Search

Evolved Multimedia Broadcast Multicast Service (eMBMS) is a 3GPP standard that improves the utilization of scarce wireless resources and the quality of the received content. eMBMS uses a Single Frequency Network (SFN) to transmit real-time videos over synchronized resources across neighboring base stations (eNBs) and allows users to share wireless spectrum across multiple cell sites. However the user with the worst channel condition and the eNB with the least available resources limit the throughput of a session. To overcome such limitations, the SFN can be divided into non-overlapping clusters of eNBs and in each cluster users can be split into groups. We formulate an optimization problem that maximizes an operator-defined utility for multiple eMBMS sessions served at multiple bitrates by choosing the optimal set of SFN clusters and user groups for each session. We propose an algorithm, RTOP, that finds the optimal or a near-optimal solution in real-time regardless of the number of eMBMS users. Our extensive simulations indicate that, in comparison to state-of-the-art schemes, RTOP improves the system utility and average user bitrate by up to 14% and 90% respectively. Additionally, we show that the utility of RTOP always stays within a 1% gap from the optimal solution.

International audience; In time varying data communication networks (TVCN), traffic congestion, system utility maximization and network performance enhancement are the prominent issues. All these issues can be resolved either by optimizing the network structure or by selecting efficient routing approaches. In this paper, we focus on the design of a time varying network model and propose an algorithm to find efficient user route in this network. Centrality plays a very important role in finding congestion free routes. Indeed, the more a node is central, the more it can be congested by the flow coming from or going to its neighborhood. For that reason, classically, routes are chosen such that the sum of centrality of the nodes coming in user’s route is minimum. In this paper, we show that closeness centrality outperforms betweenness centrality in the case of community structured time varying networks. Furthermore, Kelly’s optimization formulation for a rate allocation problem is used in order to compute optimal rates of distinct users at different time instants.

Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO) from two aspects: first, we introduce differential evolution (DE) process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS) process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.