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 this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconstructs downlink (DL) channel state information (CSI) from uplink (UL) training signals using partial reciprocity, eliminating the need for CSI feedback. To obtain the error covariance matrix of the reconstructed DL CSI, we devise anobserved Fisher information-based estimation technique. Leveraging this, to mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we propose a rate-splitting multiple access (RSMA) aided precoder optimization method. This method jointly updates the precoding vector and Lagrange multipliers by solving the nonlinear eigenvalue problem with eigenvector dependency to maximize SE. The numerical results show that the proposed design achieves precise beam pattern control, maximizes SE, and significantly improves the sensing-communication trade-off compared to the state-of-the-art methods in FDD ISAC scenarios.

Low Earth orbit (LEO) satellite networks with mega constellations have the potential to provide 5G and beyond services ubiquitously. However, these networks may introduce mutual interference to both satellite and terrestrial networks, particularly when sharing spectrum resources. In this paper, we present a system-level performance analysis to address these interference issues using the tool of stochastic geometry. We model the spatial distributions of satellites, satellite users, terrestrial base stations (BSs), and terrestrial users using independent Poisson point processes on the surfaces of concentric spheres. Under these spatial models, we derive analytical expressions for the ergodic spectral efficiency of uplink (UL) and downlink (DL) satellite networks when they share spectrum with both UL and DL terrestrial networks. These derived ergodic expressions capture comprehensive network parameters, including the densities of satellite and terrestrial networks, the path-loss exponent, and fading. From our analysis, we determine the conditions under which spectrum sharing with UL terrestrial networks is advantageous for both UL and DL satellite networks. Our key finding is that the optimal spectrum sharing configuration among the four possible configurations depends on the density ratio between terrestrial BSs and users, providing a design guideline for spectrum management. Simulation results confirm the accuracy of our derived expressions.

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.

Inter-beam interference is a core challenge in low Earth orbit (LEO) satellite communications, driven by dense constellations, aggressive frequency reuse, and overlapping beam footprints. To address this, we propose space–time beamforming, a novel approach that jointly exploits spatial and temporal channel characteristics—specifically the angle of arrival (AoA) and relative Doppler shift—to optimize transmission between moving satellites and ground users. By synthesizing a virtual array-of-subarrays across repeated transmissions, this method effectively expands the aperture and forms ultra-narrow beams, sharply suppressing interference leakage to neighboring users. We develop two strategies within this framework: space-time zero-forcing (ST-ZF) and space-time signal-to-leakage-plus-noise ratio (ST-SLNR) beamforming. In partially connected networks, ST-ZF provides a 3 dB SNR gain over conventional maximum ratio transmission (MRT). In more general interference scenarios, ST-SLNR delivers significant improvements in sum spectral efficiency. While temporal repetition introduces a rate trade-off, it also enables finer spatial discrimination through Doppler-induced temporal signatures. Our analysis and simulations demonstrate that space-time beamforming offers a powerful and adaptable solution for interference mitigation in next-generation LEO satellite systems, unlocking better spectral efficiency and more reliable connectivity in densely served orbital environments.

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.

Time-interleaved analog-to-digital converters (TI-ADCs) can achieve high sampling rates by interleaving multiple sub-ADCs in parallel. Mismatch errors between the sub-ADCs, however, can significantly degrade the signal quality, which is a main performance bottleneck in TI-ADCs. In this letter, we present a hybrid calibration approach by interpreting the mismatch problem as a tracking problem, and use the extended Kalman filter for online estimation of the mismatch errors. After estimation, the input signal is reconstructed using a truncated fractional delay filter and a high-pass filter. Simulations demonstrate that our algorithm substantially outperforms the existing hybrid calibration method in both mismatch estimation and compensation.

