Attachment Learning for Multi-channel Allocation in Distributed OFDMA Networks

Wireless technologies have gained tremendous popularity in recent years, resulting in a dense deployment of wireless devices. Therefore, it is desired to provide multiple concurrent transmissions by dividing a broadband channel into separate sub channels. This fine-grained channel access calls for efficient channel allocation mechanisms, especially in distributed networks. However, most of the current multichannel access methods rely on costy coordination, which significantly degrade their performance. Motivated by this, we propose a cross layer design called Attachment Learning (AT-learning) in distributed OFDMA (Orthogonal Frequency Division Multiple Access) based networks. AT-learning utilizes jamming technique to attach identifier signals on data traffic, where the identifier signals can help mobile stations to learn allocation strategy by themselves. After the learning stage, mobile stations can achieve a TDMA-like performance, where stations can know when exactly to transmit on which channel without further collisions. We conduct comprehensive simulations and the experimental results show that AT-learning can improve the throughput by up to 300% compared with traditional multichannel access method which asks mobile stations to randomly choose channels without learning.