Massive MU-MIMO Research Articles

Joint Data and Pilot Power Allocation for Massive MU-MIMO Downlink TDD Systems

This brief investigates the jointly optimal pilot and data power allocation among the users in a massive multiuser multiple-input multiple-output downlink system, in which the base station is equipped with a massive number of antennas and simultaneously serves single-antenna users in the same frequency band. First, a closed-form lower bound on the achievable rate is derived for each user where maximum-ratio transmission precoding is employed, and this closed form lower bound is used in defining the spectral efficiency (SE). Then, in order to maximize the SE, a method of power allocation among each of the pilot and data symbols of all the users is proposed, given the total power budget per coherence interval for all users. Numerical results show that the proposed power allocation scheme is superior to other existing methods in terms of higher SE.

Optimal ZF Precoder for MU massive MIMO Systems over Ricean Channel with per Antenna Power Allocation

Multiuser massive multiple-input multiple-output systems have the potential to increase the data rate. However, with a large base station (BS) antenna, the non-square channel matrix restricts the zero-forcing (ZF) precoder rotations to obtain the best optimal solution with the per-antenna power allocation. In this paper, we propose the beamforming and lattice reduction (LR) approach to restrain the channel matrix and transform the lattice of the channel vectors to be near orthogonal. Numerical results show that the LR-based ZF precoder outperforms other ZF precoder schemes, such as, the norm approximation of the beamforming matrix. In particular, the sum rate of the proposed optimal ZF precoder requires a small number of BS antenna. Subsequently, with the strong line of sight (LoS) channel, the optimal power allocations in the subchannels depend on the dominance of the users in order to achieve substantial multiplexing and diversity gains. Specifically, the Ricean channel gain with the water-filling allocation at high SNR is non-negligible.

An optimized hybrid beamforming for millimeter wave musassive MIMO system

Millimeter wave (mmWave) and massive multiple-input multiple-output communications (massive MIMO) have been adopted as the most important keys for the next generation (5G) wireless networks due to high spectrum availability and massive use of space diversity for high throughput communications. Massive MIMO must be implemented parallel with mmWave to overcome high path-loss issue in mmWave band by improving the antennas gain. Digital precoding in massive MIMO shows high performance at the cost of hardware complexity due to the requirement of huge number of radio frequency (RF) chains and analog-to-digital converters (ADCs). On the other hand, analog precoding has less complexity with limited performance as it supports only one data stream. In compromise between these two, a hybrid precoding for multiuser massive MIMO (MU-massive MIMO) systems with low-complexity and reasonable performance becomes necessary for next generation networks. We propose an optimized hybrid precoding and combining scheme for fully connected structure. The design of combined analog and digital precoding for the mmWave MU-massive MIMO system becomes a non-convex problem. Therefore, we proposed a suboptimal design, where we split the transmitter and receiver hybrid precoding and decoding and adopt orthogonal matching pursuit algorithm for their design. The simulation results show that the performance of proposed hybrid precoding approaches the digital precoding in the term of total sum data rate as we increase number of RF chains and SNR.

Differential Downlink Transmission in Massive MU-MIMO Systems

In this paper, a differential downlink transmission scheme is proposed for a massive multiple-input multiple-output (MIMO) system without explicit channel estimation. In particular, we use a downlink precoding technique combined with a different encoding scheme to simplify the overall system complexity. A novel precoder is proposed, which, with a large number of transmit antennas, can effectively precancel the multiple access interference (MAI) for each user, thus enhancing the system performance. Maximizing the worst case signal-to-interference-plus-noise ratio (SINR) is used to optimize the precoder for the users in which full power space profile (PSP) knowledge is available to the base station (BS). In addition, we provide two suboptimal solutions based on the matched and the orthogonality approach of the PSP to separate the data streams of multiple users. The decision feedback differential detection (DFDD) technique is employed to further improve the performance. The proposed schemes eliminate the MAI, enhance system performance, and achieve a simple low complexity transmission scheme. Moreover, transmission overheads are significantly reduced using the proposed scheme, since it avoids explicit channel estimation at both ends. The Monte Carlo simulation results demonstrate the effectiveness of the proposed schemes.

Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

This paper deals with the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink system, where a base station is equipped with a very large number of antennas and serves single-antenna users simultaneously in the same frequency band, and the beamforming training scheme is employed in the time-division duplex mode. An optimal resource allocation that jointly selects the training duration on uplink transmission, the training signal power on downlink transmission, the training signal power on uplink transmission, and the data signal power on downlink transmission is proposed in such a way that the spectral efficiency is maximized given the total energy budget. Since the spectral efficiency is the main concern of this work, and its calculation using the lower bound on the achievable rate is computationally very intensive, in this paper, we also derive approximate expressions for the lower bound of achievable downlink rate for the maximum ratio transmission (MRT) and zero-forcing (ZF) precoders. The computational simplicity and accuracy of the approximate expressions for the lower bound of achievable downlink rate are validated through simulations. By employing these approximate expressions, experiments are conducted to obtain the spectral efficiency of the massive MIMO downlink time-division duplexing system with the optimal resource allocation and that of the beamforming training scheme. It is shown that the spectral efficiency of the former system using the optimal resource allocation is superior to that yielded by the latter scheme in the cases of both MRT and ZF precoders.

Joint Quantized Constant Envelope Precoding and Antenna Selection for Massive MU-MIMO Downlink Systems Using Higher-Order QAM

This paper introduces the antenna selection (AS) mechanism into the quantized constant envelope (CE) precoding in massive multiuser multiple-input multiple-output systems to simultaneously reduce multiuser interference and hardware power consumption. However, this joint design problem requires optimizing over the precoding vector, the precoding factor, and the selected antenna subset. Unfortunately, these design parameters are connected. To address this problem, we propose an iterative algorithm developed from the alternating minimization (AltMin) framework to decouple the considered problem into two subproblems: the precoding factor design problem and the joint AS and the precoding vector design problem. The former can be easily solved approximately, whereas the latter incurs combinatorial complexity. We address this issue by using a simple yet effective coordinate update algorithm (CUA). We also develop a reduced-complexity version of CUA without performance loss. Moreover, the proposed CUA-based AltMin algorithm can be applied to the quantized CE precoding design problems without modification. The simulation results show that the proposed CUA-based AltMin algorithm considerably outperforms the existing algorithms for the quantized CE precoding design problems. In addition, when the AS mechanism is included in the quantized CE precoding design, CUA-AltMin with AS achieves the same or higher performance than CUA-AltMin without AS while providing further power savings.

Energy efficiency of massive MU-MIMO with limited antennas in downlink cellular networks

In this paper, we analyze and optimize the energy efficiency (EE) of multi-user (MU) multiple-input and multiple-output (MIMO) in downlink multi-cell networks. We first analyze the spectral efficiency (SE) of MU-MIMO systems with a huge number of base station (BS) antennas by using maximal ratio transmission (MRT) and zero-forcing (ZF) precoders in downlink cellular networks. In the analysis, we assume that the number of the BS antennas is huge but limited, which is different from the conventional analysis. Then, we use the analytical SE result and the EE definition to obtain the system EE expression and further analyze the effect of the number of BS antennas and the number of served users on system EE. Since the system EE on the numbers of BS antennas and the number of served users is approximately concave, the corresponding iterative optimization algorithms are proposed to achieve the maximum system EE. The computer simulation results show that the analytical results are accurate and each served user needs about 2 active BS antennas to obtain the maximum system EE when the channel state information is available at the transmitter.

PAPR Reduction at Large Multi-User-MIMO-OFDM using Adaptive Data Detection Algorithm

<span>Wireless communication in present era contains large-scale MIMO network architecture that need to deliver an optimize-QoS to multi-user (MU). <br /> The optimize data rate transmission in massive MU-MIMO wireless systems is one of the most difficult task due to the extremely high implementation complexity. The practical wireless system channels generally exhibits the PAPR and frequency selective fading, it is also necessary to have a precoding solution in PAPR for the selected desirable channels. A solution for the designed problem of a noble error-correcting code for OFDM process with a low PAPR, in the case of impulse noise should be considered. In this paper, Adaptive-Data-detection (ADD) algorithm is proposed to obtain lower-complexity data-detection that corresponds to high throughput design and impulse noise removal for large MUI-MIMO wireless systems by the OFDM modulation technique. That contains some steps such as; initialization, <br /> pre-processing and equalization steps in order to get no performance loss and to minimalize the recurrent amount at each iterations during operation. In order to use simplify model, here we assume suitably perfect synchronization, large cyclic prefix and perfect-CSI (channel-state-information) which has been developed through the pilot depended training. Simulation results analysis show the proposed method substantial improvement over the existing algorithm in terms of both ‘Error-rate’ minimization and PAPR reduction.</span>

