Abstract: Methods inform an eNodeB on the transmit power status of a user equipment in a mobile communication system using component carrier (CC) aggregation. Also described is an implementation of these methods by hardware and in software. The invention proposes procedures that allow the eNodeB to recognize the power usage status of a UE in a communication system using carrier aggregation. The UE indicates to the eNodeB when the UE is close to using its total maximum UE transmit power or when it has exceeded the same. This is achieved by the UE including indicator(s) and/or new MAC CEs to one or more protocol data units transmitted on respective component carriers within a single sub-frame that is providing the eNodeB with power status information. The MAC CEs may report a per-UE power headroom. Alternatively, the MAC CEs may report per-CC power headrooms and/or power reductions applied to the respective uplink CCs.

Abstract: The disclosure relates to methods for improving the DRX operation of a UE by introducing an additional DRX wake-up cycle, which runs in parallel to the short and/or long DRX cycle. The DRX wake-up cycle defines time intervals after which the UE starts monitoring the PDCCH for a wake-up duration of time; the UE does not perform any other operation during the wake-up duration apart from monitoring the PDCCH. The time intervals of the wake-up cycle between the wake-up durations are preferably shorter than the one of the DRX long cycle, and may have the same or a shorter length than the ones of the DRX short cycle. The wake-up duration may be as long as the on-duration of the DRX short/long cycle, or may be preferably much shorter, such as only one or a few subframes.

Abstract: Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification.

Abstract: Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification.

Abstract: Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification.

Abstract: Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification. Determining the training example having the closest cosine distance to the relevant document includes ranking the training examples by stretching the internal best match scores for the training examples linearly to cover a complete unit interval.