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Large intelligent surface (LIS) has recently emerged as a potential enabling technology for 6G networks, offering extended coverage and enhanced energy and spectral efficiency. In this work, motivated by its promising potentials, we investigate the error rate performance of LIS-assisted non-orthogonal multiple access (NOMA) networks. Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and L NOMA users is assisted by an LIS comprising M reflective elements. First, we derive the probability density function of the end-to-end wireless fading channels between the BS and NOMA users. Then, by leveraging the obtained results, we derive an approximate expression for the pairwise error probability (PEP) of NOMA users under the assumption of imperfect successive interference cancellation. Furthermore, accurate expressions for the PEP for M = 1 and large M values (M > 10) are presented in closed-form. To gain further insights into the system performance, an asymptotic expression for the PEP in high signal-to-noise ratio regime, the asymptotic diversity order, and a tight union bound on the bit error rate are provided. Finally, numerical and simulation results are presented to validate the derived mathematical results. Index terms-Asymptotic diversity order, bit error rate, large intelligent surfaces, NOMA, pairwise error probability, union bound, 6G.

Physical layer security is an important and timely topic in the research of future wireless systems and it constitutes a part of the Internet of Things (IoT) notion. IoT oriented systems are largely characterized by a stringent quality of service and enhanced security requirements, which comes at a cost of increased computational complexity that needs to be maintained within sustainable levels. In the present contribution, we investigate the physical-layer security of a dual-hop energy RF-Powered cognitive radio network over realistic multipath fading conditions. Assuming a spectrum sharing scenario, our analysis assumes that a source S communicates with a destination D with the aid of a multi-antenna relay R and in the presence of an eavesdropper E who is attempting to overhear the communication of both S-R and R-D links. The involved relay is powered by the renewable energy harvested from the signal sent by the source based on the power-splitting energy harvesting strategy. Furthermore, the relay uses a maximum ratio combining technique to process effectively the received signals. In addition, owing to the underlying strategy, both S and R adjust their respective transmit powers in order to avoid causing interference to the primary network. By considering both the independent identically distributed and the independent but not necessarily identically distributed flat Rayleigh fading channels, closed-form expressions for the secrecy outage probability are derived, based on which an asymptotic analysis is carried out. Our results quantify the impact of the main key system parameters and point out the optimal values ensuring a high-security performance of such a communication system. The validity of the derived results is verified extensively through comparisons with respective Monte Carlo simulation results and useful theoretical and technical insights are developed which are expected to be useful in the design of future cooperative CRNs.INDEX TERMS Cognitive radio network, energy harvesting, maximum ratio combining, physical layer security, power splitting, interference, secrecy outage probability.

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Machine Learning (ML), and Deep Learning (DL) in particular, play a vital role in providing smart services to the industry. These techniques however suffer from privacy and security concerns since data is collected from clients and then stored and processed at a central location. Federated Learning (FL), an architecture in which model parameters are exchanged instead of client data, has been proposed as a solution to these concerns. Nevertheless, FL trains a global model by communicating with clients over communication rounds, which introduces more traffic on the network and increases the convergence time to the target accuracy. In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process. Next, we propose an online stateful FL heuristic to find the best candidate clients. Additionally, we propose an IoT client alarm application that utilizes the proposed heuristic in training a stateful FL global model based on IoT device type classification to alert clients about unauthorized IoT devices in their environment. To test the efficiency of the proposed online heuristic, we conduct several experiments using a real dataset and compare the results against state-of-the-art algorithms. Our results indicate that the proposed heuristic outperforms the online random algorithm with up to 27% gain in accuracy. Additionally, the performance of the proposed online heuristic is comparable to the performance of the best offline algorithm.

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