  In this paper, we address the discrete-time average consensus problem in
strongly connected directed graphs, where nodes exchange information over
unreliable error-prone communication links. We enhance the Robustified Ratio
Consensus algorithm by exploiting features of the (Hybrid) Automatic Repeat
ReQuest - (H)ARQ protocol used for error control of data transmissions, in
order to allow the nodes to reach asymptotic average consensus even when
information is exchanged over error-prone directional networks. This strategy,
apart from handling time-varying information delays induced by retransmissions
of erroneous packets, can also handle packet drops that occur when exceeding a
predefined packet retransmission limit. Invoking the (H)ARQ protocol allows
nodes to: (a) exploit the incoming error-free acknowledgement feedback to
initially acquire or later update their out-degree, (b) know whether a packet
has arrived or not, and (c) determine a local upper-bound on the delays imposed
by the retransmission limit. By augmenting the network's corresponding weight
matrix, we show that nodes utilizing our proposed (H)ARQ Ratio Consensus
algorithm can reach asymptotic average consensus over unreliable networks,
while improving their convergence speed and maintaining low values in their
local buffers compared to the current state-of-the-art.
