Mid Sweden University

Through the lens of average and peak age-of-information (AoI), this paper takes a fresh look into the uplink medium access solutions for mission-critical (MC) communication coexisting with enhanced mobile broadband (eMBB) service. Considering the stochastic packet arrivals from an MC user, we study three access schemes: orthogonal multiple access (OMA) with eMBB preemption (puncturing), non-orthogonal multiple access (NOMA), and rate-splitting multiple access (RSMA), the latter two both with concurrent eMBB transmissions. Puncturing is found to reduce both average AoI and peak AoI (PAoI) violation probability but at the expense of decreased eMBB user rates and increased signaling complexity. Conversely, NOMA and RSMA offer higher eMBB rates but may lead to MC packet loss and AoI degradation. The paper systematically investigates the conditions under which NOMA or RSMA can closely match the average AoI and PAoI violation performance of puncturing while maintaining data rate gains. Closed-form expressions for average AoI and PAoI violation probability are derived, and conditions on the eMBB and MC channel gain difference with respect to the base station are analyzed. Additionally, optimal power and rate splitting factors in RSMA are determined through an exhaustive search to minimize MC outage probability. Notably, our results indicate that with a small loss in the average AoI and PAoI violation probability the eMBB rate in NOMA and RSMA can be approximately five times higher than that achieved through puncturing. 

Given the rising demand for low-power sensing, integrating additional devices into an existing wireless infrastructure calls for innovative energy-and spectrum-efficient wireless connectivity strategies. In this respect, wireless-powered or energy-harvesting symbiotic radio (EHSR) is gaining attention for establishing the secondary relationship with the primary wireless systems in terms of RF EH and opportunistically sharing the spectrum or schedule. In this paper, assuming the commensalistic relationship with the primary system, we consider the energy-efficient optimization of such an EHSR by intelligently making EH and transmission decisions under the inherent nonlinearity of the EH circuitry and dynamics of pre-scheduled primary devices. We present a state-of-the-art deep reinforcement learning (DRL)-engineered, energy-efficient transmission strategy, which intelligently orchestrates EHSR’s uplink transmissions, leveraging the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) scheme. We first formulate the energy efficiency (EE) optimization metric for EHSR considering the nonlinear EH model, and then we decompose the inherently complex, non-convex problem into two optimization layers. The strategy first derives the optimal transmit power and time-sharing coefficient parameters, using convex optimization. Subsequently, these inferred parameters are substituted in the subsequent layer, where the optimization problem with continuous action space is addressed via a DRL framework, named modified deep deterministic policy gradient (MDDPG). Simulation results reveal that, compared to the baseline DDPG algorithm, our proposed solution provides a 6% EE gain with the linear EH model and approximately a 7% EE gain with the non-linear EH model. 

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.

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.

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, conceives the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications such as factories-of-the-future and digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zero-defect, and mass customization-based manufacturing solutions. However, it requires merging distinctive cyber–physical worlds through intelligent orchestration of various technological enablers, e.g., cognitive cobots, human-centric artificial intelligence (AI), cyber–physical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and softwarized next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers of the Industry 5.0, which is the aim of this survey. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, elemental and holistic for designing the objectives of CI-NGWNs to fulfill the Industry 5.0 vision requirements. Furthermore, we outline and discuss ongoing initiatives, demos, and frameworks linked to Industry 5.0. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.

Integrating sensing and communication functionalities are essential for realizing the vision of ubiquitous connectivity and supporting emerging applications in future wireless networks. Integrated sensing and communication (ISAC) offers an efficient approach for sharing resources, such as spectrum and hardware, however, traditional wireless systems face significant challenges in complex real-world environments. Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution, enabling intelligent manipulation of wireless signals to enhance both sensing and communication in an energy-efficient manner. However, conventional RISs, which rely solely on reflection, are limited to half-space coverage. To overcome this limitation, simultaneously transmitting and reflecting RIS (STAR-RIS) has been introduced, expanding network coverage and providing a higher degree of freedom for signal manipulation. This article explores integrating STAR-RIS into ISAC systems, highlighting their potential to revolutionize wireless networks. We examine various deployment scenarios for STAR-RIS-enabled ISAC, discuss their advantages and challenges, and present a case study showcasing performance gains. Furthermore, we identify crucial research directions to address challenges in modeling, deployment, and application, paving the way for the future of intelligent and connected wireless systems. 

