Section: New Results

Wireless Networks

Participants : Osama Arouk, Btissam Er-Rahmadi, Adlen Ksentini, Meriem Bouzouita, Pantelis Frangoudis, Yassine Hadjadj-Aoul, César Viho, Quang Pham, Gerardo Rubino.

We are continuing our activities around wireless and mobile networks, by focusing more on leveraging the current mobile and wireless architecture toward building the 5G systems.

Congestion control for M2M applications. Machine-to-Machine (M2M) communications are expected to be one of the major drivers for the future 5G network. It is expected that M2M will come up with substantial revenue growth for Mobile Network Operators (MNO), but they represent at the same time the most important challenge they are facing. For instance, a massive number of Machine-to-Machine (M2M) devices performs simultaneously Random Accesses (RA), which causes severe congestions and reduces the RA success probability. To control the Radio Access Network (RAN) overload and alleviate the congestion between M2M devices, 3GPP developed the Access Class Barring (ACB) procedure that depends on an access probability called the ACB factor. In [48][24], we first presented a simple fluid model of M2M devices'€™ random access. This model is then used to derive an optimal regulator of the ACB factor based on nonlinear non affine control theory. The main advantages of the proposed approach are twofold. First, the proposal is fully compliant with the standard while it reduces significantly the computation and the signaling overheads. Second, it provides an efficient mean to regulate adaptively the ACB factor as it guarantees having an optimal number of M2M devices accessing concurrently to the RAN. The obtained results based on simulations show clearly the robustness of the proposed approach, and its superiority compared to existing proposals. However, such a model assumes a perfect knowledge about the number of M2M attempting the ACB and the RA, which is not possible in realistic use cases. For this reason, we proposed in [50] a system-agnostic controller, which computes the barring factor dynamically based only on the mismatch between the average number of M2M devices succedding in the RA and the optimal number of M2M which should succeed. We base our controlling algorithm in a Propostional Integral Derivative (PID)-based controller. Simulation results show that the algorithm outperforms the existing solutions by improving significantly the access success probability while minimizing radio resources'€™ underutilization.

Different schemes were proposed in the literature to solve the congestion problem by regulating the M2M devices' opportunities of transmission. Nonetheless, as revealed in [51], these schemes turn out to be ineffective in case of heavily congested M2M networks. In fact, in such a condition, the unpredictable and increasingly accumulated number of devices cannot be blocked. This augments the risk of M2M devices' synchronized access, which may result in a congestion collapse. Consequently, we proposed, in [49], a methodology for a better estimation of the number of M2M devices attempting the access. We also proposed a novel implementation of the ACB process, which dynamically computes the ACB factor according to the network's overload conditions and includes a corrective action adapting the controller work, based on the mismatch existing between the computed and the targeted mean load. The simulation results show that the proposed algorithms allow improving considerably the estimation of the number of M2M devices' arrivals, while outperforming existing techniques.

In [32], we proposed a novel approach to deal with massive synchronous access attempts, tailored for both M2M delay-sensitive applications and energy constrained ones. The main idea behind the paper is to leverage crowd sourcing data, transmitted from the devices succeeding in the RACH procedure, to tune the access parameters, without requiring too complex techniques for the estimation of the number of attempts. Simulation results show that the proposed scheme achieves sub-optimal performance in the wireless resources' utilization while reducing significantly both the number of access attempts and the access latency for delay sensitive applications. This allows guaranteeing energy conservation.

In [44], we proposed two optimal solutions that use Device-to-Device (D2D) communications to lightweight the overhead of M2M devices on 5G networks. Each scheme has a specific objective, and aims to manage the communications between devices and eNodeBs to achieve its objective. The proposed solutions nominate the devices that should communicate using D2D communications and those that should directly communicate with eNodeBs. The first solution aims to reduce the energy consumption, whereas the second one aims to reduce the data transfer delay at the eNodeBs. The performance of the proposed schemes is evaluated via simulations and the obtained results demonstrate their feasibility and ability in achieving their design goals.

Network selection and optimization. With the explosion of mobile data traffic, the Fixed and Mobile Converged (FMC) network are being heavily required. Mobile devices have the capability of connecting simultaneously to different access networks in the FMC architecture. Access network selection becomes an issue when mobile devices are under coverage of different access networks, since a bad selection may lead to network congestion and degrade the QoE of users. In order to address this problem, in [91] we modeled and analyzed the interface selection procedure using control theory. Based on our model, we designed a controller which can send to mobile devices a network selection command calculated instantaneously for the access network selection.

