Minimum Mean Square Error Precoding Research Articles

Evaluation of massive multiple-input multiple-output communication performance under a proposed improved minimum mean squared error precoding

<span lang="EN-US">The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. </span>

Compute-and-forward transmission scheme in cell-free massive MIMO systems

Cell-free massive multi-input-multi-output (MIMO) systems comprise a large number of distributed access points (APs) to serve a small number of user equipments (UEs). In this paper, compute-and-forward (CF) is investigated for uplink in cell-free massive MIMO. We propose a novel reverse CF precoding scheme for downlink transmission for cell-free massive MIMO. The APs send the linear equations of messages over a multiple access channel, and the UEs recover messages without interferences. The max–min power allocation is investigated to provide uniformly good service to all UEs. Numeral results demonstrate the performance improvement provided by the proposed reverse CF precoder against zero-forcing, conjugate beamforming, and linear minimum mean-square error precoder.

Bit Error Rate Analysis Of Multiuser Massive MIMO Wireless System Using Linear Precoding

This paper presents bit error rate (BER) analysis of multiuser (MU) massive multiple input multiple out (MIMO) wireless system using linear precoding techniques. Considering the increasing demand for wireless communication that will provide seamless performance to meet users satisfaction, various precoding schemes have been developed and with massive MIMO projected to be a promising technology for 5G and next generation network. Therefore, this study was basically designed to examine the effect of linear zero forcing (ZF) and minimum mean square error (MMSE) precoders on BER performance of MU massive MIMO system with up to 32 base station antennas (BS) and communicating with up to 5 user mobile terminals (MTs). A model that describes downlink operation of MU massive MIMO wireless communication system with spatial multiplexing employing linear ZF and MMSE precoders such that each user is equipped with single antenna MT resulting in overall access points of 5 antennas was developed in MATLAB. Computer simulations revealed that ZF outperformed MMSE. This observation was validated by similar report on massive MIMO in previous study.

Learning Linear MMSE Precoder for Uplink Massive MIMO Systems With One-Bit ADCs

In this letter, we present a deep learning based linear precoder design to improve the performance for uplink multiuser multiple-input multiple-output (MU-MIMO) systems with one-bit analog-to-digital converters (ADCs). Specifically, by utilizing the Bussgang decomposition based linear minimum mean squared error (MMSE) receiver at the base station (BS), we aim to further minimize the system MSE by optimizing the precoder at each user equipment (UE). To address this problem efficiently, we propose a deep neural network based approach by unfolding the developed projected gradient descent (PGD) algorithm and treating the step size in each PGD iteration as trainable network parameters. Simulation results show that the proposed network design outperforms the conventional PGD algorithm under both perfect and estimated channel state information (CSI) and the improvement is achieved with reduced online complexity.

Data Augmentation Empowered Neural Precoding for Multiuser MIMO With MMSE Model

Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this letter, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches.

HF Skywave Massive MIMO Communication

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

Nullforming-Based Precoder for Spectrum Sharing Between HAPS and Terrestrial Mobile Networks

Ultra-wide coverage is one of the important concepts of the sixth generation (6G) mobile communications. High-altitude platform stations (HAPSs) have attracted considerable attention as a leading candidate for coverage extension of cellular networks. HAPSs should share a frequency band with terrestrial cellular networks and provide users with mobile communication services using the common frequency band to improve spectral efficiency in service links. However, the signals from HAPS strongly interfere with terrestrial cells in the service area of HAPS, which leads to a considerable deterioration in the system capacity of terrestrial cells. This study proposes a novel two-stage precoding scheme that realizes spectrum sharing between HAPS and terrestrial networks. The proposed scheme mitigates HAPS interference with terrestrial cells using beamforming techniques in phased array antenna systems. In the first stage, nulls are created in directions of terrestrial cells. In the second stage, user-specific beams are formed in directions of users connected to HAPS. Results demonstrate that the proposed scheme improves the signal-to-interference-plus-noise ratio of users in terrestrial cells by up to 10 dB compared with a conventional minimum mean square error (MMSE) precoder without nullforming when the HAPS uses a cylindrical array antenna with 196 elements. Furthermore, for ten terrestrial cells, the proposed scheme achieves 128% of total capacity of the conventional MMSE precoder. Total capacity is defined as the sum capacity of HAPS and all terrestrial cells.

