Reconfigurable Intelligent Surfaces (RISs) have been recently proposed as an enabling technology for programmable wireless environments. In this paper, we present asymptotic closed-form expressions for the mean and variance of the mutual information for a multi-antenna transmitter-receiver pair in the presence of RISs, using statistical physics methods. While nominally valid in the large-system limit, we show that the derived Gaussian approximation for the mutual information can be quite accurate, even for modest-sized antenna arrays and metasurfaces. The above results are particularly useful when fast-fading conditions are present, which renders channel estimation challenging. We find that, when the channel close to an RIS is correlated, for instance due to small angle spread, which is reasonable for wireless systems with increasing carrier frequencies, the communication link benefits significantly from statistical RIS optimization, resulting in gains that are surprisingly higher than the nearly uncorrelated case. Using our novel asymptotic properties of the correlation matrices of the impinging and outgoing signals at the RISs, we can optimize the metasurfaces without brute-force numerical optimization. When the desired reflection from any of the RISs departs significantly from geometrical optics, the metasurfaces can be optimized to provide robust communication links, without significant need for their optimal placement.

Many channel models for MIMO systems have appeared in the literature. However, with the exception of a few recent results, they are largely focussed on two dimensional (2D) propagation, i.e., propagation in the horizontal plane, and the impact of elevation angle is not considered. The assumption of 2D propagation breaks down when in some propagation environments the elevation angle distribution is significant. Consequently, the estimation of ergodic capacity assuming a 2D channel coefficient alone can lead to erroneous results. In this paper, for cross polarized channels, we define a composite channel model and channel coefficient that takes into account both 2D and 3D propagation. Using this composite channel coefficient we assess the ergodic channel capacity and discuss its sensitivity to a variety of different azimuth and elevation power distributions and other system parameters.

While most literature on MIMO (multiple-input, multiple-output) multiantenna communications and space-time coding has considered optimal decoding, or bounded-distance approximations thereof, the current standardization trend in wireless local area network (WLAN) systems such as IEEE 802.11n, that will use MIMO as an integral component of their physical layer, focuses on simpler schemes, where a linear space-time equalizer is used to separate the signals from the M transmit antennas, thus creating a set of M "virtual" parallel channels. The output of these parallel channels is then fed to a standard SISO (single-input, single-output) decoder. Surprisingly, the diversity-multiplexing tradeoff (DMT) of this simple scheme has not been fully characterized before. We provide such characterization in this work, where we show that both linear zero-forcing (ZF) and linear minimum mean-square error (MMSE) receivers achieve the same DMT, which is equal to that of *one* of the parallel channels, even though coding and decoding is performed across the antennas. Hence, linear receivers incur a dramatic loss of diversity gain with respect to other non-linear schemes. Furthermore, we comment on the different behavior of the MMSE and ZF receivers at finite rate (zero multiplexing gain).

Unmanned aerial vehicle (UAV)-aided communications is a promising emerging technology that will be adopted in the next generation communication networks. In this paper, a number of advanced cooperative UAV-aided communication solutions is proposed and evaluated, which considerably improve the performance of traditional terrestrial networks. Specifically, two generalized multi-UAV-selection schemes are introduced, depending on whether a direct link between the source and the destination is available or not. Once the UAVs have been selected, they may retransmit the source message to the destination using distributed space-time coding, or, in the presence of channel state information, distributed beamforming. A number of metrics to evaluate the performance on a realistic channel model was adopted, including the end-to-end outage probability (OP), the so-called goodput, and the average number of path estimations, as well as the total UAV communications power consumption. Exact expressions for the OP were obtained in terms of finite sums. Moreover, convenient closed-form expressions for the asymptotic OP were derived, valid for large numbers of UAVs, which are also very accurate for few UAVs. All results were compared with numerical simulations, which clearly depict the performance improvement induced by the proposed schemes as well as the impact of various system and channel parameters to the performance.

Smart wireless environments enabled by multiple distributed Reconfigurable Intelligent Surfaces (RISs) have recently attracted significant research interest as a wireless connectivity paradigm for sixth Generation (6G) networks. In this paper, using random matrix theory methods, we calculate the mean of the sum Mutual Information (MI) for the correlated Multiple-Input Multiple-Output (MIMO) Multiple Access Channel (MAC) in the presence of multiple RISs, in the large-antenna number limit. We thus obtain the capacity region boundaries, after optimizing over the tunable RISs’ phase configurations. Furthermore, we obtain a closed-form expression for the variance of the sum-MI metric, which together with the mean provides a tight Gaussian approximation for the outage probability. The derived results become relevant in the presence of fast-fading, when channel estimation is extremely challenging. Our numerical investigations showcased that, when the angle-spread in the neighborhood of each RIS is small, which is expected for higher carrier frequencies, the communication link strongly improves from optimizing the ergodic MI of the multiple RISs. We also found that, increasing the number of transmitting users in such MIMO-MAC-RIS systems results to rapidly diminishing sum-MI gains, hence, providing limits on the number of users that can be efficiently served by a given RIS.