Abstract
Through machine learning, this paper changes the fundamental assumption of the traditional medium access control (MAC) layer design. It obtains the capability of retrieving the information even the packets collide by training a deep neural network offline with the historical radio frequency (RF) traces and inferring the STAs involved collisions online in near-real-time. Specifically, we propose a MAC protocol based on intelligent spectrum learning for the future wireless local area networks (WLANs), called SL-MAC. In the proposed MAC, an access point (AP) is installed with a pre-trained convolutional neural network (CNN) model to identify the stations (STAs) involved in the collisions. In contrast to the conventional contention-based random medium access methods, e.g., IEEE 802.11 distributed coordination function (DCF), the proposed SL-MAC protocol seeks to schedule data transmissions from the STAs suffering from the collisions. To achieve this goal, we develop a two-step offline training algorithm enabling the AP to sense the spectrum with the aid of the CNN. In particular, on receiving the overlapped signal(s), the AP firstly predicts the number of STAs involving collisions and then further identifies the STAs' ID. Furthermore, we analyze the upper bound of throughput gain brought by the CNN predictor and investigate the impact of the inference error on the achieved throughput. Extensive simulations show the superiority of the proposed SL-MAC and allow us to gain insights on the trade-off between performance gain and the inference accuracy.
Highlights
In the past few years, IEEE 802.11 based wireless local area networks (WLANs), commonly known as WiFi networks, have experienced a noticeable growth and keep pace with an ever-increasing number of mobile devices with low cost [1]
For the master-convolutional neural network (CNN) model detecting the number of STAs and the slave-CNN model-1, they contain three convolution layers and one fully connected (FC) layer, i.e., KM1 = KS1 = 3, KM2 = KS2 = 1
WORK In this paper, we propose a novel medium access control (MAC) protocol for future WLANs
Summary
In the past few years, IEEE 802.11 based wireless local area networks (WLANs), commonly known as WiFi networks, have experienced a noticeable growth and keep pace with an ever-increasing number of mobile devices with low cost [1] In such a case, how the mobile devices proficiently achieve channel coordination to improve the spectrum efficiency in the dense deployment WLANs scenarios (e.g., ultradense 5G networks [2]) are attracting significant devotion from both industry and academia [3]. IEEE 802.11 distributed coordination function (DCF) uses a binary exponential backoff (BEB) scheme as a carrier sense multiple access with collision avoidance (CSMA/CA) mechanism to decrease collisions This scheme severely degrades the network performance when there exists a large number of devices contending the channel [4].
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