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1. UAV-Based in-band Integrated Access and Backhaul for 5G Communications

   Fouda, Abdurrahman; Ibrahim, Ahmed S; Guvenc, Ismail; Ghosh, Monisha (October 2018, IEEE Vehicular Technology Conference (VTC2018-Fall))

   We introduce the concept of using unmanned aerial vehicles (UAVs) as drone base stations for in-band Integrated Access and Backhaul (IB-IAB) scenarios for 5G networks. We first present a system model for forward link transmissions in an IB-IAB multi-tier drone cellular network. We then investigate the key challenges of this scenario and propose a framework that utilizes the flying capabilities of the UAVs as the main degree of freedom to find the optimal precoder design for the backhaul links, user-base station association, UAV 3D hovering locations, and power allocations. We discuss how the proposed algorithm can be utilized to optimize the network performance in both large and small scales. Finally, we use an exhaustive search-based solution to demonstrate the performance gains that can be achieved from the presented algorithm in terms of the received signal to interference plus noise ratio (SINR) and overall network sum-rate. 

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2. A Cooperative Drone Assisted Mobile Access Network for Disaster Emergency Communications

   https://doi.org/10.1109/GLOBECOM38437.2019.9013813  

   Wu, Di; Sun, Xiang; Ansari, Nirwan (February 2020, 2019 IEEE Global Communications Conference (GLOBECOM))

   Multiple drone-mounted base stations (DBSs) are used to be deployed over a disaster struck area to help mobile users (MUs) communicate with working BSs, which are located beyond the disaster-struck area. DBSs are considered as relay nodes between MUs and working BSs. In order to relax the bottleneck in wireless backhaul links, we propose a cooperative drone assisted mobile access network architecture by enabling DBSs (whose backhaul links are congested) to offload their traffic to other DBSs (whose backhaul links are not congested) via DBS-to-DBS communications. We formulate the DBS placement and channel allocation problem in the context of the cooperative drone assisted mobile access network architecture, and design a COoperative DBS plAcement and CHannel allocation (COACH) algorithm to solve the problem. The performance of COACH is demonstrated via extensive simulations. 

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3. An FSO-based Drone Assisted Mobile Access Network for Emergency Communications

   https://doi.org/10.1109/TNSE.2019.2942266  

   Wu, Di; Sun, Xiang; Ansari, Nirwan (September 2019, IEEE Transactions on Network Science and Engineering)

   Deploying drone-mounted base stations (DBSs) can quickly recover the communications of the mobile users (MUs) in a disaster struck area. That is, the DBSs can act as relay nodes to transmit data from remote working base stations to the MUs. Since the DBSs could be deployed very close to the MUs, the access link data rates between the DBSs and the MUs are well provisioned. However, the DBSs may be far away from the remote working base stations, and thus the backhaul link data rate between a DBS and the remote working base station could be throttled. Free Space Optics (FSO), which has been demonstrated to provision high speed point-to-point wireless communications, can be leveraged to improve the capacity of the backhaul link. Since FSO requires line-of-sight between a DBS and a remote working macro base station, DBSs have to carefully deployed. In this paper, we design a QoS aware drone base station placement and mobile user association strategy (RESCUE) to jointly optimize the DBS deployment, MU association, and bandwidth allocation such that the number of the served MUs in the disaster struck area could be maximized. The performance of RESCUE is validated via extensive simulations. 

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4. An Evaluation of Strategies to Train More Efficient Backward-Chaining Reasoners

   https://doi.org/10.1145/3587259.3627564  

   Jia, Yue-Bo; Johnson, Gavin; Arnold, Alex; Heflin, Jeff (December 2023, ACM)

   Knowledge bases traditionally require manual optimization to en- sure reasonable performance when answering queries. We build on previous work on training a deep learning model to learn heuristics for answering queries by comparing different representations of the sentences contained in knowledge bases. We decompose the problem into issues of representation, training, and control and propose solutions for each subproblem. We evaluate different con- figurations on three synthetic knowledge bases. In particular we compare a novel representation approach based on learning to max- imize similarity of logical atoms that unify and minimize similarity of atoms that do not unify, to two vectorization strategies taken from the automated theorem proving literature: a chain-based and a 3-term-walk strategy. We also evaluate the efficacy of pruning the search by ignoring rules with scores below a threshold. 

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5. Organic Superbases in Recent Synthetic Methodology Research

   https://doi.org/10.1002/chem.202003580  

   Puleo, Thomas_R; Sujansky, Stephen_J; Wright, Shawn_E; Bandar, Jeffrey_S (January 2021, Chemistry – A European Journal)

   Abstract Organic superbases are a distinct and increasingly utilized class of Brønsted base that possess properties complementary to common inorganic bases. This Concept article discusses recent applications of commercial organic superbases in modern synthetic methodologies. Examples of the advantages of organic superbases in three areas are highlighted, including the discovery of new base‐catalyzed reactions, the optimization of reactions that require stoichiometric Brønsted base, and in high‐throughput experimentation technology. 

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