Hybrid Precoding Scheme Research Articles

Randomized Two-Timescale Hybrid Precoding for Downlink Multicell Massive MIMO Systems

Although massive multiple-input multiple-output (MIMO) promises high spectral efficiency, there are several issues that significantly limit the potential gain of massive MIMO, such as severe inter-cell interference, huge channel state information (CSI) overhead/delay, high cost and power consumption of RF chains, and user fairness. Several precoding schemes have been proposed for massive MIMO but there still lacks a systematic solution to address all of the aforementioned issues especially for the multicell case. In this paper, we propose a randomized two-timescale hybrid precoding (RTHP) scheme for the downlink transmission of multicell massive MIMO systems. RTHP allows time sharing among multiple sets of analog precoders to achieve better tradeoff between sum throughput and fairness. Moreover, the time-sharing factors and analog precoders are adapted to the long-term channel statistics to mitigate inter-cell interference with reduced CSI overhead and RF chains. The digital precoder is used to support multiuser MIMO at each base station based on local low-dimensional effective CSI only. As such, RTHP resolves all of the aforementioned issues. We formulate the optimization of RTHP as a general network utility maximization problem (GNUMP), which is a two-timescale stochastic non-convex problem, and consequently challenging to solve. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop an online algorithm, which is shown to converge to stationary solutions of the approximate GNUMP. Finally, simulation results verify the advantages of the proposed scheme over baseline schemes.

Coordinated Hybrid Precoding for Interference Exploitation in Heterogeneous Networks

We consider a downlink multiuser massive MIMO system comprising multiple heterogeneous base stations with hybrid precoding architectures. To enhance the energy efficiency of the network, we propose a novel coordinated hybrid precoding technique, where the coordination between the base stations is aimed at exploiting interference as opposed to mitigating it as per conventional approaches. We formulate an optimization problem to compute the coordinated hybrid precoders that minimize the total transmit power while fulfilling the required quality of service at each user. Furthermore, we devise a low-complexity suboptimal precoding scheme to compute approximate solutions of the precoding problem. The simulation results reveal that the proposed coordinated hybrid precoding yields superior performance when compared to the conventional hybrid precoding schemes.

Hybrid MU-MIMO Precoding Based on K-Means User Clustering

Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) systems have been extensively investigated over the last few years from both theoretical and practical perspectives. The low complexity Linear Precoding (LP) schemes for MU-MIMO are already deployed in Long-Term Evolution (LTE) networks; however, they do not work well for users with strongly-correlated channels. Alternatives to those schemes, like Non-Linear Precoding (NLP), and hybrid precoding schemes were proposed in the standardization phase for the Third-Generation Partnership Project (3GPP) 5G New Radio (NR). NLP schemes have better performance, but their complexity is prohibitively high. Hybrid schemes, which combine LP schemes to serve users with separable channels and NLP schemes for users with strongly-correlated channels, can help reduce the computational burden, while limiting the performance degradation. Finding the optimum set of users that can be co-scheduled through LP schemes could require an exhaustive search and, thus, may not be affordable for practical systems. The purpose of this paper is to present a new semi-orthogonal user selection algorithm based on the statistical K-means clustering and to assess its performance in MU-MIMO systems employing hybrid precoding schemes.

Channel Estimation and Transmission Strategy for Hybrid mmWave NOMA Systems

This paper presents a novel channel estimation and transmission strategy for millimeter wave (mmWave) non-orthogonal multiple access communication system with hybrid architecture. We first propose a general iterative index detection-based channel estimation algorithm that can obtain both direction of arrival and channel gain of each channel path. We then design an enhanced hybrid precoding scheme from the angle domain viewpoint to reduce the inter-beam interferences. Next, we investigate the multi-user scheduling and power allocation with the objective of maximizing the overall achievable rate. The problem turns to be non-convex and then we decompose it into two sub-problems, which separately consider user scheduling and power allocation. The former is solved by a novel algorithm based on the many-to-one two sided matching theory while the latter is solved by an iterative optimization algorithm. Simulation results show that the proposed channel estimation and user scheduling can be better than traditional methods. Finally, numerical examples are provided to corroborate the proposed studies.

