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  1. Pathloss prediction is an essential component of wireless network planning. While ray tracing based methods have been successfully used for many years, they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in 5G/B5G (beyond 5G) systems. In this paper, we propose and evaluate a data-driven and model-free pathloss prediction method, dubbed PMNet. This method uses a supervised learning approach: training a neural network (NN) with a limited amount of ray tracing (or channel measurement) data and map data and then predicting the pathloss over location with no ray tracing data with a high level of accuracy. Our proposed pathloss map prediction-oriented NN architecture, which is empowered by state-of-the-art computer vision techniques, outperforms other architectures that have been previously proposed (e.g., UNet, RadioUNet) in terms of accuracy while showing generalization capability. Moreover, PMNet trained on a 4-fold smaller dataset surpasses the other baselines (trained on a 4-fold larger dataset), corroborating the potential of PMNet.1 
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  2. This paper describes our pathloss prediction system submitted to the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. We describe the architecture of PMNet, a neural network we specifically designed for pathloss prediction. Moreover, to enhance the prediction performance, we apply several machine learning techniques, including data augmentation, fine-tuning, and optimization of the network architecture. Our system achieves an RMSE of 0.02569 on the provided RadioMap3Dseer dataset, and 0.0383 on the challenge test set, placing it in the 1st rank of the challenge. 
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  3. The availability of large bandwidths in the terahertz (THz) band will be a crucial enabler of high data rate applications in next-generation wireless communication systems. The urban microcellular scenario is an essential deployment scenario where the base station (BS) is significantly higher than the user equipment (UE). Under practical operating conditions, moving objects (i.e., blockers) can intermittently obstruct various parts of the BSUE link. Therefore, in the current paper, we analyze the effect of such blockers. We assume a blockage of the strongest beam pair and investigate the availability and extent of angular diversity, i.e., alternative beampairs that can sustain communication when the strongest is blocked. The analysis uses double-directional channel measurements in urban microcellular scenarios for 145- 146 GHz with BS-UE distances between 18 to 83 m. We relate the communication-system quantities of beam diversity and capacity to the wireless propagation conditions. We show that the SNR loss due to blockage depends on the blocked angular range and the specific location, and we find mean blockage loss to be on the order of 10-20 dB in line-of-sight (LOS) and 5-12 dB in NLOS (non-LOS). This analysis can contribute to the design of intelligent algorithms or devices (e.g., beamforming, intelligent reflective surfaces) to overcome the impact of the blockage. 
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  4. Multi-path components (MPCs) in wireless channels generally occur in clusters, i.e., groups of MPCs that have similar delay/angle characteristics. However, when those clusters are widely separated and have significantly different power, highresolution parameter extraction (HRPE) algorithms based on serial interference cancellation, such as CLEAN, can miss some of the weaker clusters because they concentrate the path search in the strongest cluster. This effect can occur particularly in the presence of calibration error and/or diffuse scattering. To solve this problem, we propose a heuristic modification, Regional CLEAN (R-CLEAN), that employs cluster identification in the Fourier domain and limits the number of MPCs per cluster. We first demonstrate the effect, and the effectiveness of our proposed algorithm, on synthetic channels with calibration error or diffuse scattering. We then demonstrate it with a THz Multiple-Input- Multiple-Output (MIMO) measurement at 145 - 146 GHz. The proposed optimization and algorithm can thus be an essential step towards evaluating channels with multiple clusters. 
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