Cognitive rate adaptation for high throughput IEEE 802.11n WLANs

Rate adaptation (RA) is responsible for dynamically adjusting transmission rates based on channel quality changes. Finding the best suited transmission rate for time varying channel conditions with RA is challenging issue in wireless communications since it has great impact to the network performance in terms of throughput and efficiency. The existing RA approaches for IEEE 802.11n (e.g., RAMAS, MHT and ARC) use inefficient mechanisms for exploring the optimal rates that lead to stuck in suboptimal rates. In this paper, we propose a cognitive rate adaptation (CRA) algorithm for high throughput IEEE 802.11n WLANs. The proposed RA employs cognitive probing, cognitive modulation and coding scheme (MCS) upgrading/downgrading mechanisms that allow discovering the optimal rates efficiently. Our case study showed that the proposed CRA outperforms the existing well-known RA algorithms under various channel conditions.