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Aprojected gradient based game theoretic approach for multi-user power control in cognitive radio network

机译:基于预测梯度的博弈论方法在认知无线电网络中的多用户功率控制

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The fifth generation (5G) networks have been envisioned to support the explosive growth of data demand caused by the increasing traditional high-rate mobile users and the expected rise of interconnections between human and things. To accommodate the ever-growing data traffic with scarce spectrum resources, cognitive radio (CR) is considered a promising technology to improve spectrum utilization. We study the power control problem for secondary users in an underlay CR network. Unlike most existing studies which simplify the problem by considering only a single primary user or channel, we investigate a more realistic scenario where multiple primary users share multiple channels with secondary users. We formulate the power control problem as a non-cooperative game with coupled constraints, where the Pareto optimality and achievable total throughput can be obtained by a Nash equilibrium (NE) solution. To achieve NE of the game, we first propose a projected gradient based dynamic model whose equilibrium points are equivalent to the NE of the original game, and then derive a centralized algorithm to solve the problem. Simulation results show that the convergence and effectiveness of our proposed solution, emphasizing the proposed algorithm, are competitive. Moreover, we demonstrate the robustness of our proposed solution as the network size increases.
机译:人们已经设想了第五代(5G)网络,以支持由于日益增长的传统高速率移动用户以及人与物之间的互连预期增长而引起的数据需求的爆炸性增长。为了用稀缺的频谱资源适应不断增长的数据流量,认知无线电(CR)被认为是提高频谱利用率的有前途的技术。我们研究了底层CR网络中辅助用户的电源控制问题。与大多数现有的研究仅通过考虑单个主要用户或渠道来简化问题的方法不同,我们调查了一个更为现实的情况,即多个主要用户与次要用户共享多个渠道。我们将功率控制问题表述为具有耦合约束的非合作博弈,其中帕累托最优性和可实现的总吞吐量可以通过纳什均衡(NE)解决方案获得。为了获得游戏的NE,我们首先提出一个基于投影梯度的动态模型,其平衡点等于原始游戏的NE,然后导出集中式算法来解决该问题。仿真结果表明,所提出的解决方案的收敛性和有效性(强调所提出的算法)具有竞争力。此外,随着网络规模的扩大,我们展示了我们提出的解决方案的鲁棒性。

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