The advent of next-generation Internet of Things (IoT) networks necessitates communication capabilities characterized by ultra-reliability and low latency (URLLC) and energy efficiency requirements that traditional wireless designs frequently struggle to satisfy under practical constraints. In this context, realizing symbiotic IoT networks using mutualism backscattering technologies with short-packet transmissions has emerged as a viable solution to achieve the goals of net zero for sustainable IoT network deployment while satisfying URLLC conditions. Following that, this paper first develops theoretical frameworks for evaluating the performance of short packet transmissions in uplink symbiotic systems by deriving formulas for the block error rate (BLER) and total symbiotic ergodic rate under two diversity techniques, namely selection combining with maximal-ratio combining (SC-MRC) and full maximal-ratio combining (Full-MRC). Then, we scale up this considered system for large-scale IoT networks, where we propose an energy-aware resource allocation method using successive convex approximation (SCA) techniques for multi-user scenarios while adhering to URLLC conditions. Finally, simulation results are provided to validate the developed theoretical analyses as well as demonstrate how our proposed optimization solution achieves substantial improvements in energy efficiency performance. These findings lay a robust groundwork for developing scalable and dependable communication systems in future energy-efficient IoT eras.

In this work, we analyze the performance of rate-splitting multiple access (RSMA) in visible light communication (VLC) systems using multicolor light emitting diodes (LEDs). The system splits data of users into a common stream decoded by all users and user-specific private streams, maps these streams onto the color channels of a multicolor LED and uses successive interference cancellation (SIC) at receiver side to exploit color diversity. Closed-form expressions are derived for the key performance metrics such as outage probability, coverage probability, and ergodic sum rate under the Lambertian channel model. To concurrently mitigate multi-user interference, which arises from cross-color coupling, and optimize the power division between the common and private data streams, a joint precoding and power allocation strategy is implemented. More specifically, the resulting non-convex optimization problem is efficiently solved by an equivalent convex reformulation to efficiently obtain the optimal power allocation coefficients. Numerical results demonstrate that in the low-to-moderate common rate regime, RSMA demonstrates superior performance over conventional scheme regarding coverage probability and ergodic sum rate. This improvement further enhances the reliability and spectral efficiency for VLC systems that employ practical multicolor LED configurations.

The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. At the same time, the rapid proliferation of resource-constrained Internet-of-Things (IoT) devices has accelerated the adoption of tiny machine learning (TinyML) for efficient on-device intelligence, while large machine learning (LargeML) models continue to require substantial computational resources to support large-scale IoT services and ML-generated content. These trends highlight the need for a unified framework that integrates TinyML and LargeML to achieve seamless connectivity, scalable intelligence, and efficient resource management in future 6G systems. This survey provides a comprehensive review of recent advances enabling the integration of TinyML and LargeML in next-generation wireless networks. In particular, we(i)provide an overview of TinyML and LargeML,(ii)analyze the motivations and requirements for unifying these paradigms within the 6G context,(iii)examine efficient bidirectional integration approaches,(iv)review state-of-the-art solutions and their applicability to emerging 6G services, and(v)identify key challenges related to performance optimization, deployment feasibility, resource orchestration, and security. Finally, we outline promising research directions to guide the holistic integration of TinyML and LargeML for intelligent, scalable, and energy-efficient 6G networks and beyond.

In this paper, we investigate the performance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled short-packet communication (SPC) systems that employ covert rate-splitting (RS) to facilitate applications of Internet-of-Things (IoT). Under the generalized model of the α-η-κ-μ fading, we analyze covertness by first deriving a closed-form expression for the warden’s detection error probability (DEP) and then deducing the optimal detection threshold at which the DEP is minimized. From this optimal DEP, we guide the choice of the feasible power-allocation (PA) region at which the warden’s produced DEP is always beyond the minimal acceptable covertness. On the other hand, we develop mathematical frameworks for evaluating the system’s block-error rate (BLER) and the ergodic rate (ER), as well as providing guidelines on how to access their performance limits at high signal-to-noise ratio, especially the diversity orders and ergodic slopes of the users. Furthermore, we also propose to enhance the system performance by jointly optimizing the PA and RS coefficients in order to: (1) minimize the maximum BLER across private users subject to a minimum covertness requirement and (2) maximize the minimum ER across users subject to covertness and decoding constraints. Numerical results confirm the analytical expressions and show that the proposed optimization efficiently tunes the PA and RS coefficients to achieve the BLER and ER fairness objectives.

