  The ever-increasing data traffic, various delay-sensitive services, and the
massive deployment of energy-limited Internet of Things (IoT) devices have
brought huge challenges to the current communication networks, motivating
academia and industry to move to the sixth-generation (6G) network. With the
powerful capability of data transmission and processing, 6G is considered as an
enabler for IoT communication with low latency and energy cost. In this paper,
we propose an artificial intelligence (AI) and intelligent reflecting surface
(IRS) empowered energy-efficiency communication system for 6G IoT. First, we
design a smart and efficient communication architecture including the IRS-aided
data transmission and the AI-driven network resource management mechanisms.
Second, an energy efficiency-maximizing model under given transmission latency
for 6G IoT system is formulated, which jointly optimizes the settings of all
communication participants, i.e. IoT transmission power, IRS-reflection phase
shift, and BS detection matrix. Third, a deep reinforcement learning (DRL)
empowered network resource control and allocation scheme is proposed to solve
the formulated optimization model. Based on the network and channel status, the
DRL-enabled scheme facilities the energy-efficiency and low-latency
communication. Finally, experimental results verified the effectiveness of our
proposed communication system for 6G IoT.
