Adaptive Silent Time for Massive MIMO in Unlicensed Bands
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In dense wireless communication networks, massive MIMO arrays face challenges in efficiently allocating temporal resources for interference suppression and data transmission in unlicensed frequency bands, as increasing silent time intervals for interference measurement reduces data transmission time, and existing methods lack adaptive resource allocation strategies to optimize performance.
Innovation Solution
The implementation of a method where a node buffers non-spatially filtered samples during LBT operations and dynamically determines the silent time interval based on the number of buffered samples, allowing for adaptive allocation of temporal resources to improve interference suppression and data transmission efficiency by adjusting the target number and validity time interval based on previous LBT outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the silent time interval is increased for interference measurement, then the accuracy of covariance matrix estimation is improved, but the data transmission time is reduced
Solution Approach 1:
The patent applies dynamics by making the silent time interval adaptive rather than fixed. The node dynamically adjusts the silent time interval based on the number of buffered non-spatially filtered samples and previous LBT outcomes. When fewer samples are buffered, the silent time interval is extended to collect more samples for accurate covariance matrix estimation. When more samples are available, the silent time interval is reduced to maximize data transmission time, thus resolving the contradiction between measurement precision and productivity through dynamic adjustment.
Solution Approach 2:
The patent changes the parameter of silent time interval duration based on the number of buffered samples. By varying this temporal parameter adaptively, the system optimizes the trade-off between acquiring sufficient interference measurements and maintaining data transmission efficiency. The parameter change is driven by the buffering status and LBT history, allowing the system to flexibly allocate temporal resources.
2Object-affected harmful factors
If the silent time interval is extended to collect more samples, then the interference suppression performance is improved, but the temporal resources for data transmission are reduced
Solution Approach 1:
The patent implements feedback mechanisms where the node uses previous LBT outcomes and current buffering status to determine the appropriate silent time interval. The feedback loop continuously monitors the number of buffered samples and adjusts the silent time interval accordingly. This feedback-driven adaptation ensures that interference suppression performance is optimized only when necessary, while maximizing data transmission opportunities when interference is low or samples are sufficient.
Solution Approach 2:
The system dynamically adjusts the silent time interval based on real-time conditions including the number of buffered samples and historical LBT outcomes. This dynamic behavior allows the system to extend the silent time interval only when additional samples are needed for improved interference suppression, while reducing it when data transmission should prioritize temporal resources.
3Productivity
If adaptive allocation strategies are implemented, then the trade-off between interference reduction and data transmission is optimized, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by buffering non-spatially filtered samples during LBT operations before they are needed for covariance matrix estimation. This advance collection of samples reduces the need for extended silent time intervals in the future, as samples are already available from previous operations. The buffering mechanism prepares resources in advance, simplifying the adaptive allocation decisions that need to be made during actual transmission periods.
Solution Approach 2:
The node performs self-service by using its own buffered samples and LBT history to autonomously determine the appropriate silent time interval without external control. The adaptive allocation strategy is implemented through the node's internal logic that monitors buffering status and LBT outcomes, allowing the system to self-regulate temporal resources based on its own operational state, thereby managing complexity through decentralized intelligence.
Data Source
AI summary
A node is configured for connection to a massive multiple-input, multiple-output (MIMO) array to provide spatially multiplexed channels in an unlicensed frequency band. The node includes a memory configured to store samples of non-spatially filtered signals received by the node during a first listen-before-talk (LBT) operation used to acquire the unlicensed frequency band. The node also includes a processor configured to determine, based on a number of previously stored samples, a duration of a silent time interval during which the node collects samples of non-spatially filtered signals and stores the samples in the memory. The node further includes a transceiver configured to perform a second LBT operation to acquire the unlicensed frequency band using a spatial filter determined based on the samples stored in the memory.