

Case I: Urban Intersection, Small Road Segment (R = 200 m)
The following short movie (~ 2 min only) shows the vehicletovehicle (V2V) communications reliability for each standalone traffic realization around a corner in an urban intersection using Monte Carlo techniques based on plausible realworld traffic scenarios, channel propagation and system parameters. Press play and see the result!
We design the vehicular ad hoc network (VANET) in such a way that it inevitably meets a predefined target reliability. In other words, we want to determine the optimum transmit probability; i.e. the percentage of vehicles that can simultaneously transmit at the same timeframe and frequencyband of the wanted TX/RX pair. The design requirements and relationships are shown by the plots below for different a priori target reliability values evaluated for the worstcase TX/RX positions around an urban intersection. For 5G communications, the maximum TX/RX separation is 100 meters; and so we consider a TX and RX where both are located 50 meters away from a junction point on orthogonal roads.
Below, we show 1,000 random instances of the considered vehicular
traffic. The intensity of each road is set to 0.01 vehicles per meter.
Each speck that is shown below represent a vehicle (say a typical car of
length ~ 5 meters) driving on one of the two roads forming the urban
intersection. Each road is finite with road segment: R = 200
meters (in other words, the street length is 2R = 400 meters).
The previous "tab" showed the physical vehicular traffic
considered. What is depicted below is the vehicular traffic composed of a TX
and RX with deterministic positions around the urban intersection.
Moreover, we show (with red specks)
interfering vehicles that are actively transmitting at the same
timeframe and frequencyband of the TX/RX pair. This graph is in fact the tolerated
vehicular traffic while target reliability of 90% at the RX is
still met. As you can see, in some instances, no interferers are tolerated by
the RX. As such, we find it insightful to display the "void
probability" of this tolerated traffic (i.e. the likelihood of not
seeing a generated red speck or specks).
We analytically derived the average V2V reliability among
communicating vehicles around an urban intersection with finite
road segments. The plot below shows the outage probability for a network
that is designed to meet a target reliability of 90% at the maximum
TX/RX separation of 100 meters; which again is prescribed by the 5G
requirements for V2V communications.
We show the meta distribution (MD) of reliability for each value of outage probability conditioned on a vehicular traffic. The accuracy of the displayed MD results improve as we consider a larger number of vehicular traffic realizations (i.e. as n_{ppp} increases). The plots utilizes 5,000 fading iterations to estimate each outage probability value associated with a particular vehicular traffic realization. These results are based on a designed vehicular network to meet a target reliability of 90% at the RX.
This meta
distribution plot is
similar to the previous "tab". The only exception is that the
considered vehicular
network is not designed to
meet a certain predefined target reliability. Here, irrespective of the
resulted reliability at the worstcase TX/RX separation at 100 meters, we
consider a fixed Aloha
transmit probability of 2%.

Case II: Urban Intersection, Sizeable Road Segment (R = 10 km)
The following short movie (~ 2 min only) shows the vehicletovehicle (V2V) communications reliability for each standalone traffic realization around a corner in an urban intersection using Monte Carlo techniques based on plausible realworld traffic scenarios, channel propagation and system parameters. Press play and see the result!
We design the vehicular ad hoc network (VANET) in such a way that it inevitably meets a predefined target reliability. In other words, we want to determine the optimum transmit probability; i.e. the percentage of vehicles that can simultaneously transmit at the same timeframe and frequencyband of the wanted TX/RX pair. The design requirements and relationships are shown by the plots below for different a priori target reliability values evaluated for the worstcase TX/RX positions around an urban intersection. For 5G communications, the maximum TX/RX separation is 100 meters; and so we consider a TX and RX where both are located 50 meters away from a junction point on orthogonal roads.
Below, we show 1,000 random instances of the considered vehicular
traffic. The intensity of each road is set to 0.01 vehicles per meter.
Each speck that is shown below represent a vehicle (say a typical car of
length ~ 5 meters) driving on one of the two roads forming the urban
intersection. Each road is finite with large road segment: R =
10 km (in other words, the street length is 2R = 20 km).
The previous "tab" showed the physical vehicular traffic
considered. What is depicted below is the vehicular traffic composed of a TX
and RX with deterministic positions around the urban intersection.
Moreover, we show (with red specks)
interfering vehicles that are actively transmitting at the same
timeframe and frequencyband of the TX/RX pair. This graph is in fact the tolerated
vehicular traffic while target reliability of 90% at the RX is
still met. As you can see, in some instances, no interferers are tolerated by
the RX. As such, we find it insightful to display the "void
probability" of this tolerated traffic (i.e. the likelihood of not
seeing a generated red speck or specks).
We analytically derived the average V2V reliability among
communicating vehicles around an urban intersection with finite
road segments. The plot below shows the outage probability for a network
that is designed to meet a target reliability of 90% at the maximum
TX/RX separation of 100 meters; which again is prescribed by the 5G
requirements for V2V communications.
We show the meta distribution (MD) of reliability for each value of outage probability conditioned on a vehicular traffic. The accuracy of the displayed MD results improve as we consider a larger number of vehicular traffic realizations (i.e. as n_{ppp} increases). The plots utilizes 5,000 fading iterations to estimate each outage probability value associated with a particular vehicular traffic realization. These results are based on a designed vehicular network to meet a target reliability of 90% at the RX.
This meta
distribution plot is
similar to the previous "tab". The only exception is that the
considered vehicular
network is not designed to
meet a certain predefined target reliability. Here, irrespective of the
resulted reliability at the worstcase TX/RX separation at 100 meters, we
consider a fixed Aloha
transmit probability of 2%.

