Intel Corporation

Apple Inc.

The Boeing Company

Navid Naderializadeh

Hosein Nikopour

Shilpa Talwar

Oner Orhan

Murali Narasimha

Nageen Himayat

Sudeep K Palat

Jaemin Han

Arnaud Lucres Amadjikpe

Mikhail T Galeev

Susruth Sudhakaran

Liang Xian

Benjamin Grewell

Wei Mao

Meryem Simsek

Carlos Cordeiro

Jing Zhu

Deepak Khosla

Bahareh Sadeghi

Hassnaa Moustafa

Yang Chen

Stefano Pellerano

Ravikumar Balakrishnan

Christopher Hull

Brent R Carlton

Ehsan Aryafar

Mustafa Akdeniz

Sean Soleyman

Sagar Dhakal

Fan Hin Hung

Arjun Anand

Mustafijur Rahman

Mark R Eisen

An apparatus of a transmitter computing node n (TX node n) of a wireless network, one or more computer readable media, a system, and a method. The apparatus includes one or more processors to: implement machine learning (ML) based training rounds, each training round including: determining a local action value function Q(h, a; θ) corresponding to a value of performing a radio resource management (RRM) action aat a receiving computing node n (RX node n) associated with TX node n using policy parameter θand based on h, hincluding channel state information at RX node n; and determining, based on an overall action value function Qat time t, an estimated gradient of an overall loss at time t for overall policy parameter θ(∇L(θ)), wherein Qcorresponds to a mixing of local action value functions Q(h, a; θ) for all TX nodes i in the network at time t including TX node n; and determine, in response to a determination that ∇L(θ) is close to zero for various values of t during training, a trained local action value function Qto generate a trained action value relating to data communication between TX node n and RX node n.

Apparatus, system, method and computer-implemented storage media to implement radio resource management policies using machine learning

Training adversarial aircraft controllers is provided. The method comprises inputting current observed states of a number of aircraft into a world model encoder, wherein each current state represents a state of a different aircraft, and wherein each current state comprises a missing parameter value. A number of adversarial control actions for the aircraft are input into the world model encoder concurrently with the current observed state, wherein the adversarial control actions are generated by competing neural network controllers. The world model encoder generates a learned observation from the current observed states and adversarial control actions, wherein the learned observation represents the missing parameter value from the current observed states. The learned observation and current observed states are input into the competing neural network controllers, wherein each current observed state is fed into a respective controller. The competing neural network controllers then generate next adversarial control actions.

Predictive modeling of aircraft dynamics

Various embodiments herein provide techniques for integrated access and backhaul (IAB) nodes. For example, embodiments include techniques associated with: rate-proportional routing for network coding; utilizing multiple routes in IAB networks; user equipment (UE) and parent selection for efficient topology in IAB networks; establishing efficient IAB topologies; and/or adaptive coded-forwarding for network coding. Other embodiments may be described and claimed.

Techniques for integrated access and backhaul (IAB) nodes

A mobile communication device that is configured to cancel interference within received millimeter wave band signals. The device includes a receiver circuit that is configured to receive a millimeter wave band signal, adjust gain provided to the millimeter wave band signal at a first amplifier, cancel interference in millimeter wave band signal after gain is adjusted by the first amplifier, and adjust gain provided to the millimeter wave band signal at a second amplifier after interference is cancelled.

Millimeter wave (mmWave) system and methods

Methods, apparatus, and computer-readable media are described to use multi-finger beamforming for multimeter wave communications. A base station associates with first and second user equipment. Weight sum rates are determined for the user equipment. Transmissions are scheduled to the user equipment based on the weight sum rates. Data is encoded for the first user equipment and transmitted based on the schedule. Data is encoded for the second user equipment and transmitted based on the schedule. The transmissions are multiplexed in the power domain.

Non-orthogonal multiple-access and multi-finger beamforming

Millimeter wave (MMWAVE) system and methods

Aspects for interference mitigation in ultra-dense networks are described.

Interference mitigation in ultra-dense wireless networks

Growing community of inventors

Santa Clara, CA, United States of America

Navid Naderializadeh