The development of the fifth generation (5G) of cellular systems enables the realization of densely connected, seamlessly integrated, and heterogeneous device networks. While 5G systems were developed to support the Internet of Everything (IoE) paradigm of communication, their mass-scale implementations have excessive capital deployment costs and severely detrimental environmental impacts. Hence, these systems are not feasibly scalable for the envisioned real-time, high-rate, high-reliability, and low-latency requirements of connected consumer, commercial, industrial, healthcare, and environmental processes of the IoE network. The IoE vision is expected to support 30 billion devices by 2030, hence, green communication architectures are critical for the development of next-generation wireless systems. In this context, intelligent reflecting surfaces (IRS) have emerged as a promising disruptive technological advancement that can adjust wireless environments in an energy-efficient manner. This work utilizes and analyzes a multi-node distributed IRS-assisted system in variable channel conditions and resource availability. We then employ machine learning and optimization algorithms for efficient resource allocation and system design of a distributed IRS-enabled industrial Internet of Things (IoT) network. The results show that the proposed data-driven solution is a promising optimization architecture for high-rate, next-generation IoE applications. 

Targeting the delay-constrained Internet-of-Things (IoT) applications in sixth-generation (6G) networks, in this paper, we study the integration of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and non-orthogonal multiple access-based backscatter communication (BAC-NOMA) under statistical delay quality-of-service (QoS) requirements. In particular, we derive the closed-form expressions for the effective capacity of the STAR-RIS assisted BAC-NOMA system under Nakagami-m fading channels and energy-splitting protocol of STAR-RIS. Our simulation results demonstrate the effectiveness of STAR-RIS over the conventional RIS (C-RIS) and show an excellent correlation with analytical results, validating our analysis. The results reveal that the stringent QoS constraint degrades the effective capacity; however, the system performance can be improved by increasing the STAR-RIS elements and adjusting the energy-splitting coefficients. Finally, we determine the optimal pair of power reflection coefficients subject to the per-BSN effective capacity requirements. 

In this paper, we investigate the reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access-based backscatter communication (BAC-NOMA) system under Nakagami-m fading channels and element-splitting protocol. To evaluate the system performance, we first approximate the composite channel gain, i.e., the product of the forward and backscatter channel gains, as a Gamma random variable via the central limit theorem (CLT) and method of moments (MoM). Then, by leveraging the obtained results, we derive the closed-form expressions for the ergodic rates of the strong and weak backscatter nodes (BNs). To provide further insights, we conduct the asymptotic analysis in the high signal-to-noise ratio (SNR) regime. Our numerical results show an excellent correlation with the simulation results, validating our analysis, and demonstrate that the desired system performance can be achieved by adjusting the power reflection and element-splitting coefficients. Moreover, the results reveal the significant performance gain of the RIS-assisted BAC-NOMA system over the conventional BAC-NOMA system. 

The industrial Internet-of-things (IIoT) paradigm is reshaping the way industrial measurement systems are designed. Industrial systems require collecting accurate and timely measurements from the field using smart sensor networks distributed in wide production areas. In this context, wireless connectivity of sensors acquires undeniable importance, and in turn, opens sig-nificant research challenges. Therefore, the research community is actively analyzing the suitability of different wireless technologies, for instance, Wi-Fi, 5G-and-beyond, and low-power wide-area networks (LPWANs), toward their possible industrial applications and optimizing them to realize high-performance and accurate smart measurement systems. In this paper, we focus on long range (LoRa)-based LPWANs (i.e., LoRaWAN), especially to overcome the duty cycle (DC) limitations of the adopted ALOHA-based medium access control (MAC) strategy in the industrial, scientific, and medical (ISM) bands. The ISM bands are subjected to an hourly constraint on the number of packet transmissions or inter-message delay, where the devices using higher spreading factors (SFs) can quickly consume the available transmission time. In this paper, we propose and assess the hybrid MAC designs in a LoRa network by combining carrier sense multiple access (CSMA) with ALOHA in two different ways i) exploiting different channel plans for the access mechanisms, ii) relay-assisted access, with devices using small SFs assisting neighboring higher-SF devices with listen-before-talk (LBT) mechanism. Our simulation results reveal that the proposed access strategies lead to a higher packet delivery rate (PDR) as well as lower mean and standard deviation of the communication delay; thus, increasing the overall measurement accuracy.