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 frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) for precoding is vastly challenging due to the tremendous feedback overhead with the growing number of antennas. Utilizing uplink pilots for downlink CSI estimation is a promising approach that can eliminate CSI feedback. However, the downlink CSI estimation accuracy diminishes significantly as the number of channel paths increases, resulting in reduced spectral efficiency. In this paper, we demonstrate that achieving downlink spectral efficiency comparable to perfect CSI is feasible by combining uplink CSI with limited downlink CSI feedback information. Our proposed downlink CSI feedback strategy transmits quantized phase information of downlink channel paths, deviating from conventional limited methods. We put forth a mean square error (MSE)-optimal downlink channel reconstruction method by jointly exploiting the uplink CSI and the limited downlink CSI. Armed with the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for phase quantization. Subsequently, we present an optimal feedback bit allocation method for minimizing the MSE in the reconstructed channel through phase quantization. Utilizing a robust downlink precoding technique, we establish that the proposed downlink channel reconstruction method is sufficient for attaining a sum-spectral efficiency comparable to perfect CSI.

Multibeam satellite communication systems are promising to achieve high throughput. To achieve high performance without substantial overheads associated with channel state information (CSI) of ground users, we consider a fixed-beam precoding approach, where a satellite forms multiple fixed-beams without relying on CSI, then selects a suitable user set for each beam. Upon this precoding method, we put forth a satellite equipped with massive multiple-input multiple-output (MIMO), by which inter-beam interference is efficiently mitigated by narrowing the corresponding beam width. By modeling the ground users’ locations via a Poisson point process, we rigorously analyze the achievable performance of the presented multibeam satellite system. In particular, we investigate the asymptotic scaling laws that reveal the interplay between the user density, the number of beams, and the number of antennas. Our analysis offers critical design insights for the multibeam satellite with massive MIMO: i) If the user density scales proportionally with the number of antennas, the considered precoding can achieve a linear fraction of the optimal rate in the asymptotic regime. ii) A certain additional scaling factor for the user density is needed as the number of beams increases to maintain the asymptotic optimality.

Nonlinear self-interference cancellation (SIC) techniques are essential for enabling full-duplex communication systems, which can offer spectral efficiencies twice that of traditional half-duplex systems. The challenge of nonlinear SIC is similar to the classic problem of system identification in adaptive filter theory, whose crux lies in constructing the optimal nonlinear basis functions of a nonlinear system. This becomes especially difficult when the system input has a non-stationary distribution, as is the case in practical wireless systems. In this paper, we propose a novel algorithm for nonlinear digital SIC that adaptively constructs orthonormal polynomial basis functions according to the non-stationary moments of the transmit signal. By combining these basis functions with the least mean squares (LMS) algorithm, we introduce a new SIC technique, called the adaptive orthonormal polynomial LMS (AOP-LMS) algorithm. To reduce computational complexity for practical systems, we augment our approach with a precomputed look-up table, which maps a given modulation and coding scheme to its corresponding basis functions. Numerical simulation indicates that our proposed method surpasses existing state-of-the-art SIC algorithms in terms of convergence speed and mean squared error when the transmit signal is non-stationary, such as with adaptive modulation and coding. Experimental evaluation with a wireless testbed further confirms that our proposed approach outperforms existing digital SIC algorithms in practical systems.

In frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) becomes challenging due to the tremendous feedback overhead that increases with the number of antennas. Using uplink pilots to estimate downlink CSI is a promising approach that can eliminate the need for CSI feedback, but its accuracy decreases significantly as the number of channel paths increases. In this paper, we propose a mean square error (MSE)-optimal downlink channel reconstruction method that jointly utilizes uplink CSI and limited downlink CSI. With the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for channel phase quantization and show the optimal feedback bit allocation method to minimize the MSE. Harnessing robust downlink precoding, we demonstrate that the proposed downlink channel reconstruction is sufficient to achieve a sum-spectral efficiency comparable to that with perfect downlink CSI.

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.

A multicell-coordinated beamforming solution for massive multiple-input multiple-output orthogonal frequency-division multiplexing (OFDM) systems is presented when employing low-resolution data converters and per-antenna level constraints. For a more realistic deployment, we aim to find the downlink (DL) beamformer that minimizes the maximum power on transmit antenna array of each basestation under received signal quality constraints while minimizing per-antenna transmit power. We show that strong duality holds between the primal DL formulation and its manageable Lagrangian dual problem which can be interpreted as the virtual uplink (UL) problem with adjustable noise covariance matrices. For a fixed set of noise covariance matrices, we claim that the virtual UL solution is effectively used to compute the DL beamformer and noise covariance matrices can be subsequently updated with an associated subgradient. Our primary contributions are then 1) formulating the quantized DL OFDM antenna power minimax problem and deriving its associated dual problem, 2) showing strong duality and interpreting the dual as a virtual quantized UL OFDM problem, and 3) developing an iterative minimax algorithm based on the dual problem. Simulations validate the proposed algorithm in terms of the maximum antenna transmit power and peak-to-average-power ratio.