In this article, we describe a novel active reconfigurable repeater-aided nonorthogonal multiple access networks within the context of the Internet of Things. The study focuses on a scenario, where a source simultaneously transmits public information to an untrusted user and a covert signal to a legitimate user in the surveillance of an external warden or eavesdropper. We develop comprehensive analytical and optimization frameworks to evaluate the reliability, security, and covertness of the proposed system’s performance, measuring three respective key metrics: 1) outage probability (OP); 2) secrecy OP (SOP); and 3) detection error probability (DEP). First, we derive exact closed-form and asymptotic expressions for OP, SOP in internal and external eavesdropping scenarios, and DEP in external monitoring situations. Based on an asymptotic analysis of the OP, we propose two optimization methods for power allocation (PA) to achieve fairness in outage among users: 1) a convex approximation method and 2) an approximate closed-form solution. We then introduce an alternative method for optimizing PA to improve SOP in both eavesdropping scenarios while maintaining minimal OP requirements. In addition, we propose an effective approach for determining the warden’s detection threshold to minimize the DEP, with low complexity and fast convergence, thereby improving communications covertness. Finally, we validate the theoretical and optimization frameworks through extensive Monte Carlo simulations, exploring the impact of key system parameters on each performance metric.

The success of artificial intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet of Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm; thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI’s applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area.

This work investigates the covertness and security performance of Internet-of-Things (IoTs) networks under Rayleigh fading environments. Specifically, a cellular source transmits covert information to cell-edge users with the assistance of an IoT master node, employing a coordinated direct and relay transmission strategy combined with non-orthogonal multiple access (NOMA). This approach not only enhances spectrum utilization but also generates friendly interference to complicate a warden’s surveillance or an eavesdropper’s decoding efforts. From a covertness perspective, we derive exact closed-form expressions for the detection error probability (DEP) under arbitrary judgment thresholds. We then identify the optimal judgment threshold for the worst-case scenario, at which the warden minimizes its DEP performance. Accordingly, we determine the effective region for user power allocation (PA) in NOMA transmission that satisfies the DEP constraint. From a security perspective, we derive analytical expressions for the secrecy outage probability under two eavesdropping strategies using selection combining and maximal ratio combining. Based on this analysis, we propose an adaptive PA scheme that maximizes covert rate while ensuring the quality-of-service (QoS) requirements of legitimate users, the system’s minimum covertness requirements, and supporting successive interference cancellation (SIC) procedures. Furthermore, we design an adaptive PA scheme that maximizes the secrecy rate while ensuring the QoS requirements of legitimate users and SIC conditions. Numerical results demonstrate the accuracy of the analytical framework, while the proposed optimization strategies effectively adjust PA coefficients to maximize either the covert rate or the secrecy rate.

This work proposes a novel semi-non-orthogonal multiple access (NOMA) and data transmission technique, called Semi-NOMA, to enhance the spectrum utilization of aerial simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided symbiotic networks without using successive interference cancellation approaches as classical NOMA. In particular, the proposed scheme is investigated with active and passive STAR-RIS models combined with infinite blocklength (IBL) and finite blocklength (FBL) regimes under discrete phase-shift alignments. For IBL scenarios, the ergodic capacity and outage probability are derived under both approximation and asymptotic frameworks. Besides, a joint optimization problem of the power allocation factor and energy splitting coefficient is also formulated to maximize the ergodic sum capacity (ESC), where closed-form solutions are derived for both active and passive STAR-RIS models. For FBL scenarios, not only the approximation and asymptotic frameworks are derived for the average achievable rate and block-error rate, but also an approximated convex form is derived for a non-convexity optimization problem of min-max blocklength. Numerical results corroborate the efficacy of the proposed Semi-NOMA over the baseline schemes, the developed mathematical frameworks, and the solutions of the ESC maximization and min-max blocklength.

