This letter casts the problem of optimum discrete beamforming as the computation of the Minkowski sum of convex polygons, which is itself a convex polygon. The number of vertices of the latter is at most the sum of the number of vertices of the original polygons, enabling its efficient computation. This original and intuitive formulation confirms that the optimum beamforming solution can be found efficiently in broad generality.

We propose a novel approach for channel state information (CSI) compression in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, where the frequency-domain channel matrix is treated as a high-dimensional complex-valued image. Our method leverages transformer-based nonlinear transform coding (NTC), an advanced deep-learning-driven image compression technique that generates a highly compact binary representation of the CSI. Unlike conventional autoencoder-based CSI compression, NTC optimizes a nonlinear mapping to produce a latent vector while simultaneously estimating its probability distribution for efficient entropy coding. By exploiting the statistical independence of latent vector entries, we integrate a transformer-based deep neural network with a scalar nested-lattice uniform quantization scheme, enabling low-complexity, multi-rate CSI feedback that dynamically adapts to varying feedback channel conditions. The proposed multi-rate CSI compression scheme achieves state-of-the-art rate-distortion performance, outperforming existing techniques with the same number of neural network parameters. Simulation results further demonstrate that our approach provides a superior rate-distortion trade-off, requiring only 6 % of the neural network parameters compared to existing methods, making it highly efficient for practical deployment.

This paper presents an algorithm for finding the optimal configuration of active reconfigurable intelligent surface (RIS) when both transmitter and receiver are equipped with a single antenna each. The resultant configuration is globally optimal and it takes linear time for the computation. Moreover, there is a closed-form expression for the optimal configuration when the direct link vanishes, which enables further analysis.

For frequency-division-duplexing (FDD) systems, channel state information (CSI) should be fed back from the user terminal to the base station. This feedback overhead becomes problematic as the number of antennas grows. To alleviate this issue, we propose a flexible CSI compression method using variational autoencoder (VAE) with an entropy bottleneck structure, which can support multi-rate and variable-length operation. Numerical study confirms that the proposed method outperforms the existing CSI compression techniques in terms of normalized mean squared error.

This paper studies the optimization of an active reconfigurable intelligent surface (RIS) under two power budget models. Two algorithms, one for each power constraint, for finding the optimal RIS configuration are proposed under the proviso that both the transmitter and receiver are equipped with a single antenna each. The computational complexities of these methods are linear in the number of RIS elements, and the resultant configurations are globally optimal.

With increasing frequencies, bandwidths, and array apertures, the phenomenon of beam squint arises as a serious impairment to beamforming. Fully digital arrays with true time delay per antenna element are a potential solution, but they require downconversion at each element. This paper shows that hybrid arrays can perform essentially as well as digital arrays once the number of radio-frequency chains exceeds a certain threshold that is far below the number of elements. This threshold is determined by only a few physical parameters—bandwidth, array size, and beamforming direction—and can be expressed in a remarkably simple closed form. The result is robust, holding also for suboptimum yet highly appealing beamspace architectures.

This paper deals with line-of-sight (LOS) MIMO communication via an intelligent reflecting surface (IRS). It is shown that the number of spatial degrees of freedom (DOF) afforded by this setting grows in proportion with the IRS aperture, as opposed to being dictated by the transmit and receive apertures; this buttresses the interest in IRS deployments at mmWave and terahertz frequencies, with wavelengths and transmission ranges small enough to enable LOS MIMO. Explicit and simple-to-implement IRS phase shifts are put forth that achieve, not only the maximum number of DOF, but the capacity, under a certain geometrical condition. The insights leading to the proposed phase shifts, and to the optimality condition itself, are asymptotic in nature, yet extensive simulations confirm that the performance is excellent for a broad range of settings and even if the optimality condition is not strictly met.

Motivated by the widespread adoption of the parabolic wavefront model for line-of-sight (LOS) multiple-input multiple-output (MIMO) communication, this paper presents a comprehensive analysis of this model’s validity and simple conditions that ensure its applicability. Then, with the model’s scope clearly delineated, the paper expounds a number of properties of the channel that results from applying it. Connections are drawn among these properties under the umbrella of a Fourier interpretation, and their significance to LOS MIMO communication is substantiated.

This paper considers line-of-sight multiple-input multiple-output communication, of interest at millimeter-wave and sub-terahertz frequencies. Building on how, with an angular rotation dependent on the signal-to-noise ratio (SNR), uniform linear arrays (ULAs) can tightly approach the capacity of such channels, we assess the performance of rotatable uniform rectangular arrays (URAs). The changeover from ULAs to URAs is motivated by the interest in reducing the array footprints. For both isotropic and directive antennas, various types of rotatable URAs are devised that-except at very low SNRs-perform remarkably close to their ULA counterparts with considerably smaller footprints.