Integrated sensing and communication (ISAC) is widely recognized as a fundamental enabler for future wireless communications. In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems. To this end, we adopt either a radar transmit beam mean square error (MSE) or receive signal-to-clutter-plus-noise ratio (SCNR) as a radar performance constraint of a sum SE maximization problem. To resolve inherent challenges such as non-convexity and imperfect CSI, we reformulate the problems and identify first-order optimality conditions for the joint radar and communication beamformer. Turning the condition to a nonlinear eigenvalue problem with eigenvector dependency (NEPv), we develop an alternating method which finds the joint beamformer through power iteration and a Lagrangian multiplier through binary search. The proposed framework encompasses both the radar metrics and is robust to channel estimation error with low complexity. Simulations validate the proposed methods. In particular, we observe that the MSE and SCNR constraints exhibit complementary performance depending on the operating environment, which manifests the importance of the proposed comprehensive and robust optimization framework.

The joint design of secure precoding and artificial noise (AN) transmission scheme is promising to improve secrecy performance. However, in downlink multi-user multiple-input multiple-output (MU-MIMO) systems with multiple eavesdroppers, joint design of secure precoding and AN structure involves several challenges: an objective function is non-convex and non-smooth, and a precoding matrix and AN matrix have different design principles. Classically, to jointly design precoding and AN covariance matrix, an alternating optimization approach is used which has limitations in terms of the secrecy rate performance since it does not offer joint and simultaneous optimization of the precoding and AN covariance matrices. In this paper, we propose a novel optimization framework that optimizes the precoder and the AN covariance matrix jointly and simultaneously to maximize the secrecy rate. First, we approximate the objective function to a tractable non-convex form. Next, we derive the first-order optimality condition by leveraging the nonlinear eigenvalue problem (NEP) form. Finally, we utilize an efficient technique with low computational complexity for identifying the major eigenvector of the NEP which corresponds to the best stationary point. Simulations illustrate that the proposed methods enhance the secrecy rate performance compared to the existing secure precoding methods.

Satellite communications have been promising to guarantee global coverage with high capacity. In this paper, we analyze coverage performance of satellite networks with a distance-dependent line-of-sight (LOS) and non-LOS (NLOS) channel propagation probability to incorporate shadowing effect. Extending the stochastic geometry-based network analysis for terrestrial networks, we model the satellite network and users as a Poisson point process and derive an theoretical coverage probability expression to provide analytical understanding of the satellite network. Simulation results verify the exactness of the derived expression. The derived expression includes network parameters for satellite density and altitude, channel fading, pathloss, and the LOS probability, and provides insights on satel-lite networks. Our key finding is that NLOS channel propagation benefits the coverage performance by reducing the interference from non-associated satellites, and the higher NLOS probability is desirable to improve the coverage performance as the network becomes denser.

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.

In this article, we propose a precoding design to maximize the sum achievable rate in downlink nonorthogonal multiple access (NOMA)-aided short-packet Internet of Things (IoT) communications, wherein each IoT device is equipped with low-resolution analog-to-digital converters (ADCs). Due to intertwined effects caused from low-resolution ADCs, short packets, and NOMA, it is challenging to find efficient precoding vectors. To resolve the difficulties, we first linearize quantization distortion by adopting an additive quantization noise model. Thereafter, we approximate nonsmooth functions by using a LogSumExp technique. With the transformed problem, we derive a first-order optimality condition and propose a novel precoding algorithm which identifies an efficient local optimal solution with low complexity. Based on the proposed algorithm, we also investigate an efficient NOMA decoding ordering method for the considered system. Via simulations, we demonstrate that the proposed method outperforms other baseline methods.

Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precoder and to minimize a maximum error probability and information leakage rate. The main challenges arise from the complicated multi-objective problem, non-tractable back-off factors from the FBL assumption, non-convexity and non-smoothness of the secrecy rate, and the intertwined optimization variables. To address these challenges, we adopt an alternating optimization approach by decomposing the problem into two phases: secure precoding design, and maximum error probability and information leakage rate minimization. In the first phase, we obtain a lower bound of the secrecy rate and derive a first-order Karush-Kuhn-Tucker (KKT) condition to identify local optimal solutions with respect to the precoders. Interpreting the condition as a generalized eigenvalue problem, we solve the problem by using a power iteration-based method. In the second phase, we adopt a weighted-sum approach and derive KKT conditions in terms of the error probabilities and leakage rates for given precoders. Simulations validate the proposed algorithm.

Satellite networks are promising to provide ubiquitous and high-capacity global wireless connectivity. Traditionally, satellite networks are modeled by placing satellites on a grid of multiple circular orbit geometries. Such a network model, however, requires intricate system-level simulations to evaluate coverage performance, and analytical understanding of the satellite network is limited. Continuing the success of stochastic geometry in a tractable analysis for terrestrial networks, in this paper, we develop novel models that are tractable for the coverage analysis of satellite networks using stochastic geometry. By modeling the locations of satellites and users using Poisson point processes on the surfaces of concentric spheres, we characterize analytical expressions for the coverage probability of a typical downlink user as a function of relevant parameters, including path-loss exponent, satellite height, density, and Nakagami fading parameter. Then, we also derive a tight lower bound of the coverage probability in tractable expression while keeping full generality. Leveraging the derived expression, we identify the optimal density of satellites in terms of the height and the path-loss exponent. Our key finding is that the optimal average number of satellites decreases logarithmically with the satellite height to maximize the coverage performance. Simulation results verify the exactness of the derived expressions.

Rate-splitting multiple access (RSMA) is a general multiple access scheme for downlink multi-antenna systems embracing both classical spatial division multiple access and more recent non-orthogonal multiple access. Finding a linear precoding strategy that maximizes the sum spectral efficiency of RSMA is a challenging yet significant problem. In this paper, we put forth a novel precoder design framework that jointly finds the linear precoders for the common and private messages for RSMA. Our approach is first to approximate the non-smooth minimum function part in the sum spectral efficiency of RSMA using a LogSumExp technique. Then, we reformulate the sum spectral efficiency maximization problem as a form of the log-sum of Rayleigh quotients to convert it into a tractable form. By interpreting the first-order optimality condition of the reformulated problem as an eigenvector-dependent nonlinear eigenvalue problem, we reveal that the leading eigenvector of the derived optimality condition is a local optimal solution. To find the leading eigenvector, we propose an algorithm inspired by a power iteration. Simulation results show that the proposed RSMA transmission strategy provides significant improvement in the sum spectral efficiency compared to the state-of-the-art RSMA transmission methods.

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.

In this paper, we consider a downlink internet-of-things (IoT) multiple-input multiple-output (MIMO) network wherein an access point (AP), multiple IoT users, and a single eavesdropper coexist. The eavesdropper attempts to wiretap confidential messages of the IoT users. In the considered system, we solve a sum secrecy rate maximization problem in the finite blocklength (FBL) regime. Due to the FBL, the secrecy rate has a back-off factor with respect to blocklength, decoding error probability, and information leakage, which makes the problem more challenging. The main challenges are: i) the problem is not tractable because of the back-off factor, ii) an objective function is inherently non-convex, and iii) information leakage by the eavesdropper needs to be considered. To address these difficulties, we first obtain a lower bound of the secrecy rate and transform the problem into a product of Rayleigh quotients form. Then, we derive a first-order Karush–Kuhn–Tucker (KKT) condition to find a local optimal solution and interpret the condition as a generalized eigenvalue problem. Consequently, we develop a low-complexity algorithm by adopting a generalized power iteration-based (GPI) method. Via simulations, we validate the secrecy rate performance of the proposed method for the short-packet IoT communication systems.

