The demand for wireless connectivity has grown exponentially over the last years. By 2030 there should be around 17 billion of mobile-connected devices, with monthly data traffic in the order of thousands of exabytes. Although the Fifth Generation (5G) communications systems present far more features than Fourth Generation (4G) systems, they will not be able to serve this growing demand and the requirements of innovative use cases. Therefore, Sixth Generation (6G) Networks are expected to support such massive connectivity and guarantee an increase in performance and quality of service for all users. To deal with such requirements, several technical issues need to be addressed, including novel multiple-antenna technologies. Then, this survey gives a concise review of the main emerging Multiple-Input Multiple-Output (MIMO) technologies for 6G Networks such as massive MIMO (mMIMO), extremely large MIMO (XL-MIMO), Intelligent Reflecting Surfaces (IRS), and Cell-Free mMIMO (CF-mMIMO). Moreover, we present a discussion on how some of the expected key performance indicators (KPIs) of some novel 6G Network use cases can be met with the development of each MIMO technology.

Telecommunications play a pivotal role in shaping today’s interconnected world by fostering global development, supporting seamless information exchange across vast distances, and revolutionizing the interactions between individuals, businesses, and governments. Accessible and reliable communication networks transcend geographical barriers, promoting economic growth, the dissemination of knowledge, and societal connectivity. The integration of artificial intelligence into telecommunications has been transformative, revolutionizing the entire industry by improving system efficiency, allowing new services, and reducing complexity. By leveraging machine learning algorithms, telecommunication operators analyze vast data sets to gain insights into customer behavior, network performance, and market trends. This data-driven approach enhances service efficiency, leading to optimized network deployment, improved customer experience, and targeted marketing strategies. Machine learning’s impact extends to resource allocation optimization. Intelligent management of network resources reduces latency, congestion, and downtime, ensuring enhanced user experiences and increased overall network capacity. This optimization is vital for integrating emerging technologies like the Internet of Things and future generations of mobile systems and promoting sustainability by reducing energy consumption, contributing to a greener future. As technology evolves, the synergy between telecommunications and artificial intelligence will pave the way for a more connected, intelligent, and prosperous future. Given the relevance of this research topic, this paper presents a comprehensive survey of machine learning techniques applied to resource allocation in wireless communication systems. The objective is to guide the scientific and industrial community in the optimized selection of machine learning techniques according to network demands and network resource allocation to be refined. Additionally, it aims to encourage an in-depth discussion regarding the limitations presented in the current literature and future challenges for researchers.

As society evolves as a whole, new demands arise with increasingly demanding prerequisites, consequently requiring more significant effort to be met. Such demands cover emerging applications, such as remote surgeries in Smart Health use cases, whose latency and reliability network requirements cannot be met by current communication systems; or simply improving current applications with more challenging requirements to be achieved, such as increasing the transmission rate in a mobile network, offering Quality of Service (QoS), and consequently, better user experience. Therefore, enabling technologies must be chosen to design an appropriate 6G architecture to address such demands. However, the explosion of emerging applications focused on different scopes and requirements to be met makes choosing these enabling technologies extremely complex and unpredictable. Thus, this article aims to create a methodology for analyzing the relevance of enabling technologies and use it to design an optimal architecture capable of meeting the 6G demands. For this purpose, two methods named as Average (AVG) and Analytic Hierarchy Process (AHP) have been selected, whose objective is to determine the relevance of an enabler for the 6G architecture, taking into account different degrees of influencing variables for this analysis, such as adherence to a certain architectural model; popularity in the research area; degree of innovation; synergy with other enablers; and support for requirements. Each of these methods presents a particular result. In the case of the AVG method, the criteria and variables are evaluated independently, and the arithmetic mean is employed to combine the evaluations into a single measure of suitability. In contrast, the AHP method considers the relative importance of criteria and variables in order to classify an optimal set of enabling technologies capable of fulfilling the key roles to be performed by a 6G architecture, and consequently meeting the main 6G demands. Our evaluation provides a unique perspective on 6G enablers, identifying issues and fostering research for future mobile architectures. The results obtained also provide researchers with the necessary information to stay updated on emerging enabling technologies and their suitability for designing new optimized 6G architectures.

This paper proposes an extension to the η-μ fading model by including a parameter that quantifies the clustering imbalance between in-phase and quadrature components. The inclusion of such a parameter renders the model enormously flexible, extending its reach of application in a wider range of scenarios. Despite the inclusion of an additional parameter, the equations obtained remain as mathematically tractable as those for the original η-μ model. Several new closed-form expressions for important statistics are then obtained, including: (i) probability density functions for both envelope and phase; (ii) cumulative distribution functions for both envelope and phase; (iii) arbitrary moments for the envelope; (iv) moment generating function for the envelope; (v) average bit error rate for coherent binary systems; (v) level crossing rate, average fading duration, and phase crossing rate; and (vi) several other joint distributions encompassing envelope, phase, and their respective time derivatives. It is noteworthy that many of the closed-form formulations presented here are not available even for the simple η-μ model.

Sub-THz communications are envisioned to unlock ultra-high data rates, ultra-low latencies, and massive connectivity in future wireless systems by utilizing bandwidths of up to tens of GHz. This paper presents the holistic perspective on sub-THz communications developed within the European sixth-generation (6G) flagship project Hexa-X-II. According to this perspective, successfully deploying sub-THz systems will require addressing several physical-layer (PHY) challenges associated with the harsh signal propagation and hardware limitations. In addition, sub-THz systems will drive advancements in essential wireless applications beyond communications, such as joint communications and sensing (JCAS). In this context, we present a collection of technical contributions and key findings from the Hexa-X-II project that have shaped this perspective. In contrast to existing works, these efforts collectively tackle the fundamental PHY challenges of sub-THz communications, i.e., understanding and modeling of radio propagation, radio-frequency (RF) power consumption and complexity, and hardware impairments. To achieve this, we present several results across three core areas, i.e., signal propagation and channel modeling, RF transceiver design, and PHY enablers, offering useful insights into the development of sub-THz systems. Lastly, we provide an overview of sub-THz JCAS, emphasizing it as one of the most promising applications in the sub-THz range.

A number of new results aiming at facilitating the use of the α-η-κ-μ fading model are presented. These results include: (i) fast convergent, with no recursions, series representations for the envelope PDF and CDF; (ii) higher order moments and (iii) moment generating function of the envelope; (iv) asymptotic behaviour of the envelope PDF and also of the envelope CDF; (v) a procedure for parameter estimation; (vi) new closed-form expressions for particular cases of the envelope distribution; (vii) new mathematical identity for the generalized Laguerre polynomial and the generalized hypergeometric function; and (viii) approximate, but tight, series representation for the phase PDF. As application examples, the following are given: (i) outage probability; (ii) outage capacity; (iii) amount of fading; (iv) bit error rate, for which the asymptotic behaviour is also presented.