Elsevier

Physical Communication

Volume 25, Part 2, December 2017, Pages 391-398
Physical Communication

Full length article
An adaptive step-size spectrum auction mechanism for two-tier heterogeneous networks

https://doi.org/10.1016/j.phycom.2017.06.012Get rights and content

Abstract

Recently, with the rapid growth of demands for wireless communications, dynamic spectrum allocation is one of the key technologies in cognitive radio networks to resolve the realistic problem of low utilization efficiency of spectrum. It mainly focuses on how the spectrum owner dynamically allocates idle spectrum to secondary users who have no licensed spectrum for communications. In this paper, a dynamic spectrum allocation model based on auction theory in a two-tier heterogeneous network is proposed, in which the primary users (PUs) are the sellers, the central processor (CP) auctioneer is the coordinator, and femtocell base station (FBS) as the buyer bids for the idle spectrum and act as a wireless access point that provides communication services for secondary users (SUs). Its basic process is as follows: the auctioneer gradually raises the spectrum price from the reserved price; each bidder decides whether participates in the purchase or not. It is characterized by distributed execution and low complexity which can reduce unnecessary information exchange between primary users or secondary users. Meanwhile it can enhance the utilization of spectrum and improve the efficiency of the auction by generate the incentive mechanism.

Introduction

With the rapidly development of wireless communication technology, especially wireless local area network (WLAN) technology and wireless personal area network (WPAN) technology which would make more and more people can wirelessly access the Internet [1], [2]. The technology called Cognitive Radio (CR) [3] allows SUs to access the channel under certain constraints which can effectively improve the spectrum utilization. In the cognitive radio network, primary users (PUs) are the authorized users who have the ownership of the channel and can access it at any time without any restrictions. The secondary users (SUs) are the unauthorized users who can use the channel only in the absence of interference to PUs. If SUs can take the opportunity to use the channel, it would get the maximum throughput and enhance the spectrum utilization. Dynamic spectrum allocation is one of the key technologies in cognitive radio networks. It can allocate the idle spectrum to the SUs and service requirements. At present, most of the analytical models of cognitive radio spectrum allocation are based on some classic theories of mathematics and microeconomics. There are three commonly theories for cognitive radio spectrum allocation models as graph theory, game theory, and auction theory. The spectrum allocation model based on graph theory [4], [5], [6] was built on corresponding interference, constraint conditions, and graphically abstracts the network topology. It generally requires center-based optimization algorithm with high complexity and signaling overhead. In the spectrum allocation model based on game theory [7], [8], [9], the players are all the cognitive radio users who want to obtain the spectrum of the PU with authorization. Its strategy contains choices of spectrum demand and spectrum rental price. And the game’s solution (Nash equilibrium) is usually locally optimal, because of the players make decisions by themselves. According to the principle of pricing in microeconomics, the spectrum allocation mechanism has been extensively studied in recent years, and has been proved to be an effective solution to the spectrum allocation problem in cognitive radio networks [10], [11], [12].

At the same time, the global femtocell base station (FBS) industry is developing rapidly, so it be cared and promoted by operators, and a number of operators such as Vodafone have deployed the commercial network. On one hand, FBS can shunt a large number of communication services of macrocell base stations, sometimes up to 70%–80%. The outdoor macrocell base stations required in the communication system will be greatly reduced. Hence, the reduction of macrocell base stations can save a large part of cost in the communication networks of operators. In addition, the reduction of business of macrocell base stations also saves the cost of backhaul transmission. FBS can also improve the overall network service quality, and the high network service quality can retain a large number of users. However the operators are willing to spend some cost on retaining users. As the survey shows that the poor service is the most important factor for the user to leave an operator. So, FBS can help operators to a large extent. On the other hand, for those who have none or poor indoor signals, FBS allows them to use mobile terminals indoors. That is not only language services are guaranteed, but also multimedia, video or high-speed data services are feasible. FBS also saves the power of the mobile terminal. Since the distance between the mobile terminal and FBS is much shorter than that between it and the macrocell base station, therefore the transmission power is reduced, which means we can saves the power of the mobile terminal and prevents the mobile terminal from radiating the human body.

