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Abstract: It is well known that neighbor discovery is a critical component of proactive routing protocols in wireless ad hoc networks. However there is no formal study on the performance of proposed neighbor discovery mechanisms. This paper provides a detailed model of key performance metrics of neighbor discovery algorithms, such as node degree and the distribution of the distance to symmetric neighbors. The model accounts for the dynamics of neighbor discovery as well as node density, mobility, radio and interference. The paper demonstrates a method for applying these models to the evaluation of global network metrics. In particular, it describes a model of network connectivity. Validation of the models shows that the degree estimate agrees, within 5% error, with simulations for the considered scenarios. The work presented in this paper serves as a basis for the performance evaluation of remaining performance metrics of routing protocols, vital for large scale deployment of ad hoc networks. [Copyright &y& Elsevier]

• We propose an improved ℓ 0 -LMS (ℓ 0 -ILMS) algorithm for sparse system identification. • The convergence condition of the proposed ℓ 0 -ILMS algorithm on step size is derived. • The parameter selection criterion for optimal steady-state mean-square deviation (MSD) is discussed. • Compared to ℓ 0 -LMS algorithm, the steady-state MSD of the proposed ℓ 0 -ILMS algorithm is lower and less sensitive to tuning parameters and measurement noise power. The ℓ 0 -norm-constraint LMS (ℓ 0 -LMS) algorithm is one of the widely used sparse LMS algorithms for the identification of sparse system, and its performance is quite attractive compared to other precursors. However, ℓ 0 -LMS is still confronted with some limitations on the optimal parameter selection and the estimated coefficient accuracy of sparse system identification. In this paper, we proposed an improved ℓ 0 -LMS (ℓ 0 -ILMS) algorithm to address these limitations. The convergence condition and the parameter selection rules for optimal steady-state mean-square deviation (MSD) of ℓ 0 -ILMS are discussed. Compared with ℓ 0 -LMS, the steady-state MSD of ℓ 0 -ILMS is lower and less sensitive to the tuning parameters and measurement noise power. Numerical simulations comparing the performance of standard LMS, ℓ 0 -LMS and ℓ 0 -ILMS demonstrate the effectiveness of ℓ 0 -ILMS. [ABSTRACT FROM AUTHOR]