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The 6th Generation (6G) radio access technology is expected to support extreme communication requirements in terms of throughput, latency and reliability, which can only be achieved by providing capillary wireless coverage. In this paper, we present our vision for short-range low power 6G ‘in-X’ subnetworks, with the ‘X’ standing for the entity in...
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... refer to our previous work [87] for a detailed description of the used interference management schemes, as well as for the results generation procedure. Figure 7 shows the PLF as a function of the spectral efficiency. As mentioned in Section II, PLF is a measure of spatial availability of the service, and reflects the risk of obtaining at a given time and location an outage probability lower than a predefined values (10 −6 in this example). ...
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... The independent and uncoordinated subnetworks in hospitals require high reliability, determinism, and semi-autonomy in hospital contexts, e.g., to control robot arms and critical on-body devices [16], and local and indoor solutions. Therefore, medical wireless devices, machines, and sensors will require the 6G network for seamless connection, transmission, and processing of real-time health data [17,18] in a secure and safe way of data exchange [19]. ...
This paper aims to understand the value-added services that the future 6G-enabled metaverse can and will bring to hospitals. This is important since most studies on 6G and the metaverse are heavily driven by technological solutions. Adopting a qualitative research approach, this paper collects experts’ opinions on the usage scenarios of the 6G-enabled metaverse in hospitals. Six use cases within hospital contexts have been identified from open-ended interviews. The analysis of each case reveals that 6G, as a general-purpose technology, offers the necessary capabilities to support the development of the metaverse in hospitals. The metaverse-enabled services are expected to design future smart hospitals and improve work processes and resource allocation in hospitals, while also promoting preventive healthcare and training and enhancing the quality of care in emergency, treatment, and rehabilitation. Consequently, the development of both metaverse and 6G will progress in tandem, hand in hand, offering local services in hospitals. From a value perspective, this paper contributes to the development of the 6G and metaverse in the hospital vertical by understanding the needs, capabilities, and key values of the future 6G-enabled hospital metaverse.
... These applications can be categorized as a "Network of Networks", integrating specialized networks. Certain networks with specific short-range, and extreme requirements such as 0.1 ms latency, 6-8 nines reliability, high data rates, and more, are classified as In-X subnetworks [2]. In-X subnetworks (SNs), including in-robots, in-vehicle, in-on-body, and in-house SNs. ...
In-X subnetworks (SN) face challenges in achieving heterogeneous extreme requirements such as 0.1 ms latency and 6-to-8 nines reliability due to high and dynamic interference caused by mobility, channel characteristics, and limited resources in ultra-dense environments. We propose interference prediction techniques, M-to-M, 1-to-1, and federated learning (FL)-based long short-term memory (LSTM) that help in learning the complex and potentially non-linear relationship from a measured interference power vector. This results in significantly lower prediction error as compared to baseline methods. The FL-based predictor enhances robustness by leveraging knowledge from other sensor/actuator pairs' learning by achieving up to 4.5 dB lower prediction error as compared to M-to-M, and one-to-one LSTM predictors in the Sensor-actuator (SA) pair.
... Some of the applications of in-X subnetworks are in-robots, in-vehicle, in-body, inhouse, etc., with different service demands. These subnetworks have their own extreme requirements in terms of low latency, high reliability, life-criticality, and data rate, etc [3]. These subnetworks can be either connected to the existing cellular network directly or via local interactive devices or may remain unconnected to the 6G network. ...
... In 6G terminology, subnetworks are also referred to as 'in-X' subnetworks, with the 'X' standing for the entity where the subnetwork is deployed, such as a production module in a smart manufacturing environment, a robot, a vehicle, a house, or even the human body in cases of wearable devices that can monitor various parameters [57]. A schematic diagram of such a network is shown in Figure 5, where data flows are categorized according to their latency requirements: low, such as in the cases of monitoring non-latency-critical key performance indicators (KPIs); medium, such as task offloading in edge servers; and high. ...
