Network Node Scheduling for Simultaneous UE Transmissions
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Solution Overview
Problem
Current wireless communication networks face inefficiencies in simultaneous data transmissions due to limited scheduling algorithms that do not account for predictive data patterns, leading to increased energy consumption and hardware size, particularly in Advanced Antenna Systems (AAS) used in 5G networks.
Innovation Solution
A network node identifies compatible UEs for simultaneous data transmissions by aligning their data requests using predictive patterns, allowing for simultaneous data transmission in a common time resource, thereby increasing spatial multiplexing and reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Advanced Antenna Systems (AAS) are used to enhance transmission quality and reliability, then performance is improved, but power consumption increases significantly
Solution Approach 1:
The network node performs predictive analysis of data patterns before scheduling transmissions. By analyzing historical data requests and predicting future transmission needs, the system prepares transmission schedules in advance, enabling simultaneous transmissions to be coordinated beforehand. This preliminary action allows the system to maximize the utilization of AAS capabilities only when predictive patterns indicate optimal conditions, rather than continuously operating at high power.
Solution Approach 2:
The system implements periodic scheduling of simultaneous transmissions based on predicted data patterns. Instead of continuous high-power AAS operation, transmissions are organized in periodic intervals where the network node identifies compatible UEs and schedules simultaneous transmissions at optimal times. This periodic approach maintains transmission quality through structured AAS usage while reducing overall power consumption by allowing the system to enter lower-power states between scheduled transmission periods.
2Productivity
If simultaneous data transmissions are implemented without predictive patterns, then resource utilization is limited, but energy consumption increases due to inefficient scheduling
Solution Approach 1:
The network node continuously monitors data request patterns from multiple UEs and uses this feedback to refine predictive models. By analyzing actual transmission outcomes and data request behaviors, the system adjusts its prediction algorithms to better identify when simultaneous transmissions will be successful. This feedback mechanism ensures that resource utilization improves over time while energy consumption is optimized based on actual network conditions rather than static scheduling rules.
Solution Approach 2:
The system dynamically changes scheduling parameters based on predicted data patterns. When predictive analysis indicates high compatibility for simultaneous transmissions, the network node adjusts transmission parameters such as timing, frequency resources, and power levels to maximize resource utilization. When patterns suggest lower compatibility, the system modifies parameters to reduce energy consumption. This dynamic parameter adjustment resolves the contradiction by adapting resource allocation to actual predictive insights rather than using fixed scheduling approaches.
3Device complexity
If conventional scheduling algorithms are used, then implementation is simple, but spatial multiplexing opportunities are reduced and heat generation increases
Solution Approach 1:
The network node performs preliminary identification of compatible UE pairs before executing simultaneous transmissions. By pre-analyzing channel conditions, UE capabilities, and data patterns, the system prepares a list of compatible combinations in advance. This preliminary action enables the relatively simple execution phase where pre-identified compatible UEs are scheduled for simultaneous transmission, maintaining low implementation complexity while significantly increasing spatial multiplexing opportunities compared to conventional algorithms that identify compatibility only at scheduling decision time.
4Productivity
If more simultaneous transmissions are scheduled, then resource utilization improves, but heat generation from equipment increases
Solution Approach 1:
The system performs preliminary thermal and power budget analysis before scheduling simultaneous transmissions. By predicting the thermal impact of proposed transmission schedules, the network node can optimize the number and configuration of simultaneous transmissions to maximize resource utilization while staying within thermal constraints. This preliminary thermal awareness allows the system to schedule more transmissions when cooling capacity is available and reduce scheduling when thermal limits are approached, resolving the contradiction between resource utilization and heat generation.
Data Source
AI summary
A method performed by a network node for handling data transmissions to two or more User Equipments, UEs, in a wireless communications network is provided. The network node decides that a first UE and a second UE are compatible for simultaneous data transmissions. The network node times the data transmissions to the first UE and the second UE in a common time resource, for simultaneous data transmissions comprising a first data transmission to the first UE and a second data transmission the second UE.