Attachment-learning for multi-channel allocation in distributed OFDMA-based networks

Wireless technology has become ever more popular in recent years, which results in a higher and higher density of wireless devices. In order to cope with this high density, researchers are proposing the provision of multiple concurrent transmissions by dividing a broadband channel into separate narrow band subchannels. In particular, a fine-grained channel access approach calls for efficient channel allocation mechanisms, especially in distributed networks. However, most of the current multi-channel access methods rely on costly coordination, which significantly degrades network performance. Motivated by this, we propose a cross layer design, termed Attachment Learning (AT-Learning), to achieve multi-channel allocation with low cost and high efficiency in distributed OFDMA based networks. AT-Learning utilizes a jamming and cancellation technique to attach identifier signals to data traffic, without degrading the effective throughput of the original data transmission. These identifier signals help mobile stations learn the allocation strategy by themselves. After the learning stage, mobile stations can achieve a TDMA-like performance, where stations will know exactly when to transmit and on which channel without further collisions. We conduct comprehensive simulations, comparing AT-Learning with a traditional multi-channel access method like Slotted ALOHA. The experimental results demonstrate that AT-Learning can improve the throughput by up to 300% over Slotted ALOHA.