This paper explores the idea of usingredirectivereconfigurable intelligent surfaces (RedRIS) to overcome many of the challenges associated with the conventionalreflectiveRIS. We develop a framework for jointly optimizing the switching matrix of the lens-type RedRIS ports along with the active precoding matrix at the base station (BS) and the receive scaling factor. A joint non-convex optimization problem is formulated under the minimum mean-square error (MMSE) criterion with the aim to maximize the spectral efficiency of each user. In the single-cell scenario, the optimum active precoding matrix at the multi-antenna BS and the receive scaling factor are found in closed-form by applying Lagrange optimization, while the optimal switching matrix of the lens-type RedRIS is obtained by means of a newly developed alternating optimization algorithm. We then extend the framework to the multi-cell scenario with single-antenna base stations that are aided by the same lens-type RedRIS. We further present two methods for reducing the number of effective connections of the RedRIS ports that result in appreciable overhead savings while enhancing the robustness of the system. The proposed RedRIS-based schemes are gauged against conventional reflective RIS-aided systems under both perfect and imperfect channel state information (CSI). The simulation results show the superiority of the proposed schemes in terms of overall throughput while incurring much less control overhead.

This paper develops a linear minimum mean-square error (LMMSE) channel estimator that takes advantage of the mutual coupling in antenna arrays. We model the mutual coupling through multiport networks and express the singleuser multiple-input multiple-output (MIMO) communication channel in terms of the impedance and scattering parameters of the antenna arrays. It is shown that appropriately accounting for mutual coupling through the developed physically consistent model leads to remarkable improvements in terms of channel estimation performance. We demonstrate the gains in our algorithm in a rich-scattering environment using a connected array of slot antennas both at the transmitter and receiver sides.

We derive an achievability bound in an uplink setting where N single-antenna devices, of which a random subset of Kausers are active in each transmission period, attempt to update a base-station (BS), equipped with M antennas, with their status packets. Motivated by emerging applications of massive connectivity we consider the asymptotic scenario where both the total number of users and the number of antennas at the BS grow large at a fixed ratio $\zeta = \frac{M}{N}$. Under maximal-ratio combining and perfect channel state information at the receiver, we find that the achievable rate approaches ${\log _2}\left( {1 + \frac{M}{{{K_a}}}} \right)$ in the large system limit. We explore the trade-offs between this achievable rate and the freshness of the status packets using the age of information (AoI) metric. In the limiting regime, we find that the penalty one pays for increasing the data rate is a rise in the minimum AoI obtainable. Finally, we compare recent massive unsourced random access (URA) schemes against the newly established bound.

We investigate the age-limited capacity of the Gaussian many channel with total$N$users, out of which a random subset of$K_{a}$users are active in any transmission period, and a large-scale antenna array at the base station (BS). In an uplink scenario where the transmission power is fixed among the users, we consider the setting in which both the number of users,$N$, and the number of antennas at the BS,$M$, are allowed to grow large at a fixed ratio$\zeta = {M}/{N}$. Assuming perfect channel state information (CSI) at the receiver, we derive the achievability bound under maximal ratio combining. As the number of active users,$K_{a}$, increases, the achievable spectral efficiency is found to increase monotonically to a limit$\log _{2}\left ({1+\frac {M}{K_{a}}}\right)$. Further extensions of the analysis to the zero-forcing receiver as well as imperfect CSI are provided, demonstrating the channel estimation penalty in terms of the mean squared error in estimation. Using the age of information (AoI) metric, first coined by Kaul et al., as our measure of data timeliness or freshness, we investigate the trade-offs between the AoI and spectral efficiency in the context massive connectivity with large-scale receiving antenna arrays. As an extension of Liu and Yu, based on our large system analysis, we provide an accurate characterization of the asymptotic (finite system size) spectral efficiency as a function of the number of antennas and the number of users, the attempt probability, and the AoI. It is found that while the spectral efficiency can be made large, the penalty is an increase in the minimum AoI obtainable. The proposed achievability bound is further compared against recent massive MIMO-based massive unsourced random access (URA) schemes.