Nokia Corporation

University of Science and Technology Beijing

Keping Long

Jian Ma

Haitao Wu

Shiduan Cheng

Haijun Zhang

Qian Wang

Dong Wang

Xiangming Wen

Runtong Zhang

Wanqing Guan

Xiangnan Liu

Tongwei Lu

An Artificial Intelligence (AI) engine-supporting downlink radio resource scheduling method and apparatus are provided. The AI engine-supporting downlink radio resource scheduling method includes: constructing an AI engine, establishing a Socket connection between an AI engine and an Open Air Interface (OAI) system, and configuring the AI engine into an OAI running environment to utilize the AI engine to replace a Round-Robin scheduling algorithm and a fair Round-Robin scheduling algorithm adopted by a Long Term Evolution (LTE) at a Media Access Control (MAC) layer in the OAI system for resource scheduling to take over a downlink radio resource scheduling process; sending scheduling information to the AI engine through Socket during the downlink radio resource scheduling process of the OAI system; and utilizing the AI engine to carry out resource allocation according to the scheduling information, and returning a resource allocation result to the OAI system.

AI engine-supporting downlink radio resource scheduling method and apparatus

A bandwidth part (BWP) and power scheduling method and apparatus for indoor terahertz are provided. The BWP and power scheduling method uses an intelligent reflecting surface (IRS) and a non-orthogonal multiple access (NOMA) to compensate for a shortcoming, wherein terahertz waves are not suitable for an indoor transmission, convert an indoor environment of an IRS-assisted terahertz propagation into a channel model, and convert an optimal wireless resource scheduling in an indoor IRS-assisted terahertz system into a functional optimization design with random constraints. Policies and corresponding actions are trained by using a proximal policy optimization (PPO) to schedule a power and a BWP. An actor part generates continuous a power scheduling, and a critic part generates a discrete BWP scheduling.

Bandwidth part (BWP) and power scheduling method and apparatus for indoor terahertz

The present invention relates to a method and network element for scheduling data transmission in a packet data network, e.g. a differentiated services network. Data traffic of a predetermined traffic class is classified based on a predetermined traffic profile, e.g. responsive or non-responsive traffic, and an in-profile rate of the data traffic is estimated. Then, a weight determining a transmit order of queued data packets of said predetermined traffic class is adjusted according to the estimated in-profile rate. Thus, the data traffic is marked into different classes based on the traffic profile and a queue scheduler improves fairness between the classified flows in the packet data network. Thereby, enhanced fairness between traffic profiles can be achieved easily and effectively.

Self-adaptive scheduling method and network element

Systems and methods are disclosed for marking a packet with a precedence value in a TCP-friendly way. One system and method marks packets with a precedence value based on a probability function. Another system and method marks packets with one of three precedence values based on network traffic but enables interleaving of differently marked packets when a certain number of packets have been successively marked with a low or medium precedence value.

TCP-friendly markers and methods using token buckets

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Beijing, China

Keping Long