Full-Duplex Cloud Radio Access Network: Stochastic Design and Analysis

Full-duplex (FD) has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in cellular networks, is however largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process (PPP)-based abstraction model of multi-antenna radio units (RUs) and user equipments (UEs). We consider different disjoint and user-centric approaches towards the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo (MC) simulations. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient-capacity fronthaul links and advanced interference cancellation capabilities

MIPS-Fusion: Multi-Implicit-Submaps for Scalable and Robust Online Neural RGB-D Reconstruction

We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method based on a novel neural implicit representation -- multi-implicit-submap. Different from existing neural RGB-D reconstruction methods lacking either flexibility with a single neural map or scalability due to extra storage of feature grids, we propose a pure neural representation tackling both difficulties with a divide-and-conquer design. In our method, neural submaps are incrementally allocated alongside the scanning trajectory and efficiently learned with local neural bundle adjustments. The submaps can be refined individually in a back-end optimization and optimized jointly to realize submap-level loop closure. Meanwhile, we propose a hybrid tracking approach combining randomized and gradient-based pose optimizations. For the first time, randomized optimization is made possible in neural tracking with several key designs to the learning process, enabling efficient and robust tracking even under fast camera motions. The extensive evaluation demonstrates that our method attains higher reconstruction quality than the state of the arts for large-scale scenes and under fast camera motions

POE Lubricant Candidates For Low GWP Refrigerants

Several series of polyol ester (POE) refrigeration lubricants have been investigated for low GWP refrigerant R32 (R-410A replacement) and HFO-1234ze (R-134a replacement). The main problem of R32/HFO refrigeration lubricant development can be summarized as balancing between miscibility, solubility and lubricity. Generally speaking, refrigerant-lubricant mixture with highly miscible property in low temperature evaporator will lead to more soluble phenomenon in high temperature compressor. Therefore, when refrigerant is well miscible with refrigeration lubricant, dissolved refrigerant will reduce working viscosity of refrigerant-oil mixture in compressor, and thus results in lower lubricity, wear of sliding parts, and compressor durability shortage. In our studies, the key factor which result in aforementioned phenomenon was found, and can be controlled independently by using optimized chemical structure. For R32 compressor system, we have successfully developed a series of POE refrigeration lubricant, with viscosities ranging from 32cSt to 90cSt at 40°C, and with a wide range of miscibility (20% oil) from -40℃ to 2℃. From results of PVT experiments and lubricity tests (Falex P/V and four ball), it demonstrated to be possible to develop a POE oil with high miscibility, low solubility and high working viscosity. All results in R32 system were better than traditional refrigeration lubricant in R410A system. Meanwhile, we also were able to identify the relationship between surface tension of chemical structure and lubricity. For HFO-1234ze compressor system, incumbent refrigeration lubricants suitable for R134a are almost fully miscible in HFO-1234ze, which could lead to severe refrigerant dilution of lubricant viscosity and poor lubricity due to high solubility. Through studies of chemical structure of refrigeration lubricants, reliable experimental tests and rigorous thermodynamic calculation, we created a range of POE lubricants (ISO68 to ISO220) with miscibility (20% oil) from -33℃ to -13℃, all the while, maintaining solubility and working viscosity on par with the common POE refrigeration lubricants currently used in R-134a system