GeRA: Generic rate adaptation for vehicular networks

Vehicular networks arc novel wireless networks particularly for inter-vehicle communications. In vehicular networks, the current rate adaptation algorithms are not applicable to the new situations (e.g., high mobility, SNR fluctuation and complicated environment). We propose a novel hybrid rate adapt it lion scheme named as GeRA (Generic Rate Adaptation). The key idea of this scheme is to make use of both context information and signal strength information to estimate current channel condition in a much more efficient and accurate way. GeRA dynamically and adaptively switches the rate selection resources between our well-designed context information empirical model and SNR prediction model according the current situation to achieve the high mobility, density and variation. In our extensive empirical experiments and performance evaluation, we compare this scheme with two types of rate adaptation algorithms and one latest vehicular networks rate adaptation. Our experiments in real vehicular environment show that GeRA performs better than other choosing algorithms under different mobility scenarios, different traffic density and different cross-layer protocols. Our scheme achieves significant higher goodput than traditional rate adaptation algorithms, up to 93%. Compared to the context information based algorithm, GeRA also has better performance in most scenarios.