Energy-Efficient Hybrid Precoding Scheme Based on Antenna Selection Technology in Massive Multiple-Input Multiple-Output Systems

Hybrid precoding is a promising technology for massive multiple-input multiple-output (MIMO) systems. It can reduce the number of radio frequency (RF) chains. However, the power consumption is still very high owing to the large-scale antenna array. In this paper, we propose an energy-efficient precoding scheme based on antenna selection technology. The precoding scheme greatly increases the energy efficiency (EE) of the system. In the first step, we derive an exact closed-form expression of EE. Meanwhile, we further study the relationship between the number of transmit antennas and EE on the basis of the exact closed-form expression of EE. We prove that there exists an optimal value. When the number of transmit antennas equals to the value, the EE of the system can reach the maximum by a proper hybrid precoding scheme. Then, we propose an antenna selection algorithm to select antennas from the transmit antennas. And the number of selected antennas equals to the optimal value. Subsequently, we design the analog precoder based on a codebook to maximize the equivalent channel gain. At last, we further improve the EE by baseband digital precoding. The precoding algorithm we proposed offers a compromise between spectral efficiency (SE) and EE in millimeter wave (mmWave) massive MIMO systems. Finally, simulation results validate our theoretical analysis and show that a substantial EE gain can be obtained over the precoding scheme we proposed without large performance loss.

Spatial Lobes Division-Based Low Complexity Hybrid Precoding and Diversity Combining for mmWave IoT Systems

This paper focuses on the design of low complexity hybrid analog/digital precoding and diversity combining in millimeter wave (mmWave) Internet of things (IoT) systems. Firstly, by exploiting the sparseness property of the mmWave in the angular domain, we propose a spatial lobes division (SLD) to group the total paths of the mmWave channel into several spatial lobes, where the paths in each spatial lobe form a low-rank sub-channel. Secondly, based on the SLD operation, we propose a low complexity hybrid precoding scheme, named HYP-SLD. Specifically, for each low-rank sub-channel, we formulate the hybrid precoding design as a sparse reconstruction problem and separately maximizes the spectral efficiency. Finally, we further propose a maximum ratio combining based diversity combining scheme, named HYP-SLD-MRC, to improve the bit error rate (BER) performance of mmWave IoT systems. Simulation results demonstrate that, the proposed HYP-SLD scheme significantly reduces the complexity of the classic orthogonal matching pursuit (OMP) scheme. Moreover, the proposed HYP-SLD-MRC scheme achieves great improvement in BER performance compared with the fully digital precoding scheme.

Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded to be an emerging solution for the next generation of communications, in which hybrid analog and digital precoding is an important method for reducing the hardware complexity and energy consumption associated with mixed signal components. However, the fundamental limitations of the existing hybrid precoding schemes is that they have high computational complexity and fail to fully exploit the spatial information. To overcome these limitations, this paper proposes, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN). Specifically, the hybrid precoder is selected through training based on the DNN for optimizing precoding process of the mmWave massive MIMO. Additionally, we present extensive simulation results to validate the excellent performance of the proposed scheme. The results exhibit that the DNN-based approach is capable ofminimizing the bit error ratio (BER) and enhancing spectrum efficiency of the mmWave massive MIMO, which achieves better performance in hybrid precoding compared with conventional schemes while substantially reducing the required computational complexity.

Low Complexity Hybrid Precoding for Multiuser Millimeter Wave Systems Over Frequency Selective Channels

We propose new hybrid precoding schemes for the downlink transmission in a multiuser millimeter wave (mmWave) system over frequency selective channels. In the system, the multiantenna base station employs the fully connected architecture and uses orthogonal frequency-division multiplexing to serve multiple mobile users. We first propose an alternating minimization based hybrid precoding scheme by transforming the wideband hybrid precoding problem to a narrowband hybrid precoding problem. We then propose a beamforming-based hybrid precoding scheme by exploiting the correlation of subchannels to profoundly reduce the computational complexity. Numerical results are presented to demonstrate the advantages of our proposed schemes relative to two existing schemes, as well as to examine the impact of system parameters on the achieved performance.

Machine Learning Inspired Hybrid Precoding for Wideband Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) has already been considered as a promising solution to meet the requirement of the higher data rate for the future Internet of Things (IoTs). Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. However, the current literature on hybrid precoding considers either the high-resolution (HR) phase shifters (PSs) with high power consumption or the impractical narrowband mmWave channel model. To this end, in this paper, we investigate an energy-efficient hybrid precoding scheme using one-bit PSs for practical frequency-selective wideband mmWave massive MIMO systems. Specifically, we provide the energy consumption analysis to reveal the fact that the energy consumed by the one-bit PSs is much lower than that by the HR-PSs, and the array gain loss incurred by using one-bit PSs is minimal. Moreover, motivated by the cross-entropy optimization (CEO) algorithm evolved for machine learning, we propose the CEO-based hybrid precoding scheme to maximize the achievable sum-rate of the considered system. In the CEO-based hybrid precoding, we update the probability distributions of the elements in the hybrid precoder to minimize the cross-entropy between the two probability distributions so that we can generate the final solution close to the optimal one. Furthermore, we extend the proposed CEO-based hybrid precoding scheme from the case with one-bit PSs to the general case with HR-PSs to show that our solution can also be applied in other scenarios. The performance evaluation demonstrates that our proposed scheme can obtain near-optimal sum-rate and considerably higher energy efficiency than some existing solutions.

A Family of Practical Hybrid Precoding Algorithms for Rank-Deficient Millimeter-Wave MIMO Systems

Hybrid precoding (HP) techniques based on partially connected structure (PCS) are widely studied in millimeter-wave (mmWave) MIMO communication systems due to low hardware cost as well as low energy consumption. Many efficient HP schemes based on PCS have recently been proposed, which require that the baseband precoding (BP) matrix of the digital domain to be a diagonal or square matrix. To avoid the interference from multiple streams of a single-user, the number of data streams for a single-user should not exceed the rank of the equivalent baseband MIMO channel (EBMC) before BP. Hence, these schemes are only reliable to the case that the EBMC matrix is full rank. However, the EBMC matrix may be rank-deficient due to the geometrical change and misplacement of the transceiver in practice. In this paper, we focus on the HP with PCS and propose a family of effective iterative matrix factorization (IMF) algorithms for HP designs, which are suitable for rank-deficient EBMC. We firstly propose an IMF-HP algorithm based on traditional design, which approximates the performance of the fully digital precoder by utilizing an alternate optimization strategy. In particular, we present a successive interference cancelation combined with IMF for HP design (SiC-IMF-HP) to further achieve higher throughput and lower complexity. Simulation results show that the proposed SiC-IMF-HP algorithm performs better than other recently proposed hybrid precoding schemes based on PCS when the number of data streams equals to the number of RF chains. The proposed algorithms both enjoy an acceptable computational complexity.

Spectral and Energy Efficiency of Hybrid Precoding for mmWave Massive MIMO With Low-Resolution ADCs/DACs

In this paper, we investigate the spectral efficiency (SE) and energy efficiency (EE) of hybrid precoding in the point-to-point mmWave massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). First, considering the quantization noise of ADCs/DACs, an approximation expression for the SE is derived. Then, in order to solve the problem of non-convex hybrid precoding design, based on the derived analytical approximation expression, we propose a two-stage alternating minimization scheme to obtain the optimal hybrid precoder matrix. And the trade-offs between SE and EE with the full digital precoding and hybrid precoding are investigated. Numerical results verify that the system with the proposed scheme has nearly the same SE performance as with the full digital precoding. The fully connected hybrid precoding can be more energy-efficient compared to the full digital precoding, and the proposed hybrid precoding scheme can achieve the better trade-off between the SE and EE. Moreover, the increase in the resolution of ADCs/DACs can effectively improve the SE performance of the system, and 4-bits ADCs/DACs achieve the best EE performance for the hybrid precoding.

Improved Hybrid Precoding Scheme for mmWave Large-Scale MIMO Systems

Due to the limited spectrum resources, communication utilizing millimeter wave (mmWave) has become a research hotspot, deemed to be an important technology of 5G, in which plenty of antennas can be deployed. However, in mmWave large-scale MIMO systems, it is not feasible to directly use full digital precoding, bringing high energy consumption and high complexity. To overcome this difficulty, hybrid precoding is proposed. In this paper, we put forward a relative weighted-based hybrid precoding scheme on the basis of an energy-efficient precoding architecture only using switches and inverters called SI-based architecture, aimed at adaptively updating the probability distribution of elements in an analog beamformer in the hybrid precoder by minimizing cross entropy. Hence, our proposed scheme can achieve higher achievable sum rate than the conventional method without relative weighted. At last, we provide some simulation results to validate the superiority of our proposed precoding scheme.

Hybrid Precoding for Beamspace MIMO Systems With Sub-Connected Switches: A Machine Learning Approach

By employing lens antenna arrays, the number of radio frequency (RF) chains in millimeter-wave (mmWave) communications can be significantly reduced. However, most existing studies consider the phase shifters (PSs) as the main components of the analog beamformer, which may result in a significant loss of energy efficiency (EE). In this paper, we propose a switch selecting network to solve this issue, where the analog part of the beamspace MIMO system is realized by a sub-connected switch selecting network rather than the PS network. Based on the proposed architecture and inspired by the cross-entropy (CE) optimization developed in machine learning, an optimal hybrid cross-entropy (HCE)-based hybrid precoding scheme is designed to maximize the achievable sum rate, where the probability distribution of the hybrid precoder is updated by minimizing CE with unadjusted probabilities and smoothing constant. Simulation results show that the proposed HCE-based hybrid precoding can not only effectively achieve the satisfied sum-rate, but also outperform the PSs schemes concerning energy efficiency.

Machine Learning-Based Spectrum Efficiency Hybrid Precoding With Lens Array and Low-Resolution ADCs

Hybrid precoding is an important issue in millimeter wave (mmWave) massive multi-input and multi-output (MIMO) system. Specially, energy-saving hybrid precoding architectures and efficient hybrid precoding schemes provide ideas for solving this issue. In this paper, we propose a hybrid precoding/combining architecture that is low-cost and easy to implement. Specifically, a hybrid precoding architecture is realized by the lens sub-arrays at the base station (BS). Moreover, a hybrid combining architecture applies the low-resolution analog-to-digital converters (ADCs) at the front end of the radio frequency (RF) chains at the receiving terminal. Based on the hybrid precoding/combining architecture, the hybrid precoder and combiner are jointly optimized to maximize the spectrum efficiency (SE) in the downlink systems, which is a combinatorial optimization problem due to hardware constraints. The cross-entropy (CE) approach in machine learning (ML) is a simple way to solve the combinatorial optimization problem benefiting from its adaptive update procedure. Therefore, we propose an adaptive hybrid precoder/combiner design scheme (AHDS), in which a hybrid precoding algorithm based on the improved CE (ICE) inspired by ML is adopted to design the optimal hybrid precoder, and an approximate optimization method (AOM) is suggested when designing the hybrid combiner. In general, compared with the existing hybrid design schemes, the proposed AHDS is demonstrated to have significant advantage in SE with low computational complexity.

Dynamically Connected Hybrid Precoding Scheme for Millimeter-Wave Massive MIMO Systems

In order to reduce the implementation complexity and hardware cost of millimeter-wave massive multiple input multiple output systems, we propose a dynamically connected hybrid precoding (DC-HP) scheme in this letter. In the scheme, to exploit the subchannels difference of the users in the system, a dynamic connection network and an analog precoding matrix are first jointly designed. After that, the matched-filter or zero-forcing criterion is used to design the digital precoding matrix with the equivalent channel after analog precoding. Simulation results show that the DC-HP scheme achieves a better performance than the traditional sub-connected hybrid precoding and adaptive hybrid precoding schemes, while it also enjoys a much lower hardware implementation complexity and hardware power consumption than the full digital baseband precoding and fully connected hybrid precoding schemes.

Efficient Optimization Algorithms for Multi-User Beamforming With Superposition Coding

Channel asymmetry and channel correlation are frequently encountered in wireless communication systems. Orthogonal transmission schemes are usually inefficient in dealing with these problems. In this paper, in order to boost the throughput performance for multiple-input multiple-output broadcast communications in the presence of channel asymmetry and/or channel correlation, we study optimization algorithms for multi-user superposition coding beamforming (SCBF). Starting with solving the minimum power optimization problem for the two-user case, we derive the optimal solution structure of the problem and two types of dedicated algorithms that could efficiently find the optimal solutions with all parameter setups. Extensions are then made to the same problem with the signals of more than two users multiplexed in the power domain as well as to the rate region computation problem. Finally, to adapt our algorithms to more general cases, novel hybrid precoding schemes are proposed, where certain user grouping strategy is used to combine zero-forcing beamforming and SCBF. Numerical simulations are provided to show that with our algorithms, a considerable performance gain is achieved by SCBF compared to the other orthogonal transmission methods.

Hybrid Precoding With Partially Connected Structure for Millimeter Wave Massive MIMO OFDM: A Parallel Framework and Feasibility Analysis

Hybrid precoding, a combination of digital and analog precoding, is an alternative to traditional precoding methods in massive MIMO systems with a large number of antenna elements and has shown promising results recently. In this paper, we implement a parallel framework to make hybrid precoding competitive in fast-fading environments. A low-complexity algorithm which exploits the block diagonal phase-only nature of the analog precoder in a partially connected structure is proposed to arrive at a hybrid precoding solution for a multi-carrier single-user system using orthogonal frequency division multiplexing (OFDM). The original problem is broken down into two subproblems of finding the magnitude and the phase components which are solved independently. A per-RF chain power constraint is introduced instead of the sum power constraint over all antennas which are much more practical in real systems. An alternating version of the same algorithm is proposed for increased spectral-efficiency gains. Complexity and run-time analysis demonstrate the advantage of the proposed algorithm over existing hybrid precoding schemes for partially connected structure in an OFDM setting. The simulation results reveal certain insights about the partially connected structure and the tradeoffs that have to be made to make it workable in a real wideband system.

Over-Sampling Codebook-Based Hybrid Minimum Sum-Mean-Square-Error Precoding for Millimeter-Wave 3D-MIMO

Hybrid precoding design is challenging for millimeter-wave massive MIMO. Most prior hybrid precoding schemes are designed to maximize the sum spectral efficiency, while seldom investigating the bit-error-rate (BER). Therefore, this letter designs an over-sampling codebook (OSC)-based hybrid minimum sum-mean-square-error (min-SMSE) precoding to optimize the BER. Specifically, given the effective baseband channel consisting of the real channel and analog precoding, we first design the digital precoder/combiner based on min-SMSE criterion to optimize the BER. To further reduce the SMSE between the transmit and receive signals, we propose an OSC-based joint analog precoder/combiner design. Simulation results show that the proposed scheme can achieve the better performance than its conventional counterparts.

Mixed-Timescale Per-Group Hybrid Precoding for Multiuser Massive MIMO Systems

Considering the expensive radio frequency (RF) chain, huge training overhead and feedback burden issues in massive MIMO, in this letter, we propose a mixed-timescale per-group hybrid precoding (MPHP) scheme under an adaptive partially-connected RF precoding structure (PRPS), where the RF precoder is implemented using an adaptive connection network (ACN) and M analog phase shifters (APSs), where M is the number of antennas at the base station (BS). Exploiting the mixed-time stage channel state information (CSI) structure, the joint-design of ACN and APSs is formulated as a statistical signal-to-leakage-and-noise ratio (SSLNR) maximization problem, and a heuristic group RF precoding (GRFP) algorithm is proposed to provide a near-optimal solution. Simulation results show that the proposed design advances at better energy efficiency (EE) and lower hardware cost, CSI signaling overhead and computational complexity than the conventional hybrid precoding (HP) schemes.

Hybrid Precoding for Millimeter Wave MIMO Systems: A Matrix Factorization Approach

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders, and we treat it as a matrix factorization problem with power and constant modulus constraints. Our work presents three main contributions: First, we present a sufficient condition and a necessary condition for hybrid precoding schemes to realize unconstrained optimal precoders exactly when the number of data streams Ns satisfies Ns = minfrank(H);Nrfg, where H represents the channel matrix and Nrf is the number of radio frequency (RF) chains. Second, we show that the coupled power constraint in our matrix factorization problem can be removed without loss of optimality. Third, we propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based algorithm to solve our matrix factorization problem using gradient and Hessian information. Several numerical results are provided to show that our proposed algorithm outperforms existing hybrid precoding algorithms.