Internet‐of‐things is an enabling technology in the fourth‐generation industrial revolution. The freshness of the data sent by a sensor node (SN) is an important parameter in the Internet‐of‐things, unlike the throughput in cellular communications. A relatively new performance metric named age of information (AoI) is used in this paper to quantify the freshness of the data. The SN, located in the transport infrastructure, harvests energy from radio frequency signals transmitted by the FD‐UAV. This is used to transmit real‐time sensor observations to the data sink via FD‐UAV. The SN generates an update after replenishing the battery and transmits it by using the harvested energy. A closed‐form expression for average AoI is derived as a function of time allocated for energy harvesting. The optimal time allocation for energy harvesting that maximizes the freshness of data update is identified. A deep learning technique namely long‐short term memory is used to predict the average AoI. Simulation results demonstrate the usefulness of the performance bounds in terms of the freshness of data updates.

An illustration of self‐energized UAV enabled cooperative communication network in industrial Internet of things.image

Channel state information (CSI), which is crucial for resource allocation and system performance in time division duplex (TDD) massive multiple‐input multiple‐output (MIMO) systems, is difficult to predict because of the channel's time‐varying nature. To overcome this limitation, a scheme for channel prediction combined with deep learning (DL) is proposed. The system uses a deep neural network (DNN) to interpolate channel estimates from a few received pilot signals and a recurrent neural network (RNN) to train through the current time and the recent historical channel estimates to predict the CSI while the channel is constantly varying. In the end, a mixed neural network of RNN and DNN, is called MRDNN. In addition, the proposed DL‐based method does not rely on the relevant feature information about the channel, such as internal characteristics and parameters of the channel itself or channel statistical information, which improves its effectiveness in practical applications. The results of the simulation show that the MRDNN‐based method is better than the existing methods, like traditional AR method and NL Kalman method, and also can be effective in improving the quality of channel prediction and the performance of the system under the dynamic change scenario of low mobility.

In this paper, we propose a channel prediction scheme combined with deep learning. This scheme is designed to predict channel state information in order to overcome the time‐varying properties of channel in time‐division duplex MIMO systems. Simulation results show that it improves the quality of channel prediction and system performance.image

In the context of federated learning, this paper focuses on managing inherent dynamics and uncertainties, and optimizing energy management for devices in real‐world industrial environments. The problem is formulated by proposing an online deep reinforcement learning algorithm that optimizes model iteration updates between clients and servers, and an energy harvesting strategy that enhances device performance and extends lifespan. Through these innovative methods, solutions are provided for the dynamics and uncertainties in federated learning and a new approach is devised for energy usage optimization and task scheduling. Numerical results demonstrate the efficiency of the approach in transitioning federated learning from theory to practical application, and offering diversified application scenarios. In summary, this research contributes to the development of federated learning by providing novel insights and methodologies, and highlights the challenges and opportunities for future studies in the field.

This paper delves into these issues and presents several innovative research outcomes. We initially propose a novel online deep reinforcement learning algorithm that efficiently tackles the dynamics and uncertainties in federated learning by optimizing model iteration updates between clients and servers.image

In a nervous system propagation of spikes among neurons can be interpreted as an information transmission process, where, a neural response carries information about the stimulus. Quantification of this information might be of theoretical and practical importance. In this paper, (i) The interaction between two neurons with an electrically coupled transport of ions is modelled mathematically, where the communication through the junction gap between very close neurons occurs with a specific time delay; (ii) then, the connection structure of neurons is proposed based on a particular Rulkov network model. The corresponding process is modelled as a non‐linear binary channel with memory; and (iii) by assuming a recurrence relation for number of available membrane potential changing due to the arrival of the spike to the axonal terminal, the mutual information between channel input and output is obtained and the input range maximizing mutual information, and hence, the information rate of channel is derived.

The information theory modelling for communications between neurons.image

Cooperative Non‐orthogonal Multiple Access (C‐NOMA) is projected as the dominant technology for 5G and beyond. Also, physical layer security, due to the presence of eaves‐dropper, is a bottleneck security concern for any open link wireless system. This manuscript presents a novel Decode & Forward (DF)‐based C‐NOMA system in the presence of two eavesdroppers. Moreover, both half duplex and full duplex schemes are included in the analysis. Thereby, separate expressions for Secrecy Outage Probability (SOP), being among the most crucial performance measures in physical layer security, are produced and examined for the system under consideration. Specifically, first of all, novel expression of SOP for C‐NOMA systems under both Half Duplex (HD) and Full Duplex (FD) schemes is derived for the case when only eavesdropper one (E1) attacks on link source to device one (S‐D1). Thereafter, expressions of SOP for the C‐NOMA system under both HD and FD schemes are derived, considering presence of E1 and E2 on the links, source to device one (S‐D1) and source to device two (S‐D2), respectively. Further, for both the duplexes, the effect of eavesdropper one (E1) and eavesdropper two (E2) on the links, S‐D1 and S‐D2, respectively, is analyzed. The presented analysis is useful in the technological development level of C‐NOMA in the presence of multiple eavesdroppers. The simulated results show perfect agreement with the theory.

Cooperative Non‐orthogonal Multiple Access (C‐NOMA) is projected as the dominant technology for 5G and beyond. Also, physical layer security, due to the presence of eaves‐dropper, is a bottleneck security concern for any open link wireless system. This manuscript presents a novel Decode & Forward (DF)‐based C‐NOMA system in the presence of two eavesdroppers.image