Affected by high data rate, long-distance transmission, and signal fading, the network planning and indoor coverage attract the attention of operators and manufacturers. At the same time, due to the requirement of full-service competition of telecom operation, FBS will attract more investment from operators and also brings many market opportunities to other parties in the industry chain. To sum up, FBS will be of great help to indoor signal coverage and have a broad application prospect.

The authors of [13] applied the concept of alliance game and Nash bargaining to the channel allocation about multiple access system of multiuser orthogonal frequency division, and this allocation solution is quite fair. In [14], the authors built an optimized model of FBS to minimize the co-channel interference received from primary user. In addition, the author proposed a distributed dynamic subcarrier allocation and adaptive power allocation solution with linear complexity which can guarantee small information exchange to get the solution close to the optimal. In [15], the authors studied the influence of FBS’s interference on the access capacity and coverage of high-speed downlink packet, meanwhile discussed the transmission power control solution required by FBS. In [16], the author considered a coexisting network that involves one spectrum provider (SP) sharing unused spectrum resources to multiple heterogeneous secondary networks, and designed Unilateral Vickrey–Clarke–Groves (VCG)-based auction for heterogeneous secondary networks. In [17], the authors proposed a method to avoid interference in the modulation and coding network by the code allocation strategy, which can improve the spectrum efficiency without reducing the PU experience. In [18], the resource allocation between FBS was described as a non-cooperative game, and the authors proposed a decentralized power control solution for reducing the system interference. In [19], the authors established a Stackelberg game to study the maximization of the combined utility of macrocell and FBS, which is limited by the maximum tolerable interference power from macrocell.

The game theory has also been widely used in spectrum leasing. Auction mechanism and linear and non-linear prices are two main tools for spectrum trading. In [20], the authors studied auction between PU and SU under the random spectrum access model. The SU shares the channel that may have interference in the tolerance threshold to the PU and pays the subscription fee to the PU to obtain other channels. In [21], the author proposed a price-based spectrum trading mechanism, which allows the SU to randomly access and use the channel in a distributed manner.

At present, the research on the spectrum auction mechanism is still in the initial stage, in which the network structure of the auction allocation model is generally centralized. The auctioneer is the CP, the bidder is the SU, and the auction item is the idle spectrum. Some scholars used the auction principle to study the spectrum allocation mechanism of cognitive radio network [22]. In [23], in order to improve the PU’s revenue, the authors set up a TRUBA and TRUBA+ model based on the form of traditional grouping auction, which can make control of the SU’s budget and the PU’s macro-distribution. In the auction mechanism in [24], the time-based SU valuation information has been proposed, which is more helpful for the PU to determine the spectrum allocation to maximize the revenue. In [25], during the long-term auction, the authors proposed an auction mechanism that adds budget constraints to ensure optimal social benefits. In [26], according to the characteristics of data stream from the secondary user, the authors proposed an auction theory based on QoS layering which can selects an optimal channel for each user to achieve dynamic spectrum allocation.

There is less research on the combination of spectrum auction mechanism and FBS which have many advantages. The purpose of this research is to design a new spectrum auction mechanism called adaptive step-size spectrum auction mechanism, which is combined with FBS in the two-tier heterogeneous network. According to the spectrum characteristics and communication scenarios, we can improve the PU’s revenue by the spectrum auction mechanism, at the same time generate the trading incentives, and make the spectrum auction be more in line with practical application. The main contributions of this work are as follows:

  • 1.

    We use auction theory to allocate the unoccupied spectrum of the licensed primary users to the unlicensed secondary users in the two-layer heterogeneous network.

  • 2.

    We design a new auction mechanism called adaptive step-size auction mechanism which can guarantee the fairness, improve the PUs revenue, and provide the incentive of PU and SU.

