As we advance towards 6G communication systems, the number of network devices continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS), Cognitive Radio (CR) exploits the frequency spectrum dynamically by detecting and transmitting in underutilized bands. The performance of 6G networks can be enhanced by utilizing Deep Neural Networks (DNNs) to perform SS. This paper provides a detailed survey of several Deep Learning (DL) algorithms used for SS by classifying them as Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, combined CNN-LSTM architectures and Autoencoders (AEs). The works are discussed in terms of the input provided to the DL algorithm, data acquisition technique used, data pre-processing technique used, architecture of each algorithm, evaluation metrics used, results obtained, and comparison with standard SS detectors. This survey further provides an overview of traditional ML algorithms and simple Artificial Neural Networks (ANNs) while highlighting the drawbacks of conventional SS approaches for completeness. A description of some publicly available Radio Frequency (RF) datasets is included and the need for comprehensive RF datasets and Transfer Learning (TL) is discussed. Furthermore, the research challenges related to the use of DL for SS are highlighted along with potential solutions.

Simultaneous localization and mapping (SLAM) algorithm has enabled the automation of mobile robots in unknown environments. It enables the robot to navigate through an unknown trajectory by employing sensors that provide measurements to infer the surrounding environment and use this information to localize the robot. Sensor technology plays a key role in defining the quality of measurements as it affects the overall performance of SLAM. While visual sensors, like cameras, can capture rich features of the environment, they, however, fail to work in low-light conditions. On the other hand, radio frequency sensors are invariant to light conditions, however, high-frequency signals such as millimeter wave (mm-wave) are prone to severe channel attenuation, therefore, they are suitable for short-range indoor applications. Despite the high localization accuracy that the mm-wave frequency band has to offer, its shortcomings have limited the amount of research work carried out to enhance the performance of SLAM. Therefore, this paper aims to provide an overview of the recent developments in radio SLAM, with a specific focus on mm-wave enabled localization and SLAM methods. However, some notable research work based on other radio frequency sensors has also been discussed. In addition, we highlight the role of deep learning-based methods for localization and identify some of the key challenges in data-driven implementation.

In this paper, we investigate a full duplex (FD) multi-user non-orthogonal multiple access (NoMA) communication system, based on the optimization of received signalto- interference-plus-noise ratio (SINR) per unit power. Since the communication system operates in FD mode, co-channel interference (CCI) and self-interference (SI) dominate the system’s performance. Accordingly, to combat the CCI, we adopt a gametheoretic approach and propose users clustering algorithms and to suppress the SI, we formulate an optimization problem to maximize the power-normalized SINR (PN-SINR). While the user clustering optimization problem is constrained by i) the successive interference cancellation (SIC) constraint and ii) two binary constraints for the allocations of UL and DL users, the PN-SINR problem is constrained by i) total transmit power budget at the base station and uplink (UL) users, ii) the fundamental condition for the implementation of successive interference cancellation in NoMA, and iii) the minimum fairness condition for UL users. The original PN-SINR problem is non-convex and hence is converted into an equivalent subtractive-form problem, after which we propose an iterative algorithm to find the optimal power allocation policy. Properties of all the proposed algorithms are thoroughly investigated and numerical results are provided. Based on the channel conditions and suppression level of SI and CCI, the superiority of the proposed FD-NoMA system over half duplex NoMA and FD orthogonal multiple access systems is verified.

In the beyond 5G networks, extraordinary demands for data rates and capacity are to be met. A possible candidate to address these challenges is Non-Orthogonal Multiple Access (NOMA) technique, which leads to higher diversity gains and massive connectivity. One caveat of NOMA is the increased receiver complexity to nullify the Inter User Interference (IUI) through Successive Interference Cancellation (SIC). In this paper, a cooperative relaying scheme is employed to improve the overall diversity gain and data rates of the NOMA system. Extrinsic Information Transfer (EXIT) charts are employed to examine the cooperative NOMA system’s user fairness as well as its performance while implementing the IRregular Convolutional Code (IRCC). The EXIT chart using IRCC evaluates the convergence analysis for the proposed system. Furthermore, the implementation of EXIT charts for optimization convergence is exploited by using power optimization and the SIC for the joint rate to evaluate the fairness of the system. Simulation results shows that cooperative NOMA helps to achieve higher diversity gain and improved data rate for the cooperative NOMA system.

We consider linear precoding in the downlink of a multiuser multiple-input-single-output (MISO) overlay cognitive radio (CR) network, wherein a primary base station (PBS) and a cognitive base station (CBS) share the same frequency band to transmit to a number of primary users (PUs) and secondary users. Conventionally, linear precoding techniques in a CR network aim to limit or completely cancel interference to the PUs while achieving maximum downlink throughput of the CR system. In this paper, we investigate novel adaptive linear precoding techniques at the CBS to exploit the interference to the primary and secondary systems, instead of its cancellation, by exploring the potential of making use of interference energy when the interference between the two systems is mutually constructive on an instantaneous basis. By doing so, the received signal-to-noise ratio (SNR) is enhanced by the signal energy gleaned from the constructive interference. In this direction, constructive-interference-based adaptive linear precoding techniques are proposed for the CR network, assuming a simple zero-forcing precoder at the PBS and noncausal knowledge of the primary message at the CBS. The presented analysis and simulations show that the proposed precoding techniques outperform conventional techniques, as the CR transmission actively enhances the performance of the PUs through constructive interference while achieving significant throughput for its own transmission.

This article examines the transformative potential of integrating reconfigurable intelligent surfaces (RISs) into sixth-generation (6G) wireless non-terrestrial networks (NTNs). The focus is on the RIS’s capability to address diverse user requirements, including secure data transmission, power efficiency, extended coverage, and enhanced data rates. The paper delves into the synergy between RISs and NTNs, emphasizing key components like multiple-input multiple-output (MIMO) systems and advanced radio communications. Additionally, it highlights the crucial role of artificial intelligence (AI) and machine learning (ML) in optimizing RIS-based beamforming to solve scientific and engineering challenges while ensuring energy efficiency and sustainability in NTN operations. By positioning RISs as a key enabler in shaping the future of wireless communication systems, this research underscores their significance in unlocking the full potential of NTNs and advancing next-generation wireless communications. This paper contributes valuable insights and projections for future research directions, highlighting RISs’ potential to revolutionize NTNs for 6G technologies.