... In 6G terminology, subnetworks are also referred to as 'in-X' subnetworks, with the 'X' standing for the entity where the subnetwork is deployed, such as a production module in a smart manufacturing environment, a robot, a vehicle, a house, or even the human body in cases of wearable devices that can monitor various parameters [57]. A schematic diagram of such a network is shown in Figure 5, where data flows are categorized according to their latency requirements: low, such as in the cases of monitoring non-latencycritical key performance indicators (KPIs); medium, such as task offloading in edge servers; and high. ...
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.
... In 6G terminology, subnetworks are also referred to as 'in-X' subnetworks, with the 'X' standing for the entity where the subnetwork is deployed, such as a production module in a smart manufacturing environment, a robot, a vehicle, a house, or even the human body in cases of wearable devices that can monitor various parameters [53]. A schematic diagram of such a network is shown in Figure 5, where data flows are categorized according to their latency requirements: low, such as in the cases of non-latency critical key performance indicators (KPI) monitoring; medium, such as task offloading in edge servers, and; high. ...
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things – IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where IoT, edge and cloud converge to form a computing continuum, is also known as IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems architectural approach, including: i) design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; ii) design of efficient task offloading algorithms aimed at optimizing services execution; iii) support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; iv) scalability, flexibility and reliability guarantees to face the expected mobility for IoT systems, and; v) design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges become even more significant towards the new era of sixth generation (6G) networks, which will be based on the integration of various cutting edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented, from an IEC continuum perspective.
... In-X sub-networks have been recently identified as an important component for 6G [14]: most of the use cases where HRLLC requirements are expected relate to short-range communications, for example communications among nearby robots and production modules in a factory or among smartphones and wearables of one person or nearby people for XR. From that observation arises the concept of sub-networks, as a sort of mobile short-range cell, where one device acts as access point (AP) within that sub-network, scheduling the transmissions of the other sub-network devices, being a gateway toward the public/private network, and with local traffic characterized by extreme requirements. ...
... From that observation arises the concept of sub-networks, as a sort of mobile short-range cell, where one device acts as access point (AP) within that sub-network, scheduling the transmissions of the other sub-network devices, being a gateway toward the public/private network, and with local traffic characterized by extreme requirements. Several challenges have already been identified for sub-networks in [14], both for the air interface and the architecture: one of the most relevant issues is the interference among sub-networks in dense and dynamic deployments. Advanced interference mechanisms are needed, that allow a sub-network, also when out-of-coverage of a public/private network, to meet the requirements of the local traffic [15]. ...
... Some of the applications of in-X subnetworks are in-robots, in-vehicle, in-body, inhouse, etc., with different service demands. These subnetworks have their own extreme requirements in terms of low latency, high reliability, life-criticality, and data rate, etc [3]. These subnetworks can be either connected to the existing cellular network directly or via local interactive devices or may remain unconnected to the 6G network. ...
Network of subnetworks" is envisioned to be a key enabler in a 6G network with extremely low (100 µs) latency and high-reliability (99.9999%-99.99999%) in demanding applications. However, to achieve this level of performance, it is necessary to introduce a proactive and robust interference estimation considering the random mobility of subnetworks in an ultra-dense environment. We propose long-short term memory (LSTM) to learn the non-linear behavior of interference power time series for robust estimation and prediction in in-X subnetworks. This proposed method empowers to prediction/estimation of interference on the subnetwork itself. The achieved estimation result is compared with the moving average-based and expectation based estimators. Furthermore, we introduce federated learning (FL) in-X subnetworks' interference estimation, which learns cooperatively from the interference power vector of subnetworks participating in training. The results indicate that the proposed FL-based estimator achieves a higher convergence speed and lower estimation error.
