## 2 Related Work and Our Motivation
### Related Work
By employing a specific procedure of dynamically adjusting the frame size, [12] proposed a method that dealt with frame-size estimation based on the extended Kalman filter for saturated networks. They derived the mathematical equation for throughput, which was a function of the frame size. The optimal frame size was obtained using differential calculus. Likewise, [PERSON]'s Markov chain model studied the relationship between the throughput and frame size in IEEE 802.11 WLANs [13]. However, the assumption of this work was an ideal channel, which was unrealistic. According to the simulation results, the throughput increased with the frame size; i.e., the larger the frame size, the better the throughput. However, the cost of delay increased when increasing the throughput [5], thus the results provided a suboptimal solution. A machine-learning-based frame-size-optimization approach that considered channel conditions and contention effects of users was proposed in [14] by extending [PERSON]'s model [13] as the main simulation environment. According to the simulation results, the frame-size optimization was effectively achieved to maximize the throughput performance of the WLAN at the expense of delay. This work did not consider frame aggregation but limited its contribution to dynamic frame fragmentation and defragmentation to maintain backward compatibility with IEEE 802.11a/b/g WLANs. An adaptive algorithm for frame-size optimization was proposed in [15] that allowed an ARQ protocol to dynamically optimize the packet size based on estimates of the channel bit errors. The main strategy of this study was to make estimates of the channel bit-error rate; the researchers considered the acknowledgment history, and based on that, the optimal packet size could be determined. However, this approach is not suitable for IEEE 802.11 WLAN environments. In general, all of the above studies mainly focused on maximizing throughput at the expense of delay. However, there is always a trade-off between maximizing throughput and minimizing delay when adopting frame-size-determination strategies [5]. A data-frame-construction scheme called DFSC was proposed in [16] to determine the length of a multiuser (MU) frame with the aim to maximize the transmission efficiency by considering the status of buffers and transmission bit rates of stations in both uplink and downlink multiuser transmissions. However, they did not consider the effects of channel errors, which can reduce the transmission performance due to excessive retransmissions of frames received in error, and the cost of this delay was not elaborated upon. A frame-size-based aggregation scheme was proposed in [17]; the authors demonstrated that both the queueing length and number of active nodes had significant impacts on the system throughput performance. The main approach of this paper was to generate the same frame length in all spatial streams that could maximize the system throughput performance. However, this study focused on maximizing throughput while ignoring the cost of delay. Some works in the literature focused on the padding problem. According to [18; 19], the authors improved the transmission efficiency in the downlink MU-MIMO channel by replacing padding bits with data frames from other users in one stream to fill the space of frame padding, violating the rules of MU transmissions. However, these approaches increased the complexity of both the transmission and reception process in wireless communication, which requires modification of the standard to allow the transmission to multiple destinations within a special stream. A frame-duration-based frame-aggregation scheme was proposed in [20] that employed criteria for selecting a receiving mobile terminal (MT). This approach provided high priority to the MT expecting high throughput in the next MU-MIMO transmission and having a large amount of data while reducing the signaling overhead.