pytwinnet.physics.ris_beam¶

Classes

`RISBeamModel`(base, ris[, extra_loss_db])	Wraps a base PropagationModel.
`SmartRISPanel`(position[, element_count, ...])	Toy RIS with a steerable mainlobe. Approximates array gain:

class pytwinnet.physics.ris_beam.RISBeamModel(base, ris, extra_loss_db=3.0)[source]¶

Bases: PropagationModel

Wraps a base PropagationModel. For a configured target UE (by id), the RIS contributes mainlobe gain on the two-hop path; others see sidelobe gain. Effective path loss = min( direct, (tx->RIS + RIS->rx - gain + extra_loss_db) ).

Parameters:

base (PropagationModel)
ris (SmartRISPanel)
extra_loss_db (float)

calculate_path_loss(tx, rx, environment)[source]¶

Return type:

Parameters:

tx (WirelessNode)
rx (WirelessNode)
environment (Environment)

set_beam(rx_node_id)[source]¶

Point mainlobe toward rx_node_id (or None for no specific target).

Return type:: None
Parameters:: rx_node_id (str | None)

class pytwinnet.physics.ris_beam.SmartRISPanel(position, element_count=64, mainlobe_gain_db=None, sidelobe_gain_db=None)[source]¶

Bases: object

Toy RIS with a steerable mainlobe. Approximates array gain:: mainlobe_gain_db ~= 20*log10(N) sidelobe_gain_db ~= mainlobe - 13 dB (typical)

Parameters:

position (Tuple[float, float, float])
element_count (int)
mainlobe_gain_db (float | None)
sidelobe_gain_db (float | None)

element_count: int = 64¶

mainlobe_gain_db: Optional[float] = None¶

position: Tuple[float, float, float]¶

sidelobe_gain_db: Optional[float] = None¶