural-light-quality illumination and visible light communication (VLC) using a unique dual-exciplex architecture. Central to this design is a versatile organic layer of PPCzTrz that serves as both an electron donor and acceptor at two distinct interfaces, establishing complementary charge-transport pathways and a voltage-controlled dynamic shift of the exciton recombination zone. This spatial redistribution balances blue and green exciplex emissions, while Förster resonance energy transfer (FRET) sequentially funnels energy to strategically positioned green, orange, and red phosphorescent ultrathin layers. The resulting multi-path exciton management strategy ensures spectrally stable white light from 400 to 700nm, yielding a record-high color rendering index (CRI = 97), a peak external quantum efficiency of 27.0%, and a power efficiency of 85.8lmW-1. The same device enables high-speed VLC with a data rate of 14.0Mbps. This work provides a scalable and energy-efficient platform that simultaneously addresses the needs of high-quality lighting and optical data transmission, paving the way for smart lighting systems and fully organic integrated optoelectronics.

Sixth-generation (6G) wireless communication represents the future evolution of mobile networks, expected to be deployed around 2030. It aims to provide extremely high data rates, ultra-low latency, and intelligent network operations beyond the capabilities of fifth-generation (5G) systems. While 5G has enabled enhanced mobile broadband and massive connectivity, emerging applications such as holographic communication, autonomous transportation, smart cities, and advanced healthcare require higher performance, reliability, and intelligence. To meet these demands, 6G introduces new technologies including terahertz communication, artificial intelligence-driven network management, intelligent reflecting surfaces, and integrated terrestrial and non-terrestrial networks. This paper presents a comprehensive overview of 6G wireless networks by discussing their vision, key enabling technologies, and potential applications. In addition, major challenges such as spectrum limitations, energy efficiency, security, and hardware complexity are analyzed. Finally, future research directions toward sustainable, secure, and intelligent 6G networks are highlighted. The study aims to provide a foundational understanding of 6G and its role in shaping next-generation wireless communication systems.

Diffractive neural networks offer a novel physical implementation for optical computing to achieve parallelism, low power consumption, and light-speed processing. However, their limited dispersion engineering necessitates increasingly complex architectures for tasks such as spectrum recognition and simultaneous multi-class classification, which in turn leads to increased energy demands. Here, we propose a spoof plasmonic neural network (SPNN) comprising cross-cascaded spoof surface plasmonic waveguides with strong engineered dispersion properties designed for operation in the terahertz regime. This compact platform efficiently separates spectral components from a broadband input signal, achieving a data rate of 22 Gbit/s across two separated spectral channels. We experimentally show that the SPNN can simultaneously classify multiple inputs from Fashion-MNIST+MNIST or Fashion-MNIST+EMNIST datasets, achieving classification accuracies of 98.3% and 97.4% or 97.4% and 93.8%, respectively. For multi-color CIFAR-10 dataset classification, the network architecture incorporating multiple cascaded SPNNs realizes over 10% higher accuracy than single-color-channel methods by leveraging distinct color channels mapped to respective spectrum channels. These findings highlight the potential of SPNNs for machine learning applications and lay the groundwork for future terahertz chip integration.

The civil Global Navigation Satellite System (GNSS) signal is broadcast with an open structure, making it vulnerable to spoofing attacks. Incorporating authentication data into GNSS signals is a significant measure to enhance system security. Precise Point Positioning (PPP) technology has garnered extensive attention for its ability to provide real-time services with centimeter-level accuracy. The PPP service features a high data update rate, with the validity period of the data being approximately ten to twenty seconds. This imposes more stringent requirements on the authentication data rate and the authentication time. Code Shift Keying (CSK) technology has emerged as a key candidate for satellite-based PPP signal design, as it can increase the data rate without requiring additional spectrum resources. This paper investigates authentication methods for CSK-modulated satellite-based PPP signals. Two approaches are proposed: phase modulation authentication and polarity modulation authentication. Simulation and analysis results indicate that the PPP signal with phase modulation authentication experiences less carrier-to-noise ratio (C/N0) loss and has a higher detection probability. In contrast, the signal with polarity modulation authentication does not suffer from C/N0 loss and achieves a higher data rate and a shorter authentication time. These findings can serve as valuable references for future GNSS signal design.

