# The Transmission Medium¶

## Overview¶

For wireless communication, an additional module is required to model the shared physical medium where the communication takes place. This module keeps track of transceivers, noise sources, ongoing transmissions, background noise, and other ongoing noises.

It relies on several models:

1. signal propagation model

2. path loss model

3. obstacle loss model

4. background noise model

5. signal analog model

With the help of the above models, the medium module computes when, where, and how signals arrive at receivers, including the set of interfering signals and noises. In addition, the medium module also contains various mechanisms and ways to improve the scalability of wireless network simulations.

The standard transmission medium model in INET is RadioMedium. RadioMedium is as an OMNeT++ compound module with several replaceable submodules. It contains submodules for each of the above models (signal propagation, path loss, etc.), and various caches for efficiency.

Note that RadioMedium is an active compound module, that is, it has an associated C++ class that encapsulates the computations.

RadioMedium contains its components as submodules with parametric types:

propagation: <default("ConstantSpeedPropagation")> like IPropagation;
analogModel: <default("ScalarAnalogModel")> like IAnalogModel;
if typename != "";
pathLoss: <default("FreeSpacePathLoss")> like IPathLoss;
obstacleLoss: <default("")> like IObstacleLoss
if typename != "";
mediumLimitCache: <default("MediumLimitCache")> like IMediumLimitCache;
communicationCache: <default("VectorCommunicationCache")> like ICommunicationCache;
neighborCache: <default("")> like INeighborCache
if typename != "";


There are many preconfigured versions of RadioMedium:

The following sections describe the parts of the medium model.

## Propagation Models¶

When a transmitter starts to transmit a signal, the beginning of the signal propagates through the transmission medium. When the transmitter ends the transmission, the signal’s end propagates similarly. The propagation model describes how a signal moves through space over time. Its main purpose is to compute the arrival space-time coordinates at receivers. There are two built-in models in INET, implemented as simple modules:

• ConstantTimePropagation is a simplistic model where the propagation time is independent of the traveled distance. The propagation time is simply determined by a module parameter.

• ConstantSpeedPropagation is a more realistic model where the propagation time is proportional to the traveled distance. The propagation time is independent of the transmitter and receiver movement during both signal transmission and propagation. The propagation speed is determined by a module parameter.

The default propagation model is configured as follows:

*.radioMedium.propagation.typename = "ConstantSpeedPropagation" # module type
*.radioMedium.propagation.propagationSpeed = 299792458 mps # speed of light


A more accurate model could take into consideration the transmitter and receiver movement. This effect becomes especially important for acoustic communication, because the propagation speed of the signal is much more comparable to the speed of the transceivers.

## Path Loss Models¶

As a signal propagates through space its power density decreases. This is called path loss and it is the combination of many effects such as free-space loss, refraction, diffraction, reflection, and absorption. There are several different path loss models in the literature, which differ in their parameterization and application area.

In INET, a path loss model is an OMNeT++ simple module implementing a specific path loss algorithm. Its main purpose is to compute the power loss for a given signal, but it is also capable of estimating the range for a given loss. The latter is useful, for example, to allow visualizing communication range. INET contains a number of built-in path loss algorithms, each comes with its own set of parameters:

• FreeSpacePathLoss models line of sight path loss for air or vacuum.

• BreakpointPathLoss refines it using dual slope model with two separate path loss exponents.

• LogNormalShadowing models path loss for a wide range of environments (e.g. urban areas, and buildings)

• TwoRayGroundReflection models interference between line of sight and single ground reflection.

• TwoRayInterference refines the above for inter-vechicle communication.

• RicianFading is a stochastical model for the anomaly caused by partial cancellation of a signal by itself.

• RayleighFading is a stochastical model for heavily built-up urban environments when there is no dominant propagation along the line of sight.

• NakagamiFading further refines the above two models for cellular systems.

The following example replaces the default free-space path loss model with log normal shadowing:

*.radioMedium.pathLoss.typename = "LogNormalShadowing" # module type
*.radioMedium.pathLoss.sigma = 1.1 # override default value of 1


## Obstacle Loss Models¶

When the signal propagates through space it also passes through physical objects present in that space. As the signal penetrates physical objects, its power decreases when it reflects from surfaces, and also when it is absorbed by their material. There are various ways to model this effect, which differ in the trade-off between accuracy and performance.

In INET, an obstacle loss model is an OMNeT++ simple module. Its main purpose is to compute the power loss based on the traveled path and the signal frequency. The obstacle loss models most often use the physical environment model to determine the set of penetrated physical objects. INET contains a few built-in obstacle loss models:

• IdealObstacleLoss model determines total or no power loss at all by checking if there is any obstructing physical object along the straight propagation path.

• DielectricObstacleLoss computes the power loss based on the accurate dielectric and reflection loss along the straight path considering the shape, position, orientation, and material of obstructing physical objects.

By default, the medium module doesn’t contain any obstacle loss model, but configuring one is very simple:

*.radioMedium.obstacleLoss.typename = "DielectricObstacleLoss" # module type


Statistical obstacle loss models are also possible but currently not provided.

## Background Noise Models¶

Thermal noise, cosmic background noise, and other random fluctuations of the electromagnetic field affect the quality of the communication channel. This kind of noise doesn’t come from a particular source, so it doesn’t make sense to model its propagation through space. The background noise model describes instead how it changes over space and time.