This paper comprehensively investigates the performance of downlink multi-user rate-splitting multiple access (RSMA) networks under Nakagami-m fading channels. We first develop the mathematical outage probability (OP) and ergodic capacity (EC) frameworks, deriving exact expressions for both, along with asymptotic analysis in high signal-to-noise ratio (SNR) and low-rate regions, which serve as the foundation for deducing the approximate and maximal energy-reliability and energy-spectral formulas. To enhance system performance, we tackle the non-convex problems of jointly optimizing common and private power allocation (PA) coefficients to minimize the maximal OP performance. Moreover, we also delve into optimizing PA coefficients and rate-splitting factors concurrently to maximize the ergodic sum capacity (ESC). Addressing the influence of modulation schemes on user error rates, we introduce mathematical frameworks for symbol error rate (SER) considering four combined modulation schemes based on binary phase-shift keying (BPSK) and quadrature-phase shift keying (QPSK), the whole cases are quantified in terms of exact and asymptotic manners. Furthermore, we present a straightforward approach to optimize the PA coefficients to minimize the maximal SER performance. Finally, Monte-Carlo simulations are presented to validate our developed frameworks and optimization solutions.

In this paper, we propose a best node and friendly jammer (bN-fJ) scheme for secure short-packet multi-hop communications in Internet-of-Things (IoT) networks. Under imperfect channel state information (CSI) conditions, the best IoT node is chosen for data transmission and a friendly jammer is chosen later to confuse the received signals at a multi-antenna eavesdropper. Approximate and asymptotic closed-form expressions for the secrecy throughput of the bN-fJ scheme are obtained, offering valuable insights into the system designs. Numerical results show that the proposed bN-fJ scheme achieves much better performance than benchmarking schemes, especially with 1 more bit/channel use and 9.4 dB enhancement in communication reliability measure. Moreover, under imperfect CSI and large antennas at the eavesdropper, the secrecy throughput and communication reliability of the system can be improved by employing the proposed node selection, e.g., a 12.5 dB improvement at 10 antennas at Eve and the imperfect CSI of 0.8. Finally, the secrecy throughput is presented as a concave curve for the number of hops and blocklengths, which enables us to identify the optimal hops and blocklength for secure multi-hop short-packet transmissions.

This paper proposes a reconfigurable intelligent surface-aided two-way full-duplex communication with non-orthogonal multiple access transmission schemes to improve spectrum utilization. Besides, the joint impact of error phase-shift quantization, imperfect successive interference cancellation, and residual loop interference on the system performance have also been investigated. Under Nakagami-m fading channels, approximate closed-form expressions for the outage performance and the ergodic rate are derived. Through asymptotic analyses, some insights are achieved, including the diversity order, the coding gain, and the ergodic slope. Moreover, three adaptive power allocation optimization problems are formulated aiming to: 1) minimize outage probability, 2) achieve max-min rate fairness, and 3) maximize the user’s sum rate subject to the quality-of-service constraint. Simulation results not only validate the theoretical analyses and the optimal/sub-optimal solutions but also reveal three following observations. First, the considered system outperforms the orthogonal multiple access baseline. Second, increasing the number of control bits for the error phase-shift quantization and/or the number of RIS’s elements can significantly reduce the impact of imperfect successive interference cancellations. Third, employing one of three adaptive power allocation solutions improves the system’s performance significantly.

This paper investigates the performance of downlink rate-splitting multiple access (RSMA)-aided unmanned aerial vehicle (UAV) communication systems, wherein a multi-antenna UAV exploits RSMA to serve multiple ground users. Considering nonline-of-sight environments, double-shadowed scattering channel modeling is adopted to generically characterize the impacts of mobility and shadowing on UAV-to-ground communications, assuming imperfect successive interference cancellation (SIC). Besides, a unified precoder design is proposed to fully capture the benefits of multi-antenna paradigms. Closed-form expressions for the users' outage probability (OP) and ergodic capacity are derived. In addition, asymptotic analysis is carried out to get further insights into the system design, such as the diversity gain and ergodic slope. Numerical results are presented, and it is shown that: 1) the effects of double-shadowed scattering on the system outage performance can be significantly reduced by increasing the number of antennas installed at the UAV; 2) the imperfect SIC error can be minimized by properly optimizing the power allocation of the common stream; and 3) RSMA provides superior users' ergodic capacity compared to its orthogonal and non-orthogonal multiple access counterparts.

