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公开(公告)号：US20220377614A1
公开(公告)日：2022-11-24
申请号：US17712050
申请日：2022-04-01
Applicant: Intel Corporation
Inventor： Ravikumar Balakrishnan , Nageen Himayat , Arjun Anand , Mustafa Riza Akdeniz , Sagar Dhakal , Mark R. Eisen , Navid Naderializadeh
Abstract: 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 Qn(hn, an; θn) corresponding to a value of performing a radio resource management (RRM) action an at a receiving computing node n (RX node n) associated with TX node n using policy parameter θn and based on hn, hn including channel state information at RX node n; and determining, based on an overall action value function Qtot at time t, an estimated gradient of an overall loss at time t for overall policy parameter θt(∇Lt(θt)), wherein Qtot corresponds to a mixing of local action value functions Qi(hi, ai; θi) for all TX nodes i in the network at time t including TX node n; and determine, in response to a determination that ∇Lt(θt) is close to zero for various values of t during training, a trained local action value function Qn,trained to generate a trained action value relating to data communication between TX node n and RX node n.
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公开(公告)号：US20230189319A1
公开(公告)日：2023-06-15
申请号：US17921549
申请日：2021-06-26
Applicant: Intel Corporation
Inventor： Mustafa Riza Akdeniz , Nageen Himayat , Ravikumar Balakrishnan , Sagar Dhakal , Mark R. Eisen , Navid Naderializadeh
IPC: H04W72/542 , H04W24/02 , G06N3/08
CPC classification number: H04W72/542 , H04W24/02 , G06N3/08
Abstract: In one embodiment, a machine learning (ML) model for determining radio resource management (RRM) decisions is updated, with ML model parameters being shared between RRM decision makers to update the model. The updates may include local operations (between an AP and UE pair) to update local primal and dual parameters of the ML model, and global operations (between other devices in the network) to exchange/update global parameters of the ML model.
