Upstream Orthogonal Frequency Division Multiple Access (OFDMA) technology in DOCSIS 3.1 is starting to be rolled out in the field. Operators are beginning to test Upstream OFDMA channels in the lab and in the field and are discovering various intricacies in getting the upstream OFDMA to work robustly.
Lower frequencies in the upstream spectrum can be noisy and making use of those portions of the spectrum tougher. Upstream RxMER looks very different than the Downstream RxMER, due to the noise funneling characteristics on the HFC plant, the additive nature of noise has a large impact at the CMTS upstream receiver.
As operators roll out OFDMA technology, they are starting to collect data on the performance of these OFDMA channels. This includes the US RxMER data, IUC usage hours, profile definitions etc. As a cable industry we are just starting to comprehend the OFDMA channel performance. Analyzing the USRxMER data and the IUC data is a powerful tool in understanding the performance of each of the node segments and the individual modems. This paper will discuss methods on how to analyze the upstream network data. It will discuss algorithms on how to logically extract the outlier modems and node segments. This paper will discuss methods for anomaly detection, historical behavior analysis, pattern recognition, classification and condition evaluation in the access network data. Combining the analysis of data along, with network topology and device location, it is possible to create a general view of the plant condition and isolate problem sources. The paper will implement methods on how to assign a health score to modems and network segments in an effort to triage which are the top priority nodes that operators need to work on. All this will enable operators to reduce upstream OFDMA troubleshooting and problem resolution time, reducing operational costs and enhancing network reliability.