Gaofei Sun

Combinatorial auction with time-frequency flexibility in cognitive radio networks

In this paper, we tackle the spectrum allocation problem in cognitive radio (CR) networks with time-frequency flexibility consideration using combinatorial auction. Different from all the previous works using auction mechanisms, we model the spectrum opportunity in a time-frequency division manner. This model caters to much more flexible requirements from secondary users (SUs) and has very clear application meaning. The additional flexibility also brings theoretical and computational difficulties. We model the spectrum allocation as a combinatorial auction and show that under the time-frequency flexible model, reaching the social welfare maximal is NP hard and the upper bound of worst-case approximation ratio is √m, m is the number of time-frequency slots. Therefore, we design an auction mechanism with near-optimal winner determination algorithm, whose worst-case approximation ratio reaches the upper bound √m. Further we devise a truthful payment scheme under the approximation winner determination algorithm to guarantee that all the bids submitted by SUs reflect their true valuation of the spectrum. To further address the issue and reach optimality, we simplify the general model to that only frequency flexibility is allowed, which is still useful, and propose a truthful, optimal and computationally efficient auction mechanism under modified model. Extensive simulation results show that all the proposed algorithms generate high social welfare as well as high spectrum utilization ratio. Whats more, the actual approximation ratio of near-optimal algorithm is much higher than the worst-case approximation ratio.

A game approach for cell selection and resource allocation in heterogeneous wireless networks

Cell selection and resource allocation (CS-RA) are processes of determining cell and radio resource which provide service to mobile station (MS). Optimizing these processes is an important step towards maximizing the utilization of current and future networks. In this paper, we investigate the problem of CS-RA in heterogeneous wireless networks. Specifically, we propose a distributed cell selection and resource allocation mechanism, in which the CS-RA processes are performed by MSs independently. We formulate the problem as a two-tier game named as inter-cell game and intra-cell game, respectively. In the first tier, i.e. the inter-cell game, MSs select the best cell according to an optimal cell selection strategy derived from the expected payoff. In the second tier, i.e., the intra-cell game, MSs choose the proper radio resource in the serving cell to achieve maximum payoff. We analyze the existence of Nash equilibria of both games, the structure of which suggests the interesting property that we can achieve automatic load balance through the two-tier games. Furthermore, we propose distributed algorithms named as CS-Algorithm and RA-Algorithm to enable the independent MSs converge to Nash equilibria. Simulation results show that the proposed algorithms converge effectively to Nash equilibria and that the proposed CS-RA mechanism achieves better performance in terms of throughput and payoff compared to conventional mechanisms.

Coalitional Double Auction for Spatial Spectrum Allocation in Cognitive Radio Networks

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve spectrum utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and the spatial reusability simultaneously, which leaves room to further enhance the spectrum efficiency. In this paper, we introduce the coalition double auction for efficient spectrum allocation in CRNs, where secondary users (SUs) are partitioned into several coalitions and the spectrum reusability can be executed within each coalition. The partition formation process is not only related to the interference condition between SUs, but also the expected economic goals. Therefore, we propose a fully-economic spatial spectrum allocation mechanism by incorporating the coalition formation approach with auction theory. With the proposed scheme, the primary operator acts as an auctioneer, who performs multiple virtual auctions to form a stable partition of SUs and conducts a final auction to decide the winning SUs. Moreover, we propose a possible operation rules for the primary operator to iteratively change the partition, and prove that the virtual auctions could converge in finite time. Comprehensive theoretical analysis and simulation results are presented to show that our scheme can satisfy the crucial economic robustness properties of double auction, and outperform existing mechanisms.

Cooperative relaying schemes for device-to-device communication underlaying cellular networks

Intra-cell interference management is one of the technical challenges for device-to-device (D2D) communication underlaying cellular networks. In this paper, we propose two superposition coding-based cooperative relaying schemes to exploit the transmission opportunities for the D2D users without deteriorating the performance of the cellular users. In the first scheme, the D2D transmitter (DT) is enabled to decode and regenerate the cellular signal, and transmit the cellular signal by superposing it with its own signal. In this way, the interference from the D2D pair to the cellular pair can be canceled by properly allocating time and power. To further exploit the transmission opportunity for the D2D pair, in the second scheme the cellular transmitter splits its signal into two parts and broadcasts these two parts in a superposition signal. DT relays only one part of the cellular signal. Analytic and numerical results confirm the efficiency of the proposed schemes.

A discriminatory pricing double auction for spectrum allocation

Cognitive radio is promising in improving spectrum efficiency by enabling unlicensed users to access to the licensed spectrum. Spectrum auction is perceived as a potential way to realize it. Primary users (PUs) act as sellers selling unused spectrum bands and secondary users (SUs) act as buyers who intend to get spectrum bands from PUs. In situations where multiple PUs and SUs exist, double auction is a paradigm to assign spectrum. Efficiency and economic robustness are considered two essential properties in the model. Previous work often employ bid-independent uniform pricing to maintain economic robust at the substantial cost of efficiency. In this paper, we investigate the tradeoff between efficiency and robustness. We propose DIPA, a DIscriminatory Pricing double Auction for spectrum, in which bidders are charged of varying prices for the same item they purchase. We demonstrate that DIPA is robust and improves efficiency largely over the previous design.

