Performance of WLAN in Downlink MU-MIMO Channel with the Least Cost in Terms of Increased Delay

Abstract

To improve the performance of IEEE 802.11 wireless local area (WLAN) networks, different frame-aggregation algorithms are proposed by IEEE 802.11n/ac standards to improve the throughput performance of WLANs. However, this improvement will also have a related cost in terms of increasing delay. The traffic load generated by mixed types of applications in current modern networks demands different network performance requirements in terms of maintaining some form of an optimal trade-off between maximizing throughput and minimizing delay. However, the majority of existing researchers have only attempted to optimize either one (to maximize throughput or minimize the delay). Both the performance of throughput and delay can be affected by several factors such as a heterogeneous traffic pattern, target aggregate frame size, channel condition, competing stations, etc. However, under the effect of uncertain conditions of heterogeneous traffic patterns and channel conditions in a network, determining the optimal target aggregate frame size is a significant approach that can be controlled to manage both throughput and delay. The main contribution of this study was to propose an adaptive aggregation algorithm that allows an adaptive optimal trade-off between maximizing system throughput and minimizing system delay in the WLAN downlink MU-MIMO channel. The proposed approach adopted different aggregation policies to adaptively select the optimal aggregation policy that allowed for achieving maximum system throughput by minimizing delay. Both queue delay and transmission delay, which have a significant impact when frame-aggregation algorithms are adopted, were considered. Different test case scenarios were considered such as channel error, traffic pattern, and number of competing stations. Through system-level simulation, the performance of the proposed approach was validated over the FIFO aggregation algorithm and earlier adaptive aggregation approaches, which only focused on achieving maximum throughput at the expense of delay. The performance of the proposed approach was evaluated under the effects of heterogenous traffic patterns for VoIP and video traffic applications, channel conditions, and number of STAs for WLAN downlink MU-MIMO channels.