An experimentally validated fading model for THz wireless systems

As the wireless world moves towards the sixth generation (6G) era, the demand of supporting bandwidth-hungry applications in ultra-dense deployments becomes more and more imperative. Driven by this requirement, both the research and development communities have turned their attention into the terahertz (THz) band, where more than $$20\,{\text {GHz}}$$ 20 GHz of contiguous bandwidth can be exploited. As a result, novel wireless system and network architectures have been reported promising excellence in terms of reliability, massive connectivity, and data-rates. To assess their feasibility and efficiency, it is necessary to develop stochastic channel models that account for the small-scale fading. However, to the best of our knowledge, only initial steps have been so far performed. Motivated by this, this contribution is devoted to take a new look to fading in THz wireless systems, based on three sets of experimental measurements. In more detail, measurements, which have been conducted in a shopping mall, an airport check-in area, and an entrance hall of a university towards different time periods, are used to accurately model the fading distribution. Interestingly, our analysis shows that conventional distributions, such as Rayleigh, Rice, and Nakagami-m, lack fitting accuracy, whereas, the more general, yet tractable, $$\alpha $$ α –$$\mu $$ μ distribution has an almost-excellent fit. In order to quantify their fitting efficiency, we used two well-defined and widely-accepted tests, namely the Kolmogorov–Smirnov and the Kullback–Leibler tests. By accurately modeling the THz wireless channel, this work creates the fundamental tools of developing the theoretical and optimization frameworks for such systems and networks.

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features.

Delay-oriented IAB Topology Optimisation

Integrated access and bachhaul (IAB), introduced in 3GPP Release 16, is expected to be one of the main enablers for dense deployments in mmWave spectrum. The IAB architecture brings in topology changes, which impact network-related aspects, such as ensuring robust routing of traffic and load-balancing. While an IAB network should be transparent to the UE, its topology design may affect the end-user performance. Of particular importance in these first releases, hence, is to determine and evaluate optimal IAB topologies. This paper addresses optimal organisation of IAB nodes by considering a number of inter-connected aspects, including maximum number of hops, path-length, load balancing between the IAB-donors and traffic demand in the access network. In that, we focus on one of the parameters most sensitive to the architectural changes in the network - delay. Our analysis relies on the connectivity graph obtained by considering some practical aspects of sectorised beamforming using real mmWave antenna patterns.

Delay-oriented IAB Topology Optimisation

Integrated access and bachhaul (IAB), introduced in 3GPP Release 16, is expected to be one of the main enablers for dense deployments in mmWave spectrum. The IAB architecture brings in topology changes, which impact network-related aspects, such as ensuring robust routing of traffic and load-balancing. While an IAB network should be transparent to the UE, its topology design may affect the end-user performance. Of particular importance in these first releases, hence, is to determine and evaluate optimal IAB topologies. This paper addresses optimal organisation of IAB nodes by considering a number of inter-connected aspects, including maximum number of hops, path-length, load balancing between the IAB-donors and traffic demand in the access network. In that, we focus on one of the parameters most sensitive to the architectural changes in the network - delay. Our analysis relies on the connectivity graph obtained by considering some practical aspects of sectorised beamforming using real mmWave antenna patterns.