Seunghyeong Yoo

We investigate full-duplex (FD) multi-user multiple input single-output systems with coarse quantization, aiming to characterize the impact of employing low-resolution analog-to-digital converters (ADCs) on self-interference (SI) and to develop a quantization- and SI-aware beamforming method that alleviates quantization-induced performance degradation in the FD systems. We first present an analysis on the perantenna signal-to-quantization noise ratio for conventional linear beamformers to provide the desired range of the number of analog-to-digital converter (ADC) bits, providing system insights for reliable FD operation in regard to the ADC resolution and beamforming strategy. Motivated by the insights, we then propose an SI-aware beamforming method that mitigates residual SI and quantization distortion. The resulting spectral efficiency (SE) maximization problem is decomposed into two tractable subproblems solved via alternating optimization: precoder and combiner design. The precoder optimization is formulated as a generalized eigenvalue problem, where the dominant eigenvector yields the best stationary solution through power iteration, while the combiner is derived as a quantization-aware minimum meansquared error (MMSE) filter. Numerical studies show that the number of required ADC bits with the proposed beamforming falls within the derived theoretical range while achieving the highest SE compared to benchmarks.

In massive multiple-input multiple-output (MIMO) systems, achieving high spectral efficiency (SE) often requires advanced precoding algorithms whose complexity scales rapidly with the number of antennas, limiting practical deployment. In this paper, we develop a scalable and computationally efficient generalized power iteration precoding (GPIP) framework for massive MIMO systems under both perfect and imperfect channel state information at the transmitter (CSIT). By exploiting the low-dimensional subspace property of optimal precoders, we reformulate the high-dimensional beamforming problem into a lower-dimensional weight optimization that scales with the number of users rather than antennas. We further extend this framework to the imperfect CSIT scenario by showing that stationary solutions reside in a combined subspace spanned by the estimated channel and error covariance matrices, enabling a robust design via low-rank approximation. To reduce computational cost, we leverage the Sherman-Morrison formula to simplify matrix inversions. Moreover, interpreting the GPIP update as a projected preconditioned gradient ascent method, we establish convergence guarantees and develop a stable and monotonic algorithm using a backtracking line search. Numerical results demonstrate that the proposed methods achieve the highest SE performance compared to state-of-the-art linear precoders with significantly reduced complexity and convergence, highlighting their suitability for large-scale MIMO systems.

Generalized power iteration-based precoding (GPIP) exhibits superior performance in maximizing spectral efficiency for multi-antenna communications. Increasing the number of antennas, however, makes GPIP less practical due to its cubic complexity with respect to the number of antennas in computing matrix inversion. To resolve this, we propose efficient GPIP methods with imperfect channel knowledge. Exploiting the GPIP matrix structure, we use two low-complexity matrix inversion approaches: the matrix inversion lemma-based low-dimension matrix inversion and Gauss-Seidel iteration. These approaches decrease complexity order to the square of the number of antennas. Simulations validate the computational efficiency of the proposed methods while maintaining performance.

We propose a joint secure max-min fairness precoding and antenna selection algorithm for multi-user systems constrained by limited active antennas, low-resolution quantizers, and imperfect channel knowledge. We first formulate an optimization problem which is non-convex and non-smooth. Then, we cast it as a tractable problem, allowing us to leverage the available channel knowledge through averaging spectral efficiency and use a Lagrangian method to handle the antenna selection constraint. We identify the first-order optimality condition for a precoder and solve the condition by employing a generalized power iteration method, which achieves a superior sub-optimal solution. Simulations validate the proposed algorithm.

We propose a max-min fairness (MMF) precoding algorithm for a multi-user multiple-input multiple-output (MU-MIMO) downlink system with different user weights, considering the quantization effects of low-resolution digital-to-analog converters (DACs). To find a precoder, we first formulate a problem, which jointly considers MMF and quantization. This joint problem presents some challenges that make finding a direct solution infeasible: non-smoothness and non-convexity. To address the non-smoothness, we use LogSumExp method to approximate the problem into a smooth problem. Subsequently, we derive the first-order optimality condition and cast it as a nonlinear eigenvalue problem. Lastly, we employ a generalized power iteration method to determine the superior stationary point. Simulation result demonstrates the superiority of our MMF precoding algorithm.

Satellite communications offer the potential of ensuring global coverage with a substantial capacity. In this paper, we examine the coverage performance of satellite networks by considering a distance-dependent line-of-sight (LOS) and non-LOS (NLOS) channel propagation probability, which takes into account the shadowing effect. Building upon the stochastic geometry-based network analysis used for terrestrial networks, we employ a Poisson point process to model both the satellite network and its users. Through this approach, we derive a theoretical expression for the coverage probability with beam gain under a nearest satellite association rule, providing an analytical understanding of the satellite network. The accuracy of the derived expression is confirmed through simulation results. The derived expression encompasses various network parameters such as satellite density, altitude, channel fading, pathloss, LOS probability, and beam gain, offering valuable insights into satellite networks.

Underwater acoustic (UWA) channel is one of the common systems for wireless underwater communication. However, the underwater environment poses significant challenges to reliable and efficient communication. This paper is a survey on channel modeling for UWA communications. We investigate various UWA channel modeling approaches that incorporate both large- and small-scale fading effects and provides a comprehensive summary of the key findings.

We consider a full-duplex (FD) multi-user multiple-input multiple-output (MU-MIMO) system with low-resolution quantizers at an access point (AP). In the considered FD system, there are main bottlenecks: self-interference (SI), co-channel interference (CCI), and quantization errors. In this paper, we propose a novel precoding and combining method to maximize the sum spectral efficiency (SE) by incorporating the effect of the quantization errors as well as the SI and CCI. Since the beamformers are intertwined with the quantization errors, SI, and CCI, it is highly challenging to solve the sum SE maximization problem. To address the challenges, we convert the problem into the Rayleigh quotient form. Then, we derive the first-order optimality condition with interpreting it as a generalized eigenvalue problem by leveraging the principle of the Rayleigh quotient problem. Accordingly, we adopt a power iteration method for identifying the leading eigenvector: the best local optimal precoding solution. Consequently, we propose an alternating algorithm to jointly optimize the precoder and combiner. Simulations validate the proposed algorithm.