In this work, we present a solution for coordinated beamforming for large-scale downlink (DL) communication systems with low-resolution data converters when employing a perantenna power constraint that limits the maximum antenna power to alleviate hardware cost. To this end, we formulate and solve the antenna power minimax problem for the coarsely quantized DL system with target signal-to-interference-plus-noise ratio requirements. We show that the associated Lagrangian dual with uncertain noise covariance matrices achieves zero duality gap and that the dual solution can be used to obtain the primal DL solution. Using strong duality, we propose an iterative algorithm to determine the optimal dual solution, which is used to compute the optimal DL beamformer. We further update the noise covariance matrices using the optimal DL solution with an associated subgradient and perform projection onto the feasible domain. Through simulation, we evaluate the proposed method in maximum antenna power consumption and peak-to-average power ratio which are directly related to hardware efficiency.

In this paper, we propose a precoding method to maximize energy efficiency (EE) in a downlink multiuser massive multiple-input multiple-output system with low-resolution quantizers. To this end, we formulate an EE maximization problem with respect to precoders by incorporating the quantization errors caused by the low-resolution quantizers. The main challenges exist: i) the quantization errors are entangled with the precoders, ii) a objective function is non-convex, and iii) unlike a spectral efficiency (SE) maximization problem, a precoding power needs to be jointly optimized. To address these challenges, we first adopt a Dinkenbach method and reformulate the EE problem to a more tractable form. We further decompose the problem into an optimal precoding direction and transmit power problems. To find the optimal direction, we derive a first-order Karush–Kuhn–Tucker (KKT) condition and interpret the condition as a generalized eigenvalue problem. Accordingly, adopting a generalized power iteration-based precoding method, we find the principal eigenvector which is the best sub-optimal precoder. Regarding the transmit power optimization, the objective function becomes concave for given other variables. Hence, the transmit power level is optimized by using a gradient descent method. Via simulations, we demonstrate that the proposed algorithm provides the highest EE performance compared to baseline methods.

Physical layer security is a useful tool to prevent illegal wiretapping to confidential information. In this paper, we consider a generalized model of conventional physical layer security, referred as hierarchical information accessibility (HIA). A main feature of the HIA model is that a network has a hierarchy in information access, wherein decoding feasibility is determined by each user’s priority. Under this HIA model, we formulate a sum secrecy rate maximization problem with regard to precoding vectors. This problem is challenging since multiple non-smooth functions are involved into the secrecy rate to fulfill the HIA conditions and also the problem is non-convex. To address the challenges, we approximate the minimum function by using the LogSumExp technique, thereafter obtain the first-order optimality condition. One key observation is that the derived condition is cast as a functional eigenvalue problem, where the eigenvalue is equivalent to the approximated objective function of the formulated problem. Accordingly, we show that finding a principal eigenvector is equivalent to finding a local optimal solution. To this end, we develop a novel method called generalized power iteration for HIA (GPI-HIA). Simulations demonstrate that the GPI-HIA significantly outperforms other baseline methods in terms of the secrecy rate.

The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise for massive multiple-input multiple-output (MIMO) systems. This paper considers a precoding optimization problem for maximizing EE in quantized downlink massive MIMO systems. To this end, we jointly optimize an active antenna set, precoding vectors, and allocated power; yet acquiring such joint optimal solution is challenging. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. To optimize precoding power for given precoding direction, we adopt a gradient descent algorithm for the EE maximization. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set, the precoding direction, and allocated power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems.

In this paper, we consider a hierarchical information accessibility (HIA) model, which generalizes conventional physical layer security. In the considered model, multiple layers with different security priorities are assumed, where only the users in a higher priority layer are permitted to decode the message intended to lower priority layers. To maximize the sum secrecy rate of the considered system, we formulate an optimization problem with regard to precoders. To solve the formulated problem, we first approximate the objective function by using the LogSumExp technique and show that finding a local optimum is equivalent to finding a leading eigenvector of the first-order optimality condition of the reformulated problem. Accordingly, we propose a novel algorithm called generalized power iteration for hierarchical information accessibility (GPI-HIA) to obtain a solution. Via simulations, we demonstrate that the proposed method significantly outperforms other baseline schemes under the considered HIA scenario.

