Department Publications

Semi-grant-free non-orthogonal multiple access (SGF-NOMA) schemes group one grant-based (GB) user with multiple grant-free (GF) users into one time/frequency resource block (RB) to enhance spectral efficiency. Due to the sporadic traffic of GF users and the stringent quality of service (QoS) requirement of the GB user, the access collision problem becomes severe in SGF-NOMA. To solve this problem, this paper firstly designs an RB-based power pool (PP), which directs GF users to adjust their transmit power without disrupting the ongoing transmission of the internal GB user. After that, this work proposes an efficient multi-agent deep reinforcement learning (MA-DRL) framework to jointly optimize the PP and access strategy for maximizing the network throughput. In particular, this work exploits the fast-response feature of the traditional competitive MA-DRL and the increased-performance feature of the …

This paper characterizes the communication–sensing rate tradeoff of an active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication system operating under residual environmental clutter. The BS beamformer and active-RIS coefficients are jointly designed while accounting for amplifier noise on the forward and return sensing paths, a global RIS power budget, and perelement amplification limits. Residual clutter introduces a beamformer-dependent term in the sensing denominator, leading to a generally indefinite, nonconvex sensing-QoS constraint in the communication-centric (C-C) design and a fractional quadratic beamformer objective in the sensing-centric (S-C) design. We develop alternating optimization procedures for both formulations based on established MM, Kronecker-vectorization, and Dinkelbach techniques. Their QoS-threshold sweeps are refined and filtered to …

This paper investigates sum-rate maximization in a multi-user downlink system assisted by a movable RIS (MRIS) under dynamic blockage conditions. Unlike conventional fixed RIS (FRIS) deployments, the RIS is modeled as a rigid panel whose anchor position can be continuously adjusted within a predefined region, introducing an additional spatial degree of freedom. In practical environments, temporary blockages arise from moving objects or environmental changes, significantly degrading communication links. To capture this effect, a geometric blockage model is incorporated into the RIS-to-user channels, where propagation paths are selectively obstructed based on line-of-sight conditions. The base station-to-RIS channel is modeled using a field-response-based multipath formulation, while RIS-to-user links follow distance-dependent path loss with phase propagation. For a fixed position, a common RIS …

Low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) are important enablers of energy-efficient wideband uplinks, but they introduce nonlinear distortions whose impact depends on the operating point and, in multi-user systems, on the time- and frequency-resource split. This paper studies quantization-aware uplink resource allocation for multi-user orthogonal multiple access (OMA), focusing on frequency division multiple access (FDMA) and time division multiple access (TDMA) with finite-resolution quantization at both the user transmitters and the base station receiver. Using Bussgang-style decompositions for power-preserving DAC quantization and mid-point ADC quantization, we derive a closed-form, conservative lower bound on the per-user achievable rate in terms of the allocation variables, channel gains, noise level, and quantization distortion factors. Under this …

Unmanned aerial vehicles (UAVs)-assisted communication systems are considered as a promising technology in diverse verticals. This paper studies the deployment of UAVs in wireless sensor network (WSN) systems. Considering the energy-constrained nature of the wireless sensors, we have proposed a multi-UAV deployment algorithm that minimizes the maximum power transmitted among the sensor nodes (SN) for given minimum data collection rate, minimum data-transferring rate, maximum power and height constraints. The problem is divided into three subproblems in order to reduce the complexity involved in solving them as a single problem. The subproblems are UAV-SN association, 2D positioning of the UAVs and the altitude optimization of the UAVs. Each subproblem is optimized by fixing other parameters as constant. First, the UAV-SN association is addressed using a customized Gale–Shapley …

It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI.

The emergence of 5G technology has transformed network architecture, with network slicing technology standing out as a crucial feature for managing diverse and demanding applications. This innovative technology allows the creation of multiple virtual networks within a single physical infrastructure, each dedicated to specific needs such as latency, bandwidth, and reliability. These capabilities are vital for supporting services like enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine-type communications. Predicting the optimal configuration of network slices is challenging due to the complex and dynamic nature of 5G environments. While traditional methods often lack the necessary accuracy, machine learning models have shown promise in addressing this challenge. However, the adoption of these models introduces the challenge of interpretability, as understanding how …

The rapid evolution of network technologies and frequent network intrusions have amplified the demand for intrusion detection systems (IDSs) powered by artificial intelligence (AI). However, the performance of IDSs is limited by labeled intrusion data and class imbalances in existing datasets. Collecting such data through live network simulations is costly, insecure, and poses regulatory challenges. This paper presents a multi-model framework that uses a conditional tabular generative adversarial network (CTGAN) to synthesize realistic network intrusion data. The framework addresses challenges such as unstable training, mode collapse, and handling mixed data types, which are common in most generative AI models when applied to non-Gaussian tabular data generation while validating the synthesized data through qualitative and quantitative analyses. Overlaid density histograms and cumulative distribution …

