Mohamed Lassaad Ammari

This study aims to review and analyze the latest scientific breakthroughs in Generative artificial intelligence (Gen-AI) published in information system (IS) journals. The goal is to consolidate and organize the collective knowledge of Gen-AI in IS. We adopted a scoping review approach and complemented it with bibliometric analyses. We selected 55 articles from a collection of scholarly publications from several databases covering 2018 to 2024. Results reveal significant trends regarding the scientific production, the influential publications and articles, the study areas and their interconnections, the bibliographic coupling, the methodologies, and the theoretical foundations employed. This comprehensive analysis foresees a productive area of study on Gen-AI in IS as it established the basis for identifying key contributions and gaps and generating novel ideas for further exploration.

Summary: In this paper, we propose two downlink multiple access architectures for networks where human‐type communication users (HTCUs) and machine‐type communication devices (MTCDs) coexist. The proposed schemes combine non‐orthogonal multiple access (NOMA), orthogonal frequency division multiplexing (OFDM), and index modulation (OFDM‐IM) concepts. In the first scheme, the base station (BS) transmits bits of HTCUs using modulated symbols and bits of MTCDs by the subcarrier activation pattern (SAP). This approach called IM‐NOMA with null subcarriers (IM‐NOMA‐NS) ensures that inactive subcarriers are always null, which improves the system bit error rate (BER) performance. To improve the spectral efficiency (SE), we propose a second approach, termed IM‐NOMA with dual‐mode modulation (IM‐NOMA‐DM), in which the HTCUs' bits are transmitted using two‐dimensional modulation and the MTCDs' bits are transmitted using one‐dimensional modulation and the SAP. For each proposed system, a near‐optimal low‐complexity detector, based on the energy‐detection (ED) and the log‐likelihood ratio (LLR) criterion, is provided to mitigate the detection burden of the optimal maximum‐likelihood (ML) detector. The BER performances and SE of the proposed schemes are investigated. The average BERs of IM‐NOMA‐NS and IM‐NOMA‐DM are derived in closed‐form expressions corroborated by the simulation results. We have proved numerically that the proposed schemes achieve a good trade‐off between BER performance, SE, and the number of supported users, making them more suitable for Internet of Things (IoT) applications.

Non orthogonal multiple access (NOMA) systems are very important for future communications which allows a huge increase in capacity gains when comparing to conventional orthogonal multiple access (OMA) techniques. On the other hand, the power consumption at the access point (AP) should be as low as possible, making single-carrier (SC) schemes, such as single carrier with frequency-domain equalization (SC-FDE), particularly interesting. In this paper, we consider power-efficient NOMA systems employing SC-FDE signals and present a generalized framework to characterize the signals at the output of a nonlinear device. We develop an accurate estimation of the power spectral density (PSD) of the signal associated to each user, showing that the nonlinear distortion effects on some users can be very high. Therefore, we introduce a joint iterative receiver for distortion compensation and interference cancellation based on SC-FDE characterized by low-complexity suitable for scenarios with strong nonlinear effects. Our performance results indicate that the proposed technique is able to reduce considerably the degradation associated to a high power amplifier (HPA) modeled as a memoryless polynomial and allow complexity improvements when compared to conventional schemes, especially when operating at low input back-off (IBO).

Non orthogonal multiple access (NOMA) is being considered a key technology for future communications. In this paper, we provide an analytical characterization of downlink NOMA orthogonal frequency-division multiplexing (OFDM) in presence of nonlinear effects associated to a high power amplifier (HPA) modeled as a memoryless polynomial. Taking advantage of the Bussgang approximation and intermodulation products (IMPs) tools, we present a generalized framework to characterize the nonlinearity for digital NOMA-OFDM systems with Nyquist-sampled signals and oversampled signals. An accurate estimation for the power spectral density (PSD) and signal-to-distortion ratio (SDR) for each user is developed and discussed. Moreover, we introduce a joint iterative approach for distortion compensation and interference cancellation suitable for scenarios with strong nonlinear effects. Our performance results indicate that the proposed technique allows significant bit error rate (BER) and complexity improvements, when compared to conventional schemes.

This paper proposes a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system based on cognitive radio (CR) networks. To reduce the users interference, we propose an efficient user clustering algorithm. Within a cluster, we consider the user with the weak channel gain as a primary user (PU) and the user with the strong channel gain as a secondary user (SU). Given the quality of service for both PUs and SUs, we propose a power allocation (PA) method that maximizes the sum rate. The PA optimization problem is discussed into three scenarios which depend on the available transmit power. Thereafter, we derive a robust solution for the PA issue under imperfect channel state information (CSI) assumptions. Simulation results demonstrate that our proposed NOMA-CR scheme can significantly improve the spectral efficiency (SE).In addition, the proposed robust PA scheme for the imperfect CSI case reduces the performance loss due to the channel uncertainty.

In this paper, a non‐orthogonal multiple access (NOMA) scheme is considered for downlink millimeter‐wave (mmWave) transmissions over beamspace massive multiple‐input multiple‐output (MIMO) channels. To improve the NOMA detection and reduce its complexity, it is assumed that users are grouped in clusters. The major benefit of the NOMA depends on the power allocation (PA) among users. In this context, to ensure the quality of service (QoS) of users, a threshold rate for each user is defined. The aim of this work is to propose a new PA strategy that takes into account the pre‐defined threshold rates and maximizes the achievable sum rate. Thereafter, considering the Karush‐Kuhn‐Tucker (KKT) conditions, a closed‐form solution for the PA issue under perfect and imperfect channel state information (CSI) assumptions is derived. Through simulation results, it is shown that the proposed scheme outperforms the conventional orthogonal multiple access techniques in terms of spectral efficiency (SE) and energy efficiency (EE). In addition, the proposed robust PA scheme for the imperfect CSI case reduces the performance loss due to the channel uncertainty.

In this paper, we provide an analytical performance assessment of downlink non-orthogonal multiple access (NOMA) systems over Nakagami-m fading channels in the presence of nonlinear high-power amplifiers (HPAs). By modeling the distortion of the HPA by a nonlinear polynomial model, we evaluate the performance the NOMA scheme in terms of outage probability (OP) and ergodic sum rate. Hence, we derive a new closed-form expression for the exact OP, taking into account the undesirable effects of HPA. Furthermore, to characterize the diversity order of the considered system, the asymptotic OP in the high signal-to-noise (SNR) regime is derived. Moreover, the ergodic sum rate is investigated, resulting in new upper and lower bounds. Our numerical results demonstrate that the performance loss in presence of nonlinear distortions is very substantial at high data rates. In particular, it is proved that in presence of HPA distortion, the ergodic sum rate cannot exceed a determined threshold which limits its performance compared to the ideal hardware case. Monte-Carlo simulations are conducted and their results agree well with the analytical results.