Ali Ranjha | École de Technologie Supérieure (original) (raw)

Control Systems by Ali Ranjha

Lab Session 2:-Q1. Develop mathematical model of the following lead-lag network. Represent this m... more Lab Session 2:-Q1. Develop mathematical model of the following lead-lag network. Represent this model in both transfer function form and pole-zero-gain form.

Apply block diagram reduction techniques on model below to show that its complete transfer functi... more Apply block diagram reduction techniques on model below to show that its complete transfer function is X(s)/R(s)=1/200s 2 +25s+10

Papers by Ali Ranjha

IEEE IoT Magazine, 2024

The applications of upcoming sixth-generation (6G) empowered vehicle-to-everything (V2X) communic... more The applications of upcoming sixth-generation (6G) empowered vehicle-to-everything (V2X) communications depend heavily on large-scale data exchange with high throughput and ultra-low latency to ensure system reliability and passenger safety. However, in urban and remote areas, signals can be easily blocked by various objects. Moreover, the propagation of signals with ultra-high frequencies, such as millimetre waves and terahertz communications, is severely affected by obstacles. To address these issues, the Intelligent Reflecting Surface (IRS), which consists of nearly passive elements, has gained popularity because of its ability to intelligently reconfigure signal propagation in an energy-efficient manner. Due to the promise of ease of deployment and low cost, IRS has been widely acknowledged as a key technology for both terrestrial and nonterrestrial networks to improve wireless coverage signal strength, physical layer security, positioning accuracy, and reduce latency. This paper first describes the introduction of 6G empowered V2X communications and IRS technology. Then, it discusses different use case scenarios of IRS enabled V2X communications and reports recent advances in the existing literature. Next, it focuses our attention on the scenario of vehicular edge computing involving IRS enabled drone communications in order to reduce vehicle computational time via optimal computational and communications resource allocation. Finally, this paper highlights current challenges and discusses future perspectives of IRS enabled V2X communications in order to improve current work and spark new ideas.

IEEE Transactions on Industrial Cyber Physical Systems, 2024

Theme: In the rapidly evolving landscape of intelligent mobile devices and the impending 6G netwo... more Theme: In the rapidly evolving landscape of intelligent mobile devices and the impending 6G networks, the Integrated Sensing Digital Framework (ISDF) emerges as a transformative force within the Internet of Things (IoT), offering unprecedented opportunities for real-time data collection. Comprising the Data Requester (DR), Sensing-Computing Provider (SCP), and Framework Executor (FE), ISDF revolutionizes data gathering by harnessing device intelligence and eliminating the need for specialized sensors, providing a rich source of realtime, diverse data at the convergence of physical and digital realms. However, the integration of ISDF presents challenges, particularly in privacy and reliability. Privacy threats such as data content, task content, and location and identity risks must be addressed for the framework's success. Additionally, the strain on network capacity due to ISDF's reliance on intelligent mobile devices requires careful management for effective support of Ultra-Reliable and Low Latency Communications (URLLC). This proposal focuses on innovative solutions that integrate principles from Integrated Sensing, Computing, and Communications (ISCC) and Zero Touch Network and Service Management (ZSM) to establish a secure and dependable ISDF framework, meeting the evolving needs of 6G systems.

IEEE Open Journal of the Communications Society , 2022

Ultra-reliable and low latency communications (URLLC) will be the backbone of the upcoming sixth-... more Ultra-reliable and low latency communications (URLLC) will be the backbone of the upcoming sixth-generation (6G) systems and will facilitate mission-critical scenarios. A design accounting for stringent reliability and latency requirements for URLLC systems poses a challenge for both industry and academia. Recently, unmanned aerial vehicles (UAV) have emerged as a potential candidate to support communications in futuristic wireless systems due to providing favourable channel gains thanks to Lineof-Sight (LoS) communications. However, usage of UAV in cellular infrastructure increases interference in aerial and terrestrial user equipment (UE) limiting the performance gain of UAV-assisted cellular systems. To resolve these issues, we propose low-complexity algorithms for intercell interference coordination (ICIC) using cognitive radio when single and multi-UAVs are deployed in a cellular environment to facilitate URLLC services. Moreover, we model BS-to-UAV (B2U) interference in downlink communication, whereas in uplink we model UAV-to-BS (U2B), UAV-to-UAV (U2U), and UE-to-UAV (UE2U) interference under perfect/imperfect channel state information (CSI). Results demonstrate that the proposed perfect ICIC accounts for fairness among UAV especially in downlink communications compared to conventional ICIC algorithms. Furthermore, in general, the proposed UAV-sensing assisted ICIC and perfect ICIC algorithms yield better performance when compared to conventional ICIC for both uplink and downlink for the single and multi-UAV frameworks. INDEX TERMS URLLC, multi-UAV, cognitive radio, intercell interference coordination (ICIC).

