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While targeting the energy-efficient connectivity of the Internet-of-things (IoT) devices in the sixth-generation (6G) networks, in this paper, we explore the integration of non-orthogonal multiple access-based backscatter communication (BAC-NOMA) and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). To this end, first, for the performance evaluation of the STAR-RIS-assisted BAC-NOMA system, we derive the statistical distribution of the channels under Nakagami- $m$ fading. Second, by leveraging the derived statistical channel distributions, we present the effective capacity analysis under the delay quality-of-service (QoS) constraint. In particular, we derive the closed-form expressions for the effective capacity of the reflecting and transmitting backscatter nodes (BSNs) under the energy-splitting protocol of STAR-RIS. To obtain more insight into the performance of the considered system, we provide the asymptotic analysis, and derive the upper bound on the effective capacity, which represents the ergodic capacity. Our simulation results validate the analytical analysis, and reveal the effectiveness of the STAR-RIS-assisted BAC-NOMA system over the conventional RIS (C-RIS)- and orthogonal multiple access (OMA)-based counterparts. Finally, to highlight the trade-off between the effective capacity and energy consumption, we analyze the link-layer energy efficiency. Overall, this paper provides useful guidelines for the performance analysis and design of the STAR-RIS-assisted BAC-NOMA systems.

Like robust connectivity, precise positioning is evolving into an innovative component of 5G service offerings for industrial use-cases and verticals with challenging indoor radio environments. Therefore, the 3GPP Rel-16 standard has been a tipping point in specifying critical innovations, followed by enhancements in Rel-17 and Rel-18. In this article, we discuss the 5G positioning framework, measurements, and procedures before shifting the focus to recently identified carrier-phase (CP) measurements in Rel-18 as a complementary measure for time- and angular-based positioning methods. We discuss the associated challenges and potential solutions for exploiting CP, including integer ambiguity, multipath sensitivity, and signaling aspects. Furthermore, we study the ways in which phase-continuous reference signaling can counter noisy phase measurements using realistic simulations to achieve centimeter-level accuracy in indoor factory (InF) scenarios.

Across all industries, digitalization and automation are on the rise under the Industry 4.0 vision, and the forest industry is no exception. The forest industry depends on distributed flows of raw materials to the industry through various phases, wherein the typical workflow of timber loading and offloading is finding traction in using automation and 5G wireless networking technologies to enhance efficiency and reduce cost. This article presents one such ongoing effort in Sweden, Remote-Timber—demonstrating a 5G-connected teleoperation use-case within a workflow of timber terminal—and disseminates its business attractiveness as well as first measurement results on network performance. Also, it outlines the future needs of the 5G network design/optimization from teleoperation perspective. Overall, the motivation of this article is to disseminate our early-stage findings and reflections to the industrial and academic communities for furthering the research and development activities in enhancing 5G networks for verticals.

To efficiently accommodate RF energy-harvesting (EH) capable device in a wireless network with prescheduled devices, this letter designs the deep reinforcement learning (DRL)-driven energy-efficient transmission strategy. The transmission strategy handles the EH-device uplink transmissions opportunistically using cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) scheme while maximizing energy efficiency (EE). In this respect, firstly, we formulate the EE optimization problem of the EH-device while considering its RF circuit power consumption. Secondly, we divide the original, non-convex problem into a two-layer optimization problem, and solve it sequentially as i) we theoretically derive the optimal transmit power and time-sharing coefficient parameters from the first layer, and ii) using the derived parameters in the second layer, we solve the one-dimensional continuous space optimization problem through a DRL technique, recognized as a combined experience replay deep deterministic policy gradient (CER-DDPG). Finally, the numerical results show that, under different operational scenarios, the proposed approach outperforms benchmark DDPG and Stochastic algorithms in terms of EE.