This paper introduces block orthogonal sparse su-perposition (BOSS) codes for efficient short-packet commu-nications over Gaussian channels. Unlike conventional sparse superposition codes, an encoder of BOSS code uses multiple unitary matrices as a fat dictionary matrix and maps information bits such that multiple subgroups of codewords are orthogonal. Exploiting this orthogonal property per group, a two-stage maximum a posteriori (MAP) decoding algorithm is presented. The key idea of the two-stage MAP decoder is to successively estimate the non-zero alphabets corresponding to the orthogonal columns in a dictionary matrix and the index of a sub-dictionary matrix containing the columns. This decoding algorithm achieves a near-optimal decoding performance while requiring polynomial time complexity in blocklength. Via simulations, we show that the proposed encoding and decoding techniques achieve enhanced block-error-rate performances in the short blocklength regime compared to the state-of-the-art coded modulation methods.

In this work, we present a solution for coordinated beamforming and power allocation when base stations employ a massive number of antennas equipped with low-resolution analog-to-digital and digital-to-analog converters. We address total power minimization problems of the coarsely quantized uplink (UL) and downlink (DL) communication systems with target signal-to-interference-plus-noise ratio (SINR) constraints. By combining the UL problem with minimum mean square error combiners and deriving the Lagrangian dual of the DL problem, we prove UL-DL duality and show there is no duality gap even with coarse data converters. Inspired by strong duality, we devise an iterative algorithm to determine the optimal UL transmit powers, and then linearly amplify the UL combiners with proper weights to acquire the optimal DL precoder. Simulation results validate strong duality and evaluate the proposed method in terms of total power consumption and achieved SINR.

In this article, we consider a multiple-input–multiple-output (MIMO) system serving Internet of Things (IoT) devices. To satisfy stringent requirements on the latency of IoT communications, the IoT devices communicate in the finite blocklength regime, wherein the achievable spectral efficiency (SE) has a backoff factor and decoding error probability is nonnegligible. Aiming to jointly optimize the sum SE and the maximum error probability, we first express the achievable SE as a function of the channel coefficients, precoders, and error probabilities. Subsequently, we formulate a problem with respect to precoders and error probabilities. A straightforward approach for the formulated problem, however, is challenging as it not only has multiple objectives but also is nonconvex. To resolve these issues, we first transform the problem as a single-objective optimization by using a weighted sum approach. Based on the reformulation, we propose an algorithm, in which error probabilities and precoders are determined by alternating two phases. Via simulations, we demonstrate that the proposed method offers significant gains compared to baseline methods, in terms of the achievable SE and the maximum error probability. In particular, we show that the communication latency is greatly reduced by using the proposed method.

In this paper, we investigate a coordinated multipoint (CoMP) beamforming and power control problem for base stations (BSs) with a massive number of antenna arrays under coarse quantization at low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Unlike high-resolution ADC and DAC systems, non-negligible quantization noise that needs to be considered in CoMP design makes the problem more challenging. We first formulate total power minimization problems of both uplink (UL) and downlink (DL) systems subject to signal-to-interference-and-noise ratio (SINR) constraints. We then derive strong duality for the UL and DL problems under coarse quantization systems. Leveraging the duality, we propose a framework that is directed toward a twofold aim: to discover the optimal transmit powers in UL by developing iterative algorithm in a distributed manner and to obtain the optimal precoder in DL as a scaled instance of UL combiner. Under homogeneous transmit power and SINR constraints per cell, we further derive a deterministic solution for the UL CoMP problem by analyzing the lower bound of the SINR. Lastly, we extend the derived result to wideband orthogonal frequency-division multiplexing systems to optimize transmit power and beamformer for all subcarriers. Simulation results validate the theoretical results and proposed algorithms.