Superimposed Pilot Optimization Design and Channel Estimation for Multiuser Massive MIMO Systems

For massive multiuser multiple input multiple output (MIMO) (MU-MIMO) systems, pilot contamination (PC) and data interference (DI) are two main factors that affect the accuracy of the superimposed pilot (SP) based channel estimation, provided that the channel coherence time is less than the total number of users. In this paper, to tackle the effect of the PC, we propose a block-diagonal Grassmannian line packing (GLP) approach, in which the specific sequences for different cells are first designed based on the GLP, and then are block-diagonally extended to form the SP matrices for the users in different cells. To also alleviate the effect of DI on the channel estimation, with the designed SP matrices, an iterative channel estimation (ICE) method based on Tikhonov regularization is presented. The impact that the proposed ICE method has on the signal-to-interference-plus-noise ratio with matched filtering receiver is theoretically analyzed. Moreover, the optimal power allocation factor to the SP by maximizing the spectral efficiency (SE) of the target cell is also derived. Numerical results show that the proposed ICE method with the designed SP matrices effectively mitigates the effects of the PC and DI, and hence, improves the accuracy of channel estimation and further the SE of the massive MU-MIMO systems.

Notice of Violation of IEEE Publication Principles: A Robust Signal Detection Scheme for 5G Massive Multiuser MIMO Systems

Notice of Violation of IEEE Publication Principles A Robust Signal Detection Scheme for 5G Massive Multiuser MIMO Systems by Imran Khan in IEEE Transactions on Vehicular Technology, July 2018, 9597-9604 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper copied portions of text from the following paper with no credit to the original work (including appropriate references to the original author(s) and/or paper title) and without permission. Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems by Lei Liu, Chau Yuen, Yong Liang Guan, Ying Li, and Yuping Su in IEEE Transactions on Wireless Communications, June 2016, pp.6487-6501 Aiming at the problem of high detection complexity at the base station in massive multiuser multiple-input multiple-output system, this paper proposes a low complexity variable node full information Gaussian message passing iterative detection based on forced convergence (VFI-GMPID-FC) algorithm. First, the traditional GMPID algorithm is improved to get the VFI-GMPID algorithm. The detection performance of the VFI-GMPID algorithm approaches the minimum mean square error (MMSE) detection algorithm, but the complexity is much lower than that of the MMSE algorithm. Combined with the idea of forced convergence and VFI-GMPID, the VFI-GMPID-FC algorithm is proposed to further reduce the complexity of the algorithm and improve the detection efficiency. Finally, the simulation results show that the proposed algorithm can effectively reduce the complexity of the algorithm while ensuring improved detection performance.

Sum Ergodic Capacity Analysis Using Asymptotic Design of Massive MU-MIMO Systems

This communication attempts to characterize the performance metrics of downlink Massive MU-MIMO systems impaired by cochannel interference and additive noise over a Rayleigh fading environment. We obtain close-form solutions for the probability density function of signal-to-interference-plus-noise ratio (SINR) and the sum ergodic capacity. The proposed work structures SINR in a quadratic form and thereby imposes a condition on its signal and interference power for a large transmit antenna diversity order; the conditional form is then analyzed using a distance correlation metric. Eventually, the sum ergodic capacity is expressed in a close-form by means of a residue theory approach and validated using the Monte Carlo simulation means.