Hybrid channel assignment in multi-hop multi-radio cognitive ad hoc network

The existence of under-utilized spectrum and the congestion of certain spectrum give rise to development on cognitive radios as a promising technology to address these problems. However, the general precondition that secondary users should evacuate from the channel which primary users are willing to use, makes the cognitive ad hoc networks vulnerable and unstable since network partition may occur since the links affected by primary users may form a cut set of the network. In this paper, we address this problem through a hybrid channel assignment scheme, where channels are carefully assigned to each link to guarantee the connectivity and the capacity based on spanning trees. Since the optimal solution is NP-hard, we propose an effective approximation algorithm. Also, dynamic channel assignment is proposed to further improve the network performance.

Efficient wireless sensor networks scheduling scheme: Game theoretic analysis and algorithm

Energy efficiency is the core issue in wireless sensor networks scheduling algorithm design, which calls up wide attention from researchers. However, few of the previous works notice the constraints on sensors caused by limited buffer size. It challenges the systems meeting deadlines performance. To alleviate this concern, we propose a scheduling policy: the least energy and free storage remaining first, which extends wireless sensor networks lifetime and provides a high real-time services quality. To make our strategy more practical, we consider the users non-cooperative behavior out of self-interests. It increases the systems packet lost in buffer overflow and reduces the energy efficiency. Based on a mixed strategy game model, we show that the non-cooperative game converges to an inefficient Nash Equilibrium, where the spectrum resource is significantly wasted. In order to eliminate users selfish manner, we design a punishment scheme via a repeated game, which considers the time-variant character of network and can detect non-cooperative action precisely and rapidly.

Incentive mechanism for access permission and spectrum trading in femtocell network

The concept of femtocell access points underlying existing macrocell network has recently emerged as a key technology that provides better coverage and improves wireless system capacity. Among all three access control mechanism adopted by Femtocell Access Points (FAPs), Hybrid access is regarded as the most desirable because this mechanism makes it possible to allow both femto and macro end users to access femtocell network, which significantly enhances overall network performance. In this paper, we propose an incentive mechanism for the two-tier macro-femtocell network under hybrid access control, in which FAPs expect to lease licensed spectrum from the coexisting Macrocell Service Provider (MSP) to serve their end users (FUs), in return FAPs grant the MSP a fraction of Access Permission (ACP) so that macro end users (MUs) can access FAPs to increase the Quality of Service and macrocell network capacity. We introduce the concept of contract to specify the relationship between FAPs and the MSP. We study how the MSP can obtain the optimal contract by designing different ACP-spectrum combinations (i.e., the amount of spectrum sold from the MSP to FAPs and the corresponding quantity of ACP sold from FAPs to the MSP) for different types of FAPs. Numerical results show that both MSP and FAPs can benefit from this incentive mechanism in term of their utilities.

A game theory approach for power control and relay selection in cooperative communication networks with asymmetric information

The performance of cooperative communication depends on elaborative resource allocation, such as power control and relay selection. Traditional centralized transmission control requires precise information of the whole networks. In this paper, we introduce a new cooperative mechanism where relay nodes take the initiative. In the system model information like channel state, energy cost and power constraints are all users private information. Thus, the game involved is under asymmetric information. We derive the Perfect Bayesian Equilibrium (PBE) via distributed computing and achieve optimal benefits for the mobile virtual network operators (MVNO) and relay nodes. The proposed approach not only helps the relay nodes with power control but also helps the operators with relay selection.We prove the game to achieve unique equilibrium in different scenarios. Moreover, numerical results and analysis show that both relay nodes and the source node benefit from the proposed game compared with none-cooperative game.

Opportunistic access for cooperative cognitive radio networks with requirement constraint

In cognitive radio network, cooperation between primary users and secondary users can improve their data rate and achieve dynamic spectrum sharing. This paper proposes a novel spectrum sharing model to solve the opportunistic access problem in cooperative cognitive radio network (CCRN). In this model, primary users select sets of secondary users as their relays and allocate channel resource to secondary users according to their cooperative transmitting power. Secondary users have their own data rate requirement, and they have to determine their optimal relay power to satisfy their requirement. Primary users have to select the best relay group and maximize their throughput. We model this problem as a Stackelberg game and prove the existence and uniqueness of Nash Equilibrium. A low-complexity algorithm is proposed to realize the cooperative transmission mechanism. Simulation results show significant throughput enhancement in the primary network and more opportunities for secondary users to get access into the spectrum.