This article studies short-packet communication (SPC) in multireconfigurable intelligent surface (RIS)-assisted multiantenna wireless systems. In this system, a sensor node communicates with another sensor node through the help of an access point (AP) and two sets of distributed RISs. Aiming to enhance system performance, we combine the best RIS selection strategies with maximum-ratio transmission (MRT) beamforming designs to improve the transmitted signal and selection combining (SC) or maximum-ratio combining (MRC) to increase the received signals. Closed-form expressions for the block error rate (BLER) throughput, latency, and reliability of the receivers over Rayleigh fading channels are derived to evaluate the system performance. Numerical results show that, in the first transmission phase, employing MRC with optimal phase shift (OPS) occurring at RIS can help AP achieve the best BLER performance, while SC with OPS provides better BLER performance than MRC and uncertain phase shift (UPS). In the second transmission phase, the reflective-path beamforming design shows better performance than the direct-path beamforming design, where OPS also attains outstanding performance when compared to UPS. Aiming for real-time system configurations with high reliability along with minimizing costs and resources, we propose a deep learning (DL) approach to optimize the number of reflective elements at each RIS or the number of RIS in a set of distributed RISs. Our work shows that the prediction results of the DL framework match the analytical derivations and the deep neural network (DNN) can help the systems save overhead and resources while satisfying the requirement of real-time communication.

In this article, we propose a cooperative nonorthogonal multiple access (NOMA)-enabled simultaneous wireless information and power transfer (SWIPT) Internet of Things (IoT) networks, where one information source harvests energy from a multiantennas power beacon (PB) to serve two IoT users via the help of multiple energy-limited relay nodes. To improve the performance of far IoT user, we propose reactive and proactive relay selection protocols together with time-power energy harvesting mechanism under imperfect successive interference cancellation. Closed-form expressions for the outage probability (OP), throughput, and energy efficiency (EE) of the proposed system are obtained, from which the asymptotic analysis for the throughput is also carried out. To further enhance the system performance, we propose a low-complexity method to optimize the outage and throughput performance subject to power allocation, time, and power splitting parameters. Toward real-time configurations in IoT networks, we design a deep learning framework for the sum-throughput and EE predictions with low computation complexity and high accuracy. The influences of antennas setting at PB, time-switching ratio, power-splitting ratio, power allocation factor, and the number of relays on the system OP, throughput, and EE are evaluated and discussed along with numerical results.

In this article, we study novel wireless powered cognitive nonorthogonal multiple access (NOMA)-based Internet-of-Things (IoT) relay networks to improve the performance of a cell-edge user under perfect and imperfect successive interference cancelation (SIC). In the secondary networks, a source node communicates with a cell-center user via direct link and with a cell-edge user through the assistance of a master IoT node under cognitive radio constraint. Exact closed-form analytical expressions for the outage probability (OP) of NOMA users and the overall system throughput are derived. To provide further insights, a performance floor analysis is also carried out considering two power-setting scenarios: 1) the transmit powers at the power beacon goes to infinity and 2) the maximum allowable power constraint goes to infinity. Moreover, we develop two iterative algorithms for minimizing OP users and maximizing system throughput subject to time-switching and power-allocation factors in two-hop transmission. Direct derivation of the closed-form expression for the ergodic capacity (EC) becomes unfeasible due to the high complexity of the proposed system model. To overcome this issue, we design a deep neural network (DNN) framework for the EC prediction toward real-time configurations. Our results show that the predicted results based on this DNN framework perfectly align with the simulations, validating our design framework. In addition, the DNN approach exhibits the lowest root-mean-square error and low run-time predictions among other regression models.

