Improving VHT MU-MIMO communications by concatenating long data streams in consecutive groups

communication mode that allows an Access Point (AP) to simultaneously transmit multiple data streams as Aggregated Multi-Protocol Data Units (A-MPDUs) to a group of multiple stations (STAs) over the same channel. This mode combines communication technologies that enable the 802.11ac protocol to use spectrum more efficiently compared to the previous standards. However, VHT MU-MIMO wastes an unused part of the Physical Protocol Data Unit (PPDU) interval when short and long data streams are grouped together. In this paper, we propose a solution that improves VHT MU-MIMO communications by reducing wasted portion of the PPDU duration of short data streams by concatenating longer data streams in consecutive groups. Simulations of the VHT MU-MIMO communication process with and without the proposed approach indicate smaller wasted part and shorter transmission time of randomly generated STAs data streams

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