IEEE Transactions on Vehicular Technology, 2024

Open radio access networks (RAN) enhance the capabilities of traditional RAN by introducing featu... more Open radio access networks (RAN) enhance the capabilities of traditional RAN by introducing features such as interoperability, open interfaces, software/hardware separation, and intelligence. Open RAN has several use cases in cellular vehicle-to-everything communications such as low-latency information exchange between vehicles and RAN intelligence controller (RIC). However, efficient data sharing between vehicles and RIC suffers from the challenges of signal loss due to mobility and dynamic channel conditions. In this regard, intelligent reflecting surfaces (IRS) have emerged as an intriguing concept of reconfigurable and smart environments to improve the performance of wireless communication systems. In the proposed research work, our main focus is on a multi-IRS aided single input single output system, where open RAN base stations (BS) convey information to a remote vehicular user. The transmitted signal is reflected by IRSs via multi-hop passive beamforming over pairwise line-of-sight links. To maximize the overall network sum-rate, we propose an IRS assignment method that allocates either a single IRS or multiple IRSs to each BS-user pair. In particular, the proposed algorithm consists of two stages, where in the first stage we perform k-means clustering to group the IRSs according to their location. In the next stage, for each group, we select the best IRS-assisted path (based on received signal strength) by transforming the original network into a trellis graph and using a trellis-search method. Simulation results show that the proposed technique outperforms existing IRS selection techniques in multiple IRS-enabled multi-hop communication systems.

IEEE Internet of Things Journal , 2021

Efficient resource allocation can maximize power efficiency, which is an important performance me... more Efficient resource allocation can maximize power efficiency, which is an important performance metric in future fifth generation (5G) communications. Minimization of sum uplink power in order to enable green communications while concurrently fulfilling the strict demands of ultra-reliability for short packets is an essential and central challenge that needs to be addressed in the design of 5G, and subsequent wireless communication systems. To address this challenge, this paper analyzes the joint optimization of various unmanned aerial vehicle (UAV) systems parameters including the UAV's position, height, beamwidth, and the resource allocation for uplink communications between ground IoT devices and a UAV employing short ultra-reliable and low-latency (URLLC) data packets. Towards achieving the aforesaid task, we proposed a perturbation-based iterative optimization to minimize the sum uplink power in order to determine the optimal position for the UAV, its height, beamwidth of its antenna, and the blocklength allocated for each IoT device. It is shown that the proposed algorithm has lower time complexity, yields a better performance than other benchmark algorithms, and achieves similar performance to exhaustive search. Moreover, the results also demonstrate that Shannon's formula is not an optimum choice for modeling sum power for short packets as it can significantly underestimate the sum power, where our calculations show that there is an average difference of 47.51% for the given parameters between our proposed approach and Shannon's formula. Lastly, our results confirm that the proposed algorithm allows ultra-high reliability for all the users, and converges rapidly.

IEEE Transactions on Industrial Informatics, 2022

In the upcoming sixth-generation (6G) networks, ultra-reliable and low-latency communication (URL... more In the upcoming sixth-generation (6G) networks, ultra-reliable and low-latency communication (URLLC) is considered as an essential service that will empower real-time wireless systems, smart grids, and industrial applications. In this context, URLLC traffic relies on short blocklength packets to reduce the latency, which poses a daunting challenge for network operators and system designers since classical communication systems are designed based on the classical Shannon's capacity formula. Therefore, to tackle this challenge, this paper considers an unmanned aerial vehicle (UAV) acting as a decode-andforward (DF) relay to communicate short URLLC control packets between a controller and multiple-mobile robots in a cell to enable a use-case of Agriculture 4.0. Moreover, this paper employs perturbation theory and studies the quasi-optimization of the UAV's location, height, beamwidth, and resource allocation, including time-varying power and blocklength for the two phases of transmission from the controller to UAV and from UAV to robots. In this regard, we propose an iterative optimization method to find the optimal UAV's height and location, the antenna beamwidth, and the variable power and blocklength allocated to each robot inside the circular cell to minimize the average overall decoding error. It is demonstrated that the proposed algorithm outperforms other benchmark algorithms based on fixed parameters and performs nearly as well as the smart exhaustive search. Lastly, our results emphasize the need to jointly optimize all of the above-mentioned UAV's system parameters and resource allocation for the two phases of transmission to achieve URLLC for multiple-mobile robots.

