Theoretical and experimental studies of multiple-input/multiple-output (MIMO) radio channels are presented in this paper. A simple stochastic MIMO model channel has been developed. This model uses the correlation matrices at the mobile station (MS) and base station (BS) so that results of the numerous single-input/multiple-output studies that have been published in the literature can be used as input parameters. In this paper, the model is simplified to the narrowband channels. The validation of the model is based upon data collected in both picocell and microcell environments. The stochastic model has also been used to investigate the capacity of MIMO radio channels, considering two different power allocation strategies, water filling and uniform and two different antenna topologies, 4 4 and 2 4. Space diversity used at both ends of the MIMO radio link is shown to be an efficient technique in picocell environments, achieving capacities within 14 b/s/Hz and 16 b/s/Hz in 80% of the cases for a 4 4 antenna configuration implementing water filling at a SNR of 20 dB.

Abstract-A simple statistical model of azimuthal and temporal dispersion in mobile radio channels is proposed. The model includes the probability density function (pdf) of the delay and azimuth of the impinging waves as well as their expected power conditioned on the delay and azimuth. The statistical properties are extracted from macrocellular measurements conducted in a variety of urban environments. It is found that in typical urban environments the power azimuth spectrum (PAS) is accurately described by a Laplacian function, while a Gaussian pdf matches the azimuth pdf. Moreover, the power delay spectrum (PDS) and the delay pdf are accurately modeled by an exponential decaying function. In bad urban environments, channel dispersion is better characterized by a multicluster model, where the PAS and PDS are modeled as a sum of Laplacian functions and exponential decaying functions, respectively.

The focus of wireless research is increasingly shifting toward 6G as 5G deployments get underway. At this juncture, it is essential to establish a vision of future communications to provide guidance for that research. In this paper, we attempt to paint a broad picture of communication needs and technologies in the timeframe of 6G. The future of connectivity is in the creation of digital twin worlds that are a true representation of the physical and biological worlds at every spatial and time instant, unifying our experience across these physical, biological and digital worlds. New themes are likely to emerge that will shape 6G system requirements and technologies, such as: (i) new man-machine interfaces created by a collection of multiple local devices acting in unison; (ii) ubiquitous universal computing distributed among multiple local devices and the cloud; (iii) multi-sensory data fusion to create multi-verse maps and new mixed-reality experiences; and (iv) precision sensing and actuation to control the physical world. With rapid advances in artificial intelligence, it has the potential to become the foundation for the 6G air interface and network, making data, compute and energy the new resources to be exploited for achieving superior performance. In addition, in this paper we discuss the other major technology transformations that are likely to define 6G: (i) cognitive spectrum sharing methods and new spectrum bands; (ii) the integration of localization and sensing capabilities into the system definition, (iii) the achievement of extreme performance requirements on latency and reliability; (iv) new network architecture paradigms involving sub-networks and RAN-Core convergence; and (v) new security and privacy schemes.

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The main goal of this letter is to obtain models for path loss exponents and shadowing for the radio channel between airborne unmanned aerial vehicles (UAVs) and cellular networks. In this pursuit, field measurements were conducted in live LTE networks at the 800 MHz frequency band, using a commercial UAV. Our results show that path loss exponent decreases as the UAV moves up, approximating freespace propagation for horizontal ranges up to tens of kilometers at UAV heights around 100 m. Our findings support the need of height-dependent parameters for describing the propagation channel for UAVs at different heights.