Visual monitoring in heavy equipment operations was critical for reducing workplace accidents caused by operator blind spots. Conventional static cameras and sensors often failed to provide adequate coverage in dynamic environments, leading to limitations in situational awareness. This study aimed to evaluate the transmission performance of the Walksnail Avatar digital FPV system using directional and omnidirectional antennas for blind spot monitoring applications. An experimental method was employed by configuring a digital FPV transmitter and receiver at a fixed distance of 20 meters with a wooden obstacle placed along the transmission path. The receiver antenna orientation was rotated at 10-degree intervals to measure variations in data rate and latency. Two antenna configurations were tested: a 9.4 dBi directional patch antenna and a standard omnidirectional antenna. The results indicated that the directional patch antenna achieved a maximum data rate of 25 MBps at frontal orientations but experienced significant degradation at lateral angles, with latency increasing up to 100 ms. In contrast, the omnidirectional antenna demonstrated more uniform performance across all directions, maintaining data rates between 19–24 MBps and stable latency below 45 ms. These findings were consistent with antenna radiation theory regarding gain concentration and angular coverage.The study implied that omnidirectional antennas were more suitable for blind spot monitoring in heavy equipment operations due to their consistent coverage and low latency, while directional antennas were better applied for focused line-of-sight monitoring scenarios. The results provided empirical guidance for selecting antenna configurations in digital FPV-based industrial safety systems.

Purpose In the past 10 years, excessive research enthusiasm has been focused on 2.5D/3D advanced packaging (e.g. CoWoS/SoIC) used for data-center artificial intelligence (AI) chips and high bandwidth memory integration. For the edge AI chips with lightweight computing power, very thin FBGA (VFBGA), low power double data rate 5/5X (LPDDR5/5X) and wafer-level (WL) packaging with the redistribution layer (RDL)-first process are the integration technology trends. This paper focuses on AI system on chip (SoC) and LPDDR5X on-package integration design only using 3 RDL on the WL package. This paper aims to examine the influence to explore the trace routing scheme and corresponding signal integrity (SI) performance of the RDL interconnection for the on-package LPDDR5X, thereby providing RDL design and simulation reference for the ever-increasing demand for AI SoC and LPDDR5X on-package integration that is an alternative for on-board interconnection. Design/methodology/approach A design practice is used with type selection design and physical design. SI design and SI simulation are used to ensure the performance of the design practice. Detailed approach for the SI analysis consists of frequency domain and time domain simulation that are transmission line characteristics with coupled frequency domain and eye diagram analysis in the time domain. Findings Through the design practice and SI simulation analysis, VFBGA LPDDR5X and RDL-first technology are suitable for integration with AI SoC using WL packaging. Sub-10 µm line width and space design rule is required for the practice. The on-package integration SI performance is higher than that of on-board. Originality/value This paper is a technology application exploration and focuses on the design and SI simulation field. The main value is two-fold: on one side, providing a new solution for AI SoC and LPDDR5X integration; on the other side, extending the application range for WL packaging technology. The design and simulation results are valuable for the reference with the similar application.

Underwater networks are crucial for monitoring the marine ecosystem, enabling data collection to support the preservation and protection of natural resources. Among the various technologies available, acoustic and optical communications stand out for their superior performance in underwater environments. Acoustic technologies are suitable for long-range communications, typically operating over hundreds of meters up to several kilometers, albeit with low data rates ranging from a few hundred bps to few tens of kbps. In contrast, optical technologies excel in providing high data rates, often between 1 and 10 Mbps, but only over short distances (e.g., 50 m) in controlled conditions. To leverage the strengths of these technologies, recent research has proposed multi-modal underwater systems; however, these solutions generally rely on single-level or at most dual-level architectures, limiting the benefits of a structured hierarchical approach. In this review paper, after discussing related work on multi-technology acoustic and optical networks, we highlight relevant design guidelines for multi-technology, multi-level underwater architectures, explicitly considering three layers: a deep acoustic layer, an intermediate optical layer, and an upper RF-enabled surface layer. For illustration, we also discuss a PoC of such a hierarchical architecture under development at the University of Catania, Italy, in the Area Marina Isole dei Ciclopi natural reserve. The PoC includes optical nodes capable of transmitting up to 10 Mbps over short ranges and acoustic nodes (both software defined and not) supporting rates of tens of kbps over hundreds of meters and being adaptive to network conditions, interconnected through hybrid multi-technology nodes deployed across the three network levels. By assigning specific technologies to appropriate layers, the architecture enhances scalability, robustness, and adaptability to dynamic underwater conditions. This design strategy not only improves data transmission efficiency but also ensures seamless operation across diverse marine scenarios, making it an effective solution for a wide range of underwater monitoring applications.

