It is crucial to consider time-critical communications in cognitive-radio-based Internet of Things (CR-IoT), which increasingly rely on the exchange of short-packet information to efficiently monitor and control. Therefore, the age of information (AoI) is exploited as a timeliness metric in this paper to investigate the freshness of received information at the remote server with dual-hop short-packet communications in underlay CR-IoT. Since short-packet communications are sensitive to time delay, the influence of outdated factors in the channel state information (CSI) cannot be ignored, so we develop the system under the condition of outdated CSI with Nakagami-m fading channels. Considering the impact of outdated CSI and packets error, we investigate the performance of dual-hop short-packet communications under the classical automatic repeat-request (ARQ) protocol and truncated ARQ (TARQ) protocol, and obtain the approximated expressions of the average AoI and the average peak AoI (PAoI), respectively. Accordingly, the blocklength and the number of retransmissions can be optimized by Fibonacci and iterative algorithms to minimize the average PAoI under two protocols, respectively. Simulation results substantiate the influence of each parameter on the system performance differently under two different protocols. Moreover, the AoI of the dual-hop is also better than that of the single-hop due to the interference threshold of the primary user.

In this paper, we investigate physical layer security (PLS) in non-orthogonal multiple access-enabled (NOMA-enabled) underlay cognitive radio networks (CRNs) with outdated channel state information (CSI). Considering the influence of outdated CSI on the interference of secondary transmitter (Alice) to primary user (PU), the constraint for the power is adopted to guarantee the quality-of-service (QoS) of PU over Nakagami-m channels. To further analyze the NOMA-enabled underlay CRNs with outdated CSI in PLS perspective, the secrecy performance is evaluated by the closed-form expressions for connection outage probability (COP), the intercept probability (IP) and effective secrecy throughput (EST). In addition, Monte Carlo simulations are provided to verify the derived analytical results. From the analytical results and simulations, it is concluded that a) with the increment of the channel parameter m, the secrecy performance of the considered networks increases in the low SNR region and decreases in the high SNR region, b) the connection performance with the outdated CSI of the interference links only reduce in the high SNR region, because the power margin factor changes significantly in this region, c) considering the impact of the constraint for the power, the secrecy performance and EST performance of the considered networks with the outdated CSI of the interference links increase in the high SNR region, d) the considered networks with NOMA scheme can achieve higher EST than that with orthogonal multiple access (OMA) scheme.

This article is motivated by the requirement of high information freshness in the industrial Internet of Things (IIoT). An industrial robot sends short status packets to a control center (CC), and the timeliness of status updates is measured by the Age of Information (AoI). Due to the dynamic change of the wireless channel, the robot needs to send a pilot for channel estimation during each coherence time. Considering the robot is energy-limited, we investigate the average AoI minimization scheme for short-packet communications under the average power consumption constraint. By rationally analyzing state transitions, we first formulate the problem as a constrained Markov decision process and obtain the optimal solution through linear programming (LP). Then, for the problem of high computational complexity caused by too many variables in LP, we propose a heuristic threshold-based status update scheme by exploiting the threshold structure of the optimal solution. Simulation results show that the LP scheme can effectively minimize the average AoI and the threshold-based scheme can achieve near-optimal performance. Interestingly, we find that when the channel suffers from severe fading, at the end of a coherence time, the robot does not send status packets even if the information at the CC is particularly stale.

Timely status updates are significant for some critical Internet of Things applications. However, due to the transmission delay of status packets, the status information arriving at the destination is not fresh enough. Especially, when the transmission delay is large, the destination cannot know the current status of the target well. In this article, the prediction technology is introduced into status update systems to solve this problem. First, we study a prediction-based status update scheme for machine-type communications (MTCs) and use the recently proposed metric, Age of Information (AoI), to characterize the status update performance. With the scarce spectrum resources, an MTC device (MTCD), as an unlicensed user, adopts the overlay mode to reuse the spectrum resource of the primary user and sends status packets to the central controller (CC). Then, we consider prediction errors and packet decoding errors to characterize the reliability of MTC, analyze the status update process in the prediction-based scheme, and derive the closed-form expression of the average AoI. Finally, in the proposed prediction-based scheme, we analyze the tradeoff between the status update performance and energy consumption and design an optimization algorithm to improve the status update performance by adjusting the transmit power and prediction horizon of the MTCD. Simulation results verify the correctness of the theoretical analysis and show that the proposed scheme can help the MTCD effectively improve the status update performance.

In the cellular Internet of Things (IoT), some machine type communication devices (MTCDs) are used to sample and send status information of monitored targets to help base station (BS) perform monitoring or controlling tasks. However, due to limited spectrum resources, the MTCDs often cannot occupy separate spectrum resources. This paper studies a cognitive IoT where the MTCDs exploit the spectrum resource vacated by cellular users to send status packets to the BS. The timeliness of status updates is characterized by the recently proposed metric, age of information (AoI). In the IoT, the packets sent by the MTCDs are often short, which means it is necessary to analyze the status updates under short packet communication theory (SPC). In this case, we adopt the automatic repeat request protocol to retransmit the erroneous packets, and the preemption policy is exploited to improve the status update performance. We first carefully analyze the evolution of AoI in the cognitive IoT, and adopt a recursive method to obtain the closed-form expression of the average peak AoI. Then, we analyze age-energy tradeoff, and propose a scheme of jointly optimizing transmit power, packet length, and spectrum sharing to reduce the average peak AoI of the network. The simulation results verify the correctness of the theoretical analysis, and show that the proposed scheme outperforms the benchmark schemes.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for collaborative search missions in complex environments. However, in the presence of interference, communication disruptions between UAVs and ground control stations can severely degrade coordination efficiency, leading to prolonged search times and reduced mission success rates. To address these challenges, this paper proposes a novel multi-agent deep reinforcement learning (MADRL) framework for joint spectrum and search collaboration in multi-UAV systems. The core problem is formulated as a combinatorial optimization task that simultaneously optimizes channel selection and heading angles to minimize the total search time under dynamic interference conditions. Due to the NP-hard nature of this problem, we decompose it into two interconnected Markov decision processes (MDPs): a spectrum collaboration subproblem solved using a received signal strength indicator (RSSI)-aware multi-agent proximal policy optimization (MAPPO) algorithm and a search collaboration subproblem addressed through a target probability map (TPM)-guided MAPPO approach with an innovative action-masking mechanism. Extensive simulations demonstrate superior performance compared to baseline methods (IPPO, QMIX, and IQL). Extensive experimental results demonstrate significant performance advantages, including 68.7% and 146.2% higher throughput compared to QMIX and IQL, respectively, along with 16.7–48.3% reduction in search completion steps versus baseline methods, while maintaining robust operations under dynamic interference conditions. The framework exhibits strong resilience to communication disruptions while maintaining stable search performance, validating its practical applicability in real-world interference scenarios.