path: root/lib/size_to_radio_energy.py

"""
Convert data length to radio TX/RX energy.

Contains classes for some embedded CPUs/MCUs. Given a configuration, each
class can convert a cycle count to an energy consumption.
"""

import numpy as np


def get_class(radio_name: str):
    """Return model class for radio_name."""
    if radio_name == "CC1200tx":
        return CC1200tx
    if radio_name == "CC1200rx":
        return CC1200rx
    if radio_name == "NRF24L01tx":
        return NRF24L01tx
    if radio_name == "NRF24L01dtx":
        return NRF24L01dtx
    if radio_name == "esp8266dtx":
        return ESP8266dtx
    if radio_name == "esp8266drx":
        return ESP8266drx


def _param_list_to_dict(device, param_list):
    param_dict = dict()
    for i, parameter in enumerate(sorted(device.parameters.keys())):
        param_dict[parameter] = param_list[i]
    return param_dict


class CC1200tx:
    """CC1200 TX energy based on aemr measurements."""

    name = "CC1200tx"
    parameters = {
        "symbolrate": [6, 12, 25, 50, 100, 200, 250],  # ksps
        "txbytes": [],
        "txpower": [10, 20, 30, 40, 47],  # dBm = f(txpower)
    }
    default_params = {
        "symbolrate": 100,
        "txpower": 47,
    }

    @staticmethod
    def get_energy(params: dict):
        if type(params) != dict:
            return CC1200tx.get_energy(_param_list_to_dict(CC1200tx, params))

        # Mittlere TX-Leistung, gefitted von AEMR
        # Messdaten erhoben bei 3.6V
        power = 8.18053941e04
        power -= 1.24208376e03 * np.sqrt(params["symbolrate"])
        power -= 5.73742779e02 * np.log(params["txbytes"])
        power += 1.76945886e01 * (params["txpower"]) ** 2
        power += (
            2.33469617e02 * np.sqrt(params["symbolrate"]) * np.log(params["txbytes"])
        )
        power -= (
            6.99137635e-01 * np.sqrt(params["symbolrate"]) * (params["txpower"]) ** 2
        )
        power -= 3.31365158e-01 * np.log(params["txbytes"]) * (params["txpower"]) ** 2
        power += (
            1.32784945e-01
            * np.sqrt(params["symbolrate"])
            * np.log(params["txbytes"])
            * (params["txpower"]) ** 2
        )

        # txDone-Timeout, gefitted von AEMR
        duration = 3.65513500e02
        duration += 8.01016526e04 * 1 / (params["symbolrate"])
        duration -= 7.06364515e-03 * params["txbytes"]
        duration += 8.00029860e03 * 1 / (params["symbolrate"]) * params["txbytes"]

        # TX-Energie, gefitted von AEMR
        # Achtung: Energy ist in µJ, nicht (wie in AEMR-Transitionsmodellen üblich) in pJ
        # Messdaten erhoben bei 3.6V

        energy = 1.74383259e01
        energy += 6.29922138e03 * 1 / (params["symbolrate"])
        energy += 1.13307135e-02 * params["txbytes"]
        energy -= 1.28121377e-04 * (params["txpower"]) ** 2
        energy += 6.29080184e02 * 1 / (params["symbolrate"]) * params["txbytes"]
        energy += 1.25647926e00 * 1 / (params["symbolrate"]) * (params["txpower"]) ** 2
        energy += 1.31996202e-05 * params["txbytes"] * (params["txpower"]) ** 2
        energy += (
            1.25676966e-01
            * 1
            / (params["symbolrate"])
            * params["txbytes"]
            * (params["txpower"]) ** 2
        )

        return energy * 1e-6

    @staticmethod
    def get_energy_per_byte(params):
        A = 8.18053941e04
        A -= 1.24208376e03 * np.sqrt(params["symbolrate"])
        A += 1.76945886e01 * (params["txpower"]) ** 2
        A -= 6.99137635e-01 * np.sqrt(params["symbolrate"]) * (params["txpower"]) ** 2
        B = -5.73742779e02
        B += 2.33469617e02 * np.sqrt(params["symbolrate"])
        B -= 3.31365158e-01 * (params["txpower"]) ** 2
        B += 1.32784945e-01 * np.sqrt(params["symbolrate"]) * (params["txpower"]) ** 2
        C = 3.65513500e02
        C += 8.01016526e04 * 1 / (params["symbolrate"])
        D = -7.06364515e-03
        D += 8.00029860e03 * 1 / (params["symbolrate"])

        x = params["txbytes"]

