Next-generation Wireless Networks Research Articles

A 3.2-3.8 GHz low-noise amplifier for 5G/6G satellite-cellular convergence applications

The rapid evolution of wireless communication systems towards 5G standards has imposed stringent requirements on the performance of radio frequency front-end components. Among these, the Low-Noise Amplifier (LNA) plays a pivotal role in determining the overall noise figure and sensitivity of the receiver chain. This paper presents a comprehensive design and analysis of a 3.2-3.8 GHz LNA tailored for 5G applications, employing a 0.3 μmm gate length Gallium Arsenide (GaAs) pseudomorphic high electron transistor (pHEMT) technology process. The proposed LNA design focuses on achieving a low noise figure (NF), high gain, and robust linearity to accommodate the dense signal environment and wide bandwidth of 5G networks. The design leverages advanced matching networks and feedback topologies to enhance stability and reduce the noise contribution from the active devices. Simulation results predict a noise figure of 1.3-1.4 dB, a gain of 20-21 dB across the band of interest, and an input-referred third-order intercept point (IIP3) of 18 dBm. The LNA demonstrates excellent performance in a 5G testbed, showing a significant improvement in the signal-to-noise ratio and the potential to enhance 5G receiver sensitivity. The research substantiates the LNA’s viability for integration into 5G base stations and user equipment, underscoring its potential to contribute to the efficient and reliable operation of next-generation wireless networks. This LNA can be used for 5G New Release (NR) band of n77 and n78 (3.5-3.7 GHz)

Design of an Iterative Method for Hybrid Device-to-Device and Network Integration via Dynamic Spectrum Sharing and Intelligent Resource Management

In the evolving landscape of wireless communication, the integration of Device-to-Device (D2D) communication within traditional network infrastructures emerges as a pivotal solution to meet the burgeoning demand for efficient, scalable, and reliable communication. Despite the promise of D2D to augment network capabilities, existing frameworks often falter in optimizing resource utilization, ensuring privacy and security, and maintaining high-quality service amidst dynamic network conditions and escalating data traffic. Addressing these limitations, this work introduces a comprehensive hybrid model that synergizes D2D communication with network integration through the innovative application of Dynamic Spectrum Sharing (DSS), Reinforcement Learning (RL)-based Resource Allocation, Mobile Edge Computing (MEC), Differential Privacy Techniques, Swarm Intelligence-based Routing, Cognitive Radio Networks, Self-Healing Network Protocols, and Low-Power Communication Protocols. DSS is employed to dynamically allocate spectrum, enhancing communication flexibility between D2D and network modes. RL algorithms adaptively manage resources, optimizing network performance in real-time scenarios. MEC decentralizes data processing, slashing latency and bandwidth demands. Differential privacy fortifies data security within D2D interactions in different communication scenarios. Swarm intelligence and cognitive radios bolster network resilience and spectral efficiency through self-organized routing and adaptive spectrum access. Self-healing protocols and energy-efficient designs ensure robust, sustainable network operations. The proposed model markedly elevates network efficiency, slashes latency, and augments scalability by intelligently offloading traffic, distributing computing tasks, and preserving user privacy. Its implementation fosters a resilient, self-organizing network fabric capable of meeting contemporary demands. The impacts of this work are profound, offering a blueprint for next-generation wireless networks that are more adaptive, secure, and sustainable, thereby significantly enhancing user experience and trust in D2D communication systems.

Optimal Beamwidth for Maximizing Uplink Coverage Probability in Quasi Earth-Fixed LEO Satellite Communication System

Satellite communication is proposed to fulfill the ubiquitous coverage for next-generation wireless networks. Considering the propagation delay and path loss, low-earth orbit (LEO) satellites are widely adopted. However, since the beam boresight directions become close in quasi-earth-fixed cells (QEFC) scenarios at low elevation angles, the interference increases and causes low communication quality. This paper introduces the optimal beamwidth maximizing uplink coverage probability scheme for quasi-earth-fixed cells in LEO satellite communication systems. The proposed scheme dynamically adjusts the beamwidth to achieve max uplink coverage probability at different elevation angles. The simulation results show that the proposed scheme matches the exhaustive search method in different scenarios and target signal-to-interference-plus-noise ratios. Furthermore, the proposed scheme significantly mitigates interference and improves the uplink coverage probability. Compared with the 3GPP setting, the proposed scheme improves the uplink coverage probability by 0.93 at time 100 s, and compared with the three-color frequency reuse, the proposed scheme improves the uplink coverage probability by 0.195 at time 100 s. The dynamic beamwidth and boresight direction adjustments enable the satellite to maintain seamless and reliable communication services across a wide range of operating conditions, ultimately realizing the goal of ubiquitous communications in the QEFC LEO satellite system.

