Antenna and Radio-Frequency Technologies for 5G and 6G Wireless Communications

1. Introduction

Fifth-generation (5G) mobile technology has firmly established itself as a critical infrastructure for the modern digital economy, attracting extensive research interest from both industry and academia, with a specific focus on its opportunities and challenges. Although 5G has successfully enabled enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) [1], as the deployment of 5G reaches maturity, the research horizon is shifting toward the sixth generation (6G). It is expected that 6G will bring forth a great revolution in communication technologies, transcending the limitations of current networks to enable the creation of an Internet of Everything (IoE). This new paradigm envisions a seamless fusion of physical, digital, and biological worlds, where billions of devices, sensors, and autonomous systems interact in real time [2].

Compared to 5G technology, future 6G networks are expected to deliver significantly higher performance metrics. Throughputs are projected to reach the terabit-per-second (Tbps) scale, while latency targets are set to sub-millisecond levels to support holographic communications and immersive extended reality (XR) [3]. Furthermore, 6G aims to support a massive component density—potentially connecting up to 10⁷ devices per square kilometer. To achieve these ambitious goals, the network architecture must evolve to utilize higher-frequency spectrums, extending from the millimeter-wave (mmWave) bands used in 5G FR2 to the sub-terahertz (sub-THz) and terahertz regimes [4]. This spectral expansion offers vast bandwidths but introduces severe physical layer challenges. High-frequency signals suffer from drastic atmospheric attenuation and are highly susceptible to blockage by common obstacles such as building materials, making reliable indoor coverage a critical bottleneck that requires precise channel modeling and material characterization [5].

Beyond propagation issues, the hardware governing radio interfaces is subject to stringent constraints. The evolution toward Massive MIMO (Multiple-Input Multiple-Output) and Ultra-Massive MIMO (UM-MIMO) necessitates the densification of antenna arrays, where hundreds or even thousands of antenna elements are packed into compact footprints. This trend creates significant challenges in terms of circuit size, thermal management, and power consumption [6]. Traditional Radio-Frequency (RF) front-end architectures, which often rely on discrete and bulky components, are no longer viable for such high-density integration. Consequently, there is an urgent need for novel co-design strategies that integrate amplifiers, phase shifters, and matching networks into miniaturized, energy-efficient modules without compromising signal integrity [7].

Moreover, the complexity of managing these heterogeneous and ultra-dense networks surpasses human operational capabilities. The mass integration of Artificial Intelligence (AI) and Machine Learning (ML) into all segments constituting the network becomes indispensable. AI is expected to transform 6G into an “intelligent native” network capable of autonomous resource allocation, dynamic spectrum sharing, and self-optimization [8]. In particular, energy efficiency has emerged as a key performance indicator (KPI) for sustainable development. “Green” network architectures that leverage AI to dynamically adjust power consumption based on real-time traffic loads—such as through intelligent network slicing—are essential to mitigate the carbon footprint of future wireless infrastructures [9].

This Special Issue addresses these multifaceted hurdles. By bringing together academic and industrial researchers, this collection highlights key opportunities for applying advanced antenna and Radio-Frequency technologies in order to understand and design modern network systems. The contributions collected herein advance the state of the art by exploring fundamental propagation characteristics in high-frequency bands, developing innovative circuit integration techniques for compact MIMO systems, and proposing AI-driven strategies for green network management. These studies collectively offer a roadmap for overcoming the physical and architectural limitations of 5G and 6G, paving the way for a ubiquitous and sustainable intelligent network.

2. An Overview of Published Articles

Recent advancements in the transition from 5G to 6G wireless communications have focused on overcoming physical layer limitations, hardware constraints, and network management complexities through innovative architectures and empirical modeling. One crucial area of research involves understanding the interaction between signals and the environment in the sub-terahertz regime, which is paramount for future network planning. To address the scarcity of empirical data for indoor channel modeling at these extremely high frequencies, the electromagnetic wave attenuation of various building materials in the W-Band (75–110 GHz) has been thoroughly investigated. Utilizing a precision measurement setup inside an anechoic chamber, it was found that, while insulation materials like Styrofoam remain nearly transparent to W-Band signals, structural materials like glass and heterogeneous wood composites exhibit profound attenuation and severe scattering effects. This comprehensive dataset provides the essential parameters required for accurate ray-tracing simulations and reliable indoor 6G coverage.

Additionally, addressing the physical constraints of Massive MIMO arrays necessitates highly integrated and low-loss transceiver hardware. A novel co-design architecture was proposed to successfully merge a microwave amplifier and phase shifter, solving the traditional trade-off between spatial footprints and electrical performance. Instead of relying on conventional cascaded setups that inevitably lead to bulky designs and significant insertion losses, this approach utilizes a reflection-type phase shifter (RTPS) to function simultaneously as the input matching network for the microwave transistor. Experimental validations demonstrated a remarkable 38.5% reduction in the overall circuit size and an enhanced gain compared to traditional cascaded architectures, offering a highly scalable and efficient solution for high-density antenna arrays.

