  Stringent constraints on both reliability and latency must be guaranteed in
ultra-reliable low-latency communication (URLLC). To fulfill these constraints
with computationally constrained receivers, such as low-budget IoT receivers,
optimal transmission parameters need to be studied in detail. In this paper, we
introduce a multi-objective optimization framework for the optimal design of
URLLC in the presence of decoding complexity constraints. We consider
transmission of short-blocklength codewords that are encoded with linear block
encoders, transmitted over a binary-input AWGN channel, and finally decoded
with order-statistics (OS) decoder. We investigate the optimal selection of a
transmission rate and power pair, while satisfying the constraints. For this
purpose, a multi-objective optimization problem (MOOP) is formulated. Based on
the empirical model that accurately quantifies the trade-off between the
performance of an OS decoder and its computational complexity, the MOOP is
solved and the Pareto boundary is derived. In order to assess the overall
performance among several Pareto-optimal transmission pairs, two scalarization
methods are investigated. To exemplify the importance of the MOOP, a case study
on a battery-powered communication system is provided. It is shown that,
compared to the classical fixed rate-power transmissions, the MOOP provides the
optimum usage of the battery and increases the energy efficiency of the
communication system while maintaining the constraints.
