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In this article we analyze the case of TD-SCDMA system standardization in China. Specifically, we focus on the leading role that government-controlled enterprises (GCEs) played in the standardization, and how the Chinese government supported GCEs. The case study findings advance current knowledge on how governments support standardization efforts in a catching-up context. We found that, under strong government support and with GCEs’ leadership, a latecomer country such as China, with certain technological capabilities and in economic, social, and institutional contexts that favor indigenous innovation, can pursue path-breaking innovation with complex technological systems. Governments should engage in various tactics to support GCEs that lead the standardization process. Most importantly, governments should intervene in scoping decisions on technological systems and research and development forces in the standard development stage, and offer moral suasion in the standard diffusion stage. We provide practical implications for governments that support GCEs in technology catching-up. [ABSTRACT FROM AUTHOR]

Phased array antennas (PAAs) play a key role in many fields such as radar systems and wireless communication systems. Since PAAs based on optical methods outperform the electronic counterparts in beam directivity and instantaneous bandwidth, optical PAAs have aroused wide research interests. Here, we report and experimentally demonstrate an integrated 4-channel optical true time delay network for PAAs. The true time delay unit is based on a grating-assisted contradirectional coupler, assisted by index chirp engineering and asymmetric apodization. A linear group delay with both positive and negative dispersion coefficients can be obtained. For the 4-channel time delay network, the measured dispersion coefficient of each channel is 10.35, 4.00, −4.32 and −14.38 ps/nm with 7 nm available bandwidth, respectively. The calculated beam scanning angle can range from 36.4 degree to −33.9 degree for the beam steering. This time delay network can be extended to large scale with more delay units and cascaded structures, thus wider scanning range and better beam directivity can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, we aim to design a fully-distributed control solution to navigate a group of unmanned aerial vehicles (UAVs), as the mobile Base Stations (BSs) to fly around a target area, to provide long-term communication coverage for the ground mobile users. Different from existing solutions that mainly solve the problem from optimization perspectives, we proposed a decentralized deep reinforcement learning (DRL) based framework to control each UAV in a distributed manner. Our goal is to maximize the temporal average coverage score achieved by all UAVs in a task, maximize the geographical fairness of all considered point-of-interests (PoIs), and minimize the total energy consumptions, while keeping them connected and not flying out of the area border. We designed the state, observation, action space, and reward in an explicit manner, and model each UAV by deep neural networks (DNNs). We conducted extensive simulations and found the appropriate set of hyperparameters, including experience replay buffer size, number of neural units for two fully-connected hidden layers of actor, critic, and their target networks, and the discount factor for remembering the future reward. The simulation results justified the superiority of the proposed model over the state-of-the-art DRL-EC $^3$ 3 approach based on deep deterministic policy gradient (DDPG), and three other baselines. [ABSTRACT FROM AUTHOR]