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This systematic review investigates compression algorithms for three-dimensional content, focusing on recent advancements. It categorizes the methodologies into traditional, learning-based, and semantic approaches. The review includes 52 studies selected based on criteria including publication date, peer review status, and relevance to the field. The analysis highlights the significant contributions of learning-based and semantic techniques in advancing 3D content compression. Notably, some reviewed learning-based methods demonstrated over 95% improvement in rate optimization compared to standard point cloud compression methods. Despite the comprehensive coverage, the review acknowledges certain limitations due to potential biases in study selection and the inherent heterogeneity of the included research. The findings underscore the importance of continued exploration in learning-based and semantic compression for enhancing the efficiency and applicability of 3D content technologies.

Holographic-type communication (HTC) is on the verge of revolutionizing current communication paradigms. It will seamlessly connect people from distant locations through a fully immersive experience that will engage all five senses: sight, hearing, touch, smell, and taste. However, the practical realization and widespread adoption of HTC impose significant demands on current networks and end-user devices. While recent studies have primarily focused on discussing the HTC challenges and potential research directions, this paper takes a further step by evaluating the capabilities of wireless networks to meet HTC requirements. Specifically, it highlights the limitations of the fifth-generation (5G) networks by identifying HTC-related Key Performance Indicators (KPIs) and explores the potential of the sixth-generation (6G) networks. Moreover, it not only proposes potential research approaches for HTC enhancement but also analyzes their impact on the challenges in HTC implementation. Finally, the paper questions the ubiquitous potential of 6G, suggesting that a coordinated approach leveraging artificial intelligence (AI) for jointly optimizing user sites and communication networks is the most promising strategy, rather than solely relying on the capabilities of specific network generations.

Radio environment maps (REMs) have been established as an important tool in spectrum occupancy characterization toward more efficient coverage planning and design of resource allocation algorithms. The utilization of deep learning (DL) techniques for REM reconstruction, particularly when working with a limited number of samples, has garnered significant research attention owing to its speed and accuracy. This is particularly relevant for spatial three-dimensional REMs, which involve an exponential increase in the number of samples compared to the two-dimensional case. This paper presents a method for determining the optimal sampling grid resolution based on two key criteria, 1) the generated map’s similarity to the covariance matrix mean (CMM) of measurements collected in real world (RW) scenarios, and 2) reduced computational complexity. Subsequently, three prominent DL models for REM reconstruction, are evaluated, with the convolutional autoencoder (CAE) achieving the best performance. To enhance its accuracy, a neural network (NN) design approach is introduced, which involves assessing the difference in CMM between the original example and the output for each layer in the NN architecture. This method identifies the layers that introduce the smallest difference and determines their optimal number of filters. Thus, the model’s complexity is reduced, and the accuracy is improved. Normalized root mean square error of as little as −35 dB is achieved for a sampling rate of 30%.

The increasing densification and diversification of modern and upcoming wireless networks have become an important motivation for the development of agile spectrum sharing. Radio environment maps (REMs) are a basic tool for spectrum utilisation characterisation and adaptive resource allocation, but they need to be estimated through accurate interpolation methods. This work evaluated the performance of two established algorithms for spatial three-dimensional (3D) data collected in two real-world scenarios: indoors, through a mechanical measuring system, and outdoors, through an unmanned aerial vehicle (UAV) for measurement collection. The investigation was undertaken for the complete dataset on two-dimensional (2D) planes of different altitudes and for a subset of limited samples (representing the regions of interest or RoIs), which were combined together to describe the spatial 3D environment. A minimum error of −9.5 dB was achieved for a sampling ratio of 21%. The methods’ performance and the input data were analysed through the resulting Kriging error standard deviation (STD) and the STD of the distances between the measurement and the estimated points. Based on the results, several challenges for the interpolation performance and the analysis of the spatial RoIs are described. They facilitate the future development of 3D spectrum occupancy characterisation in indoor and UAV-based scenarios.

In the field of holographic-type communication (HTC), this paper presents a comprehensive exploration of existing technologies and proposes a novel, modular architecture for holographic telepresence systems (HTPSs). We substantiate our architectural framework through a practical implementation, demonstrating its modularity, interoperability, and versatility. Quantitative and qualitative assessments reveal both the promise and areas for improvement within our platform. Our findings reinforce the premise that the key to unlocking HTC’s future lies in modularity and interoperability, serving as critical pillars for efficient standardization and the development of minimal viable products.

With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.

Holographic-type communication (HTC) permits new levels of engagement between remote users. It is anticipated that it will give a very immersive experience while enhancing the sense of spatial co-presence. In addition to the newly revealed advantages, however, stringent system requirements are imposed, such as multi-sensory and multi-dimensional data capture and reproduction, ultra-lightweight processing, ultra-low-latency transmission, realistic avatar embodiment conveying gestures and facial expressions, support for an arbitrary number of participants, etc. In this paper, we review the current limitations to the HTC system implementation and systemize the main challenges into a few major groups. Furthermore, we propose a conceptual framework for the realization of an HTC system that will guarantee the desired low-latency transmission, lightweight processing, and ease of scalability, all accompanied with a higher level of realism in human body appearance and dynamics.

Resource allocation (RA) has always had a prominent place in wireless communications research due to its significance for network throughput maximization, and its inherent complexity. Concurrently, graph-based solutions for RA have also grown in importance, providing opportunities for higher throughput and efficiency due to their representational capabilities, as well as challenges for realizing scalable algorithms. This article presents a comprehensive review and analysis of graph-based RA methods in three major wireless network types: cellular homogeneous and heterogeneous, device-to-device, and cognitive radio networks. The main design characteristics, as well as directions for future research, are provided for each of these categories. On the basis of this review, the concept of Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) is proposed. It describes the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks via a hypergraph and its attributes. In addition, the implementation challenges of GRIST are explained in detail. Finally, to complement GRIST, a scheme for determining the appropriate balance between different design considerations is introduced. It is described via a simplified complete graph-based design process for resource management algorithms.