Dynamic Adaptive Streaming over HTTP (DASH), with its different proprietary versions, is presently the most widely adopted technology for video delivery over the Internet. DASH offers significant advantages, enabling users to switch dynamically between different available video qualities responding to variations in the current network conditions during video playback. This is particularly interesting in wireless and mobile access networks, which present such variations in a hard to predict manner, but sometimes quite frequently. Moreover, mobile users of these networks share a common radio access link and, thus, a common bottleneck in case of congestion, which may cause user experience to degrade. In this context, the Mobile Edge Computing (MEC) emerging standard gives new opportunities to improve DASH performance, by moving IT and cloud computing capabilities down to the edge of the mobile network. In [69] and [103] we proposed a novel architecture for adaptive HTTP video streaming tailored to a MEC environment. The proposed architecture includes an adaptation algorithm running as a MEC service, aiming to relax network congestion while improving the Quality of Experience (QoE) for mobile users. Our mechanism is standards-compliant and compatible with receiver-driven adaptive video delivery algorithms, with which it cooperates in a transparent manner.

Low-rate wireless personal area networks (WPANs) (and also wireless sensor networks) suffer from many constraints. The IEEE 802.15.4 standard proposes the slotted CSMA/CA as a communication channel access mechanism with collision avoidance that takes into account the constraints of WPANs. In [22], we proposed to introduce a data fragmentation mechanism into slotted CSMA/CA to improve a bandwidth utilisation. The novelty here is the use of the fragmentation mechanism to replace an acknowledgement frame after the transmission of the fragment and the remaining frame. The beacon frame is used to confirm the success transmission of a data fragment. To evaluate the performance of our proposition, we have developped a three dimension Markov chain which modelises the behaviour of the node using IEEE 802.15.4 with data fragmentation mechanism without using an ACK frame. The analytical results concerning the network throughput and the transmission success delay demonstrate the improvement of the bandwidth occupation.

Mobile networks' improvements. In [85], we introduced the concept of elastic bearer in Evolved Packet System (EPS), which allows the users to enhance on-demand the performance of certain applications and permits the network to efficiently manage the resource allocation taking into account the application type. In particular, the paper introduces a set of mechanisms to trigger and support bearer elasticity in EPS based on the Quality of Experience (QoE) perceived by users or based on feedback from Radio Access Network (RAN). Bearer elasticity can be attained through potential Packet Data Network/Serving Gateway (PDN/S-GW) relocation to eventually improve QoE within and beyond the mobile network operator premises. The paper also introduces a set of methods to identify and cope with a storm of requests for particular applications at densely populated areas.

One important objective of 5G mobile networks is to accommodate a diverse and ever-increasing number of user equipment (UEs). Coping with the massive signaling overhead expected from UEs is an important hurdle to tackle so as to achieve this objective. In [11], we devised an efficient tracking area list management (ETAM) framework that aims for finding optimal distributions of tracking areas (TAs) in the form of TA lists (TALs) and assigning them to UEs, with the objective of minimizing two conflicting metrics, namely paging overhead and tracking area update (TAU) overhead. ETAM incorporates an online part and an and offline one, in order to achieve its design goal. In the online part, two strategies were proposed to assign in real time, TALs to different UEs, while in the offline part, three solutions were proposed to optimally organize TAs into TALs. The performance of ETAM is evaluated via analysis and simulations, and the obtained results demonstrate its feasibility and ability in achieving its design goals, improving the network performance by minimizing the cost associated with paging and TAU.

QoE aware routing in wireless networks. This year we continued our research on QoE-based optimization routing for wireless mesh networks. First, we approximate PSQA models by explicit mathematical forms, which can be used to find the optimal or near to optimal routes. Next, the hardness of the problem is studied and decentralized algorithms are proposed. The quality of the solution, computational complexity of the proposed algorithm, and the fairness are the main concerns of this work. Several centralized approximation algorithms have been proposed in order to address the complexity and the quality of the published solutions. The results can be found in the following papers: [25],[94], [95] and [26]. However, these centralized algorithms are not appropriate in large-scale networks. Thus, a distributed algorithm is necessary as a complement of the existing centralized methods. This is currently studied at the team.