Power trading in cognitive radio networks

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Abstract

Cognitive radio technology has been proposed to improve spectrum utilization by sharing the frequency spectrum bands between licensed and unlicensed users, called primary users (PUs) and secondary users (SUs), respectively. The main objective of the SUs is to achieve their quality of service (QoS) by exploiting the unused parts of the spectrum, while the PUs aim to reap extraordinary profits by leasing their unused portions of the spectrum. Pricing and transmission power are two key issues of interest to both PUs and SUs. Game theory has been considered as a useful tool for discussing the power control issue in wireless networks. In this paper, we consider the issue of power trading and propose two different models. First, a power-pricing model without game theory is developed, wherein the PUs obtain some revenue by renting their unused frequencies to SUs that use suitable power levels to transmit. The suitable power level ensures that the use of the spectrum by SUs does not interfere with other users in the network. Second, a non-cooperative game is applied to the proposed pricing model among the system users (i.e., PUs and SUs), to create balance between them. This balance point is known as the “Nash equilibrium.” Performance evaluations of the proposed models are provided, demonstrating their efficiency and how they help in using the frequency spectrum more efficiently. The developed models allow the PUs to increase their gained profit while the SUs can use the spectrum for their data transmissions.

Introduction

In modern society, people want to access the Internet from anywhere and at any time. This desire results in the augmentation of spectrum demand. Simultaneously, the number of web-based applications is rapidly increasing; yet spectrum resources remain limited. Therefore, limitations in spectrum resources and high spectrum demand lead to the problem of spectrum scarcity.

In most countries around the world, the spectrum is allocated exclusively to licensed users; however, if the licensed users do not actually use this spectrum, it is still considered used, despite the fact that it is actually unused (wasted). Recent spectrum utilization measurements have shown that the use of the spectrum is concentrated on certain portions of it, while significant amounts are severely under-utilized. The Federal Commission Communication (FCC) chart clearly shows the unused portions of the spectrum, as indicated in Fig. 1 (Mitola, 1999).

In order to increase spectrum utilization in an efficient way, new spectrum-sharing models must be produced. The FCC allows sharing the spectrum among both types of users (i.e., unlicensed users and licensed users), so long as unlicensed users respect licensed users׳ rights.

Many solutions have been introduced to overcome the spectrum scarcity problem. Dynamic spectrum access (DSA) is one such solution, wherein the spectrum is dynamically utilized. It enables users to adjust communication parameters (such as operating frequency, transmission power, and modulation scheme) in response to changes in the wireless environment (Akkarajitsakul et al., 2011, Mishra et al., 2005, Weiss et al., 2003).

Cognitive radio (CR) has two types of users: the licensed users, which are referred to as primary users (PUs), and the unlicensed users, which are referred to as secondary users (SUs). The PUs get the spectrum bands from their service providers and have the ability to use the bands whenever they want, while the SUs must first detect the absence/presence of PUs in their spectrum bands before using them.

Power is consumed when the spectrum is used by any type of user. This value of power should be in an acceptable range, so that it does not affect the performance of the whole system. The spectrum owners (i.e., PUs) use the full capabilities of their frequency bands, while the SUs can use the partial or full capability of the frequency bands of the PUs. The SUs can use low power levels to transmit over the frequency bands of the PUs; however, if they want to use the capabilities of the frequency bands, they should pay for it.

Spectrum trading is the process of leasing the frequency bands of PUs to SUs, whereby the SUs pay for the use of the bands. The PUs allow SUs to use proper power levels to achieve the QoS desired by SUs while not harming other users in the network. The PUs reap high revenues from this leasing plan.

The big challenge in CR is determining how to develop a model that represents the spectrum-trading process. Any developed model should take into consideration the different goals of the various types of users and create a balance between these conflicting goals.

Game theory is a tool widely used in both wireless networks and CR networks, and previous studies have shown that more balance is achieved between the different aims of the different users (i.e., PUs and SUs) in the CR network by applying game theory.

We have two main contributions in this paper. First we develop a model for power trading process wherein PUs lease their unused spectrum channels to SUs which are willing to pay the channels rent. Second, we apply the game theory to the power trading model taking in consideration the different users׳ requirements. The power trading model without game theory aims at increasing the PUs profit by leasing their unused spectrum channels while the power trading model with game theory makes a balance between the conflicting objectives of PUs and SUs by applying the game theory concepts. Both the models have following objectives: to let SUs use proper power levels for their transmissions which should not harm other users in the system, to increase the number of users that utilize the spectrum, to consider the requirements of the SUs in CR networks, to enhance the efficiency of the spectrum utilization, and to consider the conflicting objectives of both types of the users in the CR network.

The rest of this paper is organized as follows: Section 2 shows an overview of cognitive radio network and game theory. Section 3 presents related work. In Section 4 we propose the general view of the system developed. The non-game theoretic approach and its performance evaluation are shown in Section 5. In Section 6 the game theoretic model is shown as well as its performance evaluation. We conclude this paper in Section 7.

Section snippets

An overview of cognitive radio

The principle of cognitive radio (CR) was first mentioned and explained by Mitola (1999). CR is defined as an efficient technology that allows more users to access the available spectrum. It is a radio that can change its transmitter parameters based on interaction with the environment in which it operates. Two characteristics are identified from the previous definition: cognitive capability and re-configurability (Mitola, 1999). Cognitive capability represents the ability of the radio

Related work

Recently, many researchers focused their attention on the economic aspect of dynamic spectrum sharing, which is also referred to as spectrum trading. The authors in Weiss et al. (2003) discussed the concept of spectrum trading in the context of different spectrum sharing models, and outlined different forms of spectrum trading, the related research problems, and the different solution approaches. The spectrum trading concept with multiple PUs selling spectrum opportunities to multiple SUs are

Network overview and users requirements

This section presents the general form and assumptions of the system that is considered in this paper. The requirements of both types of users are identified.

Power trading approach without game theory

The power trading approach proposed without the game theory model is described in detail in the following section.

Power trading approach with game theory

Recently, game theory has been considered as a useful tool to discuss the power control issue in wireless networks. Each game can be defined by three major components: a set of players, a set of actions of each player, and the utility (payoff) for each player. Each player chooses an action to gain its payoff. There are two major approaches of game theory which can be used to model the different spectrum access schemes: the cooperative and the non-cooperative game approaches. In the former

Conclusion

The power management and spectrum trading issues are merged together leading to a new approach of power trading. Two new models are developed for power trading, where in the first model the power is traded during a negotiation process between the spectrum owners and the requesting users. The spectrum owners (PUs) gain more profit by trading proper levels of power to the requesting nodes (SUs) that desire to utilize the spectrum for their data transmissions. Finally, we used game theory to

Acknowledgments

The authors thank the anonymous reviewers for their valuable comments. The authors would like to acknowledge the financial support provided by MITACS – Acceleration Quebec funds, with grant number N00668.

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