Melhem El Helou

A Network-Assisted Approach for RAT Selection in Heterogeneous Cellular Networks

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.

Radio access selection approaches in heterogeneous wireless networks

Along with the rapid growth of mobile broadband traffic, multiple radio access technologies (RATs) are being integrated and jointly managed. To optimize heterogeneous network performance, efficient Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the access technology selection - a key CRRM functionality - and proposes a hybrid approach that combines benefits from both network-centric and user-centric methods. Network information, that is periodically broadcasted, assists mobile users in their decisions. By broadcasting appropriate decisional information, the network tries to globally control users decision in a way to meet operator objectives. On the other hand, mobiles also integrate their needs and preferences to select their access technology so as to maximize their own utility. In comparison with other RAT selection techniques, including network-centric, hybrid and user-centric methods, simulation results prove the efficiency of our hybrid approach in enhancing resource utilization and maximizing user satisfaction.

RRH clustering in cloud radio access networks

Cloud Radio Access Network (C-RAN) offers an evolution in base stations architecture. The base station is broken down into a Base Band Unit (BBU) and a Remote Radio Head (RRH). While BBUs are pooled in a single geographical point, RRHs are distributed across multiple sites. In conventional architectures, a one-to-one logical mapping exists between BBUs and RRHs. One BBU is assigned to one RRH, so as to maximize network capacity. However, in the C-RAN architecture, a one-to-many logical mapping may be established. One BBU is assigned to many RRHs, so as to reduce network power consumption. In this paper, we investigate the BBU-RRH mapping, also known as the RRH clustering problem, and formulate it as a bin packing problem. Optimal and heuristic solutions are derived to reduce network power consumption, without compromising user quality of service. Simulation results illustrate the benefits from clustering RRHs, and prove that our heuristic achieves close performance to the optimal solution.

Radio access technology selection in heterogeneous networks

The migration of wireless networking towards the 5G era is distinguished by the proliferation of various Radio Access Technologies (RAT). As no existing technology can be surrogated by another one, the coexistence of today wireless networks is the best solution at hand when dealing with the incessantly growing user demand for bandwidth. Hence, in this heterogeneous environment, users will be able to utilize services through diverse RATs. RAT selection is crucial and must be designed astutely to avoid resource wastage. In this paper, we consider the downlink of a heterogeneous network with two broadband RATs: a primary RAT such as LTE, and a secondary RAT such as WiFi. We start by formulating a centralized approach for the RAT selection as an optimization problem. Then, two distributed approaches are proposed for adequate RAT selection: first, we put forward distributed heuristic algorithms based on the peak rate perceived by users from available RATs. Second, we devise a distributed RAT selection scheme portrayed as a non-cooperative game with a learning-based algorithm to reach the Nash Equilibriums of the RAT selection game. Extensive simulation results show that the proposed distributed algorithms give efficient results compared to the centralized optimal approach. The analysis of the simulation results enables to define pertinent use cases that delimit the scope of the proposed optimal centralized and distributed approaches.

Optimizing network information for radio access technology selection

The rapid proliferation of radio access technologies (e.g., HSPA, LTE, WiFi and WiMAX) may be turned into advantage. When their radio resources are jointly managed, heterogeneous networks inevitably enhance resource utilization and user experience. In this context, we tackle the Radio Access Technology (RAT) selection and propose a hybrid decision framework that integrates operator objectives and user preferences. Mobile users are assisted in their decisions by the network that broadcasts cost and QoS parameters. By signaling appropriate decisional information, the network tries to globally control users decision in a way to meet operator objectives. Besides, mobiles combine their needs and preferences with the signaled network information, and select their access technology so as to maximize their own utility. Deriving network information is formulated as a Semi-Markov Decision Process (SMDP). We show how to dynamically optimize long-term network reward, aligning with user preferences.

Satisfaction-based Radio Access Technology selection in heterogeneous wireless networks

In this paper, a hybrid approach for Radio Access Technology (RAT) selection in heterogeneous wireless networks is proposed. This decision framework dynamically integrates operator objectives and user preferences, with a relatively reduced network complexity, signaling and processing load. By broadcasting cost and QoS parameters, the network assists mobile users in their decisions. Focusing on the user side, we present a satisfaction-based multi-criteria decision-making (MCDM) method. Based on their needs and preferences, individual users select their RAT avoiding inadequate decisions. Simulation results show that our MCDM method maximizes user utility and outperforms existing solutions.