The rest of this paper is organized as follow. In Section 2, system model and spectrum auction preliminaries in the two-tier heterogeneous network are introduced. In Section 3, the spectrum auction mechanism called adaptive step-size dynamic spectrum auction is presented in detail. Except that, the analysis of auction mechanism are given in Section 4. In Section 5, the performance evaluation through numerical simulation is presented and we draws the conclusions of this paper in Section 6.

Section snippets

System model

In this section, we first introduce the system model of two-tier heterogeneous network. Then, some spectrum auction preliminaries are given.

We consider a single cell employing femtocell base station (FBS) that consists of a MBS, k SUs, and d FBSs, as showed in Fig. 1. The macrocell base station (MBS) transmits data to the PUs, which own a number of licensed spectrum. While in the secondary market, the central processor (CP) is the center unit in the FBS system and the FBSs serving k SUs act as

Auction mechanism

In this section, we design a new spectrum auction mechanism called adaptive step-size dynamic spectrum auction, which can improve the PU’s revenue, and guarantee the incentive mechanism. First, in order to set different step sizes according to the utility of different FBSs, the CP must detect the utility of each FBS. Second, according to the utility of FBS the CP sets different step sizes for each FBSs before stepping auction.

Equilibrium analysis

Because of the auction is a dynamic process, it is important to analyze its convergence. This section defines two equilibrium states and proves that adaptive step-size auctions converge to an equilibrium state.

First, Weak equilibrium (WE) is defined as a state, in which the system has not excess demand, as follows.

Definition

Weak equilibrium is such a state that the demand of the FBS for the spectrum does not exceed the channel supply, which means nm.

The value of the

Simulation results

The simulation results are shown in this section. We will compare the designed action mechanism with the regular stepping auction mechanism called MAP (Multi-Auctioneer Progressive Spectrum Auction) [27]. We consider a single cell structure with radius=500 m, in which 100 FBSs are distributed to provide signal transmission for SUs. The bandwidth B is set to 0.5 MHz respectively for 80 bands, FBS’s transmission power is set as Pf=20 dBm [27], [28]. The PU’s reserve price is uniformly distributed

Conclusions

In this paper, we combine the FBS and CR which can achieve high system performance with the new auction mechanism called adaptive step-size spectrum auction. It can allocate PU’s idle spectrum bands to SUs who have no license spectrum in the two-tier heterogeneous network. The auction mechanism can advance the utilization rate of spectrum and generate the incentive mechanism which can ensure the efficiency of the auction, meanwhile boost the PU’s revenue. Moreover, the adaptive step-size

Acknowledgments

This research was supported by the National Natural Science Foundation of China (61471135, 61671165), the Guangxi Natural Science Foundation (2015GXNSFBB139007, 2016GXNSFGA380009), the Fund of Key Laboratory of Cognitive Radio and Information Processing (Guilin University of Electronic Technology), Ministry of Education, China and the Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing (CRKL160105), and the Innovation Project of GUET Graduate Education (2016YJCX87,

Feng Zhao received the Ph.D. degree in communications and information systems from Shandong University, China in 2007. Now he is a Professor with the School of Information and Communication, Guilin University of Electronic Technology, China. His research interests include wireless communications, signal processing, and information security.

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    Feng Zhao received the Ph.D. degree in communications and information systems from Shandong University, China in 2007. Now he is a Professor with the School of Information and Communication, Guilin University of Electronic Technology, China. His research interests include wireless communications, signal processing, and information security.

    Zhenyu Tan received the B.Eng. degree in communication engineering from Chongqing University of Posts and Telecommunications, China in June 2015. He is working towards the M.E. degree in Guilin University of Electronic Technology from September 2015. His research focuses on spectrum auction in heterogeneous networks.

    Hongbin Chen received the B.Eng. degree in electronic and information engineering from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2004 and the Ph.D. degree in circuits and systems from South China University of Technology, Guangzhou, China, in 2009. From October 2006 to May 2008, he was a Research Assistant with the Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong. From March to April 2014, he was a Research Associate with the same department. From May 2015 to May 2016, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. He is currently a Professor with the School of Information and Communication, Guilin University of Electronic Technology, Guilin, China. His research interests include energy-efficient wireless communications.

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