On the Performance of Packet Aggregation in IEEE 802.11ac MU-MIMO WLANs

Multi-user spatial multiplexing combined with packet aggregation can significantly increase the performance of Wireless Local Area Networks (WLANs). In this letter, we present and evaluate a simple technique to perform packet aggregation in IEEE 802.11ac MU-MIMO (Multi-user Multiple Input Multiple Output) WLANs. Results show that in non-saturation conditions both the number of active stations (STAs) and the queue size have a significant impact on the system performance. If the number of stations is excessively high, the heterogeneity of destinations in the packets contained in the queue makes it difficult to take full advantage of packet aggregation. This effect can be alleviated by increasing the queue size, which increases the chances to schedule a large number of packets at each transmission, hence improving the system throughput at the cost of a higher delay

A New Adaptive Frame Aggregation Method for Downlink WLAN MU-MIMO Channels

Accommodating the heterogeneous traffic demand among streams in the downlink MU-MIMO channel is among the challenges that affect the transmission efficiency since users in the channel do not always have the same traffic demand. Consequently, it is feasible to adjust the frame size to maximize the system throughput. The existing adaptive aggregation solutions do not consider the effects of different traffic scenarios and they use a Poison traffic model which is inadequate to represent the real network traffic scenarios, thus leading to suboptimal solutions. In this study, we propose some adaptive aggregation strategies which employ a novel dynamic adaptive aggregation policy selection algorithm in addressing the challenges of heterogenous traffic demand in the downlink MU-MIMO channel. Different traffic models are proposed to emulate real world traffic scenarios in the network and to analyze the proposed aggregation polices with respect to various traffic models. Finally, through simulation, we demonstrate the performance of our adaptive algorithm over the baseline FIFO aggregation approach in terms of system throughput performance and channel utilization in achieving the optimal frame size of the system

Proportional Fair MU-MIMO in 802.11 WLANs

We consider the proportional fair rate allocation in an 802.11 WLAN that supports multi-user MIMO (MU-MIMO) transmission by one or more stations. We characterise, for the first time, the proportional fair allocation of MU-MIMO spatial streams and station transmission opportunities. While a number of features carry over from the case without MU-MIMO, in general neither flows nor stations need to be allocated equal airtime when MU-MIMO is available