Wireless Backhaul Node Placement for Small Cell Networks

Small cells have been proposed as a vehicle for wireless networks to keep up with surging demand. Small cells come with a significant challenge of providing backhaul to transport data to(from) a gateway node in the core network. Fiber based backhaul offers the high rates needed to meet this requirement, but is costly and time-consuming to deploy, when not readily available. Wireless backhaul is an attractive option for small cells as it provides a less expensive and easy-to-deploy alternative to fiber. However, there are multitude of bands and features (e.g. LOS/NLOS, spatial multiplexing etc.) associated with wireless backhaul that need to be used intelligently for small cells. Candidate bands include: sub-6 GHz band that is useful in non-line-of-sight (NLOS) scenarios, microwave band (6-42 GHz) that is useful in point-to-point line-of-sight (LOS) scenarios, and millimeter wave bands (e.g. 60, 70 and 80 GHz) that are recently being commercially used in LOS scenarios. In many deployment topologies, it is advantageous to use aggregator nodes, located at the roof tops of tall buildings near small cells. These nodes can provide high data rate to multiple small cells in NLOS paths, sustain the same data rate to gateway nodes using LOS paths and take advantage of all available bands. This work performs the joint cost optimal aggregator node placement, power allocation, channel scheduling and routing to optimize the wireless backhaul network. We formulate mixed integer nonlinear programs (MINLP) to capture the different interference and multiplexing patterns at sub-6 GHz and microwave band. We solve the MINLP through linear relaxation and branch-and-bound algorithm and apply our algorithm in an example wireless backhaul network of downtown Manhattan.Comment: Invited paper at Conference on Information Science & Systems (CISS) 201

Anatomical Variation of Radial Wrist Extensor Muscles: A Study in Cadavers

OBJECTIVE: The tendons of the extensor carpi radialis longus and brevis muscles are quite useful in tendon transfer, such as in correction of finger clawing and restoration of thumb opposition. Knowledge of additional radial wrist extensor muscle bellies with independent tendons is useful in the above-mentioned surgical procedures. METHODS: The skin, subcutaneous tissue, and antebrachial fascia of 48 (24 on the right side and 24 on left side) male upper limb forearms were dissected. The following aspects were then analyzed: (a) the presence of additional muscle bellies of radial wrist extensors, (b) the origin and insertion of the additional muscle, and (c) measurements of the muscle bellies and their tendons. RESULTS: Five out of 48 upper limbs (10.41%) had additional radial wrist extensors; this occurred in 3 out of 24 left upper limbs (12.5%) and 2 out of 24 right upper limbs (8.3%). In one of the right upper limbs, two additional muscles were found. The length and width of each additional muscle belly and its tendon ranged between 2 - 15cm by 0.35 - 6.4cm and 2.8 - 20.8cm by 0.2 0.5cm, respectively. The additional radial wrist extensor tendons in our study basically originated either from the extensor carpi radialis longus or brevis muscles and were inserted at the base of the 2nd or 3rd metacarpal bone. CONCLUSION: The present study will inform surgeons about the different varieties of additional radial wrist extensors and the frequency of their occurrence

Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps

Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements. The map contains information about buildings and foliage. The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size. Further, our approach works with continuous transmitter and receiver coordinates without relying on discretization. In experiments, we show that the proposed model is able to efficiently learn dominant path losses from sparse training data and generalizes well when tested on novel maps.Comment: Accepted at IEEE GLOBECOM 2023, v2: Changed license on arxi

THE DESIGN AND IMPLEMENTATION OF SCORPIO - A GRAPHICAL IDE FOR SYSTEM-ON-CHIP DEVELOPMENT

Master'sMASTER OF SCIENCE IN COMPUTER SCIENC