This letter presents an optimal control framework to reduce the spread of viruses in networks. The network is modeled as an undirected graph of nodes and weighted links. We consider the spread of viruses in a network as a system, and the total number of infected nodes as the state of the system, while the control function is the weight reduction leading to slow/reduce spread of viruses. Our epidemic model overcomes three assumptions that were extensively used in the literature and produced inaccurate results. We apply the optimal control formulation to crucial network structures. Numerical results show the dynamical weight reduction and reveal the role of the network structure and the epidemic model in reducing the infection size in the presence of indiscernible infected nodes.

Several analytical models for the channel bonding feature of IEEE 802.11ac have previously been presented for performance estimation, but their accuracy has been limited by the assumptions that there are no collisions or all nodes are in saturated state. Therefore, in this letter, we develop an analytical model for the throughput performance of channel bonding in IEEE 802.11ac, considering the presence of collisions under both saturated and non-saturated traffic loads, and our numerical results were validated by a simulation study.

IEEE 802.11ay supports multi-user multiple-input-multiple-output (MU-MIMO). However, the MU-MIMO beam-forming training (BFT) is a time-consuming process for finding appropriate directional antenna patterns, and inefficient BFT results in a long training time. Thus, in this letter, we propose an algorithm that configures the transmit antenna with the aim of reducing the number of redundant transmissions during MU-MIMO BFT. Both analytic and simulation results show that our proposed algorithm can significantly reduce the training time.