Case III: Suburban Intersection, Small Road Segment (R = 200 m)
The following short movie (~ 2 min only) shows the vehicletovehicle (V2V) communications reliability for each standalone traffic realization around a corner in an suburban intersection using Monte Carlo techniques based on plausible realworld traffic scenarios, channel propagation and system parameters. Press play and see the result!
We design the vehicular ad hoc network (VANET) in such a way that it inevitably meets a predefined target reliability. In other words, we want to determine the optimum transmit probability; i.e. the percentage of vehicles that can simultaneously transmit at the same timeframe and frequencyband of the wanted TX/RX pair. The design requirements and relationships are shown by the plots below for different a priori target reliability values evaluated for the worstcase TX/RX positions around an suburban intersection. For 5G communications, the maximum TX/RX separation is 100 meters; and so we consider a TX and RX where both are located 50 meters away from a junction point on orthogonal roads.
Below, we show 1,000 random instances of the considered vehicular
traffic. The intensity of each road is set to 0.01 vehicles per meter.
Each speck that is shown below represent a vehicle (say a typical car of
length ~ 5 meters) driving on one of the two roads forming the suburban
intersection. Each road is finite with road segment: R = 200
meters (in other words, the street length is 2R = 400 meters).
The previous "tab" showed the physical vehicular traffic
considered. What is depicted below is the vehicular traffic composed of a TX
and RX with deterministic positions around the suburban intersection.
Moreover, we show (with red specks)
interfering vehicles that are actively transmitting at the same
timeframe and frequencyband of the TX/RX pair. This graph is in fact the tolerated
vehicular traffic while target reliability of 90% at the RX is
still met. As you can see, in some instances, no interferers are tolerated by
the RX. As such, we find it insightful to display the "void
probability" of this tolerated traffic (i.e. the likelihood of not
seeing a generated red speck or specks).
We analytically derived the average V2V reliability among
communicating vehicles around an suburban intersection with finite
road segments. The plot below shows the outage probability for a network
that is designed to meet a target reliability of 90% at the maximum
TX/RX separation of 100 meters; which again is prescribed by the 5G
requirements for V2V communications.
We show the meta distribution (MD) of reliability for each value of outage probability conditioned on a vehicular traffic. The accuracy of the displayed MD results improve as we consider a larger number of vehicular traffic realizations (i.e. as n_{ppp} increases). The plots utilizes 5,000 fading iterations to estimate each outage probability value associated with a particular vehicular traffic realization. These results are based on a designed vehicular network to meet a target reliability of 90% at the RX.
This meta
distribution plot is
similar to the previous "tab". The only exception is that the
considered vehicular
network is not designed to
meet a certain predefined target reliability. Here, irrespective of the
resulted reliability at the worstcase TX/RX separation at 100 meters, we
consider a fixed Aloha
transmit probability of 2%.

Case IV: Suburban Intersection, Sizeable Road Segment (R = 10 km)
The following short movie (~ 2 min only) shows the vehicletovehicle (V2V) communications reliability for each standalone traffic realization around a corner in an suburban intersection using Monte Carlo techniques based on plausible realworld traffic scenarios, channel propagation and system parameters. Press play and see the result!
We design the vehicular ad hoc network (VANET) in such a way that it inevitably meets a predefined target reliability. In other words, we want to determine the optimum transmit probability; i.e. the percentage of vehicles that can simultaneously transmit at the same timeframe and frequencyband of the wanted TX/RX pair. The design requirements and relationships are shown by the plots below for different a priori target reliability values evaluated for the worstcase TX/RX positions around an suburban intersection. For 5G communications, the maximum TX/RX separation is 100 meters; and so we consider a TX and RX where both are located 50 meters away from a junction point on orthogonal roads.
Below, we show 1,000 random instances of the considered vehicular
traffic. The intensity of each road is set to 0.01 vehicles per meter.
Each speck that is shown below represent a vehicle (say a typical car of
length ~ 5 meters) driving on one of the two roads forming the suburban
intersection. Each road is finite with large road segment: R =
10 km (in other words, the street length is 2R = 20 km).
The previous "tab" showed the physical vehicular traffic
considered. What is depicted below is the vehicular traffic composed of a TX
and RX with deterministic positions around the suburban intersection.
Moreover, we show (with red specks)
interfering vehicles that are actively transmitting at the same
timeframe and frequencyband of the TX/RX pair. This graph is in fact the tolerated
vehicular traffic while target reliability of 90% at the RX is
still met. As you can see, in some instances, no interferers are tolerated by
the RX. As such, we find it insightful to display the "void
probability" of this tolerated traffic (i.e. the likelihood of not
seeing a generated red speck or specks).
We analytically derived the average V2V reliability among
communicating vehicles around an suburban intersection with finite
road segments. The plot below shows the outage probability for a network
that is designed to meet a target reliability of 90% at the maximum
TX/RX separation of 100 meters; which again is prescribed by the 5G
requirements for V2V communications.
We show the meta distribution (MD) of reliability for each value of outage probability conditioned on a vehicular traffic. The accuracy of the displayed MD results improve as we consider a larger number of vehicular traffic realizations (i.e. as n_{ppp} increases). The plots utilizes 5,000 fading iterations to estimate each outage probability value associated with a particular vehicular traffic realization. These results are based on a designed vehicular network to meet a target reliability of 90% at the RX.
This meta
distribution plot is
similar to the previous "tab". The only exception is that the
considered vehicular
network is not designed to
meet a certain predefined target reliability. Here, irrespective of the
resulted reliability at the worstcase TX/RX separation at 100 meters, we
consider a fixed Aloha
transmit probability of 2%.