Experimentally realized physical-model-based frugal wave control in metasurface-programmable complex media

Metasurface-programmable radio environments are considered a key ingredient of next-generation wireless networks. Yet, identifying a metasurface configuration that yields a desired wireless functionality in an unknown complex environment was so far only achieved with closed-loop iterative feedback schemes. Here, we introduce open-loop wave control in metasurface-programmable complex media by estimating the parameters of a compact physics-based forward model. Our experiments demonstrate orders-of-magnitude advantages over deep-learning-based digital-twin benchmarks in terms of accuracy, compactness and required calibration examples. Strikingly, our parameter estimation also works without phase information and without providing measurements for all considered scattering coefficients. These unique generalization capabilities of our pure-physics model unlock unforeseen and previously inaccessible frugal wave control protocols that significantly alleviate the measurement complexity. For instance, we achieve coherent wave control (focusing or perfect absorption) and phase-shift-keying backscatter communications in metasurface-programmable complex media with intensity-only measurements. Our approach is also directly relevant to dynamic metasurface antennas, microwave-based signal processors and emerging in situ reconfigurable nanophotonic, optical and room-acoustical systems.

Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks

Multipath channels continue to present challenges in wireless communication for both 5G and 6G networks. A multipath channel is a phenomenon in wireless communications where signals traverse from the sender to the receiver along various paths. This end occurs due to the reflection, diffraction, and refraction of signals of various objects and structures in the environment. Such pathways can cause symbol interference in the transmitted signal, leading to communication issues. To this end, our paper proposes the integration of three algorithms: teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and artificial neural networks (ANN). This combination effectively analyzes and stabilizes the transmission channel, minimizing symbol interference. We have developed, simulated, and evaluated this hybrid approach for multipath fading channels. We apply it to various coding schemes, including tail-biting convolutional code, turbo codes, low-density parity-check, and polar code. Additionally, we have explored various decoding methods such as Viterbi, maximum logarithmic maximum a posteriori, minimum sum, and cyclic redundancy check soft cancellation list. Our study encompasses new channel equalization schemes and coding gains derived from simulations and mathematical analysis. Our proposed method significantly enhances channel equalization, reducing interference and improving error correction in wireless communication systems.

Beam-space MIMO-NOMA Technique for mm-Wave Communication Systems

The multi-input-multi-output-antenna Scheme is regarded as the most promising method for next-generation wireless communication networks that employ millimeter-wave (mm-wave) frequencies and enable them to have a large information throughput. As a result of the large number of radio frequency chains, MIMO mm-wave technology has difficulty with energy consumption. The beam space MIMO (or BS-MIMO for short) reduces the total number of radio frequency components or chains (RF-Chains) without degrading performance significantly. Yeti, VS-MIMO restricts the number of clients that can be supported at the same time-frequency domain (cannot exceed the overall RF-chains number ). In order to cope with such problems, it has been intended to include the NOMA technique (which stands for Non-Orthogonal-Multiple-Access) with the concept of beam space to create the extra model of Non-Orthogonal beam space-MIMO (NOMA-BS-MIMO). With such a scheme the base station can support the service for more than one client (supposed to have correlated channels) through the same beam employing the same radio frequency chain, causing the total clients number greater than the number of employed radio chains in the system. In this research, the Spectral Efficiency of a proposed Non-Orthogonal beam space-MIMO has been considered. More specifically, the research proposes and develops a simple iterative algorithm with near-perfect performance in terms of system Spectral Efficiency. To achieve rate balancing across users, the proposed method, SLNR-IV, employs the signal-to-leakage-plus-noise ratio (SLNR) and the intermediate value method. Considering algorithm validity and a particular parameter setting scenario, the proposed strategy improves bandwidth efficiency by around 15% in comparison to the conventional approach based on OFDM and Zero-forcing digital precoder.

Artificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges

Wireless technologies are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide. With the advancement in technology, profound techniques can potentially improve the performance of wireless networks. Besides, the advancement of artificial intelligence (AI) enables systems to make intelligent decisions, automation, data analysis, insights, predictive capabilities, learning, and adaptation. A sophisticated AI will be required for next-generation wireless networks to automate information delivery between smart applications simultaneously. AI technologies, such as machines and deep learning techniques, have attained tremendous success in many applications in recent years. Hances, researchers in academia and industry have turned their attention to the advanced development of AI-enabled wireless networks. This paper comprehensively surveys AI technologies for different wireless networks with various applications. Moreover, we present various AI-enabled applications that exploit the power of AI to enable the desired evolution of wireless networks. Besides, the challenges of unsolved research in this area, which represent the future research trends of AI-enabled wireless networks, are discussed in detail. We provide several suggestions and solutions that help wireless networks be more intelligent and sophisticated to handle complicated problems. In summary, this paper can help researchers deeply understand the up-to-the-minute wireless network designs based on AI technologies and identify interesting unsolved issues to be pursued in their research in a fast way.

Signal transmission diversity based successive interference cancellation-slotted aloha

Efficient medium access protocols are crucial in leveraging the advancement in the physical layer techniques that have been proposed for the next-generation wireless networks. However, collisions among data packets in random access protocols reduce channel efficiency and hence the throughput. In this paper, we propose a MAC protocol based on both the signal transmission diversity and Successive interference cancellation (SIC) to improve the throughput. In the proposed Medium Access Control (MAC) protocol, the receiver receives two sets of signals from the transmitters over two uncorrelated time slots. Inter slot SIC is first carried out at the receiver over two independent sets of received interfered signals. Signals are decoded by canceling those signals that have already been decoded in one set from the other set. Closed-form expressions for the packet decoding probability, system throughput, average packet transmission delay, and energy efficiency have been derived by considering order statistics of received signal power. Analytical results are validated against simulation. Maximum achievable throughput of the proposed scheme is shown to be four times that of slotted Aloha and twice that of a slotted Aloha with SIC.

AI-Assisted Enhanced Composite Metric-Based Intrusion Detection System for Secured Cyber Internet Security for Next-Generation Wireless Networks

The prevalence of distributed denial of service (DDoS) flooding assaults is one of the most serious risks to cloud computing security. These types of assaults have as their primary objective the exhaustion of the system’s available resources, that is, the target of the attack, in order to make the system in question unavailable to authorized users. Internet thieves often conduct flooding assaults of the kind known as DDoS, focusing primarily on the application and network levels. When the computer infrastructure is multi-mesh-geo distributed, includes multi-parallel services, and a high number of domains, it may be difficult to detect assaults. This is particularly true when a substantial number of domains are present. When there are a big number of independent administrative users using the services, the situation gets more complicated. The purpose of this body of research is to identify signs that may be utilized to detect DDoS flooding assaults; this is its main objective. As a result, throughout the course of our study, we established a composite metric that considers application, system, network, and infrastructure elements as possible indicators of the incidence of DDoS assaults. According to our research, DDoS assaults may be triggered by a combination of variables. Investigations of simulated traffic are being conducted in the cloud. High traffic may be the result of flooding assaults. The composite metric-based intrusion detection system will be the name of a one-of-a-kind intrusion detection system (IDS) that has been agreed upon ICMIDS. This system will use [Formula: see text]-Means clustering and the Genetic Algorithm (GA) to detect whether an effort has been made to flood the cloud environment. CMIDS employs a multi-threshold algorithmic strategy in order to identify malicious traffic occurring on a cloud-based network. Cisco has created this technology. This strategy necessitates a comprehensive investigation of all factors, which is crucial for assuring the continuation of cloud-based computing-based activities. This monitoring system involves the development, administration, and storage of a profile database, denoted as Profile DB. This database is used for recording and using the composite metric for each virtual machine. The results of a series of tests are compared to the ISCX benchmark dataset and statistical settings. The results indicate that ICMIDS has a reasonably high detection rate and the lowest false alarm rate in the majority of situations examined during the series of tests done to validate and verify its efficacy. This was shown by the fact that ICMIDS had the lowest false alarm rate among all examined conditions.

