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In this paper, we investigate the content delivery in edge caching networks where requesters can get the interested contents from surrounding helpers by device-to-device (D2D) communications. Taking the finite service capacity and cache space of helpers into account, the problem is to maximize the number of requesters obtaining all desired contents by optimizing the selection of content sources. The hypergraph theory is first applied to analyze the cumulative content effect caused by multiple content sources, and the problem is formulated as a hypergraph based cooperation cache game with at least one pure Nash equilibrium (NE). The log-linear based cooperation caching algorithm (LBCC) is proposed to find the corresponding NE points. Simulation results verify the superiority of the proposed LBCC. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the resource allocation problem, including user pairing and power allocation, for downlink non-orthogonal multiple access (NOMA) networks. Different from most of the existing studies focusing on maximizing throughput, we aim to maximize users’ quality-of-experience (QoE) considering demand diversity. We apply mean opinion score (MOS) to evaluate user’s QoE level, and the optimization target is to maximize the sum MOSs of all users, which is a complex non-convex problem. To tackle this problem, we firstly propose a linear searching algorithm to get the optimal sum MOSs for a specific 2-user NOMA system. Then, we derive the corresponding power allocation strategy. Furthermore, we extend it to a general NOMA system with 2K users, and design a fast and low complexity scheme. Results show that our proposed scheme can significantly improve the users’ sum MOSs in the NOMA networks, compared with other existing schemes. [ABSTRACT FROM AUTHOR]

Ultra-dense SCNs have been regarded as a promising technology to handle the future data traffic explosion. However, the complicated relationships among large scale users and cells practically demand a cooperative grouping mechanism to account for the shortage of resources in SCNs. A group buying market mechanism provides a win-win situation and effective resource allocation in the view of economics. With additional decision information being provided, the context awareness enhances and improves the group buying mechanism. In this article, we propose a CAGB mechanism to allocate the resources in ultra-dense SCNs. The feasibility, necessity, and effectiveness of CAGB are analyzed first. Then, we introduce the group buying mechanism and some common context awareness. The relationship between context awareness and group buying is also analyzed. Some important technologies in SCNs are discussed in the view of CAGB, such as load balancing, spectrum management, and cooperative caching. Then, the graphical CFGs of CAGB are presented to match the complicated network topologies in SCNs. Two CAGB-based use cases about spectrum market and cooperative caching are presented. Finally, future research issues about context awareness, grouping mechanism, and buying mechanism are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the hybrid access control policy in two-tier small cell networks from the perspective of incentive mechanism design, considering macro-cell base station's (MBS) private information. Then, we formulate this problem as a Stackelberg game. To be specific, the MBS and small cell base stations (SBSs) are modeled as leader and followers, respectively. A subsidy mechanism is adopted by MBS when the SBS can provide acceptable service level for macro user equipment. Moreover, we consider the impacts of MBS's private information on the Stackelberg equilibrium (SE) of the proposed game, and we present the equilibrium analysis and relationship under different available information circumstances. To obtain relatively satisfactory outcome for both MBS and SBS, we discuss the design of bargaining scheme based on the SE. Theoretical analysis and simulation results show that it is better for MBS to broadcast the private information to get more payoff from the perspective of incentive mechanism design. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the anti-jamming channel selection problem for interference mitigation (IM) based dense wireless networks in dynamic environment, in which the active user set is variable due to their specific traffic demands. We jointly consider the mutual interference among users and external jamming in IM-based dense wireless networks, and propose a generalized maximum protocol interference and jamming model to accurately capture the mutual interference and external jamming. Then, the anti-jamming channel selection problem is formulated as an anti-jamming dynamic game, and subsequently it is proved to be an exact potential game, which has at least one pure strategy Nash equilibrium (NE). Based on the stochastic learning theory, a distributed anti-jamming channel selection algorithm (DACSA) is proposed to find the NE solution. Moreover, the simulation results are presented to demonstrate the effectiveness of the proposed DACSA algorithm. [ABSTRACT FROM AUTHOR]

This article investigates the anti-jamming communications problem in wireless networks from a Stackelberg game perspective. By exploring and analyzing the inherent characteristics of the anti-jamming problem, we present and discuss some technical challenges and fundamental requirements to address them. To be specific, the adversarial characteristic, incomplete information constraints, dynamics, uncertainty, dense deployment, and heterogeneous feature bring technical challenges to anti-jamming communications in wireless networks. Then, for the purpose of improving system performance, four requirements for anti-jamming communications are presented and discussed. Following the advantages of the Stackelberg game model in the anti-jamming field, we formulate an anti-jamming decision-making framework based on the Stackelberg game for anti-jamming defence in wireless networks. Moreover, two preliminary case studies are presented and discussed for better understanding of the anti-jamming Stackelberg game problem. Finally, some future research directions are also provided. [ABSTRACT FROM AUTHOR]

