This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the cooperative awareness messages (CAMs) to their followers while ensuring timely delivery of safety-critical messages to the Road-Side Unit (RSU). To lower the computational load at the RSU and cope with the challenges of dynamic channel conditions, we exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. Motivated by the existing literature in RL, we propose two novel MARL frameworks based on the multi-agent deep deterministic policy gradient (MADDPG), named Modified MADDPG, and Modified MADDPG with task decomposition. Both algorithms train two critics with the following goals: A global critic which estimates the global expected reward and motivates the agents toward a cooperating behavior and an exclusive local critic for each agent that estimates the local individual reward. Furthermore, based on the tasks each agent has to accomplish, in the second algorithm, the holistic individual reward of each agent is decomposed into multiple sub-reward functions where task-wise value functions are learned separately. Numerical results indicate our proposed algorithms' effectiveness compared with other contemporary RL frameworks, e.g., federated reinforcement learning (FRL) in terms of AoI performance and CAM message transmission probability.

In this paper, a wide-scan angle planar patch phased array is proposed. The proposed array is composed of wideband probe-fed microstrip patches placed 0.087λ _h (λ _h is the wavelength of highest frequency) above individual ground planes and interlaced with parasitic decoupling walls. In order to cancel the feed probe inductance, the array elements comprise additional degrees of freedom to introduce controllable capacitance. Concurrently, individual ground planes and parasitic decoupling walls are proposed as an effective structure to considerably increase the E-plane scanning angle of the phased array by reducing the magnitude of near-field mutual coupling. A 544-element phased array prototype of the proposed element was designed, manufactured, and validated experimentally. The experimental results agree well with the numerical simulation ones and indicate effectiveness of the proposed design for mutual coupling reduction and wide-angle scanning. Because of the modifications, the measured mutual coupling between adjacent elements is reduced to lower than -32 dB at the center frequency. The proposed phased array has almost a constant active input impedance (active VSWR less than 2 over 20% bandwidth) up to scan angle of 65° in E-plane and 60° in H-plane, with a realized gain reduction of about 3.5 dB.

Design and implementation of a dual band and orthogonally polarized single layer microstrip reflectarray are presented in this paper. The proposed reflectarray operates in two separated broad frequency bands within X- and K-bands. The reflectarray element consists of a circular patch surrounded by a microstrip ring. Two curved phase delay lines are attached to the patch and two curved phase delay lines are attached to the ring. The ring and patch are designed for operation at X- and K-band, respectively. Phase delay lines attached to the ring are spatially rotated by 90°with respect to the delay lines attached to the patch. This produces a linear polarization at X-band which is perpendicular to the linear polarization produced at K-bands. This also provides a high polarization isolation which enables adjusting the phase of the elements at each band independent of the phase at the other band. At both operation bands, the required phase shifts are obtained by varying the lengths of the phase delay lines. The proposed element provides more than 450°linear phase range over the bands 8.7–10.7 GHz (X-band) and 18–20.4 GHz (K-band). Measurement results show the maximum gain of 27.7 dB at 9.7 GHz with 25% 1-dB gain bandwidth and the maximum gain of 31.8 dB at 19.2 GHz with 18% 1-dB gain bandwidth. By proper arrangement of the elements in the array, the cross-polarization is reduced. The measured efficiency is 63% at 9.7 GHz and 42% at 19.2 GHz with a center fed design for the reflectarray.

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime—that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecommunication vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors.

This research explores strategies to augment the connectivity among users within Hierarchical Non-Terrestrial Networks (HNTNs) dedicated to Disaster Relief Services (DRS). The primary goal is to optimize radio resources, computing capacities, and the trajectories of Unmanned Aerial Vehicles (UAVs) at each time slot, aiming to maximize the number of satisfactory connections (NSCs). UAVs function as aerial base stations (ABSs), establishing links for reduced capability (RedCap) user equipment (UE) using power domain non-orthogonal multiple access (PD-NOMA). Given the potential inoperability of terrestrial networks during disasters, swift data transmission is critical for mission-critical (MC) UEs. Therefore, end-to-end (E2E) delay is a crucial quality of service (QoS) constraint. The proposed problem is solved using a multi-agent recurrent deterministic policy gradient (MARDPG) algorithm, where the ABSs collaborate to maximize the NSCs and determine their optimal policy by interacting with the environment. Additionally, a sharing experience module (SEM) is incorporated, enabling agents to encode actions and observations using long short-term memory (LSTM), allowing each agent to utilize the historical actions and observations of other agents. To demonstrate the superiority of MARDPG, three algorithmic benchmarks and four different system models are implemented. The numerical results demonstrate the impact of various parameters, such as the number of subcarriers, users, and the maximum tolerable E2E delay on the NSCs. Furthermore, different scenarios indicate that MARDPG outperforms the benchmarks, achieving approximately a 6 percent optimality gap and a 91 percent fairness for achievable rate among users. HNTNs, DRS, UAVs, PD-NOMA, MARDPG

This paper proposes the use of unmanned aerial vehicles (UAVs) with intelligent reflecting surfaces (IRS) to reflect signals from the industrial internet of things (IIoT) to the destination, where power-domain non-orthogonal multiple access (PD-NOMA) is used in the uplink. The objective of our paper is to minimize the average age of information (AAoI) of users affected by transmit power constraint, and UAV movement restrictions. By optimizing transmit power, sub-carriers, trajectory, and phase shift matrix elements, UAV-IRS on IIoT networks can improve the freshness of the data collected from IIoT devices. The nonlinear integer optimization problem leads to an NP-hard problem, which is practically difficult to solve. We exploit the powerful reinforcement learning algorithm, i.e., the proximal policy optimization (PPO). The numerical results illustrate the benefits of IRS-enabled UAV communication systems. By using IRSs and the PPO algorithm, UAVs can achieve better performance than other methods that consider a fixed IRS, random deployment, other RL methods(A2C), and the impact of UAV jitter.