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.

This paper presents the results of field tests conducted in the project “Implementation of TV White Spaces (TVWS) for Internet Access in Brazil”. This study evaluates the feasibility and regulatory implications of TVWS in rural and remote areas. TVWS systems are promising for sensor network applications, enabling efficient and long-range connectivity. The experiments assess the coexistence of TVWS signals, applying, for example, the Remote Area Access Network System for the Fifth Generation (5G-RANGE) using the generalized frequency division multiplexing (GFDM) technique, with the Integrated Services Digital Broadcasting–Terrestrial (ISDB-T) system. Laboratory tests determined the protection ratio (PR) between digital television (DTV) signals and interfering signals, with minimum PR values of $-31.38$ dB on channel n$-1$ and $-33.24$ dB on channel $n+1$ for 5G-RANGE using GFDM, highlighting its low out-of-band emission (OOBE). Field tests confirmed the laboratory results, with the worst recorded PR causing interference being $-30.2$ dB on channel $n-1$. The power restriction to 1 Wp limited coverage, allowing 96 Mbps in 24 MHz BW at 14.7 km from the base station. These results highlight that regulatory adjustments can be made to support TVWS deployment in Brazil.

Selecting the Modulation and Coding Scheme (MCS) from the measured Signal-to-Noise Ratio (SNR) is commonly implemented via precomputed Look-Up Tables (LUTs) derived from analytical models, link-level simulations under idealized assumptions, or empirical Bit Error Rate (BER)/Block Error Rate (BLER) thresholds. In operational Software-Defined Radio (SDR) platforms, however, non-ideal channel estimation, pilot-grid interpolation, equalizer noise enhancement, decoder non-idealities, and Radio Frequency (RF) front-end impairments distort this mapping, causing conventional tables to miss the expected goodput. This paper proposes a measurement-driven Adaptive Modulation and Coding (AMC) strategy based on Contextual Bandit (CB), which learns a stationary SNR→MCS policy directly from link measurements by maximizing downlink Medium Access Control (MAC)-layer goodput. The target application is a TV White Space (TVWS) backhaul in the Ultra High Frequency (UHF) band, designed to provide Internet connectivity to remote areas: both the Base Station (BS) and the remote terminal are fixed, typically under Line-of-Sight (LoS), yielding quasi-stationary channel conditions over many transmission intervals. We implement and evaluate the proposed approach on an SDR-based radio access system in two representative deployments: (i) an RF wireless link that exercises the full Physical Layer (PHY) stack under regulatory transmit-power constraints, and (ii) a Radio over Fiber (RoF) extension enabling tens-of-kilometers reach. The context is the measured SNR (discretized into bins), the action is the MCS index, and the reward is the delivered goodput; transmission errors and retransmissions are naturally captured through reduced goodput. For controlled experimentation and reproducible operating regimes, we constrain the effective SNR within a desired interval by programmatically adjusting the transmit-chain gain. As baselines, we consider as references the link adaptation based on an Inner Loop Link Adaptation (ILLA) obtained from laboratory calibration, an Outer Loop Link Adaptation (OLLA) that periodically adjusts the selected MCS using a BER target, and a supervised Histogram-Based Gradient Boosting Regressor (HGBR) baseline, trained offline from measured samples to approximate the goodput-maximizing SNR→MCS mapping. Experimental results show that the learned CB policy improves the area under the goodput–SNR curve and the mean Spectral Efficiency (SE) over both the baselines within the operational SNR range, while satisfying the BER target. In addition, by directly learning the optimal SNR→MCS mapping from interaction with the real environment, the proposed approach avoids the approximation stage required by the offline HGBR model and eliminates the transient convergence period required by the BER-driven OLLA to adapt from the initial ILLA table toward its steady-state operating point, thereby enabling immediate near-optimal performance in quasi-stationary wireless backhaul links.

This work presents the design and experimental validation of a 2 × 2 MIMO communication system assisted by a directly modulated analog radio-over-fiber (A-RoF) fronthaul, targeting low-complexity connectivity solutions for underserved/remote regions. The study details the complete end-to-end architecture, including a wireless access segment to complement the 20-km optical fronthaul link. The system is implemented on an software defined radio (SDR) platform using GNU Radio 3.7.11, running on Ubuntu 18.04 with kernel 4.15.0-213-generic. It also employs adaptive modulation driven by real-time signal-to-noise ratio (SNR) estimation to keep bit error rate (BER) close to zero while maximizing throughput. Performance is characterized over 20 km of single-mode fiber (SMF) using coarse wavelength division multiplexing (WDM) and assessed through root mean square error vector magnitude (${\mathrm{EVM}}_{\mathrm{RMS}}$), throughput, and spectral integrity. The results identify an optimum radio-frequency drive region around $-16$ dBm enabling high-order modulation (e.g., 256-QAM), whereas RF input powers above approximately $-10$ dBm increase ${\mathrm{EVM}}_{\mathrm{RMS}}$ due to nonlinearity in the RF front-end/low-noise amplifier (LNA) and direct modulation stage, forcing the adaptive scheme to reduce modulation order and throughput. Over the optical-power sweep, when the incident optical power exceeds approximately $-8$ dBm, the system reaches ∼130 Mbps (24-MHz channel) with ${\mathrm{EVM}}_{\mathrm{RMS}}$ approaching ∼1%, highlighting the need for careful joint tuning of RF drive, optical launch power, and wavelength allocation across transceivers. Finally, the integrated access link employs diplexers for transmitter/receiver separation in a 2 × 2 configuration with 2.8 m antenna separation and low channel correlation, demonstrating a 10 m proof-of-concept range and enabling end-to-end spectrum/EVM/throughput observations across the full communication chain.