Multi-input multi-out and non-orthogonal multiple access (MIMO-NOMA)
internet-of-things (IoT) systems can improve channel capacity and spectrum
efficiency distinctly to support the real-time applications. Age of information
(AoI) is an important metric for real-time application, but there is no
literature have minimized AoI of the MIMO-NOMA IoT system, which motivates us
to conduct this work. In MIMO-NOMA IoT system, the base station (BS) determines
the sample collection requirements and allocates the transmission power for
each IoT device. Each device determines whether to sample data according to the
sample collection requirements and adopts the allocated power to transmit the
sampled data to the BS over MIMO-NOMA channel. Afterwards, the BS employs
successive interference cancelation (SIC) technique to decode the signal of the
data transmitted by each device. The sample collection requirements and power
allocation would affect AoI and energy consumption of the system. It is
critical to determine the optimal policy including sample collection
requirements and power allocation to minimize the AoI and energy consumption of
MIMO-NOMA IoT system, where the transmission rate is not a constant in the SIC
process and the noise is stochastic in the MIMO-NOMA channel. In this paper, we
propose the optimal power allocation to minimize the AoI and energy consumption
of MIMO- NOMA IoT system based on deep reinforcement learning (DRL). Extensive
simulations are carried out to demonstrate the superiority of the optimal power
allocation.

本文提出了一种基于深度强化学习的 MIMO-NOMA 物联网系统的最优功率分配策略以最小化信息时延和能耗，并通过广泛的模拟实验证明了其优越性。

基于深度强化学习的 MIMO-NOMA 物联网系统中功率分配以最小化 AoI 和能耗

Deep Reinforcement Learning Based Power Allocation for Minimizing AoI and Energy Consumption in MIMO-NOMA IoT Systems

With the accelerated development of immersive applications and the explosive
increment of internet-of-things (IoT) terminals, 6G would introduce terahertz
(THz) massive multiple-input multiple-output non-orthogonal multiple access
(MIMO-NOMA) technologies to meet the ultra-high-speed transmission and massive
connectivity requirements. Nevertheless, the unreliability of THz transmissions
and the extreme heterogeneity of device requirements pose critical challenges
for practical applications. To address these challenges, we propose a novel
smart reconfigurable THz MIMO-NOMA framework, which can realize customizable
and intelligent communications by flexibly and coordinately reconfiguring
hybrid beams through the cooperation between access points (APs) and
reconfigurable intelligent surfaces (RISs). The optimization problem is
formulated as a decentralized partially-observable Markov decision process
(Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the
diversified users' performance, via a joint RIS element selection, coordinated
discrete phase-shift control, and power allocation strategy. To solve the above
non-convex, strongly coupled, and highly complex mixed integer nonlinear
programming (MINLP) problem, we propose a novel multi-agent deep reinforcement
learning (MADRL) algorithm, namely graph-embedded value-decomposition
actor-critic (GE-VDAC), that embeds the interaction information of agents, and
learns a locally optimal solution through a distributed policy. Numerical
results demonstrate that the proposed algorithm achieves highly customized
communications and outperforms traditional MADRL algorithms.

提出了一种基于可重构智能表面技术实现的智能 THz MIMO-NOMA 通信框架，并采用分布式智能算法，完成网络中多个访问点协同优化查询的过程，通过了高定制、高效能和低延迟等测试。