Looking at NB-IoT over LEO Satellite Systems: Design and Evaluation of a Service-Oriented Solution

The adoption of the NB-IoT technology in satellite communications intends to boost Internet of Things services beyond the boundaries imposed by the current terrestrial infrastructures. Apart from link-level studies in the scientific literature and preliminary 3GPP technical reports, the overall debate is still open. To provide a further step forward in this direction, the work presented herein pursues a novel service-oriented methodology to design an effective solution, meticulously stitched around application requirements and technological constraints. To this end, it conducts link-level and system-level investigations to tune physical transmissions, satellite constellation, and protocol architecture, while ensuring the expected system behavior. To offer a real smart agriculture service operating in Europe, the resulting solution exploits 24 Low Earth Orbit satellites, grouped into 8 different orbits, moving at an altitude of 500 km. The configured protocol stack supports the transmission of tens of bytes generated at the application layer, by also counteracting the issues introduced by the satellite link. Since each satellite has the whole protocol stack on-board, terminals can transmit data without the need for the feeder link. This ensures communication latencies ranging from 16 minutes to 75 minutes, depending on the served number of terminals and the physical transmission settings. Moreover, the usage of the Early Data Transmission scheme reduces communication latencies up to 40%. These results pave the way towards the deployment of an effective proof-of-concept, which drastically reduces the time-to-market imposed by the current state of the art

A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks

This paper deals with the transmission power control problem in wireless networks. Such a problem represents a well known and relevant issue as it allows to efficiently manage the network's required energy and the interference experienced by end-users. With the widespread diffusion of smart devices, the relevance of this aspect further increased and has been identified as such also in 5G standards. The problem has been formalized as a Multi-Agent Reinforcement Learning approach (MARL) to guarantee scalability and robustness. These two aspects also drove the development of an original Distributed Average-Cost Temporal-Difference (TD) Learning algorithm. To adopt such an algorithm, a Markov Game formulation of the power control problem has also been derived. The effectiveness of the proposed distributed framework in reducing the total network's transmission power has been proved by means of simulations in a specific case study

User-aware centralized resource allocation in heterogeneous networks

In the last two years, in Europe, 5G networks and services proliferated. The integration of 5G networks with other radio access networks is considered one of the key enablers for matching the challenging 5G Quality of Service requirements. In particular, the integration with high throughput satellites promises to increase the network performances in terms of resilience and Quality of Service. The present work addresses this problem and presents a user-aware resource allocation methodology for heterogeneous networks. Said methodology is articulated in two-steps: at first, the Analytical Hierarchy Process is used for deciding the network over which traffic is steered and, then, a Cooperative Game for allocating resources within the network is set up. Simulations are presented for validating the proposed approach

Hierarchical RL for load balancing and QoS management in multi-access networks

This paper deals with the problem of resource management in Multi-Access Networks. A Reinforcement Learning based hierarchical control strategy is presented. The main contribution of the proposed approach is its capability of simultaneously tacking the load balancing and QoS management problems in a scalable, dynamic and closed-loop way. The effectiveness of the proposed solution has been proved in a specific case study in the context of which the performances of the proposed algorithm have been compared with a standard load balancing controller

Multi-Connectivity in 5G Terrestrial-Satellite Networks: the 5G-ALLSTAR solution

The 5G-ALLSTAR project is aimed at integrating Terrestrial and Satellite Networks for satisfying the highly challenging and demanding requirements of the 5G use cases. The integration of the two networks is a key feature to assure the service continuity in challenging communication situations (e.g., emergency cases, marine, railway, etc..) by avoiding service interruptions. The 5G NTN (Non-Terrestrial Network e.g., Satellite Network) would have a fundamental role in 5G [1], thanks to its characteristics exploited typically for live events broadcasting in large areas and for ultra-reliable and secure communications; The networks integration will have a great impact to the network performances. The 5G-ALLSTAR project proposes to develop Multi-Connectivity (MC) solutions in order to guarantee network reliability and improve the throughput and latency for each connection between User Equipment (UE) and network. In the 5GALLSTAR vision to easily integrate the terrestrial and satellite networks, allowing Fast Switching and User Plane Aggregation, we divide the gNB in two entities [3]: 1) gNB-CU (Centralized Unit) and 2) gNB-DU (Distributed Unit). Each gNB-CU controls a set of different gNB-DUs (see Figure 1-a). The gNB-CU integrates an innovative Traffic Flow Control algorithm able to optimize the network resources by coordinating the controlled gNB-DUs resources, while implementing MC solutions. The MC [2] permits to connect each UE (whether possible) with simultaneous multiple access points which can belong to both the same and different radio access technologies. The 5G-ALLSTAR solution for the MC deals with the possibility to have a common RRC and partial User Plane functionalities in the gNB-CU, i.e., SDAP and PDCP layers. This solution leads to have independent gNB-DU/s that contain the RLC, MAC and PHY layers. The communication between gNB-CU and the controlled gNB-DUs takes place by using the wellknown F1 interface [3]. As an example of integration between NTN and Terrestrial Networks we can consider the MC solution shown in Figure 1-b where the same packet (duplicated by the PDCP layer) are delivered independently to the two access points (gNB-DU-SAT and gNB-DU-5G) [4],[5],[6]. The 5G-ALLSTAR MC algorithms offer advanced functionalities to RRC layer [7] (in the gNB-CU) that is, in turn, able to set up the SDAP [8], the PDCP [9] and the lower layers in gNB-DU. In this regard, the AI-based MC algorithms, implemented in gNB-CU (also known as Cloud RAN), by considering the network performances in the UE surrounding environment as well as the UE QoS requirements, will dynamically select the most promising access points able to guarantee the fulfillment of the requirements (guarantying the required degree of throughput and latency) also enabling the optimal traffic splitting to cope with the connection reliability. In this paper, we present also an innovative AI-based framework, included within the Traffic Flow Control, able to address the MC objectives (as presented above), by implementing a Reinforcement Learning algorithm in charge of solving the network control problem