The downlink power allocation in a two-tier cellular network which consists of a macrocell network underlaid by multiple femtocell networks is addressed in this paper. The paper aims to maximize the transmission capacity of the femtocell networks while guaranteeing that the interference experienced at the macro base station does not exceed an interference constraint. We formulate a Bayesian Stackelberg game to model and analyze behaviors of macrocell and femtocell base stations. In this game, the macrocell base station is the leader, whereas the femtocell base stations are the followers. The channel information between a femtocell base station and its associated femtocell user is private information and is considered as the type of the follower. The leader issues the price of interference charged to the followers first to maximize its own profit. Based on the price, the followers decide the strategies to maximize their payoffs defined as the difference between the transmission capacity and the cost of interference paid to the leader. Using backward induction, the follower game is studied first: the existence and uniqueness of the Bayesian Nash equilibrium (BNE) is examined, and the methods to determine BNE for a symmetric case are provided. Then, the leader game is analyzed. Finally, the numerical analysis is provided.
The invention of cognitive radio (CR) concept aims to overcome the spectral scarcity issues of emerging radio systems by exploiting under‐utilization of licensed spectrum. Determining how to allocate unused frequency bands among CR is one of the most important problems in CR networks. Because different CRs may have different quality‐of‐service requirements, they may have different objectives. In voice communication, high‐speed transmission is the most important factor; hence, voice radios always try to maximize their transmission rate. However, in data communication, the most important factor is the bit error rate. The data radios always try to maximize their signal‐to‐interference‐plus‐noise ratio (SINR). In this paper, two non‐cooperative games named interference minimization game and capacity maximization game, which reflect the target of data radios and voice radios, respectively, are proposed. From the simulations, after these games are applied, the average SINRs of all players at each channel are improved. The average SINR of players in each channel after applying the capacity maximization game is smaller than that after applying the interference minimization game. However, in comparison with that after applying the interference minimization game, the average capacity of players after applying capacity maximization approach is larger. Copyright © 2012 John Wiley & Sons, Ltd.
This study addresses the problem of spectrum trading in a cognitive radio network with multiple primary users (PUs) competing to sell spectrum to a secondary user (SU). The spectrum trading process is modelled using a 'Cournot game model' of competition by which the PUs set the size of spectrum to sell. In this study, the spectrum requirements for the PUs' services are not fixed but time varying, and the spectrum trading process is carried out before the realisation of these values. If the spectrum retained for a PU after selling is less than the spectrum requirement for the PU's service, a cost must be charged to the PU. The Nash equilibrium (NE) for a static game when the PUs have complete knowledge on the utility functions of other PUs is studied first. A dynamic game, in which the players adaptively change their strategies to reach the NE, is discussed subsequently. Finally, the trading problem is extended to a scenario which involves multiple SUs.
The invention of cognitive radio concept is aimed to overcome the spectral scarcity issues of emerging radio systems by exploiting under-utilization of licensed spectrum. As the cognitive users (secondary users) are only allowed to use a licensed spectrum in the absence of its rightful owner, the ability to accurately sense the presence of the rightful owners (primary users) is highly essential. The traditional way of having individual secondary users perform their own spectrum sensing is vulnerable to the presence of noise and shadowing of propagation channel. Cooperative spectrum sensing emerges as an attractive alternative that exploits the inherent geospatial diversity of multiple cognitive radios to enhance the robustness of sensing accuracy. Two specific issues in cooperative spectrum sensing are discussed in this paper. The first issue is on dynamic detection of primary user's bands. A dynamic band clustering (DBC) algorithm that uses K-means clustering technique is proposed in this paper. The proposed algorithm reduces the number of erroneous narrow subbands resulting from spurious noise. This in turn minimizes the number of subbands to be detected and hence the overall sensing time. The second issue is on reducing the overheads required to facilitate fusion center operation. A novel entropy-based maximal ratio combining for decision-fusion center is also proposed in this paper. Based on extensive simulation studies, the proposed fusion technique is shown to be comparable to conventional information-fusion techniques. The performance offered by
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