ABRSL: AI based bilateral RAT selection framework for next-generation wireless networks

aBRSL: AI based bilateral RAT selection framework for next-generation wireless networks

Sixth Generation 6G to the Waying Seventh 7G Wireless Communication Visions and Standards, Challenges, Applications

The increasing need for next-generation wireless networks becomes apparent as the Internet of Everything (IoE) gains prominence in smart services, projecting widespread popularity in the future. While sixth generation (6G) networks have the capability to support a diverse range of IoE services, their potential limitations in meeting the demands of innovative applications prompt consideration for seventh generation (7G) wireless systems. This article seeks to compare the characteristics of 6G and the planned 7G wireless systems. The commercial development of fifth generation (5G) mobile communication systems is currently underway, introducing new services and enhancing user experiences. Despite these advancements, 5G encounters challenges that require on-going improvements. With the International Telecommunication Union Radio communication Sector (ITU-R) actively envisioning 6G and anticipating a consensus on 7G by October 2024, numerous unresolved questions persist in global discussions. This paper delivers a comprehensive overview of the current understanding of 7G, exploring the vision, technical requirements, and application possibilities. It presents a critical evaluation of the 7G network architecture and essential technologies. Furthermore, the article provides an in-depth examination of advanced 7G verification platforms and existing test beds, unveiling these aspects for the first time. Future research directions and lingering issues are emphasized to contribute to the on-going global discourse on 7G networks. Discussions on lessons learned from 7G networks culminate in the suggestion that 7G systems signify the pinnacle of mobile communication technology. The ongoing research on 7G, an intelligent cellular technology, holds promise for the future of wireless communication.

A mini-review of signal processing techniques for RIS-assisted near field THz communication

Reflecting Intelligent Surfaces (RISs) are reshaping the landscape of wireless communications, particularly in the terahertz (THz) frequency bands, offering promising solutions to inherent challenges in the bands. THz communication boasts bandwidths exceeding 100 GHz, leading to data rates potentially in the terabits per second (Tbps) range, thereby making it an attractive proposition for wireless communications, imaging, and sensing. However, benefits come with challenges, including significant molecular absorption, scattering, diffraction, and hardware limitations. Moreover, as bandwidth in the THz range increases, so does the difficulty of signal processing at Nyquist rate. RIS emerges as a game-changer for 6G and beyond by providing programmable reflecting elements that can adaptively modify the phases and amplitudes of incident signals, enabling precision in directing THz waves and enhancing received signal strength. Such capabilities can significantly mitigate path loss and atmospheric absorption challenges. Furthermore, inherent pencil beamforming capabilities of RIS lead to optimized energy utilization. Major challenge in THz communications is the pressing needs for efficient algorithms for robust THz transceivers and optimizing RIS elements. This review describes the integration of RIS and near-field THz communications, highlighting their future potential and challenges for the next-generation wireless networks. In this article, a comprehensive understanding of the complexities and nuances of near-field propagation in 6G networks, especially as the technology shifts towards extremely large-scale antenna arrays (ELAA). Additionally, it will introduce the transformative potential of sub-Nyquist rate signal processing and artificial intelligence (AI) offering innovative solutions to address the inherent challenges of 6G communication, especially in channel estimation and beamforming strategies.

Information Freshness for Monitoring and Control over Wireless Networks

Brief Biography: Vishrant Tripathi obtained his PhD from the EECS department at MIT, working with Prof. Modiano at the Lab for Information and Decision Systems (LIDS). He is currently working on building efficient data center networks at Google. His research interests primarily lie in the optimization of resources in resource constrained networked systems. The main applications of his work are in multi-agent robotics, federated learning, edge computing, cloud infrastructure, and monitoring for IoT. More recently, he has also been working on software defined networking and next-generation wireless networks. In 2022, he won the Best Paper Runner Up Award at ACM MobiHoc.

