Basel Barakat

Bio: Basel Barakat is an academic researcher from University of Greenwich. The author has contributed to research in topics: LTE Advanced & Computer science. The author has an hindex of 5, co-authored 14 publications receiving 68 citations. Previous affiliations of Basel Barakat include Ajman University of Science and Technology & Edinburgh Napier University.

6G opportunities arising from Internet of Things use cases: a review paper

Abstract: The race for the 6th generation of wireless networks (6G) has begun. Researchers around the world have started to explore the best solutions for the challenges that the previous generations have experienced. To provide the readers with a clear map of the current developments, several review papers shared their vision and critically evaluated the state of the art. However, most of the work is based on general observations and the big picture vision, and lack the practical implementation challenges of the Internet of Things (IoT) use cases. This paper takes a novel approach in the review, as we present a sample of IoT use cases that are representative of a wide variety of its implementations. The chosen use cases are from the most research-active sectors that can benefit from 6G and its enabling technologies. These sectors are healthcare, smart grid, transport, and Industry 4.0. Additionally, we identified some of the practical challenges and the lessons learned in the implementation of these use cases. The review highlights the cases’ main requirements and how they overlap with the key drivers for the future generation of wireless networks.

Is the Zero-Wait Policy Always Optimum for Information Freshness (Peak Age) or Throughput?

Abstract: The zero-wait (ZW) policy is widely held to achieve maximum information “freshness,” i.e., to achieve minimum peak age (PA) and maximum throughput, for real-time Internet-of-Things applications. In this letter, it was shown through a series of experiments that the ZW policy is not necessarily the optimum policy for freshness or throughput in all real-world scenarios. First, the effect of delay on the ZW policy was shown on a local area network (LAN). Afterward, the server was located on the Internet, and it was shown that the ZW policy incurred a two-fold PA and throughput performance degradation compared with continuously sending status updates.

Generalized proportional fair (GPF) scheduler for LTE-A

Abstract: The growth of wireless traffic and the demand for higher data rates motivated researchers around the world to enhance the Long Term Evolution-Advanced (LTE-A) performance. Recently, a considerable amount of the research had been done to optimise the packet schedulers. The packet schedulers distribute the radio resources among users to increase spectrum efficiency and network performance. In this paper, a Generalized Proportional Fair (GPF) scheduler is used to enhance the scheduler performance compared to the other conventional schedulers. The GPF scheduler performance is compared in terms of users' throughput, energy efficiency, spectral efficiency and fairness using system level simulations. The simulation results show that the proposed scheduler outperforms the conventional schedulers proposed for LTE-A.

An adaptive hybrid scheduling algorithm for LTE-Advanced

Abstract: The 3rd Generation Partnership Project (3GPP) introduced Long Term Evolution (LTE) in release 8, and afterwards it was updated significantly in later releases (referred to as LTE-Advanced). LTE and LTE-Advanced (LTE-A) aim to achieve higher spectral efficiency, higher data rates, robustness and flexibility. Intelligent channel-aware radio resource scheduling is one of the key features of LTE-A. A number of schedulers proposed in the literature rely on the feedback sent from the Users Equipment (UE) without considering the presence of feedback delay. In this paper, we analyse the effect of the uplink delay on the cell performance of existing schedulers, in terms of throughput and the users' fairness. We then propose an adaptive hybrid scheduler to overcome the effect of the uplink delay on the scheduler performance. The simulation results show that our proposed scheduling algorithm outperforms the existing schedulers in the presence of uplink feedback delay.

How to Measure the Average and Peak Age of Information in Real Networks

Abstract: The Age of information (AoI) was proposed in the literature to quantify the freshness of information. The majority of the work done in this area has theoretically evaluated AoI and its Peak (PAoI). In this paper, a method for obtaining the value of AoI and PAoI from experiments is proposed. We conducted an experiment emulating an M/M/1 queue and used the proposed method to evaluate AoI and PAoI. The values were compared to the expressions presented previously in the literature. Our results show that the proposed method is accurate for the M/M/1 queue. A statistical test was conducted to confirm the reliability of this conclusion.

