Frequency Diversity-Aware Wi-Fi Using OFDM-Based Bloom Filters

With the increasing move towards wide band operation in recent Wi-Fi networks, the frequency diversity awareness has become critical for throughput optimization. To exploit frequency diversity in Wi-Fi channels, the access point should measure the channel quality and coordinate the channel contention for all the stations. However, there is a tradeoff between achieving the frequency diversity gain and sustaining protocol efficiency because the channel estimation and coordination consume time and frequency resource that ideally should be used for data transfer. In this paper, we present Diversity-aware Wi-Fi (D-Fi), a novel PHY/MAC protocol, that efficiently exploits frequency diversity. In particular, D-Fi leverages an OFDM-based Bloom filter that synergistically integrates two operations: (i) the channel quality estimation and (ii) the contention based channel allocation. D-Fi also employs a machine learning (ML) method to resolve the false-positive ambiguity caused by the Bloom filter. Furthermore, we develop a decentralized algorithm, called Kε-greedy, based on the Multi-Armed Bandit (MAB) framework, so that it achieves sub-optimal performance by studying the gain for exploring new channel quality information. We implement the prototype of D-Fi on the USRP/GNURadio to validate the feasibility of our work. The experiments and trace-driven simulations show that D-Fi provides up to 3 x throughput improvement compared to the existing solutions.