no code implementations • 1 Aug 2023 • Zhaoming Hu, Ruikang Zhong, Chao Fang, Yuanwei Liu
As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm.
no code implementations • 10 May 2022 • Ruikang Zhong, Yuanwei Liu, Xidong Mu, Yue Chen, Xianbin Wang, Lajos Hanzo
Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy.
no code implementations • 23 Nov 2020 • Ruikang Zhong, Xiao Liu, Yuanwei Liu, Yue Chen, Xianbin Wang
Our simulation results demonstrate that 1) With the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.
no code implementations • 18 Oct 2020 • Ruikang Zhong, Xiao Liu, Yuanwei Liu, Yue Chen
Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
no code implementations • 18 Oct 2020 • Ruikang Zhong, Xiao Liu, Yuanwei Liu, Yue Chen
A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network.