  Effective Capacity defines the maximum communication rate subject to a
specific delay constraint, while effective energy efficiency (EEE) indicates
the ratio between effective capacity and power consumption. We analyze the EEE
of ultra-reliable networks operating in the finite blocklength regime. We
obtain a closed form approximation for the EEE in quasi-static Nakagami-$m$
(and Rayleigh as sub-case) fading channels as a function of power, error
probability, and latency. Furthermore, we characterize the QoS constrained EEE
maximization problem for different power consumption models, which shows a
significant difference between finite and infinite blocklength coding with
respect to EEE and optimal power allocation strategy. As asserted in the
literature, achieving ultra-reliability using one transmission consumes huge
amount of power, which is not applicable for energy limited IoT devices. In
this context, accounting for empty buffer probability in machine type
communication (MTC) and extending the maximum delay tolerance jointly enhances
the EEE and allows for adaptive retransmission of faulty packets. Our analysis
reveals that obtaining the optimum error probability for each transmission by
minimizing the non-empty buffer probability approaches EEE optimality, while
being analytically tractable via Dinkelbach's algorithm. Furthermore, the
results illustrate the power saving and the significant EEE gain attained by
applying adaptive retransmission protocols, while sacrificing a limited
increase in latency.
