Majed Haddad

A Hybrid Decision Approach for the Association Problem in Heterogeneous Networks

The area of networking games has had a growing impact on wireless networks. This reflects the recognition in the important scaling advantages that the service providers can benefit from by increasing the autonomy of mobiles in decision making. This may however result in inefficiencies that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized protocols keep being considered and compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal centric radio resource management schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. We derive the utilities related to the Quality of Service (QoS) perceived by the users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE systems.

Probabilistic analysis of buffer starvation in Markovian queues

Our purpose in this paper is to obtain the exact distribution of the number of buffer starvations within a sequence of N consecutive packet arrivals. The buffer is modeled as an M/M/1 queue. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit solution, but at the cost of the high complexity order in certain circumstances. The recursive approach, though not offering an explicit result, needs fewer computations. We further propose a fluid analysis of starvation probability on the file level, given the distribution of file size and the traffic intensity. The starvation probabilities of this paper have many potential applications. We apply them to optimize the quality of experience (QoE) of media streaming service, by exploiting the tradeoff between the start-up delay and the starvation.

Analysis of Buffer Starvation With Application to Objective QoE Optimization of Streaming Services

Our purpose in this paper is to characterize buffer starvations for streaming services. The buffer is modeled as a FIFO queue with exponential service time and Poisson arrivals. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches to obtain exact distribution of the number of buffer starvations, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit result. We extend this approach to the scenario with a constant playback rate using Tàkacs Ballot theorem. The recursive approach, though not offering an explicit result, allows us to obtain the distribution of starvations with non-independent and identically distributed (i.i.d.) arrival process in which an ON/OFF bursty arrival process is considered. We further compute the starvation probability as a function of the amount of prefetched packets for a large number of files via a fluid analysis. Among many potential applications of starvation analysis, we show how to apply it to optimize objective quality of experience (QoE) of media streaming, by exploiting the tradeoff between startup/rebuffering delay and starvations.

QoE analysis of media streaming in wireless data networks

The purpose of this paper is to model quality of experience (QoE) of media streaming service in a shared fast-fading channel. In this context, the arrival and the service processes of the playout buffer do not have the same job size. We present an analytical framework based on Takacs Ballot theorem to compute the probability of buffer starvation and the distribution of playback intervals. We model the arrival processes of Proportional Fair and Round Robin schedulers, and feed them into this framework to study the impact of prefetching on the starvation behavior. Our simulations match the developed model very well if the base station knows the playback rate and the channel gain. Furthermore, we make an important observation that QoE metrics predicted by users are very sensitive to the measurement error of arrival process.

A Nash-Stackelberg Fuzzy Q-Learning Decision Approach in Heterogeneous Cognitive Networks

Motivated by the fact that when selfish users choose their policies independently without any coordination mechanism, Nash equilibria could result in a network collapse, we develop in this paper a hierarchical distributed learning framework for decision-making in heterogeneous cognitive networks. We introduce the Nash-Stackelberg fuzzy Q-learning, with the network as leader that aims at maximizing its utility (revenue) and the mobiles as followers that have their individual objectives (maximizing their QoS). We validate our results through extensive simulations of the algorithm in a practical setting of a geographical area covered by a global HSDPA and 3G LTE system that serves both streaming and elastic traffic.

Joint strategic spectrum sensing and opportunistic access for cognitive radio networks

This paper deals with the problem of joint sensing and medium accessing in cognitive radio networks. We design a non-cooperative two-step game to describe the Sense-Transmit-Wait paradigm in an opportunistic point of view. We give a full characterization of the Nash equilibria and analyze the optimal pricing policy, from the network owner view, for both centralized setting and decentralized setting. Next, we propose a combined learning algorithm that is fully distributed and allows the cognitive users to learn their optimal payoffs and their optimal strategies in both symmetric and asymmetric cases. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in medium access cognitive radio networks in terms of sensing capabilities.