A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems

Abstract: In this paper, we study a real-time monitoring system in which multiple source nodes are responsible for sending update packets to a common destination node in order to maintain the freshness of information at the destination. Since it may not always be feasible to replace or recharge batteries in all source nodes, we consider that the nodes are powered through wireless energy transfer (WET) by the destination. For this system setup, we investigate the optimal online sampling policy (referred to as the age-optimal policy ) that jointly optimizes WET and scheduling of update packet transmissions with the objective of minimizing the long-term average weighted sum of Age of Information (AoI) values for different physical processes (observed by the source nodes) at the destination node, referred to as the sum-AoI . To solve this optimization problem, we first model this setup as an average cost Markov decision process (MDP) with finite state and action spaces. Due to the extreme curse of dimensionality in the state space of the formulated MDP, classical reinforcement learning algorithms are no longer applicable to our problem even for reasonable-scale settings. Motivated by this, we propose a deep reinforcement learning (DRL) algorithm that can learn the age-optimal policy in a computationally-efficient manner. We further characterize the structural properties of the age-optimal policy analytically, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. We extend our analysis to characterize the structural properties of the policy that maximizes average throughput for our system setup, referred to as the throughput-optimal policy . Afterwards, we analytically demonstrate that the structures of the age-optimal and throughput-optimal policies are different. We also numerically demonstrate these structures as well as the impact of system design parameters on the optimal achievable average weighted sum-AoI.

A Reinforcement Learning Framework for Optimizing Age-of-Information in RF-powered Communication Systems.

Abstract: In this paper, we study a real-time monitoring system in which multiple source nodes are responsible for sending update packets to a common destination node in order to maintain the freshness of information at the destination. Since it may not always be feasible to replace or recharge batteries in all source nodes, we consider that the nodes are powered through wireless energy transfer (WET) by the destination. For this system setup, we investigate the optimal online sampling policy (referred to as the age-optimal policy) that jointly optimizes WET and scheduling of update packet transmissions with the objective of minimizing the long-term average weighted sum of Age-of-Information (AoI) values for different physical processes (observed by the source nodes) at the destination node, referred to as the sum-AoI. To solve this optimization problem, we first model this setup as an average cost Markov decision process (MDP). Due to the extreme curse of dimensionality in the state space of the formulated MDP, classical reinforcement learning algorithms are no longer applicable to our problem. Motivated by this, we propose a deep reinforcement learning (DRL) algorithm that can learn the age-optimal policy in a computationally-efficient manner. We further characterize the structural properties of the age-optimal policy analytically, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. We extend our analysis to characterize the structural properties of the policy that maximizes average throughput for our system setup, referred to as the throughput-optimal policy. Afterwards, we analytically demonstrate that the structures of the age-optimal and throughput-optimal policies are different. We also numerically demonstrate these structures as well as the impact of system design parameters on the optimal achievable average weighted sum-AoI.

Spatial Distribution of the Mean Peak Age of Information in Wireless Networks

Abstract: This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates , to their corresponding destinations in a time-slotted fashion. We employ age of information (AoI) for quantifying the freshness of the status updates measured at the destination nodes under the preemptive and non-preemptive queueing disciplines with no storage facility. The non-preemptive queue drops the newly arriving updates until the update in service is successfully delivered, whereas the preemptive queue replaces the current update in service with the newly arriving update, if any. As the update delivery rate for a given link is a function of the interference field seen from the receiver, the temporal mean AoI can be treated as a random variable over space. Our goal in this paper is to characterize the spatial distribution of the mean AoI observed by the SD pairs by modeling them as a bipolar Poisson point process (PPP). Towards this objective, we first derive accurate bounds on the moments of success probability while efficiently capturing the interference-induced coupling in the activities of the SD pairs. Using this result, we then derive tight bounds on the moments as well as the spatial distribution of peak AoI (PAoI). Our numerical results verify our analytical findings and demonstrate the impact of various system design parameters on the mean PAoI.

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Abstract: Ever since the introduction of fifth generation (5G) mobile communications, the mobile telecommunications industry has been debating whether 5G is an “evolution” or “revolution” from the previous legacy mobile networks, but now that 5G has been commercially available for the past few years, the research direction has recently shifted towards the upcoming generation of mobile communication system, known as the sixth generation (6G), which is expected to drastically provide significant and evolutionary, if not revolutionary, improvements in mobile networks. The promise of extremely high data rates (in terabits), artificial intelligence (AI), ultra-low latency, near-zero/low energy, and immense connected devices is expected to enhance the connectivity, sustainability, and trustworthiness and provide some new services, such as truly immersive “extended reality” (XR), high-fidelity mobile hologram, and a new generation of entertainment. Sixth generation and its vision are still under research and open for developers and researchers to establish and develop their directions to realize future 6G technology, which is expected to be ready as early as 2028. This paper reviews 6G mobile technology, including its vision, requirements, enabling technologies, and challenges. Meanwhile, a total of 11 communication technologies, including terahertz (THz) communication, visible light communication (VLC), multiple access, coding, cell-free massive multiple-input multiple-output (CF-mMIMO) zero-energy interface, intelligent reflecting surface (IRS), and infusion of AI/machine learning (ML) in wireless transmission techniques, are presented. Moreover, this paper compares 5G and 6G in terms of services, key technologies, and enabling communications techniques. Finally, it discusses the crucial future directions and technology developments in 6G.