The demand for spectrum access continues to grow exponentially and dynamic spectrum access (DSA) is a candidate to alleviate issues with spectrum scarcity by adapting spectrum resources in real-time. Despite the benefits of intelligent DSA protocols, many algorithms are complex, and thus prohibitively difficult to implement in a real-time system. This paper proposes a contention-based access method named Guided-Aloha (or G-Aloha), that stems from the simple and well-known slotted-Aloha (S-Aloha) access method, for DSA in the context of primary users (PU) and secondary users (SU) of the network. The G-Aloha method is proposed for SU access to provide extended protection to the PU compared to the S-Aloha method by being guided by the knowledge from spectrum prediction (or spectrum forecasting), considering any potential regulatory requirements for protecting the PU. Using Markov modeling, an …

The energy consumption of telecommunication networks has been rising steadily due to the expansion of networks, power-hungry equipment and ever-increasing consumer demand. This contributes significantly to the world’s carbon footprint and greenhouse emissions. In particular, radio access networks (RANs) have been identified to have a larger contribution to the energy consumption of telecommunication networks, and a thorough evaluation is required to understand the avenues for energy reduction. In light of that, this study discusses various strategies to lower RAN power usage to minimize telecommunication’s environmental effect. Furthermore, a two-pronged strategy that combines dynamic operation management of base stations (BSs) and renewable energy integration is introduced to reduce the environmental impacts of RANs. With this hybrid strategy energy consumption and carbon footprint may be …

Virtual cycling is an engaging way to promote health and fitness while addressing the safety challenges of outdoor cycling. This paper introduces a Competitive Virtual Cycling (CVC) system that enhances user experience through immersive visuals, real-time competition, and low-latency networking. The system integrates virtual reality (VR) headsets, smart bicycle trainers, and real-world 360 ° videos to create a realistic gaming environment. A dynamic resistance feedback mechanism, driven by real-time sensor data from smart trainers and actuators, adjusts pedal resistance based on terrain variations, ensuring an authentic riding experience. To enhance engagement, we propose a novel avatar placement algorithm that optimizes competitor visibility in immersive, interactive races. Performance evaluations show that the system achieves a mean latency of 60 ms on a 4G network and 30 ms on a private 5G network …

The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for distributed ML, as it protects privacy by design. However, many practical challenges exist before FL can be fully utilized as a key technology for these future networks. We consider the vision of a 6G layered architecture to evaluate the applicability of FL-based distributed intelligence. In this paper, we highlight the benefits of using FL for 6G and the main challenges and issues involved. We also discuss the existing solutions and the possible future directions that should be taken toward more robust and trustworthy FL …

In this paper, we design a resource block (RB) oriented power pool (PP) for semi-grant-free non-orthogonal multiple access (SGF-NOMA) in the presence of residual errors resulting from imperfect successive interference cancellation (SIC). In the proposed method, the BS allocates one orthogonal RB to each grant-based (GB) user, and determines the acceptable received power from grant-free (GF) users and calculates a threshold against this RB for broadcasting. Each GF user as an agent, tries to find the optimal transmit power and RB without affecting the quality-of-service (QoS) and ongoing transmission of the GB user. To this end, we formulate the transmit power and RB allocation problem as a stochastic Markov game to design the desired PPs and maximize the long-term system throughput. The problem is then solved using multi-agent (MA) deep reinforcement learning algorithms, such as double deep Q …

Cycling is a popular leisure and fitness activity, making it a prime candidate for emerging virtual and augmented reality applications. This paper details the design, development, and testing of a competitive virtual cycling platform that allows two players to compete regardless of their geographical location on customized stationary bicycles. They wear head-mounted Virtual Reality (VR) devices to immerse themselves in a 360° video experience. Video playback responds to their cycling speed and head orientation. Competitors' locations are displayed on a map within their VR dashboard. The platform's functionality is tested on 4G and 5G networks and an experimental 4G network implemented on software-defined radios. To evaluate the system's usability, the critical metric of end-to-end delay is measured for the different communication architectures. Delays of under 100ms, crucial for preventing motion sickness, are …

We present SimuSIL, a software-in-the-loop (SIL) simulation platform using open-source tools to capture the behavior of connected vehicles in intelligent transportation system (ITS) applications. Connected cooperative vehicles use vehicle-to-everything (V2X) communication to enable ITS. Real-world testing of ITS applications involves significant risks. Simulating true vehicle dynamics and real-time behavior is challenging in discrete time step environments. We extend the Veins platform, integrating SUMO and OMNet++, with Webots, a simulator for robots and vehicles. Webots implements engine control unit (ECU) models in software to mimic physical vehicle behavior. We validate SimuSIL with a car-following application using Webots and demonstrate its ability to capture communication anomalies like packet losses. Additionally, we demonstrate fuel consumption and CO 2 emissions in autonomous eco-driving …

Metaverse is the latest technological hype in the modern world due to its potential for revolutionizing the digital visual perspective. With the COVID-19 pandemic, most industries have moved towards digitization, and the metaverse is identified as one of the most promising platforms for such a transition, as it provides a three-dimensional (3D) immersive experience for the users. Currently, most digital service providers and organizations are actively working on metaverse- based applications. In addition, there has been a rapid increase on research work involving metaverse realization. Launching a large scale metaverse in the real world is a challenging task. However, fifth-generation (5G) and beyond 5G (B5G) technologies are envisioned to improve the feasibility of pragmatic deployments. Although, there are several conceptual designs available, actual adaptations of the concepts are still limited. This survey …