Generalized Space–Time Line Code With Receive Combining for MIMO Systems

In this article, a generalized space–time line code (GSTLC) is proposed. The proposed GSTLC concurrently transmits multiple data streams obtained by space–time line code encoding and precoding to provide spatial multiplexing and diversity. When full channel state information (CSI) is available to the transmitter and limited CSI is available to the receiver, an optimal precoding matrix is derived for GSTLC with linear combining that is based on the minimum mean square error (MMSE) criterion. Moreover, the detection signal-to-interference-plus-noise ratio (SINR) is derived corresponding to the optimal MMSE precoder. Using the SINR, we propose a receiver combining scheme that selects the best combining matrix, thereby minimizing the bit error rate (BER) from a codebook that includes candidate combining matrices. Through numerical simulations, we demonstrate that the proposed GSTLC with the optimal combiner achieves the theoretical lower bound of BER. In addition, we show that the GSTLC with combiner selection performs better than conventional precoded spatial multiplexing methods in terms of the BER.

Iterative AP selection, MMSE precoding and power allocation in cell‐free massive MIMO systems

In this work, the authors proposed the iterative access point (AP) selection (APS), linear minimum mean-square error (MMSE) precoding and power allocation techniques for cell-free massive multiple-input multiple-output (MIMO) systems. They considered the downlink channel with single-antenna users and multiple-antenna APs. They derive sum-rate expressions for the proposed iterative APS techniques followed by MMSE precoding and optimal, adaptive, and uniform power allocation schemes. Simulations show that the proposed approach outperforms existing conjugate beamforming and zero-forcing schemes and that performance remains excellent with APS, in the presence of perfect and imperfect channel state information.

Block diagonalization precoding and power allocation for clustering small-cell networks

The clustering network is a solution to improve data-rate transmission in small-cells. In this case, clustering small-cells (CSCs) adopt a multiple antennas concept. The multiple antennas are used to maximize the downlink data-rate transmission at the users, but it requires precoding techniques to minimize interference among CSC users. This paper proposes a block diagonalization (BD) as a precoding technique for minimizing interference among CSC users. The performance of the BD precoding implemented on the clustering network under various numbers of small-cells. The CSC also implements a water-filling power allocation (PA-CoopWF) to distribute the available transmission power along with the CSCs antennas. To show the performance, our paper simulates two types of precoding techniques; those are the proposed BD and minimum mean square error (MMSE) in CSCs. Based on the receiver user parts under the overlapping coordination of CSCs, our method based on the BD precoding achieves considerably higher data-rate transmission compared to the MMSE precoding, especially on larger clusters. The simulation also shows that by implementing CSC with the BD in short-range distances and higher numbers of antennas, it promotes better data-rate performances compared to the MMSE precoding by 2.75 times at distance 100m and 67% at 50 antennas.

Two dimension angle of arrival-based precoding for pilot contamination reduction in multi-cell massive MIMO systems

