Channel-Aware Slotted ALOHA Networks Assisted by Intelligent Reflecting Surfaces.

The paper proposes resource allocation for slotted ALOHA networks assisted by intelligent reflecting surfaces (IRS). The base station (BS) adjusts the reflection parameters of the IRS to randomly direct the beam to a single user in a time slot. The end users have knowledge of their respective channels to the BS, and attempt channel access only in favorable channel conditions while using truncated channel inversion policy. We maximize the network throughput under the proportional fairness criterion by determining optimal values of the users’ channel access threshold and the IRS beam steering probability towards different users. The numerical results show performance improvement due to the use of IRS, while the optimization of the beam steering probability is important in the case of high discrepancy among the users’ signal to noise ratios.