IEEE 802.11ah Restricted Access Window Surrogate Model for Real-Time Station Grouping

Abstract

The Restricted Access Window (RAW) mechanism proposed by IEEE 802.11ah promises to address one of the major problems of the Internet of Things (IoT): high channel contention in large-scale densely deployed sensor networks. The RAW feature allows the Access Point (AP) to divide stations into different groups, with only the stations in the same group being allowed to access the channel simultaneously. Existing station grouping strategies only support homogeneous scenarios, where all sensor stations have the same fixed data transmission interval, modulation and coding scheme (MCS) and packet size. In this paper, we present two contributions to address this issue. First, a surrogate model that predicts RAW performance given specific network conditions and RAW configuration parameters. It is fast to train and can be solved in real-time. Second, the Model-Based RAW Optimization Algorithm (MoROA), which uses the surrogate model to determine the optimal RAW configuration in real-time, for heterogeneous stations and dynamic traffic. We compare the accuracy of our surrogate model to simulation results. Performance of MoROA is compared to existing RAW optimization algorithms and traditional 802.11 channel access methods. The results shows that the trained surrogate model can accurately predict RAW performance with a relative error less than 7% and 10% for 95% and 98% of the RAW configurations respectively. MoROA achieves a throughput up to twice as high as traditional 802.11 channel access functions in dense heterogeneous networks.

Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

“…However, the model does not take the finite length of the RAW slot into account. Recently, we proposed a new RAW performance model based on supervised surrogate modeling [11], [12]. The model is trained on a limited set of labeled data samples from ns-3 simulation results, supports realistic channel conditions, including communication errors, propagation delays and capture effects.…”

“…For training simplicity, we assume each station sends one packet per second and a small buffer size of 10 packets is used. The built model can be further used by the RAW optimization algorithms, such as TAROA [9], [10] and MoROA [11], [12], to calculate RAW performance under arbitrary data transmission intervals.…”

“…1) RAW parameters I r design As mentioned in Section II-A, the related parameters of a RAW group r are: number of stations assigned to the RAW groups n r , duration of the RAW group d r , and number of slots in the RAW group s r . In our previous work [11], we selected the appropriate range and step size for each RAW parameter, which leads to high model accuracy and only a limited number of training data points. Therefore, in this paper, we use the same RAW parameter values, as listed in Table III.…”

“…It should be noted that, as the minimal value of s r is 1 according to the IEEE 802.11ah specification, we use step size of 4 for s r = 1 and the next value (i.e., s r = 5), and step size of 5 for other s r values. For more details, the readers are referred to our previous work [11]. 2) Network condition parameter I n design As in the IEEE 802.11ah network environment, the common settings for an IoT scenario are represented by the parameters listed in Table II, the remaining variables are the packet size range [ps − , ps + ] and coverage range [d − , d + ] (determining the MCS), which leads to a variety of heterogeneous networks.…”

“…To the best of our knowledge, this is the first RAW model that supports heterogeneous stations in terms of different modulation and coding schemes (MCSs) and packet sizes. The built model can be used as an input for real-time optimization algorithms such as [9], [10], [11], [12] to get optimal RAW configurations for IEEE 802.11ah heterogeneous networks, achieving highest performance.…”