This paper proposes a new precoding approach that utilizes two-dimension (2D) Angle of Arrival (AOA) based precoding (beamforming) approach for multi-cell massive Multiple Input Multiple Output (m-MIMO) systems. The proposed approach enhances the performance of m-MIMO systems by overcoming pilot contamination that is caused by corrupted Channel State Information. It exploits the spatial properties of the propagated signals to distinguish between various users in the multi-cell m-MIMO system and selects the partial space for beamforming based on AOA information to suppress inter-cell and intra-cell interference. We mathematically analyze the impact of pilot contamination in the uplink training and determine the closed-form approximation of uplink Minimum Mean Square Error (MMSE) estimator. By using MMSE estimator as well as 2D AOA and channel estimation, a corresponding MMSE precoding which minimizes the effect of pilot contamination and improves the downlink achievable rate of the system is proposed. Furthermore, a comparison is made of the resolution between the various 2D AOA estimations for an m-MIMO and the optimum values closest bound for the proposed precoding was chosen to reduce pilot contamination. The performance of the proposed MMSE precoding based on 2D AOA estimation methods: 2D Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (2D UESPRIT), 2D Fourier Domain Line Search MUSIC (2D FDLSM), and 2D Propagator Method (2D PM)) were evaluated and compared with pilot contaminated system and deterministic MMSE precoding in terms of their respective achievable rate. Simulation results reveal that the achievable sum rate gains of 2D UESPRIT, 2D FDLSM, and 2D PM based precoding which achieved 97.6%, 92.4%, 86.3% of the desired deterministic performance, respectively, compared to pilot contaminated system, which achieved only 90% even when in the best case of having only one contaminated user out of all users within the activated cell. Findings from our research indicate the feasibility of utilizing the AOA based beamforming to reduce pilot contamination effect that is inherent in a larger order m-MIMO system for the Fifth Generation (5G) wireless transmissions.

A wireless precoding technique for millimetre-wave MIMO system based on SIC-MMSE

A communication method is proposed using Minimum Mean Square Error (MMSE) precoding and Successive Interference Cancellation (SIC) technique for millimetre-wave multiple-input multiple-output (mm-Wave MIMO) based wireless communication system. The mm-Wave MIMO technology for wireless communication system is the base potential technology for its high data transfer rate followed by data instruction and low power consumption compared to Long-Term Evolution (LTE). The mm-Wave system is already available in indoor hotspot and Wi-Fi backhaul for its high bandwidth availability and potential lead to rate of numerous Gbps/user. But, in mobile wireless communication system this technique is lagging because the channel faces relative orthogonal coordination and multiple node detection problems while rapid movement of nodes (transmitter and receiver) occur. To improve the conventional mm-wave MIMO nodal detection and coordination performance, the system processes data using symbolized error vector technique for linearization. Then the MMSE precoding detection technique improves the link strength by constantly fitting the channel coefficients based on number of independent service antennas (M), Signal to Noise Ratio (SNR), Channel Matrix (CM) and mean square errors (MSE). To maintain sequentially encoded user data connectivity and to overcome data loss, SIC method is used in combination with MMSE. MATLAB was used to validate the proposed system performance.

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

The capacity of a massive MIMO cellular network depends on user and antenna selection algorithms, and also on the acquisition of perfect Channel State Information (CSI). Low computational cost algorithms for user and antenna selection significantly may enhance the system capacity, as it would consume a smaller bandwidth out of the total bandwidth for downlink transmission. The objective of this paper is to maximize the system sum-rate capacity with efficient user and antenna selection algorithms and linear precoding. We consider in this paper, a slowly fading Rayleigh channel with perfect acquisition of CSI to explore the system sum-rate capacity of a massive MIMO network. For user selection, we apply three algorithms, namely Semi-orthogonal user selection (SUS), Descending Order of SNR-based User Scheduling (DOSUS), and Random User Selection (RUS) algorithm. In all the user selection algorithms, the selection of Base Station (BS) antenna is based on the maximum Signal-to-Noise Ratio (SNR) to the selected users. Hence users are characterized by having both Small Scale Fading (SSF) due to slowly fading Rayleigh channel and Large-Scale Fading (LSF) due to distances from the base station. Further, we use linear precoding techniques, such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Ratio Transmission (MRT) to reduce interferences, thereby improving average system sum-rate capacity. Results using SUS, DOSUS, and RUS user selection algorithms with ZF, MMSE, and MRT precoding techniques are compared. We also analyzed and compared the computational complexity of all the three user selection algorithms. The computational complexities of the three algorithms that we achieved in this paper are O(1) for RUS and DOSUS, and O(M2N) for SUS which are less than the other conventional user selection methods.

