Dr Faouzi Bouali

Research Fellow in Advanced Radio Resource Management for Next-generation Networks
PhD (2013), MSc (2006), Dipl-Ing (2004)

Academic and research departments

Institute for Communication Systems, Faculty of Engineering and Physical Sciences.

About

Areas of specialism

Wireless Communications and Networking; Innovative Architectures for Next-generation Networks; Advanced radio resource management (RRM); Vehicular communications; Spectrum sharing; Quality of service/experience (QoS/QoE) provisioning; Network slicing

University roles and responsibilities

  • Research Fellow
  • Teaching Assistant
  • Delegate in various committees of the British Standards Institution (BSI).

    My qualifications

    2013
    Ph.D Degree in Signal Theory and Communications (Excellent Cum Laude qualification)
    University Polytechnica de Catalunya (UPC), Barcelona'Tech, Spain
    2006
    Master’s degree in Telecommunications (EUR-ACE® labelled).
    High School of Communications of Tunis, SUP’COM, Tunis
    2004
    Diplome d'Ingenieur, First-in-class - National Excellence prize (EUR-ACE® labelled).
    High School of Communications of Tunis, SUP’COM, Tunis.

    Affiliations and memberships

    5G Infrastructure Public Private Partnership (5G PPP)
    Delegate in the Technology Board (TB)
    5G Infrastructure Public Private Partnership (5G PPP)
    Delegate in the Automotive Work Group (WG)
    5G Infrastructure Public Private Partnership (5G PPP)
    Delegate in the Pre-standards Work Group (WG)

    Research

    Research interests

    Research projects

    Teaching

    Publications

    Highlights

    F. Bouali, K. Moessner, and M. Fitch, “Energy-efficient QoE-driven Strategies for Context-aware RAT Selection,” IEEE Transactions on Green Communications and Networking, January 2020 (accepted)  

    M. Lu, F. Bouali, “Potential 5G Applications for Connected Vehicles: Use Cases, Opportunities and Challenges”, The 27th ITS World Congress, Los Angeles, USA, October 4-8th, 2020 (accepted).