This paper establishes an upper bound on the capacity of line-of-sight multiantenna channels over all possible antenna arrangements and shows that uniform linear arrays (ULAs) with an SNR-dependent rotation of transmitter and/or receiver can closely approach such capacity-and in fact achieve it at low and high SNR, and asymptotically in the numbers of antennas. Then, as an alternative to mechanically rotating ULAs, we propose to electronically select among multiple ULAs having a radial disposition at either transmitter or receiver, and we bound the shortfall from capacity as a function of the number of such ULAs. With only three ULAs, properly angled, 96% of the capacity can be achieved. Finally, we further introduce reduced-complexity precoders and linear receivers that capitalize on the structure of the channels spawned by these configurable ULA architectures.

We establish an upper bound on the information-theoretic capacity of line-of-sight (LOS) multiantenna channels with arbitrary antenna arrangements and identify array structures that, properly configured, can attain at least 96.6% of such capacity at every signal-to-noise ratio (SNR). In the process, we determine how to configure the arrays as a function of the SNR. At low- and high-SNR specifically, the configured arrays revert to simpler structures and become capacity-achieving.

A novel channel estimation framework is presented for orthogonal frequency division multiplexing (OFDM) system that operates with low-precision analog-to-digital converters (ADCs). The framework is based on concatenated minimum mean square error (MMSE) estimation, which consists of an inner and an outer MMSE estimation blocks. The outer MMSE estimation finds the compound signal, i.e., the unknown channel multiplied by a pilot signal, from the nonlinear distortion of the received signal by the quantization and its mean square error (MSE). Using the estimated compound signal and its MSE, the inner MMSE estimation estimates the desired signal, i.e., the unknown channel value, assuming the resulting estimation error of the outer MMSE block follows the Gaussian distribution. One major finding is that the proposed framework is analytically tractable for quantifying the effective quantization error, unlike the widely-used Bussgang-based approach. From simulations, it is shown that the proposed channel estimation framework provides a significant gain over conventional Bussgang-based methods using the approximated covariance matrix of the quantization error.

In this paper, we analyze the information-theoretical limits of a multiple-input single-output (MISO) channel with one-bit transceiver. In particular, we present a capacity expression in a closed form when perfect channel state information (CSI) is available at both a transmitter and a receiver. One major finding is that the capacity-achieving transmission strategy is to uniformly use four multi-dimensional signal points. The four signal points are chosen as a function of the channel and the signal-to-noise ratio (SNR) from the channel input set constructed by a spatial lattice modulation (SLM) method. From our analysis and simulation results, we also demonstrate that the capacity loss caused by the use of one-bit DACs is sufficiently small throughout the entire SNR regime even with a few-bit CSIT feedback that suffices to achieve the capacity.

A one-bit wireless transceiver is a promising communication architecture that not only can facilitate the design of mmWave communication systems but also can extremely diminish power consumption. The non-linear distortion effects by one-bit quantization at the transceiver, however, change the fundamental limits of communication rates. In this paper, the capacity of a multiple-input single-output (MISO) fading channel with one-bit transceiver is characterized in a closed form when perfect channel state information (CSI) is available at both a transmitter and a receiver. One major finding is that the capacity-achieving transmission strategy is to uniformly use four multi-dimensional constellation points. The four multi-dimensional constellation points are optimally chosen as a function of the channel and the signal-to-noise ratio (SNR) among the channel input set constructed by the spatial lattice modulation method. As a byproduct, it is shown that a few-bit CSI feedback suffices to achieve the capacity. For the case when CSI is not perfectly known to the receiver, practical channel training and CSI feedback methods are presented, which effectively exploit the derived capacity-achieving transmission strategy.

In this paper, we propose computationally efficient yet near-optimal soft-output detection methods for coded millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) systems with low-precision analog-to-digital converters (ADCs). The underlying idea of the proposed methods is to construct an extremely sparse inter-symbol-interference channel model by jointly exploiting the delay-domain sparsity in mmWave channels and a high quantization noise caused by low-precision ADCs. Then, we harness this sparse channel model to create a trellis diagram with a reduced number of states and a factor graph with very sparse edge connections, which are used for the computationally efficient soft-output detection methods. Using the reduced trellis diagram, we present a soft-output detection method that computes the log-likelihood ratios (LLRs) of coded bits by optimally combining the quantized received signals obtained from multiple receive antennas using a forward-and-backward algorithm. To reduce the computational complexity further, we also present a low-complexity detection method using the sparse factor graph to compute the LLRs in an iterative fashion based on a belief propagation algorithm. Simulations results demonstrate that the proposed soft-output detection methods provide significant frame-error-rates gains compared with the existing frequency-domain equalization techniques in a coded mmWave MIMO system using one- or two-bit ADCs.