Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

Abstract

The new medium access method of IEEE 802.11ah, called Restricted Access Window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this paper, we present a surrogate model that can accurately predict RAW performance under a given Restricted Access Window (RAW) configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10 17 possible data points. This is too big to achieve feasible training convergence. In this paper, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10 5 data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.Index Terms-IEEE 802.11ah, RAW, surrogate model, heterogeneous networks.

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

“…This observation is reflected in a recent survey on 802.11ah research [318] where, out of about 200 cited references, only three consider multi-hop scenarios. Moreover, surprisingly, only two papers by Tian et al [319], [320] deal with applying ML: both use a form of SL to optimize the parameters of 802.11ah's grouping functionality, restricted access window (RAW). A similar problem is also addressed by Mahesh and Harigovindan [321], where an MLP NN configures these parameters considering, i.a., network size and MCS values used.…”

Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges

“…Related studies concerning RAW optimization are presented here, and details are shown in Table 5 . The current solutions for RAW optimization in the literature primarily focus on enhancing the network performance in terms of throughput [ 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 115 ], channel efficiency/utilization [ 97 , 104 , 116 , 117 , 118 ], and energy consumption [ 59 , 102 , 105 , 111 , 112 , 119 , 120 , 121 , 122 , 123 , 124 ].…”

Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks

“…Normal data is divided into non-regular data that is transmitted at a certain amount of data, depending on the nature of the signal, and regular data that the data transmission time is fixed according to the time limit. Redundant data reduces the efficiency of the network at the time of transmission, thereby reducing network efficiency overall [17]. It is removed immediately if duplicate data is found using the cosine duplication function, and it also improves transmission efficiency by eliminating redundant data in the incoming data.…”