        # in pJ
        de_dx = A * D + B * C * 1 / x + B * D * (np.log(x) + 1)

        # in µJ
        de_dx = 1.13307135e-02
        de_dx += 6.29080184e02 * 1 / (params["symbolrate"])
        de_dx += 1.31996202e-05 * (params["txpower"]) ** 2
        de_dx += 1.25676966e-01 * 1 / (params["symbolrate"]) * (params["txpower"]) ** 2

        # de_dx = (B * 1/x) * (C + D * x) + (A + B * np.log(x)) * D

        return de_dx * 1e-6


class CC1200rx:
    """CC1200 RX energy based on aemr measurements."""

    name = "CC1200rx"
    parameters = {
        "symbolrate": [6, 12, 25, 50, 100, 200, 250],  # ksps
        "txbytes": [],
        "txpower": [10, 20, 30, 40, 47],  # dBm = f(txpower)
    }
    default_params = {
        "symbolrate": 100,
        "txpower": 47,
    }

    @staticmethod
    def get_energy(params):
        # TODO
        return params["txbytes"] * CC1200rx.get_energy_per_byte(params)

    @staticmethod
    def get_energy_per_byte(params):
        # RX        : 0 + regression_arg(0) + regression_arg(1) * np.log(parameter(symbolrate) + 1)
        # [84414.91636169   205.63323036]

        de_dx = (
            (84414.91636169 + 205.63323036 * np.log(params["symbolrate"] + 1))
            * 8000
            / params["symbolrate"]
        )

        return de_dx * 1e-12


class NRF24L01rx:
    """NRF24L01+ RX energy based on aemr measurements (using variable packet size)"""

    name = "NRF24L01"
    parameters = {
        "datarate": [250, 1000, 2000],  # kbps
        "txbytes": [],
        "txpower": [-18, -12, -6, 0],  # dBm
        "voltage": [1.9, 3.6],
    }
    default_params = {
        "datarate": 1000,
        "txpower": -6,
        "voltage": 3,
    }

    @staticmethod
    def get_energy_per_byte(params):
        # RX        : 0 + regression_arg(0) + regression_arg(1) * np.sqrt(parameter(datarate))
        #     [48530.73235537   117.25274402]

        de_dx = (
            (48530.73235537 + 117.25274402 * np.sqrt(params["datarate"]))
            * 8000
            / params["datarate"]
        )

        return de_dx * 1e-12


# PYTHONPATH=lib bin/analyze-archive.py --show-model=all --show-quality=table ../data/*_RF24_no_retries.tar
class NRF24L01tx:
    """NRF24L01+ TX(*) energy based on aemr measurements (32B fixed packet size, (*)ack-await, no retries)."""

    name = "NRF24L01"
    parameters = {
        "datarate": [250, 1000, 2000],  # kbps
        "txbytes": [],
        "txpower": [-18, -12, -6, 0],  # dBm
        "voltage": [1.9, 3.6],
    }
    default_params = {
        "datarate": 1000,
        "txpower": -6,
        "voltage": 3,
    }

    # AEMR:
    # TX power / energy:
    # TX        : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * (19.47+parameter(txpower))**2 + regression_arg(3) * 1/(parameter(datarate)) * (19.47+parameter(txpower))**2
    #            [6.30323056e+03 2.59889924e+06 7.82186268e+00 8.69746093e+03]
    # TX        : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * (19.47+parameter(txpower))**2 + regression_arg(3) * 1/(parameter(datarate)) * (19.47+parameter(txpower))**2
    #            [7.67932887e+00 1.02969455e+04 4.55116475e-03 2.99786534e+01]
    # epilogue  : timeout   : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))
    #            [  1624.06589147 332251.93798766]

    @staticmethod
    def get_energy(params):
        if type(params) != dict:
            return NRF24L01tx.get_energy(_param_list_to_dict(NRF24L01tx, params))

        # TX-Leistung, gefitted von AEMR
        # Messdaten erhoben bei 3.6V
        power = 6.30323056e03
        power += 2.59889924e06 * 1 / params["datarate"]
        power += 7.82186268e00 * (19.47 + params["txpower"]) ** 2
        power += (
            8.69746093e03 * 1 / params["datarate"] * (19.47 + params["txpower"]) ** 2
        )

