A Hybrid Model-Based and Data-Driven Approach to Spectrum Sharing in mmWave Cellular Networks

Abstract

Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference. Unfortunately, traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms in terms of latency and protocol overhead, while being sensitive to missing channel state information. In this paper, we propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms, which incorporate model-based beamforming and user association complemented by data-driven model refinements. Our solution has the same computational complexity as a model-based approach but has the major advantage of having substantially less signaling overhead. We discuss how limited channel state information and quantized codebook-based beamforming affect the learning and the spectrum sharing performance. We show that the proposed hybrid sharing scheme significantly improves spectrum utilization under realistic assumptions on inter-operator coordination and channel state information acquisition.

CRC‐AFC‐based dynamic spectrum resource optimisation for URLLC access in 5G‐advanced and 6G

“…However, considering the occupation of available spectrum by many mobile operators, even mmWave with abundant spectrum resources is not unlimited [11]. Radio spectrum resources need to be reasonably allocated and managed to improve mmWave spectrum utilisation and ensure URLLC performance [12].…”

Traffic Load-Based Spectrum Trading between Two Mobile Network Operators in HetNets Using Interference Management Technique

“…In addition, a hybrid modelbased and data-driven multi-operator spectrum sharing mechanism was proposed in Ref. [32]. e technique incorporates model-based beam forming and user association complemented by data-driven model refinements having substantially less signalling overhead but at the expense of increased interoperator interference.…”

Machine Learning: A Catalyst for THz Wireless Networks

“…Scanning the technical literature, we can identify several contributions that employ ML for user association in sub-THz and THz wireless networks (Zhang H. et al, 2019;Liu R. et al, 2020;Khan L. U. et al, 2020;Chou et al, 2020;Li et al, 2020c;Elsayed et al, 2020;Ghadikolaei et al, 2020;Hassan et al, 2020). In more detail, in (Liu R. et al, 2020), the authors employed multi-label classification ML that takes as input both topological as well as network characteristics and returns a user association policy that satisfy users' latency demands.…”

“…In (Hassan et al, 2020), two clustering approaches, namely least standard deviation user clustering and redistribution of BSs load-based clustering were presented that take into account the characteristics of both radio frequency (RF) and THz as well as the traffic load across the network in order to provide appropriate associations in RF and THz heterogeneous networks. Furthermore, in (Ghadikolaei et al, 2020), a transfer learning methodology was employed for inter-operator spectrum sharing in mmW cellular networks. The aforementioned methodology takes as input the network topology, the association matrix, the coordination matrix, the effective channels and outputs approximate the achievable data-rate.…”

“…Similarly, the contributions in (Liu et al, 2007;Bin and Hao, 2010;Noorbehbahani and Mansoori, 2018;Zhang H. et al, 2019;Khan L. U. et al, 2020;Chou et al, 2020) and (Kumari et al, 2016) can be applied in any femto-cell network that support high data traffic demands, such as the THz wireless networks, independently from the operation frequency. Moreover, (Auld et al, 2007;Wang et al, 2013;Kruber et al, 2018;Su et al, 2018;Burghal et al, 2019;Guo et al, 2019;Yan et al, 2019;Elsayed et al, 2020;Ghadikolaei et al, 2020) and (Wang and Yu, 2008) refer to mmW networks that support data-rates in the order of 100 Gb/s in 60 GHz. In such networks, both the AP and UEs employ high-gain antennas.…”