Joint $\alpha$-Fair Allocation of RAN and Computing Resources to URLLC Users in 5G

5G networks have emerged as the only viable solution to accomplish a satisfying performance level for various types of services, where each of them has very challenging traffic requirements. One of those services are Ultra Reliable Low Latency Communications (URLLC), which are characterized by the stringent demand to deliver packets within a very short time with high reliability. Besides being successfully transmitted, the data must be processed as well. To maximize the number of users the network can serve, the interplay of the different resources must be understood and adequate resource allocation schemes must be devised. In this paper, we consider the joint allocation of uplink and downlink Radio Access Network (RAN) and edge computing resources such that the traffic requirements of individual users are met and the network utility is maximized for different types of fairness. To this end, an optimization problem for the general case of
$\bm {\alpha }$
-fairness is formulated and its properties are explored. For the special cases of no fairness, proportional fairness, minimum potential delay fairness, and max-min fairness, polynomial-time allocation approximation algorithms are proposed. Using data from real traces, it is shown that the performance deviation of these approaches from the continuous optimum (upper bound) rarely exceeds
$\bm {2}$
%.