In this article, we study wireless-powered cognitive nonorthogonal multiple access (NOMA) Internet of Things (IoT) networks with short-packet communications to improve spectrum utilization and sustainability, as well as reduce the latency under imperfect channel state information (CSI) and successive interference cancelation (SIC). For performance evaluation, closed-form expressions for the block error rate (BLER) of the NOMA users, goodput, energy efficiency, latency, and reliability are derived. To gain some further insights into the system design, two scenarios can be taken into account for the positions of the primary receivers: 1) they are located near the secondary network and 2) they are located far away from the secondary network. Moreover, we propose an effective algorithm to minimize the BLERs of the NOMA users by optimizing power allocation coefficients. In addition, a novel multi-output deep-learning (DL) framework is designed to simultaneously predict the BLERs and goodputs of users towards real-time configurations for IoT systems. Numerical results show the outstanding performance of the proposed system over the orthogonal multiple access (OMA) one in terms of the BLER and goodput. Moreover, the proposed system achieves a lower latency and higher reliability compared to the long packet communications under the same channel settings. Furthermore, our designed multioutput DL also exhibits the lowest error performance and a short run-time prediction compared to the other multioutput regression models, while the predicted results using the DL model are almost matched with the simulation ones.

This paper investigates reconfigurable intelligent surface (RIS)-aided cognitive non-orthogonal multiple access (NOMA) systems, where an RIS is deployed to serve two users under multi-primary users’ constraints. Our analysis assumes imperfect channel state information and successive interference cancellation under scenarios with and without line-of-sight (LoS) link between source and users. We derive exact closed-form expressions for the outage probability, throughput, and an upper bound for the ergodic capacity (EC). To provide further insights, an asymptotic analysis is carried out by considering two power settings at the source. It is also determined the optimal data rate factors of all users that maximize the system throughput. In addition, a deep learning framework (DLF) for EC prediction is designed. Numerical results show that: i) compared to the system without LoS link, the performance of the proposed system with LoS link can significantly improve when the number of reflecting elements at the RIS increases, and ii) the proposed system has superior performance compared to its orthogonal multiple access counterpart. Furthermore, our proposed DLF exhibits the lowest root-mean-square error and low execution-time among other approaches, verifying the effectiveness of this method for future analysis.

In this article, we investigate power beacon (PB)-assisted wireless-powered nonorthogonal multiple access (NOMA) Internet-of-Things (IoT)-based systems, where all transmitters harvest energy from a PB to transmit their signals to a destination by employing a time-splitting mechanism. In order to utilize spectral efficiency and enhance the quality of service of an edge user (EU), a cellular base station can communicate with the EU thank to the help of one IoT node in a cellular network by employing the NOMA protocol. To characterize the performance of the proposed systems, we derive the exact closed-form expression for the outage probability, throughput, energy efficiency, and the approximate closed-form expression for the ergodic capacity. To further improving the system performance, we present two algorithms that are to minimize the outage performance of users by optimizing the time-splitting factor and to maximize sum throughput via jointly optimal power allocation and time-spitting factor. Our numerical results show that the proposed system has outstanding performance in comparison with simultaneous wireless information and power transfer NOMA systems under time switching mechanism based on the IoT relay.

In this paper, we investigate an underlay cognitive non-orthogonal multiple access (NOMA)-based coordinated direct and relay transmission network with imperfect successive interference cancellation, imperfect channel state information, and co-channel interference caused by a multi-antenna primary transmitter. In the secondary network, a source communicates with a near user via direct link and with a far user through the assistance of multiple relays subject to transmit power constraints. Four relay selection schemes are proposed to enhance the performance of NOMA users and the overall system throughput. In our analysis, exact closed-form expressions for the outage probability (OP) of NOMA users and for the overall system throughput are derived. To provide further insights, a performance floor analysis is carried out considering two power-setting scenarios: (i) the transmit powers at the secondary source and relays go to infinity and (ii) the peak interference constraint goes to infinity. Towards real-time configurations, we also design a deep learning (DL) framework for the OP and system throughput prediction. Our results show that the deep neural network exhibits the lowest run-time prediction and root-mean-square error among the proposed DL models. Furthermore, the predicted results based on DL framework match with those of the analysis and simulation.