Brn.Phy - Revision history

Kurth: /* Receiver Models */

2008-03-16T13:23:36Z

Receiver Models

← Older revision		Revision as of 13:23, 16 March 2008
Line 62:		Line 62:

	[[Image:dist-sens-simplepathloss-3.5-ber.png]]		[[Image:dist-sens-simplepathloss-3.5-ber.png]]

			== Diversity Transceiver ==
			Tbd.

	= References =		= References =

Kurth: /* Propagation */

2008-03-16T13:22:54Z

Propagation

← Older revision		Revision as of 13:22, 16 March 2008
Line 31:		Line 31:
	=== Jakes Model ===		=== Jakes Model ===
	We ported Jakes model from [http://www.dei.unipd.it/wdyn/?IDsezione=5529] to Brn.Sim.		We ported Jakes model from [http://www.dei.unipd.it/wdyn/?IDsezione=5529] to Brn.Sim.

			== Propagation Delay ==
			Tbd.

	== Implementation Details ==		== Implementation Details ==

Kurth: /* Jakes Model */

2008-03-16T13:20:51Z

Jakes Model

← Older revision		Revision as of 13:20, 16 March 2008
Line 30:		Line 30:

	=== Jakes Model ===		=== Jakes Model ===
			We ported Jakes model from [http://www.dei.unipd.it/wdyn/?IDsezione=5529] to Brn.Sim.

	== Implementation Details ==		== Implementation Details ==

Kurth: /* Small Scale Fading */

2008-03-16T13:19:26Z

Small Scale Fading

← Older revision		Revision as of 13:19, 16 March 2008
Line 24:		Line 24:

	== Small Scale Fading ==		== Small Scale Fading ==
			JiST/SWANS comes with probabilistic models for Rayleigh and Rician Fading. Additionally we realized the Rician fading model from Punnoose and Jakes model.

			=== Punnoose Rician Fading ===
			The Punnoose Model uses a pre-calculated fading realization with reasonable statistical distribution and timely correlation and only adjusts the variability (the Rician K factor) and evolution speed (via maximum velocity, i.e. doppler). Details can be found in [http://scholar.google.com/scholar?q=Punnoose+%22Efficient+Simulation+of+Ricean+Fading+within+a+Packet+Simulator%22 Punnoose "Efficient Simulation of Ricean Fading within a Packet Simulator"]. Furthermore, we applied [http://www.winlab.rutgers.edu/~zhibinwu/html/ns_fading_error.html this patch].

			=== Jakes Model ===

	== Implementation Details ==		== Implementation Details ==

Kurth: /* Shadow Fading */

2008-03-16T13:09:10Z

Shadow Fading

← Older revision		Revision as of 13:09, 16 March 2008
Line 20:		Line 20:
	== Shadow Fading ==		== Shadow Fading ==
	Brn.Sim supports log-normal shadowing to account for possibly time-varying obstacles in the propagation path. The model was derived from [http://www.isi.edu/nsnam/ns/ ns-2] and [http://www.eg.bucknell.edu/swan/ SWAN]. The shadowing variability can be changed by modifying the standard deviation of the underlaying normal distribution. Furthermore, it supports different types of timely correlations. When setting the coherence time to 0, a new shadowing value is realized for each transmission (the ns-2 approach). By setting the coherence time to a fixed positive value, the shadowing coefficient remains constant for the specified duration, before it is realized anew. Additionally there is the possibility to use an exponential distribution for coherence times.		Brn.Sim supports log-normal shadowing to account for possibly time-varying obstacles in the propagation path. The model was derived from [http://www.isi.edu/nsnam/ns/ ns-2] and [http://www.eg.bucknell.edu/swan/ SWAN]. The shadowing variability can be changed by modifying the standard deviation of the underlaying normal distribution. Furthermore, it supports different types of timely correlations. When setting the coherence time to 0, a new shadowing value is realized for each transmission (the ns-2 approach). By setting the coherence time to a fixed positive value, the shadowing coefficient remains constant for the specified duration, before it is realized anew. Additionally there is the possibility to use an exponential distribution for coherence times.

			In Brn.Sim, shadowing is realized as a composite path loss model which can be used with any other path loss model.

	== Small Scale Fading ==		== Small Scale Fading ==

Kurth: /* Shadow Fading */

2008-03-16T13:07:22Z

Shadow Fading

← Older revision		Revision as of 13:07, 16 March 2008
Line 19:		Line 19:

	== Shadow Fading ==		== Shadow Fading ==
			Brn.Sim supports log-normal shadowing to account for possibly time-varying obstacles in the propagation path. The model was derived from [http://www.isi.edu/nsnam/ns/ ns-2] and [http://www.eg.bucknell.edu/swan/ SWAN]. The shadowing variability can be changed by modifying the standard deviation of the underlaying normal distribution. Furthermore, it supports different types of timely correlations. When setting the coherence time to 0, a new shadowing value is realized for each transmission (the ns-2 approach). By setting the coherence time to a fixed positive value, the shadowing coefficient remains constant for the specified duration, before it is realized anew. Additionally there is the possibility to use an exponential distribution for coherence times.

