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This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-inputsingle-output (SISO) near-field communications. By incorporating the finite antenna size constraint using Chu's theory and under the assumption of canonical-minimum scattering, we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we establish the achievable rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

We put forward a new algorithmic solution to the massive unsourced random access (URA) problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays. This paper makes an observation that spatial signature is key to URA in massive connectivity setups. The proposed scheme relies on a slotted transmission framework but eliminates completely the need for concatenated coding that was introduced in the context of the coupled compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA rely on an inner/outer tree-based encoder/decoder to stitch the slotwise recovered sequences. This paper takes a different path by harnessing the nature-provided correlations between the slot-wise reconstructed channels of each user in order to put together its decoded sequences. The required slot-wise channel estimates and decoded sequences are first obtained through the powerful hybrid generalized approximate message passing (HyGAMP) algorithm which systematically accommodates the multiantenna-induced group sparsity. Then, a channel correlation-aware clustering framework based on the expectation-maximization (EM) concept is used together with the Hungarian algorithm to find the slotwise optimal assignment matrices by enforcing two clustering constraints that are very specific to the problem at hand. Stitching is then readily accomplished by associating the decoded sequences to their respective users according to the ensuing assignment matrices. Exhaustive computer simulations reveal that the proposed scheme outperforms by far the only known work from the open literature, which investigates the use of large-scale antenna arrays in the context of massive URA. More precisely, it will be seen that our scheme can accommodate a very large number of active users with much higher total spectral efficiency while using a remarkably smaller number of receive antennas and achieving a low decoding error probability.

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In this paper, we investigate the age-limited capacity of the Gaussian many channel with total N users, out of which a random subset of Ka users are active in any transmission period and a large-scale antenna array at the base station (BS). Motivated by IoT applications and promises of the massive MIMO technology, 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 ζ = 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, Ka, increases, the achievable spectral efficiency is found to increase monotonically to a limit log 2 1 + M Ka . Using the age of information (AoI) metric, first coined in [1], as our measure of data timeliness/freshness, we investigate the trade-offs between the AoI and spectral efficiency in the context massive connectivity with large-scale receiving antenna arrays. Based on our large system analysis, we provide an accurate characterization of the asymptotic spectral efficiency as a function of the number of antennas/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.