A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs

<div><p>In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.</p></div

Results of the <i>CL</i> forecasting.

<p>Results of the <i>CL</i> forecasting.</p

<i>CL</i> in different traffic flows in on highway.

<p>According to CLF-BTPC, as the beacon message transmission power changed, the <i>CSR</i> and number of shared nodes in a channel for each vehicle also changed; therefore, the wireless spatial multiplexing rate changed. When the transmission power changed by <i>kε</i>, the <i>CSR</i> was adjusted to (1±<i>kε</i>)·<i>CSR</i>_<i>ini</i>, where <i>CSR</i>_<i>ini</i> is the initial <i>CSR</i>. The target node sends beacon messages at its highest transmission power, assuming that the <i>CL</i> fell within the predefined range. As shown in Fig 6, the <i>CL</i> is decreased to predefined threshold range of 3–6 Mbps after a period of oscillation. The simulation results show that CLF-BTPC algorithm sufficiently solves the <i>CL</i> congestion control problem in a VANET.</p

Relative error of the results.

<p>Relative error of the results.</p

<i>CL</i> in the entrance road section.

<p>As shown in Fig 4, when vehicles approach the intersection, the <i>CL</i> increases with an increase in speed. At 600 m, the maximum vehicle speed is attained. The maximum <i>CL</i> is attained near the intersection, and the <i>CL</i> value is less than 6 Mbps after the CLF-BTPC algorithm is applied. The results indicate that the CLF-BTPC algorithm is more effective for frequent changes in vehicle speeds (e.g., near the intersection).</p

Parameters for the eight-lane highway simulation.

<p>Parameters for the eight-lane highway simulation.</p

Relationship between <i>PA</i> and distance.

<p>Relationship between <i>PA</i> and distance.</p

<i>CL</i> in exit road section.

<p>As shown in Fig 3, after the CLF-BTPC algorithm is applied, the <i>CL</i> converged to values within the predefined threshold range of 3–6 Mbps after a period of oscillation in the exit road section. The closer the distances to the exit intersection, the larger the <i>CL</i>. When the vehicle moves away from the intersection, the <i>CL</i> will significantly decrease. The <i>CL</i> begins to increase when the distance from the intersection is 400 m.</p

Parameters for the urban intersection simulation.

<p>Parameters for the urban intersection simulation.</p

<i>CL</i> in different position on highway.

<p>As shown in Fig 5, after the CLF-BTPC algorithm is employed, the <i>CL</i> converged to a value within the predefined threshold range of 3–6 Mbps after a period of oscillation, which is determined by the distribution and fairness of the algorithm. When the run time of the algorithm is increased, the rate of convergence to the results increases.</p