Comiqual: collaborative measurement of internet quality

With the continuous growth of both fixed and mobile Internet usage, measuring the Internet QoS (quality of service) becomes of vital interest for all involved Internet stakeholders, mainly consumers, operators, and regulators. In this paper, we describe in detail, Comiqual (collaborative measurement of Internet quality), a crowd-sourced large-scale Internet measurement platform that coordinates and collects measurements from measurement agents (MAs) installed on fixed and mobile end user devices. Although the initial and main target of Comiqual is Lebanon, the platform is generically designed to measure the Internet access quality from the user’s perspective anywhere on the globe. The MAs that execute mainly active measurements are jointly controlled by users and by a measurement center (MC); the latter sends measurement instructions to MAs and collects the measurement results. The communication protocol between MC and MAs uses JSON messages that are exchanged via HTTP through REST calls and secured by HTTPS. Measurement results could be openly accessed in a raw format or viewed as an aggregation via a Google map. Moreover, an online statistical tool allows user-defined statistics computation and visualization. All these features combined with the flexibility of the platform management are the main drivers that will allow Comiqual to reach its ultimate goal, which is to create a collaborative, neutral, and transparent observatory of the Internet.

Centralized and distributed RRH clustering in Cloud Radio Access Networks

Cloud Radio Access Network (C-RAN) is a promising technology to improve user quality of service and reduce network capital and operating costs. The key concept behind C-RAN is to break down the conventional base station into a Base Band Unit (BBU) and a Remote Radio Head (RRH), and to pool BBUs from multiple sites into a single geographical point. Moreover, to achieve statistical multiplexing gain, RRHs should be efficiently clustered: many RRHs may be mapped into a single BBU. In this article, RRH clustering is formulated as a coalition formation game where RRHs collaborate and organize themselves into disjoint independent clusters, in a way to optimize network throughput, power consumption, and handover frequency. An optimal centralized solution, based on exhaustive search, is presented. We also propose a distributed algorithm, based on the merge-and-split rule, to form RRH clusters. Simulation results show that our centralized solution adapts to network load conditions and outperforms the no-clustering method, where only one RRH is assigned to each BBU, and the grand coalition method, where all RRHs are assigned to a single BBU. More importantly, our distributed algorithm achieves very close performance to the optimal solution, with significantly lower computational complexity.

Interference-aware clustering in cloud radio access networks

Cloud Radio Access Network (C-RAN) is an evolution in base station architecture. The key concept is to break down the conventional base station into a Base Band Unit (BBU) and a Remote Radio Head (RRH). While BBUs are pooled in a cloud data center, RRHs are distributed across multiple sites. In this context, resource utilization can be enhanced through statistical multiplexing: many RRHs may be clustered and associated with a single BBU. In this paper, RRH clustering is formulated as a Set Partitioning Problem, considering inter-cluster interferences. Optimal and heuristic solutions are derived to reduce network power consumption with minimum throughput requirements. Simulation results show that our interference-aware solutions outperform the no-clustering method, where only one RRH is associated with each BBU, and the interference-unaware Bin Packing algorithm. Moreover, our heuristic achieves performance very close to the optimal solution.

A Hybrid Approach for RRH Clustering in Cloud Radio Access Networks based on Game Theory

Cloud Radio Access Networks (C-RAN) is an evolution in the base station architecture, mainly composed of two elements: The Base Band Unit (BBU) and the Remote Radio Head (RRH). The BBU is a centralized pool of computational resources to provide the signal processing and coordination functionality required by all cells, while the RRHs are light radio units that User Equipment (UE) connects to via the RAN. Many advantages are derived from this architecture, such as dynamic BBU-RRH associations and statistical multiplexing gains. In particular, the BBU-RRH association problem is crucial for reducing power consumption. In this paper, we focus on decentralized BBU-RRH association, which has not received attention in the literature. Therefore, the aim of this work is to propose a hybrid two-stage approach that includes a game theoretic framework for the BBU-RRH association, and a centralized scheme to set the adequate number of available BBUs. The game among RRHs is solved by two different algorithms. The first relies on the best response algorithm, namely H-BR-IACA. The second is based on a reinforcement learning method (the replicator dynamics), namely H-DR-IACA. We compare our devised solution to a centralized approach proposed in a previous work. The results of our proposition show close performance to the centralized method.