Multi-operator Spectrum Research Articles

Multi-Operator Spectrum and MEC Resource Sharing in Next Generation Cellular Networks

Multi-Operator Spectrum and MEC Resource Sharing in Next Generation Cellular Networks

Deep Reinforcement Learning Assisted Multi-Operator Spectrum Sharing in Cell-Free MIMO Networks

Deep Reinforcement Learning Assisted Multi-Operator Spectrum Sharing in Cell-Free MIMO Networks

Multi-Operator Spectrum Sharing for Massive IoT Coexisting in 5G/B5G Wireless Networks

With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.

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

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.

Quantifying Benefits in a Business Portfolio for Multi-Operator Spectrum Sharing

Benefits of multi-operator spectrum sharing in wireless networks heavily depend on the traffic misbalance in the networks belonging to different operators. In this paper, we study the likelihood that such misbalance occurs in networks with high traffic dynamics. An extensive business portfolio for heterogeneous networks is presented to analyse the benefits due to multi-operator cooperation for spectrum sharing. High resolution pricing models are developed to dynamically facilitate price adaptation to the system state. By using queuing theory, we quantify the operators' gains in cooperative arrangements as opposed to non-cooperative independent operation. In addition, Markov model is used that can handle wider range of different distributions of traffic arrivals and service rates. A tractable analysis and quantitative results are provided for those gains as a function of the number of cooperating operators. Under the condition that there is a traffic underflow in one band, it has been shown that with capacity aggregation model, the operator operating in other band can take advantage of additional channels with probability close to 1. In capacity borrowing/leasing model, this advantage is not unconditional, and there is a risk that the operator leasing the spectra will suffer temporary packet losses. When cognitive models are used in a network with high traffic dynamics, 50–70% of the spectra may be lost due to channel corruptions caused by the return of primary users. The gains of traffic offloading from a cellular network to a WLAN are quantified by an equivalent increase in opportunistic capacity proportional to the ratio of aggregate coverage of cellular networks and WLANs. The results provide guidelines for business decision in multi-operator network management.