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Distributed average consensus with bounded quantization

This paper considers distributed average consensus using bounded quantizers with potentially unbounded input data. We develop a quantized consensus algorithm based on a distributed alternating direction methods of multipliers (ADMM) algorithm. It is shown that, within finite iterations, all the agent variables either converge to the same quantization point or cycle with a finite period. In the convergent case, we derive a consensus error bound which also applies to that of the unbounded rounding quantizer provided that the desired average lies within quantizer output range. Simulations show that the proposed algorithm almost always converge when the network becomes large and dense.