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The 60 GHz band plays an important role for wireless signalling with extremely high data rates, both for WiFi and cellular applications. For the design and performance analysis of this band, the impact of human interactions on the propagation from transmitter to receiver has to be taken into account. While the impact of a single human body blocking the line-of-sight (LOS) has been investigated as a deterministic effect, statistical models describing the effect of multiple human bodies, acting as reflectors, on received power and delay spread are still lacking. To close this gap, this paper analyzes measurements of 60 GHz channel impulse responses in static but “evolutionary” office scenarios that involve one and two people and uses them to calibrate a ray tracer that allows the generation of a larger number of channel realizations. Regression fits are applied to the resulting channel responses to obtain an accurate characterization of human-induced power and delay variations in proximity situations where humans give rise to additional multipath. 

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. 

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. 

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. 

Low-Earth orbit (LEO) satellite (SAT) networks exhibit ultra-wide coverage under time-varying SAT network topology. Such wide coverage makes the LEO SAT network support the massive IoT, however, such massive access put existing multiple access protocols ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for massive IoT in LEO SAT networks. Not only showing the performance of our proposed approach (see, Table II), but we also discuss the issue of scalability of deep reinforcement learning (DRL) by showing the convergence behavior (see, Table III and IV). 

The worlds of computing, communication, and storage have for a long time been treated separately, and even the recent trends of cloud computing, distributed computing, and mobile edge computing have not funda-mentally changed the role of networks, still designed to move data between end users and pre-determined compu-tation nodes, without true optimization of the end-to-end compute-communication process. However, the emergence of Metaverse applications, where users consume multime-dia experiences that result from the real-time combination of distributed live sources and stored digital assets, has changed the requirements for, and possibilities of, systems that provide distributed caching, computation, and com-munication. We argue that the real-time interactive nature and high demands on data storage, streaming rates, and processing power of Metaverse applications will accelerate the merging of the cloud into the network, leading to highly-distributed tightly-integrated compute- and data-intensive networks becoming universal compute platforms for next-generation digital experiences. In this paper, we first describe the requirements of Metaverse applications and associated supporting infrastructure, including rele-vant use cases. We then outline a comprehensive cloud network flow mathematical framework, designed for the end-to-end optimization and control of such systems, and show numerical results illustrating its promising role for the efficient operation of Metaverse-ready networks.