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.

When Spectrum Sharing in Cognitive Networks Meets Deep Reinforcement Learning: Architecture, Fundamentals and Challenges

Next-generation wireless networks require the integration of cognitive networks (CNs) and decision-making techniques to improve the spectrum efficiency. The conventional spectrum sharing schemes require full channel state information of CNs and cannot satisfy the low-latency requirement of nextgeneration wireless networks. Artificial intelligence has shown its high potential to perform decision-making and improve resource utilization efficiency. Hence, we propose a multi-agent reinforcement learning (MARL)-based scheme for spectrum sharing, which is a promising self-decision technique in highly dynamic and complex wireless networks. We analyze several key challenges when MARL is applied to spectrum sharing, such as multi-objective function formulation, multi-dimensional action space, and partial channel state information. Then, we propose efficient solutions and apply the explainable DRL to improve the convergence efficiency in spectrum sharing. The proposed architecture, fundamentals, and challenges provide a clear vision for MARL in CNs.

Joint Beamforming Design in Reconfigurable Intelligent Surface-Assisted Rate Splitting Networks

Reconfigurable Intelligent Surface (RIS) is an emerging technology that can improve the spectrum and energy efficiency of next-generation wireless networks. However, attaining accurate channel state information (CSI) for the cascaded RIS channel is particularly challenging. Imperfect CSI is a major bottleneck to achieving the spectral efficiency benefit of RIS-assisted networks. Rate splitting (RS), a promising multiple access technology, has been shown to be able to achieve an improved spectrum efficiency and be robust to channel uncertainties. This paper investigates the interplay between RIS and RS by considering a RIS-assisted RS beamforming problem. Active beamforming at the base station (BS) and passive beamforming at the RIS are jointly considered to maximize the minimum user rate under both the perfect CSI case and the imperfect CSI case. A block coordinate descent (BCD) algorithm is developed to solve this non-convex problem. Compared with the conventional semi-definite relaxation (SDR) approach, the proposed method does not require that the covariance matrix of the common beamforming vector be rank-one. Moreover, we present theoretical results to help reveal the impact of the system parameters and explain the performance gain resulting from the integration of RIS and RS. Extensive simulation results are also provided to show that RIS-assisted RS can improve the max-min rate performance significantly compared with conventional multiple access technologies, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) with/without RIS, especially in overloaded systems. With the proposed method, RS shows great potential in combating realistic CSI errors in RIS-assisted networks.

PERFORMANCE ANALYSIS OF ACHIEVABLE BIT RATES IN RIS-ASSISTED MASSIVE MIMO NETWORKS AT 28 GHz BAND

Reconfigurable intelligent surface (RIS) technology is a smart way of controlling the radio signal propagation to improve the capacity and coverage of wireless networks. RIS tunes the phase shifts of the incident signals in a dynamic fashion. Channel modeling is an important aspect in RIS-based mmWave communication for the next-generation wireless networks. However, to achieve maximum benefit from RIS-assisted wireless systems, it is essential to provide accurate channel state information (CSI). But, it is very challenging to get accurate CSI because of large number of RIS elements, their passive nature, and the training overhead involved during the channel estimation. To overcome the higher training overhead, in this article we aimed to take advantage of the correlation and sparsity of channels in RIS-assisted channel estimation. The objective of this work is to propose a simplified channel model for the RIS-assisted physical channel of a massive multi-input-multi-output (mMIMO) wireless communication system and analyze its performance in terms of transmitted powers, MIMO configurations, and achievable bit rates. The simulated results proved that the strategic placement of RIS with optimal phase shifts and optimal MIMO configuration can enhance the maximum achievable rate. The achievable rates of the proposed channel modeling are compared with the existing state-of-the-art methods to prove its efficiency. Also, the combination of mMIMO technology along with RIS-assisted communication provides degrees of freedom in terms of signal coverage, energy consumption, and system complexity.

