当前位置: X-MOL 学术IEEE Trans. Wirel. Commun. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Online Learning-Based Reconfigurable Antenna Mode Selection Exploiting Channel Correlation
IEEE Transactions on Wireless Communications ( IF 10.4 ) Pub Date : 2021-05-12 , DOI: 10.1109/twc.2021.3076760
Tianchi Zhao , Ming Li , Yanjun Pan

Reconfigurable antennas (RAs) emerged as a promising technology that can deal with channel variations and enhance the capacity and reliability of the wireless channel. To fully exploit the advantage of RAs, optimal antenna modes need to be selected in an online manner. However, the channel statistics are unknown a priori. Multi-armed bandit-based online learning algorithms were proposed to address this challenge, but the main drawback of existing approaches are that their regret scales linearly with the number of antenna modes, which converges slowly when the latter is large. To improve the scalability, we first apply an existing algorithm: Thompson sampling via Gaussian process (TS-GP), and propose two new algorithms for antenna mode selection: upper confidence bound with channel prediction (UCB-CP) and Thompson Sampling with channel prediction (TS-CP). TS-GP uses Gaussian prior to model the reward distribution of each antenna mode, as well as the correlation among them. UCB-CP and TS-CP exploit channel modeling to predict the channel conditions of unexplored antenna modes at each time step, by relating the correlation between different channel states to the underlying antenna modes. We prove the finite-time regret bound of UCB-CP and show that it is independent from the number of arms, when the expected channel estimation errors are small enough. We also extend the algorithms to the mobile setting. Both simulation results and real-world experiments show that all of our proposed learning algorithms can significantly improve the convergence rate and yield much lower regret (thus higher throughput) than existing schemes.

中文翻译:

利用信道相关性的基于在线学习的可重构天线模式选择

可重构天线 (RA) 作为一种很有前途的技术出现,可以处理信道变化并提高无线信道的容量和可靠性。为了充分发挥 RA 的优势,需要在线选择最佳天线模式。然而,信道统计是先验未知的。提出了基于多臂老虎机的在线学习算法来解决这一挑战,但现有方法的主要缺点是它们的遗憾与天线模式的数量成线性关系,当天线模式很大时收敛缓慢。为了提高可扩展性,我们首先应用现有算法:通过高斯过程的汤普森采样(TS-GP),并提出两种新的天线模式选择算法:通道预测置信上限 (UCB-CP) 和通道预测 Thompson Sampling (TS-CP)。TS-GP 使用高斯先验对每个天线模式的奖励分布以及它们之间的相关性进行建模。UCB-CP 和 TS-CP 利用信道建模,通过将不同信道状态之间的相关性与基础天线模式相关联,来预测每个时间步未探索的天线模式的信道条件。我们证明了 UCB-CP 的有限时间遗憾界,并表明当预期的信道估计误差足够小时,它与臂的数量无关。我们还将算法扩展到移动设置。
更新日期:2021-05-12
down
wechat
bug