Spectrum Cost Optimization for Cognitive Radio Transmission over TV White Spaces using Artificial Neural Networks

Abstract

In this paper, the use of TV White Spaces (TVWS) by small cognitive radio wireless network operators (SCWNOs) is considered in order to support the growing demands for IoT applications in smart grid and smart cities. In order to support the wide range of services and applications that are being offered by SCWNOS, spectrum leasing could be considered as an alternative solution to achieve improved Quality of Service (QoS). We consider a situation whereby in order to satisfy the QoS requirements, SCWNOs can decide to lease a certain part of the TVWS spectrum that is referred to as high priority TVWS channel (HPC) for a certain period and pay a fee depending on the duration of HPC spectrum usage. We develop an Artificial Neural Networks (ANN) based online algorithm to determine the optimal transmission decision per time slot that would minimise the overall HPC leasing cost of the SCWNOs while satisfying the QoS constraints. The simulations results shows that our proposed ANN based online algorithms outperforms the Lyaponuv based online algorithm while its performance is very close to the optimal offline solution with 99% accuracy.

Revenue Maximization Through Cell Switching and Spectrum Leasing in 5G HetNets

“…In this regard, various research works using techniques such as game theory, matching theory, and machine learning techniques, etc., have been proposed [17]- [22]. The authors in [17] proposed a traffic-adaptive spectrum leasing scheme whereby the SUs are able to negotiate the duration of channel leasing with the PUs in order to ensure their continual utilization of the leased channel for the complete transmission of the data in their buffer.…”

“…These methods comprise analytical, heuristic and machine learning-based approaches. Similar optimization techniques have also been proposed for spectrum leasing [17]- [22]. However, very few research works have considered both cell switching and spectrum leasing for maximizing the revenue of the PN [23], [24], even though only a homogeneous network deployment scenario as well as a fixed electricity and spectrum pricing policy were considered, thereby making their work quite simplistic.…”

Revenue Maximization through Cell Switching and Spectrum Leasing in 5G HetNets

“…In this regard, various research works using techniques such as game theory, matching theory, and machine learning techniques, etc., have been proposed [16]- [22]. The authors in [16] proposed a traffic-adaptive spectrum leasing scheme whereby the SUs are able to negotiate the duration of channel leasing with the PUs in order to ensure their continual utilization of the leased channel for the complete transmission of the data in their buffer.…”

“…Their goal is to find a suitable paring between the MNOs and MVNOs that would maximize the revenue of the MNOs as well as the bandwidth allocated to the MVNOs. The work in [22] considered the problem of spectrum leasing optimization for CRN transmission over TV white spaces. A neural network based solution was proposed to determine the optimal transmission policy that would result in minimal spectrum leasing cost while considering the QoS of the CRN.…”

“…These methods comprise analytical, heuristic and machine learning based approaches. Similar optimization techniques have also been proposed for spectrum leasing [16]- [22]. However, very few research works have considered both cell switching and spectrum leasing for maximizing the revenue of the PN [23] and [24], even though only a homogeneous network deployment scenario as well as a fixed electricity and spectrum pricing policy were considered, thereby making their work quite simplistic.…”