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Non-orthogonal multiple access (NOMA) has been identified as one of the promising technologies to enhance the spectral efficiency and throughput for the fifth generation (5G) and beyond 5G cellular networks. Alternatively, Coordinated multi-point transmission and reception (CoMP) improves the cell edge users' coverage. Thus, CoMP and NOMA can be used together to improve the overall coverage and throughput of the users. However, user grouping and pairing for CoMP-NOMAbased cellular networks have not been suitably studied in the existing literature. Motivated by this, we propose a user grouping and pairing scheme for a CoMP-NOMA-based system. Detailed numerical results are presented comparing the proposed scheme with the purely OMA-based benchmark system, NOMA only, and CoMP only systems. We show through simulation results that the proposed scheme offers a trade-off between throughput and coverage as compared to the existing NOMA or CoMP based system.Index Terms-Coordinated multi-point transmission and reception (CoMP), Non-orthogonal multiple access (NOMA), User grouping, User pairing schemes, fifth generation (5G) and beyond 5G cellular networks.

In this paper, we analyze the performance of an energy harvesting (EH)-assisted overlay cognitive non-orthogonal multiple access (NOMA) system. The underlying system consists of a primary transmitter-receiver pair accompanied by an energy-constrained secondary transmitter (ST) with its intended receiver. Accordingly, ST employs a time switching (TS) based receiver architecture to harvest energy from radio-frequency signals of the primary transmissions, and thereby uses this energy to relay the primary information and to transmit its own information simultaneously using the NOMA principle. For this, we propose two cooperative spectrum sharing (CSS) schemes based on incremental relaying (IR) protocol using amplify-and-forward (AF) and decode-and-forward (DF) strategies, viz., CSS-IAF and CSS-IDF, and compare their performance with the competitive fixed relaying based schemes. The proposed IR-based schemes adeptly avail the degrees-of-freedom to boost the system performance. Thereby, considering the realistic assumption of the NOMA-based imperfect successive interference cancellation, we derive the expressions of outage probability for the primary and secondary networks under both CSS-IAF and CSS-IDF schemes subject to the Nakagami-m fading. In addition, we quantify the throughput and energy efficiency for the considered system. The obtained theoretical findings are finally validated through numerous analytical and simulation results to reveal the advantages of the proposed CSS schemes over the baseline direct link transmission and orthogonal multiple access schemes.Index Terms-Amplify-and-forward, cognitive radio, decode-and-forward, energy harvesting, incremental relaying, non-orthogonal multiple access (NOMA), overlay spectrum sharing, simultaneous wireless information and power transfer (SWIPT).Recently, the NOMA technique has been commonly debated in the context of cognitive radio (CR), which is another potential This work is carried out under BRICS Multilateral R&D Project with No.

The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network.

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