In INET, a background noise model is an OMNeT++ simple module. Its main purpose is to compute the analog representation of the background noise for a given space-time interval. For example, IsotropicScalarBackgroundNoise computes a background noise that is independent of space-time coordinates, and its scalar power is determined by a module parameter.

The simplest background noise model can be configured as follows:

*.radioMedium.backgroundNoise.typename = "IsotropicScalarBackgroundNoise" # type
*.radioMedium.backgroundNoise.power = -110 dBm # isotropic scalar noise power


## Analog Models¶

The analog signal is a complex physical phenomenon which can be modeled in many different ways. Choosing the right analog domain signal representation is the most important factor in the trade-off between accuracy and performance. The analog model of the transmission medium determines how signals are represented while being transmitted, propagated, and received.

In INET, an analog model is an OMNeT++ simple module. Its main purpose is to compute the received signal from the transmitted signal. The analog model combines the effect of the antenna, path loss, and obstacle loss models. Transceivers must be configured transmit and receive signals according to the representation used by the analog model.

The most commonly used analog model, which uses a scalar signal power representation over a frequency and time interval, can be configured as follows:

*.radioMedium.analogModel.typename = "ScalarAnalogModel" # module type


## Neighbor Cache¶

Transceivers are considered neighbors if successful communication is possible between them. For wired communication it is easy to determine which transceivers are neighbors, because they are connected by wires. In contrast, in wireless communication determining which transceivers are neighbors isn’t obvious at all.

In INET, a neighbor cache is an OMNeT++ simple module which provides an efficient way of keeping track of the neighbor relationship between transceivers. Its main purpose is to compute the set of affected receivers for a given transmission. All built-in models in INET provide a conservative approximation only, because they update their state periodically:

• NeighborListNeighborCache takes a range as parameter, and for each transceiver it maintains the list of receivers within range (neighbor list).

• GridNeighborCache organizes transceivers in a 3D grid with constant cell size.

• QuadTreeNeighborCache organizes transceivers in a 2D quad tree (ignoring the Z axis) with constant node size.

The following example sets QuadTreeNeighborCache as neighbor cache:

**.radioMedium.neighborCache.typename = "QuadTreeNeighborCache" # module type


How should one decide which neighbor cache to choose for a given simulation? As the sole purpose of the neighbor cache is to speed up the simulation, one should choose the one that leads to the best performance for that particular network. Which one performs best is best determined by experimentation, as it depends on many factors: number of nodes, their spatial distribution, their speed and movement pattern, their communication pattern, and so on. Note that not only the choice of neighbor cache but also its parameterization can affect performance.

## Medium Limit Cache¶

The medium limit cache (and its default implementation MediumLimitCache) keeps track of certain thresholds and minimum/maximum values of quantities related to layer 1 modeling. Some of these limits can be gathered from other modules in the network, but still, all of them can be explicitly specified by the user. The quantities include:

• maximum speed (can be gathered from mobility models)

• maximum transmission power

• minimum interference power and reception power

• maximum antenna gain (can be computed from antenna models)

• minimum time interval to consider two overlapping signals interfering

• maximum duration of a transmission

• maximum communication range and interference range (can be computed from transmitter and receiver models)

These limits allow the transmission medium model to make assumptions about the locations of nodes (i.e. the maximum distance they can move during some interval), about the possibility of interference, and about the possibility of a signal being receivable.

## Communication Cache¶

The communication cache is used to cache various intermediate computation results related to the communication on the medium. The main motivation to have multiple implementations is that different implementations may be the most efficient in different simulations. Also, a conservative (simple but robust) implementation may be used for validating new (more efficient but also more complex) implementations.

Implementations include:

## Improving Scalability¶

The simulation of wireless networks is inherently less scalable than that of wired networks. In wired networks, a transmission only affects the host’s neighbors on the link, which is usually 1 in modern networks that are dominated by point-to-point links. The wireless medium, however, is a broadcast medium. Any transmission is “heard” by all nodes within interference range, not only the intended recipients. The signal may be receivable by them (and must be indeeded received before the destination address field in it can be examined), or may interfere with the reception of other transmissions. Whichever the case, the transmission must be evaluated or processed by a much larger number of nodes than in the wired case. This makes the computational complexity at least $$O(n^2)$$ ($$n$$ being the number of nodes.) Other effects may further increase the exponent.

The medium module provides a set of parameters that can be used to alleviate the scalability issue. These filter parameters that can be used to reduce the amount of processing at nodes that are not the indended recipients of the frame, increasing simulation performance.

There are several filters that can be enabled/disabled individually:

• Range filter. When this filter is active, the medium module does not send signals to a radio if it is outside interference range (or communication range, this option can also be selected.)

• Radio mode filter. When this filter is active, the medium module does not send signals to a radio if it is neither in receiver nor in transceiver mode.

• Listening filter. When this filter is active, the medium module does not send signals to a radio if it listens on the channel in incompatible mode (e.g. different carrier frequency and bandwidth, or different modulation)

• MAC address filter. When this filter is active, the radio medium does not send signals to a radio if it the destination MAC address does not match

The corresponding module parameters are called rangeFilter, radioModeFilter, listeningFilter and macAddressFilter. By default, all filters are turned off.