        # TX-Dauer, gefitted von AEMR
        duration = 1624.06589147
        duration += 332251.93798766 * 1 / params["datarate"]

        # TX-Energie, gefitted von AEMR
        # Achtung: Energy ist in µJ, nicht (wie in AEMR-Transitionsmodellen üblich) in pJ
        # Messdaten erhoben bei 3.6V
        energy = 7.67932887e00
        energy += 1.02969455e04 * 1 / params["datarate"]
        energy += 4.55116475e-03 * (19.47 + params["txpower"]) ** 2
        energy += (
            2.99786534e01 * 1 / params["datarate"] * (19.47 + params["txpower"]) ** 2
        )

        energy = power * 1e-6 * duration * 1e-6 * np.ceil(params["txbytes"] / 32)

        return energy

    @staticmethod
    def get_energy_per_byte(params):
        if type(params) != dict:
            return NRF24L01tx.get_energy_per_byte(
                _param_list_to_dict(NRF24L01tx, params)
            )

        # in µJ
        de_dx = 0


class NRF24L01dtx:
    """nRF24L01+ TX energy based on datasheet values (probably unerestimated)"""

    name = "NRF24L01"
    parameters = {
        "datarate": [250, 1000, 2000],  # kbps
        "txbytes": [],
        "txpower": [-18, -12, -6, 0],  # dBm
        "voltage": [1.9, 3.6],
    }
    default_params = {
        "datarate": 1000,
        "txpower": -6,
        "voltage": 3,
    }

    # 130 us RX settling: 8.9 mE
    # 130 us TX settling: 8 mA

    @staticmethod
    def get_energy(params):
        if type(params) != dict:
            return NRF24L01dtx.get_energy(_param_list_to_dict(NRF24L01dtx, params))

        header_bytes = 7

        # TX settling: 130 us @ 8 mA
        energy = 8e-3 * params["voltage"] * 130e-6

        if params["txpower"] == -18:
            current = 7e-3
        elif params["txpower"] == -12:
            current = 7.5e-3
        elif params["txpower"] == -6:
            current = 9e-3
        elif params["txpower"] == 0:
            current = 11.3e-3

        energy += (
            current
            * params["voltage"]
            * ((header_bytes + params["txbytes"]) * 8 / (params["datarate"] * 1e3))
        )

        return energy


class ESP8266dtx:
    """esp8266 TX energy based on (hardly documented) datasheet values"""

    name = "esp8266"
    parameters = {
        "voltage": [2.5, 3.0, 3.3, 3.6],
        "txbytes": [],
        "bitrate": [65e6],
        "tx_current": [120e-3],
    }
    default_params = {"voltage": 3, "bitrate": 65e6, "tx_current": 120e-3}

    @staticmethod
    def get_energy(params):
        # TODO
        return 0

    @staticmethod
    def get_energy_per_byte(params):
        if type(params) != dict:
            return ESP8266dtx.get_energy_per_byte(
                _param_list_to_dict(ESP8266dtx, params)
            )

        # TX in 802.11n MCS7 -> 64QAM, 65/72.2 Mbit/s @ 20MHz channel, 135/150 Mbit/s @ 40MHz
        # -> Value for 65 Mbit/s @ 20MHz channel
        return params["tx_current"] * params["voltage"] / params["bitrate"]


class ESP8266drx:
    """esp8266 RX energy based on (hardly documented) datasheet values"""

    name = "esp8266"
    parameters = {
        "voltage": [2.5, 3.0, 3.3, 3.6],
        "txbytes": [],
        "bitrate": [65e6],
        "rx_current": [56e-3],
    }
    default_params = {"voltage": 3, "bitrate": 65e6, "rx_current": 56e-3}

    @staticmethod
    def get_energy(params):
        # TODO
        return 0

    @staticmethod
    def get_energy_per_byte(params):
        if type(params) != dict:
            return ESP8266drx.get_energy_per_byte(
                _param_list_to_dict(ESP8266drx, params)
            )

        # TX in 802.11n MCS7 -> 64QAM, 65/72.2 Mbit/s @ 20MHz channel, 135/150 Mbit/s @ 40MHz
        # -> Value for 65 Mbit/s @ 20MHz channel
        return params["rx_current"] * params["voltage"] / params["bitrate"]