Abstract: A wireless device includes a preamble detector configured to identify preambles transmitted via a random access channel of a wireless network. The preamble detector includes preamble false alarm logic. The preamble false alarm logic is configured to set a preamble false alarm detection window, and compare, to one another, preambles identified in the false alarm detection window. The preamble false alarm logic is configured to identify, based on the comparison, a largest of the preambles in the false alarm detection window, and reject all but the identified largest of the preambles as false alarm detections.

Abstract: A wireless device includes a preamble detector configured to identify preambles transmitted via a random access channel of a wireless network. The preamble detector includes preamble false alarm logic. The preamble false alarm logic is configured to set a preamble false alarm detection window, and compare, to one another, preambles identified in the false alarm detection window. The preamble false alarm logic is configured to identify, based on the comparison, a largest of the preambles in the false alarm detection window, and reject all but the identified largest of the preambles as false alarm detections.

Abstract: A computer implemented method selects K extreme elements of a list of N elements by partitioning each of the N elements into a plurality of sections. For each section the method selects a threshold selection determining at least K extreme entries from the list. This iteratively compares a corresponding section to a section threshold, counts a number of sections which are more extreme than the section threshold, increasing (or decreasing) the section threshold if the count is greater than K and decreasing (or increasing) the section threshold if the count is less than K. The method forms a combined threshold by concatenation of said section thresholds in order, compares each of the N elements to the combined threshold, and selects at least K elements from the set of N elements more extreme than the combined threshold.

Abstract: A computer implemented method selects K extreme elements of a list of N elements by partitioning each of the N elements into a plurality of sections. For each section from a most significant section to a least significant section the method selects a threshold selection determining at least K extreme entries from the list. This iteratively compares a corresponding section to a section threshold, counts a number of sections which are more extreme than the section threshold, increasing (or decreasing) the section threshold if the count is greater than K and decreasing the section threshold if the count is less than K. After finding the section threshold for the corresponding section the method forms a combined threshold by concatenation of said section thresholds in order from a most significant section to a least significant section, compares each of the N elements to the combined threshold, and selects at least K elements from the set of N elements more extreme than the combined threshold.