Medium Access Control (MAC) protocols determine how multiple devices share a single communication channel. This started with Additive Links On-Line Hawaii Area (ALOHA) channel protocol and advanced to Carrier Sense Multiple Access (CSMA) protocols, variants of which are used today as WiFi standards. Such random access protocols are generally divided into contention-based methods like ALOHA and CSMA which are simple yet can have collisions at high traffic loads, and contention-free methods like Time Division Multiple Access (TDMA) which offer high efficiency but require complex clock synchronization and inflexible time slotting. While distributed queuing concepts have been pitched to help bridge this gap (e.g., DQDB or DQRAP) they have traditionally relied on physical time slots, dual buses, and/or complex signaling that makes them less suitable for the modern demands of wireless networks.

Historically, radio frequency and microwave switches have historically relied on either electromechanical switches (which suffer from limited speed and reliability) or solid-state switches such as PIN diodes and field-effect transistors (FETs), both of which require continuous bias current to maintain their states, consuming significant power in modern communication systems. In particular, solid-state switches (PIN diodes and FETs) require continuous DC power to maintain their ON or OFF states, leading to substantial energy consumption particularly problematic for battery-operated devices and large-scale systems like 5G/6G base stations and Internet of Things networks. Emerging non-volatile RF switches based on phase-change materials (PCM) and other memristive devices have shown promise but are constrained by large switching energies, limited resistance modulation ratios (typically < three orders of magnitude), volatile behavior requiring thermal maintenance above transition temperatures, and low endurance.

Traditional approaches to indoor mapping relied heavily on manual floor plan tracing or rule-based computer vision algorithms, which proved fragile when confronted with the wide variety of graphical representations used in architectural drawings. While Computer-Aided Design (CAD) floor plans in formats like DWG or DWF exist for most modern buildings, these detailed technical drawings are typically proprietary and inaccessible to the public. Mappers often work with low-quality images (JPEG or PDF format) of floor plans, necessitating manual digitization processes. RGB-D cameras, which capture both color and depth information, emerged as promising tools for 3D indoor scanning, though they face limitations including restricted range (typically less than 5 meters), sensitivity to lighting conditions, noisy point clouds at object edges, and computational demands for real-time processing. Automatic floor plan vectorization algorithms remain highly sensitive to image quality and graphical symbol variations, often requiring substantial manual editing even with state-of-the-art deep learning approaches.

A novel method to extract navigation observables from Starlink LEO satellite signals enabling precise positioning without additional infrastructure.

A novel framework enabling submeter-level accurate unmanned aerial vehicle (UAV) navigation using cellular carrier phase measurements with and without a base station.

A novel navigation framework enabling accurate positioning using unknown signals of opportunity without relying on Global Navigation Satellite System (GNSS).

A novel navigation framework utilizing low Earth orbit (LEO) satellite signals to provide accurate positioning where traditional Global Navigation Satellite System (GNSS) signals fail.

This technology enables precise navigation by opportunistically using 5G new radio (NR) signals without requiring dedicated positioning transmissions or direct network communication.

A novel navigation framework leveraging LTE cellular signals enables sub-meter level accurate UAV positioning in GNSS-challenged environments.

WiFi-based indoor positioning has been a widely researched area for the past five years, with systems traditionally relying on signal telemetry data including Received Signal Strength Indicator (RSSI), Channel State Information (CSI), and Fine Timing Measurement (FTM). However, adoption in practice has remained limited due to environmental challenges including signal fading, multipath effects, and interference that significantly impact positioning accuracy. Existing machine learning approaches typically require extensive manual feature engineering, preprocessing steps like filtering and data scaling, and struggle with missing or incomplete telemetry data while lacking flexibility across heterogeneous environments. Furthermore, there is currently no unified model capable of handling variations in telemetry data formats from different WiFi device vendors, use-case requirements, and environmental conditions, forcing practitioners to develop separate models for each specific deployment scenario.

This technology introduces a novel approach for bridging force sensing with wireless communication through direct analog force-to-RF conversion provides lower power consumption and lower costs.

A groundbreaking algorithm that significantly reduces communication time and message size in distributed machine learning, ensuring fast and reliable model convergence.

An innovative technology that dynamically manipulates electromagnetic waves for improved wireless communication and interference management.

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An innovative approach to vehicle localization and mapping using lidar and cellular LTE data, enhancing accuracy without relying on GNSS signals.

This technology offers a novel approach to navigation by using LTE signals, providing a viable alternative to traditional GPS systems.

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Researchers at the University of California, Davis have developed a reversed feedback amplifier design for enhanced mm-wave signal amplification.

This technology introduces a flexible, secure, and scalable approach to creating body area networks (BANs) using textile-integrated metamaterials for advanced healthcare monitoring.

This technology introduces a revolutionary receiver architecture capable of demodulating high-order QAM signals without the need for high-speed analog-to-digital converters (ADCs), significantly enhancing communication speed and efficiency.

Researchers from UC San Diego have developed RadSenNet, a new approach of sequential fusing of information from radars and cameras. The key idea of sequential fusion is to fundamentally shift the center of focus in radar-camera fusion systems from cameras to radars. This shift enables their invention (RadSegNet) to achieve all-weather perception benefits of radar sensing. Keeping radars as the primary modality ensures reliability in all situations including occlusions, longrange and bad weather.

Researchers from UC San Diego present a fully-passive, miniaturized, flexible form factor sensor interface titled ZenseTag that uses minimal electronics to read and communicate analog sensor data, directly at radio frequencies (RF). The technology exploits the fundamental principle of resonance, where a sensor's terminal impedance becomes most sensitive to the measured stimulus at its resonant frequency. This enables ZenseTag to read out the sensor variation using only energy harvested from wireless signals. UCSD inventors further demonstrate its implementation with a 15x10mm flexible PCB that connects sensors to a printed antenna and passive RFID ICs, enabling near real-time readout through a performant GUI-enabled software. They showcase ZenseTag's versatility by interfacing commercial force, soil moisture and photodiode sensors. 

Researchers from UC San Diego developed a patent-pending novel Synthesis of Virtual Array Manifold (SVAM) sensing approach for the mmWave single RF chain systems. More specifically, this new technology for sensing leads to faster and more robust beam alignment. UCSD believes this contribution will have significant impact on the traditional paradigm for sensing in mmWave systems.

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