Backscatter Communication (BackCom), which is based on passive reflection and modulation of an incident radio-frequency (RF) wave, has emerged as a cutting-edge technological paradigm for self-sustainable Internet-of-things (IoT). Nevertheless, contemporary BackCom systems are limited to short-range and low data rate applications only, rendering them insufficient on their own to support pervasive connectivity among the massive number of IoT devices. Meanwhile, wireless networks are rapidly evolving toward the smart radio paradigm. In this regard, reconfigurable intelligent surfaces (RISs) have come to the forefront to transform the wireless propagation environment into a fully controllable and customizable space in a cost-effective and energy-efficient manner. Targeting the sixth-generation (6G) horizon, we anticipate the integration of RISs into BackCom systems as a new frontier for enabling 6G IoT networks. In this article, for the first time in the open literature, we provide a tutorial overview of RIS-assisted BackCom (RIS-BackCom) systems. Specifically, we introduce the three different variants of RIS-BackCom and identify the potential improvements that can be achieved by incorporating RISs into BackCom systems. In addition, owing to the unrivaled effectiveness of non-orthogonal multiple access (NOMA), we present a case study on a RIS-assisted NOMA-enhanced BackCom system. Finally, we outline the way forward for translating this disruptive concept into real-world applications.

Reconfigurable intelligent surfaces (RISs), with the potential to realize smart radio environments, have emerged as an energy-efficient and a cost-effective technology to support the services and demands foreseen for coming decades. By leveraging a large number of low-cost passive reflecting elements, RISs introduce a phase-shift in the impinging signal to create a favorable propagation channel between the transmitter and the receiver. In this article, we provide a tutorial overview of RISs for sixth-generation (6G) wireless networks. Specifically, we present a comprehensive discussion on performance gains that can be achieved by integrating RISs with emerging communication technologies. We address the practical implementation of RIS-assisted networks and expose the crucial challenges, including the RIS reconfiguration, deployment and size optimization, and channel estimation. Furthermore, we explore the integration of RIS and non-orthogonal multiple access (NOMA) under imperfect channel state information (CSI). Our numerical results illustrate the importance of better channel estimation in RIS-assisted networks and indicate the various factors that impact the size of RIS. Finally, we present promising future research directions for realizing RIS-assisted networks in 6G communication.

The scale of wireless technologies' penetration in our daily lives, primarily triggered by Internet of Things (IoT)-based smart cities, is bea-coning the possibilities of novel localization and tracking techniques. Recently, low-power wide-area network (LPWAN) technologies have emerged as a solution to offer scalable wireless connectivity for smart city applications. LoRa is one such technology, which provides energy efficiency and wide-area coverage. This article explores the use of intelligent machine learning techniques, such as support vector machines, spline models, decision trees, and ensemble learning, for received signal strength indicator (RSSI)-based ranging in LoRa networks on a training dataset collected in two different environments: indoors and outdoors. The suitable ranging model is then used to experimentally evaluate the accuracy of localization and tracking using trilateration in the studied environments. Later, we present the accuracy of a LoRa-based positioning system (LPS) and compare it with the existing ZigBee, WiFi, and Bluetooth-based solutions. In the end, we discuss the challenges of satellite-independent tracking systems and propose future directions to improve accuracy and provide deployment feasibility.

No relevant conflicts of interest to declare.

Introduction:BCR-ABL mutations are the major background players in manifestation of resistance to all FDA approved tyrosine kinase inhibitors (TKIs) including imatinib, dasatinib, nilotinib, Bosutinib and ponatinib 1. Detection of mutations is a vital part of European or North American clinical guidelines at the time of resistance and/or drug switching, because resistance-causing mutations appearing as a result of one drug are sensitive to other in many instances and mutational data can therefore help in prescription of better alternative TKI in case of resistance or unsatisfactory response 1,2. Although BCR-ABL sequencing protocols are reported, they either lack the experimental details or are not cost-effective to be used in third world countries 1’3. Therefore, objective of this study was to develop a cost-effective protocol for BCR-ABL mutation detection in TKI resistant CML.