申公臣靈王 (二) : 遇於枥隧

《上海博物館藏戰國楚竹書·申公臣靈王》篇首句“[吾戈]於枥隧”之首字“[吾戈]”，多數學者釋爲“禦”，獨周鳳五釋爲“圉”。唯不論“禦”或“圉”，皆無“禦於”或“圉於”辭例。本文釋該字爲“晤”，即“遇”，二者聲義既通，又有“遇”辭例，故無滯礙；因而遂論“遇”字，以明其事本末云

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 (D$^{3}$QN) 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, D$^{3}$QN 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 D$^{3}$QN 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

A hybrid lattice Boltzmann and finite difference method for two-phase flows with soluble surfactants

A hybrid method is developed to simulate two-phase flows with soluble surfactants. In this method, the interface and bulk surfactant concentration equations of diffuse-interface form, which include source terms to consider surfactant adsorption and desorption dynamics, are solved in the entire fluid domain by the finite difference method, while two-phase flows are solved by a lattice Boltzmann color-gradient model, which can accurately simulate binary fluids with unequal densities. The flow and interface surfactant concentration fields are coupled by a modified Langmuir equation of state, which allows for surfactant concentration beyond critical micelle concentration. The capability and accuracy of the hybrid method are first validated by simulating three numerical examples, including the adsorption of bulk surfactants onto the interface of a stationary droplet, the droplet migration in a constant surfactant gradient, and the deformation of a surfactant-laden droplet in a simple shear flow, in which the numerical results are compared with theoretical solutions and available literature data. Then, the hybrid method is applied to simulate the buoyancy-driven bubble rise in a surfactant solution, in which the influence of surfactants is identified for varying wall confinement, Eotvos number and Biot number. It is found that surfactants exhibit a retardation effect on the bubble rise due to the Marangoni stress that resists interface motion, and the retardation effect weakens as the Eotvos or Biot number increases. We further show that the weakened retardation effect at higher Biot numbers is attributed to a decreased non-uniform effect of surfactants at the interface