Showing papers by "Mohammad Patwary published in 2020"

A New CPW-Fed Diversity Antenna for MIMO 5G Smartphones

Abstract: In this study, a new coplanar waveguide (CPW)-fed diversity antenna design is introduced for multiple-input–multiple-output (MIMO) smartphone applications. The diversity antenna is composed of a double-fed CPW-fed antenna with a pair of modified T-ring radiators. The antenna is designed to cover the frequency spectrum of commercial sub-6 GHz 5G communication (3.4–3.8 and 3.8–4.2 GHz). It also provides high isolation, better than −16 dB, without an additional decoupling structure. It offers good potential to be deployed in future smartphones. Therefore, the characteristics and performance of an 8-port 5G smartphone antenna were investigated using four pairs of the proposed diversity antennas. Due to the compact size and also the placement of the elements, the presented CPW-fed smartphone antenna array design occupies a very small part of the smartphone board. Its operation band spans from 3.4 to 4.4 GHz. The simulated results agree well with measured results, and the performance of the smartphone antenna design in the presence of a user is given in this paper as well. The proposed MIMO design provides not only sufficient radiation coverage supporting different sides of the mainboard but also polarization diversity.

An Improved Switch Migration Decision Algorithm for SDN Load Balancing

Abstract: Dynamic and Adaptive Load Balancing (DALB) and Controller Adaption and Migration Decision (CAMD) frameworks are the recently developed efficient controller selection frameworks that solved the challenge of load-imbalance in Software-Defined Networking (SDN). While CAMD framework was established to be efficient over DALB framework yet it was not efficient when the incoming-traffic load was elephant flow, hence, leading to a significant reduction in the overall system performance. This study had proposed an Improved Switch Migration Decision Algorithm (ISMDA) that solved the network challenge when the incoming load is elephant flow. The balancing module of the switch migration framework, which runs on each controller, is initiated during the controller load imbalance phase. The improved framework used the controller variance and controller average load status to determine the set of underloaded controllers in the network. The constructed efficient migration model was used to, simultaneously, identify both the migration cost and load-balancing variation for the optimal selection of controller among the set of underloaded controllers. The controller throughput, response time, number of migration space and packet loss were used as the performance comparison metrics. The average controller throughput of ISMDA increased with 7.4% over CAMD framework while average response time of the proposed algorithm improved over CAMD framework with 5.7%. Similarly, the proposed framework had 5.6% average improved migration space over CAMD framework and the packet-loss of ISMDA had average 6.4% performance over the CAMD framework. It was concluded that ISMDA was efficient over CAMD framework when the incoming traffic load is elephant flow.

Spatial Modeling of Interference in Inter-Vehicular Communications for 3-D Volumetric Wireless Networks

Abstract: Unmanned aerial vehicle (UAV) assisted cell-free communications hold promise for enhancing the coverage and capacity of heterogeneous cellular networks. However, the network interference in such scenarios must be accurately modeled for efficient system design. The spatial characteristics of the desired and interfering signals can be jointly modeled by considering the characteristics of the signal-to-interference ratio (SIR). This work proposes a generalized framework for modeling the spatial statistics of the SIR encountered in 3-D volumetric inter-vehicular communication channels. Though the novel paradigm of UAV-assisted cell-free vehicular communications is analyzed in particular, the proposed framework is more general in that it incorporates 3-D mobility at both link ends. Also, this framework is shown to include as its special cases, several notable 2-D propagation models of network interference including those for terrestrial vehicle-to-vehicle and fixed-to-vehicle scenarios. Analytical expressions are derived for the SIR level-crossing-rate (LCR), average-fade-duration (AFD), spatial auto-covariance (SAC), and coherence distance (CD). Both single- and multi-cluster scattering environments are analyzed and the impact of channel parameters such as the direction and velocity of mobile nodes as well as the altitudes of the UAV and scattering cluster(s) on the SIR fading statistics is investigated. Finally, some future extensions of this work are also discussed such as the integration of intelligent reflective surfaces in the propagation scenario to generate favorable channel conditions.

Towards the Mobility Issues of 5G-NOMA Through User Dissociation and Re-association Control

Abstract: Non-Orthogonal Multiple Access (NOMA) system is considered as core enabler for the Fifth-Generation (5G) of the wireless cellular networks, contributing to the improvement of spectral efficiency. NOMA groups users into clusters, based on the maximum channel gain-difference. However, user mobility continuously changes the channel gain and requires re-clustering, depending on the percentage of mobile users and the environment in which they operate. In this paper, we propose a set of re-clustering methods: arbitrary, one-by-one and simultaneous, that expedite link re-establishment and keep the clusters interference-free, taking into account the mobility of users. The methods are applied to dissociate identified users within clusters, when the gain-difference is lower than a given threshold, followed by re-association procedure, which integrates users into different clusters, maintaining an appropriate gain-difference. The proposed methods are based on mathematical formulation and algorithms and address many technical and computational challenges associated with the clustering techniques. Numerical and experimental investigation has been carried out to test their performance and the results show that the simultaneous method can provide lower number of clusters, making it more suitable in dense and highly mobile scenarios. Our findings also demonstrate that this method has the potential to minimize the number of reclustering, improving resource utilization and lowering signaling loads.