Dynamic spectrum access is recognized as a potential solution to the problem of spectrum scarcity. Consequently, a significant amount of research has been conducted on spectrum allocation. In this study, we present a multi-traffic class queue-based spectrum allocation solution. The proposed solution is designed for a scenario where communication and other systems do not necessarily require agility in the frequency bands they operate in. The spectrum sensing and forecasting processes are carried out over relatively long time periods compared to the actual transmission rates. The study considers two traffic classes: priority and non-priority. Three packet handling strategies are presented and their average queuing delay performances are compared. While all three strategies yield satisfactory results under low/medium traffic conditions, Strategy 3 exhibits the greatest flexibility in terms of delay performance …

This paper addresses the problem of deploying unmanned aerial vehicles (UAVs) to assist data collection and transmission in wireless sensor networks (WSNs). A multi-UAV deployment algorithm is proposed for an energy-constrained network to minimize the maximum transmit power of the sensor node (SN), while satisfying data rate and transmit power constraints. The problem is divided into two subproblems, namely, UAV-SN association, and UAV positioning. First, the UAV-SN association is addressed using a customized Gale-Shapley algorithm. Then, the 2D positions of the UAVs are decided using a modified pattern search algorithm. The two subproblems are solved alternatively in an iterative manner to obtain a near-optimal solution. Compared to baseline algorithms, the proposed solution results in at least 4-fold and 4.5-fold reductions in the number of iterations required for convergence, and the maximum …

Network slicing enables the creation of multiple logical independent networks on physical networking infrastructure. Network slice deployment in a Fifth Generation (5G) mobile network can be classified as vertical and horizontal slicing. The paper compares the performance of the two slicing methods through solving two convex optimization problems, considering several smart hospital scenarios that differ from each other based on their medical speciality. The results are used to draw insights on the most appropriate slicing approach for each setup.

Unmanned aerial vehicles (UAVs) commonly referred to as drones are becoming the attractive solution that provides wireless connectivity in a situation wherever the standard terrestrial base station is burden with high data traffic or unreachable in a disaster scenario. Our work examines the problem of best deployment of the UAVs as an aerial base station to empower wireless coverage to the users in the coverage less area. The foremost contributions of this study incorporate couple of strategies to deploy the ABSs and associate the UEs to the best ABS, where the total spectral efficiency of the network is maximized. Approaches will be chosen considering the prevailing environmental condition and system constraints. The first strategy uses an unsupervised clustering algorithm to determine the 2D positioning of the UAV ((x, y) coordinates) and a stable marriage approach is used for UE assignment by …

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

A variety of applications and forwarding protocols have been proposed for opportunistic networks (OppNets) in the literature. However, the methodology of evaluation, testing and comparing these forwarding protocols are not standardized yet, which leads to large levels of ambiguity in performance evaluation studies. Performance results depend largely on the evaluation environment, and on the used parameters and models. More comparability in evaluation scenarios and methodologies would largely improve also the availability of protocols and the repeatability of studies, and thus would accelerate the development of this research topic. In this survey paper, we focus our attention on how various OppNets data forwarding protocols are evaluated rather than what they actually achieve. We explore the models, parameters and the evaluation environments and make observations about their scalability, realism and comparability. Finally, we deduce some best practices on how to achieve the largest impact of future evaluation studies of OppNets data dissemination/forwarding protocols.

Due to the increasing heterogeneity in network user requirements, dynamically varying day to day network traffic patterns and delay in-network service deployment, there is a huge demand for scalability and flexibility in modern networking infrastructure, which in return has paved way for the introduction of Software Defined Networking (SDN) in core networks. In this paper, we present an FPGA-based switch that is fully compliant with OpenFlow; the pioneering protocol for southbound interface of SDN. The switch architecture is completely implemented on hardware. The design consists of an OpenFlow Southbound agent which can process OpenFlow packets at a rate of 10Gbps. The proposed architecture speed scales up to 400Gbps while it consumes only 60% of resources on a Xilinx Virtex-7 featuring XC7VX485T FPGA.

In this letter an intuitive measurement based approximation for packet switched network traffic in backbone links is presented. It consists of a sum of independent non-identical Bernoulli sources. The resulting sum has a Poisson binomial distribution which can be approximated to a skewed Gaussian distribution at the asymptote. It can be further approximated to a binomial distribution for predicting congestion by estimating the number of users of the network. The results are verified using samples of traffic data from 42 academic and research institutions.

This paper proposes and analyzes a distance-based duplex mode switching scheme for maximizing the average throughput of a cellular network and its underlay device-to-device (D2D) network. For each duplex mode of the cellular network, a node distance based duplex mode selection scheme is proposed to maximize the throughput of the D2D network. The proposed duplex mode selection scheme provides average throughput improvements in the range of 10%-200% for the D2D network compared to the always half-duplex and always full-duplex communications for high D2D user scatter variances.