    F. Bouali, O. Sallent, J. Pérez-Romero, and R. Agustí (2013) Exploiting Knowledge Management for Supporting Multi-Band Spectrum Selection in Non-Stationary Environments, IEEE Transactions on Wireless Communications, vol.12, no.12, pp. 6228–6243, December 2013, doi: 10.1109/TWC.2013.101713.130165.
    This paper proposes a new knowledge management framework for spectrum selection under non-stationary conditions supporting a set of applications with heterogeneous requirements. In this respect, an optimization problem is formulated to maximize an aggregate utility function that captures the suitability of spectrum portions with respect to the various application requirements and a set of preferences for using the different spectrum bands. Motivated by the practical limitations when solving the considered problem directly, an alternative solution is proposed. It exploits a statistical characterization of the environment retained in the knowledge database of the proposed framework. To cope with the non-stationarity of the environment, a reliability tester is proposed to detect relevant changes in radio conditions, and update database statistics accordingly. Then, a knowledge manager exploiting these statistics is developed to monitor the time-varying suitability of spectrum resources. Based on this, a novel spectrum management is proposed to approximate the optimal solution of the considered problem. The results obtained in a realistic digital home reveal that, under stationary conditions, the proposed strategy performs very close to the optimal solution with much less requirements in terms of spectrum reconfiguration and measurement reporting. Furthermore, thanks to the reliability tester support, substantial robustness is shown when interference conditions become non-stationary, thus proving the practicality of the proposed solution.
    F. Bouali, K. Moessner, and M. Fitch (2018) Spectrum Utility: a Novel Metric for Efficient Spectrum Usage in Next-generation Networks, IEEE 87th Vehicular Technology Conference (VTC2018-Spring), June 2018
    This paper proposes a novel spectrum utility (SU) metric that assesses the efficiency of spectrum usage by a set of heterogeneous applications. Unlike the traditional spectrum efficiency (SE), the proposed metric does not blindly consider the achievable bit- rate, but captures the most relevant performance metrics for each of the considered applications. Specifically, it is formulated as an aggregated utility that combines the satisfaction level with respect to the various requirements with an innovative pricing model based on it to derive the total revenue generated for the spectrum owner. To get insight into the usefulness of the proposed metric, the proposed methodology is instantiated for an illustrative use case, where a mixture of delay- sensitive (i.e., interactive video) and -tolerant (i.e., file transfer) applications are established in dense indoor deployments. The obtained results reveal that the proposed SU significantly outperforms the legacy SE in assessing how efficiently a limited frequency spectrum is utilised from the perspective of the total revenue, particularly when the quality- of- experience (QoE) perceived during video sessions is degraded. This calls for a novel SU-aware ecosystem, where the spectrum sharing models, billing policies and resource allocation mechanisms (e.g., medium access control (MAC) and radio resource management (RRM)) are jointly revisited to maximise the overall SU.
    F. Bouali, O. Sallent, J. Pérez-Romero, and R. Agustí (2013) A Fittingness Factor-based Spectrum Management Framework for Cognitive Radio Networks, Wireless Personal Communications pp. 1–15, 2013.
    In order to increase cognitive radios (CRs) operation efficiency, there has been an increasing interest in strengthening awareness level about spectrum utilisation. In this respect, this paper proposes to exploit the fittingness factor concept to capture the suitability of spectral resources exhibiting time-varying characteristics to support a set of heterogeneous CR applications. First, a new knowledge management functional architecture for optimizing spectrum management has been constructed. It integrates a set of advanced statistics capturing the influence of the dynamic radio environment on the fittingness factor. Then, a knowledge manager (KM) exploiting these statistics to monitor time-varying suitability of spectrum resources has been proposed to support the spectrum selection (SS) decision-making process. In particular, a new Fittingness Factor-based strategy combining two SS and spectrum mobility (SM) functionalities has been proposed, following either a greedy or a proactive approach. Results have shown that, with a proper fittingness factor function, the greedy approach efficiently exploits the KM support at low loads and the SM functionality at high loads to introduce significant gains in terms of the user dissatisfaction probability. The proactive approach has been shown to maintain the introduced performance gain while minimizing the signalling requirements in terms of spectrum handover rate.
    F. Bouali, O. Sallent, J. Pérez-Romero, and R. Agustí (2013) A Cognitive Management Framework for Spectrum Selection, Computer Networks Journal, ISSN: 1389-1286, Vol: 57, Issue: 14, Page: 2752-2765.