Layer division multiplexing for 5G DL transmission within ultra-dense heterogeneous networks

Abstract: To date, the main focus of the wireless network has been considered human-centric end-use. Fifth-generation (5G) wireless technology is now evolving towards the era, where the focus is on large scale machine-type communication. 5G & beyond wireless networks are focused on enabling their new forms of usage of wireless connectivity, which are expected to deliver a large number of new applications & services into reality. This will result in a massive increment in terms of the number of users and heterogeneous service requirements. While providing such new applications and services, it is expected to ensure optimal utilization of resources. The challenges to be addressed to attain optimal resource utilization includes meeting heterogeneous capacity requirement to serve a massive number of devices with limited spectrum. The work presented in this paper is focused on exploring the possibility of layer division multiplexing (LDM) as a potential tool to resolve these challenges. While analysing the feasibility of LDM for 5G downlink transmission framework, we have considered unicast & multicast transmission scenarios. This paper presents an analytical framework to define the performance bounds of LDM in the context of 5G downlink transmission. The robustness of our analytical model has been validated with simulation results of LDM. We also have considered specific urban & rural use-case scenarios for 5G with LDM downlink transmission. Our analytical results suggest LDM core layer transmission is one of the strongest candidates for massive machine-type communication (mMTC) within 5G networks to attain enhanced coverage range compared to its existing counterpart.

Uncertainty-aware RAN Slicing via Machine Learning Predictions in Next-Generation Networks

Abstract: Network slicing enables 5G network operators to offer diverse services in the form of end-to-end isolated slices, over shared physical infrastructure. Wireless service providers are facing the need to plan and rapidly evolve their slices configuration to meet the varied tenants’ demand. Network slicing unfolds a new market dimension to the infrastructure providers as well as to the tenants, who may acquire a network slice from the infrastructure provider to deliver a specific service to their respective subscribers. In this new context, there is a growing need for new network resource allocation algorithms to capture such proposition. This paper addresses this problem by introducing a family of online algorithms with the aim to (i) minimize tenants spectrum allocation costs, (ii) maximize radio resource utilization and (iii) ensure that the service level agreements (SLAs) provided to tenants are satisfied. We focus on improving the performance of prediction-based decisions that are made by a tenant when prediction models lack the desired accuracy. Our evaluations show that the proposed probabilistic approach can automatically adapt to prediction error variance, while largely improving network slice acquisition cost and resource utilization.

End-use Aware Optimized Control Signaling for User Admission within 5G and Beyond Networks.

Abstract: The exponential growth of wireless network technologies (e.g., 5th generation (5G) and beyond wireless communication) demand to provide a reliable backhaul to accommodate the ever-increasing use-cases, with better QoE beyond the current networks. The increasing demand for services from these use-cases is starting to drive new control signaling traffic, primarily due to a rapid increase in the number of devices, both individuals and machines. This constant connectivity demand tends to occupy significant Erlang capacity of the network. In this paper, we have proposed a dynamically reconfigurable cluster-based end-use aware control signaling optimization scheme to accommodate more user-specific data traffic. In this approach, it is proposed to exploit pre-clustering end-use analysis, usage specific clustering, and clustering based on end-use application and device-specific resource demand. In doing so, a dynamically reconfigurable QoE based slice performance bounds are considered for the user admission to the network. For the performance evaluation for the proposed signaling optimization and user admission framework, a set of comparative results have been attained and compared with the existing work. The achieved results suggest that the proposed signaling optimization and admission control scheme is superior in performance compared to the existing results. This is due to the attainment of reduced control signaling and a softer approach in user admission with the proposed framework.

Towards an Integrated In-Vehicle Isolation and Resilience Framework for Connected Autonomous Vehicles

Abstract: Connected Autonomous Vehicles (CAV) have attracted significant attention, specifically due to successful deployment of ultra-reliable low-latency communications with Fifth Generation (5G) wireless networks. Due to the safety-critical nature of CAV, reliability is one of the well-investigated areas of research. Security of in-vehicle communications is mandatory to achieve this goal. Unfortunately, existing research so far focused on in-vehicle isolation or resilience independently. This short paper presents the elements of an integrated in-vehicle isolation and resilience framework to attain a higher degree of reliability for CAV systems. The proposed framework architecture leverages benefits of Trusted Execution Environments to mitigate several classes of threats. The framework implementation is also mapped to the AUTOSAR open automotive standard.