Performance Of Precoding Research Articles

Raptor Codes with LT-LDPC Precoding Performance Analysis and FPGA Optimization

Raptor codes are designed to effectively handle packet loss and erasures as a type of fountain code, capable of generating an unlimited number of encoded symbols from source informations, which is particularly useful in scenarios where the number of transmissions is unknown or variable. Luby Transform (LT) codes, a kind of fountain code encoding method, exhibit linear complexity, and Low-Density Parity-Check (LDPC) codes can be constructed by the generator matrix and the parity-check matrix. The LT-LDPC pre-coding method combines the advantages of both, enhancing encoding and decoding efficiency. Deploying code on Field-Programmable Gate Arrays (FPGAs) involves several steps: synthesis, place and route, and bitstream generation, followed by loading the bitstream onto the FPGA to configure its internal logic according to the coding methods. This paper introduces a method of encoding and decoding Raptor codes, utilizing the intimal LT codes and the peripheral LDPC codes constructed by PEG. It employs a lop-BP decoding method over noise channels based on the Tanner graph for decoding and deploys the optimized algorithm on FPGA to enhance performance.

Numerical Evaluation of the MU-MIMO Beamforming Performance with Channel Estimation

This paper presents the numerical evaluation of the ZF beamforming algorithm and DFT precoding using the LS channel estimation in the multiuser multiantenna (MU-MIMO) downlink system. The sum rate performance depending on a number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. For MIMO channel simulation the QuaDRiGa channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared with DFT precoding based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder outperforms the DFT precoder in channel with less spatial correlation. The DFT precoder in spatially more correlated channel has advantage over ZF precoder.

Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach

This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability performance, pragmatic precoder design is an effective and indispensable solution. However, the design requires accurate instantaneous channel state information at the transmitter (ICSIT) which is not always available in practice. Motivated by this, we adopt a deep learning (DL) approach to exploit implicit features from estimated historical delay-Doppler domain channels (DDCs) to directly predict the precoder to be adopted in the next time frame for minimizing the frame error rate (FER), that can further improve the system reliability without the acquisition of ICSIT. To this end, we first establish a predictive transmission protocol and formulate a general problem for the precoder design where a closed-form theoretical FER expression is derived serving as the objective function to characterize the system reliability. Then, we propose a DL-based predictive precoder design framework which exploits an unsupervised learning mechanism to improve the practicability of the proposed scheme. As a realization of the proposed framework, we design a DDCs-aware convolutional long short-term memory (CLSTM) network for the precoder design, where both the convolutional neural network and LSTM modules are adopted to facilitate the spatial-temporal feature extraction from the estimated historical DDCs to further enhance the precoder performance. Simulation results demonstrate that the proposed scheme facilitates a flexible reliability-latency tradeoff and achieves an excellent FER performance that approaches the lower bound obtained by a genie-aided benchmark requiring perfect ICSI at both the transmitter and receiver.

On the Common AOA Error in CKM-Based Integrated Sensing and Communications

In integrated sensing and communications (ISAC), the channel information can be acquired via the channel knowledge map (CKM). However, due to the lack of the precise knowledge of the user equipment’s heading, angle of arrival (AOA) estimations of all paths share an identical error, which is defined as the common AOA error (CAE). In this letter, we prove that the CAE imposes rotational effects on the unitary matrices of the singular value decomposition (SVD) of the channel matrix, leading to non-orthogonality among data layers for the SVD-based precoding. Then we develop two algorithms utilizing the rotational effect to estimate and eliminate the CAE. The two algorithms are suitable for scenarios with low and high Rician K-factors, respectively. By applying the proposed algorithms, accurate wideband channel information can be attained based on narrowband reference signals. Simulation results show that the proposed algorithms obtain higher precoding performance than the baseline method.

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>

Numerical Evaluation of the MU-MIMO Beamforming Performance with User Selection Algorithm

This paper presents the numerical evaluation of the ZF beamforming algorithm using the user selection in the multiuser multiantenna (MU-MIMO) downlink system. Two user selection algorithm – semiorthogonal user selection and greedy user selection are numerically evaluated based on the open source MIMO channel model. The sum rate performance depending on number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. The selection of users is based on the orthogonality of the channels among selected users. For MIMO channel simulation the QUADRIGA channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder using user selection (G ZF) outperforms the ZF precoder with random user selection in spectral efficiency. The greedy user selection in spatially correlated channel has advantage to semi-orthogonal user selection. It isobserved that as the increasing the number of served users used for selection the greedy user selection gives better performance than semi-orthogonal algorithm.

