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.

A simple amide compound (QLBA), prepared from quinoline-2-carboxylic acid and 2-(1H-benzo[d]imidazol-2-yl)aniline, was synthesized and evaluated as an efficient fluorescence chemosensor for the selective recognition of copper ions (Cu²⁺). This sensor could detect Cu²⁺ in CH₃OH/HEPPES buffer in a wide pH range (4.0–10.0), with obvious fluorescence quenching. The obtained QLBA–Cu²⁺ complex could be used as a new cascade sensor for detecting sulfide anions (S²⁻). The corresponding detection limits were found to be 2.2 × 10⁻⁷ M for Cu²⁺ and 4.6 × 10⁻⁷ M for S²⁻. The sensing mechanisms of QLBA toward Cu²⁺ were systematically investigated in detail by NMR, HR-MS analysis, and density functional theory (DFT) calculations. In addition, this sensor was verified to be of low cytotoxicity and to have good imaging characteristics for the detection of Cu²⁺ and further for the recognition of S²⁻ in living cells, suggesting that QLBA was a useful tool for tracking Cu²⁺ and S²⁻ ions in vivo.

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.

Because of the influence of wind, unmanned aerial vehicles (UAVs) inevitably experience different degrees of wobble while flying. Considering the impact of effective apertures (EA) on the performance of array antennas, this letter proposes a channel model for UAV-enabled communication systems, which incorporates the UAV’s pitch and yaw, as well as the randomly varying EA caused by the UAV’s wobble and flight posture. The general expression for the average signal-to-noise ratio (ASNR) is derived based on this model. From this general expression, we derive a closed-form expression for the case when the receiver is located directly below the hovering UAV. In deriving this closed-form expression, a method for approximating the general expression using a finite power series is proposed. The numerical results show that even slight wobble of the UAV can severely degrade the performance of the communication system. Moreover, owing to the wobble, increasing the number of antenna elements does not narrow the beamwidth and can’t lead to a significant improvement in ASNR. Furthermore, we find that in a UAV-enabled communication system, neglecting the EA also results in a severe overestimation of the ASNR.

To effectively capture the inherent near-field effects and spatial non-stationarity across extremely large antenna arrays (ELAAs), this letter develops a novel analytical channel model tailored for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. In the proposed framework, spatial non-stationarity is first characterized using a 0-1 diagonal matrix, after which the composite XL-MIMO channel matrix is formulated as a linear combination of structured matrix components. Leveraging this representation, we perform a comprehensive performance analysis, evaluating key metrics including the downlink ergodic capacity, an efficiently computable upper bound derived from eigenvalue matrix, and the symbol error probability (SEP). The analysis demonstrates that the proposed scheme not only achieves performance comparable to benchmark methods but also substantially reduces computational complexity. Furthermore, the analysis reveals a pronounced performance degradation in the presence of increasing channel estimation errors.