Non-Alternating Globally Optimal MMSE Precoding for Multiuser VLC Downlinks

We study the minimum mean-squared error (MMSE) precoding for multiuser visible light communication downlink systems. Different from radio frequency systems, this problem does not admit a closed-form solution due to the light-emitting diode optical power constraints. To handle the difficult non-convex problem, we transform it into an equivalent convex form via the Charnes–Cooper transformation. In this way, we are able to achieve the globally optimal MMSE precoder by solving only one convex problem, thus requiring much lower complexity than the existing alternating algorithm. The proposed non-alternating method is also extended to robust the MMSE precoder optimization in the presence of statistical channel-state information errors.

SIC-MMSE Method based Wireless Precoding Technique for Millimetre-Wave MIMO System

Objectives: A communication method is proposed using Minimum Mean Square Error (MMSE) precoding and Successive Interference Cancellation (SIC) technique for millimetre-wave multiple-input multiple-output (mm-Wave MIMO) based wireless communication system. Background: The mm-Wave MIMO technology for wireless communication system is the base potential technology for its high data transfer rate followed by data instruction and low power consumption compared to Long-Term Evolution (LTE). The mm-Wave system is already available in indoor hotspot and Wi-Fi backhaul for its high bandwidth availability and potential lead to rate of numerous Gbps/user. But, in mobile wireless communication system this technique is lagging because the channel faces relative orthogonal coordination and multiple node detection problems while rapid movement of nodes (transmitter and receiver) occur. Methods/Improvement: To improve the conventional mm-wave MIMO nodal detection and coordination performance, the system processes data using symbolized error vector technique for linearization. Then the MMSE precoding detection technique improves the link strength by constantly fitting the channel coefficients based on number of independent service antennas (M), Signal to Noise Ratio (SNR), Channel Matrix (CM) and mean square errors (MSE). To maintain sequentially encoded user data connectivity and to overcome data loss, SIC method is used in combination with MMSE. Improvements: MATLAB was used to validate proposed system performance. Simulation analysis has shown that, with the increase number of antenna use, the spectral efficiency also increased and higher then millimetre-wave MIMO or Single MMSE system. This research observed that, hybrid controller or combined control method have the better efficiency then single method, where SIC-MMSE based hybrid controller is a good example. Keywords: Channel Matrix, Long-term Evolution, Millimetre-wave, Minimum Mean Square Error, Quantized System, Successive Interference-Cancellation, Wireless Communication

The Spatial Dimming Scheme for the MU-MIMO-OFDM VLC System

Multiuser visible light communication (MU-VLC) systems utilizing multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) are gaining increased attentions recently. Visible light communication (VLC) links are expected to work under different illumination conditions and, thus, the need for dimming control mechanisms. However, the traditional analog- and digital-based dimming schemes have adverse effects on the data communications performance, such as clipping distortion and the variation of the duty cycle. In this paper, spatial dimming schemes based on the zero-forcing and the minimum mean-squared error precoding schemes are proposed for direct-current biased optical OFDM based indoor MU-MIMO VLC system, and the bipolar optical OFDM signal is biased by a fixed dc level. Transmit antenna selection algorithms are designed for the optimum working light emitting diodes (LEDs) subset at each dimming level. Owing to the simultaneously exploration of the selection diversity of LEDs-based lights and the channel state information, the proposed spatial dimming schemes outperform the traditional dimming schemes, which is also verified by simulation results. Thus, the proposed schemes are shown to have a great potential to be applied in practical MU-MIMO-OFDM VLC systems.

