Distributed LMS for consensus-based in-network adaptive processing
IEEE Transactions on Signal Processing
Performance analysis of the consensus-based distributed LMS algorithm
EURASIP Journal on Advances in Signal Processing
Steady-state analysis of the long LMS adaptive filter
Signal Processing
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New and weak conditions are given under which the LMS algorithm is exponentially convergent with probability one in a stochastic setting. These results show that LMS works under very broad conditions: a small gain, input signal with finite fourth moments; time varying persistence of excitation; and weak assumptions on the correlation structure of the input signal. Previous results at this level of generality linked convergence to peak signal amplitude rather than average amplitude. Under the same conditions stochastic boundedness of a forced LMS system is also established