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Large-scale channel prediction, i.e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning. Ray tracing (RT)-based methods have been widely used for many years, but they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in B5G/6G systems. In this paper, we propose a data-driven, model-free pathloss map prediction (PMP) method, called PMNet. PMNet uses a supervised learning approach: it is trained on a limited amount of RT data and map data. Once trained, PMNet can predict pathloss over location with high accuracy (an RMSE level of 10−2 ) in a few milliseconds. We further extend PMNet by employing transfer learning (TL). TL allows PMNet to learn a new network scenario quickly ( ×5.6 faster training) and efficiently (using ×4.5 less data) by transferring knowledge from a pre-trained model, while retaining accuracy. Our results demonstrate that PMNet is a scalable and generalizable ML-based PMP method, showing its potential to be used in several network optimization applications. 

Since the terahertz frequency band (0.1–1 THz) has attracted considerable attention for the upcoming sixth-generation (6G) wireless communication systems, accurate models for multipath propagation in this frequency range need to be established. Such models advantageously use the fact that multi-path components (MPCs) occur typically in clusters, i.e., groups of MPCs that have similar delays and angles. In this paper, we first analyze the limitations of a widely used clustering algorithm, Kernel-Power-Density (KPD), in evaluating an extensive THz outdoor measurement campaign at 145–146 GHz, particularly its inability to detect small clusters. We introduce a modified version, which we term multi-level KPD (ML-KPD), iteratively applying KPD to detect whether a cluster determined in the previous round is made up of multiple clusters. We first apply the method to synthetic channels to demonstrate its efficacy and select suitable values for the adaptive hyperparameters. Then, multi-level KPD is applied to our channel measurements in line-of-sight (LOS) and non-line-of-sight (NLOS) environments to determine statistics for the number of clusters and the cluster spreads. 

Pathloss is one of the essential characteristics of wireless propagation channels. It is usually captured from channel measurements with (quasi)isotropic antennas. To characterize the wireless channels at high frequencies, beamforming or directional antennas are commonly used, in which case a method for estimating the isotropic pathloss is needed. The method should account for the possible spatial overlap of the different directional measurements while including the received signal from all the multipath components in the channel. In this letter, we propose an efficient method that uses a weighted sum of the powers received from the directional measurements. The weights can be calculated using matrix inversion. We verify the solution using synthetic data and demonstrate the usage with measurements at sub-THz frequencies. 

Data-intensive augmented information (AgI) services (e.g., metaverse applications such as virtual/augmented reality), designed to deliver highly interactive experiences resulting from the real-time combination of live data-streams and pre-stored digital content, are accelerating the need for distributed compute platforms with unprecedented storage, computation, and communication requirements. To this end, the integrated evolution of next-generation networks (5G/6G) and distributed cloud technologies (mobile/edge/cloud computing) have emerged as a promising paradigm to address the interaction- and resource-intensive nature of data-intensive AgI services. In this paper, we focus on the design of control policies for the joint orchestration of compute, caching, and communication (3C) resources in next-generation 3C networks for the delivery of data-intensive AgI services. We design the first throughput-optimal control policy that coordinates joint decisions around (i) routing paths and processing locations for live data streams, with (ii) cache selection and distribution paths for associated data objects. We then extend the proposed solution to include a max-throughput data placement policy and two efficient replacement policies. Numerical results demonstrate the superior performance obtained via the novel multi-pipeline flow control and 3C resource orchestration mechanisms of the proposed policy, compared with state-of-the-art algorithms that lack full 3C integrated control. 

Design and standardization of future millimeter-wave (mmWave) wireless communications systems require accurate models of wireless propagation channels. In particular, comprehensive statistical models describing the effect of human bodies moving randomly in the surrounding environment, acting as reflectors or absorbers, on the received power and delay spread are urgently needed. To enable these, new measurements campaigns are required based on channel sounders designed specifically to capture the realtime dynamics of the channel responses. This paper proposes a new methodology to enable fully dynamic measurements with a pseudonoise (PN)-sequence channel sounder by means of quasi-perfect transmitter-receiver (Tx-Rx) synchronization and suppression of probing signal effects in the post-processed channel impulse responses (CIRs). This approach allows the identification of the weak multipath components (MPCs) originated by reflections on the human body. The approach is validated by analysing CIRs collected in an indoor environment with one person moving close to the 60 GHz link. The results also demonstrate that future mmWave systems could exploit these additional MPCs and benefit from human interactions.