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Advances, Systems and Applications

Table 2 Parameters setting about simulation

From: Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Description

Parameter

Value

Maximum episodes

epimax

200

Length of time slot

ε

100 ms

Maximum time slots

t

186

Number of RSUs

m

1

Number of SeV

n

2

Coverage radius of RSU

r1

300 m

Communication radius of vehicle

rv

130 m

Number of applications

z

6

Tolerance time of applications

li

[50,100] slots

Data size of tasks

\(d_{j}^{i}\)

[1,2] Mb

Channel bandwidth

b

1 MHz

Transmission power of vehicles

ptr

30 dBm

Channel fading coefficient

h

1

White Gaussian noise power

γ

-100 dBm

Path loss exponent

Ï–

2

Switched capacitance coefficient of vehicle

κtav

10−24

Switched capacitance coefficient of RSUs

κi

10−29

Processing capability of vehicle

\(f_{i}^{l}\)

1.4 G cycles/s

Processing capability of RSU

\(f_{1}^{r}\)

10 G cycles/s

Processing density of data

c

100 cycles/bit