Remarkable research activities and major advances have been occurred over the past decade in multiuser multiple-input multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.

High-resolution delay-Doppler estimation is an important requirement for automotive radar systems, especially in multi-target scenarios that require better target separation performance. Orthogonal frequency division multiplexing (OFDM) is a promising candidate waveform for future intelligent transport networks, since it enables the integration of both radar and communication functionalities. Exploring the dual functionality enabled by OFDM, this paper presents a new cooperative method for high-resolution delay-Doppler estimation. The proposed subspace-based method exploits the combination of both the radar and received communication signals to estimate target parameters. The procedure achieves high-resolution delay-Doppler estimation for both uncorrelated, partially correlated and coherent signals, and enables a significant reduction in the required bandwidth when compared to previous approaches which did not exploit the knowledge of the communication signals. Laboratory measurements at 24 GHz and simulation results demonstrate the efficacy of the proposed method for the estimation of multiple targets.

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks.

MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.

The distributed antenna system concept promises to enhance the capacity and diversity of next-generation wireless communication networks, due to the inherently added micro and macro diversity. In this article we first give an overview of the main benefits of a DAS in relation to a collocated antenna system. Next we study the sum-capacity scaling of a multi-user DAS with the number of jointly processed transmit antennas in the downlink. In a practical system this scaling will have implications on the number of antennas worth jointly processing, since the costs of processing an additional antenna can be higher than the additional benefits obtained. Results show that the most important system property to attain the highest capacity gains is symmetry, and the users that attain the maximum gain are those at cell borders. They also confirm that the main DAS feature that makes possible its gains over the CAS architecture are the additional degrees of freedom/diversity provided by such an architecture, which increase the probability of finding a system state with high symmetry and of each user being near one of the transmit antennas.