| /* |
| * Copyright 2019 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package com.android.server.wifi; |
| |
| import android.annotation.NonNull; |
| |
| import com.android.server.wifi.WifiCandidates.Candidate; |
| import com.android.server.wifi.WifiCandidates.ScoredCandidate; |
| |
| import java.util.Collection; |
| |
| /** |
| * A CandidateScorer that weights the RSSIs for more compactly-shaped |
| * regions of selection around access points. |
| */ |
| final class BubbleFunScorer implements WifiCandidates.CandidateScorer { |
| |
| /** |
| * This should match WifiNetworkSelector.experimentIdFromIdentifier(getIdentifier()) |
| * when using the default ScoringParams. |
| */ |
| public static final int BUBBLE_FUN_SCORER_DEFAULT_EXPID = 42598152; |
| |
| private static final double SECURITY_AWARD = 44.0; |
| private static final double CURRENT_NETWORK_BOOST = 22.0; |
| private static final double LAST_SELECTION_BOOST = 250.0; |
| private static final double LOW_BAND_FACTOR = 0.25; |
| private static final double TYPICAL_SCAN_RSSI_STD = 4.0; |
| private static final boolean USE_USER_CONNECT_CHOICE = true; |
| |
| private final ScoringParams mScoringParams; |
| |
| BubbleFunScorer(ScoringParams scoringParams) { |
| mScoringParams = scoringParams; |
| } |
| |
| @Override |
| public String getIdentifier() { |
| return "BubbleFunScorer_v2"; |
| } |
| |
| /** |
| * Calculates an individual candidate's score. |
| * |
| * Ideally, this is a pure function of the candidate, and side-effect free. |
| */ |
| private ScoredCandidate scoreCandidate(Candidate candidate) { |
| final int rssi = candidate.getScanRssi(); |
| final int rssiEntryThreshold = mScoringParams.getEntryRssi(candidate.getFrequency()); |
| |
| double score = shapeFunction(rssi) - shapeFunction(rssiEntryThreshold); |
| |
| // If we are below the entry threshold, make the score more negative |
| if (score < 0.0) score *= 2.0; |
| |
| // The gain is approximately the derivative of shapeFunction at the given rssi |
| // This is used to estimate the error |
| double gain = shapeFunction(rssi + 0.5) |
| - shapeFunction(rssi - 0.5); |
| |
| // Prefer 5GHz/6GHz when all are strong, but at the fringes, 2.4 might be better |
| // Typically the entry rssi is lower for the 2.4 band, which provides the fringe boost |
| if (candidate.getFrequency() < ScoringParams.MINIMUM_5GHZ_BAND_FREQUENCY_IN_MEGAHERTZ) { |
| score *= LOW_BAND_FACTOR; |
| gain *= LOW_BAND_FACTOR; |
| } |
| |
| // A recently selected network gets a large boost |
| score += candidate.getLastSelectionWeight() * LAST_SELECTION_BOOST; |
| |
| // Hysteresis to prefer staying on the current network. |
| if (candidate.isCurrentNetwork()) { |
| score += CURRENT_NETWORK_BOOST; |
| } |
| |
| if (!candidate.isOpenNetwork()) { |
| score += SECURITY_AWARD; |
| } |
| |
| return new ScoredCandidate(score, TYPICAL_SCAN_RSSI_STD * gain, |
| USE_USER_CONNECT_CHOICE, candidate); |
| } |
| |
| /** |
| * Reshapes raw RSSI into a value that varies more usefully for scoring purposes. |
| * |
| * The most important aspect of this function is that it is monotone (has |
| * positive slope). The offset and scale are not important, because the |
| * calculation above uses differences that cancel out the offset, and |
| * a rescaling here effects all the candidates' scores in the same way. |
| * However, we choose to scale things for an overall range of about 100 for |
| * useful values of RSSI. |
| */ |
| private static double unscaledShapeFunction(double rssi) { |
| return -Math.exp(-rssi * BELS_PER_DECIBEL); |
| } |
| private static final double BELS_PER_DECIBEL = 0.1; |
| |
| private static final double RESCALE_FACTOR = 100.0 / ( |
| unscaledShapeFunction(0.0) - unscaledShapeFunction(-85.0)); |
| private static double shapeFunction(double rssi) { |
| return unscaledShapeFunction(rssi) * RESCALE_FACTOR; |
| } |
| |
| @Override |
| public ScoredCandidate scoreCandidates(@NonNull Collection<Candidate> candidates) { |
| ScoredCandidate choice = ScoredCandidate.NONE; |
| for (Candidate candidate : candidates) { |
| ScoredCandidate scoredCandidate = scoreCandidate(candidate); |
| if (scoredCandidate.value > choice.value) { |
| choice = scoredCandidate; |
| } |
| } |
| // Here we just return the highest scored candidate; we could |
| // compute a new score, if desired. |
| return choice; |
| } |
| |
| } |