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.

Analysis of Optimal Spectral-energy Efficiency Tradeoff Linear Precoding Massive MIMO 5G and Beyond

In this article, we consider cellular wireless communication with a large number base station antennas deployement, called Massive-MIMO systems, are inspected. Massive MU (multi-user), (MIMO) multiple-input-multiple-output technique gives a significant SE (spectral efficiency) using easy techniques precoding while high EE (energy efficiency) is expected to be offered. Our paper works on two famous schemes, linear precoding Zero-Forcing and Maximum-RatioTransmission taking into account of system circuit power consumption, where that important aspect was ignored in the literature. Both EE (energy efficiency) and SE (spectral efficiency) are considered to be the important key indicators in 5G and beyond communications, which need to be taken into account at the same moment with respect to nowadays environmental consideration. To realize an optimal tradeoff between SE-EE, that precoding schemes are been formulated under CSI-imperfection (channel state information) in a realistic way. Simulation results illustrate that our proposed method achieves better optimal balance between Energy Efficiency and Spectral Efficiency. Under Matlab experimentation, simulation result shows an optimal performance, while tradeoff aspect is observed clairly between SE and EE using such two precoders algorithms. Although such tradeoff SE-EE coincide at first but later on their interests become conflicting and divergent then leading EE to so gradually decrease while SE continues increasing logarithmically. ZF pre-coding method is the appropriate scenario to achieve higher pre-coder performance.

A Statistical Linear Precoding Scheme Based on Random Iterative Method for Massive MIMO Systems

In this paper, the random iterative method is introduced to massive multiple-input multiple-output (MIMO) systems for the efficient downlink linear precoding. By adopting the random sampling into the traditional iterative methods, the matrix inversion within the linear precoding schemes can be approximated statistically, which not only achieves a faster exponential convergence with low complexity but also experiences a global convergence without suffering from the various convergence requirements. Specifically, based on the random iterative method, the randomized iterative precoding algorithm (RIPA) is firstly proposed and we show its approximation error decays exponentially and globally along with the number of iterations. Then, with respect to the derived convergence rate, the concept of conditional sampling is introduced, so that further optimization and enhancement are carried out to improve both the convergence and the efficiency of the randomized iterations. After that, based on the equivalent iteration transformation, the modified randomized iterative precoding algorithm (MRIPA) is presented, which achieves a better precoding performance with low-complexity for various scenarios of massive MIMO. Finally, simulation results based on downlink precoding in massive MIMO systems are given to show the system gains of RIPA and MRIPA in terms of performance and complexity.

CNN Based Hybrid Precoding for MmWave MIMO Systems With Adaptive Switching Module and Phase Modulation Array

Hybrid precoding is considered as a promising candidate to balance between the sum rate and the power and cost in the mmWave MIMO systems. However, it still suffers from huge power consumption due to considerable radio frequency links and phase shifters. In this study, an energy-efficient adaptive switching module (ASM) and phase modulation array (PMA) assisted hybrid precoding scheme is proposed, in which ASM is introduced to enhance the energy efficiency while PMA is employed to implement analog precoding to reduce power consumption. The ASM-PMA hybrid precoder design problem related to three matrix variables is formulated. Besides, the joint ASM-PMA hybrid precoder/combiner design at both transmitter and receiver is also investigated. Owing to the tough task of joint optimization of matrix variables, we can divide it into two sub-problems. Correspondingly, alternating optimization hybrid precoding (AOHP) algorithm is suggested to solve the PMA. Then, matching theory and convolutional neural network (CNN) based algorithms are respectively proposed to seek the optimal ASM. For the CNN based scheme, relaxed adaptive switching CNN (RAS-CNN) is designed to solve the binary integer problem in the offline train stage, while in the online deployment stage, ideal adaptive switching CNN (IAS-CNN) is adopted. Furthermore, CNN accepts estimated channel as input to yield robust precoding performance and lower computational complexity. Simulation results verify that, the proposed hybrid precoding can outperform other schemes in energy efficiency with satisfactory spectral efficiency.

