D2D网络中基于噪声势博弈学习的分布式信道分配
Optimal Distributed Channel Assignment in D2D Networks Using Learning in Noisy Potential Games
摘要：
我们提出了一种新的解决方案，在设备到设备（D2D）无线网络中的信道分配问题（CAP），考虑到吞吐量估计噪声。CAP在文献中被认为是NP难的，并且没有考虑估计噪声的实用最优学习算法。在本文中，我们首先制定CAP作为一个随机优化问题，以最大限度地提高预期的总和数据率。为了捕捉估计噪声，CAP被建模为一个嘈杂的潜在的游戏，本文介绍了一个新的概念。然后，我们提出了一个分布式的二进制对数线性学习算法（BLLA），收敛到最佳的信道分配。对于有界和无界噪声，证明了BLLA的收敛性。给出了BLLA定温和降温参数的证明。给出了保证收敛到最优状态的足够数量的估计样本。我们评估BLLA的性能进行了大量的模拟，结果表明，总和数据率的信道和用户的数量增加。与较好的响应算法相反，该算法即使在存在估计噪声的情况下也能分布式地实现最优信道分配。
英文摘要：
We present a novel solution for Channel Assignment Problem (CAP) in Device-to-Device (D2D) wireless networks that takes into account the throughput estimation noise. CAP is known to be NP-hard in the literature and there is no practical optimal learning algorithm that takes into account the estimation noise. In this paper, we first formulate the CAP as a stochastic optimization problem to maximize the expected sum data rate. To capture the estimation noise, CAP is modeled as a noisy potential game, a novel notion we introduce in this paper. Then, we propose a distributed Binary Log-linear Learning Algorithm (BLLA) that converges to the optimal channel assignments. Convergence of BLLA is proved for bounded and unbounded noise. Proofs for fixed and decreasing temperature parameter of BLLA are provided. A sufficient number of estimation samples is given that guarantees the convergence to the optimal state. We assess the performance of BLLA by extensive simulations, which show that the sum data rate increases with the number of channels and users. Contrary to the better response algorithm, the proposed algorithm achieves the optimal channel assignments distributively even in presence of estimation noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