... For example, the recent concept of 6G in-X subnetworks envisions short-range radio cells to be installed inside entities like industrial robots, production modules, vehicles or even human bodies, for critical control applications [4]. Specifically, in-X subnetworks may replace hard real time industrial communication protocols such as PROFINET Isochronous Real Time (IRT) and Ethernet Powerlink, characterized by strictly periodic transmissions with deterministic cycle times [5]. ...
... In spite of its simplicity, we believe that the packet erasure channel model is well-suited for the basic analysis of an interference-limited in-X subnetwork scenario. This is because in short range subnetworks the receive signal strength of the desired transmitter is expected to be high; moreover, the AP can use techniques such as power control and link adaptation to counteract fading and ensure correct reception for interference-free transmissions [4]. This means, transmission failures are only caused by collisions. ...
Future wireless systems are expected to support mission-critical services demanding higher and higher reliability. In this letter, we dimension the radio resources needed to achieve a given failure probability target for ultra-reliable wireless systems in high interference conditions, assuming a protocol with frequency hopping combined with packet repetitions. We resort to packet erasure channel models and derive the minimum amount of resource units in the case of receiver with and without collision resolution capability, as well as the number of packet repetitions needed for achieving the failure probability target. Analytical results are numerically validated and can be used as a benchmark for realistic system simulations
... That reduces the over-the-air latency down to 100 μs for highly critical applications and imposes great challenges on system designs for 6G networks. Recently, the concept of in-X subnetworks has been identified as a potential solution for such extreme applications [3]. Controllers are co-located with access points (APs) and installed in specific entities e.g., industrial production lines, vehicles, etc., to provide shortrange capillary coverage for critical services for a small group of devices. ...
... In particular, the macrocells generally utilize sub-6 GHz bands for long transmissions to provide conventional wireless connections in indoor and outdoor environments, whereas the small cells utilize high frequencies such as mmWave and THz bands for robust data rates over short distances. As a result, a variety of user services are enabled such as mobile broadband Internet access for rich media content (e.g., videos on-demand, social media, multiplayer interactive online gaming, distance learning) (Nallappan et al., 2018), Internet of things (IoT) services (Petrov et al., 2020), industrial IoT networks (Vijayakumar et al., 2022;Ju et al., 2021), smart home/buildings applications (Berardinelli et al., 2021), precision agriculture applications (Ndassimba et al., 2021), and vehicular ad-hoc network (Singh et al., 2021;Eckhardt et al., 2021). Moreover, low-latency remote services benefit from diverse access technologies for stable connections, such as telehealthcare (i.e., e-Health and m-Health) services (e.g., telemedicine, medical transportation services, Internet of medical things, online training sessions for healthcare professionals) (Yu and Zhou, 2021;Chen et al., 2021b;Ye et al., 2022;Juwono et al., 2021;Chen et al., 2021a), and disease detection using in-body nano-sensor communication (Simonjan et al., 2021;Saeed et al., 2021b;Kabir et al., 2021;Javaid et al., 2022). ...
In recent years, we have witnessed a major expansion of wireless communications, extending from terrestrial to aerial spaces to facilitate novel user services. While aerial access infrastructure is widely accepted to be an essential part of a comprehensive sixth-generation (6G) network, underwater wireless communications (UWC), unfortunately, are neglected. This observation questions us about the maturity of UWC technologies and its potentials for 6G. First, we describe an overview of 6G access infrastructures, revealing the gaps concerning underserved spaces and services. Next, we investigate current developments of cutting-edge technologies that enable heterogeneous multiaccess UWCs. Then, we analyze a feasible approach for a UWC integration to complement the 6G infrastructures, achieving a fully comprehensive framework. Furthermore, we introduce UWC-enabled application scenarios that possibly improve the popularity of 6G in aquatic environment. Finally, open challenges are discussed to drive future studies toward the maturation of UWC. This study is expected to facilitate interested researchers and engineers with a systematical reference framework of state-of-the-art UWC knowledge and information.