IEEE Transactions on Vehicular Technology, 2022

Ultra-reliable and low-latency (URLLC) traffic in the upcoming sixth-generation (6G) systems util... more Ultra-reliable and low-latency (URLLC) traffic in the upcoming sixth-generation (6G) systems utilize short packets, signalling a distancing from traditional communication systems devised only to support long data packets based on Shannon's capacity formula. This poses a formidable challenge for system designers and network operators. Many URLLC scenarios involve infrastructure-less unmanned aerial vehicle (UAV)-assisted communications. One of the biggest challenges with UAVs is their limited battery capacity, which can cause abrupt disruption of UAV-assisted communications. To overcome these limitations, we consider URLLC-enabled over-the-air charging of UAV relay system using a laser transmitter. Furthermore, we formulate a non-convex optimization problem to minimize the total decoding error rate subject to optimal resource allocation, including blocklength allocation, power control, trajectory planning, and energy harvesting to facilitate URLLC in such systems. In this regard, given its lower complexity, a novel perturbation-based iterative method is proposed to solve the optimization problem. The proposed method yields optimal blocklength allocation and power control for the two transmission phases, i.e., from the source node to the UAV and from the UAV to the robot acting as a ground station. It also maps the UAV trajectory from the initial position to the final position, and the UAV completes the flight using the laser's harvested energy. It is shown that the proposed algorithm and fixed baseline scheme, named fixed blocklength (FB), yield a similar performance as the exhaustive search in terms of UAV energy consumption. In contrast, fixed trajectory (FT) delivers the worst performance. Simultaneously, the proposed method yields the best performance in terms of the lowest average overall decoding error compared to fixed baseline schemes, including FB and FT, showing the efficacy of the proposed technique.

IEEE Wireless Communications Letters, 2020

Achieving ultra-high reliability for short packets is a core challenge for future wireless commun... more Achieving ultra-high reliability for short packets is a core challenge for future wireless communication systems, as current systems are designed only to transmit long packets based on classical information-theoretic principles. To tackle this challenge, this letter relies on multi-hop unmanned aerial vehicle (UAV) relay links to deliver short ultra-reliable and low-latency (URLLC) instruction packets between ground Internet of Things (IoT) devices. To accomplish this task, we perform non-linear optimization to minimize the overall decoding error probability in order to find the optimal values of the distance and the blocklength. In this vein, a novel, semi-empirical based non-iterative algorithm is proposed to solve the quasi-optimization problem. The algorithm executes in quasilinear time and converges to a globally optimal/sub-optimal solution based on the chosen parameters. Simulation results demonstrate that our algorithm allows operation under the ultra-reliable regime (URR), and yields the same performance as exhaustive search algorithms.

IEEE Transactions on Reliability , 2024

The futuristic sixth-generation (6G) networks will empower ultra-reliable and low latency communi... more The futuristic sixth-generation (6G) networks will empower ultra-reliable and low latency communications (URLLC), enabling a wide array of mission-critical applications such as mobile edge computing (MEC) systems, which are largely unsupported by fixed communication infrastructure. To remedy this issue, unmanned aerial vehicle (UAV) has recently come to the limelight to facilitate MEC for internet of things (IoT) devices as they provide desirable line-of-sight (LoS) communications compared to fixed terrestrial networks, thanks to their added flexibility and three-dimensional (3D) positioning. In this paper, we consider UAV-enabled relaying for MEC systems for uplink transmissions in 6G networks, and we aim to optimize mission completion time subject to the constraints of resource allocation, including UAV transmit power, UAV CPU frequency, decoding error rate, blocklength, communication bandwidth, and task partitioning as well as 3D UAV positioning. Moreover, to solve the non-convex optimization problem, we propose three different algorithms, including successive convex approximations (SCA), altered genetic algorithm (AGA) and smart exhaustive search (SES). Thereafter, based on time-complexity, execution time, and convergence analysis, we select AGA to solve the given optimization problem. Simulation results demonstrate that the proposed algorithm can successfully minimize the mission completion time, perform power allocation at the UAV side to mitigate information leakage and eavesdropping as well as map a 3D UAV positioning, yielding better results compared to the fixed benchmark sub-methods. Lastly, subject to 3D UAV positioning, AGA can also effectively reduce the decoding error rate for supporting URLLC services.