Currently, 6G lies at the forefront of wireless communications research. It has many additional features compared to those of previous generations. In particular, new key performance indicators (KPIs) are defined, such as latency at the microsecond level and transmission of a higher amount of data, among others. Ensuring these KPIs will undoubtedly boost the development of immersive applications, such as the metaverse, digital twin and others related to Industry 6.0. For this purpose, technologies like edge computing, THz communications and cell-free systems need to be exploited. To further optimize these technologies, Artificial Intelligence (AI) / Machine Learning (ML) are expected to play a pivotal role. Another important aspect is the increased security and privacy in both the infrastructure and the communications between the system and the underlying users. To support these, the current research suggests the network’s architecture to be more user-centric, meaning it should be tailored to each user’s requirements rather than be service-oriented, as was the case with 5G. Therefore, the main goal of this survey article is to review the various aspects of the 6G network architecture from a user-centric standpoint.

Next-generation wireless communications aim to utilize mmWave/subTHz bands. In this regime, signal propagation is vulnerable to interferences and path losses. To overcome this issue, a novel technology has been introduced, which is called reconfigurable intelligent surface (RIS). RISs control digitally the reflecting signals using many passive reflector arrays and implement a smart and modifiable radio environment for wireless communications. Nonetheless, channel estimation is the main problem of RIS-assisted systems because of their direct dependence on the system architecture design, the transmission channel configuration and methods used to compute channel state information (CSI) on a base station (BS) and RIS. In this paper, a concise survey on the up-to-date RIS-assisted wireless communications is provided and includes the massive multiple input-multiple output (mMIMO), multiple input-single output (MISO) and cell-free systems with an emphasis on effective algorithms computing CSI. In addition, we will present the effectiveness of the algorithms computing CSI for different communication systems and their techniques, and we will represent the most important ones.

Extensive research in telecommunications and especially in wireless systems assisted by reconfigurable intelligent surfaces (RIS) has emerged at the forefront of cutting-edge wireless communications nowadays. RISs are composed of several arrays of passive elements, where their purpose is to receive the transmitted signal and send it to the corresponding receiver. One of the main disadvantages of RIS, due to the various processes performed in the RIS controller, is the channel estimation time. Therefore, research has focused on optimized channel estimation algorithms in multiple input- multiple output (MIMO) systems to reduce channel overhead and estimation time. In a previous publication, we presented the methodologies that have been used for channel estimation and our goal in this research is to simulate the proposed algorithms on a common system and common parameters. The presentation of the operation mode of the optimal channel estimation methodologies is implemented in a multiple input- single output (MISO) system and in a Base Station (BS) -RIS-user channel. Then we will present in detail the results of the comparison of the methods and mention future scenarios that can be tested.