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The Fourth Industrial Revolution (Industrial 4.0) is coming, and thisrevolution will fundamentally enhance the way the factories manufactureproducts. The conventional wired lines connecting central controller to robotsor actuators will be replaced by wireless communication networks due to its lowcost of maintenance and high deployment flexibility. However, some criticalindustrial applications require ultra-high reliability and low latencycommunication (URLLC). In this paper, we advocate the adoption of massivemultiple-input multiple output (MIMO) to support the wireless transmission forindustrial applications as it can provide deterministic communications similaras wired lines thanks to its channel hardening effects. To reduce the latency,the channel blocklength for packet transmission is finite, and suffers fromtransmission rate degradation and decoding error probability. Thus,conventional resource allocation for massive MIMO transmission based on Shannoncapacity assuming the infinite channel blocklength is no longer optimal. Wefirst derive the closed-form expression of lower bound (LB) of achievableuplink data rate for massive MIMO system with imperfect channel stateinformation (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF)receivers. Then, we propose novel low-complexity algorithms to solve theachievable data rate maximization problems by jointly optimizing the pilot andpayload transmission power for both MRC and ZF. Simulation results confirm therapid convergence speed and performance advantage over the existing benchmarkalgorithms.

Joint Pilot and Payload Power Allocation for Massive-MIMO-enabled URLLC IIoT Networks