Interference Rejection Combining for Asynchronous Interference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional interference rejection combining techniques perform poorly in the presence of asynchronous interference, where the desired signal and interference only partially overlap in time, leading to non-stationary interference properties that are challenging to mitigate, especially in dense wireless networks with poor synchronization between adjacent small cells.
Innovation Solution
The proposed solution involves an interference rejection combining module that determines a covariance based on the Hermitian transpose of signals received on non-pilot subcarriers, allowing for the computation of equalizer weights on a per-resource block basis to mitigate asynchronous interference in LTE systems, using a minimum mean square error receiver that jointly estimates noise and interference covariance with data, leveraging good time-frequency coherence in dense deployments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional interference rejection combining techniques are used, then the system can mitigate synchronous interference, but it performs poorly in the presence of asynchronous interference where desired signal and interference only partially overlap in time
Solution Approach 1:
The patent applies dynamics by making the IRC system adaptive to time-varying interference conditions. The covariance matrix is computed dynamically based on received signals rather than assuming stationary interference. The system adjusts its interference rejection combining weights in real-time to match changing interference characteristics, enabling effective mitigation of asynchronous interference where traditional static IRC fails.
Solution Approach 2:
The patent changes the fundamental parameter assumption from synchronous to asynchronous interference modeling. By computing the covariance matrix using actual received signals including asynchronous components, and by adjusting the IRC weights based on these computed parameters, the system adapts to time-varying interference conditions. This parameter adaptation allows the system to maintain reliability across different interference scenarios.
2Productivity
If small cells are deployed in dense networks to increase capacity, then network capacity increases, but co-channel interference between adjacent cells becomes a limiting factor
Solution Approach 1:
The patent converts the harmful co-channel interference into a useful signal for covariance estimation. By using the received signals that contain interference to compute the covariance matrix, the system transforms the interference from a detrimental factor into a source of information about interference characteristics. This computed covariance is then used to design IRC weights that reject the same interference, effectively converting the harmful interference into a benefit for interference mitigation.
3Device complexity
If IRC assumes interference and desired signal are synchronous, then the processing is simplified, but this assumption is highly idealized and might not be true in practice
Solution Approach 1:
The patent applies preliminary action by computing the covariance matrix from received signals before applying IRC processing. This pre-computation of interference statistics allows the system to prepare appropriate IRC weights that account for actual interference conditions rather than relying on idealized synchronous assumptions. The preliminary covariance estimation enables subsequent interference rejection to be more accurate without significantly increasing overall system complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An apparatus includes an interference rejection combining module, at least partially implemented in hardware. The interference rejection combining module determines a covariance based on a Hermitian transpose of a signal received on a subcarrier of a symbol that is not a pilot symbol.