    To increase cognitive radio (CR) operation efficiency, there has been an interest in enhancing the awareness level of spectrum utilization. In this context, this paper builds a new cognitive management functional architecture for spectrum selection (SS). It relies on a knowledge manager (KM) retaining a set of advanced statistics that track the suitability of spectral resources to support a set of heterogeneous applications under varying interference conditions. Based on this architecture, a novel proactive strategy is proposed for both SS and spectrum mobility (SM) functionalities. The required interactions between the proposed decision-making processes are described, and their capability to exhibit robustness to unexpected changes in the radio environment is highlighted. The results show that the proposed strategy efficiently exploits the KM support for low loads, while the SM functionality introduces significant gains for high loads with respect to other strategies. Finally, to assess the practicality of the proposed approach, the signaling requirements in the radio interface are evaluated.
      F. Bouali, O. Sallent, and J. Pérez-Romero (2014) Exploiting Cognitive Management for Supporting Multimedia Applications over Cognitive Radio Networks, Book Chapter in Multimedia Over Cognitive Radio Networks, Algorithms, Protocols, and Experiments, Taylor & Francis LLC, CRC Press, New York, USA, December, 2014.
      F. Bouali, K. Moessner, and M. Fitch (2017) A Context-aware QoE-driven Strategy for Adaptive Video Streaming in 5G multi-RAT Environments, IEEE Wireless Personal Multimedia Communications (WPMC) 2017, December 2017.
      This paper extends traditional dynamic adaptive streaming over HTTP (DASH) to efficiently exploit all available bands and licensing regimes in a given context. A novel objective quality-of-experience (QoE) metric is proposed to capture the most relevant factors that impact user perception during streaming sessions. Based on it, a QoE-driven adaptation strategy is devised to jointly select the best radio access technology (RAT) and quality for each video segment depending on the various components of the context. It relies first on fuzzy logic to estimate the QoE provided by each available RAT subject to the uncertainty level associated with DASH clients. Then, a fuzzy multiple attribute decision making (MADM) methodology is developed to combine the QoE estimates with the heterogeneous components of the context to assess the in-context suitability levels. The proposed approach is applied to adapt video streaming across available RATs in dense deployments for a set of Bronze and Gold subscriptions. The results reveal that the proposed strategy always assigns Gold clients to the well-regulated licensed band, while switches Bronze clients between licensed and unlicensed bands depending on the operating conditions. It strikes a balance between maximising video quality and reducing playback stalling, which significantly improves the perceived QoE compared to the traditional DASH approach.
      F. Bouali, K. Moessner, and M. Fitch (2017) A Context-aware User-driven Strategy to Exploit Offloading and Sharing in Ultra-dense Deployments, IEEE International Conference on Communications (ICC) 2017, Paris France, May 2017.
      This paper proposes a novel context-aware user- driven strategy to efficiently exploit all available bands and licensing regimes in ultra-dense deployments without prior knowledge about each combination. It relies first on fuzzy logic to estimate the suitability of each radio access technology (RAT) to support the requirements of various applications. Then, a fuzzy multiple attribute decision making (MADM) approach is developed to combine these estimates with the heterogeneous context components to assess the in-context suitability. Based on this metric, a spectrum management strategy is proposed to support interactive video sessions for a set of Bronze and Gold subscriptions. The results reveal that the proposed approach always assigns Gold users to the well-regulated licensed band, while switches Bronze users between licensed and unlicensed bands depending on the operating conditions. This results in a significant improvement of the quality-of-experience (QoE) compared to a baseline that exploits only licensed bands. Then, a comparative study is conducted between the available options to exploit unlicensed bands, namely Offloading and Sharing. The results show that the best option strongly depends on the existing load on WLAN. Therefore, a combined approach is proposed to efficiently switch between both options, which achieves the best QoE for all considered loads.
      F. Bouali, K. Moessner, and M. Fitch (2016) Context-Aware User-Driven Framework for Network Selection in 5G Multi-RAT Environments, The Vehicular Technology Conference (VTC)-Fall 2016, Montreal Canada, September 2016
      To improve the inter-working of future 5G systems with existing technologies, this paper proposes a novel context-aware user-driven framework for network selection in multi-RAT environments. It relies on fuzzy logic to cope with the lack of information usually associated with the terminal side and the intrinsic randomness of the radio environment. In particular, a fuzzy logic controller first estimates the out-of-context suitability of each RAT to support the QoS requirements of a set of heterogeneous applications. Then, a fuzzy multiple attribute decision making (MADM) methodology is developed to combine these estimates with the various components of the context (e.g., terminal capabilities, user preferences and operator policies) to derive the in-context suitability level of each RAT. Based on this novel metric, two spectrum selection (SS) and spectrum mobility (SM) functionalities are developed to select the best RAT in a given context. The proposed fuzzy MADM approach is validated in a dense small-cell environment to perform a context-aware offloading for a mixture of delay-sensitive and best-effort applications. The results reveal that the fuzzy logic component is able to efficiently track changes in the operating conditions of the different RATs, while the MADM component enables to implement an adjustable context-aware strategy. The proposed fuzzy MADM approach results in a significant improvement in achieving the target strategy, while maintaining an acceptable QoS level compared to a traditional offloading based on signal strength.