Femtocells are considered as promising technologies for the next generation wireless networks to improve system capacity and enhance indoor coverage. The dense deployment of femtocells brings a lot of challenges in terms of interference management and energy consumption. To alleviate co-channel interference, we investigate interference management based on hierarchically joint user scheduling and power control in downlink femtocell networks considering the impact of energy cost. Then, we formulate this problem as a Stackelberg game with one leader and multiple followers. Specifically, macro base station and femtocell base stations select their optimal scheduled users and determine the optimal transmission power, based on their own net utilities considering power cost. To obtain the Stackelberg equilibrium (SE) in a distributed manner, a hierarchical and iterative user scheduling and power update framework is proposed. Moreover, we prove that the optimization of user-scheduling and power control can be decoupled in our scheme and the proposed scheme converges to a unique SE under mild conditions. Simulation results are presented to show the convergence and effectiveness of the proposed hierarchical game. [ABSTRACT FROM AUTHOR]

In this paper, we investigate channel allocation for ultradense device-to-device (D2D) communications. Different from both binary graph and undirected hypergraph interference model, an improved directed hypergraph is applied to simultaneously represent cumulative and asymmetric interference aspects in the context of ultradense communications. We formulate the channel access problem in cloud D2D communication networks as a directed-hypergraph-based local altruistic game, which is proved to be an exact potential game. Then, a multiagent concurrent learning scheme in centralized-distributed fashion is proposed to search the optimal pure Nash equilibrium, which can also maximize the normalized network capacity. Finally, simulation results are presented to validate the proposed learning scheme. [ABSTRACT FROM AUTHOR]

In this letter, we propose a learning solution for distributed channel access in device-to-device communications based on a hypergraph interference model. We first define a new interference metric for a hypergraph model, and then formulate this distributed channel access problem as a local altruistic game, which is proved to be an exact potential game admitting at least one pure strategy Nash equilibrium (PNE). A distributed learning algorithm is designed to quickly achieve the optimal PNE, which can minimize the defined networks’ interference metric. Simulation results show that the proposed algorithm outperforms the existing schemes and significantly improves the spectrum efficiency. [ABSTRACT FROM PUBLISHER]

Spectrum auction is one of the most effective solutions to allocate the spectrum resource following the market rules and has attracted much attention from both academia and industry. However, most of the existing studies assume that the spectrum buyers’ demands are homogeneous and the interference relationship is fixed without any change with the variation of spectrum. Furthermore, the economical efficiency of auction outcome has not drawn enough attention. That motivates us to design an auction scheme to jointly consider the multi-demand of buyers, heterogeneous spectrum, and economical efficiency. In this paper, we propose a novel overlapping coalition formation-based double auction, called VERACITY, to address this problem. The auctioneer groups the conflict free buyers into the same coalition and allows a buyer to join multiple coalitions based on the heterogeneous demand. Dynamic overlapping coalition formation implemented by the auctioneer is to find the approximately optimal coalition structure corresponding to the economical efficiency outcome, i.e., maximizing the social welfare. Furthermore, we prove that VERACITY is individually rational, budget balanced, truthful, and economically efficient. Simulation results are presented to show the convergence and effectiveness of the proposed VERACITY. [ABSTRACT FROM PUBLISHER]

The smart jammers pose a severe threat to wireless communications due to their abilities of learning the users’ transmission strategies. A Stackelberg game can be formulated to model and analyze the hierarchical interactions between the user and the smart jammer. In this letter, an antijamming Bayesian Stackelberg game with incomplete information is proposed. In the proposed game, the user who acts as the leader has the privilege over the smart jammer and takes actions first, whereas the smart jammer acting as the follower moves subsequently. Moreover, the Stackelberg equilibrium (SE) is derived, and the existence and the uniqueness of SE are demonstrated. Simulation results are presented to validate the effectiveness of the proposed antijamming Bayesian Stackelberg game. [ABSTRACT FROM PUBLISHER]

A modified bit-reliability based decoding algorithm is presented based on a recent work by Huang et al. For the presented algorithm, only one Galois field element is passed and exchanged along the edges of the Tanner graph. At variable nodes, full messages rather than extrinsic messages are processed to further reduce the computational complexity. At check nodes, only hard reliability is considered and the main operation is to compute the check-sum and send one extrinsic symbol back to variable nodes. Simulation results show that, even with lower complexity and less memory consumption, the presented algorithm still can perform very closely to the original wBRB algorithm with low quantization bits (3 \sim 4\,\textbits) when decoding the majority-logic decodable nonbinary LDPC codes. For codes constructed in high order fields, the presented algorithm can even outperform the original wBRB algorithm. [ABSTRACT FROM PUBLISHER]