	== Small Scale Fading ==		== Small Scale Fading ==

Kurth: /* Large Scale Path Loss */

2008-03-16T12:53:48Z

Large Scale Path Loss

← Older revision		Revision as of 12:53, 16 March 2008
Line 8:		Line 8:

	=== Free Space Path Loss ===		=== Free Space Path Loss ===
			The model is shipped with vanilla JiST/SWANS. It is a special case of the Log-Distance Model with path loss exponent 2.0.

	=== Two Ray Path Loss ===		=== Two Ray Path Loss ===
			The model is shipped with vanilla JiST/SWANS.

	=== Log-Distance based Path Loss ===		=== Log-Distance based Path Loss ===

Kurth: /* Path Loss */

2008-03-16T12:51:01Z

Path Loss

← Older revision		Revision as of 12:51, 16 March 2008
Line 1:		Line 1:
	Details about the physical layers in Brn.Sim. Tbd.		Details about the physical layers in Brn.Sim. Tbd.

	= ~~Path Loss~~ =		= Propagation =

	== ~~Log-Distance~~ ~~based~~ Path Loss ==		== Large Scale Path Loss ==

			Currently Brn.Sim supports the path loss models FreeSpace, TwoRay and LogDistance.

			=== Free Space Path Loss ===

			=== Two Ray Path Loss ===

			=== Log-Distance based Path Loss ===
	[[Image:dist-snr-simplepathloss-3.5.png]]		[[Image:dist-snr-simplepathloss-3.5.png]]

	Pathloss using the log-distance based model (see Goldsmith Wireless Communication p. 40) and path loss exponent 3.5 (1sec == 1m).		Pathloss using the log-distance based model (see Goldsmith Wireless Communication p. 40) and path loss exponent 3.5 (1sec == 1m).

	== ~~Shadowing~~ ==		== Shadow Fading ==

			== Small Scale Fading ==

	== ~~Fading~~ ==		== Implementation Details ==

	== Hierarchical Binning ==		=== Hierarchical Binning ===
	JiST/SWANS use spatial binning to improve simulation performance, similar to SNS ([http://www.cs.cornell.edu/people/egs/sns/ STAGED NETWORK SIMULATOR]). The idea is to partition the simulation area into bins. If a node at position X emits a signal, that a bin is visited only if the signal strength at a defined reference location (the location inside the bin nearest to X) exceeds a certain threshold. Thus, spatial binning prunes all bins in which none of the nodes would receive the signal.		JiST/SWANS use spatial binning to improve simulation performance, similar to SNS ([http://www.cs.cornell.edu/people/egs/sns/ STAGED NETWORK SIMULATOR]). The idea is to partition the simulation area into bins. If a node at position X emits a signal, that a bin is visited only if the signal strength at a defined reference location (the location inside the bin nearest to X) exceeds a certain threshold. Thus, spatial binning prunes all bins in which none of the nodes would receive the signal.

Kurth: /* Path Loss */

2008-02-28T09:29:50Z

Path Loss

← Older revision		Revision as of 09:29, 28 February 2008
Line 11:		Line 11:

	== Fading ==		== Fading ==

			== Hierarchical Binning ==
			JiST/SWANS use spatial binning to improve simulation performance, similar to SNS ([http://www.cs.cornell.edu/people/egs/sns/ STAGED NETWORK SIMULATOR]). The idea is to partition the simulation area into bins. If a node at position X emits a signal, that a bin is visited only if the signal strength at a defined reference location (the location inside the bin nearest to X) exceeds a certain threshold. Thus, spatial binning prunes all bins in which none of the nodes would receive the signal.

			However, there is a problem with shadowing and fading, because signal strength and distance are not monotonically any more. So there is a probability that the binning prunes bins because of severe fading at the reference location, whereas other nodes within the bin would receive the signal. We address this problem by adding a fading margin to the reference signal strength calculation. In detail, we use the 95. percentile for log-normal fading and the 99. percentile for Rician fading.

	= Receiver Models =		= Receiver Models =

Kurth at 14:45, 30 January 2008

2008-01-30T14:45:03Z

← Older revision		Revision as of 14:45, 30 January 2008
Line 25:		Line 25:

	== BER based Receiver ==		== BER based Receiver ==
			The BER receiver model calculates Bit and Packet Error Rates, which are used as probability whether a frame is received without error. Frame reception becomes a stochastic process depending on the used modulation and coding scheme (i.e. bit rate), the SNR (distance) and number of bits.

			[[Image:dist-per-simplepathloss-3.5-ber.png]]

			Both figures show the number of received and sensed frames for the BER receiver in the same scenario described in the previous section. Note that the thresholds of the SNR receiver caused sharp fall-offs: At a certain point the PER for a given bit rate changed abruptly from 0 to 1. Using the BER receiver we now have soft transition. Note further that the sensing range is larger compared to the SNR receiver, because the model still uses thresholds for preamble detection. This may not be very realistic and will likely be changed in future versions.

			[[Image:dist-sens-simplepathloss-3.5-ber.png]]

			= References =
			* Goldsmith Wireless Communication
			* Paper yans physical layer