Low Complexity Multi-User MIMO Detection for Uplink SCMA System Using Expectation Propagation Algorithm

Sparse code multiple access (SCMA), which combines the advantages of low density signature (LDS) and code-division multiple access (CDMA), is regarded as one of the promising modulation technique candidate for the next generation of wireless systems. Conventionally, the message passing algorithm (MPA) is used for data detector at the receiver side. However, the MPA-SCMA cannot be implemented in the next generation wireless systems, because of its unacceptable complexity cost. Specifically, the complexity of MPA-SCMA grows exponentially with the number of antennas. Considering the use of high dimensional systems in the next generation of wireless systems, such as massive multi-user MIMO systems, the conventional MPA-SCMA is prohibitive. In this paper, we propose a low complexity detector algorithm named the expectation propagation algorithm (EPA) for SCMA. Mainly, the EPA-SCMA solves the complexity problem of MPA-SCMA and enables the implementation of SCMA in massive MU-MIMO systems. For instance, the EPA-SCMA also enables the implemantation of SCMA in the next generation wireless systems. We further show that the EPA can achieve the optimal detection performance as the numbers of transmit and receive antennas grow. We also demonstrate that a rotation design in SCMA codebook is unnecessary, which is quite rather different from the general assumption

Adaptive Preconditioned Iterative Linear Detection and Architecture for Massive MU-MIMO Uplink

Being the enabling technique for 5G wireless communications, massive multiple-input multiple-output (MIMO) system can drastically increase the capacity efficiency. However, a few hundreds of antennas will inevitably introduce notable complexity and therefore hinders its direct adoption. Though the state-of-the-art (SOA) iterative methods such as conjugate gradient (CG) detection show complexity advantage over the conventional ones such as MMSE detection, their convergence rates slow down if the antenna configurations become more complicated. To this end, first this paper devotes itself in exploring the convergence properties of iterative linear solvers and then leverages the proposed adaptive precondition technique to improve the convergence rate. This adaptive precondition technique is general and has been incorporated with steepest descent (SD) detection as a show case. An approximated calculation for log-likelihood ratios (LLRs) is proposed for further complexity reduction. Analytical and numerical results have shown that with the same iteration number, the adaptive preconditioned SD (APSD) detector outperforms the CG one around 1 dB when BER = 10−3. Hardware architecture for the APSD detector is proposed based on iteration bound analysis and architectural optimization for the first time. Architectures for other adaptive preconditioned iterative linear detectors can be easily derived by following similar design flow. Compared with the SOA designs, FPGA implementations have verified the APSD detector’s advantage in balancing throughput and complexity, and guaranteed its application feasibility for 5G wireless.

1-bit Massive MU-MIMO Precoding in VLSI

Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in fifth-generation (5G) wireless systems as it offers significant improvements in spectral efficiency compared to existing multi-antenna technologies. The presence of hundreds of antenna elements at the base station (BS), however, results in excessively high hardware costs and power consumption, and requires high interconnect throughput between the baseband-processing unit and the radio unit. Massive MU-MIMO that uses low-resolution analog-to-digital and digital-to-analog converters (DACs) has the potential to address all these issues. In this paper, we focus on downlink precoding for massive MU-MIMO systems with 1-bit DACs at the BS. The objective is to design precoders that simultaneously mitigate MU interference and quantization artifacts. We propose two nonlinear 1-bit precoding algorithms and corresponding very large-scale integration (VLSI) designs. Our algorithms rely on biconvex relaxation, which enables the design of efficient 1-bit precoding algorithms that achieve superior error-rate performance compared with that of linear precoding algorithms followed by quantization. To showcase the efficacy of our algorithms, we design VLSI architectures that enable efficient 1-bit precoding for massive MU-MIMO systems, in which hundreds of antennas serve tens of user equipments. We present corresponding field-programmable gate array (FPGA) reference implementations to demonstrate that 1-bit precoding enables reliable and high-rate downlink data transmission in practical systems.

Decentralized Baseband Processing for Massive MU-MIMO Systems

Achieving high spectral efficiency in realistic massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems requires computationally-complex algorithms for data detection in the uplink (users transmit to base-station) and beamforming in the downlink (base-station transmits to users). Most existing algorithms are designed to be executed on centralized computing hardware at the base-station (BS), which results in prohibitive complexity for systems with hundreds or thousands of antennas and generates raw baseband data rates that exceed the limits of current interconnect technology and chip I/O interfaces. This paper proposes a novel decentralized baseband processing architecture that alleviates these bottlenecks by partitioning the BS antenna array into clusters, each associated with independent radio-frequency chains, analog and digital modulation circuitry, and computing hardware. For this architecture, we develop novel decentralized data detection and beamforming algorithms that only access local channel-state information and require low communication bandwidth among the clusters. We study the associated trade-offs between error-rate performance, computational complexity, and interconnect bandwidth, and we demonstrate the scalability of our solutions for massive MU-MIMO systems with thousands of BS antennas using reference implementations on a graphic processing unit (GPU) cluster.

Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Massive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While data-detection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require the solution to large linear systems at high throughput and low latency, which results in excessively high receiver complexity. In this paper, we investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector). We analyze the associated performance/complexity trade-offs, and we show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity. In contrast, implicit data detection using approximate equalization methods results in the best trade-off for large BS-to-user-antenna ratios. By combining the advantages of exact, approximate, implicit, and explicit matrix inversion, we develop a new frequency-adaptive e qualizer (FADE), which outperforms existing data-detection methods in terms of performance and complexity for wideband massive MU-MIMO systems.

Quantized Precoding for Massive MU-MIMO

Massive multiuser (MU) multiple-input multiple-output (MIMO) is foreseen to be one of the key technologies in fifth-generation wireless communication systems. In this paper, we investigate the problem of downlink precoding for a narrowband massive MU-MIMO system with low-resolution digital-to-analog converters (DACs) at the base station (BS). We analyze the performance of linear precoders, such as maximal-ratio transmission and zero-forcing, subject to coarse quantization. Using Bussgang’s theorem, we derive a closed-form approximation on the rate achievable under such coarse quantization. Our results reveal that the performance attainable with infinite-resolution DACs can be approached using DACs having only 3–4 bits of resolution, depending on the number of BS antennas and the number of user equipments (UEs). For the case of 1-bit DACs, we also propose novel nonlinear precoding algorithms that significantly outperform linear precoders at the cost of an increased computational complexity. Specifically, we show that nonlinear precoding incurs only a 3 dB penalty compared with the infinite-resolution case for an uncoded bit-error rate of 10−3, in a system with 128 BS antennas that uses 1-bit DACs and serves 16 single-antenna UEs. In contrast, the penalty for linear precoders is about 8 dB.

Self-interference suppression with imperfect channel estimation in a shared-antenna full-duplex massive MU-MIMO system

In this paper, we consider a shared-antenna full-duplex massive MU-MIMO system and prove that the self-interference (SI) at the base station (BS) can be suppressed, though the direct-path SI exists and the signal/SI channel is imperfectly estimated. The BS is assumed to employ zero-forcing (ZF) or maximal-ratio transmission/maximal-ratio combining (MRT/MRC) method to linearly process signals. We propose a precoded SI channel training scheme by employing orthogonal sequences and downlink precoding, so the SI channel can be estimated like the signal channel with a much lower dimension. Based on the channel estimation, we further analyze the SI suppression by combining the SI removal and the large-scale antenna linear processing (LALP) method. Numerical results show that an additional 36 dB SI can be suppressed by combining the SI removal and the LALP suppression. We derive the tight closed-form lower bounds of the achievable rates for the combined suppression. Finally, we maximize the system spectral efficiency and energy efficiency based on the simulation and closed-form approximations.

Impact of Frequency Selectivity on the Information Rate Performance of CFO Impaired Single-Carrier Massive MU-MIMO Uplink

In this letter, we study the impact of frequency-selectivity on the gap between the required per-user transmit power in the residual carrier frequency offset (CFO) scenario i.e., after CFO estimation/compensation at the base-station (BS) and that in the ideal/zero CFO scenario, for a fixed per-user information rate, in single-carrier massive MU-MIMO uplink systems with the time-reversal maximum ratio combining receiver. Information theoretic analysis reveals that this gap decreases with increasing frequency-selectivity of the channel. Also, in the residual CFO scenario, an $ {\mathcal {O}(\sqrt {M})}$ array gain is still achievable ( $ {M}$ is the number of BS antennas) in frequency-selective channels with imperfect channel estimates.

High-Throughput Data Detection for Massive MU-MIMO-OFDM Using Coordinate Descent

Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8-user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than $2.6 \times $ in terms of throughput per FPGA look-up tables.