Penguin search with Harris-Hawk optimization algorithm to improve clustering performance in wireless network

<span>Integrating optimal search algorithm concepts across the wireless core and cluster structure enables next-generation wireless networks to effectively provide reliable low-delay communications and connectivity for internet of things (IoT) devices. This article describes penguin search with the Harris-Hawk optimization algorithm (PHHO) to improve clustering performance in wireless networks. The penguin search optimization algorithm (PSO) algorithm computes the fitness value for feature selection from the database. Harris-Hawk optimization (HHO) algorithm to reduce the time and energy required for network transmission. This mechanism builds the clusters based on node communication range. The node direction, node mobility, node bandwidth availability, and energy parameters to decide the cluster head (CH) by applying the HHO algorithm. This approach uses a PSO algorithm fitness function to select the feature subset to minimize error and overhead in the network. Using a network simulator (NS)-3, this method assesses and chooses the most efficient way for data transmission, and the result is compared to a baseline mechanism.</span>

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification

Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques.

(Invited) Recent Advances and Challenges of MOCVD-Grown AlScN/GaN HEMTs

The allocated frequency bands of next-generation wireless communication networks will be shifting further up in the spectrum to be able to meet the stringent data rate requirements. Doing so, demands higher bandwidth capabilities from the electronic devices in the RF chain. Numerous cutting-edge systems are using AlGaN/GaN-HEMT based components, in particular in the transmit chain, due to their favorable combination of output power and bandwidth capability. Still, this semiconductor technology is approaching an upper bound in this regard, known as the Bode-Fano limit. Devices based on an AlScN/GaN heterointerface have been identified as candidates to push these boundaries and enable components that deliver higher power over a broader frequency range. We report on the status of MOCVD-grown AlScN/GaN HEMTs, their current capabilities but also on challenges that still hamper the exploitation of the material system's full potential.The first reported AlScN/GaN HEMTs were fabricated on epitaxial stacks grown by MBE. However, the majority of commercial GaN-based HEMT technologies is relying on MOCVD-grown epitaxy due to higher growth rate and lower cost of operation. We managed to grow AlScN/GaN heterostructures showing sheet carrier densities (nS) of up to 3·1013cm-2 and mobilities of 1100 cm2/Vs. These results were achieved with a MOCVD reactor that had been modified to cope with the low vapor pressure of the used precursors Cp3Sc and (MCp)2ScCl. Depending on the temperature, the obtained growth rates were as high as 0.015 nm/s. Currently, further precursors with allegedly higher vapor pressure are evaluated that should allow for even higher growth rates, further improving the quality of the fabricated heterostructures. Furthermore, HEMT devices were fabricated on top of the epitaxial wafers that showed power densities (Pout) of up to 8.4 W/mm and power-added efficiencies (PAE) of more than 48 % at a frequency of 30 GHz, even surpassing the current state-of-the-art in terms of combined Pout and PAE of Ga-polar devices.However, there are still a number of unsolved issues visible in most reported AlScN/GaN HEMT devices, regardless of the used growth method. One is concerning the level of leakage currents and the consequential premature soft breakdown. The root cause of this phenomena is not yet fully understood but is believed to originate from poor dielectric/isolating properties of the Sc-containing barrier. This hypothesis is supported by a comparably high level of impurites that is found in the AlScN layers. Minimizing theses residuals by purification of the precursors is, therefore, necessary to further improve the electrical device characteristics. Secondly, transport properties of AlScN/GaN heterointerfaces are not as well studied as more established junctions, e.g., AlGaN/GaN and AlN/GaN. An important parameter that has not been experimentally identified for 2DEGs induced by AlScN/GaN heterojunctions yet, is the saturation drift velocity (vsat). It is of particular importance since the Bode-Fano limit, among other, depends on it. Since the extension of the latter is one of the great promises of AlScN/GaN devices, it is clear that the value of vsat is essential to know in order to make quantitative predictions of the level of improvement that is achievable in terms of bandwidth capability but also with regard to power density.We will give an overview of recent advances of MOCVD-grown AlScN/GaN HEMTs with respect to their capability to extend on the achievable performance level of present GaN-based devices, in particular in the millimeter-wave frequency range. Further, we will benchmark the results against other competing technologies, i.e. GaAs pHEMT and Si-CMOS, to give an outlook of where optimized AlScN/GaN devices can takes us and where they cannot.