Hybrid Precoder Design Using Alternating Minimization Algorithm in MultiCell Multi-User Millimeter-Wave Communications for Massive MIMO

In the field of upcoming generation in wireless communication technology there is an extreme demand for higher bandwidth which is expected to be fulfilled by the frequency spectrum ranging from 30GHz to 300GHz which in other words can be defined as millimeter wave (mmWave). But it is a fact that, the mmWave technology lacks the simplicity in the design of precoder. There is higher power loss and higher cost in the baseband precoding in the case of fully digital baseband precoder. As the supported number of analog-to-digital (ADC) converters is very few, the mmWave technology requires ‘Hybrid Precoder’ which may reduce the loss of extensive power and lessen cost. The cost-effective hybrid precoder transceiver formulation has not been standardized yet. In this paper, we proposed the design of an optimal hybrid precoder in fully connected structure which has a higher complexity but lower cost and lower power consumption in the case of multi-cell multi-user massive MIMO. We have used Alternating Minimization Algorithm for getting the optimal Reimannian manifold solution. Here the comparison between the Digital Baseband precoder performance and the Hybrid Precoder performance has been done using simulation tools.

Neural-Network-Based Nonlinear Tomlinson-Harashima Precoding for Bandwidth-Limited Underwater Visible Light Communication

Underwater visible light communication (UVLC) based on light-emitting diodes (LEDs) is considered a potential candidate for underwater wireless data transmission. However, the implementation of high-speed UVLC remains a challenge due to bandwidth limitation and nonlinear effects. In this paper, we investigate the performance of Tomlinson-Harashima precoding (THP) in the bandwidth-limited UVLC system for the first time. Apart from research on linear and Volterra series-based nonlinear THP, neural network (NN)-based THP is first proposed and conducted. At the transmitter, the intersymbol interference (ISI) and nonlinearity can be partially mitigated through a feedback neural network (FBN) without error propagation. A modulo operator is employed to eliminate instability. In addition, for the precoded signals, the power spectral density (PSD) is near-white. Therefore, it is possible to avoid the spectrum-shaping-induced signal-to-noise ratio (SNR) loss problem that is common in pre-emphasis methods. At the receiver, another modulo operator is used for decoding. An adaptive feedforward neural network (FFN) is applied to compensate for the channel state information (CSI) mismatch of the FBN and mitigate the remaining ISI and nonlinear impairments. In the demonstrated UVLC system, the experimental results prove the feasibility of the proposed method. The Q factor is increased by 3.39 dB at a data rate of 2.2 Gbps. 630 MBd CAP-16 transmission under a 7% HD-FEC threshold is realized, which is 90 MBd higher than that when employing linear postequalization only.

HP‐SPC algorithm with dynamic partially connected structure for mmWave MIMO systems

AbstractThis article proposes a low complexity hybrid precoding algorithm for the switch network‐based dynamic partially connected (SPC) structure, that is, named as HP‐SPC algorithm. First, via a new defined effective optimal precoding matrix, the analog switch precoding matrix optimization problem is transformed into a sparse representation problem. Thus, invoking that the key characteristic of only one nonzero entry in each row of the analog dynamic switch precoding matrix, the analog dynamic switch precoding matrix can be accurately and effectively solved. Second, the digital precoding matrix optimization problem is modeled as a dictionary update problem by the defined effective optimal precoding matrix and the combining matrix. Further, the digital precoding matrix is easily optimized based on the defined effective optimal precoding matrix, since the measurement vectors are sparsely represented by a single dictionary atom. Third, the analog phase shifter precoding matrix is given by the phase rotation method. Finally, the precoding matrices are alternant updated until convergence, a near optimal solution of the precoding matrix is obtained. Compared with the previous works, the proposed HP‐SPC algorithm provides better hybrid precoding performance, for examples: (1) it avoids complexity computation such as matrix inversion and singular value decomposition, so that it presents a low computational complexity; (2) it has a favorable property of convergence since a stable point can be reached with about 10 loop iterations. Based on the simulation results, the effectiveness of HP‐SPC algorithm is further demonstrated.

Performance Analysis of Linear Precoding in Downlink Based on Polynomial Expansion on Massive MIMO Systems

The performance of linear precoding schemes in downlink Massive MIMO systems is dealt with in this paper. Linear precoding schemes are incorporated with zero-forcing (ZF) and maximum ratio transmission (MRT), truncated polynomial expansion (TPE), regularized zero force (RZF) in Downlink massive MIMO systems. Massive MIMO downlink output is evaluated with linear precoding included. This paper expresses the performance of achievable sum-rate linear precoding with variable signal-to-noise (SNR) ratio and achievable sum rate and several transmitter-receiver antennas, such as imperfect CSI, less complex processing, and inter-user interference. The transmitter has complete state information on the channel. The information narrates how a signal propagates to the receiver from the transmitter and reflects, for example, the cumulative effect of distance scattering, fading, and power decay. They show that the performance analysis of two linear precoding techniques, i.e., Maximum Ratio Transmission (MRT) and Zero Forcing (ZF) for downlink mMIMO output network over a perfect chain. The results show the improved ZF precoding achievable sum rate compared to the MRT precoding schemes and compared the average achievable rate RZF and TPE.

