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

Abstract

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.

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

“…A dynamic adaptive aggregation selection algorithm was proposed by [11]. This approach employed different aggregation policies to determine the optimal aggregation frame size on each spatial stream.…”

“…In the WLAN downlink MU-MIMO channel, a group of multiplexed frames are constructed such that all users' frames have an identical transmission duration. However, if the data transmission duration among spatial streams is different, 'space channel time' will occur [5,[7][8][9][10][11]. Space channel time is a time duration where some of the streams carry user data while the others do not.…”

“…For example, ref. [11] provides a frame aggregation scheme to reduce space channel time and system throughput performance in the WLAN downlink MU-MIMO channel. However, these approaches only consider VoIP traffic, and the effect of channel error is not studied.…”

“…The main contribution of this paper is to achieve an efficient adaptive frame aggregation algorithm for downlink MU-MIMO transmission in terms of system throughput performance and space channel time by extending our previous work [11]. Since the performance of different aggregation policies perform differently under the dynamic effects of traffic patterns and channel conditions, the determination of the optimal aggregation policy which provides the maximum system performance is challenging.…”

“…The detailed description of the proposed approach is elaborated in Section 4. In this study, a system-level simulation is conducted using MATLAB programming language and the performance of the proposed approach is evaluated considering the effects of various channel conditions, traffic models (such as Pareto, Weibull, and fractional Brownian Motion (fBM) [11]), and the number of stations as compared to the baseline FIFO aggregation algorithm.…”