      F. Bouali, O. Sallent, and J. Pérez-Romero (2013) Knowledge Management Framework for Robust Cognitive Radio Operation in Non-Stationary Environments,” in Personal Indoor and Mobile Radio Communications (PIMRC), 2013 IEEE 24th International Symposium on, Sept 2013, pp. 3022–3027.
      To increase cognitive radio operation efficiency, this paper proposes a new knowledge management functional architecture, based on the fittingness factor concept, for supporting spectrum management in non-stationary environments. It includes a reliability tester module that detects, based on hypothesis testing, relevant changes in suitability levels of spectrum resources to support a set of heterogeneous applications. These changes are captured through a set of advanced statistics stored in a knowledge database and exploited by a proactive spectrum management strategy to assist both spectrum selection and spectrum mobility functionalities. The results reveal that the proposed reliability tester is able to disregard the changes due to the intrinsic randomness of the radio environment and to efficiently detect actual changes in interference conditions of spectrum pools. Thanks to this support, the proposed spectrum management strategy exhibits substantial robustness when the environment becomes non-stationary, obtaining performance improvements of up to 75% with respect to the reference case that does not make use of the reliability tester functionality.
      J. Pérez-Romero, O. Sallent, F. Bouali et al. (2012) A spectrum selection framework for Opportunistic Networks, Future Network & Mobile Summit (FutureNetw), , Berlin, 2012, pp. 1-9.
      This paper presents a framework for including cognitive management functionalities in the spectrum selection process for Opportunistic Networks (ONs).The framework is based on a decision making functionality interacting with a knowledge management block that stores and processes information about the spectrum use. Different approaches for spectrum selection are discussed covering specific cases including the capability to aggregate different bands and the possibility to jointly select the spectrum and the network interface. Illustrative results of the proposed framework are presented.
      F. Bouali, O. Sallent, J. Pérez-Romero, and R. Agustí (2012) Exploiting knowledge management for supporting spectrum selection in Cognitive Radio networks, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Stockholm, 2012, pp. 259-264.
      In order to increase Cognitive Radio operation efficiency, this paper builds up a new knowledge management functional architecture for supporting spectrum management. It integrates the fittingness factor concept proposed by the authors in a prior work and includes a set of advanced statistics capturing the influence of the radio environment. Then, a Knowledge Manager (KM) exploiting these statistics and observed fittingness factor values has been developed to monitor the time-varying suitability of spectrum resources to support heterogeneous services. Based on estimated suitability levels, a new strategy combining Spectrum Selection (SS) and Spectrum Mobility (SM) functionalities has been proposed. Results have shown that the proposed strategy efficiently exploits the KM support at low loads and the SM functionality at high loads to introduce significant gains (ranging from 85% to 100%) w.r.t. a pure random selection while exhibiting substantial robustness to changes in interference levels.
      F. Bouali, O. Sallent, J. Pérez-Romero, and R. Agustí (2011) A framework based on a fittingness factor to enable efficient exploitation of spectrum opportunities in Cognitive Radio networks, 2011 The 14th International Symposium on Wireless Personal Multimedia Communications (WPMC), Brest, 2011, pp. 1-5.
      In order to increase CRs (Cognitive Radios) operation efficiency, there has been an interest in increasing awareness level about spectrum utilisation. In this respect, this paper proposes a new fittingness factor concept that captures the suitability of spectral resources exhibiting time-varying characteristics to support a set of heterogeneous CR applications. Different fittingness factor functions to track unknown variations of interference levels are formulated and analysed. First, the dependency with traffic load is studied and second, the impact over the spectrum selection decision-making process in a multi-service CR context is evaluated. Results show that, even with a simple greedy approach, the fittingness factor concept can result in an efficient matching of spectral resources to the requirements of CR applications, thus resulting in significant reduction in the user dissatisfaction probability.
