Bernard Cousin

Abstract: When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.

Abstract: In this paper, we study the multicast-capable routing, modulation, and spectrum assignment (MC-RMSA) schemes that consider the physical impairments from both the transmission and light splitting in elastic optical networks (EONs). Specifically, we propose to provision each multicast request with a light forest, which consists of one or more light trees to avoid the dilemma that because of the accumulated physical impairments, a relatively large light tree may have to use the lowest modulation level, and, hence, consume too many frequency slots (FS'). In order to further improve the spectral efficiency and compensate for the differential delays among the light trees, we incorporate the rateless network coding (R-NC) in the multicast system. We first formulate an integer linear programming (ILP) model to solve the problem for static network planning. Then, we propose three time-efficient heuristics that leverage the set-cover problem and utilize layered auxiliary graphs. The simulation results indicate that in both the ILP and heuristics, the MC-RMSA with R-NC can achieve better performance on the maximum index of used FS' than that without. After that we evaluate the heuristics in a dynamic network provisioning. The results show that the MC-RMSA with R-NC can effectively improve the performance of all-optical multicast in EONs to reduce the blocking probability.

Abstract: LTE networks' main challenge is to efficiently use the available spectrum, and to provide satisfying quality of service for mobile users. However, using the same bandwidth among adjacent cells leads to occurrence of Inter-cell Interference especially at the cell-edge. Basic interference mitigation approaches consider bandwidth partitioning techniques between adjacent cells, such as frequency reuse of factor m schemes, to minimize cell-edge interference. Although SINR values are improved, such techniques lead to significant reduction in the maximum achievable data rate. Several improvements have been proposed to enhance the performance of frequency reuse schemes, where restrictions are made on resource blocks usage, power allocation, or both. Nevertheless, bandwidth partitioning methods still affect the maximum achievable throughput. In this proposal, we intend to perform a comprehensive survey on Inter-Cell Interference Coordination (ICIC) techniques, and we study their performance while putting into consideration various design parameters. This study is implemented throughout intensive system level simulations under several parameters such as different network loads, radio conditions, and user distributions. Simulation results show the advantages and the limitations of each technique compared to frequency reuse-1 model. Thus, we are able to identify the most suitable ICIC technique for each network scenario.

Abstract: In this paper we study different options for the survivability implementation in MPLS over optical transport networks (OTN) in terms of network resource usage and configuration cost. We investigate two approaches to the survivability deployment: single layer and multilayer survivability and present various methods for spare capacity allocation (SCA) to reroute disrupted traffic. The comparative analysis shows the influence of the offered traffic granularity and the physical network structure on the survivability cost: for high bandwidth LSPs, close to the optical channel capacity, the multilayer survivability outperforms the single layer one, whereas for low bandwidth LSPs the single layer survivability is more cost-efficient. On the other hand, sparse networks of low connectivity parameter use more wavelengths for optical path routing and increase the configuration cost, as compared with dense networks. We demonstrate that by mapping efficiently the spare capacity of the MPLS layer onto the resources of the optical layer one can achieve up to 22% savings in the total configuration cost and up to 37% in the optical layer cost. Further savings (up to 9 %) in the wavelength use can be obtained with the integrated approach to network configuration over the sequential one, however, at the increase in the optimization problem complexity. These results are based on a cost model with different cost variations, and were obtained for networks targeted to a nationwide coverage

Abstract: In this paper, we present a new approach to construct multicast trees in MPLS networks. This approach utilizes MPLS LSP between multicast tree branching node routers in order to reduce forwarding states and enhance scalability. In our approach only routers that are acting as multicast tree branching node for a group need to keep the forwarding state for that group. All other non-branching node routers simply forward data packets over traffic engineered unicast routes using MPLS LSP. We can deduce that our approach can be largely deployed because it uses for multicast traffic the same unicast MPLS forwarding scheme. In this paper we briefly discuss MPLS, the multicast scalability problem, merging the two technologies, related works and different techniques for forwarding state reduction. We evaluate the approach and present some related issues to conclude finally that it is feasible and promising.

Abstract: To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

Abstract: Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.