Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Wireless Networks

Participants: Yue Li, Imad Alawe, Quang Pham, Patrick Maillé, Yassine Hadjadj-Aoul, César Viho, Gerardo Rubino

Mobile wireless networks' improvements. Software Defined Networking (SDN) is one of the key enablers for evolving mobile network architecture towards 5G. SDN involves the separation of control and data plane functions, which leads, in the context of 5G, to consider the separation of the control and data plane functions of the different gateways of the Evolved Packet Core (EPC), namely Serving and Packet data Gateways (S and P-GW). Indeed, the envisioned solutions propose to separate the S/P-GW into two entities: the S/P-GW-C, which integrates the control plane functions and the S/P-GW-U that handles the User Equipment (UE) data plane traffic. There are two major approaches to create and update user plane forwarding rules for such a partition: (i) considering an SDN controller for the S/P-GW-C (SDNEPC) or (ii) using a direct specific interface to control the S/P-GW-U (enhancedEPC). In [38], we evaluate, using a testbed, those two visions against the classical virtual EPC (vEPC), where all the elements of the EPC are virtualized. Besides evaluating the capacity of the vEPC to manage and scale to UE requests, we compare the performances of the solutions in terms of the time needed to create the user data plane. The obtained results allow drawing several remarks, which may help to dimension the vEPC's components as well as to improve the S/P-GW-U management procedure.

One of the requirements of 5G is to support a massive number of connected devices, considering many use-cases such as IoT and massive Machine Type Communication (MTC). While this represents an interesting opportunity for operators to grow their business, it will need new mechanisms to scale and manage the envisioned high number of devices and their generated traffic. Particularity, the signaling traffic, which will overload the 5G core Network Function (NF) in charge of authentication and mobility, namely Access and Mobility Management Function (AMF). The objective of [37] is to provide an algorithm based on Control Theory allowing: (i) to equilibrate the load on the AMF instances in order to maintain an optimal response time with limited computing latency; (ii) to scale out or in the AMF instance (using NFV techniques) depending on the network load to save energy and avoid wasting resources. Obtained results indicate the superiority of our algorithm in ensuring fair load balancing while scaling dynamically with the traffic load. In [64] we are going further by using new advances on machine learning, and more specifically Recurrent Neural Networks (RNN), to predict accurately the arrival traffic pattern of devices. The main objective of the proposed approach is to early react to congestion by pro-actively scaling the AMF VNF in a way to absorb such congestion while respecting the traffic constraints.

Energy consumption improvements. Recently in cellular networks, the focus has been moved to seeking ways to increase the energy efficiency by better adapting to the existing users behaviors. In [17], we are going a step further in studying a new type of disruptive service by trying to answer the question “What are the potential energy efficiency gains if some of the users are willing to tolerate delays?”. We present an analytical model of the energy usage of LTE base stations, which provides lower bounds of the possible energy gains under a decentralized, noncooperative setup. The model is analyzed in six different scenarios (such as micro-macro cell interaction and coverage redundancy) for varying traffic and user-tolerable delays. We show that it is possible to reduce the power consumption by up to 30%.

Computation offloading in mobile network. Mobile edge computing (MEC) emerges as a promising paradigm that extends the cloud computing to the edge of pervasive radio access networks, in near vicinity to mobile users, reducing drastically the latency of end-to-end access to computing resources. Moreover, MEC enables the access to up-to-date information on users' network quality via the radio network information service (RNIS) application programming interface (API), allowing to build novel applications tailored to users' context. In [25] and [49], we present a novel framework for offloading computation tasks, from a user device to a server hosted in the mobile edge (ME) with highest CPU availability. Besides taking advantage of the proximity of the MEC server, the main innovation of the proposed solution is to rely on the RNIS API to drive the user equipment (UE) decision to offload or not computing tasks for a given application. The contributions are twofold. First, we propose the design of an application hosted in the ME, which estimates the current value of the round trip time (RTT) between the UE and the ME, according to radio quality indicators available through RNIS API, and provides it to the UE. Second, we present a novel computation algorithm which, based on the estimated RTT coupled with other parameters (e.g., energy consumption), decide when to offload UE's applications computing tasks to the MEC server. The effectiveness of the proposed framework is demonstrated via testbed experiments featuring a face recognition application.

