LTE Bursty WiFi Interference Detection via Energy and Periodicity Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing traffic in cellular networks due to offloading from WiFi networks leads to interference issues in unlicensed spectrum usage, which affects the performance of LTE/LTE-A communications, as existing methods fail to effectively detect and mitigate bursty WiFi interference.
Innovation Solution
The method involves detecting bursty WiFi interference in LTE/LTE-A communications using techniques such as energy detection and second-order periodicity analysis, allowing for interference cancellation or suppression, and employing null tones and reserved resource blocks to identify and mitigate interfering signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LTE/LTE-A communications operate in unlicensed spectrum to increase capacity, then network capacity is improved, but interference from WiFi networks increases
Solution Approach 1:
The system performs Clear Channel Assessment (CCA) and Listen Before Talk (LBT) procedures before transmitting in unlicensed spectrum. The eNB and UE conduct energy detection and periodicity analysis in advance to identify WiFi interference patterns, then adjust transmission timing or frequency to avoid detected interference periods, thereby maintaining capacity while preventing harmful interference
2Measurement precision
If energy detection and periodicity analysis are used to detect WiFi interference, then interference detection accuracy is improved, but device complexity increases
Solution Approach 1:
The detection process is segmented into distinct phases: energy detection phase where total received power is measured, followed by periodicity analysis phase where correlation calculations are performed on detected energy values. This segmentation allows the system to only perform complex periodicity analysis when energy detection indicates potential interference, reducing overall computational complexity while maintaining detection accuracy
Solution Approach 2:
The system performs partial periodicity analysis by calculating correlation only for specific hypothesized WiFi periodicities (e.g., 3.2 microseconds for 802.11a/n/ac) rather than analyzing all possible signal characteristics. This partial action approach achieves sufficient detection accuracy for the specific interference type without the excessive complexity of comprehensive signal analysis
3Reliability
If null tones and reserved resource blocks are used for interference detection, then interference mitigation is improved, but spectral efficiency decreases
Solution Approach 1:
Null tones and reserved resource blocks are allocated in advance at known positions within the resource grid before data transmission begins. The eNB signals the locations of these detection resources to the UE, allowing the UE to perform interference detection at predetermined opportunities without disrupting the main data transmission flow, thus balancing mitigation capability with spectral efficiency
Solution Approach 2:
Null tones serve as intermediary elements that do not carry data but enable interference detection. By placing these non-data-carrying tones at specific positions, the system creates dedicated detection opportunities that act as mediators between the data transmission process and interference mitigation requirements, allowing channel quality assessment without sacrificing data-carrying resources
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances system performance by accurately detecting and mitigating interfering signals, improving channel estimation and throughput in LTE/LTE-A communications operating in unlicensed spectrum.
Implementation Method 1
In another embodiment, an energy of a received signal that is received during the interference detection opportunity is computed. An interfering signal is determined to be present when the computed energy is greater than a noise floor threshold.
Implementation Method 2
In another embodiment, a second order periodicity of one or more signals modulated onto each of the received carrier frequencies is determined. The presence of one or more interfering signals is determined based on the second order periodicity of the one or more signals modulated onto each of the received carrier frequencies and based on the transmission characteristics of the wireless signal transmission and interfering signal transmission.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods, systems, and devices are described for detection of one or more interfering signals in a particular frequency spectrum. Signal characteristics may be identified for a signal of interest in the spectrum, such as a signal that is desired to be received at a wireless communications device. Based at least in part on the characteristics, one or more interference detection opportunities may be identified, during which interfering signals in the spectrum may be detected. Interference detection opportunities may include, for example, periods when the signal of interest may be absent from the particular frequency spectrum. Transmissions in the frequency spectrum may be monitored during the interference detection opportunity to determine the presence of one or more interfering signals.