Programmable ASIPs for Multimode MIMO Transceiver

Application specific instruction-set processors (ASIP) are a programmable and flexible alternative of traditional finite state machine (FSM) controlled register-transfer level (RTL) designs for multimode basedband systems. In this paper, we present two ASIPs for small scale multiple-input multiple-output (MIMO) wireless communication systems that demonstrate the soundness and effectiveness of ASIPs for this type of applications. The first ASIP is programmed with multiple MIMO symbol detection algorithms for 4 × 4 systems. The supported detection algorithms are minimum mean-square error (MMSE), two variants of the selective spanning with fast enumeration (SSFE) and K-best list sphere detection (LSD). The second ASIP supports MMSE and zero-forcing dirty paper coding (ZF-DPC) algorithms for a base station (BS) with 4 antennas and for 4 users. Both ASIPs are based on transport triggered architecture (TTA) and are programmed with a retargetable compiler with high level language to meet the time-to-market requirements. The detection and precoding algorithms can be switched in the respective ASIPs based on the error-rate requirements. Depending on the algorithms, MIMO detection ASIP delivers 6.16–66.66 Mbps throughput at a clock frequency of 200 MHz on 90 nm technology. The precoder ASIP provides a throughput of 52.17 and 51.95 Mbps for MMSE and ZF-DPC precoding respectively at a clock frequency of 210 MHz on 90 nm technology.

Performance Analysis of MMSE Pre-Coding Aided Spatial Modulation

Pre-coding aided spatial modulation (PSM) constitutes a flexible closed-loop multiple-input multiple-output transmission scheme, which attracts substantial research interest recently. In this paper, an upper bound of the average bit error probability (ABEP) for the PSM system based on the minimum mean square error (MMSE) pre-coding is investigated. In addition, a theoretical analysis is given based on the assumption that the numbers of transmit and receive antennas are large but their ratio $\theta $ is constrained. Furthermore, the theoretical analysis shows that the ABEP upper bound of the PSM system based on MMSE pre-coding depends only on ratio $\theta $ . Simulation results demonstrate that the upper bound is asymptotically tight as the signal-to-noise ratio increases, and the proposed system outperforms its benchmark that is based on zero-forcing pre-coding.

Hybrid MMSE Precoding and Combining Designs for mmWave Multiuser Systems

Hybrid analog/digital precoding architectures are a low-complexity alternative for fully digital precoding in millimeter-wave (mmWave) MIMO wireless systems. This is motivated by the reduction in the number of radio frequency and mixed signal hardware components. Hybrid precoding involves a combination of analog and digital processing that enables both beamforming and spatial multiplexing gains in mmWave systems. This paper develops hybrid analog/digital precoding and combining designs for mmWave multiuser systems, based on the mean-squared error (MSE) criteria. In the first design with the analog combiners being determined at the users, the proposed hybrid minimum MSE (MMSE) precoder is realized by minimizing the sum-MSE of the data streams intended for the users. In the second design, both the hybrid precoder and combiners are jointly designed in an iterative manner to minimize a weighted sum-MSE cost function. By leveraging the sparse structure of mmWave channels, the MMSE precoding/combining design problems are then formulated as sparse reconstruction problems. An orthogonal matching pursuit-based algorithm is then developed to determine the MMSE precoder and combiners. Simulation results show the performance advantages of the proposed precoding/combining designs in various system settings.

Interference Coordination Using Precoding Schemes for Indoor Hotspots

This paper investigates an interference coordination method for indoor hotspot deployment which includes cooperative communication between access points. Coordination between indoor hotspots access point with a method of coordination strategy is used by using of multiple-input multiple-output (MIMO) antennas technique. In order to achieve the capacity, some preceding technique is implemented in MIMO such as Minimum Mean Square Error (MMSE) and Block Diagonalization (BD). MMSE and BD precoding is used to coordinate the transmissions across multiple antenna in transmitter and receiver in case of multiple antenna transmissions. Water-Filling (WF) power allocation is also implemented in this scheme. This schemes is proposed to mitigate inter user interference. Based on the simulation, the results show that coordination scheme in indoor hotspots deployment using BD pre-coding demonstrates a higher data rate than MMSE. The achievable sum rate of hotspot with coordination three access points using BD better around 5 Bps/Hz compared to MMSE method in the same cases.