A compact, low-power frequency modulated continuous wave (FMCW) radar system enabling high-resolution direction-of-arrival (DoA) detection using a single leaky-wave antenna and transceiver chain

A pioneering framework that enables formal verification of Large Language Model (LLM) compliance with specified interaction protocols using natural language and Finite State Machine (FSM) structures. It is complemented by methods for rapid protocol development and scalable multi-agent coordination.

An algorithm that predicts missing or unobserved connections in any type of complex network by analyzing local structural patterns.

A novel mm-wave transmitter architecture that integrates load-modulated balanced power amplifiers (LMBPA) and double quadrature (DQ) up-conversion for enhanced efficiency, image suppression, and antenna resilience.

The transition to 5G and 6G networks has led to a widespread adoption of machine learning (ML) for critical functions like modulation classification, channel estimation, resource management, and spectrum sensing. While ML has enhanced operational efficiency, it has simultaneously expanded the attack surface for adversarial ML at the Physical Layer (PHY), for example, from Generative Adversarial Networks (GANs). While techniques like radio frequency (RF) fingerprinting have emerged as a PHY-level authentication method based on hardware-induced signal traits (such as in-phase/quadrature (I/Q) imbalance and error vector magnitude), GANs can synthesize RF signals to mimic legitimate hardware-induced features up to 95% similarity. This is close enough to evade most detection schemes. Existing defenses to GANs based on convolutional neural networks, deep neural networks, supervised retraining, and/or heuristics do not generalize well across different modulations, protocols, channel conditions, or unseen attack types. Autoencoder and reconstruction-based approaches are often limited to clean reference signals, which are not always available in dynamic wireless environments. While GANs are excellent at mimicking low-order statistics (mean/variance), they fail to replicate complex signal structures.

An optical circuit switch having a distinct-layered architecture provides a highly scalable design for high-radix switching matrices. To achieve this, the platform features an out-of-plane spatial separation between an underlying optical layer and a distinct overlying micro-electromechanical systems layer. This structural separation allows mechanical components and electrical interconnects to cross directly over the optical waveguides without planar routing interference, which significantly reduces the overall footprint of the optical circuit switch.

The recent state of the art in network routing has been dominated by the Border Gateway Protocol (BGP). While BGP is the standard for inter-domain routing, it primarily relies on single-path propagation based on shortest-path or policy-driven criteria and metrics. Traditional multi-path approaches to network routing are challenged by routing loops and slow convergence in changing network topology environments. More recently, Software Defined Networking (SDN) and Segment Routing have attempted to provide more granular control; however, ensuring loop-free paths across multiple autonomous systems without impractical overhead remains a stubborn issue. In considering larger-scale network communications involving internet protocols (IP), these modern networks require higher resilience and bandwidth, making the ability to utilize multiple paths simultaneously without the risk of circular routing increasingly desirable.

Researchers at the University of California, Davis in collaboration with Deutsche Telekom AG have developed a system and method for monitoring network traffic by dynamically routing traffic sub-populations over fixed monitoring locations without violating traffic engineering policies. This approach leverages existing routing flexibility to collect high-quality flow data without disrupting normal traffic engineering policies

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.

QAFeL is a novel asynchronous federated learning framework that combines buffered aggregation with bidirectional quantized communications, achieving up to 8× lower communication costs while preserving convergence speed and accuracy.

Outdoor wireless sensor and camera networks are important for environmental monitoring and public-safety surveillance, yet their real-world deployment still relies heavily on expert intuition and exhaustive simulations that fail to scale in many landscapes. Traditional coverage-maximization techniques evaluate every candidate position for every node while factoring in every other node, the task complexity becomes intractable as node count or terrain granularity grows. The challenge is sharper in three-dimensional topographies where ridges, valleys, and plateaus block line-of-sight and invalidate two-dimensional heuristics. Moreover, once nodes are in the field, relocating them is slow and costly if new blind spots emerge or missions evolve.

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

Brief description not available

Innovative methods and devices for improving error correction and reducing repair bandwidth in distributed systems using enhanced Reed-Solomon codes.

This technology enhances indoor pedestrian localization accuracy using LTE signals by mitigating multipath errors through synthetic aperture navigation.

An innovative approach to indoor localization using LTE signals and IMU data, enhancing accuracy and reliability for navigation.

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.

The DDFB laser introduces a novel feedback mechanism for enhanced frequency selectivity and stability in laser oscillation.

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.

This technology introduces an advanced scheduling strategy for optimizing multi-model AI workloads on heterogeneous chiplet-based multi-chip modules (MCMs), aiming at maximizing performance efficiency.

Brief description not available

This technology revolutionizes health monitoring by integrating smart textiles with body area networks for real-time biometric data collection.

Metadata is used to summarize basic information about data that can make tracking and working with specific data easier. Today’s communication systems, like WhatsApp, iMessage, and Signal, use end-to-end encryption to protect message contents. Such communication systems do not hide metadata, which is the data providing information about one or more aspects of such contents, like messages. Such metadata includes information about who communicates with whom, when, and how much, and is generally visible to systems and network observers. As a result, cyber risk associated with metadata leakage and traffic analysis remains a significant attack vector in such modern communication systems. Previous attempts to address this risk have been generally seen as not secure or prohibitively expensive, for example, by imposing inflexible bandwidth restrictions and cumbersome synchronous schedules globally, which cripples performance. Moreover, prior approaches relied on distributed trust for security, which is largely incompatible with conventional organizations hosting or using such apps.