Low Earth orbit (LEO) satellite constellations are a key enabler for non-terrestrial networks (NTNs) in sixth-generation (6G) systems. However, achieving high reliability over NTN links for mission-critical applications remains challenging, particularly for high-mobility terminals. Existing transmission diversity techniques based on selection combining or maximal ratio combining (MRC) provide no guaranteed recovery under simultaneous channel outages. Moreover, conventional retransmission schemes in the NTN are incompatible with strict latency constraints in 6G. This paper introduces a diversity transform that integrates forward error correction (FEC) coding with transmission diversity to improve the reliability of orthogonal time frequency space (OTFS)-modulated LEO NTN downlinks. It guarantees block-erasure recovery up to a prescribed number of subchannels, with residual errors corrected by the outer FEC, thereby eliminating retransmissions. The transform is inserted between the FEC encoder and the modulator, enabling compatibility with various coding schemes and channel models. In this system, a data word is first partitioned into subwords, each independently FEC encoded. A diversity mapper then distributes these encoded subwords across the available subchannels using a binary transform matrix derived from a Reed–Solomon code. This structure guarantees block-erasure recovery whenever a sufficient number of subchannels survive, with residual errors corrected by the outer FEC. An ephemeris-driven reliability prediction framework is developed, combining orbital mechanics, atmospheric models, and a finite-blocklength error probability analysis derived for OTFS transmission over transparent satellite relays. The framework enables proactive outage prediction without channel state information feedback. Simulation results show that the proposed diversity transform provides lower block error rate and reduced latency compared to standalone FEC and retransmission schemes, especially under blockage and high-Doppler conditions characteristic of LEO NTN links.

This paper proposes a deep joint source-channel coding (DJSCC) to minimize the age of information (AoI) for image transmission. A new content-based AoI metric called age of misclassified information (AoMI) is introduced to estimate the freshness of the information in an image classification system. AoMI is a critical metric in timely information delivery, measuring the age of the most recently received and correctly classified image at the receiver. The proposed system leverages a deep neural network at the transmitter to map image pixels directly to channel input symbols, eliminating the need for separate source and channel coding. At the receiver, the channel output is processed to perform image classification. To analyze the AoMI performance of the system, a stochastic hybrid systems (SHS) approach is employed. Closed-form expressions for the average AoMI (AAoMI) are derived, providing insights into the impact of system parameters on the AoMI. Simulation results demonstrate the effectiveness of the proposed DJSCC-based system in achieving lower AoMI compared to traditional separate source and channel coding schemes. The findings highlight the potential of deep learning techniques to maintain the freshness of the information in wireless communication systems. This work paves the way for the design of wireless communication systems that prioritize the freshness of delivered information – this is crucial in applications such as real-time monitoring, surveillance, and control systems.

Simultaneous wireless information and power transfer (SWIPT) enabled wireless cooperative communication system is an emerging technology for future wireless communication applications. Furthermore, the Internet of Things (IoT) serves a diverse range of purposes, some of which are mission-critical and require constantly evolving real-time data. Thus, the information received at the destination must be updated timely manner to ensure its freshness. A performance metric named age of information (AoI) has been introduced to measure the freshness of received information. This study estimates the AoI of a SWIPT assisted decode and forward two-way relay assisted status update system in which two sources attempt to exchange status updates as quickly as possible to the destination. The relay system employs short packet communication to adhere to the latency and reliability requirements of the wireless communication system. We study the average Age of Information (AAoI) at the destination in the proposed relay network and derive approximations for the weighted sum AAoI under two different types of transmission scheduling policies at the relay: transmit without waiting (TWW) and wait until charged (WUC). Furthermore, the effects of transmission power, packet size, the distance between relay and sources and block-length on the weighted sum AAoI of the proposed SWIPT assisted short packet relay network are extensively investigated. The performance differences of the considered transmission policies are compared and insights are provided. Numerical simulations using the Monte Carlo method have been employed to validate derived analytical expressions.

This article considers an unmanned aerial vehicles (UAVs) communication network, where UAV operate as an aerial mobile relay between a source and a destination of the network. To capture the “timeliness” of the received information at the destination node, a new performance metric named Age of Information (AoI) is considered. In addition, a short packet communication scheme maintains low latency in the proposed UAV wireless communication system. The finite block-length theory investigates the performances of the short packet communications scheme in the UAV-assisted wireless communications system. In this article, the Average AoI (AAoI) is estimated by applying the stochastic hybrid systems (SHSs) model. The SHS model comprises discrete states represents events that reset the AoI process and continuously dynamics that represent linearly growing age processes. Finally, a closed-form expression for the AAoI of the proposed UAV wireless communication system is derived and it can be used to estimate the optimal altitude, block length, and other parameters.

Age of Information (AoI) measures the freshness of data in mission critical Internet-of-Things (IoT) applications i.e., industrial internet, intelligent transportation systems etc. In this paper, a new system model is proposed to estimate the average AoI (AAoI) in an ultra-reliable low latency communication (URLLC) enabled wireless communication system with decode- and-forward relay scheme over the quasi-static Rayleigh block fading channels. Short packet communication scheme is used to meet both reliability and latency requirements of the proposed wireless network. By resorting finite block length information theory, queuing theory and stochastic processes, a closed-form expression for AAoI is obtained. Finally, the impact of the system parameters, such as update generation rate, block length and block length allocation factor on the AAoI are investigated. All results are validated by the numerical results.