The practical deployment of Orthogonal Frequency Division Multiplexing Passive Optical Networks (OFDM-PONs) is hindered by the lack of a Medium Access Network (MAC) protocol capable of managing their flexible, distance-dependent data rates, despite their high spectral efficiency. This paper proposes and validates a novel rate-adaptive, Time Division Multiplexing (TDM)-based MAC protocol for OFDM-PON systems. A key contribution is the design of a three-layer header frame structure that supports multi-ONU data scheduling with heterogeneous rate profiles. Furthermore, the protocol incorporates a unique channel probing mechanism to dynamically determine the optimal transmission rate for each Optical Network Unit (ONU) during activation. The proposed Optical Line Terminal (OLT) side MAC protocol has been fully implemented in hardware on a Xilinx VCU118 FPGA platform, featuring a custom-designed ring buffer pool for efficient multi-ONU data management. Experimental results demonstrate robust upstream and downstream data transmission and confirm the system's ability to achieve flexible net data rate switching on the downlink from 8.1 Gbit/s to 32.8 Gbit/s, contingent on the assigned rate stage.

Free-space optical (FSO) communications represent an attractive technology for future high-capacity wireless and satellite networks, offering multi-Gbps data rates, unlicensed spectrum, and built-in physical-layer security. However, their performance is severely affected by atmospheric turbulence, misalignment errors, and noise, which limit reliability and throughput. Hybrid automatic repeat request (HARQ) protocols provide a powerful mechanism to mitigate such impairments by combining forward error correction with retransmissions. In this paper, we investigate the fundamental performance limits of HARQ applied to FSO systems employing On–Off Keying (OOK) modulation. Using information-theoretic tools, we characterize the achievable rate and the finite-blocklength performance by resorting to channel dispersion, which plays a crucial role in quantifying rate–reliability tradeoffs. We further examine the interaction between HARQ retransmissions, turbulence-induced fading, and feedback delay, providing insights into the design of low-latency, high-reliability optical links. This analysis highlights how HARQ improves the robustness of OOK-based FSO systems and provides guidelines for parameter selection in next-generation space and terrestrial optical networks.

The Satellite Internet of Things (IoT) sector is undergoing rapid transformation, driven by breakthroughs in satellite communications and the pressing need for seamless global coverage—especially in remote and poorly connected regions. In locations where terrestrial infrastructure is limited or non-existent, Low Earth Orbit (LEO) satellites are proving to be a game-changing solution, delivering low-latency and high-throughput links well-suited for IoT deployments. While North America currently dominates the market in terms of revenue, the Asia-Pacific region is projected to lead in growth rate. Nevertheless, the development of satellite IoT networks still faces hurdles, including spectrum regulation and international policy alignment. In this evolving landscape, the LoRa and LoRaWAN protocols have been enhanced to support direct communication with LEO satellites, typically operating at altitudes between 500 km and 2000 km. This paper offers a comprehensive review of current research on LoRa/LoRaWAN technologies integrated with LEO satellite systems, also providing a performance assessment of this combined architecture in terms of theoretical achievable bitrate, Bit Error Rate (BER), and path loss. The results highlight the main performance trends of LoRa LR-FHSS in direct-to-LEO links. Path loss increases sharply with distance, reaching approximately 150 dB at 500 km and 165–170 dB at 2000 km, significantly reducing achievable data rates. At 500 km, bitrates range from approximately 7–8 kbps for SF7 to below 2 kbps for SF12. BER follows a similar trend: below 200 km, values remain low (10−4–10−3) for all spreading factors. At 1000 km, BER rises to approximately 3.9×10−3 for SF7 and 1.5×10−3 for SF12. At 2000 km, BER reaches approximately 4.7×10−2 for SF7 but stays below 2×10−2 for SF12, showing a 2–3× improvement with higher spreading factors. Overall, many links exhibit path loss above 160 dB and BER in the 10−3–10−2 range at long distances. These results underscore the importance of adaptive spreading factor selection and LR-FHSS gain for reliable long-range satellite IoT connectivity, highlighting the trade-off between robustness and spectral efficiency.

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems.