Precoder Design Research Articles

An Information-Theoretic Branch-and-Prune Algorithm for Discrete Phase Optimization of RIS in Massive MIMO

In this paper, we consider passive RIS-assisted multi-user communication between wireless nodes to improve the blocked line-of-sight (LOS) link performance. The wireless nodes are assumed to be equipped with Massive Multiple-Input Multiple-Output antennas, hybrid precoder, combiner, and low-resolution analog-to-digital converters (ADCs). We first derive the expression for the Cramer-Rao lower bound (CRLB) of the Mean Squared Error (MSE) of the received and combined signal at the intended receiver under interference. By appropriate design of the hybrid precoder, combiner, and RIS phase settings, it can be shown that the MSE achieves the CRLB. We further show that minimizing the MSE w.r.t. the phase settings of the RIS is equivalent to maximizing the throughput and energy efficiency of the system. We then propose a novel Information-Directed Branch-and-Prune (IDBP) algorithm to derive the phase settings of the RIS. We, for the first time in the literature, use an information-theoretic measure to decide on the pruning rules in a tree-search algorithm to arrive at the RIS phase-setting solution, which is vastly different compared to the traditional branch-and-bound algorithm that uses bounds of the cost function to define the pruning rules. In addition, we provide the theoretical guarantees of the near-optimality of the RIS phase-setting solution thus obtained using the Asymptotic Equipartition property. This also ensures near-optimal throughput and MSE performance.

Robust Downlink Multi-Antenna Beamforming With Heterogenous CSI: Enabling eMBB and URLLC Coexistence

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from the heterogeneous and conflicting requirements of different nodes sharing the same network resources. In this paper, we leverage the I-CSI of multiple eMBB links and the channel measurement’s history of a URLLC user for multi-antenna beamforming design. Specifically, we propose a precoding design that minimizes the transmit power of a base station (BS) providing eMBB and URLLC services with signal-to-interference-plus-noise ratio (SINR) and outage constraints, respectively, by modifying existing I-CSI-based precoding schemes to account for URLLC channel history information. Moreover, we illustrate and validate the proposed method by adopting zero-forcing (ZF) and the transmit power minimization (TPM) precoding with SINR constraints. We show that the ZF implementation outperforms TPM in adverse channel conditions as in Rayleigh fading, while the situation is rapidly reversed as the channel experiences some line-of-sight (LOS). Finally, we determine the confidence levels at which the target outage probabilities are reached. For instance, we show that outage probabilities below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> are achievable with more than 99% confidence for both precoding schemes under favorable LOS conditions with 16 transmit antennas and 500 samples of URLLC channel history.

QoS-Aware Precoding in Downlink Massive MIMO LEO Satellite Communications

Satellite communications (SATCOM) are essential parts of future wireless networks and are moving towards standardization in 6G. Low Earth Orbit (LEO) SATCOM show significant performance gain in global wireless access services, due to their minor path loss and short propagation delay, compared with other SATCOM counterparts. A quality of service (QoS)-aware precoding design is presented in this work to enhance the performance of massive multiple-input multiple-output (MIMO) LEO SATCOM. In particular, we set up a multi-objective optimization problem to balance the system energy efficiency (EE) and the percentage of users achieving their QoS requirements. An efficient algorithm is developed for obtaining the precoding vectors. Numerical results show that the proposed algorithm leads to a larger percentage of users achieving their QoS requirements and brings improvements in EE performance, compared with conventional approaches.

Wideband Precoding for RIS-Aided THz Communications

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communication has been considered as a promising technology for enabling future sixth-generation (6G) wireless systems. Due to the exploitation of extremely large bandwidth and large scale of RIS, RIS-aided THz communications would suffer from the beam split effect, where the generated beams cannot be aligned with the target physical direction in the whole bandwidth, so a severe array gain loss will be introduced. In this paper, the beam split effect is first analyzed in the existence of RIS. Then, a novel sub-connected RIS architecture is proposed to mitigate the beam split effect. The crux is to introduce additional time-delay (TD) modules and phase shifters into RIS elements so as to convert the classical phase-only precoding to the joint phase and delay precoding. Accordingly, a wideband precoding design is proposed to compensate for the severe array gain loss, and the performance analysis on the array gain is also provided. After that, we extend our discussions to the emerging scenarios with massive antennas equipped at the base station (BS), where the effect of “double beam split”, i.e., the coupling of beam split at the BS and the RIS, occurs. We prove the decomposability of the array gain, based on which the double beam split effect can be addressed by separately optimizing the wideband precoding at the BS and the RIS. Simulation results demonstrate that our proposed sub-connected RIS significantly alleviates the beam split effect with a small number of TD modules, and it is capable of achieving sub-optimal achievable rate performance with acceptable hardware cost and power consumption.

Hybrid precoding design for secure smart-grid enabled MIMO wireless communications in Industry 5.0

Secure and reliable wireless communication systems are essential for the successful integration of smart grid-enabled wireless communications and Industry 5.0 applications. This work proposes a hybrid precoding design for secure smart-grid multiple-input multiple-output (MIMO) wireless communications in Industry 5.0 to maximize the average secrecy capacity of the system while minimizing user outages crucial for maintaining the security and privacy of sensitive data in critical infrastructure. To achieve this, we develop a mathematical model that considers the impact of various system parameters, such as eavesdropping and interference caused by the active eavesdropper and noise, on the secrecy capacity. To find an efficient solution to the optimization problem, we propose an algorithm that decouples the original optimization problem into a series of subproblems and uses iterative techniques to find the optimal values, thereby improving computational efficiency. The hybrid precoding scheme is an effective technique for optimizing the design parameters of MIMO-based secure wireless communication systems. Our proposed approach provides a practical solution for achieving this optimization. Our numerical results demonstrate that our proposed scheme outperforms traditional benchmark schemes, maximizing the average secrecy capacity while minimizing user outages. Our work highlights the importance of secure wireless communication systems in Industry 5.0 and smart grid applications. The proposed approach provides an efficient method for designing secure wireless communication systems that can effectively address the unique challenges posed by these critical infrastructure systems.

Performance Evaluation of Hybrid Precoder Design for Multi-User Massive MIMO Systems with Low-Resolution ADCs/DACs

This paper presents a comprehensive analysis and design of a hybrid precoding system tailored for mmWave multi-user massive MIMO systems in both downlink and uplink scenarios. The proposed system employs a two-stage precoding approach, incorporating UQ and NUQ techniques, along with low-resolution DACs in downlink and ADCs in uplink to address hardware limitations. The system considers Zero Forcing and Minimum Mean Square Error algorithms as digital precoding methods for the uplink scenario, while exploring the impact of different DAC resolutions on system performance. Extensive simulations reveal that the proposed system surpasses conventional analog beamforming methods, particularly in multi-user scenarios involving inter-user interference. In downlink, the system demonstrates a trade-off between SE and EE, achieving higher Energy Efficiency with NUQ. In uplink, NUQ and UQ converters exhibit similar performance trends regardless of the chosen combiner algorithm. The proposed system attains enhanced Spectral and Energy Efficiency while maintaining reduced complexity and overhead. The study significantly contributes to the advancement of efficient and effective mmWave multi-user massive MIMO systems by providing a thorough analysis of various quantization schemes and precoding techniques. The findings of this research are expected to aid in the optimization of 5G and beyond technologies, particularly in high-density deployment scenarios.

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

AbstractMillimeter‐Wave (mmWave) massive Multi‐User Multiple Input Multiple Output (MU‐MIMO) utilizes the hybrid analog precoding design helps to minimize the amount of Radio‐Frequency (RF) chains without causing loss of sum‐rate performance. The Channel Estimation (CE) is designed to demand the conditions of channels with high time‐varying features. The existing CE frameworks are extremely complex. The accurate CE is still challenging in mmWave massive MIMO system based on the hardware constraints, a maximum number of antennas, and so on. Various proficient algorithms were combined to yield reliable channel estimates. The mmWave massive MIMO system utilizes hybrid precoding to minimize energy consumption loss and reduce hardware complexity. The fully connected layer in hybrid precoding results in high energy efficiency loss. This proposed work aims to develop two ensemble deep learning models for CE and hybrid precoding of the “mmWave massive MIMO system.” Here, the estimation of the channel is performed by a “deep Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN).” With the reconstructed channel, the hybrid precoding phase is performed by deep CNN with AdaBoost. The performance of CE and hybrid precoding is improved with Self Adaptive Searched Galactic Swarm Optimization (SAS‐GSO) by attaining the maximum spectral efficiency and also minimizing the Normalized Mean‐Squared Error (NMSE). Hence, the simulation result is shown that the proposed model achieves enhanced performance. Throughout the analysis, at the 20th iteration, the SAS‐GSO‐ensemble approach gives a lower NMSE rate, which is 31%, 25.9%, 20%, 19.6%, 19.3%, 18.6%, and 18.3% superior to OMP‐Ensemble, AMP‐Ensemble, DLCS‐Ensemble, DGMP‐Ensemble, QALS‐Ensemble, DLQP‐Ensemble, and CSO‐Ensemble, respectively, for hybrid precoding. Therefore, the offered mmWave massive MIMO is achieved better performance regarding diverse error metrics.

Rate Splitting in FDD Massive MIMO Systems Based on the Second Order Statistics of Transmission Channels

In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output system (MIMO) operating in frequency-division-duplex (FDD) mode. We propose a statistical precoding design relying on the channels’ second-order information when one-layer RS is implemented. The advantage of the statistical precoding lies in simplifying the precoder design by basing the optimization objective on the slowly-varying channel covariance matrices instead of the fast-changing instantaneous channel estimates. To this end, we use the so-called bilinear precoder, which was shown in [1] and [2] to have limited performance in the high SNR regime due to the imperfect channel state information (CSI) available at the base station (BS). We formulate the DL throughput maximization problem based on a widely used lower bound on the achievable sum rate and propose an iterative approach to solve the underlying optimization problem. Numerical results demonstrate the benefit of implementing one-layer RS. Furthermore, the proposed iterative approach achieves excellent results in terms of spectral efficiency compared to the state-of-the-art techniques.

Capacity Enhancement for Reconfigurable Intelligent Surface-Aided Wireless Network: From Regular Array to Irregular Array

Reconfigurable intelligent surface (RIS) is promising for future 6G wireless communications. However, the increased number of RIS elements results in the high overhead for channel acquisition and the non-negligible power consumption. Therefore, how to improve the system capacity with limited RIS elements is essential. Unlike the classical regular RIS whose elements are arranged on a regular grid, in this paper, we propose an irregular RIS structure to improve the system capacity. The key idea is to irregularly configure a given number of RIS elements on an enlarged surface, which provides extra spatial degrees of freedom compared with the regular RIS. In this way, the received signal power can be enhanced, and thus the system capacity can be improved. Then, we formulate a joint topology and precoding optimization problem to maximize the capacity for irregular RIS-aided communication systems. Accordingly, a joint optimization algorithm with low complexity is proposed to alternately optimize the RIS topology and the precoding design. Particularly, a tabu search-based method is used to design the irregular RIS topology, and a neighbor extraction-based cross-entropy method is introduced to optimize the precoding design. Simulation results demonstrate that, subject to the constraint of limited RIS elements, the proposed irregular RIS can significantly enhance the system capacity.

Asynchronous Cell-Free Massive MIMO With Rate-Splitting

In practical cell-free (CF) massive multiple-input multiple-output (MIMO) networks with distributed and low-cost access points, the asynchronous arrival of signals at the user equipments increases multi-user interference that degrades the system performance. Meanwhile, rate-splitting (RS), exploiting the transmission of both common and private messages, has demonstrated to offer considerable spectral efficiency (SE) improvements and its robustness against channel state information (CSI) imperfection. The signal performance of a CF massive MIMO system is first analyzed for asynchronous reception capturing the joint effects of propagation delays and oscillator phases of transceivers. Taking into account the imperfect CSI caused by asynchronous phases and pilot contamination, we derive novel and closed-form downlink SE expressions for characterizing the performance of both the RS-assisted and conventional non-RS-based systems adopting coherent and non-coherent data transmission schemes, respectively. Moreover, we formulate the design of robust precoding for the common messages as an optimization problem that maximizes the minimum individual SE of the common message. To address the non-convexity of the design problem, a bisection method is proposed to solve the problem optimally. Simulation results show that asynchronous reception indeed destroys both the orthogonality of the pilots and the coherent data transmission resulting in poor system performance. Besides, thanks to the uniform coverage properties of CF massive MIMO systems, RS with a simple low-complexity precoding for the common message obtained by the equal ratio sum of the private precoding is able to achieve substantial downlink sum SE gains, while the application of robust precoding to the common message is shown to be useful in some extreme cases, e.g., serious oscillator mismatch and unknown delay phase.

Precoder Design to Improve Performance of Multi-User OQAM DCO-FBMC Underwater Visible Light Communication

Precoder Design to Improve Performance of Multi-User OQAM DCO-FBMC Underwater Visible Light Communication

Multi-Dimensional Joint Constellation and Precoding Design for MIMO Systems

Digital modulation and precoding are two key modules for increasing the data rate and reducing the symbol error rate in multiple-input multiple-output (MIMO) systems. Traditionally, these two modules are designed separately, and regular constellations like <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> -ary phase-shift keying ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> -PSK) and quadrature amplitude modulation (QAM) are used for modulation. However, significant gains can be achieved if modulation and precoding are designed jointly for all antennas. Motivated by this, a multi-dimensional joint constellation and precoding design is proposed to optimize the in-phase and quadrature components for all sub-channels of a MIMO channel at once. The objective is to maximize the minimum distance among the symbols. Extensive simulation results indicate that this approach can significantly improve the performance of the state-of-the-art solutions by reducing the symbol error rate and bit error rate.

Fair multigroup multicast precoding design based on traffic demands for multibeam satellite systems

Onboard resources are limited for multibeam satellite communication systems, while differences exist in traffic demands among users. Since conventional multigroup multicast precoding methods do not take traffic demands for users into consideration, it is difficult to flexibly adjust the offered throughput to users’ demands. Users with higher demand may obtain lower throughput while users with lower demand may be over-satisfied, which results in the requested-offered throughput mismatch and resource waste. This paper proposes a fair multigroup multicast precoding design based on traffic demands. To obtain the precoding design, the optimization problem aimed at maximizing the minimum throughput satisfaction ratio, which is defined as the ratio of the offered throughput to the traffic demand, is formulated, so as to provide fair service for all users while satisfying per-feed power constraints and traffic demand constraints. To address this problem, an auxiliary variable is first introduced to equivalently simplify it. Then, the semidefinite relaxation and the bisection search strategies are adopted to further handle the problem. Finally, the optimal precoding vectors are obtained by employing eigenvalue decomposition or Gaussian randomization. Simulation results indicate the effectiveness of the proposed precoding algorithm and demonstrate that it can better adapt to the scenarios with different traffic demands.

Downlink NOMA for Short-Packet Internet of Things Communications With Low-Resolution ADCs

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.

Developing novel channel estimation and hybrid precoding in millimeter-wave communication system using heuristic-based deep learning

The digital or analog precoders are not carried the optimal energy efficiency in the mm-wave massive MIMO and so, every antenna is needed with one radio frequency chain in the system. Based on this view, a cost-efficient technique is developed for hybrid precoding. From this technique, the short dimensional precoding is obtained from the high dimensional beam-formers in the digital domain for steering antenna elements. Therefore, the main intention of this paper is to develop a channel estimation and hybrid pre-coding model for the mm-wave massive MIMO communication system. The channel estimation phase is performed by the Adaptive Deep Convolutional Neural Network (A-Deep CNN), which covers both channel estimation and channel reconstruction. The introduction of a hybrid meta-heuristic algorithm with Forest-Tunicate Swarm Algorithm (F-TSA) is used for enhancing A-Deep CNN that is adaptable for efficient channel estimation. Once the channel estimation is done, the deployment of Optimized Recurrent Neural Networks (O-RNN) is used for hybrid precoding. Simulation results demonstrate that the proposed A-Deep CNN-based channel estimation scheme outperforms the existing schemes in terms of the Normalized Mean-Squared Error (NMSE) and spectral efficiency, while the O-RNN-hybrid precoder design method has better spectral efficiency performance than other methods.

Precoding Design for Multi-Group MIMO-NOMA Scheme With SIC Residual Analysis

This paper presents a precoder design for multiple-input multiple-output (MIMO) uplink communications with a multi-group non-orthogonal multiple access (NOMA) scheme. In this scheme, QR-decomposition-based detection is used to decode user signals of each group under imperfect successive interference cancellation (SIC). In the uplink, transmitted signals are encoded with the constellation of a symmetric quadrature amplitude modulation (QAM), and SIC residual errors are generated from symbol demodulation. The statistics of the SIC errors are derived, with which expressions of approximate SIC-residual-power coefficient and signal-to-interference-plus-noise ratio (SINR) are developed. Consequently, the effect of the SIC errors can be explicitly addressed in the precoder design for minimizing the total transmit power subject to users' symbol error probability (SEP) constraints. A suboptimal design algorithm with efficient “logarithmic” golden-section search is developed. Simulation results show the effectiveness of the design criterion and algorithm, and validate the detection error analysis. Unlike the conventional fractional-SIC-error model that suffers from underestimation/overestimation of SIC errors and infeasibility, our SIC error model can provide accurate effect of the SIC errors in the SINR analysis and enable the proposed method to give design solutions to satisfy the SEP constraints.

Deep neural network design with SLNR and SINR criterions for downlink power allocation in multi-cell multi-user massive MIMO systems

In this paper, we propose a deep learning approach for solving power allocation problems in massive MIMO networks. We use signal-to-interference-plus-noise-ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design to define the max–min and max-prod power allocation challenges. The power allocation process to each user equipment in the base station coverage takes a long time and is inefficient, hence numerous base stations are deployed to serve multiple user equipments. As a result, we develop a deep neural network (DNN) framework in which the user’s equipment position is utilized to train the deep model, which is then used to forecast the ideal power distribution depending on the user’s location. Compared to the traditional optimization approach, the DNN design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning achieves excellent results in forecasting power allocation with an accuracy of 85% for the max–min strategy and 99% for the max-product approach.

Hybrid Beamforming for Multi User Massive MIMO Systems

The increasing demand in wireless communications for enhanced spectral efficiency (SE) and throughput makes massive multiple input multiple output (MIMO) a great choice for meeting those demands by using a vast set of antennas. Despite the advantages of massive MIMO, to implement such systems comes with a huge price tag and consumes a lot of power. Hybrid beamforming (HBF) architecture has drawn considerable attention in the past few years, by significantly decreasing the amount of employed radio frequency (RF) chains and combining high dimensional analog beamforming (ABF) using phase shifters (PS) together with low dimensional digital beamforming (DBF). However, because of the extreme energy consumption and hardware complexity, traditional precoding designs are difficult to implement. In this paper, two HBF techniques are proposed to address the above issue. (i) low complexity precoding known as phased zero forcing (PZF) precoding, which controls phase only in the RF domain (ii) singular value decomposition (SVD) based optimal unconstrained precoding, that can be implemented on inexpensive RF components.

Hybrid Precoder Design Based on mm-Wave MIMO System with GMD Method

Hybrid Precoder Design Based on mm-Wave MIMO System with GMD Method

Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing

In this letter, we address blockage detection and precoder design for multiple-input multiple-output (MIMO) links, without communication overhead required. Blockage detection is achieved by classifying light detection and ranging (LIDAR) data through a physics-based graph neural network (GNN). For precoder design, a preliminary channel estimate is obtained by running ray tracing on a 3D surface obtained from LIDAR data. This estimate is successively refined and the precoder is designed accordingly. Numerical simulations show that blockage detection is successful with 95% accuracy. Our digital precoding achieves 90% of the capacity and analog precoding outperforms previous works exploiting LIDAR for precoder design.