Services improvement in wireless heterogeneous networks. With the rapid growth of HTTP-based Adaptive Streaming (HAS) multimedia video services on the Internet, improving the Quality of Experience (QoE) of video delivery will be highly requested in wireless heterogeneous networks. Various access technologies such as 3G/LTE and Wi-Fi with overlapping coverage is the main characteristic of network heterogeneity. Since contemporary mobile devices are usually equipped with multiple radio interfaces, mobile users are enabled to utilize multiple access links simultaneously for additional capacity or reliability. However, network and video quality selection can have notable impact on the QoE of DASH clients facing the video service's requirements, the wireless channel profiles and the costs of the different links. In this context, the emerging Multi-access Edge Computing (MEC) standard gives new opportunities to improve DASH performance, by moving IT and cloud computing capabilities down to the edge of the mobile network. In [45], we propose a MEC-assisted architecture for improving the performance of DASH-based streaming, a standard implementation of a HAS framework in wireless heterogeneous networks. With the proposed algorithm running as a MEC service, the overall QoE and fairness of DASH clients are improved in a real time manner in case of network congestion.

QoE aware routing in wireless networks. This year we continued our research on QoE-based optimization routing for wireless mesh networks. The difficulties of the problem are analyzed and centralized and decentralized algorithms are proposed. The quality of the solution, the computational complexity of the proposed algorithm, and the fairness are our main concerns. Several centralized approximation algorithms have been already proposed in order to address the complexity and the quality of possible solutions. This year, we focused mainly on distributed algorithm to complement of the existing centralized algorithms. We propose decentralized heuristic algorithms based on the well-known Optimized Link-State Routing (OLSR) protocol. Control packets of OLSR are modified so as to be able to convey QoE-related information. The routing algorithm chooses the paths heuristically. After that, we studied message passing algorithms in order to find near optimal routing solutions in cooperative distributed networks. These algorithms have been published in [27], [13].

Sensors networks. In the literature, it is common to consider that sensor nodes in a clustered-based event-driven Wireless Sensor Network (WSN) use a Carrier Sense Multiple Access (CSMA) protocol with a fixed transmission probability to control data transmission. However, due to the highly variable environment in these networks, a fixed transmission probability may lead to a significant amount of extra energy consumption. In view of this, three different transmission probability strategies for event-driven WSNs were studied in [51]: the optimal one, the “fixed” approach and a third “adaptive” method. As expected, the optimum strategy achieves the best results in terms of energy consumption but its implementation in a practical system is not feasible. The commonly used fixed transmission strategy (the probability for any node to attempt transmission is a constant) is the simplest approach but it does not adapt to changes in the system’s conditions and achieves the worst performance. In the paper, we find that our proposed adaptive transmission strategy, where that probability is changed depending on specific conditions and in a very precise way, is pretty easy to implement and achieves results very close to the optimal method. The three strategies are analyzed in terms of energy consumption but also regarding the cluster formation latency. In [28], we also investigate cluster head selection schemes. Specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms, and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and some selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different considered transmission probability schemes.

Transmission delay, throughput and energy are also important criteria to consider in wireless sensor networks (WSNs). The IEEE 802.15.4 standard was conceived with the objective of reducing resource's consumption in both WSNs and Personal Area Networks (WPANs). In such networks, the slotted CSMA/CA still occupies a prominent place as a channel control access mechanism with its inherent simplicity and reduced complexity. In [26], we propose to introduce a network allocation vector (NAV) to reduce energy consumption and collisions in IEEE 802.15.4 networks. A Markov chain-based analytical model of the fragmentation mechanism, in a saturated traffic, is given as well as a model of the energy consumption using the NAV mechanism. The obtained results show that the fragmentation technique improves at the same time the throughput, the access delay and the bandwidth occupation. They also show that using the NAV allows reducing significantly the energy consumption when applying the fragmentation technique in slotted CSMA/CA under saturated traffic conditions.