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Vehicle-to-vehicle (V2V) communication plays an important role in next generation mobile communications, where security is a critical issue. Most previous works relied on encryption/authentication algorithms to ensure V2V communication security. In this paper, we explore physical-layer security in V2V communications. We formulate a secrecy capacity optimization problem, which is solved via decomposing a joint optimization problem into two subproblems, e.g., optimal subcarriers and power allocation problems. Because of the co-channel interferences from different users, the subcarriers allocation subproblem is a three-dimensional (3D) search problem. We develop an iterative algorithm, based on which we transform a non-convex power allocation problem to a convex form before solving it using an alternating maximization (AM) algorithm. Simulations are used to validate the performance of the proposed physical-layer security scheme.

Performance of automatic speech recognition system drops dramatically in the presence of background noise unlike the human auditory system which is more adept at noisy speech recognition. This paper proposes a novel auditory modelling algorithm which is integrated with a feature extraction front-end. The proposed algorithm simulates properties of the human auditory system and applies it to the speech recognition system to enhance its robustness. It integrates simultaneous masking, forward masking and temporal integration effects into traditional mel-frequency cepstral coefficients (MFCC) feature extraction algorithm for robust speech recognition. The proposed method sharpens the power spectrum of the signal in both the frequency domain and the time domain. Experiments carried out on AURORA2 database show that the word recognition rate using our proposed feature extraction method has been effectively increased.