In this project, we propose an online distributed neighbor distribution estimation scheme, called PeerProbe, in which vehicles collaborate with each other to probe their own neighborhood via simultaneous symbol-level wireless communication. An adaptive compressive sensing algorithm is developed to recover a neighbor distribution based on a small number of random probes with non-negligible noise. Moreover, the needed number of probes adapts to the sparseness of the distribution. We further conduct extensive simulations with VENUS simulator and the results demonstrate that PeerProbe is lightweight and can accurately recover highly dynamic neighbor distributions in critical channel conditions. This work is presented in IEEE INFOCOM 2021 (Distributed Neighbor Distribution Estimation with Adaptive Compressive Sensing in VANETs).

Then we modify the proposed method to be more robust and implement a prototype system with four USRP N210 to verify the feasibility of PeerProbe in various typical vehicular channel conditions. Besides, we show two typical use cases to show the potential of the proposed method. This work is accepted to appear in IEEE/ACM TON (PeerProbe: Estimating Vehicular Neighbor Distribution With Adaptive Compressive Sensing). Moreover, one patent of the scheme has been authorized.

We extend the scenario of neighnor discovery problem from V2X communication with DSRC or LTE devices to millimeter-wave vehicular networks and design a one-hop multicasting media access control (MAC) protocol. This work is presented in IEEE ICDCS 2022(mmV2V: Combating One-hop Multicasting in Millimeter-wave Vehicular Networks).