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Recently, intelligent reflective surface (IRS)-aided systems are becoming a prospective technology in realizing for sixth generation (6G) wireless communication era because of extremely low power transmission, seamless coverage and their superiority. These network systems can allow many users and devices to connect to each other, extending the coverage. To empower IRS-aided systems, non-orthogonal multiple access (NOMA) can be leveraged to work with IRS technique enabling further benefits such as mass connectivity, flexible resource allocation and improved performance. Increasing connected devices and expanding coverage means devices have the potential to interfere with each other. Recent studies focusing on researching and analyzing the performance of the IRS-supported NOMA network have not taken into account or not fully calculated the impact of interference on system performance. In this study, we first analyze the effect of co-channel interference (CCI) at users in downlink IRS-NOMA systems. In particular, the CCIs generated by the terminals deployed randomly in the coverage area affect the signal reception at the user in the downlink. In this network model, the channel conditions that follow the Rayleigh distribution and the CCI statistical model are independent and identically distributed. We analyze and evaluate network performance by extracting closed-form expressions of outage probability, ergodic capacity, total achievable rate then highlighting the adverse effects of CCI on IRS-NOMA. In addition, to improve the performance of the IRS-NOMA downlink, we present a framework of theorical analysis to look more insights of users’ performance, i.e. diversity order. Our analytical derivatives are verified through computer simulations based on Monte-Carlo and intuitive comparisons with the benchmarks.

This paper provides a study of the latest target (object) detection algorithms for underwater wireless sensor networks (UWSNs). To ensure selection of the latest and state-of-the-art algorithms, only algorithms developed in the last seven years are taken into account that are not entirely addressed by the existing surveys. These algorithms are classified based on their architecture and methodologies of operation and their applications are described that are helpful in their selection in a diverse set of applications. The merits and demerits of the algorithms are also addressed that are helpful to improve their performance in future investigation. Moreover, a comparative analysis of the described algorithms is also given that further provides an insight to their selection in various applications and future enhancement. A depiction of the addressed algorithms in various applications based on publication count over the latest decade (2023-2013) is also given using the IEEE database that is helpful in knowing their future application trend. Finally, the challenges associated with the underwater target detection are highlighted and the future research paradigms are identified. The conducted study is helpful in providing a thorough analysis of the underwater target detection algorithms, their feasibility in various applications with future challenges and defined strategies for further investigation.