# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 2 Related Work
Second, the inter-station traffic differentiation is either homogeneous (i.e., all stations have the same traffic intensity) or heterogeneous (i.e., different stations may have different traffic intensities). The considered objectives are contention minimization (i.e., through hidden node mitigation), throughput maximization, energy consumption minimization, or a combination. It is clear that, to be applicable to real scenarios, supported traffic conditions should be both dynamic and heterogeneous. The algorithms presented in the table are broadly based on two approaches: (i) analytical modeling and (ii) set partitioning. The analytical models make use of different techniques, such as probability theory [22; 27; 28], Markov chains [23; 29; 30], multi-objective game theory [25], and maximum likelihood estimation [26]. They all aim to optimize throughput, energy consumption, or a combination of both. Generally, they are computationally hard. This makes it infeasible to execute them in real time on actual AP hardware, where, at most, a few milliseconds are available at the start of the beacon interval to calculate a new RAW configuration. Moreover, such models require information about the station's traffic demand that is not readily available on the AP side. To simplify the modeling process, all existing models for RAW focus on homogeneous traffic with either one packet per station or under saturation. The combination of these factors make such models useful only from a theoretical point of view, in order to analyze the effectiveness of RAW under a variety of conditions. However, they cannot be used for real-time station grouping under dynamic and realistic traffic conditions. The set partitioning algorithms are much simpler. They assume the number of RAW slots and groups is given, and decide how to partition the associated stations among them, according to some metric. Their simplicity makes it computationally feasible to deploy them in real networks. Older work assumes simplified saturated and homogeneous network conditions. Moreover, it focuses on simple partitioning metrics, such as fully random [31] or based on the back-off timer value [24], which, in reality, is not known to the AP. Recently, some more interesting approaches have popped up. Several algorithms focus on mitigating hidden node collisions by splitting mutually hidden nodes into orthogonal groups [18; 19; 20]. Additionally, [PERSON] et al. proposed a set partitioning algorithm that assumes the (static) traffic demand of each station is known by the AP and load balances them across groups [21]. Most of these recent algorithms still assume simplified homogeneous traffic [18; 20]. However, two algorithms [19; 21] focus on more realistic traffic, where stations have heterogeneous, static and non-saturated traffic. Although the algorithms of [PERSON] [19] and [PERSON] [21] take a step in the right direction by supporting more realistic traffic and real-time execution, they still have several shortcomings that we aim to address in this article. First, none of the presented algorithms take into account traffic dynamics. In a real network, the upstream traffic intensity of stations may change over time for a variety of reasons, and the algorithm should therefore adapt to these changes. Second, they expect all information, such as the exact traffic intensity of each station, to be readily available on the AP side, which, in reality, is not the case. Third, they assume that the number of groups and slots as well as their duration are given, and only the partitioning of stations among them needs to be solved. The number of groups and their duration, however, significantly influence RAW optimality [1]. All parameters should therefore be jointly optimized. In this paper, we present a station grouping algorithm for the IEEE 802.11 ah RAW mechanism that addresses these shortcomings. It supports both dynamic and heterogeneous traffic, real-time execution, estimation of station traffic intensity on the AP side, and optimization of all RAW parameters