Session 17: WOS-3: Beamforming and energy efficiency
Thursday, 9 June 2022, 16:00-17:30
Session Chair: TBD ( , )
Unsupervised Learning for User Scheduling in Multibeam Precoded GEO Satellite Systems
Flor Ortiz, Eva Lagunas and Symeon Chatzinotas (University of Luxembourg, Luxembourg)
Future generation SatCom multibeam architectures will extensively exploit full-frequency reuse schemes together with interference management techniques, such as precoding, to dramatically increase spectral efficiency performance. Precoding is very sensitive to user scheduling, suggesting a joint precoding and user scheduling design to achieve optimal performance. However, the joint design requires solving a highly complex optimization problem which is unreasonable for practical systems. Even for suboptimal disjoint scheduling designs, the complexity is still significant. To achieve a good compromise between performance and complexity, we investigate the applicability of Machine Learning (ML) for the aforementioned problem. We propose three clustering algorithms based on Unsupervised Learning (UL) that facilitate the user scheduling decisions while maximizing the system performance in terms of throughput. Numerical simulations compare the three proposed algorithms (K-means, Hierarchical clustering, and Self-Organization) with the conventional geographic scheduling and identify the main trade-offs.
Energy Consumption of DECT-2020 NR Mesh Networks
Timo Nihtilä (Magister Solutions Ltd., Finland); Heikki Berg (Nordic Semiconductor, Finland)
ETSI DECT-2020 New Radio (NR) is a new flexible radio interface targeted to support a broad range of wireless Internet of Things (IoT) applications. Recent reports have shown that DECT-2020 NR achieves good delay performance and it has been shown to fulfill both massive machine-type communications (mMTC) and ultra-reliable low latency communications (URLLC) requirements for 5th generation (5G) networks. A unique aspect of DECT-2020 as a 5G technology is that it is an autonomous wireless mesh network (WMN) protocol where the devices construct and uphold the network independently without the need for base stations or core network architecture. Instead, DECT-2020 NR relies on part of the network devices taking the role of a router to relay data through the network. This makes deployment of DECT-2020 NR network affordable and extremely easy but due to the nature of the medium access protocol, the routing responsibility adds an additional energy consumption burden to the nodes, who in the IoT domain are likely to be equipped with limited battery capacity. In this paper we analyze by system level simulations the energy consumption of DECT-2020 NR networks with different network sizes and topologies and how the reported low latencies can be upheld given the energy constraints of IoT devices.
Energy-Efficient Dynamic Edge Computing with Electromagnetic Field Exposure Constraints
Mattia Merluzzi (CEA-Leti, France); Serge Bories (CEA, France); Emilio Calvanese Strinati (CEA-LETI, France)
We present a dynamic resource allocation strategy for energy-efficient and Electromagnetic Field (EMF) exposure aware computation offloading at the wireless network edge. The goal is to maximize the overall system sum-rate of offloaded data, under stability (i.e. finite end-to-end delay), EMF exposure and system power constraints. The latter comprises end devices for uplink transmission and an Edge Server (ES) for computation. Our proposed method, based on Lyapunov stochastic optimization, is able to achieve this goal with theoretical guarantees on asymptotic optimality, without any prior knowledge of wireless channel statistics. Although a complex long-term optimization problem is formulated, a per-slot optimization based on instantaneous realizations is derived. Moreover, the solution of the instantaneous problem is provided with closed form expressions and fast iterative procedures. Besides the theoretical analysis, numerical results assess the performance of the proposed strategy in striking the best trade-off between sum-rate, power consumption, EMF exposure, and E2E delay. To the best of our knowledge, this is the first work addressing the problem of energy and exposure aware computation offloading.
Beam Alignment Strategy Under Hardware Constraints for D-Band Communications
Johan Laurent (CEA-Leti, Université Grenoble Alpes, France); Nicolas Cassiau (CEA-Leti Minatec Campus, France); Loic Marnat (CEA, LETI, Minatec, France); David del Río and Juan F Sevillano (CEIT and TECNUN, Spain); Alessandro D’Acierno (NOKIA Italy, Italy); Maurizio Moretto (Nokia, Italy); Ivan Caballero (TTI Norte, Santander, Spain); Stefano Chinnici (HCL Technologies, Italy); Fabrizio Ronchi (HCL Technologies Italy SpA, Italy)
This paper describes the antenna beam alignment strategy of a D-band backhaul link adopted in the DRAGON project. The aim is to continuously compensate for antenna vibrations. This strategy is based on a periodic assessment of beams stored in a codebook, and can be implemented independently in both ends of the link. Hardware requirements coming from the envisioned architecture of the whole transceiver are taken into consideration. In particular, communications among the different involved boards restrict the periodicity at which the beams of the codebook can be assessed. The design of the codebook and the algorithm for beams selection are also discussed. Simulations based on a software specifically developed demonstrate that the solution meets the tracking specifications for an D-band link associated to realistic antenna movements.
[VAP]Environment-Aware Hierarchical Codebook Design for mmWave Massive MIMO System
Jing Jiang (Xi’an University of Posts and Telecommunications, China); Runqiu Han (Xi’an University of Posts and Telecommunication, China)
Hierarchical codebook is a primary enabler to acquire the channel state information in millimeter-wave (mmWave) massive MIMO wireless communication systems. However, the existed hierarchical codebook was designed by the uniform quantization of the total angular domain that may be incompatible with variable wireless environments. To tackle this issue, we propose an environment-aware hierarchical codebook design method. Specifically, it leverages the federated clustering to learn and extract the key features of complicate wireless environments. Firstly, each user equipment (UE) collects the downlink channel state information (CSI) and acquire key features of optimal precoding codebook vectors through local clustering. And then, the global model extracts the key features of the centroids for all users and construct the environment-aware hierarchical codebook. Both the theoretical analysis and simulation results prove that the proposed hierarchical codebook is closer to the optimal precoding vectors and can obtain higher achievable rate than the existed uniform codebook.