WOS2 – Energy Efficiency and Architectures of 6G
Thursday, 5 June 2024, 11:00-12:30, room 1.B
Session Chair: Anna Tzanakaki (National and Kapodistrian Univ. of Athens, GR)
Subnetworks: a Novel Architectural Paradigm for 6G
Dimitrios Alanis and Christian Hofmann (Apple, Germany); Sameh Eldessoki and Panagiotis Botsinis (Apple Technology Engineering, Germany); Tarik Tabet and Sree Vallath (Apple Inc., USA)
The anticipated collaborative use cases in 6G come with extreme requirements in latency and throughput. At the same time, network densification is expected to continue, thus making those more stringent requirements tougher to satisfy. To accommodate this, we propose a novel architecture for subnetworks (SNs), a paradigm shift compared to the more centralized cellular systems today. These SNs are formed among user equipments (UEs) with aid of a new UE role, the Management Node (MgtN), which is also introduced. As a result, a novel SN architecture is presented, where the user and control planes of the UEs and the MgtN are described. With SNs, the functionalities are distributed among the different nodes in the SN. Additionally, new processes are defined to maintain network connectivity within the SN in the form of the SN tunneling (SN-TP) and routing (SN-RP) protocols. The new architecture is designed with the aim of enabling a higher degree of independence of the SN in a network of networks.
Power-Efficient Network Design with Adaptive Antennas and Power Boosting
Lucas Ribeiro and Ehsan Moeen Taghavi (University of Oulu, Finland); Muhammad Tayyab (Nokia, Finland); Dileep Kumar and Kamakshi L Krishnakumar (Nokia, Espoo, Finland); Daniela Laselva (Nokia, Denmark); Markku Juntti (University of Oulu, Finland)
To address the challenge of reducing base station energy consumption, the 3rd generation partnership project (3GPP) has carried out a comprehensive study on energy-saving techniques in Release 18. Among others, the study has investigated the effects of adaptive transmit antenna muting and power control strategies on energy consumption and user throughput under different traffic loads. In this work, our goal is to explore the trade-offs between network energy consumption and throughput. To that end, we consider a set of configurations with a fixed number of active antennas and distinct transmit power. Results show that applying 3 dB transmit power boost together with transmit antenna muting brings a good balance between energy savings and throughput degradation, effectively reducing the throughput losses caused by antenna muting while achieving significant energy savings. Moreover, we demonstrate that the downlink (DL) throughput for the proposed solution under low traffic load conditions remains higher than the baseline throughput observed at medium and high traffic loads. This is achieved despite some throughput reduction caused by halving the number of transmit antennas.
User Plane Radio Protocol Concepts for 6G
Mai-Anh Phan, Mehdi Abad and Torsten Dudda (Ericsson Research, Germany); Erik Eriksson (Ericsson AB, Sweden); John Skördeman (Ericsson, Sweden)
In the user plane, the role of radio protocols to reliably convey data packets over the physical radio channel, is key also in 6G. In each 3GPP generation they evolved, incorporating an increasing number of variants of the radio access network (RAN) architectures and target use-cases. This added complexity, but also reduced their processing efficiency. For 6G, we revisit these shortcomings and propose a leaner protocol stack capable of achieving the same functionality in a more processing and latency efficient way. We show how to design a simplified uplink control structure and reduce uplink access latency, how to optimize hybrid automatic repeat request (HARQ) in terms of reliability, and how this information can be exploited at higher layers, enabling the introduction of a unified 6G radio bearer protocol (RBP).
Comparative Analysis of Reconfigurable Intelligent Surfaces and Relay Nodes for Energy Saving
Jordi Pérez-Romero (Universitat Politècnica de Catalunya (UPC), Spain); Josep Xavier Salvat (NEC Labs Europe, Germany); Jose A. Ayala-Romero (NEC Laboratories Europe, Germany); Oriol Sallent (Universitat Politècnica de Catalunya, Spain); Anna Umbert (Universitat Politècnica de Catalunya (UPC), Spain); Juan Sanchez-Gonzalez (Universitat Politecnica de Catalunya (UPC), Spain); Xavier Costa Perez (NEC Laboratories Europe, Germany)
Different technologies are currently being investigated to reduce the energy consumption of beyond 5G systems. Two of the most preeminent ones are the Reconfigurable Intelligent Surfaces (RISs) and the relays, as both of them allow reducing the overall transmitted power levels at the base stations. However, due to the different nature of the two approaches, RISs being mostly passive while relays being active devices, it is unclear in which conditions one or the other leads to higher energy savings. This paper intends to shed light on this question by presenting a thorough analytical study of both approaches. Compared to the current literature, this study goes a step further by considering a more accurate RIS modeling based on actual RIS equipment, a thorough comparison of different energy consumption models, and a comprehensive study in a realistic environment of a university campus including indoor and outdoor propagation.
Learning Power Control Protocol for in-Factory 6G Subnetworks
Uyoata Etuk Uyoata (Modibbo Adama University, Nigeria); Gilberto Berardinelli and Ramoni O Adeogun (Aalborg University, Denmark)
In-X Subnetworks are envisioned to meet the strin- gent demands of short-range communication in diverse 6G use cases. In the context of In-Factory scenarios, effective power con- trol is critical for mitigating the impact of interference resulting from potentially high subnetwork density. Existing approaches to power control in this domain have predominantly emphasized the data plane, often overlooking the impact of signaling overhead. Furthermore, prior work has typically adopted a network-centric perspective, relying on the assumption of complete and up-to- date channel state information (CSI) being readily available at the central controller. This paper introduces a novel multi-agent reinforcement learning (MARL) framework designed to enable access points to autonomously learn both signaling and power control protocols in an In-Factory Subnetwork environment. By formulating the problem as a partially observable Markov decision process (POMDP) and leveraging multi-agent proximal policy optimization (MAPPO), the proposed approach achieves significant advantages. The simulation results demonstrate that the learning-based method reduces signaling overhead by a factor of 8 while maintaining a buffer flush rate that lags the ideal “Genie” approach by only 5%.
