Carrier Sensing via Periodic STF Sample Combining
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Solution Overview
Problem
In wireless local area networks (WLANs) operating under the 802.11 standards, carrier sensing performance is compromised in undesirable channel conditions with low signal-to-noise ratio (SNR), making it difficult to distinguish real data from noise, especially due to the periodic nature of the short training field (STF) in OFDM schemes.
Innovation Solution
The method involves periodically combining data samples from a wireless receiver to generate auto-correlated outputs, estimating phase differences, and using these to enhance carrier sensing by compensating for carrier-frequency-offset caused phase differences, thereby improving the signal-to-noise ratio (SNR) through periodic STF combining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional carrier sensing is used in low SNR conditions, then the system operates with standard detection capability, but the ability to distinguish real data from noise deteriorates
Solution Approach 1:
The patent combines multiple data samples periodically to enhance the signal detection capability. By merging samples that are separated by integer multiples of the STF period, the system accumulates signal energy while averaging out noise, thereby improving carrier sensing accuracy in low SNR conditions
Solution Approach 2:
The patent employs periodic sampling and combining of data samples based on the known periodic structure of the STF sequence. The combining operation is performed periodically with a period equal to an integer multiple of the STF period, exploiting the periodic nature of the training sequence to enhance detection performance
2Reliability
If periodic STF combining is applied to improve SNR, then carrier sensing performance is enhanced, but computational complexity increases
Solution Approach 1:
The patent divides the received signal into multiple data samples and processes them in periodic groups. By segmenting the combining operation into discrete periodic steps rather than continuous processing, the system achieves improved reliability while keeping the computational complexity manageable through structured, periodic operations
Solution Approach 2:
The patent changes the processing parameters by introducing a configurable combining period (integer multiple of STF period) and weighting factors. These parameter adjustments allow the system to optimize the trade-off between reliability improvement and computational complexity based on specific channel conditions and performance requirements
Data Source
AI summary
In accordance with embodiments of the present disclosure there is provided a method for carrier sensing. The method includes receiving, at a wireless receiver, an input signal, and generating, based on a sampling period, a plurality of data samples from the input signal. The method further includes periodically combining a first data sample from the plurality of data samples with a second data sample that is one or more sampling periods before the first data sample to generate a combined data sample. The method further includes generating an auto-correlated output for carrier sensing based on the combined data sample. The auto-correlated output is provided to generate an estimate of phase difference between the first sample and the second sample for the periodic combining.