Multi-Slot AI Precoding for Real-Time Broadband Transmission
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Solution Overview
Problem
Conventional precoding technologies fail to meet the low-latency and high-reliability requirements of real-time broadband communication (RTBC) services, particularly in maximizing system utility and user capacity due to the need for optimizing precoding across multiple transmission slots, which is an NP-hard problem.
Innovation Solution
An intelligent precoding method using a deep reinforcement learning (DRL) network concatenated with conventional precoding to adaptively regulate precoding schemes, optimizing precoding across multiple slots to enhance user capacity and system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional precoding technology is used to maximize transmission rate in current slot, then transmission rate is improved, but system long-term utility is degraded
Solution Approach 1:
The patent applies preliminary action by performing precoding optimization across multiple transmission slots in advance rather than making single-slot decisions. The base station determines precoding matrices for multiple slots ahead of time, considering future transmission requirements and channel conditions, thereby maximizing system long-term utility while maintaining good transmission rates.
2Reliability
If precoding optimization across multiple slots is implemented, then system long-term utility is improved, but computational complexity is increased
Solution Approach 1:
The patent applies segmentation by dividing the multi-slot precoding optimization problem into manageable components. The base station processes different terminal groups separately, determines precoding matrices for each terminal group based on their specific channel conditions and QoS requirements, and combines these segmented solutions into a complete multi-slot precoding scheme, thereby reducing computational complexity.
Solution Approach 2:
The patent applies local quality by customizing precoding matrices for different terminal groups according to their specific local conditions. Each terminal group receives precoding optimized for its particular channel characteristics, data amount, and QoS requirements rather than using a uniform approach, which improves system long-term utility while managing complexity through localized optimization.
3Device complexity
If single-slot precoding optimization is used, then computational complexity is reduced, but adaptability to dynamic transmission requirements is degraded
Solution Approach 1:
The patent applies dynamics by making precoding adaptive to changing transmission requirements across multiple slots. The base station determines precoding matrices dynamically for each slot based on current channel conditions, terminal buffer states, and QoS requirements, allowing the system to adapt to dynamic transmission requirements while managing complexity through structured decision-making.
Solution Approach 2:
The patent applies feedback by using information from previous slot transmissions and current channel conditions to inform precoding decisions in subsequent slots. The base station continuously monitors transmission outcomes, channel state information, and terminal data amounts, using this feedback to adjust precoding matrices for future slots, thereby achieving adaptability to dynamic requirements.
Data Source
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AI summary
This application discloses an intelligent precoding method and apparatus in a real-time broadband communication scenario. The method includes the following steps of: obtaining a precoding matrix of first to-be-transmitted data of at least one terminal, where the precoding matrix has a correspondence with a priority weight of the at least one terminal, the priority weight corresponds to a first factor, the first factor has a correspondence with first state information, and the first state information corresponds to the first to-be-transmitted data of the at least one terminal; completing precoding of the first to-be-transmitted data of the at least one terminal based on the precoding matrix; and outputting first data, where the first data is data obtained by precoding the first to-be-transmitted data of the at least one terminal. In this application, an adaptively regulated precoding scheme trained by using a deep reinforcement learning network is used to obtain a global optimal solution of precoding in a span of a plurality of slots, to solve a problem that a single-slot optimal solution cannot meet a dynamically changing transmission requirement of a user between slots.