Furthermore, the efficient management of network resources is critical for supporting the massive, heterogeneous connectivity demanded by the Internet of Things (IoT). Another innovative study tackled the systemic challenge of dynamic user association and resource allocation by formulating the complex network slicing problem as an Unconstrained Markov Decision Process (U-MDP). By leveraging Software-Defined Networking (SDN) and Network Function Virtualization (NFV), this “Green” slicing architecture dynamically assigns incoming IoT devices to the most appropriate network slices based on real-time energy efficiency and Quality of Service (QoS) criteria. This AI-driven dynamic approach significantly outperforms traditional static allocation methods, effectively mitigating energy waste and ensuring optimal power levels across the physical infrastructure.

Numerous studies have been reported on mmWave antenna array design due to their high gain and good return loss, while single-element microstrip line antennas have not been studied extensively. The fourth paper in this Special Issue, however, presents data suggesting that single-element mmWave antennas can obtain a performance comparable to or even exceeding that of antenna arrays. After careful optimization, the authors show that the proposed single-element mmWave antenna achieved an excellent gain and return loss, making it competitive with traditional array-based solutions. The authors of this paper, therefore, address the research gap in the existing literature on the scarcity of high-performance single-element microstrip antennas tailored for commercial mmWave 5G frequency bands, such as dual-band 28 GHz and 38 GHz operations. This paper thus challenges the common wisdom that prefers the mmWave array antennas solutions, instead showcasing that a single-element mmWave antenna can indeed achieve a high gain and low S11 after optimization while still remaining compact and cost-effective. This work also reports the effects that antenna connectors can have on antenna performance and utilizes a parametric analysis of manufacturing variations to enable potential robust and practical single-element mmWave antenna solutions.

In the fifth and final paper, the authors discuss the important issue of which semiconductor IC (integrated circuit) technology, silicon or III–V, may be better for realizing real-world mmWave 5G phased arrays. To achieve the same transmitter (Tx)-equivalent isotropic radiated power (EIRP) requirement spec (e.g., 65 dBm per 100 MHz), the system designers would need to decide on the number of total antenna arrays (which affects cost and system complexity), while the output power per channel and its linearity specs determine the Tx power amplifier (PA) design requirements for front-end modules (FEMs). The authors thus compare the simulated and measured performance of two broadband mmWave monolithic PAs designed in the lab for the 5G FR2 band: one silicon PA in GF’s 22 nm FD-SOI CMOS process and one III-V PA in HRL’s 40 nm GaN/SiC HEMT process.

For mmWave 5G phased array systems, the HPBW (half-power beamwidth) of the phased array can be important, as narrow HPBWs like a “pencil-beam” can be easily blocked with physical LOS (line-of-sight) obstacles. If one reduces the phased array size, it will obviously increase the array’s HPBW, thus mitigating the LOS blocking/shadowing effects. Therefore, for the same EIRP, using high-power GaN PAs can reduce the number of channels needed and the array size vs. an all-Si large array, which mitigates mmWave LOS shadowing. The measurement data from the authors’ medium-power GaN PAs expected to realize an array with a ~2× wider HPBW and a ×4 reduction in channel numbers compared with an array using CMOS-SOI PAs. By using a newly designed Watt-level GaN PA whose simulated output power was ~ 15 dB greater than that of the CMOS-SOI PAs, the authors expect that the HPBW can be widened further by ~ 3× for GaN-based FEMs (i.e., GaN PAs with silicon control circuits) compared to all CMOS-SOI FEMs that use higher-power Watt-level GaN PAs, reducing the array size further by ×8 times with better LOS blocking behaviors. Somewhat surprisingly, GaN-based mmWave FEMs may even be significantly cheaper than an all-silicon FEMs if GaN devices are processed on 8″ silicon wafers (instead of on the traditional 4″–6″ pricy SiC wafers) to reduce die cost, as the industry will be shifting towards the production of RF GaN-on-silicon processes within the next few years.

3. Conclusions

5G/6G mobile technologies promise higher throughputs, shorter latencies, greater coverage and component density, and the mass integration of AI in all segments of communication networks. This technology has been the subject of extensive research in both both industry and academia, with scholars focusing on both its opportunities and challenges.

This Special Issue consists of high-quality papers that provide state-of-the-art research, while also addressing potential practical applications for 5G and 6G wireless communication technologies. These studies collectively showcase significant advancements in 5G/6G networks and technology, highlighting a technical revolution that requires synchronous innovations in materials science, hardware engineering, and AI-driven software architecture. We encourage academic and industrial researchers to join together in the development of novel antennas and RF technologies in order to realize and advance future 5G/6G mobile network systems.