Kim Yonggang, Kim Yohan
Division of Computer Science and Engineering, Kongju National University, Cheonan 31080, Republic of Korea.
Department of Software, Dongseo University, Busan 47011, Republic of Korea.
Sensors (Basel). 2024 Apr 9;24(8):2399. doi: 10.3390/s24082399.
In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays.
在具有多个接入点（AP）和站点的高密度网络环境中，每个AP进行单独的上行链路调度可能会严重干扰相邻AP及其关联站点的上行链路传输。在并发上行链路传输可能导致严重干扰的拥堵区域，部署一个协作调度器或一个中央协调实体将是有益的，该实体负责通过分配几个相邻AP来协调协作上行链路调度，以支持邻近服务区域内单个站点的上行链路传输，从而减轻过度干扰。通过协作信息共享和管理实现的协作上行链路调度，有望提高AP和站点高度集中区域中上行链路传输成功的可能性。尽管如此，考虑站点的队列稳定性以及上行链路调度中信息交换和决策过程产生的潜在延迟，对于维持协作方法的整体有效性至关重要。在本文中，我们提出了一种基于李雅普诺夫漂移加惩罚框架的协作上行链路调度方法，用于人口密集的Wi-Fi网络。协作调度器聚合诸如信号与干扰加噪声比（SINR）和队列状态等信息。在聚合过程中，还会估计传播延迟，并将其用作调度决策中预期协作延迟的值。聚合信息后，协作调度器计算李雅普诺夫漂移加惩罚值，并纳入预定义的模型参数以相应地调整系统。在所提出的方法中，在可能的调度候选方案中，进行上行链路决策，旨在降低李雅普诺夫漂移加惩罚值的上限，从而在高度拥堵区域中提高网络性能和稳定性，而不会显著增加协作延迟。通过全面的性能评估，所提出的方法通过适当的模型参数有效地提高了网络性能。性能提升在高度拥堵区域尤为显著，并且是在不显著增加协作延迟的情况下实现的。
