Dengue fever, malaria, Zika, West Nile virus and other mosquito-borne diseases may have finally met their match in crowded cities across the tropics. An unmanned, subterranean, robotic probe dispatched into the sewers of Kaohsiung City, Taiwan has proven lethally effective at locating the hidden pools of stagnant water where mosquitos breed. The sewer robot searches, so Taiwan's exterminators can destroy it. Researchers with Taiwan's National Mosquito-Borne Diseases Control Research Center found that their robotic hunter helped dramatically curb the city's mosquito population -- dropping the number of blood-sucking bugs by nearly 70 percent. Researchers with Taiwan's National Mosquito-Borne Diseases Control Research Center found that their robotic hunter helped dramatically curb the city's mosquito population, dropping the number of blood-sucking bugs by nearly 70 percent, based on their'gravitrap index' Researchers designed an unmanned ground vehicle (top) to scour cracks and crevices deep in the sewers of Kaohsiung.

KAOHSIUNG, Taiwan--(BUSINESS WIRE)--At the Grand Finale of X-PITCH 2022 held on November 10, fifteen finalists selected from more than 4,000 startups in 51 countries showcased their business in front of hundreds of guests in the Metaverse powered by Venu. 10 startups emerged as award winners and the top 3 startups will receive US$1 million investment in total. "As the X Games for Startups, we try something new and exciting every year. In past competitions, we've done it in high-speed elevators and self-driving cars. This year, the local semi-finals were played on the Kaohsiung Light Rail. In today's finals, we came to the Metaverse. This is an unprecedented experience for all of us."

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overheads. In this paper, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce these overheads. Unlike most existing works that focus only on channel estimation or feedback modules, to the best of our knowledge, this is the first study that considers the entire downlink CSI acquisition process, including downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Next, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Lastly, we combine the above two modules and compare two popular downlink channel acquisition frameworks. The former framework estimates and feeds back the channel at the user equipment subsequently. The user equipment in the latter one directly feeds back the received pilot signals to the base station. Our results reveal that, with the help of uplink, directly feeding back the pilot signals can save approximately 20% of feedback bits, which provides a guideline for future research. J. Guo and S. Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, P. R. C.-K. Wen is with the Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan (email: chaokai.wen@mail.nsysu.edu.tw). Since the standardization of the fifth generation (5G) communication system has gradually been solidified, researchers in the communication community are beginning to turn their attention to 5G evolution and 6G [1]. Further advancement, such as massive multiple-input and multipleoutput (MIMO) with increased antennas, distributed antenna arrangement combined with new network topology, and increased layers for spatial multiplexing, is expected [2]. A massive MIMO architecture is integral to 5G networks, especially as a key technology to utilize millimeter waves effectively [3], [4]. In massive MIMO systems, base station (BSs) are equipped with a large number of antennas to improve spectral and energy efficiencies through relatively simple (linear) processing.

Orthogonal frequency division multiplexing (OFDM) is one of the key technologies that are widely applied in current communication systems. Recently, artificial intelligence (AI)-aided OFDM receivers have been brought to the forefront to break the bottleneck of the traditional OFDM systems. In this paper, we investigate two AIaided OFDM receivers, data-driven fully connected-deep neural network (FC-DNN) receiver and model-driven ComNet receiver, respectively. We first study their performance under different channel models through simulation and then establish a real-time video transmission system using a 5G rapid prototyping (RaPro) system for over-the-air (OTA) test. To address the performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and real environments, we develop a novel online training strategy, called SwitchNet receiver. The SwitchNet receiver is with a flexible and extendable architecture and can adapts to real channel by training one parameter online. The OTA test verifies its feasibility and robustness to real environments and indicates its potential for future communications systems. At the end of this paper, we discuss some challenges to inspire future research. P. Jiang, T. Wang, B. Han, X. Gao, J. Zhang and S. Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (email: wangtianqi@seu.edu.cn; C.-K. Wen is with the Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan (email: ckwen@ieee.org). G. Y. Li is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (email: liye@ece.gatech.edu). I. INTRODUCTION By introducing artificial intelligence (AI), intelligent communications can potentially address manychallenging issues in traditional communication systems. There have been many achievements in intelligent communications recently [1], [2], [3], including using AI for signal classification [4], multiple-input multiple-output (MIMO) detection [5], channel state information (CSI) feedback [6], [7], novel autoencoder-based end-to-end communication systems [8] and [9]. Orthogonal frequency division multiplexing (OFDM) has been proved to be an effective technique to deal with delay spread of wireless channels [10], [11]. OFDM receivers can be classified into two categories: linear and nonlinear receivers.