This paper addresses the impact of asymmetric jitter, which arises from air turbulence or mechanical vibrations, on the three dimensional (3D) attitude angles of aerial intelligent reflecting surface (AIRS) mounted on unmanned aerial vehicle (UAV). To characterize these effects, a physics-based channel model based on the spherical wavefront assumption (SWA) is established. To mitigate the resulting performance degradation, we propose a novel continuous reflection phase based on the planar wavefront assumption (PWA), leveraging the concept of the Zadoff-Chu sequence. This design integrates a conventional reflection phase component with a spatial-frequency-bandwidth-dependent term, effectively broadening the bandwidth of the passive beam. Using the proposed continuous reflection phase, we analyze the normalized array gain function and average received signal power under UAV jitter, deriving approximate expressions for these metrics using Fresnel functions. The analysis demonstrates that the proposed reflection phase can expand the beam bandwidth to cover the potential range of jitter angles. Furthermore, a discrete reflection phase is designed based on the continuous version. Numerical results confirm that the beam bandwidth remains stable as the severity of asymmetric jitter increases, illustrating the effectiveness of the designed phases in mitigating UAV platform instability. Additionally, the results indicate that the proposed reflection phase can effectively compensate for performance degradation caused by UAV jitter.

Reconfigurable intelligent surfaces (RIS) have attracted significant attention due to their capability of establishing virtual line-of-sight (VLoS) links. This paper proposes a channel model for RIS-assisted millimeter wave (mmWave) communication systems that incorporate the effective aperture (EA) of RIS elements, the horizontal and vertical rotation angles of the RIS, the servomechanism limitations associated with these rotation angles and the activation criteria to constrain the feasible range of these rotation angles. To enhance the system performance, we jointly optimize the horizontal and vertical rotation angles of the RIS with the objective of maximizing the signal-to-noise ratio (SNR) based on the proposed model. An alternating optimization (AO) algorithm is developed to solve this problem efficiently. Specifically, the original optimization problem is decomposed into two subproblems corresponding to independent optimization of the horizontal and vertical angles, and closed-form optimal solutions are derived for each subproblem. Updating iteratively these closed-form solutions yields suboptimal horizontal and vertical rotation angles for the RIS. Moreover, a global optimal solution of closed-form to the original optimization problem is derived for the special case where the base station (BS) is positioned directly in front of the RIS. Numerical results demonstrate that the suboptimal rotation angles obtained by the AO algorithm closely approximate the optimal solutions. Furthermore, the proposed AO algorithm, which jointly optimizes both rotation angles, significantly outperforms the methods that individually optimize either the horizontal or vertical angle.

Due to the large path loss in millimeter wave (mmWave) band, the transmission path between transmitter (Tx) and intelligent reflecting surface (IRS) is considered as a Rayleigh fading channel, and a physics-based channel model is proposed for IRS-assisted mmWave communication system in urban scenario. Also, the horizontal and vertical rotation angles of IRS and the relationship between the scattering gain of IRS reflecting unit and its effective aperture in the incident direction and the desired reflection direction are considered in the proposed model. For the considered communication scenario, the existing reflection phases, which are designed to align the virtual line-of-sight (VLoS) components among Tx, IRS, and receiver (Rx) with the LoS components between Tx and Rx, are not the appropriate reflection phases. Based on the proposed model, we first obtain the statistical phases of the virtual scattering components within a cluster by minimizing phase differences between different IRS reflection units, and then obtain the reflection phases by minimizing the phase differences of the derived statistical phases for all clusters. By comparing with the existing reflection phases, the designed reflection phases can significantly enhance the system performance gains of mmWave communications. Using the designed reflection phases, the expressions of received signal power and upper bound of ergodic sum capacity are derived in this paper, which are validated by using Monte-Carlo simulation results. Numerical results show that the proposed mmWave channel model could accurately simulate the propagation characteristics of IRS. Also, numerical results show that the performance gains of IRS-assisted systems are equivalent to that of large-scale communication systems without using IRS.

MmWaves have been envisioned as a promising direction to provide Gbps wireless access. However, they are susceptible to high path losses and blockages, which can only be partially mitigated by directional antennas. That makes mmWave networks coverage-limited, thus requiring dense deployments. Integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Resource allocation in mmWave IAB networks must face big challenges originated by heavy temporal dynamics, such as intermittent links caused by user mobility and blockages from moving obstacles. This makes it extremely difficult to find optimal and adaptive solutions. In this article, exploiting the distributed structure of the problem, we propose a Multi-Agent Reinforcement Learning (MARL) framework to optimize user throughput via flow routing and link scheduling in mmWave IAB networks characterized by mobile users and obstacles. The proposed approach implicitly captures the environment dynamics, coordinates the interference, and manages the buffer levels of IAB relay nodes. We design different MARL components, respectively for full-duplex and half-duplex networks. In addition, we propose an online training algorithm, which addresses the feasibility issues of practical systems, especially the communication and coordination among RL agents. Numerical results show the effectiveness of the proposed approach.

MmWave communications are expected to provide huge wireless access data rates. However, mmWave signals are strongly affected by high path losses and blockages, which can only be partially alleviated by directional phased-array antennas. This makes mmWave networks coverage-limited, thus requiring network densification. 3GPP has introduced Integrated Access and Backhaul (IAB) architecture as a cost-effective solution. Resource allocation in IAB networks is complicated because it has to cope with directional transmissions, device heterogeneity, intermittent links, and mobile users. While traditional optimization techniques usually struggle in these scenarios, we believe Reinforcement Learning (RL) techniques, especially Multi-Agent RL (MARL), can implicitly capture environment dynamics and lead to interference coordination among nodes. In this paper, we propose an MARL-based framework that shows remarkable effectiveness in addressing flow allocation and link scheduling for mmWave 5G IAB networks in scenarios with random obstacles and mobile users.