/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package com.android.server.wifi;
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import android.net.wifi.WifiInfo;
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import com.android.server.wifi.util.KalmanFilter;
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import com.android.server.wifi.util.Matrix;
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/**
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* Class used to calculate scores for connected wifi networks and report it to the associated
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* network agent.
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*/
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public class VelocityBasedConnectedScore extends ConnectedScore {
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private final ScoringParams mScoringParams;
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private int mFrequency = ScoringParams.BAND5;
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private double mThresholdAdjustment;
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private final KalmanFilter mFilter;
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private long mLastMillis;
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public VelocityBasedConnectedScore(ScoringParams scoringParams, Clock clock) {
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super(clock);
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mScoringParams = scoringParams;
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mFilter = new KalmanFilter();
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mFilter.mH = new Matrix(2, new double[]{1.0, 0.0});
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mFilter.mR = new Matrix(1, new double[]{1.0});
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}
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/**
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* Set the Kalman filter's state transition matrix F and process noise covariance Q given
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* a time step.
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*
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* @param dt delta time, in seconds
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*/
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private void setDeltaTimeSeconds(double dt) {
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mFilter.mF = new Matrix(2, new double[]{1.0, dt, 0.0, 1.0});
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Matrix tG = new Matrix(1, new double[]{0.5 * dt * dt, dt});
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double stda = 0.02; // standard deviation of modelled acceleration
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mFilter.mQ = tG.dotTranspose(tG).dot(new Matrix(2, new double[]{
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stda * stda, 0.0,
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0.0, stda * stda}));
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}
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/**
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* Reset the filter state.
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*/
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@Override
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public void reset() {
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mLastMillis = 0;
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mThresholdAdjustment = 0;
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mFilter.mx = null;
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}
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/**
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* Updates scoring state using RSSI and measurement noise estimate
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* <p>
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* This is useful if an RSSI comes from another source (e.g. scan results) and the
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* expected noise varies by source.
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*
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* @param rssi signal strength (dB).
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* @param millis millisecond-resolution time.
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* @param standardDeviation of the RSSI.
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*/
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@Override
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public void updateUsingRssi(int rssi, long millis, double standardDeviation) {
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if (millis <= 0) return;
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if (mLastMillis <= 0 || millis < mLastMillis || mFilter.mx == null) {
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double initialVariance = 9.0 * standardDeviation * standardDeviation;
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mFilter.mx = new Matrix(1, new double[]{rssi, 0.0});
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mFilter.mP = new Matrix(2, new double[]{initialVariance, 0.0, 0.0, 0.0});
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} else {
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double dt = (millis - mLastMillis) * 0.001;
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mFilter.mR.put(0, 0, standardDeviation * standardDeviation);
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setDeltaTimeSeconds(dt);
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mFilter.predict();
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mFilter.update(new Matrix(1, new double[]{rssi}));
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}
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mLastMillis = millis;
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mFilteredRssi = mFilter.mx.get(0, 0);
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mEstimatedRateOfRssiChange = mFilter.mx.get(1, 0);
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}
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/**
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* Updates the state.
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*/
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@Override
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public void updateUsingWifiInfo(WifiInfo wifiInfo, long millis) {
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int frequency = wifiInfo.getFrequency();
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if (frequency != mFrequency) {
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mLastMillis = 0; // Probably roamed; reset filter but retain threshold adjustment
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// Consider resetting or partially resetting threshold adjustment
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// Consider checking bssid
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mFrequency = frequency;
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}
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updateUsingRssi(wifiInfo.getRssi(), millis, mDefaultRssiStandardDeviation);
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adjustThreshold(wifiInfo);
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}
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private double mFilteredRssi;
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private double mEstimatedRateOfRssiChange;
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/**
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* Returns the most recently computed extimate of the RSSI.
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*/
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public double getFilteredRssi() {
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return mFilteredRssi;
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}
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/**
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* Returns the estimated rate of change of RSSI, in dB/second
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*/
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public double getEstimatedRateOfRssiChange() {
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return mEstimatedRateOfRssiChange;
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}
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/**
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* Returns the adjusted RSSI threshold
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*/
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public double getAdjustedRssiThreshold() {
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return mScoringParams.getExitRssi(mFrequency) + mThresholdAdjustment;
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}
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private double mMinimumPpsForMeasuringSuccess = 2.0;
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/**
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* Adjusts the threshold if appropriate
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* <p>
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* If the (filtered) rssi is near or below the current effective threshold, and the
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* rate of rssi change is small, and there is traffic, and the error rate is looking
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* reasonable, then decrease the effective threshold to keep from dropping a perfectly good
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* connection.
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*
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*/
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private void adjustThreshold(WifiInfo wifiInfo) {
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if (mThresholdAdjustment < -7) return;
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if (mFilteredRssi >= getAdjustedRssiThreshold() + 2.0) return;
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if (Math.abs(mEstimatedRateOfRssiChange) >= 0.2) return;
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double txSuccessPps = wifiInfo.txSuccessRate;
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double rxSuccessPps = wifiInfo.rxSuccessRate;
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if (txSuccessPps < mMinimumPpsForMeasuringSuccess) return;
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if (rxSuccessPps < mMinimumPpsForMeasuringSuccess) return;
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double txBadPps = wifiInfo.txBadRate;
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double txRetriesPps = wifiInfo.txRetriesRate;
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double probabilityOfSuccessfulTx = txSuccessPps / (txSuccessPps + txBadPps + txRetriesPps);
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if (probabilityOfSuccessfulTx > 0.2) {
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// May want this amount to vary with how close to threshold we are
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mThresholdAdjustment -= 0.5;
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}
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}
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/**
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* Velocity scorer - predict the rssi a few seconds from now
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*/
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@Override
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public int generateScore() {
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if (mFilter.mx == null) return WIFI_TRANSITION_SCORE + 1;
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double badRssi = getAdjustedRssiThreshold();
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double horizonSeconds = mScoringParams.getHorizonSeconds();
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Matrix x = new Matrix(mFilter.mx);
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double filteredRssi = x.get(0, 0);
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setDeltaTimeSeconds(horizonSeconds);
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x = mFilter.mF.dot(x);
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double forecastRssi = x.get(0, 0);
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if (forecastRssi > filteredRssi) {
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forecastRssi = filteredRssi; // Be pessimistic about predicting an actual increase
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}
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int score = (int) (Math.round(forecastRssi) - badRssi) + WIFI_TRANSITION_SCORE;
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return score;
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}
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}
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