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.

To utilize the full potential of the available power at a base station (BS), we propose a joint precoding, antenna selection, and transmit power control algorithm for a total power budget at the BS. We formulate a sum spectral efficiency (SE) maximization problem for downlink multi-user multiple-input multiple-output (MIMO) rate-splitting multiple access (RSMA) systems with arbitrary-resolution digital-to-analog converters (DACs). We reformulate the problem by defining the ergodic sum SE using the conditional average rate approach to handle imperfect channel state information at the transmitter (CSIT), and by using approximation techniques to make the problem more tractable. Then, we decompose the problem into precoding direction and power control subproblems. We solve the precoding direction subproblem by identifying a superior Lagrangian stationary point, and the power control subproblem using gradient descent. We also propose a complexity-reduction approach that is more suitable for massive MIMO systems. Simulation results not only validate the proposed algorithm but also reveal that when utilizing the full potential of the power budget at the BS, medium-resolution DACs with 8 ∼ 11 bits may actually be more power-efficient than low-resolution DACs.

This paper explores the joint optimization of precoding, antenna selection, and transmit power within a fixed power budget at a base station (BS). We aim to maximize the sum spectral efficiency in downlink multi-user multipleinput multiple-output systems. The problem is split into two separate sub-problems: joint optimization of antenna selection and precoding direction, and optimization of the transmit power. We then vectorize the precoding matrix and apply approximation techniques to handle the challenges of the problem. We find a superior Lagrangian stationary point to solve the precoding direction sub-problem, and use gradient descent to solve the transmit power sub-problem. Our simulations confirm the algorithm's effectiveness and show that medium-resolution digital-to-analog converters (DACs) with$6 \sim 10$bits can be more powerefficient than the commonly assumed$3 \sim 5$bits when the total power consumption at the BS is considered.

Employing low-resolution quantizers increases energy efficiency (EE) while reducing spectral efficiency (SE) and deteriorating channel estimation accuracy, which induces higher inter-user interference. To overcome these drawbacks, we develop a rate-splitting multiple access (RSMA) precoding method in the low-resolution quantization system with imperfect channel state information at the transmitter (CSIT), which optimizes a balance between two critical yet often competing aspects: maximization of the SE to increase data rate and the EE to manage the power consumption. We first average the sum rate to properly define the SE and EE with the imperfect CSIT and error covariance matrices. Then we formulate a weighted SE and EE optimization problem and divide it into two sub-problems adopting a Dinkelbach approach: precoding direction and transmit power optimization. For precoding direction, we derive the first-order optimality condition. Casting the condition to a generalized eigenvalue problem, we propose an algorithm to identify the principal eigenvector which corresponds to the superior stationary point. Furthermore, we utilize a gradient method for transmit power optimization and update the precoding direction and transmit power alternately. Simulations validate the benefits of the proposed method in enhancing the SE and EE trade-off and reveal the superiority of RSMA over spatial-division multiple access.

In this paper, we utilize rate-splitting multiple access (RSMA) by expanding the achievable degrees of freedom in downlink multiuser multiple-input multiple-output (MIMO) systems that incorporate mixed-resolution quantizers at an access point (AP). Since the quantized RSMA precoder is required to consider both quantization error and the minimum rate of the common stream, optimizing the RSMA precoder is highly challenging for maximizing the sum spectral efficiency (SE). Addressing these difficulties, we introduce a new promising quantized RSMA pre coding algorithm aimed at maximizing the sum SE. To achieve a more tractable form, we first approximate the rate of the common stream with a smooth function. Subsequently, we derive the first-order optimality condition, which is cast as a nonlinear eigenvalue problem (NEP). Accordingly, we introduce a promising algorithm that can find the principal eigenvector of the NEP, which corresponds to the best local optimal solution. Numerous simulation results demonstrate that the advantages of RSMA in quantized multiuser MIMO systems are present in the proposed method.

In this paper, we consider a sum secrecy spectral efficiency (SE) maximization problem in a downlink rate-splitting multiple access (RSMA) system with multiple antennas. We also assume two types of users: secret users whose private streams require information security and normal users whose private streams do not require security. Due to its max and min operations in the wiretap SE and the SE of the common stream, respectively, which makes the non-convex problem even non-smooth, solving the problem is highly challenging. To deal with the difficulties, we reformulate the optimization problem into an approximated smooth problem using a LogSumExp approach. Then, we identify the first-order optimality condition and develop formulas into a generalized eigenvalue problem. We use a power iteration based algorithm to find the best local optimal solution. Simulations validate the proposed secure RSMA precoding method.

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.

This paper investigates the sum spectral efficiency maximization problem in downlink multiuser multiple-input multiple-output systems with low-resolution quantizers at an access point (AP) and users. We consider rate-splitting multiple access (RSMA) to enhance spectral efficiency by offering opportunities to boost achievable degree-of-freedom. Optimizing RSMA precoders, however, is highly challenging due to the minimum rate constraint when determining the common rate. The quantization errors coupled with the precoders make the problem more complicated. In this paper, we develop a novel RSMA precoding algorithm incorporating quantization errors for maximizing the sum spectral efficiency. To this end, we first obtain an approximate spectral efficiency in a smooth function. Subsequently, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We propose a computationally efficient algorithm to find the principal eigenvector of the NEP as a sub-optimal solution. We also extend the weighted minimum mean square error-based RSMA precoding to the considered quantization system. Simulation results validate the proposed methods. The key benefit of using RSMA over spatial division multiple access (SDMA) comes from the ability of the common stream to balance between the channel gain and quantization error in multiuser MIMO systems with different quantization resolutions.