Showing papers by "Mohammad Patwary published in 2021"

Non-Coherent and Backscatter Communications: Enabling Ultra-Massive Connectivity in 6G Wireless Networks

Abstract: With the commencement of the 5th generation (5G) of wireless networks, researchers around the globe have started paying their attention to the imminent challenges that may emerge in the beyond 5G (B5G) era. Various revolutionary technologies and innovative services are offered in 5G networks, which, along with many principal advantages, are anticipated to bring a boom in the number of connected wireless devices and the types of use-cases that may cause the scarcity of network resources. These challenges partly emerged with the advent of massive machine-type communications (mMTC) services, require extensive research innovations to sustain the evolution towards enhanced-mMTC (e-mMTC) with the scalable network cost in 6th generation (6G) wireless networks. Towards delivering the anticipated massive connectivity requirements with optimal energy and spectral efficiency besides low hardware cost, this paper presents an enabling framework for 6G networks, which utilizes two emerging technologies, namely, non-coherent communications and backscatter communications (BsC). Recognizing the coherence between these technologies for their joint potential of delivering e-mMTC services in the B5G era, a comprehensive review of their state-of-the-art is conducted. The joint scope of non-coherent and BsC with other emerging 6G technologies is also identified, where the reviewed technologies include unmanned aerial vehicles (UAVs)-assisted communications, visible light communications (VLC), quantum-assisted communications, reconfigurable large intelligent surfaces (RLIS), non-orthogonal multiple access (NOMA), and machine learning (ML)-aided intelligent networks. Subsequently, the scope of these enabling technologies for different device types (e.g., UAVs, body implants, etc), service types (e.g., e-mMTC), and optimization parameters (e.g., spectrum, energy, cost) is analyzed. Finally, in the context of the proposed non-coherent and BsCs based framework for e-mMTCs, some promising future research directions and open research challenges are highlighted.

Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks

Abstract: Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN.

Next-Generation Consumer Electronics for 6G Wireless Era

Abstract: The upcoming beyond 5G (B5G)/6G wireless networks target various innovative technologies, services, and interfaces such as edge computing, ultra-reliable and low-latency communication (URLLC), backscatter communications, and TeraHertz (THz) technology-enabled inter-chip high-capacity communications, to name a few. Although there are ongoing advances in the system/network level, it is crucial to advance the device-level design to efficiently support these novel technologies by addressing various practical constraints in terms of power, computational capacity, and storage capacity limitations. This need for device-level innovation ultimately demands significant enhancements in today’s consumer electronics (CE) framework, i.e., CE advancement towards “Consumer Electronics 2.0” . Considering the contemporary latency requirements of future CE applications (e.g., entertainment, gaming, etc), to enhance the commercial potential of “edge processing as a service”, it is envisioned that URLLC will further evolve as enhanced-URLLC (e-URLLC) in the B5G era. In this regard, this paper proposes a novel edge computing-enabled e-URLLC framework, named edge computing for CE (ECCE), to support advancements and to initiate discussions on the need for the next-generation CE. Starting with the discussion on recent trends and advances in CE, the proposed framework and its importance in the 6G wireless era are described. Subsequently, several potential technologies and tools to enable the implementation of the proposed ECCE framework are identified along with some interesting open research topics and future recommendations.

Clustering-based Redundancy Minimization for Edge Computing in Future Core Networks

Abstract: The increasing demand for new and heterogeneous services generates redundant signalling, leading to communication overheads and congestion in the network’s core. We propose a novel AI-enabled edge architecture to minimize signalling redundancy. We deploy a cluster-based signalling and admission control framework to maximize the efficiency of link (or bandwidth resources) between the edge and core networks. We minimize redundant signalling using two classical unsupervised machine learning algorithms (K-mean and Ranking-based clustering). Our results show that the proposed framework provides substantial latency reduction while maximizing resource utilization. The proposed approach is 35% superior in reducing redundant signalling compared to recent work.

Efficient Small Angle-of-Arrival Array Sensor for Intelligent Localisation and Tracking Systems

Abstract: In dynamic environments, it is expected both vehicular transmitter and receiver are moving and therefore the Angle of Arrival (AOA) information is changed during communication links. Thus, a continuous measurement for data obtaining should be made to predict the new direction and location of the targeted person accurately. In this regard, a fast AOA method that can use a single snapshot instead of multiple snapshots without sacrificing estimation accuracy is an urgent demand for direction estimation information. To this end, an efficient direction-finding method called, Inverse of Subtracting (IOS), which exploits a single eigenvector that associates with the maximum eigenvalue is proposed to overcome the intensive computations of the conventional AOA estimation techniques. An Orthogonal Frequency Division Multiplexing (OFDM) based on a wideband spectrum is adopted here as a modulation technique and then integrated with the IOS method to exploit the available bandwidth efficiently and to resist frequency selective fading and multipath effects. To cover 360 degrees during the tracking process, a compact-size helical sensor with an Omni-directional radiation pattern is selected and then a ring array is modelled based on five helical sensors. The sensor array is then combined with the OFDM-IOS scheme to determine the direction of the vehicle movement based on the most dominant received sub-carrier. The proposed system is applied to track and detect the location of a hidden object within an urban area and the results demonstrate the efficiency and superiority of the proposed AOA sensor array with less computational complexity.

TRACTS-Net: An Intelligent Road Damage Detection System using 5G Integrated Team-forming Network

Abstract: In the era of fifth generation of cellular communication (5G), connected vehicles are expected to play a crucial role in transportation and road safety. Every year, road accidents cause numerous injuries and deaths all over the world. One of the various reasons for these accidents is the damaged roads. However, recent technological advancements have provided us with the opportunity to overcome these challenges and mitigate the number of accidents drastically. Thus, in this manuscript, we developed a cost-effective IoT device to capture information of potholes on the roads and alert the authority through gateways with the aid of our proposed architecture which integrates 5G networks. Experimental investigations have been carried out to test the performance of our model and our findings demonstrate that the proposed device performs significantly well in the testbed with an accuracy of little less than cent percent in team-forming network.