基于契约的合作频谱共享

Lingjie Duan, Lin Gao, Jianwei Huang
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引用次数: 111

摘要

提供适当的经济激励对动态频谱共享的成功至关重要。合作频谱共享是实现这一目标的有效途径之一。在合作频谱共享中,辅助用户(su)为主用户(pu)中继流量,以换取专用的传输时间以满足其自身的通信需求。本文研究了不完全信息下的合作频谱共享,其中单元的类型(捕获中继信道增益和单元的功率成本)是私有信息,不为单元所知。受契约理论的启发,我们将网络建模为劳动力市场。PU是向su提供合同的雇主。该合同由一组代表频谱接入时间(即奖励)和中继功率(即贡献)组合的项目组成。SUs是雇员,他们每个人都选择最好的合同项目来最大化自己的收益。研究了弱信息不完全和强信息不完全情况下的最优契约设计。首先,我们为两种情况下的可行合同提供了必要和充分条件。在弱不完全信息场景中,我们进一步推导出与完全信息基准测试中实现相同最大PU效用的最优契约。在强不完全信息场景下,我们提出了一种接近最优契约的分解与比较算法。我们进一步表明,由于次优算法和强不完全信息导致的PU的预期效用损失都相对较小(在我们的两种SU类型的数值结果中分别小于2%和1.3%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contract-based cooperative spectrum sharing
Providing proper economic incentives is essential for the success of dynamic spectrum sharing. Cooperative spectrum sharing is one effective way to achieve this goal. In cooperative spectrum sharing, secondary users (SUs) relay traffics for primary users (PUs), in exchange for dedicated transmission time for the SUs' own communication needs. In this paper, we study the cooperative spectrum sharing under incomplete information, where SUs' types (which capture the relay channel gains and the SUs' power costs) are private information and are not known to the PU. Inspired by the contract theory, we model the network as a labor market. The PU is an employer who offers a contract to the SUs. The contract consists of a set of items representing combinations of spectrum access time (i.e., reward) and relay power (i.e., contribution). The SUs are employees, and each of them selects the best contract item to maximize its payoff. We study the optimal contract design for both weakly and strongly incomplete information scenarios. First, we provide necessary and sufficient conditions for feasible contracts in both scenarios. In the weakly incomplete information scenario, we further derive the optimal contract that achieves the same maximum PU's utility as in the complete information benchmark. In the strongly incomplete information scenario, we propose a Decompose-and-Compare algorithm that achieves a close-to-optimal contract. We further show that the PU's expected utility loss due to the suboptimal algorithm and the strongly incomplete information are both relatively small (less than 2% and 1.3%, respectively, in our numerical results with two SU types).
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