Scheduling and Power Control for V2V Broadcast Communications With Co-Channel and Adjacent Channel Interference

Abstract

This paper investigates how to mitigate the impact of both co-channel interference and adjacent channel interference (ACI) on vehicle-to-vehicle (V2V) broadcast communication by scheduling and power control. The optimal joint scheduling and power control problem, with the objective to maximize the number of connected vehicles, is formulated as a mixed integer programming problem with a linear objective and a quadratic constraint. From the joint formulation, we derive (a) the optimal scheduling problem for fixed transmit powers as a Boolean linear programming problem and (b) the optimal power control problem for a fixed schedule as a mixed integer linear programming problem. Optimal schedules and power values can, for smaller-size instances of the problem, be computed by solving first (a) and then (b). To handle larger-size instances of the problem, we propose heuristic scheduling and power control algorithms with reduced computational complexity. Simulation results indicate that the heuristic scheduling algorithm yields significant performance improvements compared to the baseline block-interleaver scheduler and that performance is further improved by the heuristic power control algorithm. Moreover, the heuristic algorithms perform close to the optimal scheme for small instances of the problem.

Radio Resource Management for V2V Multihop Communication Considering Adjacent Channel Interference

“…This implies that the received power from a far-away transmitter is quite low compared to the received power from a near-by transmitter, especially when there are many blocking vehicles. Consequently, if the desired transmitter is far-away compared to the interferer (resulting in a so-called near-far situation), the ACI from the interferer can be a significant problem and determining factor for SINR [10], [11].…”

“…Furthermore, there is no study which combines multihop scheduling and AoI. Our previous studies [10], [11] try to find efficient scheduling and power control algorithms while taking into account the effects of ACI. In this paper, we generalize our previous works in mainly four directions: 1) allowing for multihop communication, 2) considering AoI as a performance metric, 3) introducing clustering to ensure scalability, 4) proposing a distributed scheduling algorithm.…”

On the Potential of V2X Message Compression for Vehicular Networks

“…To derive the compression limits, we compute the entropy H(X) for V2X each message type and value of K using equation (3) and the PDF of the symbols (Figures 7, 8 and 9). Then, we compute with equation (6) the theoretical limit of the compression gain achievable with entropy compression, i.e.…”

“…These protocols normally adapt the communication parameters, e.g. the message rate [1] [2], the transmission power [3] [4], or the data rate [5] [6] to control the load. These solutions reduce the channel load at the expense of modifying the amount of information exchanged, the communication range or the protection against transmission errors [7].…”

Multiclass information flow propagation control under vehicle-to-vehicle communication environments

“…The SINR is the ratio of the power of a certain signal of interest over the sum of the interference power (from all the other interfering signals) and the power of some background noise (for details, see Wang et al 2018). The threshold of SINR is set as 5 dB (Hisham et al, 2016;Hisham et al, 2017), indicating that the V2V communication is considered to be successful if SINR is larger than 5 dB; otherwise, it will be considered as a communication failure. Recall that all equipped vehicles within communication range are assumed to share the bandwidth equally.…”