Cloud Gaming is a cutting-edge paradigm in the video game provision where the graphics rendering and logic are computed in the cloud. This allows a user’s thin client systems with much more limited capabilities to offer a comparable experience with traditional local and online gaming but using reduced hardware requirements. In contrast, this approach stresses the communication networks between the client and the cloud. In this context, it is necessary to know how to configure the network in order to provide service with the best quality. To that end, the present work defines a novel framework for Cloud Gaming performance evaluation. This system is implemented in a real testbed and evaluates the Cloud Gaming approach for different transport networks (Ethernet, WiFi, and LTE (Long Term Evolution)) and scenarios, automating the acquisition of the gaming metrics. From this, the impact on the overall gaming experience is analyzed identifying the main parameters involved in its performance. Hence, the future lines for Cloud Gaming QoE-based (Quality of Experience) optimization are established, this way being of configuration, a trendy paradigm in the new-generation networks, such as 4G and 5G (Fourth and Fifth Generation of Mobile Networks).

Extended Reality (XR) arises as one of the current cutting-edge educational and entertainment emergent technologies. This service differs from traditional video streaming approaches due to its immersive experience, which allows the user to enjoy omnidirectional multimedia. However, the service experience must be guaranteed to avoid side effects such as cybersickness or disorientation. This work presents a framework to assess 360-video service streaming performance over mobile networks through Key Quality Indicators (KQIs) using VR (Virtual Reality) HMD (Head Mounted Device). The testbed is composed of a 360-video client for DASH (Dynamic Adaptive Streaming over HTTP), which playbacks multimedia content from a video server located in the cloud while KQI measuring tasks are performed in the user as on the network sides. Various metrics are collected such as resolution, frame rate, initial playback time, throughput, stall events, and round trip time (RTT), among various others. Finally, a performance comparison between LTE and 5G technologies is provided. Results from the KQI measurement highlight the potential of the new generation of mobile networks in the provision of service with high-quality levels of experience.

The emergence of the novel approach of Industry 5.0 entails the utilization of immersive technologies such as XR (Extended Reality), which promise to transform the conceptualization of industry. The advent of new technology enablers, such as next-generation mobile networks, including 5G and B5G (Beyond 5G), plays a pivotal role in enhancing productivity, optimizing operators’ training programs, and minimizing unnecessary risks. This study presents a performance assessment of cloud VR services over 5G and WiFi in industrial scenarios. This scenario involves several factors that can degrade radio links, including fading and interference. To assess the suitability of 5G for the Industry 5.0 hot topic of XR, a 5G SA/WiFi-6 Edge Cloud VR setup was developed in the Smart Production Lab at Aalborg University. The results demonstrate that 5G outperforms WiFi in terms of latency and throughput consistency during the experiments in DL (Downlink), which belongs to the streaming channel with UDP (User Datagram Protocol). In the uplink (UL), WiFi generally exhibits lower latency than 5G when employing Transmission Control Protocol (TCP) for the control channel. To provide a comprehensive End-to-End (E2E) analysis, an objective Quality of experience (QoE) metric has been estimated. The outcomes demonstrate that 5G achieves higher QoE values for mobility than WiFi, which shows a decline in performance. The results indicate that 5G performance is within theoretical limits for extended reality (XR) experiences and suggest that it is a promising candidate for enabling Industry 5.0 and its use cases.

The emergence of cutting-edge technologies and services such as Extended Reality (XR) promises to change how people approach everyday living. At the same time, the emergence of modern and decentralized architectural approaches has ushered in a new generation of mobile networks, such as 5G, as well as outlining the roadmap for B5G (Beyond-5G) and further advancements. These networks are expected to be the enablers for the realization of the metaverse and other futuristic services. In this context, quantifying the service performance is a key enabler for dynamic, environment-adaptive, and proactive network management. This work presents an ML-based (Machine Learning) framework that uses data from the network, such as radio measurements, statistics, and configuration parameters to infer the best ML models that fit diverse XR Key Quality Indicators (KQIs). The output models integrate feature engineering techniques that enhance model size and performance. The proposed framework comprises data preprocessing, model definition, training, tuning, and validation. Additionally, to select the best combination algorithm this work introduces a metric called PET score, which evaluates algorithm candidates in terms of error performance and prediction time. These are considerations that are needed for time-sensitive services like XR’s. To validate our proposal, the 360-video service has been chosen to demonstrate the potential of this ML framework with a real XR use case. In addition, the dataset generated for the use case evaluation is now available. Similarly, this work serves as a foundation for future research on end-to-end quality of experience (QoE)-based network management in conjunction with other enabling technologies, including network slicing, virtualization, and multi-access edge computing (MEC).

In this document is presented a study about TV White Spaces (TVWS) current state in Ecuador, how this can solve coverage problems in difficult to reach geographic zones in comparison with other kind of technologies, facilitating the access to basic services such as Internet and Smart Grids support. Later the adopted standards by FCC in order to design White Spaces Devices are mentioned and the TVWS indoor and outdoor advantages and disadvantages in function of the date rate requirements. Later the current situation of the Ecuadorian Radioelectric spectra is exposed with ARCOTEL (Agency of Regulation and Control of the Telecommunications) data, specially focused in VHF and UHF TV bands in order to determine free and licensed channels in Azuay province to present a proposal of data service implementation through TVWS such a sub used spectra application in the country. Finally, some alternatives for the use of available spectrum are set in order to be applied in short, medium and long term in all Ecuadorian territory.

The information generated from Smart Meters in advanced measurement infrastructure (AMI) in a Smart Grid, needs real-time data acquisition and processing, this could generate high redundancy of data in an uplink in the communication network compared to the number of smart meters in a city. In this sense, the application of Compressive Sensing (CS) can improve the efficiency in the transfer of data and reduce a possible overload in the system. However, it is necessary to establish the minimum conditions required for the use of CS for data related to the Electrical Energy Consumption Profile. Thus, this article presents the analysis of the use of CS as a source compression method to reduce the data to be transmitted on a network. So, in order to evaluate the quality of the load profile reconstruction using CS despite the transformations that the data undergoes in a real wireless communication system, this technique has been implemented on a Software Defined Radio System (SDR). For this, the measurement matrix implemented with Gauss and Bernoulli distributions is analyzed. The algorithms for the reconstruction are: OMP (Paired orthogonal search), BP (Base search) and GPSR (Gradient Projection Sparse Recovery) in different compression ratios (10 - 80%). It also shows the comparison of errors generated in the uses for the representation of signals in the Gaussian campaign, the DCT transform, the Wavelet Meyer transform and Wavelet Biorthogonal 3.9.