Joint α-Fair Allocation of RAN and Computing Resources to Vehicular Users with URLLC Traffic

5G networks have emerged as the only viable solution to render a satisfying level of performance to different types of services, each of them with very stringent traffic requirements. One of those services are Ultra-Reliable Low-Latency Communications (URLLC). A use case where these services are especially sensitive are vehicular networks. Therefore, in order to satisfy their traffic requirements, adequate resource allocation schemes should be devised. However, the time-varying nature of the channel conditions in wireless networks renders this process challenging. In this paper, we consider the problem of jointly allocating Radio Access Network (RAN) resources and computing resources (to process the data from vehicles) such that all the traffic requirements of individual users are met and the utility is maximized for different types of fairness. We formulate an optimization problem for the general case of
$\alpha$
-fairness, explore its characteristics, and consider in more detail the opposite sides of fairness; the case of no fairness provided
$(\alpha=0)$
and the max-min fair allocation
$(\alpha\rightarrow\infty)$
. For each of these problems, we propose polynomial-time assignment heuristics. Using data from real traces, we show that the performance achieved with our approaches is not more than 1% away from the optimum.