MCS Selection via Multi-Field Decoding for Sensor Stations
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Solution Overview
Problem
Existing methods for selecting modulation and coding schemes (MCS) in wireless communication, such as the Minstrel algorithm, are inadequate for devices that are seldom awake, like sensor stations, as they require extensive data samples and fail to adapt quickly to changing channel conditions.
Innovation Solution
A method where a transceiver receives a data sequence with multiple MCS-coded fields, decodes each field, and selects the most suitable MCS based on the decoding results, allowing for quick and accurate MCS selection even in devices that are not actively connected often.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Minstrel algorithm is used for MCS selection, then the algorithm converges to correct throughput, but a lot of data samples are needed before convergence
Solution Approach 1:
The patent applies preliminary action by having the transmitter encode test data with multiple MCS schemes in advance within a single packet structure. The receiver decodes these pre-prepared alternatives and provides feedback, eliminating the need for gradual convergence through multiple packets. This allows the system to jump directly to the optimal MCS without the time-consuming iterative process required by the Minstrel algorithm.
2Reliability
If the Minstrel algorithm is used for MCS selection, then statistical accuracy is improved, but the algorithm is not feasible for wireless devices that wake up seldom
Solution Approach 1:
The patent applies partial action by transmitting test data encoded with multiple MCS schemes simultaneously in a single packet, rather than requiring hundreds of packets for statistical convergence. This partial sampling approach provides sufficient information for reliable MCS selection without the excessive data collection needed by the Minstrel algorithm, making it feasible for sensor STAs that wake up seldom.
3Device complexity
If SNR estimation is used to suggest MCS, then the process is simplified, but accurate SNR estimation is not trivial and does not give the full picture of channel conditions
Solution Approach 1:
The patent applies parameter changes by transitioning from estimating a single SNR parameter to evaluating multiple MCS parameters simultaneously. Instead of attempting to accurately estimate one SNR value that may not fully represent channel conditions, the system encodes test data with different MCS schemes and measures actual decoding performance. This changes the measurement parameter from theoretical SNR to practical decoding success, providing a more comprehensive assessment of channel conditions.
Data Source
AI summary
A method is disclosed performed by a second transceiver (140) of a packet based wireless communication network (100) for wireless communication with a first transceiver (130) of the network. The method comprises receiving, from the first transceiver (130), a data sequence comprising a first data field comprising data coded with a first modulation and coding scheme, MCS, and a second data field comprising data coded with a second MCS different from the first MCS, wherein the second transceiver (140) has information indicating that the data of the first data field is coded with the first MCS and the data of the second data field is coded with the second MCS. The method further comprises performing a decoding operation on the data of the first data field using the first MCS and a decoding operation on the data of the second data field using the second MCS, and selecting the first MCS or the second MCS based on a result of the performed decoding operations. Thereby a most suitable MCS can be efficiently found for communication between a first and a second transceiver.