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This paper investigates the impact of authentication on effective capacity(EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel isunder impersonation attack by a malicious node (Eve) present in the closevicinity of the legitimate node pair (Alice and Bob); Eve tries to inject itsmalicious data into the system by making Bob believe that she is indeed Alice.To thwart the impersonation attack by Eve, Bob utilizes the distance of thetransmit node as the feature/fingerprint to carry out feature-basedauthentication at the physical layer. Due to authentication at Bob, due to lackof channel knowledge at the transmit node (Alice or Eve), and due to thethreshold-based decoding error model, the relevant dynamics of the consideredsystem could be modelled by a Markov chain (MC). Thus, we compute thestate-transition probabilities of the MC, and the moment generating functionfor the service process corresponding to each state. This enables us to derivea closed-form expression of the EC in terms of authentication parameters.Furthermore, we compute the optimal transmission rate (at Alice) throughgradient-descent (GD) technique and artificial neural network (ANN) method.Simulation results show that the EC decreases under severe authenticationconstraints (i.e., more false alarms and more transmissions by Eve). Simulationresults also reveal that the (optimal transmission rate) performance of the ANNtechnique is quite close to that of the GD method.

On the Effective Capacity of an Underwater Acoustic Channel under Impersonation Attack