  This paper considers distributed average consensus using finite-bit bounded
quantizer with possibly unbounded data. Under the framework of the alternating
direction method of multipliers (ADMM), we develop distributed averaging
algorithms where each node iteratively updates using only the local information
and finitely quantized outputs from its neighbors. It is shown that all the
agent variables either converge to the same quantization level or cycle around
the data average after finite iterations. An error bound for the consensus
value is established, which turns out to be the same as that of using the
unbounded rounding quantizer provided that an algorithm parameter (i.e., ADMM
step size) is small enough. We also analyze the effect of the algorithm
parameter and propose an adaptive parameter selection strategy that only
requires knowledge of the number of agents in order to accelerate the algorithm
with certain consensus accuracy guarantee. Finally, simulations are performed
to illustrate the effectiveness of the proposed algorithms.