Spectrum Coordination in Energy-Efficient Cognitive Radio Networks

Device coordination in open spectrum systems is a challenging problem, particularly since users experience varying spectrum availability over time and in different locations. In this paper, we propose a game-theoretic approach that allows cognitive radio (CR) pairs, namely the primary user (PU) and the secondary user (SU), to update their transmission power and frequencies simultaneously. Specifically, we address a Stackelberg game model in which individual users attempt to hierarchically access to the wireless spectrum while maximizing their energy efficiency. A thorough analysis of the existence, uniqueness, and characterization of the Stackelberg equilibrium (SE) is conducted. In particular, we show that a spectrum coordination naturally occurs when both actors in the system decide sequentially about their power and their transmitting carriers. As a result, spectrum sensing in such a situation turns out to be a simple detection of the presence/absence of a transmission on each subband. We also show that when users experience very different channel gains on their two carriers, they may choose to transmit on the same carrier at the SE as this contributes enough energy efficiency to outweigh the interference degradation caused by the mutual transmission. Then, we provide an algorithmic analysis on how the PU and the SU can reach such a spectrum coordination using an appropriate learning process. We validate our results through extensive simulations and compare the proposed algorithm to some typical scenarios, including the non-cooperative case in the work of Meshkati et al. and the throughput-based-utility systems. Typically, it is shown that the proposed Stackelberg decision approach optimizes the energy efficiency while still maximizing the throughput at the equilibrium.

Towards efficient disaster management: 5G and Device to Device communication

Recent events of multiple earthquakes in Nepal and resulting loss of life and resources bring our attention to the ever growing significance of disaster management, especially in the context of large scale nature disasters such as earthquake and Tsunami. In this paper, we focus on how disaster management can benefit from recent advances in wireless communication technologies and protocols, especially mobile technologies and devices. The paper provides an overview of how the new generation of telecommunications and technologies such as 5G, Device to Device, 4G/LTE, and software defined radio can improve the potential of disaster management networks. Our survey is different from existing surveys in that we focus on recent advances and ongoing research directions in disaster management with the focus being on the use of ubiquitous mobile devices and applications.

NEWCAST: Anticipating resource management and QoE provisioning for mobile video streaming

The knowledge of the future capacity variations in wireless networks using smartphones becomes more and more possible by exploiting the rich contextual information from smartphone sensors through mobile applications and services. It is entirely likely that such contextual information, which may include the traffic, mobility and radio conditions, could lead to a novel agile resource management not yet thought of. Inspired by the attractive features and potential advantages of this agile resource management, several approaches have been proposed during the last period. However, agile resource management also comes with its own challenges, and there are significant technical issues that still need to be addressed for successful rollout and operation of this technique. In this paper, we propose an approach (called NEWCAST) for anticipating throughput variation for mobile video streaming services. The solution of the optimization problem realizes a fundamental trade-off among critical metrics that impact the users perceptual quality of the experience (QoE) and system utilization. Both simulated and real-world traces collected from [1] are carried out to evaluate the performance of NEWCAST. In particular, it is shown that NEWCAST provides the efficiency, computational complexity and robustness that the new 5G architectures require.

The association problem with misleading partial channel state information

It has been known that the throughput of the 802.11 WLAN is much smaller than the nominal bit rate offered when attempting to connect to an access point. A user may discover the quality of service offered by an access point only after taking the decision of which of the access points to connect to. In fact, the actual throughput of a user is a function of not only his channel state but also of that of the other connected users. This could likely lead to congestion and overload conditions in the Access Point (AP) in question (which offers the best signal strength) and all users would lose. The information available to users attempting to connect to an AP is thus misleading. In this paper, we develop a Nash-Bayesian game framework where users compete to maximize their throughput by picking the best locally serving radio access network (RAN) with respect to their own measurement, their demand and a partial statistical channel state information (CSI) of other users. We derive analytically the utilities perceived by users to obtain the equilibria. In particular, it is shown that equilibria strongly depend on the channel quality indicator.