      F. Bouali, O. Sallent, J. Pérez-Romero, and R. Agustí (2011) A novel spectrum selection strategy for matching multi-service secondary traffic to heterogeneous primary spectrum opportunities, 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, Toronto, ON, 2011, pp. 417-422.
      In order to increase spectrum utilization efficiency, CRs (Cognitive Radios) have been introduced to reuse white spaces left unused by legacy services under the strict constraint of not interfering them. In this context, this paper proposes to exploit a statistical characterisation of Primary User (PU) activity to be retained in Radio Environment Maps (REMs) for spectrum selection purposes. The objective is to match multi-service secondary traffic to heterogeneous primary spectrum opportunities minimizing the SpHO (Spectrum handOver) rate. Specifically focusing on dependence structures potentially exhibited by primary ON/OFF periods, two spectrum selection criteria have been first proposed to benchmark the utility of the embedded statistical patterns in the REM. Results have shown that the one or the other criterion can introduce significant gains with respect to a random selection depending on the secondary configuration and characteristics of PUs. Therefore, a novel pro-active spectrum selection strategy combining the proposed criteria has been developed and proven to achieve in most of the cases the best performance for a given secondary service mix and the dependence level between primary ON/OFF periods.
      F. Bouali, O. Sallent, J. Pérez-Romero and R. Agustí (2011) Strengthening Radio Environment Maps with primary-user statistical patterns for enhancing cognitive radio operation, 2011 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Osaka, 2011, pp. 256-260.
      In order to increase spectrum utilization efficiency, CRs (Cognitive Radios) have been introduced to reuse white spaces left unused by legacy services under the strict constraint of not interfering them. In order to fulfill this constraint while optimising spectrum utilisation, it is important to get knowledge about primary-user activity in order to devise proper strategies for secondary-user operation. In this context, this paper proposes to strengthen Radio Environment Maps (REM) with statistical patterns of primary systems that capture among others temporal dependence structures between activity (ON) and inactivity (OFF) periods. Convergence times for the different statistics are analysed. Then, a set of novel spectrum selection criteria exploiting these statistics are proposed and assessed to benchmark the usefulness of primary statistical patterns retained in the REM. Results show that significant performance gains can be achieved in terms of a reduction in the number of required spectrum hand-overs.
      F. Bouali, K. Moessner, and M. Fitch (2020) Energy-efficient QoE-driven Strategies for Context-aware RAT Selection, IEEE Transactions on Green Communications and Networking, January 2020 (accepted)
      This paper formulates an optimization problem that maximizes an aggregate utility that captures the ``in-context'' suitability of available radio access technologies (RATs) to support adaptive video streaming subject to a single-homing constraint. To efficiently solve the considered problem, a novel network-assisted quality-of-experience (QoE)-driven methodology is devised, and its impact on the end-user devices is evaluated. The proposed approach is evaluated and benchmarked against its distributed and centralized counterparts from a cost-benefit perspective. The results reveal that the proposed strategy significantly outperforms its distributed counterpart, and performs differently with respect to its centralized counterpart depending on the number of video clients. At low loads, it performs similarly with much less control overhead. At high loads, the proposed strategy scales up well, while the centralized approach gets overwhelmed by an increasing uplink signaling. A practicality analysis of the proposed strategy for battery-powered devices reveals that its gain in terms of uplink signaling outweighs its cost in terms of processing load, which results in a drastic reduction of the consumed energy. Therefore, the proposed solution provides a win-win situation, where the video clients can sustain good QoE levels at reduced energy consumption, while the network can accommodate more users with existing capacity.
      M. Lu, F. Bouali (2020) Potential 5G Applications for Connected Vehicles: Use Cases, Opportunities and Challenges”, The 27th ITS World Congress, Los Angeles, USA, October 4-8th, 2020 (accepted).
      The fifth generation of wireless networks (5G) is not just an evolutionary upgrade of the previous generations of cellular communications, but rather a revolutionary technology envisioned to meet the access, bandwidth, performance, and latency requirements associated with various vertical industries. In this context, this paper makes an analysis of the capability of 5G systems to support various advanced use cases of the Intelligent Transport Systems (ITS) industry. First, the most representative ITS use cases and associated scenarios are selected and described together with their key challenges. To efficiently trial the considered use case scenarios, a three-step method is proposed, where the focus will be initially on performing local tests and eventually evolve towards interconnected multi-site setups.Finally, the technical opportunities and business challenges associated with the future application of 5G to intelligent road transport are discussed and some of the future directions are highlighted.