Prioritized Random MAC Optimization Via Graph-Based Analysis
Motivated by the analogy between successive interference cancellation and iterative belief-propagation on erasure channels, irregular repetition slotted ALOHA (IRSA) strategies have received a lot of attention in the design of medium access control protocols. In this work, we consider generic systems where sources in different importance classes compete for a common channel. We propose a new prioritized IRSA algorithm and derive the probability to correctly resolve collisions for data from each source class. We then make use of our theoretical analysis to formulate a new optimization problem for selecting the transmission strategies of heterogenous sources. We optimize both the replication probability per class and the source rate per class, in such a way that the overall system utility is maximized. We then propose a heuristic-based algorithm for the selection of the transmission strategy, which is built on intrinsic characteristics of the iterative decoding methods adopted for recovering from collisions. Experimental results validate the accuracy of the theoretical study and show the gain of well-chosen prioritized transmission strategies for transmission of data from heterogenous classes over shared wireless channels.