786 research outputs found
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Competitive MA-DRL for Transmit Power Pool Design in Semi-Grant-Free NOMA Systems
In this paper, we exploit the capability of multi-agent deep reinforcement
learning (MA-DRL) technique to generate a transmit power pool (PP) for Internet
of things (IoT) networks with semi-grant-free non-orthogonal multiple access
(SGF-NOMA). The PP is mapped with each resource block (RB) to achieve
distributed transmit power control (DPC). We first formulate the resource
(sub-channel and transmit power) selection problem as stochastic Markov game,
and then solve it using two competitive MA-DRL algorithms, namely double deep Q
network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the
optimal transmit power level and RB to form the desired PP. With the aid of
dueling processes, the learning process can be enhanced by evaluating the
valuable state without considering the effect of each action at each state.
Therefore, DDQN is designed for communication scenarios with a small-size
action-state space, while Dueling DDQN is for a large-size case. Our results
show that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the
SGF-NOMA system with the fixed-power-control mechanism and networks with pure
GF protocols with 17.5% and 22.2% gain in terms of the system throughput,
respectively. Moreover, to decrease the training time, we eliminate invalid
actions (high transmit power levels) to reduce the action space. We show that
our proposed algorithm is computationally scalable to massive IoT networks.
Finally, to control the interference and guarantee the quality-of-service
requirements of grant-based users, we find the optimal number of GF users for
each sub-channel
Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications
A novel reconfigurable intelligent surface-aided multi-robot network is
proposed, where multiple mobile robots are served by an access point (AP)
through non-orthogonal multiple access (NOMA). The goal is to maximize the
sum-rate of whole trajectories for multi-robot system by jointly optimizing
trajectories and NOMA decoding orders of robots, phase-shift coefficients of
the RIS, and the power allocation of the AP, subject to predicted initial and
final positions of robots and the quality of service (QoS) of each robot. To
tackle this problem, an integrated machine learning (ML) scheme is proposed,
which combines long short-term memory (LSTM)-autoregressive integrated moving
average (ARIMA) model and dueling double deep Q-network (DQN) algorithm.
For initial and final position prediction for robots, the LSTM-ARIMA is able to
overcome the problem of gradient vanishment of non-stationary and non-linear
sequences of data. For jointly determining the phase shift matrix and robots'
trajectories, DQN is invoked for solving the problem of action value
overestimation. Based on the proposed scheme, each robot holds a global optimal
trajectory based on the maximum sum-rate of a whole trajectory, which reveals
that robots pursue long-term benefits for whole trajectory design. Numerical
results demonstrated that: 1) LSTM-ARIMA model provides high accuracy
predicting model; 2) The proposed DQN algorithm can achieve fast average
convergence; 3) The RIS with higher resolution bits offers a bigger sum-rate of
trajectories than lower resolution bits; and 4) RIS-NOMA networks have superior
network performance compared to RIS-aided orthogonal counterparts
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