For low-resolution analog-to-digital converter (ADC) systems, only high-complexity receive antenna selection has been developed and transmit antenna selection has been limited to a single antenna selection in prior work. In this paper, we propose low-complexity receive antenna selection algorithms and analyze transmit antenna selection by considering antenna selection at a base station with large antenna arrays and low-resolution ADCs. For downlink antenna selection, we show a selection criterion with zero-forcing precoding equivalent to a perfect quantization system; sum rate increases with number of selected antennas; derivation of the sum rate loss function from using a antenna subset; and sum rate loss reaches a maximum at a point of total transmit power and decreases beyond that point to converge to zero. For wideband orthogonal-frequency-division-multiplexing (OFDM) systems, our results hold when entire subcarriers share a common subset of antennas. For uplink antenna selection, we generalize a greedy antenna selection criterion; propose a quantization-aware fast antenna selection algorithm using the criterion; and derive a lower bound on sum rate achieved by the proposed algorithm. For wideband OFDM systems, we extend our algorithm and derive a lower bound on its sum rate. Simulation results validate theoretical analyses and show increases in sum rate over conventional algorithms.

In this paper, we investigate learning-based maximum likelihood (ML) detection for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog- to-digital converters (ADCs). To overcome the significant dependency of learning-based detection on the training length, we propose two one-bit ML detection methods: a biased-learning method and a dithering-and-learning method. The biased-learning method keeps likelihood functions with zero probability from wiping out the obtained information through learning, thereby providing more robust detection performance. Extending the biased method to a system with knowledge of the received signal-to-noise ratio, the dithering-and- learning method estimates more likelihood functions by adding dithering noise to the quantizer input. The proposed methods are further improved by adopting the post likelihood function update, which exploits correctly decoded data symbols as training pilot symbols. The proposed methods avoid the need for channel estimation. Simulation results validate the detection performance of the proposed methods in symbol error rate.

In this paper, we propose a two-stage analog combining architecture for millimeter wave (mmWave) communications with hybrid analog to digital beamforming and low-resolution analog-to-digital converters (ADCs). We first derive a two-stage combining solution by solving a mutual information (MI) maximization problem without a constant modulus constraint on analog combiners. With the derived solution, the proposed receiver architecture splits the analog combining into a channel gain aggregation stage followed by a spreading stage to maximize the MI by effectively managing quantization error. We show that the derived two-stage combiner achieves the optimal scaling law with respect to the number of radio frequency (RF) chains and maximizes the MI for homogeneous singular values of a MIMO channel. Then, we develop a two-stage analog combining algorithm to implement the derived solution under a constant modulus constraint for mmWave channels. Simulation results validate the algorithm performance in terms of MI.

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments. By leveraging the ability of Q-learning to estimate future performance improvement rewards, we propose two algorithms: 1) closed loop power control (PC) for downlink voice over LTE (VoLTE) and 2) self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power so that the signal-to-interference plus noise ratio (SINR) of a user equipment (UE) meets the target SINR. It does so without the UE having to send power control requests. The SON fault management algorithm uses RL to improve the performance of an outdoor base station cluster by resolving faults in the network through configuration management. Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex performance optimization problem using RL. Simulation results show that our proposed RL-based algorithms outperform the industry standards today in realistic cellular communication environments.

We investigate uplink user scheduling for millimeter wave (mm-wave) hybrid analog/digital beamforming systems with low-resolution analog-to-digital converters (ADCs). Deriving new scheduling criteria for the mm-wave systems, we show that the channel structure in the beamspace, in addition to the channel magnitude and orthogonality, plays a key role in maximizing the achievable rates of scheduled users due to quantization error. The criteria show that to maximize the achievable rate for a given channel gain, the channels of the scheduled users need to have 1) as many propagation paths as possible with unique angle-of-arrivals (AoAs) and 2) even power distribution in the beamspace. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mm-wave zero-forcing receivers with low-resolution ADCs. We further propose a chordal distance-based scheduling algorithm that exploits only the AoA knowledge and analyze the performance by deriving ergodic rates in closed form. Based on the derived rates, we show that the beamspace channel leakage resulting from phase offsets between AoAs and quantized angles of analog combiners can lead to sum rate gain by reducing quantization error compared to the channel without leakage. The simulation results validate the sum rate performance of the proposed algorithms and the derived ergodic rate expressions.