The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

Imperfections to be Considered in mmWave Systems for Hybrid Processing / Imperfeições a serem consideradas em sistemas mmWave para processamento híbrido

This paper describes a methodology to generate mmWave scenarios, where both imperfect channel estimation and noised phase shifters are considered. This methodology is greatly useful to evaluate hybrid precoder performance. Numerical results evidence that the number of antennas required to obtain a workable performance in mmWave systems with no ideal assumptions can be huge, whereas in mmWave systems with perfect conditions only a few dozens are needed. Furthermore, there are mmWave systems on ideal cases with a comparable performance of other systems on no ideal cases.

A New Optimization Technique in Massive MIMO and LSAS using Hybrid Architecture and Channel Estimation Algorithm for 5G Networks

Massive Multiple Input Multiple Output (m-MIMO) or Large Scale Antenna System (LSAS) is the latest version of cellular network technology with the purpose to send data from the base station to different users. The huge determined expansion in the mobile equipment and data rate requirement has created the rigid necessity for future wireless communication networks. The m-MIMO envisages a significant increase in the capacity but at the verge of excessive hardware complication. In this paper, we put forward a less complex precoding scheme based on the hybrid architecture to achieve a performance higher than the conventional baseband Minimum Mean Square Estimation (MMSE) precoding. In this paper, we propose a precoding schema namely amplitude and phase MMSE (APMMSE) to attain the performance of traditional MMSE precoding. The informed greedy best-first search has offered to estimate the baseband precoding matrix. It formulates the amplitude of the baseband precoder. Radio Frequency Precoder modifies the phase and provides an optimal signal-to-interference -noise ratio SINR and thus improves spectral efficiency of hybrid architecture.

Precoded OVSB-OFDM transmission system using DML with Kramers-Kronig receiver

Different from the use of IQ modulator or a dual-drive Mach-Zehnder modulator, we investigate an optical vestigial sideband orthogonal frequency division multiplexing (OVSB-OFDM) transmission system using a low-cost directly modulated laser. Precoding techniques and Kramers-Kronig (KK) algorithm are employed to enhance the bit error rate (BER) performance. The KK receiver is applied to eliminate the signal-signal beat interference caused by the square-law detection of the photodetector. Discrete Fourier transform (DFT) and orthogonal circulant matrix transform (OCT) precoding techniques can effectively average the signal-to-noise ratio over the data-carrying subcarriers and improve the BER performance. Through numerical simulation, we discuss the relationship between the order and center frequency of the optical filter and the carrier signal power ratio (CSPR). Furthermore, we study the influence of the KK algorithm and precoding techniques on receiver sensitivity under different transmission distances. The results show that both KK algorithm and precoding technologies can improve receiver sensitivity and extend the transmission distance. The performance of DFT precoding is better than that of OCT precoding. When there is no KK algorithm, the transmission distance of OCT precoding or no precoding is only 20 km at the BER of 3.8e-3; while DFT precoding can extend the transmission distance up to 60 km. With the help of the KK algorithm, OCT precoding and DFT precoding can further extend the transmission distance by up to 40 and 60 km, respectively, compared to the DFT precoding and without the KK algorithm case. The DFT precoding with the KK algorithm can improve the receiver sensitivity of more than 7 dB in terms of received optical power, compared to the no precoding and without the KK algorithm case.

On the effect of shadowing correlation and pilot assignment on hybrid precoding performance for cell-free mmWave massive MIMO UDN system

One of promising transmission technologies for beyond-5G wireless communication is cell-free massive MIMO where all the access nodes (ANs) cooperate to coherently serve all the user equipments (UEs). Based on the benefits from massive MIMO, the novel notion of cell-free network architecture is adequate to overcome the inter-cell interference limitation in ultra-dense network (UDN) environments, owing to the full cooperation among ANs. In this paper, we develop synergetic operations between the cell-free massive MIMO and mmWave ultra-wide frequency band, which is able to provide a genuine high-quality and robust wireless pervasive service by fully utilizing the basic three pillars for wireless capacity, such as bandwidth, spectral efficiency and network densification. For this goal, we study the impacts of analog precoding schemes to be combined with zero-forcing digital precoding on cell-free mmWave massive MIMO UDN systems. In addition, we investigate the effect of correlated shadowing and pilot assignment strategies on the average spectral efficiency performance for the proposed hybrid precoding schemes in both outdoor and indoor UDN environments.