Compact ultra-massive antenna arrays (CUMA) share key characteristics with holographic communication systems, featuring densely spaced and individually controlled antenna elements that enable precise manipulation of electromagnetic waves. In this paper, we investigate the spectral efficiency (SE) of CUMA deployed within some constrained physical space. Departing from prior works that assume ideal isotropic antennas, we derive a closed-form expression for the SE assuming a line-of-sight (LoS) channel at the electromagnetic level, explicitly accounting for mutual coupling and antenna orientation. The analysis reveals that the channel gain is highly sensitive to both the array orientation and individual antenna directions. In the single-user case, our results show that the optimal orientation of the user array is either aligned parallel or perpendicular to the signal direction, depending on the inter-element spacing. Notably, near-optimal channel gain is achieved when individual antennas are oriented perpendicular to the signal direction. In the multi-user case, we further optimize transceiver configurations under mutual coupling constraints. Simulation results confirm that SE is strongly influenced by the directional alignment of user antennas and array placement in the near-field regime. CUMA significantly outperforms traditional half-wavelength spaced arrays in terms of SE when constrained to the same physical aperture.

By decoupling the dedicated radio frequency (RF) chain into transmit RF (TX RF) chain and receive RF (RX RF) chain, the asymmetrical system can flexibly equip the downlink/uplink array with different number of TX/RX RF chain according to the practical demand in a massive multiple-input multiple-output Internet of Things (IoT) network. To reduce cost and power consumption, this paper maximizes the uplink resource efficiency (RE) under Weichselberger channel model by designing transmit covariance matrices and receive antenna selection (RAS). In IoT networks with multiple IoT nodes, we propose an alternate optimization algorithm to iteratively optimize transmit covariance matrices and RAS by exploiting statistical channel state information. Specifically, for correlated channels, we propose a penalty method-based algorithm for RAS which utilizes Dinkelbach’s transform and linear relaxation to tackle the intractable fractional function and binary constrain, respectively. Compared with greedy search, the proposed algorithm has lower complexity without much loss of performance. For independent identically distributed channels, we simplify the RE maximization problem and provide the necessary conditions of the optimal number of receive antennas and transmit power. Finally, the validness of our conclusions as well as the effectiveness of proposed algorithms are illustrated by numerical simulations.

In “Channel Estimation for Massive MIMO-OTFS System in Asymmetrical Architecture,” by Chen et al., a two-stage channel estimation scheme is proposed based on the input-output relationship of orthogonal time frequency space (OTFS) modulation in asymmetrical architecture. This correspondence provides some comments and corrections to the derivation of the OTFS input-output relationship published in [1].

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multi-user XL-MIMO scenario, where only partial antennas can receive users’ signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

The orthogonal time frequency space (OTFS) is poised to become a pivotal technology for the next generation of mobile communications, due to its inherent robustness against Doppler shift. By combining OTFS technology with massive multiple-input multiple-output (MIMO) technology, users can experience high-quality communication services even in highly mobile scenarios. In this letter, we extend the massive MIMO-OTFS system to an asymmetrical architecture with unequal number of transceiver radio frequency chains. To overcome the channel inconsistency and recover the downlink channel by partial uplink channel, we utilize coprime patterns and propose a channel estimation algorithm that firstly extracts the angle parameters from the virtual array and then estimates the remaining channel parameters, which effectively reduces the three-dimensional search space to two dimensions. Our numerical simulations demonstrate that the proposed algorithm enhances the accuracy of channel estimation with much lower complexity.