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

Millimeter wave (mmWave) spectrum has drawn attention due to its tremendous available bandwidth. The high propagation losses in the mmWave bands necessitate beamforming with a large number of antennas. Traditionally each antenna is paired with a high-speed analog-to-digital converter (ADC), which results in high power consumption. A hybrid beamforming architecture and one-bit resolution ADCs have been proposed to reduce power consumption. However, analog beamforming and one-bit quantization make channel estimation more challenging. In this paper, we propose a narrowband channel estimation algorithm for mmWave communication systems with one-bit ADCs and hybrid beamforming based on generalized approximate message passing (GAMP). We show through simulation that 1) GAMP variants with one-bit ADCs have better performance than do least-squares estimation methods without quantization, 2) the proposed one-bit GAMP algorithm achieves the lowest estimation error among the GAMP variants, and 3) exploiting more frames and RF chains enhances the channel estimation performance.

In millimeter wave (mmWave) systems, we investigate uplink user scheduling when a base station employs low-resolution analog-to-digital converters (ADCs) with a large number of antennas. To reduce power consumption in the receiver, low-resolution ADCs can be a potential solution for mmWave systems in which many antennas are likely to be deployed to compensate for the large path loss. Due to quantization error, we show that the channel structure in the beamspace, in addition to the channel magnitude and beamspace orthogonality, plays a key role in maximizing the achievable rates of scheduled users. Consequently, we derive the optimal criteria with respect to the channel structure in the beamspace that maximizes the uplink sum rate for multi- user multiple input multiple output (MIMO) systems with a zero-forcing receiver. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mmWave systems with low-resolution ADCs. Numerical results validate that the proposed algorithm outperforms conventional user scheduling methods in terms of the sum rate.

A spectrum- and energy-efficient system is essential for millimeter wave communication systems that require large antenna arrays with power-demanding ADCs. We propose an ADC bit allocation (BA) algorithm that solves a minimum mean squared quantization error problem under a power constraint. Unlike existing BA methods that only consider an ADC power constraint, the proposed algorithm regards total receiver power constraint for a hybrid analog-digital beamforming architecture. The major challenge is the non-linearities in the minimization problem. To address this issue, we first convert the problem into a convex optimization problem through real number relaxation and substitution of ADC resolution switching power with constant average switching power. Then, we derive a closed-form solution by fixing the number of activated radio frequency (RF) chains M. Leveraging the solution, the binary search finds the optimal M and its corresponding optimal solution. We also provide an off-line training and modeling approach to estimate the average switching power. Simulation results validate the spectral and energy efficiency of the proposed algorithm. In particular, existing state-of-the-art digital beamformers can be used in the system in conjunction with the BA algorithm as it makes the quantization error negligible in the low-resolution regime.

Millimeter wave (mmWave) systems operating over a wide bandwidth and using a large number of antennas impose a heavy burden on power consumption. In a massive multiple-input multiple-output (MIMO) uplink, analog-to-digital converters (ADCs) would be the primary consumer of power in the base station receiver. This paper proposes a bit allocation (BA) method for mmWave multi-user (MU) massive MIMO systems under a power constraint. We apply ADCs to the outputs of an analog phased array for beamspace projection to exploit mmWave channel sparsity. We relax a mean square quantization error (MSQE) minimization problem and map the closed-form solution to non-negative integer bits at each ADC. In link-level simulations, the proposed method gives better communication performance than conventional low-resolution ADCs for the same or less power. Our contribution is a near optimal low-complexity BA method that minimizes total MSQE under a power constraint.

In this paper, we propose space-time fronthaul compression of baseband uplink LTE signals for cellular networks, in which baseband units (BBUs) support remote radio heads (RRHs) through fronthaul links. In particular, we assume massive antenna arrays in which the number of antennas in a RRH is much larger than the number of active users. The proposed method consists of two phases: dimensionality reduction phase and individual quantization phase. The key idea of the first phase is to apply principal component analysis (PCA). It performs low-rank approximation of a matrix - composed of received signals - by exploiting the correlation of the received signals across space and time. In the second phase, our method individually quantizes the dimensionality-reduced signal by applying transform coding with bit allocation to reduce the number of quantization bits. An LTE link-level simulator provides numerical results which show that the method achieves up to 8 × compression ratio for the uplink with 64 antennas and 4 active users, along with improvement in communication system performance as a result of denoising.