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.

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Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of reflecting elements at the IRS and its passive operation represent an important challenge in the acquisition of the instantaneous channel state information (I-CSI) of all links as it adds a very high overhead to the system and requires equipping the IRS with radio-frequency chains. To overcome this problem, a new approach is proposed in order to optimize beamforming at the BS and the phase shifts at the IRS without considering any knowledge of I-CSI but while only exploring the statistical channel state information (S-CSI). We aim at maximizing the user-achievable rate subject to a maximum transmit power constraint. To achieve this goal, we propose a new two-phase framework. In the first phase, both the beamforming at the BS and IRS are designed based only on S-CSI and, in the second phase, the previously designed beamforming pair is used as an initial solution, and beamforming at the BS and IRS is designed only by considering the feedback of the SNR at UE. Moreover, for each phase, we propose new methods based on Genetic Algorithms. Results show that the developed algorithms can approach beamforming with I-CSI but with significantly reduced channel estimation overhead.

5G and beyond 5G (B5G) wireless systems promise to support services with different requirements in the same network, as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communication (mMTC). One alternative is to consider the network slicing paradigm, where the wireless network resources are shared (or sliced) among active services with different requirements. In addition, another emerging technology, that is considered as a key enabler for B5G wireless systems, is the intelligent reflecting surfaces (IRS). From the deployment of an IRS, it is possible to improve the received signal quality and consequently increase the overall network capacity. Therefore, in this paper, we investigate the use of IRS to support simultaneous eMBB and URLLC services. We evaluate the achievable rate of an IRS-aided radio access network, where the uplink resources are shared between eMBB and URLLC users either under heterogeneous orthogonal multiple access (H-OMA) or heterogeneous non-orthogonal multiple access (H-NOMA) techniques. Results show that exploiting an IRS can considerably increase the eMBB rate and the URLLC reliability simultaneously, regardless of whether operating under H-OMA or H-NOMA. Moreover, we also provide some insights on the best user pairing strategy, showing that higher rates are achieved by matching many eMBB users near to the IRS with a URLLC user close to the base station.INDEX TERMS Enhanced Mobile Broadband (eMBB), Intelligent Reflecting Surfaces (IRS), Network Slicing, Ultra-Reliable Low-Latency Communications (URLLC).

The deployment of satellite networks is key to providing global wireless connectivity for the Internet of Things (IoT). In this line, we consider a cluster of IoT devices served by a constellation of low Earth orbit (LEO) satellites, while slotted Aloha is used as a medium access control technique in the uplink. To characterize the channel, we employ an On-Off fading channel model that estimates the quality of the links between the cluster of IoT devices and the LEO satellites within the constellation, by taking into account their relative positions. Since each relative position of the constellation with respect to the cluster of IoT devices leads to a different throughput for a given traffic load, we propose a novel traffic load distribution strategy based on successive convex approximation (SCA) to maximize the system throughput. The method adequately allocates the traffic load among the different constellation positions with respect to the IoT cluster. Finally, the results show that the proposed method outperforms other recently proposed strategies based on heuristics for traffic load allocation, while it also achieves a stable non-zero throughput even for large traffic loads.