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.

With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the first AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement 13 (PS-013), organized by Universitat Pompeu Fabra (UPF), which primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we overview the ML models proposed by participants (including Artificial Neural Networks, Graph Neural Networks, Random Forest regression, and gradient boosting) and analyze their performance on an open dataset generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

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With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the First AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement 13 (PS-013), organized by Universitat Pompeu Fabra (UPF), whose primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we provide an overview of the ML models proposed by participants (including artificial neural networks, graph neural networks, random forest regression, and gradient boosting) and analyze their performance on an open data set generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

Next-generation communication systems will face new challenges related to efficiently managing the available resources, such as the radio spectrum. DL is one of the optimization approaches to address and solve these challenges. However, there is a gap between research and industry. Most AI models that solve communication problems cannot be implemented in current communication devices due to their high computational capacity requirements. New approaches seek to reduce the size of DL models through quantization techniques, changing the traditional method of operations from a 32 (or 64) floating-point representation to a fixed point (usually small) one. However, there is no analytical method to determine the level of quantification that can be used to obtain the best trade-off between the reduction of computational costs and an acceptable accuracy in a specific problem. In this work, we propose an analysis methodology to determine the degree of quantization in a DNN model to solve the problem of AMR in a radio system. We use the Brevitas framework to build and analyze different quantized variants of the DL architecture VGG10 adapted to the AMR problem. The evaluation of the computational cost is performed with the FINN framework of Xilinx Research Labs to obtain the computational inference cost. The proposed design methodology allows us to obtain the combination of quantization bits per layer that provides an optimal trade-off between the model performance (i.e., accuracy) and the model complexity (i.e., size) according to a set of weights associated with each optimization objective. For example, using the proposed methodology, we found a model architecture that reduced 75.8% of the model size compared to the non-quantized baseline model, with a performance degradation of only 0.06%.

Tele-education went from being an option to becoming a necessity given the current public health problems. Due to this, the educational sector has included blended courses as part of its training offer. Mobile Ad Hoc Networks (MANETs) will therefore, become an important resource to work, under this class format, with students outside the classroom, in accordance with the standards recommended inside a university campus. MANETs require certain parameters that ensure the quality of communications and identify the factors that influence Quality of Service (QoS). The purpose of this study is to determine the factors that affect the quality of communications in a tele-education environment. For this purpose, the QoS of a MANET supporting a real-time video streaming is evaluated based on throughput. As a result, the main factors that directly or indirectly affect the operation of the network could be identified, which may help in making decisions regarding aspects such as the number of nodes and the node mobility speed. Moreover, the versatility and scalability of the MANET was proven, as when the number of nodes went from 5 to 10, throughput increased by 14 %. This also occurred with the transmission rate factor when the video was streamed in a channel with a variable bitrate (64–4096 kbps).

The objective of this study, reflected in this document, was to implement a rectenna for 2.45 GHz and 5.38 GHz wireless local area applications. For this purpose, the antenna dimensions were set to 18 mm × 44 mm, which is simulated using the optimization software CST Studio, manufactured on FR4 substrate with a thickness of 1.6 mm, where the conductive material has a thickness of 0.035 mm. Likewise, the rectangular slot technique was used to improve the bandwidth of the antenna; this technique consists of inserting slots in the structure to modify the displacement of the surface current. The above presented a gain of 2.49 dB at the 2.45 GHz frequency and 4.01 dB at the 5.38 GHz frequency. The proposed antenna for RF energy harvesting applications exhibits a dipole type radiation pattern, which enhances the capture of RF energy from various directions. The triple slotted-band rectifier with T-shaped impedance matching network was designed in FR4, using a Schotkky HSMS-286C diode for AC to DC RF control switching. A tp-link TL-WR940N wireless router was used as the RF emitting source at 30 cm separation between it and the proposed rectenna. The DC output of the rectenna is 3 volts with a generated signal power of 20 dBm at 2.4 GHz. The low-cost rectenna can be used for power-charging applications in the Internet of things (IoT) systems.